diff --git a/README.md b/README.md
index 28309cc..a446f44 100644
--- a/README.md
+++ b/README.md
@@ -237,1266 +237,10 @@ This cell instantiates and trains the baseline classification model using Scikit
 ```python
 from sklearn.linear_model import LogisticRegression
 mylr = LogisticRegression(solver='lbfgs', random_state=42)
-mylr.fit(x,y)
+mylr.fit(x,y);
 ```
 
 
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-  border: 1px solid rgba(106, 105, 104, 0.232);
-  white-space: nowrap;
-  color: var(--sklearn-color-text);
-}
-
-.total_features {
-  display: flex;
-  justify-content: center;
-  margin-top: 0.5em;
-}
-</style><body><div id="sk-container-id-1" tabindex="0" class="sk-top-container sk-global"><div class="sk-text-repr-fallback"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually sk-global" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix="">
-        <div class="estimator-table">
-            <details>
-                <summary>Parameters</summary>
-                <table class="parameters-table">
-                  <tbody>
-
-        <tr class="user-set">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('random_state',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-random_state;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone">
-            random_state
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-random_state;">
-            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>
-        </a>
-    </td>
-            <td class="value">42</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('penalty',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-penalty;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27">
-            penalty
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-penalty;">
-            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing &#x27;multinomial&#x27; + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>
-        </a>
-    </td>
-            <td class="value">&#x27;deprecated&#x27;</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('C',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-C;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0">
-            C
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-C;">
-            C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>
-        </a>
-    </td>
-            <td class="value">1.0</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('l1_ratio',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-l1_ratio;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0">
-            l1_ratio
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-l1_ratio;">
-            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>
-        </a>
-    </td>
-            <td class="value">0.0</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('dual',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-dual;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse">
-            dual
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-dual;">
-            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>
-        </a>
-    </td>
-            <td class="value">False</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('tol',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-tol;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4">
-            tol
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-tol;">
-            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>
-        </a>
-    </td>
-            <td class="value">0.0001</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('fit_intercept',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-fit_intercept;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue">
-            fit_intercept
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-fit_intercept;">
-            fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>
-        </a>
-    </td>
-            <td class="value">True</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('intercept_scaling',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-intercept_scaling;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1">
-            intercept_scaling
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-intercept_scaling;">
-            intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a &quot;synthetic&quot; feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>
-        </a>
-    </td>
-            <td class="value">1</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('class_weight',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-class_weight;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone">
-            class_weight
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-class_weight;">
-            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight=&#x27;balanced&#x27;*</span>
-        </a>
-    </td>
-            <td class="value">None</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('solver',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-solver;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27">
-            solver
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-solver;">
-            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>
-        </a>
-    </td>
-            <td class="value">&#x27;lbfgs&#x27;</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('max_iter',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-max_iter;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100">
-            max_iter
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-max_iter;">
-            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>
-        </a>
-    </td>
-            <td class="value">100</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('verbose',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-verbose;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0">
-            verbose
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-verbose;">
-            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>
-        </a>
-    </td>
-            <td class="value">0</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('warm_start',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-warm_start;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse">
-            warm_start
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-warm_start;">
-            warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>
-        </a>
-    </td>
-            <td class="value">False</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('n_jobs',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-n_jobs;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone">
-            n_jobs
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-n_jobs;">
-            n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>
-        </a>
-    </td>
-            <td class="value">None</td>
-        </tr>
-
-                  </tbody>
-                </table>
-            </details>
-        </div>
-
-        <div class="estimator-table">
-            <details>
-                <summary>Fitted attributes</summary>
-                <table class="parameters-table">
-                    <tbody>
-                        <tr>
-                        <th>Name</th>
-                        <th>Type</th>
-                        <th>Value</th>
-                        </tr>
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-classes_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29">
-            classes_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-classes_;">
-            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">ndarray[int64](2,)</td>
-           <td>[0,1]</td>
-
-
-       </tr>
-
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-coef_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29">
-            coef_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-coef_;">
-            coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">ndarray[float64](1, 1)</td>
-           <td>[[-4.53]]</td>
-
-
-       </tr>
-
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-intercept_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29">
-            intercept_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-intercept_;">
-            intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">ndarray[float64](1,)</td>
-           <td>[3.42]</td>
-
-
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 ```python
 Xnew = np.linspace(-1,3,100).reshape(-1,1)
 yPred = mylr.predict_proba(Xnew)
@@ -1538,1266 +282,10 @@ y = (iris.target == 0).astype(int)
 ```python
 from sklearn.linear_model import LogisticRegression
 mylr = LogisticRegression(solver='lbfgs', random_state=42)
-mylr.fit(x,y)
+mylr.fit(x,y);
 ```
 
 
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-</style><body><div id="sk-container-id-2" tabindex="0" class="sk-top-container sk-global"><div class="sk-text-repr-fallback"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually sk-global" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix="">
-        <div class="estimator-table">
-            <details>
-                <summary>Parameters</summary>
-                <table class="parameters-table">
-                  <tbody>
-
-        <tr class="user-set">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('random_state',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-random_state;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone">
-            random_state
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-random_state;">
-            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>
-        </a>
-    </td>
-            <td class="value">42</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('penalty',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-penalty;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27">
-            penalty
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-penalty;">
-            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing &#x27;multinomial&#x27; + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>
-        </a>
-    </td>
-            <td class="value">&#x27;deprecated&#x27;</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('C',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-C;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0">
-            C
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-C;">
-            C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>
-        </a>
-    </td>
-            <td class="value">1.0</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('l1_ratio',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-l1_ratio;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0">
-            l1_ratio
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-l1_ratio;">
-            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>
-        </a>
-    </td>
-            <td class="value">0.0</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('dual',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-dual;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse">
-            dual
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-dual;">
-            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>
-        </a>
-    </td>
-            <td class="value">False</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('tol',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-tol;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4">
-            tol
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-tol;">
-            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>
-        </a>
-    </td>
-            <td class="value">0.0001</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('fit_intercept',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-fit_intercept;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue">
-            fit_intercept
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-fit_intercept;">
-            fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>
-        </a>
-    </td>
-            <td class="value">True</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('intercept_scaling',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-intercept_scaling;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1">
-            intercept_scaling
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-intercept_scaling;">
-            intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a &quot;synthetic&quot; feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>
-        </a>
-    </td>
-            <td class="value">1</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('class_weight',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-class_weight;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone">
-            class_weight
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-class_weight;">
-            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight=&#x27;balanced&#x27;*</span>
-        </a>
-    </td>
-            <td class="value">None</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('solver',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-solver;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27">
-            solver
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-solver;">
-            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>
-        </a>
-    </td>
-            <td class="value">&#x27;lbfgs&#x27;</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('max_iter',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-max_iter;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100">
-            max_iter
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-max_iter;">
-            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>
-        </a>
-    </td>
-            <td class="value">100</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('verbose',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-verbose;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0">
-            verbose
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-verbose;">
-            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>
-        </a>
-    </td>
-            <td class="value">0</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('warm_start',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-warm_start;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse">
-            warm_start
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-warm_start;">
-            warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>
-        </a>
-    </td>
-            <td class="value">False</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('n_jobs',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-n_jobs;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone">
-            n_jobs
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-n_jobs;">
-            n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>
-        </a>
-    </td>
-            <td class="value">None</td>
-        </tr>
-
-                  </tbody>
-                </table>
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-
-        <div class="estimator-table">
-            <details>
-                <summary>Fitted attributes</summary>
-                <table class="parameters-table">
-                    <tbody>
-                        <tr>
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-                        <th>Type</th>
-                        <th>Value</th>
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-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-classes_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29">
-            classes_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-classes_;">
-            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">ndarray[int64](2,)</td>
-           <td>[0,1]</td>
-
-
-       </tr>
-
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-coef_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29">
-            coef_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-coef_;">
-            coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">ndarray[float64](1, 1)</td>
-           <td>[[-2.92]]</td>
-
-
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-
-
-       <tr class="default">
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-            style="anchor-name: --doc-link-intercept_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29">
-            intercept_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-intercept_;">
-            intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">ndarray[float64](1,)</td>
-           <td>[7.92]</td>
-
-
-       </tr>
-
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-n_features_in_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int">
-            n_features_in_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-n_features_in_;">
-            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">int</td>
-           <td>1</td>
-
-
-       </tr>
-
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-n_iter_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29">
-            n_iter_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-n_iter_;">
-            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">ndarray[int32](1,)</td>
-           <td>[17]</td>
-
-
-       </tr>
-
-                    </tbody>
-                </table>
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 ```python
 Xnew = np.linspace(0,8,100).reshape(-1,1)
 yPred = mylr.predict_proba(Xnew)
@@ -2835,1264 +323,9 @@ x = iris.data[:, 0:1]
 y = (iris.target == 0).astype(int)
 from sklearn.linear_model import LogisticRegression
 mylr = LogisticRegression(solver='lbfgs', random_state=42)
-mylr.fit(x,y)
-```
-
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-  --sklearn-color-unfitted-level-0: #fff5e6;
-  --sklearn-color-unfitted-level-1: #f6e4d2;
-  --sklearn-color-unfitted-level-2: #ffe0b3;
-  --sklearn-color-unfitted-level-3: chocolate;
-  /* Definition of color scheme for fitted estimators */
-  --sklearn-color-fitted-level-0: #f0f8ff;
-  --sklearn-color-fitted-level-1: #d4ebff;
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-</style><body><div id="sk-container-id-3" tabindex="0" class="sk-top-container sk-global"><div class="sk-text-repr-fallback"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually sk-global" id="sk-estimator-id-3" type="checkbox" checked><label for="sk-estimator-id-3" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix="">
-        <div class="estimator-table">
-            <details>
-                <summary>Parameters</summary>
-                <table class="parameters-table">
-                  <tbody>
-
-        <tr class="user-set">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('random_state',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-random_state;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone">
-            random_state
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-random_state;">
-            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>
-        </a>
-    </td>
-            <td class="value">42</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('penalty',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-penalty;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27">
-            penalty
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-penalty;">
-            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing &#x27;multinomial&#x27; + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>
-        </a>
-    </td>
-            <td class="value">&#x27;deprecated&#x27;</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('C',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-C;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0">
-            C
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-C;">
-            C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>
-        </a>
-    </td>
-            <td class="value">1.0</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('l1_ratio',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-l1_ratio;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0">
-            l1_ratio
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-l1_ratio;">
-            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>
-        </a>
-    </td>
-            <td class="value">0.0</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('dual',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-dual;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse">
-            dual
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-dual;">
-            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>
-        </a>
-    </td>
-            <td class="value">False</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('tol',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-tol;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4">
-            tol
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-tol;">
-            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>
-        </a>
-    </td>
-            <td class="value">0.0001</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('fit_intercept',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-fit_intercept;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue">
-            fit_intercept
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-fit_intercept;">
-            fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>
-        </a>
-    </td>
-            <td class="value">True</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('intercept_scaling',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-intercept_scaling;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1">
-            intercept_scaling
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-intercept_scaling;">
-            intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a &quot;synthetic&quot; feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>
-        </a>
-    </td>
-            <td class="value">1</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('class_weight',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-class_weight;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone">
-            class_weight
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-class_weight;">
-            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight=&#x27;balanced&#x27;*</span>
-        </a>
-    </td>
-            <td class="value">None</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('solver',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-solver;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27">
-            solver
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-solver;">
-            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>
-        </a>
-    </td>
-            <td class="value">&#x27;lbfgs&#x27;</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('max_iter',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-max_iter;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100">
-            max_iter
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-max_iter;">
-            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>
-        </a>
-    </td>
-            <td class="value">100</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('verbose',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-verbose;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0">
-            verbose
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-verbose;">
-            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>
-        </a>
-    </td>
-            <td class="value">0</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('warm_start',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-warm_start;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse">
-            warm_start
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-warm_start;">
-            warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>
-        </a>
-    </td>
-            <td class="value">False</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('n_jobs',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-n_jobs;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone">
-            n_jobs
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-n_jobs;">
-            n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>
-        </a>
-    </td>
-            <td class="value">None</td>
-        </tr>
-
-                  </tbody>
-                </table>
-            </details>
-        </div>
-
-        <div class="estimator-table">
-            <details>
-                <summary>Fitted attributes</summary>
-                <table class="parameters-table">
-                    <tbody>
-                        <tr>
-                        <th>Name</th>
-                        <th>Type</th>
-                        <th>Value</th>
-                        </tr>
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-classes_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29">
-            classes_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-classes_;">
-            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">ndarray[int64](2,)</td>
-           <td>[0,1]</td>
-
-
-       </tr>
-
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-coef_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29">
-            coef_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-coef_;">
-            coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">ndarray[float64](1, 1)</td>
-           <td>[[-3.46]]</td>
-
-
-       </tr>
-
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-intercept_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29">
-            intercept_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-intercept_;">
-            intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">ndarray[float64](1,)</td>
-           <td>[18.55]</td>
-
-
-       </tr>
-
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-n_features_in_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int">
-            n_features_in_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-n_features_in_;">
-            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">int</td>
-           <td>1</td>
-
-
-       </tr>
-
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-n_iter_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29">
-            n_iter_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-n_iter_;">
-            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">ndarray[int32](1,)</td>
-           <td>[17]</td>
-
-
-       </tr>
-
-                    </tbody>
-                </table>
-            </details>
-        </div>
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-            console.error('Failed to copy:', err);
-            element.style.color = 'orange';
-            element.innerHTML = "Failed!";
-            setTimeout(() => {
-                element.innerHTML = originalHTML;
-                element.style = originalStyle;
-            }, 1000);
-        });
-    return false;
-}
-/**
- * Adapted from Skrub
- * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789
- * @returns "light" or "dark"
- */
-function detectTheme(element) {
-    const body = document.querySelector('body');
-
-    // Check VSCode theme
-    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');
-    const themeNameAttr = body.getAttribute('data-vscode-theme-name');
-
-    if (themeKindAttr && themeNameAttr) {
-        const themeKind = themeKindAttr.toLowerCase();
-        const themeName = themeNameAttr.toLowerCase();
-
-        if (themeKind.includes("dark") || themeName.includes("dark")) {
-            return "dark";
-        }
-        if (themeKind.includes("light") || themeName.includes("light")) {
-            return "light";
-        }
-    }
-
-    // Check Jupyter theme
-    if (body.getAttribute('data-jp-theme-light') === 'false') {
-        return 'dark';
-    } else if (body.getAttribute('data-jp-theme-light') === 'true') {
-        return 'light';
-    }
-
-    // Guess based on a parent element's color
-    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');
-    const match = color.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)\s*$/i);
-    if (match) {
-        const [r, g, b] = [
-            parseFloat(match[1]),
-            parseFloat(match[2]),
-            parseFloat(match[3])
-        ];
-
-        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness
-        const luma = 0.299 * r + 0.587 * g + 0.114 * b;
-
-        if (luma > 180) {
-            // If the text is very bright we have a dark theme
-            return 'dark';
-        }
-        if (luma < 75) {
-            // If the text is very dark we have a light theme
-            return 'light';
-        }
-        // Otherwise fall back to the next heuristic.
-    }
-
-    // Fallback to system preference
-    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';
-}
-
-
-function forceTheme(elementId) {
-    const estimatorElement = document.querySelector(`#${elementId}`);
-    if (estimatorElement === null) {
-        console.error(`Element with id ${elementId} not found.`);
-    } else {
-        const theme = detectTheme(estimatorElement);
-        estimatorElement.classList.add(theme);
-    }
-}
-
-forceTheme('sk-container-id-3');</script></body>
-
+mylr.fit(x,y);
 
+```
 
 
 ```python
@@ -4169,1265 +402,9 @@ mylrvir = LogisticRegression(
     max_iter=100,
     solver='newton-cg'
 )
-mylrvir.fit(X,y)
+mylrvir.fit(X,y);
 ```
 
-
-
-
-<style>.sk-global {
-  /* Definition of color scheme common for light and dark mode */
-  --sklearn-color-text: #000;
-  --sklearn-color-text-muted: #666;
-  --sklearn-color-line: gray;
-  /* Definition of color scheme for unfitted estimators */
-  --sklearn-color-unfitted-level-0: #fff5e6;
-  --sklearn-color-unfitted-level-1: #f6e4d2;
-  --sklearn-color-unfitted-level-2: #ffe0b3;
-  --sklearn-color-unfitted-level-3: chocolate;
-  /* Definition of color scheme for fitted estimators */
-  --sklearn-color-fitted-level-0: #f0f8ff;
-  --sklearn-color-fitted-level-1: #d4ebff;
-  --sklearn-color-fitted-level-2: #b3dbfd;
-  --sklearn-color-fitted-level-3: cornflowerblue;
-}
-
-.sk-global.light {
-  /* Specific color for light theme */
-  --sklearn-color-text-on-default-background: black;
-  --sklearn-color-background: white;
-  --sklearn-color-border-box: black;
-  --sklearn-color-icon: #696969;
-}
-
-.sk-global.dark {
-  --sklearn-color-text-on-default-background: white;
-  --sklearn-color-background: #111;
-  --sklearn-color-border-box: white;
-  --sklearn-color-icon: #878787;
-}
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-.sk-global {
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-}
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-}
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-  margin: -1px;
-  overflow: hidden;
-  padding: 0;
-  position: absolute;
-  width: 1px;
-}
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-.sk-global div.sk-dashed-wrapped {
-  border: 1px dashed var(--sklearn-color-line);
-  margin: 0 0.4em 0.5em 0.4em;
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-  padding-bottom: 0.4em;
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-}
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-.sk-global div.sk-container {
-  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-     but bootstrap.min.css set `[hidden] { display: none !important; }`
-     so we also need the `!important` here to be able to override the
-     default hidden behavior on the sphinx rendered scikit-learn.org.
-     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-  display: inline-block !important;
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-}
-
-.sk-global div.sk-text-repr-fallback {
-  display: none;
-}
-
-div.sk-parallel-item,
-div.sk-serial,
-div.sk-item {
-  /* draw centered vertical line to link estimators */
-  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
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-}
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-/* Parallel-specific style estimator block */
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-.sk-global div.sk-parallel {
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-.sk-global div.sk-parallel-item {
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-/* Serial-specific style estimator block */
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-  padding-right: 1em;
-  padding-left: 1em;
-}
-
-
-/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
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-- Pipeline and ColumnTransformer use this feature and define the default style
-- Estimators will overwrite some part of the style using the `sk-estimator` class
-*/
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-/* Pipeline and ColumnTransformer style (default) */
-
-.sk-global div.sk-toggleable {
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-}
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-/* Toggleable label */
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-.sk-global label.sk-toggleable__label-arrow:hover:before {
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-/* Toggleable content - dropdown */
-
-.sk-global div.sk-toggleable__content {
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-.sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
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-/* Estimator-specific style */
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-/* Colorize estimator box */
-.sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
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-
-.sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
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-}
-
-.sk-global div.sk-label label.sk-toggleable__label,
-.sk-global div.sk-label label {
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-}
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-/* On hover, darken the color of the background */
-.sk-global div.sk-label:hover label.sk-toggleable__label {
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-
-/* Label box, darken color on hover, fitted */
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-}
-
-/* Estimator label */
-
-.sk-global div.sk-label label {
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-.sk-global div.sk-label-container {
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-  /* fitted */
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-}
-
-/* Specification for estimator info (e.g. "i" and "?") */
-
-/* Common style for "i" and "?" */
-
-.sk-estimator-doc-link,
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-a:visited.sk-estimator-doc-link {
-  float: right;
-  font-size: smaller;
-  line-height: 1em;
-  font-family: monospace;
-  background-color: var(--sklearn-color-unfitted-level-0);
-  border-radius: 1em;
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-div.sk-label-container:hover .sk-estimator-doc-link:hover,
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-*/
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-*/
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-a.param-doc-link:visited {
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-
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-</style><body><div id="sk-container-id-4" tabindex="0" class="sk-top-container sk-global"><div class="sk-text-repr-fallback"><pre>LogisticRegression(C=100, random_state=22, solver=&#x27;newton-cg&#x27;, tol=1e-05)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually sk-global" id="sk-estimator-id-4" type="checkbox" checked><label for="sk-estimator-id-4" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix="">
-        <div class="estimator-table">
-            <details>
-                <summary>Parameters</summary>
-                <table class="parameters-table">
-                  <tbody>
-
-        <tr class="user-set">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('C',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-C;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0">
-            C
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-C;">
-            C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>
-        </a>
-    </td>
-            <td class="value">100</td>
-        </tr>
-
-
-        <tr class="user-set">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('tol',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-tol;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4">
-            tol
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-tol;">
-            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>
-        </a>
-    </td>
-            <td class="value">1e-05</td>
-        </tr>
-
-
-        <tr class="user-set">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('random_state',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-random_state;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone">
-            random_state
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-random_state;">
-            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>
-        </a>
-    </td>
-            <td class="value">22</td>
-        </tr>
-
-
-        <tr class="user-set">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('solver',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-solver;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27">
-            solver
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-solver;">
-            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>
-        </a>
-    </td>
-            <td class="value">&#x27;newton-cg&#x27;</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('penalty',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-penalty;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27">
-            penalty
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-penalty;">
-            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing &#x27;multinomial&#x27; + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>
-        </a>
-    </td>
-            <td class="value">&#x27;deprecated&#x27;</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('l1_ratio',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-l1_ratio;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0">
-            l1_ratio
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-l1_ratio;">
-            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>
-        </a>
-    </td>
-            <td class="value">0.0</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('dual',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-dual;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse">
-            dual
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-dual;">
-            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>
-        </a>
-    </td>
-            <td class="value">False</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('fit_intercept',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-fit_intercept;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue">
-            fit_intercept
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-fit_intercept;">
-            fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>
-        </a>
-    </td>
-            <td class="value">True</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('intercept_scaling',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-intercept_scaling;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1">
-            intercept_scaling
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-intercept_scaling;">
-            intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a &quot;synthetic&quot; feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>
-        </a>
-    </td>
-            <td class="value">1</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('class_weight',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-class_weight;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone">
-            class_weight
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-class_weight;">
-            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight=&#x27;balanced&#x27;*</span>
-        </a>
-    </td>
-            <td class="value">None</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('max_iter',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-max_iter;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100">
-            max_iter
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-max_iter;">
-            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>
-        </a>
-    </td>
-            <td class="value">100</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('verbose',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-verbose;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0">
-            verbose
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-verbose;">
-            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>
-        </a>
-    </td>
-            <td class="value">0</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('warm_start',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-warm_start;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse">
-            warm_start
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-warm_start;">
-            warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>
-        </a>
-    </td>
-            <td class="value">False</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('n_jobs',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-n_jobs;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone">
-            n_jobs
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-n_jobs;">
-            n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>
-        </a>
-    </td>
-            <td class="value">None</td>
-        </tr>
-
-                  </tbody>
-                </table>
-            </details>
-        </div>
-
-        <div class="estimator-table">
-            <details>
-                <summary>Fitted attributes</summary>
-                <table class="parameters-table">
-                    <tbody>
-                        <tr>
-                        <th>Name</th>
-                        <th>Type</th>
-                        <th>Value</th>
-                        </tr>
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-classes_;"
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-            classes_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-classes_;">
-            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>
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-
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-            style="position-anchor: --doc-link-coef_;">
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-            intercept_
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-            style="position-anchor: --doc-link-intercept_;">
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-        </a>
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-            n_features_in_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-n_features_in_;">
-            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>
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-           <td>2</td>
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-            style="position-anchor: --doc-link-n_iter_;">
-            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>
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-
 #### Coordinate Grid Generation & Probability Mapping
 
 This block builds the mathematical testing grid used to map out the complete probability landscape:
@@ -5476,19 +453,12 @@ This plot visualizes the continuous probability space generated by the trained b
 ```python
 fig, ax =plt.subplots(subplot_kw={"projection": "3d"})
 surf = ax.plot_surface(x0,x1,zz, cmap='jet')
-ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or')
+ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or');
 ```
 
 
-
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-    <mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x2a3c5a946e0>
-
-
-
-
     
-![png](README_files/README_42_1.png)
+![png](README_files/README_42_0.png)
     
 
 
@@ -5517,1267 +487,9 @@ lrmc = LogisticRegression(
     C=100,
     random_state=22
 )
-lrmc.fit(X,y)
+lrmc.fit(X,y);
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-</style><body><div id="sk-container-id-5" tabindex="0" class="sk-top-container sk-global"><div class="sk-text-repr-fallback"><pre>LogisticRegression(C=100, random_state=22)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually sk-global" id="sk-estimator-id-5" type="checkbox" checked><label for="sk-estimator-id-5" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix="">
-        <div class="estimator-table">
-            <details>
-                <summary>Parameters</summary>
-                <table class="parameters-table">
-                  <tbody>
-
-        <tr class="user-set">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('C',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-C;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0">
-            C
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-C;">
-            C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>
-        </a>
-    </td>
-            <td class="value">100</td>
-        </tr>
-
-
-        <tr class="user-set">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('random_state',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-random_state;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone">
-            random_state
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-random_state;">
-            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>
-        </a>
-    </td>
-            <td class="value">22</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('penalty',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-penalty;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27">
-            penalty
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-penalty;">
-            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing &#x27;multinomial&#x27; + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>
-        </a>
-    </td>
-            <td class="value">&#x27;deprecated&#x27;</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('l1_ratio',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-l1_ratio;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0">
-            l1_ratio
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-l1_ratio;">
-            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>
-        </a>
-    </td>
-            <td class="value">0.0</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('dual',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-dual;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse">
-            dual
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-dual;">
-            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>
-        </a>
-    </td>
-            <td class="value">False</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('tol',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-tol;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4">
-            tol
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-tol;">
-            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>
-        </a>
-    </td>
-            <td class="value">0.0001</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('fit_intercept',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-fit_intercept;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue">
-            fit_intercept
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-fit_intercept;">
-            fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>
-        </a>
-    </td>
-            <td class="value">True</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('intercept_scaling',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-intercept_scaling;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1">
-            intercept_scaling
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-intercept_scaling;">
-            intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a &quot;synthetic&quot; feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>
-        </a>
-    </td>
-            <td class="value">1</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('class_weight',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-class_weight;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone">
-            class_weight
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-class_weight;">
-            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight=&#x27;balanced&#x27;*</span>
-        </a>
-    </td>
-            <td class="value">None</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('solver',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-solver;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27">
-            solver
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-solver;">
-            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>
-        </a>
-    </td>
-            <td class="value">&#x27;lbfgs&#x27;</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('max_iter',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-max_iter;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100">
-            max_iter
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-max_iter;">
-            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>
-        </a>
-    </td>
-            <td class="value">100</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('verbose',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-verbose;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0">
-            verbose
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-verbose;">
-            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>
-        </a>
-    </td>
-            <td class="value">0</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('warm_start',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-warm_start;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse">
-            warm_start
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-warm_start;">
-            warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>
-        </a>
-    </td>
-            <td class="value">False</td>
-        </tr>
-
-
-        <tr class="default">
-            <td><i class="copy-paste-icon"
-                 onclick="copyToClipboard('n_jobs',
-                          this.parentElement.nextElementSibling)"
-            ></i></td>
-            <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-n_jobs;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone">
-            n_jobs
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-n_jobs;">
-            n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>
-        </a>
-    </td>
-            <td class="value">None</td>
-        </tr>
-
-                  </tbody>
-                </table>
-            </details>
-        </div>
-
-        <div class="estimator-table">
-            <details>
-                <summary>Fitted attributes</summary>
-                <table class="parameters-table">
-                    <tbody>
-                        <tr>
-                        <th>Name</th>
-                        <th>Type</th>
-                        <th>Value</th>
-                        </tr>
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-classes_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29">
-            classes_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-classes_;">
-            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">ndarray[int64](3,)</td>
-           <td>[0,1,2]</td>
-
-
-       </tr>
-
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-coef_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29">
-            coef_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-coef_;">
-            coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">ndarray[float64](3, 2)</td>
-           <td>[[-9.33, 8.18],
- [ 3.73,-4.3 ],
- [ 5.61,-3.88]]</td>
-
-
-       </tr>
-
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-intercept_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29">
-            intercept_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-intercept_;">
-            intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">ndarray[float64](3,)</td>
-           <td>[ 25.2 , -6.11,-19.1 ]</td>
-
-
-       </tr>
-
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-n_features_in_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int">
-            n_features_in_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-n_features_in_;">
-            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">int</td>
-           <td>2</td>
-
-
-       </tr>
-
-
-       <tr class="default">
-           <td class="param">
-        <a class="param-doc-link"
-            style="anchor-name: --doc-link-n_iter_;"
-            rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29">
-            n_iter_
-            <span class="param-doc-description"
-            style="position-anchor: --doc-link-n_iter_;">
-            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>
-        </a>
-    </td>
-           <td class="fitted-att-type">ndarray[int32](1,)</td>
-           <td>[59]</td>
-
-
-       </tr>
-
-                    </tbody>
-                </table>
-            </details>
-        </div>
-    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers
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-
-
-
 #### Multi-Class Grid Generation and Probability Evaluation
 
 This cell sets up the coordinate testing matrix to evaluate the multi-class prediction behavior across the entire sepal feature space:
@@ -6858,19 +570,12 @@ This plot visualizes the ultimate classification boundaries by partitioning the
 ```python
 fig, ax =plt.subplots(subplot_kw={"projection": "3d"})
 surf = ax.plot_surface(x0,x1,zz, cmap='jet')
-ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or')
+ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or');
 ```
 
 
-
-
-    <mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x2a3c02451d0>
-
-
-
-
     
-![png](README_files/README_53_1.png)
+![png](README_files/README_53_0.png)
     
 
 
diff --git a/README_files/README_42_0.png b/README_files/README_42_0.png
new file mode 100644
index 0000000..cbf3479
Binary files /dev/null and b/README_files/README_42_0.png differ
diff --git a/README_files/README_53_0.png b/README_files/README_53_0.png
new file mode 100644
index 0000000..3839455
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diff --git a/Regresion_Logistica/main.ipynb b/Regresion_Logistica/main.ipynb
index 1046f60..98f28e7 100644
--- a/Regresion_Logistica/main.ipynb
+++ b/Regresion_Logistica/main.ipynb
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "cd073961",
    "metadata": {},
    "outputs": [
@@ -368,1277 +368,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 37,
    "id": "1069ed1e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "}\n",
-       "\n",
-       ".sk-global.light {\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: black;\n",
-       "  --sklearn-color-background: white;\n",
-       "  --sklearn-color-border-box: black;\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "}\n",
-       "\n",
-       ".sk-global.dark {\n",
-       "  --sklearn-color-text-on-default-background: white;\n",
-       "  --sklearn-color-background: #111;\n",
-       "  --sklearn-color-border-box: white;\n",
-       "  --sklearn-color-icon: #878787;\n",
-       "}\n",
-       "\n",
-       ".sk-global {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".sk-global pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip-path: inset(100%);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       ".sk-global div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       ".sk-global label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label label.sk-toggleable__label,\n",
-       ".sk-global div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       ".sk-global div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       ".sk-global div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       ".sk-global div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       ".sk-global a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       ".sk-global a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-top-container.sk-global {\n",
-       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
-       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
-       "   to allow keyboard-only users to navigate the display. */\n",
-       "  overflow-x: scroll !important;\n",
-       "  max-width: 100%;\n",
-       "}\n",
-       "\n",
-       ".estimator-table {\n",
-       "    font-family: monospace;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary::marker {\n",
-       "    font-size: 0.7rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "    margin-top: 0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover td {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table :is(td, th) {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    `table td`is set in notebook with right text-align.\n",
-       "    We need to overwrite it.\n",
-       "*/\n",
-       ".estimator-table table td.param {\n",
-       "    text-align: left;\n",
-       "    position: relative;\n",
-       "    padding: 0;\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".default td, .estimator-table th {\n",
-       "    color: black;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       "td.fitted-att-type {\n",
-       "    white-space: preserve nowrap;\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    Styles for parameter documentation links\n",
-       "    We need styling for visited so jupyter doesn't overwrite it\n",
-       "*/\n",
-       "a.param-doc-link,\n",
-       "a.param-doc-link:link,\n",
-       "a.param-doc-link:visited {\n",
-       "    text-decoration: underline dashed;\n",
-       "    text-underline-offset: .3em;\n",
-       "    color: inherit;\n",
-       "    display: block;\n",
-       "    padding: .5em;\n",
-       "}\n",
-       "\n",
-       "@supports(anchor-name: --doc-link) {\n",
-       "    a.param-doc-link,\n",
-       "    a.param-doc-link:link,\n",
-       "    a.param-doc-link:visited {\n",
-       "    anchor-name: --doc-link;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
-       "a.param-doc-link::before {\n",
-       "    position: absolute;\n",
-       "    content: \"\";\n",
-       "    inset: 0;\n",
-       "}\n",
-       "\n",
-       ".param-doc-description {\n",
-       "    display: none;\n",
-       "    position: absolute;\n",
-       "    z-index: 9999;\n",
-       "    left: 0;\n",
-       "    padding: .5ex;\n",
-       "    margin-left: 1.5em;\n",
-       "    color: var(--sklearn-color-text);\n",
-       "    box-shadow: .3em .3em .4em #999;\n",
-       "    width: max-content;\n",
-       "    text-align: left;\n",
-       "    max-height: 10em;\n",
-       "    overflow-y: auto;\n",
-       "\n",
-       "    /* unfitted */\n",
-       "    background: var(--sklearn-color-unfitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       "@supports(position-area: center right) {\n",
-       "    .param-doc-description {\n",
-       "    position-area: center right;\n",
-       "    position: fixed;\n",
-       "    margin-left: 0;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* Fitted state for parameter tooltips */\n",
-       ".fitted .param-doc-description {\n",
-       "    /* fitted */\n",
-       "    background: var(--sklearn-color-fitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".param-doc-link:hover .param-doc-description {\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
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-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".features {\n",
-       "  font-family: monospace;\n",
-       "  cursor: pointer;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: .20em;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  font-size: inherit; /* Needed for jupyter */\n",
-       "}\n",
-       "\n",
-       ".features.fitted {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features summary {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  margin-bottom: 0;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "  padding: .25em;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features.fitted details[open] > summary {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features details > summary .arrow::before {\n",
-       "  content: \"▸\";\n",
-       "  color: grey;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary .arrow::before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       ".features details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features.fitted details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features .features-container {\n",
-       "  max-width: 15em;\n",
-       "  max-height: 10em;\n",
-       "  overflow: auto;\n",
-       "  scrollbar-width: thin;\n",
-       "  padding: .25em 0.1rem;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 0 0 .5em .5em;\n",
-       "}\n",
-       "\n",
-       ".features.fitted .features-container {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features .image-container {\n",
-       "  block-size: 1em;\n",
-       "  inline-size: 1em;\n",
-       "  padding: 0;\n",
-       "  margin: 0%;\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  align-items: center;\n",
-       "}\n",
-       "\n",
-       ".features .copy-paste-icon {\n",
-       "  background-size: 1em 1em;\n",
-       "  width: 1em;\n",
-       "  height: 1em;\n",
-       "  filter: grayscale(100%) opacity(60%);\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  width: 100%;\n",
-       "  margin: 0.01em;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(odd) {\n",
-       "  background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(even) {\n",
-       "  background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:hover {\n",
-       "  background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  table-layout: inherit;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table td {\n",
-       "  text-align: left;\n",
-       "  padding: 0 0.5em;\n",
-       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "  white-space: nowrap;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".total_features {\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  margin-top: 0.5em;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-random_state;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
-       "            random_state\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-random_state;\">\n",
-       "            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-penalty;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
-       "            penalty\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-penalty;\">\n",
-       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing &#x27;multinomial&#x27; + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-C;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
-       "            C\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-C;\">\n",
-       "            C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('l1_ratio',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-l1_ratio;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
-       "            l1_ratio\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-l1_ratio;\">\n",
-       "            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-dual;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
-       "            dual\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-dual;\">\n",
-       "            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-tol;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
-       "            intercept_scaling\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
-       "            intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a &quot;synthetic&quot; feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
-       "            class_weight\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
-       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight=&#x27;balanced&#x27;*</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-solver;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
-       "            solver\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-solver;\">\n",
-       "            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
-       "            max_iter\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
-       "            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-verbose;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
-       "            verbose\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-verbose;\">\n",
-       "            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
-       "            warm_start\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
-       "            warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
-       "            n_jobs\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
-       "            n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    \n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Fitted attributes</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                    <tbody>\n",
-       "                        <tr>\n",
-       "                        <th>Name</th>\n",
-       "                        <th>Type</th>\n",
-       "                        <th>Value</th>\n",
-       "                        </tr>\n",
-       "                        \n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-classes_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29\">\n",
-       "            classes_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-classes_;\">\n",
-       "            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
-       "           <td>[0,1]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1, 1)</td>\n",
-       "           <td>[[-4.53]]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[3.42]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int\">\n",
-       "            n_features_in_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
-       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>1</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29\">\n",
-       "            n_iter_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
-       "            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
-       "           <td>[9]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "                    </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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-      ],
-      "text/plain": [
-       "LogisticRegression(random_state=42)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
     "mylr = LogisticRegression(solver='lbfgs', random_state=42)\n",
-    "mylr.fit(x,y)"
+    "mylr.fit(x,y);"
    ]
   },
   {
@@ -1713,1277 +450,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 38,
    "id": "167b1fa6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>.sk-global {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "}\n",
-       "\n",
-       ".sk-global.light {\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: black;\n",
-       "  --sklearn-color-background: white;\n",
-       "  --sklearn-color-border-box: black;\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "}\n",
-       "\n",
-       ".sk-global.dark {\n",
-       "  --sklearn-color-text-on-default-background: white;\n",
-       "  --sklearn-color-background: #111;\n",
-       "  --sklearn-color-border-box: white;\n",
-       "  --sklearn-color-icon: #878787;\n",
-       "}\n",
-       "\n",
-       ".sk-global {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".sk-global pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip-path: inset(100%);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       ".sk-global div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       ".sk-global label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label label.sk-toggleable__label,\n",
-       ".sk-global div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       ".sk-global div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       ".sk-global div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       ".sk-global div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       ".sk-global a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       ".sk-global a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-top-container.sk-global {\n",
-       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
-       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
-       "   to allow keyboard-only users to navigate the display. */\n",
-       "  overflow-x: scroll !important;\n",
-       "  max-width: 100%;\n",
-       "}\n",
-       "\n",
-       ".estimator-table {\n",
-       "    font-family: monospace;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary::marker {\n",
-       "    font-size: 0.7rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "    margin-top: 0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover td {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table :is(td, th) {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    `table td`is set in notebook with right text-align.\n",
-       "    We need to overwrite it.\n",
-       "*/\n",
-       ".estimator-table table td.param {\n",
-       "    text-align: left;\n",
-       "    position: relative;\n",
-       "    padding: 0;\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".default td, .estimator-table th {\n",
-       "    color: black;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       "td.fitted-att-type {\n",
-       "    white-space: preserve nowrap;\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    Styles for parameter documentation links\n",
-       "    We need styling for visited so jupyter doesn't overwrite it\n",
-       "*/\n",
-       "a.param-doc-link,\n",
-       "a.param-doc-link:link,\n",
-       "a.param-doc-link:visited {\n",
-       "    text-decoration: underline dashed;\n",
-       "    text-underline-offset: .3em;\n",
-       "    color: inherit;\n",
-       "    display: block;\n",
-       "    padding: .5em;\n",
-       "}\n",
-       "\n",
-       "@supports(anchor-name: --doc-link) {\n",
-       "    a.param-doc-link,\n",
-       "    a.param-doc-link:link,\n",
-       "    a.param-doc-link:visited {\n",
-       "    anchor-name: --doc-link;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
-       "a.param-doc-link::before {\n",
-       "    position: absolute;\n",
-       "    content: \"\";\n",
-       "    inset: 0;\n",
-       "}\n",
-       "\n",
-       ".param-doc-description {\n",
-       "    display: none;\n",
-       "    position: absolute;\n",
-       "    z-index: 9999;\n",
-       "    left: 0;\n",
-       "    padding: .5ex;\n",
-       "    margin-left: 1.5em;\n",
-       "    color: var(--sklearn-color-text);\n",
-       "    box-shadow: .3em .3em .4em #999;\n",
-       "    width: max-content;\n",
-       "    text-align: left;\n",
-       "    max-height: 10em;\n",
-       "    overflow-y: auto;\n",
-       "\n",
-       "    /* unfitted */\n",
-       "    background: var(--sklearn-color-unfitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       "@supports(position-area: center right) {\n",
-       "    .param-doc-description {\n",
-       "    position-area: center right;\n",
-       "    position: fixed;\n",
-       "    margin-left: 0;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* Fitted state for parameter tooltips */\n",
-       ".fitted .param-doc-description {\n",
-       "    /* fitted */\n",
-       "    background: var(--sklearn-color-fitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".param-doc-link:hover .param-doc-description {\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".features {\n",
-       "  font-family: monospace;\n",
-       "  cursor: pointer;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: .20em;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  font-size: inherit; /* Needed for jupyter */\n",
-       "}\n",
-       "\n",
-       ".features.fitted {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features summary {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  margin-bottom: 0;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "  padding: .25em;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features.fitted details[open] > summary {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features details > summary .arrow::before {\n",
-       "  content: \"▸\";\n",
-       "  color: grey;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary .arrow::before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       ".features details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features.fitted details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features .features-container {\n",
-       "  max-width: 15em;\n",
-       "  max-height: 10em;\n",
-       "  overflow: auto;\n",
-       "  scrollbar-width: thin;\n",
-       "  padding: .25em 0.1rem;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 0 0 .5em .5em;\n",
-       "}\n",
-       "\n",
-       ".features.fitted .features-container {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features .image-container {\n",
-       "  block-size: 1em;\n",
-       "  inline-size: 1em;\n",
-       "  padding: 0;\n",
-       "  margin: 0%;\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  align-items: center;\n",
-       "}\n",
-       "\n",
-       ".features .copy-paste-icon {\n",
-       "  background-size: 1em 1em;\n",
-       "  width: 1em;\n",
-       "  height: 1em;\n",
-       "  filter: grayscale(100%) opacity(60%);\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  width: 100%;\n",
-       "  margin: 0.01em;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(odd) {\n",
-       "  background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(even) {\n",
-       "  background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:hover {\n",
-       "  background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  table-layout: inherit;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table td {\n",
-       "  text-align: left;\n",
-       "  padding: 0 0.5em;\n",
-       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "  white-space: nowrap;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".total_features {\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  margin-top: 0.5em;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-2\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-random_state;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
-       "            random_state\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-random_state;\">\n",
-       "            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-penalty;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
-       "            penalty\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-penalty;\">\n",
-       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing &#x27;multinomial&#x27; + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-C;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
-       "            C\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-C;\">\n",
-       "            C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('l1_ratio',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-l1_ratio;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
-       "            l1_ratio\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-l1_ratio;\">\n",
-       "            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-dual;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
-       "            dual\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-dual;\">\n",
-       "            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-tol;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
-       "            intercept_scaling\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
-       "            intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a &quot;synthetic&quot; feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
-       "            class_weight\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
-       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight=&#x27;balanced&#x27;*</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-solver;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
-       "            solver\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-solver;\">\n",
-       "            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
-       "            max_iter\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
-       "            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-verbose;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
-       "            verbose\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-verbose;\">\n",
-       "            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
-       "            warm_start\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
-       "            warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
-       "            n_jobs\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
-       "            n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    \n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Fitted attributes</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                    <tbody>\n",
-       "                        <tr>\n",
-       "                        <th>Name</th>\n",
-       "                        <th>Type</th>\n",
-       "                        <th>Value</th>\n",
-       "                        </tr>\n",
-       "                        \n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-classes_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29\">\n",
-       "            classes_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-classes_;\">\n",
-       "            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
-       "           <td>[0,1]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1, 1)</td>\n",
-       "           <td>[[-2.92]]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[7.92]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int\">\n",
-       "            n_features_in_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
-       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>1</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29\">\n",
-       "            n_iter_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
-       "            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
-       "           <td>[17]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "                    </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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-       "    } else {\n",
-       "        const theme = detectTheme(estimatorElement);\n",
-       "        estimatorElement.classList.add(theme);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "forceTheme('sk-container-id-2');</script></body>"
-      ],
-      "text/plain": [
-       "LogisticRegression(random_state=42)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.linear_model import LogisticRegression\n",
     "mylr = LogisticRegression(solver='lbfgs', random_state=42)\n",
-    "mylr.fit(x,y)"
+    "mylr.fit(x,y);"
    ]
   },
   {
@@ -3048,1279 +522,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 39,
    "id": "78ea8db6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>.sk-global {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "}\n",
-       "\n",
-       ".sk-global.light {\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: black;\n",
-       "  --sklearn-color-background: white;\n",
-       "  --sklearn-color-border-box: black;\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "}\n",
-       "\n",
-       ".sk-global.dark {\n",
-       "  --sklearn-color-text-on-default-background: white;\n",
-       "  --sklearn-color-background: #111;\n",
-       "  --sklearn-color-border-box: white;\n",
-       "  --sklearn-color-icon: #878787;\n",
-       "}\n",
-       "\n",
-       ".sk-global {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".sk-global pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip-path: inset(100%);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       ".sk-global div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       ".sk-global label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label label.sk-toggleable__label,\n",
-       ".sk-global div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       ".sk-global div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       ".sk-global div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       ".sk-global div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       ".sk-global a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       ".sk-global a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-top-container.sk-global {\n",
-       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
-       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
-       "   to allow keyboard-only users to navigate the display. */\n",
-       "  overflow-x: scroll !important;\n",
-       "  max-width: 100%;\n",
-       "}\n",
-       "\n",
-       ".estimator-table {\n",
-       "    font-family: monospace;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary::marker {\n",
-       "    font-size: 0.7rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "    margin-top: 0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover td {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table :is(td, th) {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    `table td`is set in notebook with right text-align.\n",
-       "    We need to overwrite it.\n",
-       "*/\n",
-       ".estimator-table table td.param {\n",
-       "    text-align: left;\n",
-       "    position: relative;\n",
-       "    padding: 0;\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".default td, .estimator-table th {\n",
-       "    color: black;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       "td.fitted-att-type {\n",
-       "    white-space: preserve nowrap;\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    Styles for parameter documentation links\n",
-       "    We need styling for visited so jupyter doesn't overwrite it\n",
-       "*/\n",
-       "a.param-doc-link,\n",
-       "a.param-doc-link:link,\n",
-       "a.param-doc-link:visited {\n",
-       "    text-decoration: underline dashed;\n",
-       "    text-underline-offset: .3em;\n",
-       "    color: inherit;\n",
-       "    display: block;\n",
-       "    padding: .5em;\n",
-       "}\n",
-       "\n",
-       "@supports(anchor-name: --doc-link) {\n",
-       "    a.param-doc-link,\n",
-       "    a.param-doc-link:link,\n",
-       "    a.param-doc-link:visited {\n",
-       "    anchor-name: --doc-link;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
-       "a.param-doc-link::before {\n",
-       "    position: absolute;\n",
-       "    content: \"\";\n",
-       "    inset: 0;\n",
-       "}\n",
-       "\n",
-       ".param-doc-description {\n",
-       "    display: none;\n",
-       "    position: absolute;\n",
-       "    z-index: 9999;\n",
-       "    left: 0;\n",
-       "    padding: .5ex;\n",
-       "    margin-left: 1.5em;\n",
-       "    color: var(--sklearn-color-text);\n",
-       "    box-shadow: .3em .3em .4em #999;\n",
-       "    width: max-content;\n",
-       "    text-align: left;\n",
-       "    max-height: 10em;\n",
-       "    overflow-y: auto;\n",
-       "\n",
-       "    /* unfitted */\n",
-       "    background: var(--sklearn-color-unfitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       "@supports(position-area: center right) {\n",
-       "    .param-doc-description {\n",
-       "    position-area: center right;\n",
-       "    position: fixed;\n",
-       "    margin-left: 0;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* Fitted state for parameter tooltips */\n",
-       ".fitted .param-doc-description {\n",
-       "    /* fitted */\n",
-       "    background: var(--sklearn-color-fitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".param-doc-link:hover .param-doc-description {\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".features {\n",
-       "  font-family: monospace;\n",
-       "  cursor: pointer;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: .20em;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  font-size: inherit; /* Needed for jupyter */\n",
-       "}\n",
-       "\n",
-       ".features.fitted {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features summary {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  margin-bottom: 0;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "  padding: .25em;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features.fitted details[open] > summary {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features details > summary .arrow::before {\n",
-       "  content: \"▸\";\n",
-       "  color: grey;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary .arrow::before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       ".features details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features.fitted details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features .features-container {\n",
-       "  max-width: 15em;\n",
-       "  max-height: 10em;\n",
-       "  overflow: auto;\n",
-       "  scrollbar-width: thin;\n",
-       "  padding: .25em 0.1rem;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 0 0 .5em .5em;\n",
-       "}\n",
-       "\n",
-       ".features.fitted .features-container {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features .image-container {\n",
-       "  block-size: 1em;\n",
-       "  inline-size: 1em;\n",
-       "  padding: 0;\n",
-       "  margin: 0%;\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  align-items: center;\n",
-       "}\n",
-       "\n",
-       ".features .copy-paste-icon {\n",
-       "  background-size: 1em 1em;\n",
-       "  width: 1em;\n",
-       "  height: 1em;\n",
-       "  filter: grayscale(100%) opacity(60%);\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  width: 100%;\n",
-       "  margin: 0.01em;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(odd) {\n",
-       "  background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(even) {\n",
-       "  background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:hover {\n",
-       "  background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  table-layout: inherit;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table td {\n",
-       "  text-align: left;\n",
-       "  padding: 0 0.5em;\n",
-       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "  white-space: nowrap;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".total_features {\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  margin-top: 0.5em;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-3\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-random_state;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
-       "            random_state\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-random_state;\">\n",
-       "            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">42</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-penalty;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
-       "            penalty\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-penalty;\">\n",
-       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing &#x27;multinomial&#x27; + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-C;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
-       "            C\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-C;\">\n",
-       "            C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('l1_ratio',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-l1_ratio;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
-       "            l1_ratio\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-l1_ratio;\">\n",
-       "            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-dual;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
-       "            dual\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-dual;\">\n",
-       "            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-tol;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
-       "            intercept_scaling\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
-       "            intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a &quot;synthetic&quot; feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
-       "            class_weight\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
-       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight=&#x27;balanced&#x27;*</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-solver;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
-       "            solver\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-solver;\">\n",
-       "            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
-       "            max_iter\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
-       "            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-verbose;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
-       "            verbose\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-verbose;\">\n",
-       "            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
-       "            warm_start\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
-       "            warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
-       "            n_jobs\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
-       "            n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    \n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Fitted attributes</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                    <tbody>\n",
-       "                        <tr>\n",
-       "                        <th>Name</th>\n",
-       "                        <th>Type</th>\n",
-       "                        <th>Value</th>\n",
-       "                        </tr>\n",
-       "                        \n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-classes_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29\">\n",
-       "            classes_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-classes_;\">\n",
-       "            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
-       "           <td>[0,1]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1, 1)</td>\n",
-       "           <td>[[-3.46]]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[18.55]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int\">\n",
-       "            n_features_in_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
-       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>1</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29\">\n",
-       "            n_iter_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
-       "            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
-       "           <td>[17]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "                    </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
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-       "        ];\n",
-       "\n",
-       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
-       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
-       "\n",
-       "        if (luma > 180) {\n",
-       "            // If the text is very bright we have a dark theme\n",
-       "            return 'dark';\n",
-       "        }\n",
-       "        if (luma < 75) {\n",
-       "            // If the text is very dark we have a light theme\n",
-       "            return 'light';\n",
-       "        }\n",
-       "        // Otherwise fall back to the next heuristic.\n",
-       "    }\n",
-       "\n",
-       "    // Fallback to system preference\n",
-       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
-       "}\n",
-       "\n",
-       "\n",
-       "function forceTheme(elementId) {\n",
-       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
-       "    if (estimatorElement === null) {\n",
-       "        console.error(`Element with id ${elementId} not found.`);\n",
-       "    } else {\n",
-       "        const theme = detectTheme(estimatorElement);\n",
-       "        estimatorElement.classList.add(theme);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "forceTheme('sk-container-id-3');</script></body>"
-      ],
-      "text/plain": [
-       "LogisticRegression(random_state=42)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "x = iris.data[:, 0:1]\n",
     "y = (iris.target == 0).astype(int)\n",
     "from sklearn.linear_model import LogisticRegression\n",
     "mylr = LogisticRegression(solver='lbfgs', random_state=42)\n",
-    "mylr.fit(x,y)"
+    "mylr.fit(x,y);\n"
    ]
   },
   {
@@ -4433,1273 +644,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 40,
    "id": "ce1caddc",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>.sk-global {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "}\n",
-       "\n",
-       ".sk-global.light {\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: black;\n",
-       "  --sklearn-color-background: white;\n",
-       "  --sklearn-color-border-box: black;\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "}\n",
-       "\n",
-       ".sk-global.dark {\n",
-       "  --sklearn-color-text-on-default-background: white;\n",
-       "  --sklearn-color-background: #111;\n",
-       "  --sklearn-color-border-box: white;\n",
-       "  --sklearn-color-icon: #878787;\n",
-       "}\n",
-       "\n",
-       ".sk-global {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".sk-global pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip-path: inset(100%);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       ".sk-global div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       ".sk-global label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label label.sk-toggleable__label,\n",
-       ".sk-global div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       ".sk-global div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       ".sk-global div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       ".sk-global div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       ".sk-global a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       ".sk-global a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-top-container.sk-global {\n",
-       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
-       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
-       "   to allow keyboard-only users to navigate the display. */\n",
-       "  overflow-x: scroll !important;\n",
-       "  max-width: 100%;\n",
-       "}\n",
-       "\n",
-       ".estimator-table {\n",
-       "    font-family: monospace;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary::marker {\n",
-       "    font-size: 0.7rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "    margin-top: 0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover td {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table :is(td, th) {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    `table td`is set in notebook with right text-align.\n",
-       "    We need to overwrite it.\n",
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-       ".estimator-table table td.param {\n",
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-       "@supports(anchor-name: --doc-link) {\n",
-       "    a.param-doc-link,\n",
-       "    a.param-doc-link:link,\n",
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-       "\n",
-       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
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-       "    width: max-content;\n",
-       "    text-align: left;\n",
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-       "    overflow-y: auto;\n",
-       "\n",
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-       "\n",
-       "@supports(position-area: center right) {\n",
-       "    .param-doc-description {\n",
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-       "    margin-left: 0;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* Fitted state for parameter tooltips */\n",
-       ".fitted .param-doc-description {\n",
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-       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".param-doc-link:hover .param-doc-description {\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".features {\n",
-       "  font-family: monospace;\n",
-       "  cursor: pointer;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: .20em;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  font-size: inherit; /* Needed for jupyter */\n",
-       "}\n",
-       "\n",
-       ".features.fitted {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features summary {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  margin-bottom: 0;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "  padding: .25em;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features.fitted details[open] > summary {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features details > summary .arrow::before {\n",
-       "  content: \"▸\";\n",
-       "  color: grey;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary .arrow::before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       ".features details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features.fitted details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features .features-container {\n",
-       "  max-width: 15em;\n",
-       "  max-height: 10em;\n",
-       "  overflow: auto;\n",
-       "  scrollbar-width: thin;\n",
-       "  padding: .25em 0.1rem;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 0 0 .5em .5em;\n",
-       "}\n",
-       "\n",
-       ".features.fitted .features-container {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features .image-container {\n",
-       "  block-size: 1em;\n",
-       "  inline-size: 1em;\n",
-       "  padding: 0;\n",
-       "  margin: 0%;\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  align-items: center;\n",
-       "}\n",
-       "\n",
-       ".features .copy-paste-icon {\n",
-       "  background-size: 1em 1em;\n",
-       "  width: 1em;\n",
-       "  height: 1em;\n",
-       "  filter: grayscale(100%) opacity(60%);\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  width: 100%;\n",
-       "  margin: 0.01em;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(odd) {\n",
-       "  background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(even) {\n",
-       "  background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:hover {\n",
-       "  background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  table-layout: inherit;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table td {\n",
-       "  text-align: left;\n",
-       "  padding: 0 0.5em;\n",
-       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "  white-space: nowrap;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".total_features {\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  margin-top: 0.5em;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-4\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=100, random_state=22, solver=&#x27;newton-cg&#x27;, tol=1e-05)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-C;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
-       "            C\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-C;\">\n",
-       "            C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-tol;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1e-05</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-random_state;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
-       "            random_state\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-random_state;\">\n",
-       "            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">22</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-solver;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
-       "            solver\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-solver;\">\n",
-       "            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;newton-cg&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-penalty;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
-       "            penalty\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-penalty;\">\n",
-       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing &#x27;multinomial&#x27; + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('l1_ratio',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-l1_ratio;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
-       "            l1_ratio\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-l1_ratio;\">\n",
-       "            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-dual;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
-       "            dual\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-dual;\">\n",
-       "            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
-       "            intercept_scaling\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
-       "            intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a &quot;synthetic&quot; feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
-       "            class_weight\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
-       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight=&#x27;balanced&#x27;*</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
-       "            max_iter\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
-       "            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-verbose;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
-       "            verbose\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-verbose;\">\n",
-       "            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
-       "            warm_start\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
-       "            warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
-       "            n_jobs\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
-       "            n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    \n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Fitted attributes</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                    <tbody>\n",
-       "                        <tr>\n",
-       "                        <th>Name</th>\n",
-       "                        <th>Type</th>\n",
-       "                        <th>Value</th>\n",
-       "                        </tr>\n",
-       "                        \n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-classes_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29\">\n",
-       "            classes_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-classes_;\">\n",
-       "            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
-       "           <td>[0,1]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1, 2)</td>\n",
-       "           <td>[[ 2.6 ,-0.74]]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
-       "           <td>[-14.16]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int\">\n",
-       "            n_features_in_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
-       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>2</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29\">\n",
-       "            n_iter_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
-       "            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
-       "           <td>[11]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "                    </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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-       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
-       "\n",
-       "    if (themeKindAttr && themeNameAttr) {\n",
-       "        const themeKind = themeKindAttr.toLowerCase();\n",
-       "        const themeName = themeNameAttr.toLowerCase();\n",
-       "\n",
-       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
-       "            return \"dark\";\n",
-       "        }\n",
-       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
-       "            return \"light\";\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    // Check Jupyter theme\n",
-       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
-       "        return 'dark';\n",
-       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
-       "        return 'light';\n",
-       "    }\n",
-       "\n",
-       "    // Guess based on a parent element's color\n",
-       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
-       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
-       "    if (match) {\n",
-       "        const [r, g, b] = [\n",
-       "            parseFloat(match[1]),\n",
-       "            parseFloat(match[2]),\n",
-       "            parseFloat(match[3])\n",
-       "        ];\n",
-       "\n",
-       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
-       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
-       "\n",
-       "        if (luma > 180) {\n",
-       "            // If the text is very bright we have a dark theme\n",
-       "            return 'dark';\n",
-       "        }\n",
-       "        if (luma < 75) {\n",
-       "            // If the text is very dark we have a light theme\n",
-       "            return 'light';\n",
-       "        }\n",
-       "        // Otherwise fall back to the next heuristic.\n",
-       "    }\n",
-       "\n",
-       "    // Fallback to system preference\n",
-       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
-       "}\n",
-       "\n",
-       "\n",
-       "function forceTheme(elementId) {\n",
-       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
-       "    if (estimatorElement === null) {\n",
-       "        console.error(`Element with id ${elementId} not found.`);\n",
-       "    } else {\n",
-       "        const theme = detectTheme(estimatorElement);\n",
-       "        estimatorElement.classList.add(theme);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "forceTheme('sk-container-id-4');</script></body>"
-      ],
-      "text/plain": [
-       "LogisticRegression(C=100, random_state=22, solver='newton-cg', tol=1e-05)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "X = iris.data[:,0:2]\n",
     "y = (iris.target==2).astype(int)\n",
@@ -5710,7 +658,7 @@
     "    max_iter=100,\n",
     "    solver='newton-cg'\n",
     ")\n",
-    "mylrvir.fit(X,y)"
+    "mylrvir.fit(X,y);"
    ]
   },
   {
@@ -5786,23 +734,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 43,
    "id": "0b4ce6e6",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x2a3c5a946e0>"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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LSJGC+GtF6ecmkqWmXbTROMnCocmGiId6qBDMCYlIZy3Kwpsdm+VzrdVdVgkJnNI+OnT/0TFUs2NK4WyUi3Ux0hkeHmZuQHJ1v1m6htoks0G1L6UrvfVQTl7sXCvFWsiK/OeUnkzjpcm/EgRjotyYaKz0HrRREgF9fgogE+FMT08zgia3iulaW0rkcTnYLBPBZozJ3KxrQxM1uZBNNzI9c6dPn2bjoSQCcjWbEjimtbORagRc8bqYpQrms/pm6Bpqk8w6obTCeLHal3LEUKqcTIHy5bRTXuhcG+Uuo89LwdnBwUHs3r2bua9ef/31Rd9jpWNa7jWgIj/ayJdvulXompoij0RIppWzkZPN7e4uW40m3XqBrBbayE1LFi8t2szMNVp40MROblXTtUY/NyKup6rqvL44ppVDf6ONiMd0pd8OpHNrPl23Ue0L3TyVUE6+mYF/eijIeiE32f33388eXrImNoPUv9WtQt+LdbIxq9DpmKWq0DeT9UDYLJPOZiI8E6UxGVqoUS0ZbQT63ukeIBcbxXOIhDZCAkctksxyFKZLSecnfuIn8KEPfYj9vNVgk8w6t0Ve6sGrlHLyzUphNq0uejAp6820CiqRoWYdUyVAq9tSva3SKnRyT9J3Ya1C32yT52ac2NdT5n+1WCzwT0kCpXE9k3RKJXDIGqd7YTmKFGshGSvKkQ5l1N2qFvetOerbSBqGjqPXvfbaa2tSTi4910a4y+jzksVFltdCVtdiJLOaCWk9rAir9AmlR5uZa2ZBYGlR6GbCZrKqNloRutIV/9b7wEwmMSVw6FxmBiNtq81gVFV12e7v0vHReCiB4VaETTI3URqGblyq+yBQDQkFrtcCet/SIsf1smRKkxLuvfde9vCVO0clV9sbsUourUI3CwLN9sSkEUfKwrQKJWuIkgqsTbvezJbMZhrLcrTLFoM1maScBA6h1OJdrgyStooCUfP9idhuRdgksw61LytRTjaJicz2tWIjUpgpiE5ZO3TDL5aUsB4ks9Erd2t7YrJSiWiIXCnBgeJOREimlbNRwePNOLHfau6ylaCcBI6pRkAySLRQXK4EjrpK4iOSqWSm5kbCJpkK1L4s1RZ5MeVkqn958cUXKyKBsZ7ZZWaOP1VaL7dmx3zdreDuWUlRKH02yqAji850rZnBY2tR6Hq7kDbLxH4ru8tWc83NDEaSd1pMAqe6RI2AXrecmMxCi1LbknkTYaVtkRdTTjYnc/rbWovF1qtOxpS0oYforrvums3UWuocixHJSglmM0r9kxVHKbK0lUrZm+nSpqqwSTorFXhcCpvpetzOlsxKJXDoWba6WV0uF7N0KMFgpc8oLV7IW2L26bnVYFsyq6h9oZuHBB8ffvjhNSsnm6+/2UWUC7nLaIVG7jEaL417uYWMy7FkbuVJdTlS9mZRKAmgmsrSpmuNfq61KHQzucs2myVjPqs3Y0xkqVglcOLxONuIMIh0yCtQauksFtuj+4hgk8ybKLhvpjwu5wFfjnJyJetbKklWNG4qXqTVGY17JRNapUlms0ymyx1Lqaowfe/m6pZIx7wfiJBK4zmrsWQ3y3XZbJbMRsvcLEU6tbW1bCMvBmVjEswkAnKdmwXC5e4FIhlzEXMrwrZkVlH7QitSk2wWe7DIzUQ3EAUHF1JOJmw2kqHPZVbJU+8XSq1eKd5slsxKV7d0P5laW2TlkKuNCKm0KHQp3/1ms2Q2y1gI5nOwGUimXAozfb+lEjhm5pqpMm5K4FAaPbnhiGDW+lk++9nP4l/+5V9Y/ND0TPzBH/wBy2xdDP/8z/+M3/zN32TJLtu2bWOvec973rPs97VJZhW1L+bDv9iDtVzl5M2iOWaCbnC6mehz07hNt95qxnK7WjKVAK1UibxNAi9VlqaFiVkMaFo65eoyNhPpbjZ32WYmGcGyeLAWCNO9QAsQWpz++q//OrN26HN88pOfxNve9jb2XK7Gqnn55Zfxi7/4i7jnnnvY/UXne+c73zkrtVQOR44cYSoDRFDve9/78Hd/93f4wAc+gFOnTjF19eXAJplV1L6YN265dMSVKidvJkuGqoopwE+TG51ntQRzO1sy6zWGcsrSJukQ6dP1LC0Kpe9mM1kPm81dtlEkk08n4fYHVxQnEpawUOleMCVwaKFKE/unPvUp5mr7yEc+wpIJyCKhSX8l+MEPfjDn/9/4xjcYsZ08eRKPPvpo2dd86Utfwrvf/W5GdoTf+Z3fwXPPPYevfOUrePLJJ5f1vjbJlAHdCBSgW6j2xbxJrBP6apSTNwPJ0GsoGEmxAsp6I/Kk7Ki1wLZk1nbtzGJAqssoTZGliYbSyGkiMl1uZOXczKLQzWrJrFVQUtWK0lDgEA6Po66uhe2ffOpz4Pou4mRVM3o/+Q0Em1rQ8dhjaKftkUfgWSD7Uis+mytNYSbXGmUwfv3rX2fXmVxqFN9ZK8jbQlgsW5REbj/2sY/N2feud70L//qv/7rs97FJZoG2yIvVvpTKdls1vMgKWIly8s12l1EsgLLe6HU0bjLDaaW03hpom0VW5lZAuRRZIhdyq5HyAFnOREilSQQbrXO1GS2ZpUhPh84IhHD94nE0d+/G0PnXsfWux+EQHRgfuoqGn3s/vv7WfweZc+Bn3vo+hB58BNy1c3j1yGkMPTsM3umA6HIhcvUqEsPDePojH8GBn/95PPZ7vzfv/dQSr8hKUNoyg67xclq1L+f6/Mqv/ApzvS3m9iLvhikqaoL+T/uXC5tkVlH7YsZl6DVrVU62KjGvBSslK7P3C62SqOOkucJajdT/ete2bKYJbLNkKxGR0PdGExCRDi10SpWlTdJZSlm6EthMrjsryWSyKfi8hiTLpSM/gDQxjGxNA4KCgDsefT/bX/XJ/4Z/ddfg2mQaT1RXoe0970fmwXvwF/VbIP3RX7Jj/vJzT6LKKcDx/rsxlVLBNXqhTWURI8tixw6MvvIKdv/UT+HhT3+67JjUVZIMZZdVutqfYjPkGn/11Vex3njTk0xp3xfzQVmusKXZAXItysnmuTaSZOgYqkqmFfCePXuY8oD1PBut5rwcvFktmYVg3q9kNZdTlibSMcUdTckTIp7l6mytBDerJoUQCU+gpq4JQ9fOomPbneA5HuMjvWj96m/gHy4+ATWVwo///MchBkMonHoNV19+DSOHziLgdWGy5TeZm+v7TdvB/f3ToCf4NTrpoSNoaajCWGsrpMfuR3dvP/TxaXihYvzbx2B+0uo7diHQ0AyJXOS//Mt45Ld/e8FrqxaD/iu99ma2WaXwS7/0S/j+97+Pw4cPL6mXSAtQctGWgv6/knbqb2qSsbZFXmnTJXKP0YO9FuXkje4DY7YUoAyWBx54oOzNW8mWAav5WyWOf7Og3HWxKgrTSrg0RdYqebKWBI/1tmQUVYEoGM9W79lXoZNbzukBVAnb7nyI7Rc+9E78S1MPLick/NjWnWj/mQ9DSidwWhIw+snPQZBl/OnvfBntDdXo+08/hfhIEtWKgmxCxrlEGvHr/eAkINHdDqm5AfvfOAmeA0am46ifjhvj8HnQUCxdCe5tRSKXBSQFciKF4YtX8Ojv/i7u/uhHF/0s2iqlo8hdVgmSoe/oox/9KL7zne/gpZdeYklJS4HmiBdeeIG51kxQ4J/2LxdvWpIprX0p7d2wnNdR4JVeS1/SUjnmm6mjJaVEEjGSq2UxYtwod9lKJyXbkpl/PZa6hqVFoaXK0mTlmJInpKtV2p56NUWhqw38U3Bd4AXMzIyhvr6V7RvsPYvkPzyJsX0PwT0xhLf8199g+zM//A4mjhzDqXNj6GyoRXpLF9o++GP414YtiH/nMLyKgqd++Brwp3+Jjp42nGhrgXBgD5rPX4GSy0ONxSD80VdAIfNCKIBOB4dAOomwscZEaGAEUiwBCUBYAoLVAVzeu5sK3tDTN4jJTM448PwYsJPOIiLY0IbHfud3seODH1z6s6o3VxyTXGSUqfbd736X3Q9mXIVifeZC42d+5meYYgWlLBN++Zd/GY899hg+//nP473vfS/+4R/+ASdOnMCf//mfL/t933Qks9q+L6XKyfQ6s0K3Elhvd5mZkUIbkSJlLC1lZWw2d5ltyczHaq5tqbI0gZ4DsyiU4orkpy8tCqUJaDmr76UC/0PXz6O6vhWJyCREpxvNrT1sf+Gdu/D9h96BiZk4fvp9P47aJ96PXGQKQ2cu4tKf/AscuTzGPvNFdO7eidff+26oJwbgSaQwPRPB9MVr6Hz2h4h4Q4jevRcCz2Pr6ycRcgLD/aNo7R813tztRGexTE3oaUd+fBqqKEIq5NGbN/ZnG2oR3rcLAUlG/pVjoCubjKXgmY7AHYlBisbQ6ADcPDC1tw2562FA0XD/5/9fdL3lrcu69uoqxTHJEq2EOOZXv/pV9vPxxx+fs5+y1n72Z3+W/U5yN6VESMlAZgo11dVQMSZlli23RuZNRzKr7ftSWkNCLgiaqI8fP14RYlhvkqG0anKP0Wpood4v5c6zGUlms1gym4XwKuGiImuWKs/N6nNyo5quNZK+oefFTCIwlaWt75nNGq22aX/v+deRvnoOasc2iBND2P8jP2cc86e/j77+IbwS1XFfRweU++5H009/CE9Vt2Hwq/8EMV/A//e334NP5FD/+EN4reBEsDoERzaH6XQBnrNnIR4/C87hQHjvTuybGIMnlUJ/AXAWEmg6ehqK1wOvCERloL6hGme3bYF7OoLmdBpDEzPGYPtHkNnaiVx9LbZfvQYy5mcUIF9bjfrTl+AJR5Eil5gAuLZ0YqyhFu5wDLqqYUoF2p1A/uw40BYA/52fQFfP8ghmLSRDz24lWoEs5/khN5oVP/ZjP8a21eJNQzJL1b4spZxMJGMqJ1eyC2Ulz2WSg/nA0wqV0pNpkqAVyXLdIJvRkrGx8DWuJKj+xqosPTIzgFQyhcsX3oDbX4famloIggrf534dJx55N9SxUez7wIcher1IXTuPiW/+Dc4f64PP5UTks3+M9re8Bc8GGiEf/iGCsozLpy/i8nefQdfv/jZObd8B6b4DqB6fgrd3AHWCjsHnX4XhOAOc9dXoSMWQp7WT3wchnUFgaAwSp2KsADg4YPLgXhSqgvBnc5h4/RR7XXo6Bp9vCjy14pBkdLgAVQeGO7vhisVR1TeEop2DFr8TEwE/cg21qB8chXNoDCEBGO0bQes1o0GZtyaE1mwCWQ3gt9dB/t5/gLezHcgs/9pqa4jJ3Kq6ZW8KkjHdY+QKoKA3mXurUU62SqxUKu240jEZAp2LiikpnZWELam2YqXilpsxhXkzENZmGMNGpQ2boowNZ34SL0xvQ9/zWbz/jrvgfscTGMsmcDmtoP8Tf8CskJn/+1dobahG9Cf+IwZHUqhOZyGlszgbiSN+/TpUiUNqSwcyLU2469RpuGUJg1kV9WcusffS3C5mJSg6UNdajz6XB65YAh6HA31FlxbkLCbv2w8pFED70RNs8pJ1wBlPwjs5Df/YFNwcUOsAci1NCPt9qDt/hcVYholMnAA3OoFcVxuie3bg4MnTcBYKGMtIaDp+ZvZzd7uM8/p62jEuKfCPjMPjc6M3mkCrExCe+o+QO/1wqisrgFXXYMncqgrMtz3JlLrH6CetCCqhnEygm6WSlkyl3GUEihtRNe9KVAes59mMlsxmmuA3A9Z6PaJDRxFo2g2H60bq/ZWnfhuv/vJX0LqjDp2PP4r2938I/3xpKxKfOwSnouDZZ14F/7kvYcu2Drze0gLnjh7UXbwGSVGhxWJIfOGrqC7GOFpdDtRNT2JCBjhdZ9aDZypCVZAYLgD1bhFn7rsLQiaLqmQaI9cGjEGMzUDoaEGmvRmNyRTqnEBMARJtzcyK8U3OYJruUyKEoAuHu9rgmYnCOxVGXjGeyfjoDOqUSWSaG9DiFFE/NY5JCeClPGou9wGX++AMujAuAzVBD84c2Ad3NIYqUcTA6YvGOPqG4HU6Mf7ofWjovY79PuCJWg6/22EE4V1YOcnwqwz82yRzC7RFJmtkORP5Risnm+cy06jXAroZCXQusrxWKzWyEe4yGitlOC23r8pmiYNsJizXkuk79CVIqRl0PvJz8NUY1eJn/u6jeOmjfw1OBJofqkP7W+4B79Tw2v/6AaAByelRXH7l79D593+HGakZsQcOgpck9Bw7g1on0Ns7jNZesg8oPSmA7nwKNL2766uRm4mBU1RIqozegnGI3NKI8T3bja6Sz79Cb4GZvALfyARESYYzHEWzA3DyQKyhCblMDoHhcSQpAE/tyR1AlOcR396NVGcrOk+cRb2gYzglof0FVtkCzePGNlGFpAOOqiDy4Rh8E9Pgu1pmraFcRysi3W2QAj64nj7ExhxO5tB09BTC+3ZDOH+FWTzkhhtuaEaspgptLx9FaFcX3i/MABSAL15zh74xlky2QinMNwvi7V77UqqcvJTlsVLl5M1iyZRaXoQ777xzTVpW6+0uo/gWabxR6izJjpt9VWijrKeFHkTbkrkBRTVm71KSOf23/wNDzx9Cx1sfQfejP4Wqrodw9Ms/gaO/+TR0DeD4P0btAT9aHm7D2OFeZgqoOR1jL81Azv8QU68qcNVwqK31QtQy4AVg8BrgwQQ8wxPIVwURJFeUBrTWBXG6vR2uSBxVmoqBBIXLiTliiOzdiUJNCG19AxCEJJIqoLgcqD93mVkb5L6qE4FgyIfXmupRf/I8+IKECYC5o9KTYSDox8QDB7FrfAJVkxPM8nEOjqJ10IikNAZdyGYLaK724VRXF6ov90HbvRW9Jy8Y4yjEkG2sw8zBO1Bz8QoL5NM4HOEIHI21aHv52Ow4nA21eH3vHrS8fAxcvoBxAD4eLNjvH5vEa3/0KXz46jnwzw5CKUkldq6QZLRVxGTonq9UCvPNgng71r6YE2RpceViJFOqnNzT08O2pVaImyUmQ5+JLC/qvkiWF+Wwr3UytiYQVIpkzBojqkKnJAoiFBq/WZ1ONRuU3WRWp1M9D63gVlokezuD9LZ++MUP4+r/+Q5q7w7i5Nvvw5a3/TgufeurOPuFN9gx/f/yTbzs+Cba3u6FnJZQf08Q8WtJEC9xYh7nvnSFHSe4eTQ8yMNZxSHVTywEFKI64moBXjcQngKCVcDVO+6GyovwRqOYvEB2BVv+Q6zJMeUvzulkgfWMCqSam+AfnWCxkEhJjON0Qz1yVOGoqPBGYvDwwGA0g+YjJ6G4nFC2d2PX8ADiCqCpCjyRONyvnwLf2YzBAhCgQPxd+5ARRZZFhueLcijRNGoy1zBz117Uz0RZqnJWBWZEF7KtTeh65iVEi+OoF4FTD9wFMZdHtr4W3pkIChopKefR+exhNg6+ux1d4yPMGkpPh3H8S5/GxV/4GTg+9F/YOTTvjcnehZV1NlVVdVVF25VKYb5ZEG83aZjFlJPLkcxalJNvtiVjJiZQ1hhlj5FlUAk3XqmCcqVIhsiDrES63lQtTNYi/V7aS8PMZiLCoc3so0EWDllmlbrea8XNILzsd/49ZMGJF04mMPgnr4NmwanDcUROPov+7z3PSKLhfic4UUNuRoHoEjDyTLb46iQ8LRyqdvLgnUDtAQGxSyrcjW5kx/OYPmJcV2c1h4b7BIhjHkRHDcskGQccyQyqRydYIL5WBPwCIFVXI0dkEYkx1xflYFHgPhpPIb57GzSnE9vOXkRtPsNIourYaVBljsbz6PGLUGUFNSEfJiQNQi4P2eWcrVnh3C6MH9jD0op9rx5j+1Iq4LtwFfk929H8winUuAT4oSLnC+LCli5GViiOg6wQvr4Gmihi7NH7sP3sRYjpNLNcTNcaod0NCDoQ93kgaYAznQGnqRiXAPKDhP/vJxnBEMSCYTmqPu+qLRm12LBspbBjMrdI7Us5UliLcjKRViXiKObYVkoMVK1NdTtUxU0Zc2ZAsRLxFPNcaz2PORYzlZosFFNpoNznNbOZaCNNpdLqdFIbpuzAY8eOzalOX42P+5ZCPgW4A8gN9eM735lB8nQanIdHzQEvnLV56BKHycPGfTgdkRDo4aEpPLScjqZHaDIHlJwH+ck0pl+/cf/X3SNAV11wBCSIbiB+VUNwG4/JVxQoGYNgyIppbOcxHvCx4kQK3kcUYxKfnIrB4XRiZv9uNMsyOvt6cZ3mYSmDpmNnoFG7guZaxDIZtDmBkwf2wzs8jlRPB/jXThiDKGSgVgUx+cBBtIajzNKg6nudA7M2ur//Aogm/TxQ7wAOP3ofak5fZOOIFlTI5NKSFdSducxqZ+p4Dm2XLjO3mDYygcYRcsABQk0QAgd4nQIu33cQwf5hZJvq4DpzEQX6YBNhCKKIoXc/hu3DYyw77Z1VwP/4d++YvV6CJBVJxrMmkhFW4S4rVWG+FSHeTm2RF1thlrq3KqGcXMnA/0pcb3QcxTHGx8exb9++WVHESo6rkr1gyI1HhEhESJl6K7nOpdXptNHnJikfcq+R242sI7N75EKFgrc6/P97KyYSbjwV85FUFiAAek6DJumIndGQD+tw13EIbhfg8LmQGswi0298b+lhsm5EpPqy8HdyCO0UICU0iAEgfFSFJsdm36fl7SKUtI66gyIyFzmkojKq64De8xpacAI6xyG5vRt3Dw+wSZyjZJqChJqLveB62hnBuDhA3taD4eZ6aA4H+KJLi6yQ4OXrLIXZMxVGp4eHrGiIOz2INjfMWhdULlkjAomeDkhBP+JbOlB1fRgiB0Zu7T94mR1Hfz84OQpF0TCWlyAQ0Zy/wsZBhZV096vbuzHZ3MDIsfO1N6AqQFRR0fLaG5h48C6WoUYV/ES7k4IL43fvQ/sPX4FaFcC/qwEanEC4ZGIXJcOSkQMlJLNCd5m2ipgMLaxofrPdZbeANIzpLjMFIteqnHwzUphpRUMWAcHs/bLacy233ma1oGtDri8y9e+6665FGyMtd0y0laoNl3aPNF1rphwKbeQ+vNVxeYDHv74Qg6wYhMAHBPgerYYrJUHJc4xkaKvaDYwfykBXgUA3D187D9EPFsxXMkCcylE4DU2Pigi/oaB6rwDBXYNceAbeRgHjz9+wying37WNvn+gpceB/qgHvKxA83lxLX/jmPEHDqJQHcK2s0atC7MKpmfgFXiWJuz1ulGr5MHxwMmtHag/dXHWpVUlEFFx0Bwixh67H63Do6gbHmVJBerF3tlizPrmWngiEWiCgIlQLXyT08jXhDA8MMxqWThORWTPdiZuufVKH9tHd61wbQBcYx2aj5yCKApo5jXoHg+O3Hs3Op5/lVlDxdw4+Lc1wz0TwfX/8B78yIGdaPjqH7H9M4EbJCMUjA8uU7BqAy2ZTDFj1M4uuwWkYUz31muvvcb6qm8W5eTlnovcRUSOJGtDPUQWyrffDO4ykwzpOyILZq0EY8I6HqtrjeJqlHpuCj9S3McknFvOtabrOPl7P4orJwpoqQHCVX5k+tLwHAwi9VSYSZ8QvK1A7X4HCjENrhoR+RkF6VEN3hYeo08rLEW5ancI7sY0BDeH8ecU6AoQOaVCDMQQ2iYgcVlDw4MiI4PctAJn1o1BM0ACGUpLDVJdbXDpOpocwIwMOHxeZg20vH6KxS+oCJJSf3u39UCcCrNXZrN5JvOSVTlGMPFt3axu5cGjx5hlkkllUXfOSELgt3UhpwMhEUju3o1UKsMq8PnjZ6CxR12FLzeNoXc/CjFbYO6ziETEpiNfV43upw5B03Vm9TSKwOUH74ObanI0DYWCyqr7W/0COp97Ban2ZsitTbjz5Gk4eGCgdxD5h+/B4Sd/Hz/9zX8yLj+lS3tuEIpQjMnIfteaSIZfYZ0MkQy9phJK2TcL4q1c+7Jc1wgdT33SaZIi3bGleihspsA/7aesNxKuI1G6pTSMbra7jNxjFOeicRKJV6pD41LfdWn3SILZmri0kdet4lrT5RwO/fx9OPvtwdl9XDoL34Mh6AUNvkeqgMEspIQET5OAkafk2eNqD/DwNPLIzegQSBE/B+RnCtA1HYkrKrNuqnYKcIQAOVWF8HGDEPJhxYjnyDyknIqOLWAuprzmREbWZgPrpNtLRJMN+pDuaIHqcqLmynVUicCYBAhvnGU9WdKtTdjNKcB0mGWdEap6BxghjEqsHhOBxlpc2bkVssuFrkOvIaMBGTrHmUvIPHYfqq72o64uBBdpqUHA1QfuQecPDrNzUZoxfXvVO7oxRu2R9+1G/fkrRgYXB1S9bCQM5Guq0MWpCGRSmMgbcZXAyATUcAwqB4wWAM+7HsH3/vVrUN1uuCTjWupe/2w9DIEvusskXwnJYGMsGd869P/ZSIi3Wu3LYm2RF1NONglhJc12bnYKM02MNHaaMBfq/bLcc623u6xU6Xn37t1MLrwS6dSlY1rJuShrjSxW2sz7wEyVJsKm863GtbYRtTr89Hm898wg9twvoH9Mx3BaQ7Teg8yrRk92gq/dieBWAQ4vx+Iu8SsKnNVu5CMSIqeLNWJOI9aiytUozEyzuhglDah5IDWooRAOw9PEIbhFgOAFktdVZEbo82lIRoCWDiAVVpDvqUZ8xxaEpsPY2dvHKvjV0Sm0jhrNrLy7tkC8fp3FOK7X1MExGUZiSydGSM2YXS4dsR09iG3vRmBskpEDPY0psjR2bkPXDw+zjLJmXoWoA5fuuwetRZKgyn46vrnKhcDQKMYevRfObB6dJ86h2gmMXR1A61VDKYDze9ElZ5m7LLWtC76+IZYyLWlyMXNNYuQX3bkFQVVF4uWjuNsPnP3VDzOCIbiKbjHVN/dZM0mm4LtBLE7dte4kky52xbRJZpO0RV5MOZncNtR8Z7W56hsdkyGLgNxjpDhAMY3ljvlmSMIQCZpp4KVxrs2iN1bqWiPyM11rpT1VSl1rlGRQKQtsVZBScMjAtnEVHhG4kAOUmAzfQ4aVpsk65N4sMiMl2WJ3CVAKIty1ClzVQOyShtr9AqZfp5iMkWVF9TCNDwuQ04CcBGjazE3qCG4FJl403GjBrSKCvANuMYfh64BU0FB78Rp7feK+A5hWjaB4qqMDo+CRaWtC60tHWRYYQZyKYPgdDzM9s4aaIBLRJIvVkOpxz/deYMfoxQr+3LatiMxEWVozny8wQml0Av5X32CFlImtXdh/rQ+uRBzD8SyCtA2Osl4wAQdY2rFnRw+GPW4myV+r6xiYMEYS6B1kxEbuuZ6z55ngZYKyziiZpG8QwaEx3BcCHg0BL5fUoDhns8jmkgxXdJcV/DdIxrUBxZiZW7wQc1NbMqtti0ygyZ8ykWgCIReTqShLr7/ZtS1Lnas0823Xrl1sUrwZ4pbLJSuarE+fPs0mcLK2StPAN6tAZqlrjbLVaBFjWjmbwbUmTL0O/DRw8awLh14uIE2L67wEZUaC7+Eq5M8k4W9zoWYvoGR1iD4dU6+q0NX07Dla3ipCSuqou1tEatiLzGASNfsEjHz/RoDf0+JF3UEZhbAOhx+Q4kCyX2FE03fJCO7XdvlwtmsvK4Ds/MHLrEhxgubhgTHk7t2H6ou98G7pQO3IMBIyMLp/NzqeM7LKyM6htX5LrR+jAo/pA3sYYbnI5U36Ypf6ZhuIhZrr0TbQP9tAjJQB6Nsu5AuYogZiPjcu33UndTlDIBzF1NViVOpqP1ydbaxzJREVFWOmVeB6VxecyTSqr/Yz1xqhprkOoz0dEBQFhQ/9KB79xh+z/eMl3y3V7BDkkqJLAle0cPKBEktmA7TLMra7bH1AkwnVR9AERlXfKyGYhZSTze6XlSSGShMWFSeSe4yyslab+VZJReelJnWzVodUnklE1PodbSZLZjHQvVLqWistCLW61oiQ1p1wImN4qR94+Slj9RzycVAOhsAHRWReiUGXdKT680gNAk0Pi6z+pfoOAYKvClI8Cnctj/FDN8iEc6bR9JgITQYaHxKR6FOYJeNu9GHk+0afFQr617B4jg/SxQIjGAq4J6fzcFWn0PbSUah+HzqkDFuZTvZ0orkoq5+diTCflk8AhFiCubTEXAHbL15hqb9jkfSs+yvT1Yau8VHmAnM010OamEG+OgTfdAR9RZ2zXHMDpvftAi+rqH3lKNuXzOQZ8VBBqDsaZ62QSTVgZOs2iCSOORRBrpi51ujiwcsyUp1tzBracewU/LqC6UgCjW+cxQ//6U9wX1MdUCSZbN2NxJRCxIhRJUstDlqgyoaFk6MPuYrAv15cNNuWzCaAab2Y/ciJKJaDjVZOrnRMxsx8I1cNWQSraYG7Ua2caT+5mOh6l6vV2eyWzFKghQlZkOVca6RvR6RE41kP15qcieDFvz2N8CjgCfLIJTVkCzocBY1llVFVoXtPAKF6Dg5PFmPFbLHoWRViMIlQD4/YBaqPEcA7OGSnFeioxuRLpsgL4GkAs2o0XUDd3QLil1U4ghzUDDD2A8NCEB1AezcgB2swnc4xq4J6uaSLc2zhSj+LbyS727BjchruwWFWo+IbHGVbbHsPVE1jtTPNjdU430B9mHQEx6cxYszXwMQMpu4/AI1SjCcFeOIJpDQg3VCLpiOn4EqmkC/WznhaGzFRWw3/yAR4RWWuNcpm0/qG4BBFTN99J7ZGY6gZG8FIQWPuMNoIwYAL6ayC0NY2/ONffxHT9+7H40dO3CjKDtxwjdUVYzNSibvMX7RuCNk5JLP8mIxanHdWG5O5lSFu1toXmmSXSwjLVU6udBylEueiz02FlfSTXDcrLVhcr8l4IXcZFUCSpWgmIyz2ANwOUv9W1xoVgZKlQ2OptGstM3Ia//bjT2DiQjHCwWlorQVcXicmWOMUjsnJqAkFmayG9IBiZIvtEuAMAbloEJFTBplMH1Vnq/9VRbpR/Z9RUYgCM0dZGSI7tuoOHg4PB8HLAbwbiatZVog5Mwlk+2ZQjRnk6mqg7tqKruPHES4ms5F4ZK6+BtnRKaQUMMmZcw/dB1UQWGpyOi+DOfCmYlA6Oox4SlsTtqVjrKAzuaUHdWcusfiNGdPp8XAYCwUQ3b0V1Veuwx1PMoWB0ZFpNA9NQXU4kNu5BXcOXkeC1AE0GY6ChIYT58B3tzIJG4+DR/89B6DzHGRKQ37uVVaX0/rj72EEQ/AULRNCpEQqxlHcr9femEMCxXgMO5an3w0CysQycIQcy3KBacUF22osmVu5RmbTkEy52hdaHS5nEl+JcvJms2ToM5O7iVJtCatRHthIdxmNk+IvNJkuJxlhKZJZaaxpM8CsWSCliHKuNYJJOCvJWkuf+xYGfuGX0erKwtHjx8R4GqFGN+KTeWSoIGREYnIy/nfVAIoIDBmZZpQtpklA+KQGKRaBt5lDgLLFPECiV0VmmK5/CpPjQO3dAjLDgLeNY5lpmckgXFVxxC9orGDTgIL27U7okgSPFwh7fchOZxDf1oWmY6cxaM7NTfXov+dOeGZi0EnimSZgBeBUDa2vnYQu8GgIuOGR8sg3NEI9fZHFQjATYS4tEtSMzUQxc3Avc7W1n72EVjmL/pyO1sPH2fkoSN9d64NG3TDraxCNpSBKEvSAb1bnTPd5MLVnG7JN9djzivG6nKyh8fVTmHjkHnQ+9yq2BBz4QEDG093ts9fbXQzwE2ZKSaa4P1cSkwkW05rZ+9UbSQKCKuDihYtsPiFr1qzFMgVdrVBVdVVCr7e6pMymIJmF2iIvpQ22WuXkSloypp91NY2IiBzJIqAb6N5778Urr7yy6nOtt7uMPid12iQX2UrkYZZjyaxEhHMzxncWc62tJGtt8vc+Cf1ciolIViGNlv3AQIpHXQhwq05EohI8BwJIvxRjtTIEkpOpv1+AlKR0ZePaZCdKssVUILSDBx9oga92EhOvKswlJiWMNOXqe5ysEJMsIdHLITumwdVYhZHXo0YKGEMGw+96hBVASju3IHT+CvJUVd9UfyNbjOfRIAIht4hwIsliGCQ5M10AC8RPjUxBrqnCzPYe+GUZd144j34yCApxNB85wcjE29WK5HAW7QEnTuzchdDV64hSXOYVQ1kaUxGoAR/G3vIAGsNRZjURqaEgQRdF9Pzb8ywmQzpnweoAXrv/XjQeP4ORtz2Ej4+dgycrI1kyWXsKBnFoHh8bvwlTCDPrv3Gsv3gs209y0Kz41IOHH36YWRpm0ggl65SqTtBPTzEmvBzpq4XcZbYls0b3mJk9Zv0CTEIoNwmtVjm50pYMYaXEUDphm+RojmkztTw2SbS0lcBK5WGWGstKCGazWDLrlbWmFxQ0vt2H7GgG/VwAZ89QbMR0Iknwv60GWkaF954gpOsFyBM5VO2xZIt1BlG3N4fcpAaedLmom+Q1DVUHOYw+q0BwA7UHBTiCJPPPYewHhrssfEJlmmhND4qIXcmgrUcEBXpiM4Cjqxl49pXZ96DptscFXOE5JPbvQfWlXoiKAhcP9KZJQ+wq0x2b2rMdj1y9ikjCMJEoWF975iKU7T2MYCilePrOPYi6XExahi9aIbEU6aFdQ2TvDjgTKbSH3Mhn8oiITiS296DzWUO/jJyCQarWf/Au6ETurU2sBofSmguqju6nXsC1n/h3OPSNz+Mv9t7JXpMssVg8xbqXUul+AllKhExpTKbEXZYOcLOZZfTdEQHQ1t7ePivoSt+5uchwuVyzjflW2xVzNd1t8Wa3ZJYjDWOu+KwZGWtVTq6kJWOOb7kojR2RrA1lzq32XIuNq1JkRa4gikHQOc1WAis9x60ek1nPrDWCueLVZRUtnXk8kwF6z2TRuhfg1SCSg0n4anwYf8HsigJwHgGNj/iha3nU3SsgfkllTckcQQ9Gvp+cLcSkv7kbeEQvZplLioowE9dUVvE/+ZICR7Ub1bsUcILOdMQmXybCUjA6AzjdQE09kIjnMfbYfXCks6gbGEZTKsFIwnnqIuhTpZsb0OF2gB8fQ7VbRDSvsJiMM53F9ahBMM6AD9fv2cfUAdqLGWlUsyIMjMDZ2sQajvkaalCXiCLncuNSZydTciaMUDozBzgbq5kLjjLXmqjOZXKaScLUHjay1ghV9VWoScaRdIg49T9/Hkf/8JNG1X6RUBIlJOMuusBUC8kIxWNTcyyZG661jI9bsEamVNDVXGTE4/FZ0qE57/jx47Pf+WIN+kzQPUMEdivjppCMSSiL+SjNi29WyVZKObnSJLPc85HZS/EMIkXrhG2S7GYiGVPtmdxAi2mlLYZKZ5dtBqzl85S61sw0/Wh4Bt1PPoSTQwl8uyCi/zjdTyrGzlN2WQ5+ke4dmUnJ6AUdcrgAweHB1Cs3FJQpxhLoEpDPO5h1Q9li1OFSTugIH6fJNML+X3NAgEA9X84Z96wcyyM9yDFXWbLPSBRw1NXDE55BIathkmZ4xNA6coxZCjpNvKkEc4FdbeuAJMnMVRUdGCk2BlOYBZKvq4EgyfA5eCZJA5FHqH+YBf5JzZlcXRSIP7V7G4KnLoDXNGSmo3AIgMJrCF0fwvRdeyH7vLjrjZPIFBRkx6bQOGYoDOjNDfDygJsHLj5wEMHeIUghHzyT04wA92/vxNHP/QY7VlRUcMXnIV4iKOsqWieKpejS1ChLlRzrK9bI6G4PVOrLvMz0ZVpk1NXVsY1Ipa+vj7maSxv00WLZdK+RZWt9zmjeKCeEeyvhpsVklir2My+2mW1WKeXkSqUdm3U3yyEZyh4jC2ah1OqNrm9ZDCaZU8CRiJwkYm7WWMqN7XYBXRvmWnPIiF2N4tURIDcsIdgI+H0eiNU5JEZ4kBoMucvwigTXdi/LLiPfFFkyhViOaZJJUSrGJCvEaE1MLjGR5iWOY8WaJBXjaeAQv6AiN2VcQ3+nCHdXI0RuCuGThsuNGp/5uALyMzryWaCxFcg0duKapx6hvkEIY+FiPhqg8gJ0J4dsQy26FAnK2BQcXhdyE9PMZUYgO4YIKdJQh1wwCPdMFM5MltRtMC4DoWJXTOpJszuZhGNkFOM5CZS833DyPMJ37EBcUtnxjh3dGKqvBSepaL50jaVLE5peP4XwnTsBTUco5McT7jzUhw/MXmevfCOeEqeOmhZLRrYE1c2+MYkSS8Zrxm/W0HpZLSqNNDY2ss1s0EdWDm3kQqd9Zp8kitWSpVOJ7LLDhw/jc5/7HE6ePMksqu985zv4wAc+sODxL730Et7ylrfM20+vXY0s100P/C/2ENKXQrEAWgHQamCtysmVdJeZ51uMGOi9qG5namoK+/fvn3WblEOlSGYt5yFznsjcbPdKN/lacbtZMpXGwHf/AqdOgUnUE5JTgK8LmLgEKIUCGkIAX+NHutuB7MkktIQKGQVWP1J/r4D0sA5/h4DQNiDa60OoI43YRbUkWwwI3NUDt3+YiWIquWIhZoOI2PFJqDmVKTWT9L+vg0P8ugOZtM4C/1NjgLtaQ/2Viyxuou3eiq7rfUyPbHB0glXHk9VBNlWrE0jrHKK7tkEoSCz9uFvJYSynQLk6AKqSofRj+YGD8NEHZiRk9KRRnU5EhydYDMnlcmDowB2sQLPq2gDyavHCXB0AXxVCLfWNCfnRIWcg8wLeOLgfjScvsP41+x/ag72Xwjgyxwq5EU+JlhCHyxS8tLjLTI2y+BySmR+/WWkvGdWiW1baoM+0bOm5IyuH3Om///u/j2eeeYbt37JlC2tZvlphXyIqqmf78Ic/jA9+8IPLfh1ZW6UL+oXq4W5ZkqGJki4wuWz27NnDvoi1otIks9iEbsrd081E7rGlpLorSTJEFiuFKQ9DKyga76lTpyrSMmCh670a0ridLBmaqU//wY/hpf/zQxZPoatRv1VAdauKeJ8GhZSKNWA6AbQ0iiyrjPcL8N4bhKb7EPIlMH04y47JTRrLev/+auSmUqzxGNXEJC4rqNkvYvKVAaQ049pxAtD2HmpQJsDb4UaqL86KOV3VHMaeUaBrM3C5gbpGgISI+2NpOLKUt0XL+xTNrkxFudBahTh1uZRl3DE2jvGRKehSHq3FAD71iGk79gYjn/HGFuTHpjD5wAG0vPoGBou3uRL0svbIzlQaLq8b2WQGhYIM1elAx7OHmRstKHKo4nUM7d+HUO8gHLk89FzeaPfsF9D0xjnWWfPVL/wWPvrFLwKXgGzJs+Zjnd4MhEvdZUWLJb+AEGasZL9JMqR4sF69ZDiOYws72sjj8fnPfx7ve9/78Eu/9EvMsvirv/or5gX5T//pP+E3fsNwBS4XTzzxBNtWCiKVSiw0bxrJLDbJmMrJNOlWimAI9CWTH7RSWIi0yHKh7DcaN7UWWE4842a6y0x3Xqk8TKWENm+3OplKQXv6t6C9/EO0drkxNpBn1oEnAFxjyVMFuHw00QfhzScxEc6zTlxaUmXWjPeuEKZeyrLGZP4ukufXWVxl4pDRGiA9pDHFZZKcIV0y7x3dcOlDiF9V0Xi/yPrMGF3swawbkppRUoC3nUNmSAeFIKjiv5f1GIsh3dbMmoK1yhKmj51h2c3esUm4x6cw8ei9CI9Os9bJQl0Nq+wv1ISYDI0hWANgeBzjb30QznQG2LsDVReuIK4CMwfuYC2WUcyjC/CAv6sVE7LC9Mw8sQSSio5qF8AdPwsXzyO6Zxuziqjdc39aQmpbNw59/Y+Q6u6AUFxcpUvIxFtSDxP2e+cJYeYt8Q6TZEqtHk+hXOvl9e2K6fV68Z73vIe5VL/yla+wMoeXX36ZhQw2CuR9ofmS9B8//elPL1t9ZdNbMqXKyWblf6Ww3pYM/U7ZWGTamsKcKzlXJca2ErJaTB6mEokI6xH4X0na83qhEu/vSUzi8RHgceQR6wD+iYIdnIZgE5Ck30mWX9bQy9Qd83B0uOHa6gHn5pF+hQVqkBnRUIhrCG0XWGaYo70etT0xSHEVvIvD5GFzFd8PiUjroAAlp7PumKlBAbkZBaEdKJKOAUdzDTqqoywm4/YA+RzgCkchtDYCx84w5eY6B3D64Qehup1of/41ZpVRjISbjkHt6mLV91P33Il7Ll5EKq8ivmMr2g8dmX0Pyn/r9AkYV1VMPHAQoWsD8EVjrCfNSP8YmvvHWIIAdby8v+8ak5rRKc1ZVhC60g9uWyfTOWur9uEvjnwbhTojS9MkmRRVkVpiMlQLk3SX9IIxiy4tMRmuuD9akolmWjIkwrmR4pgEMyZDFsWP/MiPYCNAvaCefPJJVh5CJPO1r30Njz/+OI4dO8ZCFrcsyZRTTqYPVWlSWK/z0QrD7Aa5lNzKQueqVH3LcsjBlIehxIpyrZwrQRC3ikDmzYAeUIE6ICw78ff9EqI0j50yrlVTuxOeZgn5aQ5OEWAeH12HNJiH1J8DBB5Ve1zwtqjM1TVBZKIC8sgMEgoHh59DbkBD/b0ieBcwM1ANnzdSlJIx4K7nENwehOBMzfaj4UVAEx24bnRTNnp23dmNsZYO+CamaZUGXVVZEzLvTAS1F66yDLD6aj8CiRiyDfXQjxrxlsY3zmGKmofROafCmHj4bnCajua+QTTEIhjKqMx1RigE/ahvqgUfjSDQUo9YJAFNFKG5nOgtOh50t4Cpu/YyeZttJ89hqwt4xyN34JNFgiEIRUmYuZlhUtkmZI5i4D9jJZliJlmp1eMxu2LOsWTWv2HZzZKVIe8LbSZofqBkoC984Qv4m7/5m1vTXVaqnFw64a1HoH49Av9mN0iyBCgbazU300a6yyibha431emQO7LceCtBererJVMJCKdOYWA78MxrAmg+I4XkjABUtwHxMaWYPpwCT22N99cjWadCnigwC4fk7ikWEz2jskwwErek2pdUYQs8et9sivLMcQWh7TyoVwDxFNMvkwA5qyI/zSN+9kYadHArz/rNpLIBNLZOYXoCCFFfmnAKXeeMAki4XegSVfBOByZyRpzGkckiBw2aAsRHppFpqmcNy6hW5u6jx5hMPxeNo+XVE8g0NYATRZbk0B504+TWrSzjTPW4MX2t33iP8Rnk2pqQ7OmEoGmzlf2Kx81iN01HT8P30F34CXEGMyV9YAgUHyIkvTfKA7yyUrbo0ll0i2VK9lOqM6cY55gucZe5y7ReXmkvGVVVV+yVoXt9s/STIXfdq68aLRxuKUumVDmZ0mVJsqTUpFxKWuZmpjATaLIjy4syQqj3y1raOm+Eu4yuNxUBkktvqVqjzWrJ3OqWkftb/wVq3RacnnbgB98CNKUYVCc9rwNAIcuhoVlEVpMQHgATyBy7GIFG9Sb0/YZEuPZtgwdDLEuMICfpmugoXB5AXlLh7+Dh7+RJ6xKx8wq0eAqZOJAZ1Vh6c3YEcNY7UN3pBZdPgnPoSPXpSPbRe/SxPjDNHcxgwnRjA3IuD0IDIxAaajE1Oo5cQUZVapgRysyBPbjv7Hkks4YF4JucgRQKwBVPMoIhkhjbtxdhjxvB4XHow2OsWj+SzMMZT0IXBSh+L5oaqyFHYkgEKZ2OR2uxyJKOpXjPxT3bGFGe+NRH8ZH2BvC/dxIFi/VtWjKJUvmYIpkoFpIxNcrSJW4xs+iSXHWxknRnM0mgtPWyY4XuMm0VMv8Um6b7nZIBbjZoUbpU6/dNRzJmuuxiysnLFcm8GZYMuZsoI4sm0vvvv3/NN8J6pzCb8jB0vZcjxbNZYzK3OoQ3vo9DhzJ44zqHhq2Akw8hPZwAJwPDp+kIul7GpNa1JQglnkTtjmpmUSiTErz7A0gfvoJc8bpS4J/Sj/NTGpscCelhje2fKPaUEdtbUNczzWRlJl5SoBWoQZnR4IyC/vHLGoLbeYgeDpPnA2irimFixJClcQ5eYtMpaYD1jE8y0girHEstTre3sD4xU8XsM099NS7dewDuSAyBQaNmh6wQPZtDfe8gq0ERe9rROjbCUofz1BsmlgAGRlj9DZEJJwos0SDV0YLq3kG0R8LIaEDdkVN49Y8/jQu/+CH89z/9S3ZuK8mYlkyiZL+nmF22EMkk/fPlY6yuNdOSkfw3LCTXBrRezmSMPPS1usvIU0RlICaooJ1IgwpAaaH5iU98gi32//qv/5r9/Ytf/CJTLCAvB4UBKCZz6NAh/PCHP7y1SIYGT1bKYsrJm9VdZkrb0PlI8qESK431JBlaEVF6MpE2xYuWIw+z2dxlt4MlIyUncegVEWdYraKOySuA4EiiKQiEc0D7fkpbdiA2IKOmBxg8WzRVIhFaOjP9Ml3W4djRDm1omLm+/O08Rr5XTFknd9s+nu1L9evMpUacpYyMAz0ixp5VmGutdj8PVauF6NUx9bKRA1aIGs8F19OAZCyGlk4wgc3BqA9T++9A64uvQ9a0YrmnjsEnHmfpxPHt3Wg4e4llx4kBH0s9JrVlcm9Ri2Vqv3JWVuBJGn1qlP4RKE5Kzc5Br6nCBGmPcTwOXLmKaDQJeTqK5mlDPyC5dycQCbOWzN/+u69i4N+/m+13FuMpuQUyw+Il8RR3cZ+8gEZZsuQcphCmZklrNtUBCn7HmmIy/Cq6YtIcs5iy/HJw4sSJOcWVH/vYx9jPD33oQ/jGN77BvDGmzJGpDflrv/ZrjHgodHHnnXfi+eefL1ugualJhop8SHBxMWw2kqEJjlYBFAQjdxMpKVeyJfB6xGRmZmaYxUjZestNpy53nkqMZa3n2gxYbUwoNXgc3/0P78P0tTwCHiDU7AMXzEBO+DE2YEzAI2cAT0hBwMcjG9fQ0emHlEgjCieELhfSs/plMThreDQ8wLPAP1khyWsaC8yLLm1WNNNZzaF6twOJ/HYkrl2cda2FT6kI7eUw/fL0bBq0Kmlw+Mj6ucqyv5LFcE3mwW3whKOYeORetF8fgD46heZqH/DMS7OfTRB5dIkaJgQRqc4WVF0fhpjLg2QF+/KAr2+QKQNQl8qeTAbx81dYQzM6L23jD9+DmWiKiWYmd2zDhMcNye1Gy7HTrJXye9t9+PUiwRAccnmS4Yr7Y3MsGbls0SV17LQWXfqLxKMsQDJ5n2NN2WXCKiwZisesVZmdMsMWew6JaErx8Y9/nG2VwqbJLisHWnlvRF3LStx75CKjIBjlr5P7qdItmCt1nlKtNzJ7iWRWgs3mLrulLZlIL07+0nsQpypGIpwcIKZlFlPJRFKoC1LQmYOzR0dqmsf0pHFPRZBG0EvikC6oAuB7tAryaAFywgVXVQYTh24U3ZKkf1LfAU68BHc9kJ+h669DSgD5CxfYMaRN5u/imALz+KHIjTToqCH3TynPekMnuoJDyGUBB82jRwyhSqAXmsCzPjBZTWdClf7hcdapsqHKh75wEugdZK0KSDQzeedOtL94mIlbZqkGaDqC2K6tyJ29ws4WDPlwef8dkPw+tB06Ak3XESNuvNgL+bH7WIA/fe9+/ML1E6itnSsj5SgG53MlqcoEvhiTic6xZIxjCyWxl1JLJl4mJiNbSGa2pmaOJbO+dTK3i8z/psku28i6ltWcz+z9Ql94qfJzpYihkkkJ5mekin1aCa02XlQpd9lipD04OMhWauQbXk4DtFsV/MxlvH9QwhONwCgv4ExBxaTOIWZ0BwbNz1U+IEkJVrqO1k4OQq0OZcKFWKSAHFk6A8axrl0+cF4vPI0FuBs0JC6rcNfzkNM69LHzmL5GFwtoeFCE6COy4QGHCMgK1IKO9CBplKngHAKq7gjA25iDpoKpMhO46SFMZwA/ufCmgMi9d6LgcsEzOYPGgoTh4XHKTmCNxSSfF1MPHETzpctoIyUA0Q1Z05HuaEHLs4eZgjIhsns7a9PsG58GRFLBVJFMZFg2V/dTh5h+WX3QA08+h/6DB9Dy8jHEdm9D5Hf/Jxo/9B9RsJCJaclkSoLzbOyzJFOSGSbPb0JWSjLRkiQBsx7G6lpzFvfnSK10A1OYM5sks+y2tmQqTTI0kZmr/OVMWqW9X0g/iIJh1p43q5FwKYdKERbFuigGQ75Uir+stpi1UpZMuXOYEjb0fZA6AlmEZp8VSqteqLvgLWvJqEZfGFEFxqHj3Bh9jgI8IaCmNgh3OonxuE79t1gzsbEM0FZFyhEFUKzZva8WBY4Uj4HCmTS0bAbTxRm87m6Bdcb0tvBIK23gw4OoKSoxSzG6Vgo4pwMt7xRZjCY9UGxEJ6tQsio7Ljuuw1XLsaLO0eHdaMQ5pltGqD1+jikwa24XtEyWWTK5QAi9gSB0l5PVuhilocSAAqbv2gPKu07s3oaqS70g0WLN5WBkQuAcIlqo1017K+KTRjyI9Muo+SftF4+exuB73oLn//aLeFtRdVmxkIxYTEvOWKSayF1GyQ/JMplhuQUk/UutHt+s1WNJEjBJhgJMG1iMmS0+x7fyAuumk8xS7pRKpzCX9m1ZalVB70vKA1RTslCzLjpHpWQeKjGpkzwMpYPTuKgydy03Z6ViMlYQqZDbkfSZaCPQNTSFAanbKY2/tIUxWY638oMmvvY0tMeA58ddGD4rwSUCeRnIJQGhgcP14ixd2wn4gj44tQwGLrNyGCSIn96IwP9YNTJHE3Dv9CIvdSCo98Fdr2L6daMgk8BjEE2PiVDzOss4ywyqTEDT2dWMqVeHoRR7oHmaOIhdW+Hhw0hcMXYWIjrklA4xPICpAlBdDwRCwJHW+xAcGGFCmPQ2FB72u2TwqgbJ4cDoY/dj76kzUDN5Nrmb2mUEirEEXDwG/D4kutoQGhxlJCPJCmb6x5hrLdtYh+iurXjk3HlMxTJIve0hPPvtJ6G63XANGUynWMjEUabokkGWoHt8czLDXLNJAp6yasvhMmKaVj0zk2Qyxa6YDt0BnhUsra8lk7bdZeuP9UhhXs4XTittco9RVge5xxbK7qi0u2y1VpHZ+4WyRKjWiNxQa52UK+UuK23hbMaI9u7dy+TOza6o1hbG5J40G3tdunSJJYmYKdeVrHNaLZZ7bfnBV6HVbUdekvCPZ4GBc8ZkRS9vaHMg2C0j0XfjO4+NAx63jr6rgOgCmutFOKEgsbce8UPGqj93llKPL8H9iAfxSzLq7xFZgWbyuoqUYy8mXz43ZwzNb6tCfFJA8A4Bscsq0ygLbhUw9fp1pIjFOCC0k2f7qKMmJxnpyNQV0+3h0HjiPDhFYfUwXekMaocGMZGXERwumjr03fpcrGhUq63C5PYeBPqHIYgiAqOjGMlqs8QzTeeIJ+AcGwXvdkLLS8zy8I9NYSCawd0+4MWv/QEjGIK7+DxI8yyZonxMCUEI1DdGVec1ITMtmdKiS/bdFPfPzKnsN6VmLCRjFm5Sb2dGMiuzYlYbk7HdZRuA9YjJEBY7J6Xt0cRWKha52PluduDfKmdDFhhN5GtFpdxlBBqTmTRhxogWIjCzRzpt5KKkxA/TyiGQ1BC51EzX2lrTO9cT7u98DPETV/DU5Wpo5BrrcCA6LMPrAlRZRx/rapxl0jHV1QICO1RMXTQmUKUARGIKagNgBCM2O+Ha4oVO9Snhdky9YgTzp19XmFQ/ScOkz0eYJUMWCRFKwz0iJl6IU3gbRB3UkpkpMGcAR0sD5NFJluLsaeQx8pTMftcFN1pbFdA8G6b2yxnD0qk/fRHoaceYBAheARP33GkcTy05jp6iMAv4S32g9JKZO3fBn8miWgT8HieG4GC1L/7RSWRnIkZTaU5mCsyqywHPdBQPtQbxdiGJ3y0JxLuKbjHJW55kSt1ipkaZaiEklymaaXGBUZIA6ZklPCXqAEXiyVqTBExLxuyKuUJXma7rqyYZO/C/Ae6ySpIMvd9C51xJ75fS8VVqZb0akiknD0MT+WZofmaeg6yVo0ePMjIgElxJu2wCvY4qjUnLbnp6msn2kBuBXIMUKyOftUk6JCK41nTPSmJoVMC3ngVykinfIqOlBkYHSC+lKwO5BOBxAOksNR4zjqmqB2p6HNCHFYxMG9+BMiEZNTKNTnB9l1C1ywF3gw45SbM7x5qWiRjD5MuA4AHq9gssGaD+wRrEBwFlJoq6e00FZsIkHNUCmh7gICUNF1puQgev5llWWS/TL9ORbm1EsqcDhYAfW196nSTSoGbzaH7tJEs9biSZF5eIKlXCtZ6tiFQH0XDyAouzsMqLgoTovbuYuGVs91bUX9ZZF0x3cwPqzl2GI5XBy0/+Hj73Z18CppOIuG7cHwKpcxLJkFLnMknGWnTpKsyv7J8lGUvRpWnJZCyWjEky6cDyu2KWQi1pM78S2JbMBqDSMRnznFaSoS+TJmv623J6v9xsS4Ymf4pd9Pb2stoXKgg1rYZKpkKvlWSI8OjaUuyF6orWQgDm5yMriBYAlIRBBEZES1YOWZ90r5AFZJLOzWxbO/OPH8Pov/ThjhAw7fRhIpZBXYcHM705yHTLMNlioHtrEGo0iTQ4kDILFUCKTmDymopsTGfSLp4DAQj1IrSkhuzRBOs9Q1X63iQHwcUhH9ZRf5+A4cG9aKo5Aw4Ci9UYiILzuVB/t8Be1/CAgMRVFYWUA8EeN0aeNmp0CMHtHEYiD0OWXmFS/5Qp7B2fZlpk3U+/CJXnUO8A5LY2nN61FR3PvgJBVZFQgATT9vKh/sxlxPZsh1fg0X76HNwBH9RTF1mBJoFq2DuoGNPrxsyOHpz92H/B0PveBseTX2R/T5ZkhoFaA1DchNx6ZUgmXpoZNlvZP/c7d5ZRATALN616ZqYETco/v6ZGpzYh7rWRjGBbMps3JlNJUUQryZitBVbS++VmWjI0kVI2FrmQysnDbIY2zvQ6igvRRpbIzp071zyecmOizDkSJaXNFBMkwqECVCJgUjYw3Wpk5aylq+pKMfWJr0OIqAhRABwZ1N4NjMXALJl0nkMkpbMK/6GzZHnSK1Q4vUBbjx+ankZBMe53ml/pE2ePpqAlFYgNTmTdO9C5pRfpkQJSTG8MmDmmgvfPQBY58A5Dzp9k/XMzHsgFJ6ZfNws5AU8DwLV3Q3BPomafwDppUrtmKsYMXXsF4xFW14mmbhde3/MwqnoH2BhIRTmsAX7o6H76JUhBP2Z2bcXBy5fhzhfQ/8ZZdv76U8W6HFKAzkqYvo+kDHQm6d/qc2FkaALcVBhH//ZLmL7PaJVMwpS628NcWCaqi65QtYqu4g0UkkRpQKREut9bTAaQFhDCTFosGZL0l0kBtARmTU1qXkvmAnSybopz0Gq6YnLFHk0rAVns5eS2bjXc9OyyxWAy/2r8mYudk7700l4qZiB6pdhoS4YmUUr9pcl1oYSESqkVrzYmQ9fDzMqjJARya20EaLzkv6aNLCci43g8zkiHCIdiV0Q0JulQ/cF6ZqxxbqBhrx+JgTT6av24eoIC9qYYpo7u3RwUXUdLiwfhySzyCtC8C+g/lTZYBSpCXqChVsSYk4NeXM0r0xL0Zg3TRwssi4yC9hRT0Tlg7Ega2TFDLDM9qCG0i4eSI/JxslhNbkpjtTK6wkHvv4bpovBxYAsPXxvPGqPlHC3wyONwumlBw802FSOJ/cagD41jI+ifMdx/zmQadacvQXdx6JeAXGsTYtQtsyAhBB1Dx4l0ZDS/dmK2W2b9+cvY7gHG/vATswTDrpciQ7O4xczMMNXSndFVXGjMOG+k57vyRekXqyUjzbd62PtJhXmuNU8xU9RKSHyhMEdqZqWWjLbK+YtSmO2YzDpjudlgK53Mc7kcCyDTRFyul8rNsmQWIyyKR1DwfCmLayVp2kuNZ6WWDF1XIkF6LcVfkslkRetaVmJdkdVCq0BzJUgPrJlAQIkR9HciG9rIGlxuPdFy358nq2RnFq9PA9GEhM6DgJTyITaYQW2VAwOXzKyyLHNjtW2hBUMB9U1OhIlIVKNAs3dEAUaS4JwcPPsDcLS7MPNqBmrOGEfiigZ3Lc9k/TldQt29JC3DAZyO8EkVSiYHTOUweR2ouVNg4phe0jbjdsCb7YWzRmNSM9T0jODBOJrbjYZlWU1AIRiAK5liAX45lTH6uxSyTLMs2dYM3emA8wdGKwDP2CTESAyRfbvgPnEBDQ6Ar6vFhZpa1i2z9eWjEBtq8N4a4EvbuuZeMEWF5ptb2e80K/st5OMprg1SVTeON1tERyzPo5nuHCslDlqASYV5Vo9JaqVSMwShkIdW8vqN0C0z71m7GHOdQV8MTSy0Kl1pwHghmBYMBZNJnn+tE/F6u8toUiMFVXI9UTO3peS2K0UyK3WX0QROcS2yCOm6mt/dZpGVoYUEbdSOga4NWTk0ZiIccj9SrMckHfp9tVaOeOHbcBz5c+QKKv7qBQfCUfpOJSROAd5gASEfoGgcOncBqSyQmeAQdOoYvW7KJ0nwBIGm7X6oQ2kEApTMQbUwOviggOT3wnAhDFcdj6odRg+YmeMy60cjIIfwcTBX2dSrKrytPPw7GyFleQTqJhA+rkItAKkBDTwuw3vAC03KI7RDgLdZZ26zNL8F8eh15Fg4JMPIeOzx+6E6HKg7a+ifERzJDJPur+obBM9zaBR0nHjofmaEUetlwrQKCDNxqK2tqDt/FZc+8h/xCR+PwHf+DilrIaUiQ7Xsc5pFl1ZJ/2J8J1KyP1T8vtSquS4wKW5YXdGSJIFZPTOLdeMqutZilsA/s3oabjRHW427TFjFs2jXyWwAFssGWyloYqHJmlxOpONFE/ZaUcnst3IkQ2qoZL3Qima58jAmyVSyxmW5qghkYZF0eOk5lhrHStx6lXJv0TUyCz0pTd0sBqWNPgu9T2kCwUrSpPmpyxj/wes4MgxwThVtNDf5vZDdWWRneEyx0IjEGqUE6oGqkIO5l5p6XIiHC4DMIVCrY4C51wgqnDu9cHV7oIRlcF4eelaDlKDvpph6TLUuO5yYiO/B9rvOY+w5hVX3ky4ZRibg39eJmWMaa2xGopn5aQ1T8T2Inb/AyIv5yVhSgBvJc27UUwvoBBAPg+mN1Z69zCT5KfWYYt/qnbsQaahFzYVe9jqFpGQ4MC2zUP8wMs0NcNXXoOvyFWRqq6CeOIfeH38/Dv/J7+Azn/g0e02yJHXYOIk8r7LfVSSTtKWQkmpcdIcTMsnlFOEpEpLVteYuuhknS1xrs0WXC1gypbI0xvsV5rZe3qCumFnbXXbr6JdRrQVJ89NPmjQq1QRoPWMy5Goi1xONdSXyMOY1XauFtRx3Gb0HZXWRK69cEsJmsmQWAyUI0MKDNvpMlBVHbjWzoR75xU3CWer9L58K47nDgESHFTSMFoBGl8YmbI+fR2c97XZB8xeQTgJTMcOdg2QBVa2AR3PAGZJQ3+XBzGAOPp8A4oHUM0adELnNEtX7sfv+K0hdL76W/j4kgQ9ILEVZ9ANV+wWWypzLtyF5fIgdZnbMpPiM2D+Euru84MUCkr0KQjtETB3Jw69exPB147TNd9ZiLBRkLZarL/fBnUgiJAJTvYPoOHuZHZPo6cD+mXEoioYotWimSXxiGpqiIkWKBVNRHPvtj+Hkp/4HC5yLReIorU9hhZS6Pq+y3xSmzFgISZBlaBZC8pg1Lhbi8BcXXcnqG8kDnmKDtRnLM+Iqtl6OWpMECnNJxrWBrZd9tnbZ5k9jpsmCrAGaIEhqhSbFShEDjY0mnUpkv5WSjFkQ2tPTw7aVnJuO3QgFZSJsIkF6HyLBcmnflSaZjZCWoe+BdNRoo2tP1qRp5ZBbjdKmiZTo89I9Nfu5dR3HfvMJvPbHR1j1e9BB0ixOCKqEgck8sywKmTyoNLKlgUM8AlS3c6hzi4hHZbianEhFJcRTEjBBJ8yhsQpwBN2INTigplQo4wWI9Q44olGMPWNMiO4GDtV3COAcHoy8ZAiaKWkw11f1HgHJN4bgbAiiZkcWakGD4OWYGKaAJGaM5pNoelxEdlRDw4N+DJ5ugz9/BW1dwPCFCFo1g9w0QUD91nZ4RkaAxlpoQ+PgNQ2FUABjgwokEg9waUwZgGT9OwZGkZiJYNsTD+FPf/OX57m6EqXuq2KasWyJvTiKx5b2fGHfESUJzEs/Li+aKZaRjwkVnw3VIhUlxwzX2nQJoZjxG9nnXlPgn19hTMbMltwMXTFva4HMtUjL0JfU39/PttJakkq7uCqV/WaOi8iF5GGWWxC60LjWS9ySQLIvpPJcWgS60DluBUtmMVAskApBaaP3psw5Ihoq2qU21kQy1QEnCn/+X3H+KdaNDJoOJGWgStfQPwV4vDzqtmnQcyGI8QQGpw2CmLhMn0VGkx/I5jTWKVMeFxFJK6hp8iM6lIYUzxiCYWQhPFoFiByk4yEmnKnljcJLSmNOD6fAg2OS/b5WjsVGxl8sVsxPJxHOgglghl9VWQJA/+gBdDSeg7tanVVgTval4cYVtG0lVy1Qt6cBvRk3qvqHMfHw3eBfPgYmbjMwCjkYwPij97BiShcPRjJUgEkJAs1HToLL5fGjtcD0Ox6acz0FardJwfWSxnnu4vMoecqTTMrqLpMpfmOxZMzK/nnqABJLjVZJ/bkIn9k3xmJ5e4rjmCp5P38x40wKuDc8JpOxK/43r7uMVp/nz59ngTOz98t6uLgqmf1GExdtFJAmy2AthYSVmNwXcpeZVhbFMkh6Z7Hv8Fa0ZJZ6fyId+m5I8oYsbErVjv7SA8Az07iX7r1aJ1KchPEsMDZlTJK5rIbRC5RBBgxOA7UhJ/wdEnJ5AZ6khqEpHUgrSDJvk8Iy0XIRHc01QAoeRKdz8N4bQu5EElpWQxViLEW69d0iE8c0lZWJWihFOXZZY6nMjiAgNLXD4QfcrnHMFAs0yW3m469AbOeRuCqztgB0aZMDPKKZ7RjqM+pcgGkEOQ7Db38YYr6AmpYGZCamUdCB8IHd6HrqRebmonLOoEvE2Xc8Bs9MFLLHA/8nfgF3PPk5fN9CHDzpzzB3mWte7MUq6W8WUibLJAlYa1xcC4hmkhCm1erxSuUJiT4rkWQicCPw7y2STITS8opwaI4Ni8n4bHfZ5nOX0SRNWU4kqkjpydZYBp2PSKjSlkwl2jkT7rvvvjUT1npYMmZWHtW9HDhwYFlFYreDJbOUlU3WZnggyYLvZD4Iso5RjYoQjdYpzdWAGHJBaChg8HixiDAhIXYZaNktYHJARQM1JtvmQmKigGpOxNAput+NandymwXeXgM1q7LK//y1LNQZGTX7HZh8SYZaFAH3NDoRdR9EU/sJRM8Yzws1RdO9OajRHFJhFf5OHr4OHrqm4dqJFkTPUvMZYPoItWUGfB0BZml1bgUoPDEdETG57050PM/0bkA5C/QxMw/dzX6L7NmOmku9zG0W8DjR9dQh5Brq8P1nvoH/PGnI9OdclnhKMS15jiVTrNYvzFNbLlo9VncZLcgsxGGew1pISeoA6gKutdQ8IUxDxbm0ILRKKi5Ia26kS186cxlpZGaTR8iaXWwBpK6CZOh5sy2ZDcJyLZlSqZXFVtmVrG0x4x+rtYxKx0z+f/pZCe2tSpGMOaETKRNx08+VWFm3myWzIHQdd/8kj+sZDSev+JG6bvj2adGeJpcWFKYf5gs5UOuUoZBPv07C6DljsTOtAMKVApq2A4l+DR13AVLEj/BwGg1bqjD+/I1qfZrlh9zvQJPzZdYNM3ZRY9aMt8OL7MmTmBxSwAlg8RhfJ4fJyz7Ig2H20vSQBt5ltGAOFAZRs88FZ5XK9M9Ivyx+IQUHUhjqA1weIp0apsBMQpb+kXFUDY2iVdQx+toJmNN2viqIO6QUMjyPqXv34fm/+zLTOnP8m1GEm7UE7cmSmZcZVlxE5q0aZSYhed3zmpNZlZlNSyZuTRIoU3TpNeVjLAF+UlsuLbokBIp6ZnrNjXPcse0OcNMcU5agjFVayJpFvuVqrrRVimOy97djMus/aSwnJkOWDrnHyIopl+W0nqKbqyWt0n4199xzD1sNEclUIomgku4yyrSi+AtZhpQ4sRJpltvdkkGeLBOe/RtQRfzrsxLkXIyt9oMeDg0hnfWNGY0a91smITP5Fp1Tke4F6nuc8KYkJFOA2CZg7DwdpyFxEhAdGTRVk76kBt9j1ZBH85AGcvA9VIXOV57D5GFjCJRJ1vy4A9mwANVXDTE1zQo5HVXA+HMKNHkIYtCFmj0qs1aSvSpykzoESIieJb0y3rB4NKDmnlpMXm9Ck3iR/T92dRqNxZZk+WAANY214KNhJA/sgev6MIR8gaU4971+CvUu4Onv/RXy9UY9iaMYe0mXWCwEXlWgWywW011m7fkyq1FmIQ5yl1kr+83044SFOCgTzZoa7S0SR8Jq9bAssrkkM5vuXNJ6udpbjVBniClL0FxSWnNFzzQRg2nlhEIhdsxKmweaJGO7yzYAS5ECpfrSKpsm6YceemjJos31IJmVns+Uh6GxmvIwZi+Z1WSirJclQ0RICsokRkkxiJWS32IkY1qBKz3fzUbp5xEvfhvur/0KhkdpVexgqspyzlCFoYX64IxhzbjcHOq26XBK1UiMxxBNGffLTL/EqvplDciHNaZlpo3wyHo0CKoHoxNZIJIEhgDOx8P3YBU7+Sh3H7oDxyCngboDIka+T/dOhGXxkEus5k4e2UkdHM1rMqAkC+B4EROHiHSonwyP89cfxP1vOYno2RwKRgIZ8tMR8H43yPNERoX/YCcGFS+csTg0lwuRvkEq70Hw9EXkqqsw88BB9ro9XmD/2+/CZ4oEw65NMfYyL/1YUaC5y9fD5BYI/JcWUpodMK2uNTN+E5tXU1NAPhgqm+4cL+klw8YsSWVIxuwxU771Mj3/ZhEvwWxNYWYjqkVXGREPPffL7XRJ8Rgips3cyuJNEZMZHR1ldQwrmQTXo0fNSiZ0Ux6GKs9LlYkrFd9ZzZgWKrCk607WCwlQrga3uyWj53J4+jhwkSLfJ4xJLlTtQp1YQIxiJcWhFvJknQLD1+OgBX51QERwmwJd9SF8JYMszWOSjpEzQMinsTbLZEpQTU3SHUBSKUAMOZB5lRKggTYcY26z5sdFqtVE1S4e8SsadBgxl+HvGc8LT3NhSzdqtgK5ySFGMIRkn8aSBKZeybEeM9TCWfRyyGVqET8boxwEVsODqSGIW7ugO53I11ahPluH/GQY03W1KFQF0fbyUVz50I/ig/1Av6WA0VF8xspZMqpVo6wYe8laiMNss1xa2c8gS8gvYMnMkY8pxm+s7ZRNkrEeSyQjWUjGX3St5fwlJLNIPxmzNQVtZhryhQsXmOTSG2+8MetaM7eFLBxKWlpvfb2NwqYWyCSQe4ZWB6UwU33JJ7rcIPR6ksxyY0aLycNUkmTWMrmTRWU2GKNrtVqCWes4FjrfZkEhPopvfuLLCMUBvwCkVbBYSLDKhesDdL/qs4F/X6PIGpCprLoeiKUU+FVg/GIWvA7UBTh4t1JjK2DmItXT0FF5UGTH1yBDrBIhhET4Hq5C4XoOsYkqtDWFmWViwtXkQ8q1E9DOwl1HlhGgqYDm8CB6mDWHgaeRQ3CbAGdIQOapy4x0aAufUNHwoIDk2Rnojmp0dmYZGU772+AZiTDtMmq/TOnLVQKQaWpAoSqAaz/1AZz55EfBb/k2JAuZmEWXKWtMhqUfzyUTtykfYzkH6Y5Z4zecprEOmLkFJP2j5SwZa+C/UDx2nrssj0z13NoZX75Y5FlsvSzqIgQIKxJtdbvd7DkiySWz6yvFYk05o1LXmjkPmCRzO+CWsGRKJ3G6+OQeM5WI6QtcCSqZwmyObyliWI48TKWKKNdiydC1JTceuR6pTodWXmtBpRShN5sl82t//Tze/91PItEvs+JKAlXr13YA+TENLhEoKIZMvygK6LtiTKKBoBPVLgkOhxMjfRIjHboTwyT7rxPpAAEnh6Y7dcTGPPBJOUynZKgkAlaEc4sXaTTD3RiF6DOKLl0hgPM44Bw4ScYHs3LIZUbaZYMn8+AF0oIh2X8doe3AyFMFeKg52m4HPPU6BC8w+kMaMAXmI6ziv60bUPqnkdrWg3B1FdzTEWyZmkQ+nkLNhat49Uu/hQu/9LPwmerHVoKYJRnLpK+qUCwZZzfkY+ZrlFnjN2b6cW5eurNBBpF5HTDl+SRTtE6sJENyNXmLJeOd7YrJr6oQs1Qgk+YKk1BKXWsUlzVda6QU/vLLLzOLqBKWzOHDh/G5z30OJ0+eZPV33/nOd/CBD3xg0de89NJL+NjHPsbGRPWFn/rUp/CzP/uzty7JLKc7pukuo4tEpifpY5GM/GpiF6st7lwtaa1EHqZSBLgakjHdeHRTkRuPCLFS7ZcXIpm1xmQ+9a+n8N2LpsbXDZz/jUexXvixb0/juyN/gESLjDM5N+KTeTDRYw64/jodQe3HgNoqAdUeFXGjezFDOiWhyglcH5fAC0BDtxeeTBaqxmHkTFFVWdGZmGZVM/WcoTbJXni6BUhjBfABEdL1HJpxBlPFwD9J/btqOORzXqiUKp2YgruWLBQOo89Qt8x+CF4Havdr7LjomWKXRmis8Zm7TmTHOUMcHG0tSE0HsaX6MgZIloxUnc9dYcdP3rsf0pCEBreAP/vbP8XAv3832+8o3iNWkhGL93GipOcLgbLVFP/cidxVzCLLzHOXkdVjyQyTy1f2z1oypdX6xUw0q9Vj9o0J++fX1OQsYzNdaxl/kWRW2Hp5sRTmcq41KhH47ne/y+YMut//83/+z3jnO9+Jt73tbavqLUPn3LdvHz784Q/jgx/84JLHU/LCe9/7Xvy3//bf8M1vfhMvvPACPvKRj7Axvutd78ItSTJLwSQZco/RF0AXbC0unEoqJ5vjW+h8K5WHuRkNx0qVEUrdeGZ22VqskFKSqQRKP9ffnipPMIS9v3d4XYmmI51H9TDwqCOP613A02OArw6QSFU5CvhdQEHW0cfMHBUU527cDeSmvIhMG6xDrqyZ4SzaqoHRsA5fDRCini4jGpzbgcFjRXYaTrGKf+9DIcgjBXj3VuHya1vREzyKQDeP9KDO5P6BcebEqbtHgDPAsdbLlHlGEjOck4eUUDF9xJj4/Z0OnBt6CI898TrGXzRW6lJCh5QYA9cYwsA1oLoOiPfswJTigOT3ouXVE1BEHo91+vH/FglmTiHlPEvGeK+khWR4RZnXTtlZPNbqWiOX27zK/tmEgvl9Y3SPd06Ni9mcLGNVWy5aJzPW+I2UR9ZyrLdoqWX8q+uKudw6GdO1Rgs8mtj/8i//km2UUPDZz34WP/mTP8nmElIvWQmeeOIJti0XTz75JItxf/7zn2f/J0X1V199FV/4whduX5KhL4jiA2vt/bKRMRkiCkpIoK6bK4kZVbJ18nK7bJJlSCmYVARKacomKuHeWopkVkM+5mv+4JnyBFMJEElZUUpaSosA9AJHHF682JtlGWTRM8bfOjt90BIZpDkeXFajEho4nEByAoiNGcRR5edRvVtDIhHA6FWj/TGRUzahoTUIDL0B+KudqBUlTHp8EHscyLxkOObk4TzaEUb1XhFKSkfNfh65aQ2JyxqUhi4k+4YgxYxrREH9ZM0BdO6cghaenB0/EY4XUYw8XYDgAmoPCiy9OZHsQe6UEb+JUWlN+Cqkx+5D4/GzGHnHw/g1l4K2aUMfzYRpyeQtVogpH5OwVuuryrwaF1PS31rZT+nH1iQBb9GySFksForfWEUz2fvJEtK++ZaMLjqQdTnnucsy1pqaIiGli7tXQzLaKupk6Nmk5KA//MM/ZBvNJWtZXC8Xr7/+Ot7+9rfP2Ufk8iu/8iurPudNJ5nFVt3kwrly5QqbNCmWUYlCRZNkKhUnsFoyJBtPpq5JiuWEIzcDyZA7rLTLpjX1u1TNebUKBCu1ZMg6OXZFwpd/8v4Fz/f4kzd6mqyVOP6/J/wse26p48z9RDROVUL9ZAKv7gaOnwBaqoGZOJBTgLY7gZGLWWalkDyMUwRqa1xwVxcQLqmnlBUd8XEgNpqCywGEgiLcXQokScTo+WJ8Iiaxmn/3HgfyZ9PwPhACx3MoXMujb+Y+4MjLs9lrBP/+LiQiXlRv5RG/pqIQBgvyF3oHEDtiEJS7nkPNfrpffYi9MMr2UX+ZRK+K0FYBudO9gKcKHS1x0LppsOUOtL18DNFdW/Hyn/8f/P5v/G9oSaubylhg5ecF/o398RL5GFPc0pok4CojmmmqA8gLuLpSFkKhan1rZT8TtySSmZckMF9qho1NKiBtyZJzFwqGhSSa7rKVpxSrq6j4tyowk3beRoDIzNolmP5Pbn/KkFvJfLZpSKYcaGKjwsTh4WFmulE6bSUIZj1aOpdO6KT4TPIwJDWye/fuFZ9/o9xlNE5KniDX2M6dO8te20r0pVkuyRz8vcNUzjFvore6vD7y/Nwsw+ViIeL4EFlDzxxe3jn0a3AeOowqeTv+edyBS2doxFmm20XoeQBQJCBUJSIZlSnOjvoQMBkuQDXqGeHzC2gOqMhIHKZIr4zcTDKQzqqQp4HYiIJqHxDcCSQGHfDqMsYvGgSRfT3Bij79DzTBPzOBpkcdkOIqq/hvfEjE5CtDEDQdU2RocEDru0Ro1BJ5qBEOLc6C+q5qDrFzKnJTCXjAo2q3IaZJpDhRFMlELo7xYaChGRCPXcK1n/wRvPLl30ahporJx6jOuZOss2ix5ObFZJQFCinVeVaP2QHT2mNGkBXIFjJxm8rM1mr9ckWXs/Ix1r4x0rzKfja2QgFJy7Fuy7FrCfyvBLeLpMymJBmyBGiiplRaCpSbwajN2tKZbh4ybWmMlKJMkzaZuauxkiolebNYl01TxoZ8rTTO9YynlDvHvt96HlRC8vc/UoO9e/fiZ7/x2hyCWSi28rWvLY8MSl9L2FWhO/xu7TL0v/kL/MKrPKbdHDrvAqb6HCgkZNR4gX4W9CfIoEVvfbMLolKA2y0gkzVW9W4Xh6FpQFY1ONyAr96DOjWHhCwgSm2WyU2VAaRByorSMZMB3LtDEOo4SKM5OGpdSL82iTpMYpI6HlOtzFtEFndx7+xGbjQBLhlB48Mixp+nOhwgiKsQ/Q40PapDzVEL6BuBfzXPI3bJENOkDptyVRf0vANNnmsYHQDS774fL37tD6EWJ3+WGWYhE2exKVjWMsGTJaM7XfNjJKoyj2TMOhmra40smfk1LnJZq4eRjMU6MdWWE1ZLhoouvWVIRiogWcaSmdt6eWWWjKaRy1RfsyWzUSCLiRTGS0H/J1f6aqyYTUEypZOxaQlQDOOuu+5imWBkolXSvWWuKCoVl6Ex0ZdANxPJw1AK4lrOtV4kQ5+XUhLpGi9nnJVoflZKMjt+6/k5f/uJ70aB7xq94RfD4587jMga9Ewvr74V0RykozL+8jkgnDSuR3gIEJwKWmuAnAS03gFMXQeUHFDvAybGTatLRbAGqN8DJC7d8HDJeWp+RUF/Ih0FIbIcgi54wwWkXEA8UhS5vJSAUOeAo94JZUaC/8EWXD3Sgs7QCVTvEkpqZfpZilvrO0Ummlm7X0D0ggoSDw5sr8b4c9OzxZi+Lh9ODT6GR5qeRfiE8XmkOCV5xMEpHEYngYffCXz+G787SzAETqP0Y1fZosuMxS1GMRnNEk9h51Dkea41M0kgNk/nTJmnzOxeQDST3GWSlWRM8vLNJxmrG47V3yjyPKkZIhm1ZN9Ks8u04vOzUpIhd/ZGxGCsoIX9008/PWffc889N7vgvyVJxpyErl+/zqwBWmG3trbOTlCVdm9VsqcM1ZWQD5MmdVMeZi1YL0vGjBMR6GZZTm1Rpdo4s/f8rKHiuxqshWAqhXeoz+O93/omNB6YrhMx41ChqDrkggOj0eIALwCuAPmvReg5BZ6AiFzKmOSqGoDrr9BvCgSyckI8qvZpFFdnRZyExAQQcuiYSgAqT7EcAX5BRaQ5iEKsgPxlQ8tKGhxHNbIIbRGYO6zpURHpEQ1pqo9p346xHxopxwTqNTPofhdqxDPwtnIsE43grHahZ/AQyzYjLTXqL0NtmcfOk39vEI+/F3jsCeC3nHNJQqO4R+BGckgpyWTLxGSoj4sVZMnk5mWXlU8/FmRpnkaZ2TcmXkbnTLIcawbtrfIxzsL8yn5TCDNWJhNNXWPrZcJK3WU0t1BG6lpB5yEPiwmaY8lVTrU6VAryiU98gmXB/vVf/zX7O6Uuf+UrX8HHP/5xlvZ86NAh/NM//ROeeuqpW5dkqCCJPjQxtzXDiWAKMpJLqhIkU6l6FLJeSJST/KaU8VYJjaH1iMlQoRddX7IOVxInqpQlQ1tJqcgtiQ9e/Cc4qAsZzeNQ0NAGXHYBvCCiipMwnQQ8NYDg5jE2aloWCtMlq6n2oiBnIXoMK4e8SxTs72XevBwCHsARcKBmm4zBUzJMBaVIVIVQBeSHc4Zu2SNV0As65DCPQn8VIqfMMlBADABCzzYoCof6ewXELqlQMkD9PSLUw88ietw4ztfOMwsnOULOMuM+ULJgbrWZo6QlM4zgO/8zHnviL9nfcu65E66czSFmKaR0LqRRVkY+xnhDdZ58jFm4aa2pIUvGSkjuBTTKhDJFl96iJROd5y4rQLKQiSmEGbW4y1wFCUoJSa2m9TLHcSsmmUr1kjlx4gTe8pa3zP6fiiwJH/rQh/CNb3yD1R5S7NsExcCJUH71V38VX/rSl5hL/Wtf+9qq05c3BcmQzApNfAsVKq5VTr/SFgNN3hTToNgGxRToZqDMi0qgktlldL0oYYKy8yj3nlYtlRS4XMk5FoZ57s0jF1MOvKpB3OOHayyNUc6Bs9MyC/JT4J+mepqDgk2AxvPgExpiVETp5iEKGvpHDYrlRaCRamHcwHTR5UZI5YCaEND3GsnS6GgisurgwY3rmIzoUFQZ9CbyWAGuXT7oaQ4FBNH0mIjsuAYppcMV5JC41suu4sygQTotbxWhKUAY21BH+da0Wu8hXTOapMMQ4WCZZr42HqnrKiOaxPt/G61byUVjkExSnDvp+11OZGvnpuNrRbXghLVvk6JCLWMxk0vKKv9PKcxk9VjjN1Stb1VmNuM3EYv8P3XATFnU1021ZWtlP7nWrIQUKErNRCwk47S2Xt6grpjpdLoigf/HH3980WeYiKbca0zPRyVw00mGJkCT7cuh0i2TCas9H8nDUMyI3E9EinQTENlUqrizkpYMxV5olUIpuqZC7GrGs34ko296cin112/ZJuGaA7h4RUXzNiIUYPIKh3qvjpk0kGbNJI0JsM4LVn3vlnJwOjhIsg7RbVgM14w2K/DVAqLLiwY9i/6wMcHR3yejQHM7h4lpDTS3ue6vgq7q0DkdhXMZaBkVTTjLAv+BLTzctRxcVTwS+a3A9Cicnix8bQLGXyiOBb1wNXlQf1BGIWp0yyRpfwEynCERI0/JTAngiUNB/PFfvQUCZzTPI6RES1q7pkGxuqSKxBCztoCg4LrF6mFQlXmV/VStX67GhSwZa0KB6S6zWidkyVgr+82amojFXUYkk/XPlXbyFy2ZiMVl5yyQ1eNatbtMW6WbnxavdnZZhbCciXWl3THXg2RI2I7YnUTsiGBMN14ltdAqQTLkfiRlBBrTZmjjvDDJLIdgbi4RVetxfKHwf8EnMvjeuAvDl2jS0jBSLNWpaXZDTedQXcVD92uIDIH1gIkkiq4l9vl1dN4BFEjosfdGgIkC/zyvoXcG4HigcRu5hN2oVvLoO1t0QVEa3mtx5irLHU/Btd0LzlOH4eNubN93DZlBqtKna0T3jFGmH9pZgODkULXTUGYuwIeqeidGn86xc5KIp3drLc4N3ouq6Wfh7+Tw7h+EULVDQPaPXRBDxnvrvBM6DawEJEwpWdzClLzAPk/1XDn9XCKBeDHzbBaUvKOqSFvlY1S1bJIAL0vzXGtU40KFlAWnY54lk7XGb0z5GKuSAMnHWI71FY+dsapJE8lQsxzz/VfhLhNWQTI3K7tsPXDTLZmbIc+/0vOZLQWonQD5LEvHXMlOm2slGZMIye1I2WNrVUeoRLYbneONX7sH93y+GBhYAo93O/HSgDkZczeVdBr0GB4YO4VvnAOigoiOeiAtO5GWJDTvAoZOGhM3m+QjQMOdLjiphbIbiKs8pIKGkF/A9LCKXNL4TLQorgsC4jZg8ISxeqYGYZFhIFCno28McId41Ds0aJyA+N4AMoeM+Ev+PKkcpJHH/SjM6Ki+Q4Cu6YhfUZHl2iBKScy8fuO+Dm4Xcfra/Wh2noGrBihEDZLh3W5sVZ4B7xTw/heC8DUbk2A67WLEx8YklHF1aer8zLBiPUzSsr/a54NocV+Zcvxxi2vNUUY+hr2fIiNdxl2ml7mvybWWKZMkoAsC0pZYjygV5knNmDGZ8LxumRIKpXUyK8wuU1dRI2PqmN0OXTE3BcncDFHL5ZIMTbCkF0RB/oXcTpW2ZFZ7LrJeKEXZ7KtDUjGVGE8lLBnyL//ZW0T81xeXtkbNav/yBZTchhKOnFHw9eeBKTb/KBjO0jWR0LILyKeA2iY31EQe8RxY3czQyRvFojyvY2szUFB5CE4iGWM/CSuEU0DmiGHlNIQAtcqFQEMBg28UuzAmNIxSQWUjh8zLCbjv8EOsdUCelgBvI9pOHkN2XEd2vOgSu1tAesAJrrEVoY4M4pdVZqGoWWALXkD0pGEt1R7k4WniEb4sYdT5GH7m8Dk4gzcmwFTKDYE37j+tDMlQnUzB4gJzKFp53TEK/FsyuFzFezts+doE6khZzpJRlPkkI80XzWTnkImQLCRTkKBT7MWykCVLJmWxWKg5meb1z4sLOQp5JPw1q66TUVdpydxO7rLKlNGvM26GJUP1OceOHWNBfUpPXiiucbMtGSKBq1evMjIkeX5Ke6QxVSL1uBLuMvo8pmT4Uiit8P9fTyz1gFkJp/LYHRjHTzwKHAgAtVuCCHkBv8+B0YvA1DUgMplHMgfUNYnMGqna4mJSMoTWzgD6JoCRaRmRUcAVFLCtGfC6qHi3OGrdaAkgJVUMvgG4fTzaW4HWPW5WfzM6SfL7OvIX0kgfjkGsd0K+PoVR7n7W/8VZDVbtHzmjwhnph379MuKXVNTfYwT0fe1OZGAE6l31YJ00R59W4OxoxvNN/ziHYAhpRjLG/aeRqJn1imsa8hZ3mVnZPy8zjFxrFuvGU/zgat3cZykXiYJ5/azvp8hIWZuTkZ5ZWUkY0iizFG6yY+ffR0Qy6TLxm3JSM9SSubT1su0uu00tmUrHZJayGMyiUCqGorqdxVYilVR1NtUDlgtSRaBxEiFS/MX04VaqqHMt5zHVnenzUFsGsrCufmbLvKJMQpMXeO5X50rI/PTBg/jpg8CpU6fw9SsSfm6n05CBKT9S67tXxMpx6ApCPqDbDQR/pBuPjJ7FK2PVkDqn0fs6kBjlUOXSMUNkwPQnC6wOppXNazpq/EC0OGRvoxPX+3LQdA08Z8RgXJIHcjKHiZjxneczGkjizMXpiEWB+joncnt8kMIFiAERmcOxYhr165g+YnTGzM0YsjKDpzvgTvYzFebYBZWlJlP2mxc51N5XC1cwgeyEjj2/7ELhLR8Cfm3+o59MuBa1ZCiespBGWcKyn3rB5CzuHlcxRpOwWD211DclNDemw65goYC4RVPPSa0CyiUJyPK83jUkCVOOkKj+xipLQ1aP1fJin69QmNt6eRXFmMIKLRmam+iZvl0smZtOMsvtjrkRKcw0MVJKtSkPs5zVdyWtrJUQFilTU/yFiKU0EWGl51lqPKuxZEx1AWrIRMKb1SW++aufuaHwSgRERLnYQ0huylkNy2XqjFXCrfYje/xwFMUq2Rl1+q6BpnoPdv4q8JaP8bj82XokLkzhouREdFJiopKNfmCMmCJtqJr53TxaDmpIRwVkBEBSAE0HyzYb788zBWd3QED9FhWIuZBJFRAdLUrRhyXwZzU4290o9OXge6gKUqIG8QsJ9DySuKE3Bg1u9IPfsRu84ypqDghIXDVEMqewB84rVyEnFdzzf7zY93EPnv0nN0Rx/j2bSrohcMZ+tYwlQyJnOYtyMcVkKEEgZwnEC0RI1poaswLfKgmjqdDLNfJTZEwRI1ur9cuSjDSvsp/ENK1SM+xYSULcMoGTJSOXIRmBOmuWtl5eReCfX0UhJuF2icnY7rKSCY+KFikl+d57710WwayHJbOcc1GM6OjRo0zgkibhUoIxz3Oz3GWU3Xb8+HEWuCTl7KVcd/R5K9nfZz5uTFJ0lf74QRVtjht/OffJR/Bz986fXKhXTTtvTIo6DwjFj8ATaTGycCDk47C3Bbj3g9X4+MvAA7/SxZIDqBaGnZ8DQj4e144A41fSoPBFXbUD26iDZoLqEo2T5lMqyzabmpQQiwG13R5wu3chvXMXhBqRBfzVsIzMa3HIvSNI8D2QojqrlanaxbMPEqu6F9rVS6yKf/o1FYUI0Pz2GmRRD0+rB4/9tZ8RDHu/nAuCOP+aJ+I33GXlSIYsmXmNxVQSzXTOi3vwpFFmcaGZ4pbxMq2aC+ViMqoK2dImIxeeQbzM/USWjJW83GWkZszmZFY9M08ZFQA2NqmALPXXptfpAsQVrsvVVcRkKB5DsC2ZWzwmQytoa9thklspJ3u/GDYyhblUfocKQReS/75Z7jKKX5F767+9qBhRkh8U5WQOnZ5jwRDovKYmnekipM8/M5NDTY0HLuphbMHfvi+EL38/gWOr+CxmvIce4D/fGmEuUUqO+NM/fQVfXyBHYqY4LgqaU0GmAZ0tzZycA04oLBbDeUVQScnjj7ZDHBnE9joH0jtaoF4cwoVBKsw0UpbpUjrcOnqLBdZeL4c6nw65WURsUIFUMCbPyEAOM+0aqjIz0EUNvoeNWhl5vABVr0XL8BuImR0PBMNtFj2hsKp/Md7LsshIJHPyxRha8Qb2f/oObPsxoyCTkM+7IFpJRhcgSY5Zd5lCEgVlSCZr1S4j9xU1zLGA3GV5i8XhMuX/5xVSyvPkY1i6syLPO7bO44UWLOdayyFWJn5jlZoxJf1jVncZqQCUO7Zwg2TWq2GZFbRAIwUR6+LxVsUtk8Jc6ToZKqgkkPYYNe4iy2U1LZ1N11slBDwXIxn6/NQemdxkZCEsZkrfDHcZXUeS2fmlV8tfA4rFENGY3TZNYiZCp99pvIcODeJ//+/XMDiYwKOPtqPPrSHXHGTCkQSa9v7lQz1z1KMXkvEvRen6lNK6aaPvm973o6+9Bs4LeOqAzLAOrmj5uJqA/dsmgG8a9r7LaVxPzmXcH4JAUkIyC967qovdIVWVPVC6wKGhIYPGHLCtwQHXpxQc+nY73M+M4NTwje8lm9XBkZjmeYW1Y66tE+BzqkiHvCgMjsORS7HyTmVagqPdxTpcQuExIj6MO7YeQXpYQ/VuARMvKqjBKagpo0i07QmRxWSq91Xhs6eew8+85TNgXdaKyGVdcDisJGNcYzOFWVnAksla04Ep/dgi/8/OQ3EFy7GmRllsnkAmWT3lu2Va5WOIqPQy8RsUCpgUuHm6Y1YVZzY2WZonH0OutXw5d1khj+waWy+7Vig5RYtecoNXQhB4M+Cmk8xyQIxObphKwSQtysoi6RVqO7zapkCVFPBciBxoZUOWFt2sFH9ZytLaSHdZafvmO++8E3j1/ILHEtFc/M0bMhd0vUxFh69+9RT+1/86BFXV0dYWwEtXwkb21feG4Kj2wLMlhPzWEN799eu48JttZTPSqOnZHzyfZ+4jTVJRaGqCZ3IC6SIZHfqNe/DP6kXsV7vwgKOOvS+RyVs/fBX1wQhi2SoMDbdgoj+EB95yEf5XDN84J3AIBHPU4Rheb3FxklYRJAFHsk6CBsmMc+PYRr+IgJ4rThC6DlJn6X5AwLYRYNt9tXCrM7hQLyL6fR0XrqrMOCIJmEhYRay7Gcq1aRSqGhHv6kF3YBBqXoEyLkEdoWdgBO0YQX4GqN4jsMLLhgcEjBz1wYUUyzobfUZBcCuP+7/xACYeucsIKJUgl3VCEObeZ3pxlW7GZOSy2WVEMvMD/0o5kmHV+hb3FRGEwwlVFOb1jUlbYiRmZb+VZBghlbE4HLqGTO2NVGNCPhxGpMztS5ZMxGrJ5PPIlQm0U/vmtEkyq+yKya8iJnO7FGJuGpJZajKrtLuM3ouEI824wVp8n+YNVAmSKZeQEA6HWQYZKVOTBM9ybtiNcpfRd0JWIF1LEjf97/+wtN4Rna9UMFBRNHzqUy/jzJlJdHdXoa8vhsmcBC2nQssq4Kg5C0mzhHNIXYgwt9Fjxwbxrkcb8C/tjSiMFeC/wwvX+QRe+vVH8YWLp9H9UxEErmWQmCgg3MQhelaEr0PD//V8AzViAq8BeLYQhJy4E3f/9BSCXqOIpdobR6Y5gD3brrH/55LFCVUA3LxBLqKjaIH5NDg0CXmdYjbGY6SqxcfJSe40o/jSnN81yVS15lBdC+z/cSecMzLevVPFqf0hxC8nMPOaAwMDEyxQGoyMs02/OwhluABnlwf8Hj8iF5woRB1o9Y8j/EbpM6FB3LkDnNCHtnc7WAwmlTVShTlu7neYzbrnxWR0zSAK011WjmQosyFtCfxTtb7sKm/JWAmJCEIv05OE0qAXlI8pU/FvrdY3BiwhaiGkWocTkkU1mqFQwLRFaobqb7LlrB4pj5R/dTUya4nJ+GxLZmNRSXcZVcXTqptgzcpaa3+acgKfKz2XOamXZrqRejKRzGrOs9bxLET+ZvsAIgy6jmRlTaflZZ3TdAMkEgX89E9/F2N7c5C3cBg6mYC7MwBN1eHsCiF7doa5qrp+807krmVRc7cbfX80hEtjeVz+hwk8etKB1qlJCJyM5F7gXc8mICr12JoeQO09kdn3HP8PjVAhQBJdkFUnHIKEeD6ExtqjqBYUKJqAiVQzEoUgGrzTcAjGvRZ0FvteCuTzLy4m9GKPHHBs0qTLzBcnIV0t/k3gwRmcBM68fpRSRj8UgS3tVF2AQ8pB44Bd75JQ9e+A8Bd9+PyWn8OhmW5sO/o0AqkpNJ05Ayg6cmeMsWh1rVA4D1NU9jRyiF1UQXwwE+uA68oliF0OvO1bATh8HKJ9zrIkk8u4IBbdYlaS4YvH0rUqZ8mkrKnKqgLFWa5wkzTKrG2WST5mPkEw3TELmXiKVo/sEOfFb6yEZPSCURCxBvN1DarFumEo5BGeV+RJKgBlFpuaBtm9NneZcIs0LLutLZmNSmE2VYmpZzWZpJUIrJmTZiX7wJRaCJTpRnppKz1Ppdxl5T4XETUF+KlAdc+ePbMP0XMfe3vZOhgT7e8o4LcKz+EOfSt2TQTwsz/+b9jyp0H8P48Z/ealJx3oO+PDdKwWg4dUSNfj4P1O1N0N7HrHBFq3jiHxfjdGzzvQ+y8ctqb6UbuN8oWBZjr/zgT0TB+iA43Ixnyo3zqJuLsKDSKpDhfrPzge18Lb4RQkJAtB1Hqj4KFCUpzYWXfV+Hz5ECK5GtwdO2lcB4FWsUXLpFj4KWsCeIovUeDfXXT/FRdCVDVu1o8wnx9ZbDljAeJwyeypk3UBvKazjDPeQ/4yNjhc3v4wjj/6QRz/0Y9i++WX8Kn2jyH5TBjJp8MQqkWo58LwagVMFXMqAt0cPM0CrhypQ/vjj+Kd37sAXjTITpENouDYyW8gk3FDsMRkNNU1x10mlamToT4F8yRaKA29nCVD1foWQnJR/KZMFhldNyshuRcQzaQOmFGr2rIksy6cVkKidOeMNbuMYqcOBxTLgrAwM4OM1eqh767kuI0K/KcrpMC8WfCmcJfRa0l7bHp6mqX80sRJ8ZhKoVIZZmYxJikN0O+mhbAZ+tIQ+pIa/NlpFuA3ddz6lCS28qHZDFY6uuf9Bdyxrw9DY60YvxhE9Byw5/9J4cBuIyWqH2/gfMCD3d9vhb+6gETSi1AwC1lxoK47j933nQbeDaQ/40L/cR5edRDN3YZ8cSiYh+MhDtsfkKCknRi50AGOVxDcnYAfWfA+Hb47jPStoeuN8G2RMY0GeJFBACkkM63YXme4wwiRbA2iqSp43XlIqgNOQYbAKQi40qj1GsWPVD2pZE1B1OJ1Lana59zGh1e14mTipNoPw6ojIjGOL1o5dJuI5CYsuthEDrogAhLwP3/qD/Bd9wdnx6bAidB76tmGLwP5a2l87+d3QjwygC38i/B38SjEdKSOKBjFg7j/wz3gxU/Mvl6WipOiJSaTTTvnZZeZJENyOARpgZhMykoyKq30yxdupi3EQQ3OVE95krGKZpK4ZTmSIUsmPa8Dply2Wt8pyZixKHXQefUy560SBPRbpWbyhTnjda3CXaatwo1eqV4ymwWbgmSWwlpIhipnS9061KeaigQ3S38a6wqG3FD19fVMaWClAUMTq7WsxjMyWnwOfHc4gR/pCLH3fzGi4Nv8UXRPenGe8+ETsX7s37cP9Q31+CPxEAqu15CRQuCz23GHvg0f/LiCQI1hAVTvSGDnFidm3tbI3DDj8Q743HH2e171YE/3jY59Q0OdbOL1+TJQVUoG0MF5eOx+LAMRSeRSboSH6+FuiyEUysPBq3AEc2i/YxhDZ9vhRwZ5uJGZEoGcC9mEH537qF2kAQkiptAIh6RhLNmGOu8UJNUJHTy2NRruU0UTcT26hbnCar1hVlDIIAAuvph4UnSXSbqxwiVLxhksKhfLxUZvvMhqMYydxg+ynAhk/TB3mWpMPKpLhCOjABeBNzrntrjVKeBTAvd2P4Z2fxBnL92Pc4PbMfGyjJGnJAS3e/DpX/ksfoz78znHS0WSIY20UmQyFPife/+rJslwxUw5sXxMZh7JKPI8ZWZ2HkWZ51qjYsxyGmV0DqvMC7N6FrBkUhY5fmpOVtbqIY0yy2TtX0AFwKfrkOvn1uT483lkSghCziqQNXlFbnF1FcWYtrvsJoDcWquJyVAtBBVYUuZY6aRd6USCtVoyZC2QK4+sKxobuaDWgmm5ANkyHrbiLlobfz8QxWMNAXxrNIp3Noaws8rN/v7fg88hdMwDyZlBN+nTc4CkaQikx9D48EU0AnhZ9+OIngPpD3v5c2yV4nclkOXP40hhFLWeGYwlW+HURah8Dk5RQXtoZHYcM5k6qJqIvOpmE229bwqTEzVoapqAy2VMxPm8G5MTbgTaivU2NMH68lBlEfWhNNuXkVyYutACtSBi631Gwy4v8hCreUQSQdS2RzDS1wjnVg0uUICERwtpvxQXttOZBhQUF4uNCJqEan+S7WsLjsIlGoSiUok+QTAq2EtjFnyxkJKumwrHHB0v3qXNqhNzRUtGzhrn0smioV+LawCVF/EXf/sR/Nj938UMVz/3O5PmW/dy3slIQ/RyaH/CybZYKgD8CtXzzD3+9V5DJbo/ksaeEo9rOu2Gzz83W9N0rZnuskI5d5mmzlNb7h3P4GCZvjHUZnkeyZBrrYzVwwhpnrusvHwMWTIpa5IA9a4po1HmIPkYi4Xko/OWs3pIwsbiogpJCpwlOmvZaAavnH+Fde+l9sXkLqbfF0s1Vm132eawZJbKB18pKdCkTQWLVLhI5FJaV7Ga862nJWMqPZMrj8iFfl8ujuSnUICGR11NEIrX8FvZflyo/Raktwk4PCFjF7ZhRPWiVvTgvzcZk9g3XZfxr4Nh1Pgj8LrboXI7sFVrQfBiGjseOsGO+R5OoXCPG1y6GtUTHBRJgOhUafZAihtCDWKQISKdb8BUxguvM4fmwAR7bWtwDIl0AxyuNHKyB4PxbnjELKQsh8baCHNJmeiP9sChpDA11YTa2hl4PDlMTjajq2uA/Z2mzamBWsQnalHTGp0lSzqHN5hD09ZJpKM+6DXGsVpKRfN2w7Xmr0kjhiqI0JCHE2NohjeWRE4ModYThst3o79L31Q3RJeOSK4W9d4pOAQVao6fTWEWimrDWtHtpVFBCvsFcHiMNGRdLWbM6TdWv2bgf9Ztps61ZLxiDv/ryc/hqx/8AmYo+FMCardshZJ3zCMTcjOy97AE8yXJVTbwn065EAyZbQqKw1Lcc86RtzQsM3vBWIUwdVnBhayjbHaZlTgMkimvtjxPd4yRTBmrR5aRtATtvZICuVxaszRfo8xboE6X8wmJ4jdxyzmokVlp6+Xulm7cGdzPPCG0gKUWIAQiHJN0rO5t1Q78bw6SWQomKSyn4JEsHooZUHB6oaD5Ss63njEQqv0hVx69llx5C0ms/FtmEBcxhseE7XjAVc8m2b/KXMJI6LsQBRnPSQHome1wKvXQqg/BLUosXql5T+PV4TjaOocRlRvxdbEFB9Vd6EyOo+XgZXbuKYziWziGgu6DR6/FxNVm1HdPQXUI0Hlq7TuBliAg5RwYv9YJdXsBNUzGEXBAgaRraA2Nwy3mEcnWIiUFoagCuqsGmEvG68ii1hthRNMYmsBUspEuGKo9YUSyDeip6QeKCUCFghODgz1soksmPGwiVCDQ4djxoBFHKUgick4P1JyO6mZjHP6aDLK6Cw5OhVClYqK3GYWMC3Dn0LozwsZJ7jTCULwBwaYcJtMtLMuMCGVssglbWw1SI+QUFwYjPXiX8kNjh3Cj4l8spviaJKEJHDxOmbQxIRSP4Rwc+z8Rjzm/i5rxOgdJ1YgUnzCINqUEIcsuDJD+f/3cAls9V8aSkQxLphQkcUM4NZLAE6X7C+XdZUQy5I6cc96iJcMXLZm8xZIxP1vSQjJMo6xsm2VlHiE5VQVSGetEUOR5opnkLit7bBmSoXTnsiRDUjPWvjESqQD4yhKStUCTjlUsrZeJREjOiTaaP0jlgkiHWm2QJ4IKfYlsiHSop5O2ypgMnf92wS1DMgQihsUywpYrD1PJAsrluMtSkoqAU5h1VU3TQ5F34kg4iU/XeWcztP5GP4Wzj4zgTPoo3u7YhoPOWnwpcwLJ6h8wTakX8QL+LV+FXHInZPcImoozmEvMYJwPo6P+JGRVRDLZjEjExeIH3T3FyVMcxyCmcV24gKoOFwbPdMPty6G2JwFNUOHjUvDtN9Jkxy63IrQzziwDqoMQBQXT/U1o2jYCEcUiOVQhjiAauSycgpGvW+OJMJLZWtsHSfZgLF0/GxDvrjbG0e4aRV5xIZKrg1vMYTDeBb8UAWXB0sq7p8eIjxAy8DCSkfIOFLIOuLwyCgURfmcWol9l1tXEtWakwgG03z8IB8UTSGZl2wSGznShffs0MvBhBgGIkCDBhc5iAkG1JwZF4zGWbIdDyGJoqhVVoRRzlcVzNdhW24ug2yAmnefAma6romWimenKDm6WgBSlaPk4qYqUXGHcbNBdKxSPJ+NJBGoUgyC5kPF3Cw8YrymmQZdCzovgLBaLXEwisO4v5E3imHvyqbAD/kQxPbsIRZp7bM4Sk3HSooyKLi2LMiKZ3ALusrjFEnHI8+X/2TmIkCzEMToex8miOrU1fmO1OMhdVo44SHcsarFkfNSErBzJFAqIWEjGnyerx71gdhktUGkRSxslwZBUFREObeSRoAWvrutMa5AU3ZfbRJBiMnZ2WYWxlDVhEstiJGPKmnR2djJ5mMXOWVrbslaSoTmH5w3L6FQ4i4PU4B3APw/G8N3cOLblXbjmzOGbe/ay/SeySQwJF7B7z0XsiDbi5eat4JUovu84DYU/yroqKhjHU9rz+LP4NtK1gDPTiAbfFAtMJyUfWuuPsnMpqgMDsXbImhMt/jG2j2o8FEcWVaE8qmtiiERqkUoF0dyUBOdOwEPxieo8uqoTGLvSwmIMBTgg607wKRUzgw1o3TkKsbiiJXHI3qPbIbpkpCIhVDWHoYFDDh50YxhwA1nZi+l0AyTVhY6qQfY6pyPH4jNpKYj64Awm002QMiIEQYLHp6A1YIyXMDMVZHpauZyX/WxomEIq7kaoIQsfNIR2pBmhXD2yHbU7xuCiwkOozH1XyLqw7f5e5kIqwIn4iAvxkWZsufs6y5QKIA0PsgijDo2YxthgDZSqAHyOJBTdhc6qG8kBiXwQ4Ww9u86TqSbwctEyEQGnZimuLFoyVCgqFlPNqG6G3VdFd5kEB4tJsevBF7PN6L8CMKPVoR1TyKM4mZfJrtSy5dxl8xdOslx0l1ktHNOSsZBPPuOZV/F/csBgNKGY6p2zZJc5SDqpLEGQJVNegibhscrKkJ5ZuTbLMnqLrsbZYyUF2XI1NRKRzHzXWmGB+I1Vo8xbIPmY8oQUsRRo+knPjFqZLjOFmRICqDyCNiIXM9U/Go0y9z0tfk0rh5TJF5p7bJK5CSDCoK2ctUDWSG9vLwuck6wJfcFLodSSWSg4buKp7Ahel19HXboH7wxuw+5ACF+bmsFHGo1V+vGZDD6zNYOWK0NIBPrxzboH4YSIOPmUcQ3CvRewQxXwe+Jr6FS3wuNM4MAdRiqvyzuMMMbx58IFuJFHBs3wpMnVlUNGDqCz5kaadSxTh7gcYIHZrOyBl47Jh1DnjSDgSrFK8plsPWLZajQ5UqiqNuIjtbURZDM+OJ0xqOCQgg/ycBCxaTe6DwyyidiPArNaBq93wxvKYvRSB1r2TUDk8pDkILbdfyPlN4Eg21wosIwtEomkYka/K4067yCzWibTzchNcXDWcyw+Q2jyT2IqX4tAIMNSc4fiXRB5CarOo6lqEk5XsXUkgIGBbgR8YdYNhd7HBRmjFztmXWaEbN6BkVM9CNYlmVUh8DqzspwuNzuO3HtjfbXQt/IQoKMZU+x1rV1RxPPkKuXB6TJz47nzcfAuwOuTWeDfhJQkBWlDi0yQi/de8ZYRKK2sWBPDEX9wN1xqPFkydChpjRXdUkrKWBwJREgOcnG5kfe48LFrXyjefPPv07Lustx8d5lJMrO+OXP8hfKWjFxwzSOebNHqOTMWxd4OIGuxZCj9WHOVq9Zf2F1mVVumZIhyJJPKZJANeOcRRDkL6dpoFL1mqngRXlkqTxwFCRFrJhqpKpclGWle62VvXoLkLyGZFRRj0nxFmayEAwcOsLmLBFkplnPt2jXmKid3mkk6pRX+NsncBJj6VlaSoS+KJFckSVqRPIwpa1J6vsv8JXyL+wFSmQ50qdvwXudWfKdwGcnqZ+HhVWQaruDbGvDFvjuQcnDYze3EffoOWsiiLjyN7rcahPB/9Ffg0JuBNi+28MNshS3wCmREcFlMwbMnh5nBemTiPlR35KDX5NgKmxBCEkmuCjnVy6yCkUQ7y76K5WpR446h2heerTq/FtmOEC9DLE4gNPFkJB9zVZF1kk77EQnXIl9wo7u7n00qNM35kEFcD6Fr/xCm+ptQ21GA4iLXiQPdB27EJRTdwSZ5UeeYxdPQPYVsPACuUUUHjImYXFmJWCfCushSfk1ZEhpfS+sMXG6ZVdYn8tWQVBEdwUG4nLSUzyPoSjJXWbN/HDNjQSgIIhSKIh6vQXf3jXHQVDhwqotdx4neJjRumYTG6olE7HjQuOaFrBNJ1QHOzTMXIMHpkeEN5FnQn6yZOELIqB4ofQpqu7PM5UageNHYWB1LXIjlahDN1TALLJJrgM+VNyxVp1EPwq5z0eLQi54cTeCN9GT6czGfQZKNyUjlBVbIya5LMSbDCEkA0/X61Z/7bfzDoz+9MpKh7DJLRqxUJBnrAsl0l5WSUoEdy4G3pDAXZgnJ+JxZoRzJzLdYyF2W9c4Xa9VFYZ5GmUMuTzKJhIRsaO6z6yhQZf/8Y8latB7rlhTkysZviGSsloyEbBm3FVky0/NiMhIKpSSzitbLfLFYmzwwdXV1bDPjLmYCASmQkBVEcR0iGCKjSrjL/uRP/gSf+9znmJdn3759+PKXv8zi1OXwjW98Az/3cz83Zx/Fn0wh4Vs+JrMaJWb6Iig9mVYD5XqqLOd8TP0XGg4LL+E18VWmGRUKXcKMdhW/l2yH25VDLtmBBj4B3plBJFKHzu0X2OtfxHk8o3sgNAXhSzrYypkmNoXjIXMzcFcV4FZ5TPY1gdOc8G8Pw4csW/HWd81AvuaA1x+HBAHjaGLuHwUcGtwRBEsmgIFYNwuqJzKNcLrjCLoSGE50YXvtjVV9LFeF6Uwjs2hogqdQgMMhQXQo2NF1lU3IhVwt8p48S/Wt6TTiEhS7UGU3NPDgoGDwTBdc3gJ4UUVdVxx+Lgvy5rTuTGD0cht0jYPQKLPVuA85hFGLhuAYQkUZFpqgZ9INCLoTEB3GjBtyJRDPU4yD4jQOTMw0oeBwMYtnCwX9WaviKCQphZnpRgQCKQwOdiPgDyNYl4EMF7oPGi44s0WwGiAFSh2ZuBe+qizSkQACLQlDet8LzAzVIzpejUB9Ak2NhgVThQTyuhMN3THInBMjiQ42r1MMq7tpALygI1hMDhiId8PnyECO86wOBo4bs7o5CZvxF1plCKRuWRIcd/gVIEXp38ROxjldfLFOhsiG/hwI4Jzjjhs3ZLmYjMGDZVKYtbIkY619KeTc80jGTBIgy68cIZkpzKfDMrD1xt/zWRmDZcZjSPrPn3x1cX7GGRFVOeuErMK83xK/kaR58jHGOYhkrArKZJ2UTxKIzCvylJApM4FrooNZpXOOZSrOa2u9zC9QI2OqgVPmK3lUzPnsz/7sz5ic1Gc+8xnmoXnXu96Fu+66a8Vu/X/8x3/Exz72MTz55JNMV/CLX/wiOxclJ1B8qBwoHbu0SL1SKtCbgmRWIi1Dvk5KHSR5mK1bt6Krq2tVF4O+tIyWxvcd38UwN4wmrQmTeRcmtRwETkdnMbZAyKarEIvVQFEcmJhoZjEDmXcxzSyHOIW23eSCENF7dBsa7p9gri8CL2iQck607BhjJEIxA2SDGD5bja6D/XA4jQoLihlMoBkNmEEqVYMkvAi5YkhK1bMBc0JecTKCoRmJYgYN/inkZQ+zenYUJVEKihOJSDticQeamg1XlSgqUGUZHkhwQkY6XIXwaBWbQDvuHIObluAc0LV/EKMX2xGoT0LV3cjCAa9awOiFVrTtGZkVVaTpaRhtzH9fyAbh8lNqMYdUIYgd9VeMceQcGC00s/G1VxkV+E6HjKpAFHG5Fk2hScxk6pGRfXBEY3AGHGhtG5118VF2GbnVVPAYudCOuo4ZpCJBhJqj8NP1DRjB995jW1n1ursxzeJRdCvkkh5sues6i9nk4EYcVShARBM/BYdI1zwHT3AYQ4kudFcPYmYihJzmha+6gJziZ5lxBJ/HsGRoVW5iVovMJBkXGSZzrRvG8syS4ZmEv/HlGT+0PA9tGBB2alDNNGj24vkso2fKWTKO2Qy3ee4yC24E/rV5hMQXY0XWY82mZVmLHhmRSaFM3xjKmrPqjrHPWeZYZoWUObaeFg2WZ5gyw3KWjDMDOmQLqVEVfzlCKmdNEclYFQPYeMuQojc/tyumy4yfLRPLjfnyPM9cZr/4i7+IX/iFX/j/2/sOMDmuKuvTOXdPjtKMkhWcjXH2Ek0yJgfjJRhYMiYsaTE/GBbDEteYHEzcxV7iD8tPNDY2OOGEsyVLVhhpcuqc0/+dV/W6q6trpJme1DOu831tS62Z7urqqnfevffcc7Fjxw684AUvEDXmq666Ci9/+ctxzTXXLOi9+XtvfvObK9EJyeZ3v/sdvv/97+PDH/6w4e9wHW3UjX5dkIwcNEZPr6mpKRG9MJ/ZKNKhFP639deYsSppnnHLOKy2HgQd1d4OryMpFu1e7yR6/dXJVgcPbkHv4BQyFqvo4Obub2T3Bmw943FlcqLYIXtx4O8b0doXhk0ddetADgVLGcedsw+ZhAvx6R5k+nmzW9APpYbS2jIFR9YrajIeR0qklLir9lmzbDXEJg35zcZ7kS26ULAUkc674HFkEc60oT00jvZOLpAWTE11Yna2FQMbpuFR8zn+jghmRkLYeNKwQg7widLB2L3d2PKkg5U+DN52mXwInmAaRx4ehL81jpaBMKYtHZWUGRd7RlIzqQ6hzCIRUkKdz1nhcWQw2HJYRFcTiW4kZhXblo39ymft9E0ByTLcbVlY7AUcHhpU+jQsRXR2TIlajHiLE5OCaOyuAhIxL0rujCBMWspsOf1AhfzYtzPxWKuoMxULVkEybMTkp9mEYRQtFowfCiEdahXFfUZXRHdfBJlsEuFcB1rdsxiKDMJuLaCcVjr6ZY1FSzJFtbu/rPqE1RT+S8ptxVqVaLwUVvTKZ8lnHXiv/QqkT21H8c7yUSOZojJpoAaFtAsOVfUmIdNz+v6ZCslonq+QjF4MkHHXEFKGk9Y0sBaKSDvqF9mT22zIbKhPl5UMMgsKydQTh8PAKdkhXJHriSNvqycDDidLGPxs0YDoPFk6Bhj9bP3xMpJJBxYXydgaEBYx/U9iYWqL2RsKCBb6+/feey8uv/zyGiK74IILcMcddxxVnUvhFCMrrq//8R//sejG8KYhmflGIiyYUZZMeTKVGo3iIeuDuP+Mf4g+kM5SFzxlN2yw4aBzCF5LSfR2UI7LnW6HdwqpSDdi2TLa2ycxOdkrahwEj4A77f33bBVEEhlvQVt/GAWwb8aCHecp0QXrL4UWhX4YoYjf9WdRyNEypQg/EphGmyhxe2fysPsz6PYraZ5O75RwCC6jgEQhIIr+VJpNJHrR6x8XfTIEd8Ws0ziseeSRhsOZUeo0SR+2b98n/sxFOA0XEo92o2/HYdhU23ofCsjALyKZ8f09osek/8RJ5OyAj4q0zcp5S4a9iKJNqMsm0YlWzCI940DZaxW1IOU4bMKaxTaWQmhLulJ4plVLb+cUvJ4s4tmASK0xVdUbGIXPp2zz/f6kIJqe3nEkYn7k3Hmh6hp7sA+DJw9VFlH+d3aWC5NFpCNZL+JnyVhd2LBjSvwM1WjDu/tQ2GVHu9rXwwg12J6CxeIW55dqMqbybLMJOIMF9HYo5Me042yqVSinREe/QSTjVNNKRUrPVFjVhk3pYeZwF4UnGfswbXHl39jUGS0FMBXYgMxQNd9dztaLWoq1XCLAyNjrrZUfc6KlYTOmmi7TMpgkpLqaTFrX8a+LZOy5AjIGJGObIzoximRoGhoxGLZXMrBpsecLhrWTnEHEQZt+vS3NXETHCZj63pm5CInjm9M+5bu0likgWRhhNNoikUwmK0MJmcFZ6Gaa40FIcHoRFP/ODJARGD0xyqF4iqT2xS9+UayzjzzySF0z+5okmfmcNE6EZP3ljDPOaNjTi/WX22234h7b3SipO7ZZzKAXfThsPQhHIYR9iVY4LFmULLZq1NCZhC3pRTjcDoejgKGhTdiwcRQFK5dOm5DLSqRLfuSsFrDSUSxYYLOXkZjxoy00I/o46JU1sqcfqYgXXVumEWLynmNlMYtxdCPYEkY8HxARjBIZuDAQPCzSGD6ExaLHKIvFZkYyAfcsnM4khmMDNXUaFv1nZqnmigvCE15gKInUTt/xB0S/BY+jrSePQksSAUtCXA2s00QmQqJ2Q2luOh1C5IgXNkdBEGO7RVnEiUl0IDeTR9EWgN2SR9AdF1LlDcEjcLUpNYhIpgXjiW4EnAm4PLnKIs7OejZiCjXagTbkLD7hJbZtm3Iu2zrCopaeKPkQ6o5i6MFBuHxZtA+MI+fyoK0tJh5EfMaPossCm7+EZMQFX0sWmbhbHG8HJ42xiRABRJJ+lA4VsPEE5TOE3DEh//Z0JOD0FIQdDiMcC4qC3F2MZkoQVvrSEFPaxAijSy4+qgMz13HpCiC6R9VxAFyXKGn22BXCtdkKyORVSX66ugAVIvVD+coGJHM0dVldk6ZKMsaRjD7q0UUyepIpFg3TZVTU6R2U51rgabujN8IkigbEcVa3B0lPrdsyUaCZqEEkEzMgGSPicOULmG2rJ7qCESFls0gylacqy+TU1OX0LctzFHU2u+J9MmwG50OCBEO3FNaIrrzyyvVLMtqpiyxKsWDVKMEkkcT/dfwCh61KX4Qv5UOgHITT7cRhm/Jc3h5F0Ek1COBxpEVxmEtFSz4LuNLo61N2uuJnC0EUrXlQOBs95EfHwDRGdm9E3/Ej8AivESCTcOPxBwbhDrDgrqqTrCWRVxepNVtZNDWmRCLLhkEMCzVSiy0CvzOGkfhG9PrHMBrvRyBfgtUfQyzXUlOnyeXcOBIbFH+eTHSKFFQ24xONmNu3K6TD3pPoTBesXRGhLiMc7gKc3iwcHrY8ppEtuzH2QI8gse7NE7Db08rF4clhpuhHoD2NyHgr8m1eeG1hUedgDcm2nZ9LCef3zRwnGjdplc8IkOeRJCMt9LNZB8ZGW8XiQSJihGO1FdDeHxZeZSQYkmM8EYDXHYGvJ4sgC+e9SbT2hkUklYr5YO8sIzzjh9NaQiFvF55mbe1KtIIWYOiBQRRydvjb4+Ks84phFNflmoH7pJxIgY4lupHOu8Vx+oJKRNHvGBHfeW9gXESAhbhFkEeRC1tBEZDJxVl6kRVV+RijV0tBjXK8CpkW8ursGNgq/0Y12pi1R4yELqtNlMoP6/JlhSKKMYPoJueoJ5M5OvuNazLGkUwmrSMZV326TE88czkok3gKBgV+YW45T0Jy5vMYM5gFY0QyHAsw1RKcVwqMI5nDRpGMwTG4xXAzy4rb/BNHG69+LFDBxvdlE6gW/Pt8ay5Uu1F6zXlWi0XTkox2pj3VEUNDQw37jY1aRnCb7RaELepCJCSlNsx6ZpGzZeEv+9FaakOyYEPKMS56UIiNocM4HB2ENxTG5HSHaGwMhSKIRlswODgEh/ANATwb4zhw71bYnQXkC144HAnRtDexv6eSMmPtg22B9mRQ7MxJMAQt6PNwiJoMySY7HkDc44LdUsBgSCE/Gkxmkn7Eix7YrUpvR8gVgZ02WJZiTZ3mSHQDfFkrCvmCSKM4nexCbkNnxyQcaj9MZLQTk0OtaOmOwO5SPqvLkhHpqI27hpFJupBLtyPnScOecaFv+6g4Xn97QpDQKHqFuIGuxt7xGAJdaQzHB0SXvERsyoUJ64BQxsk6jZADWwpCpMA6DQ0pE1k/7KNxDJyoRBztHTNCmcYJmC7kEM/7ENnbBqcrA3cwi5YutZ+mHZg40CUIhikkZ2sSPm8WM8MtQubMKIZgs+MYusVr+cQgZjok5ER6cUvrfkF0M6NBpIo+pD1+bGvfJ+r2Xa5JuJ35ioSZHfx5q0NhLC46auHc6pXzZeyVXpqymv6lGo93GB2baTsf9/jxmv4fYV+uD6dhFCVNJMMJoFqKsOQKSiesBqxziQtOTzLSbVnf8a8SR3ewukBuamlRrn9dM2ZOpy7L6kiCqcOsgYMGh5PpLf3dBbot15MJoyG9Rxmhn+0i6zdGP3v8pjZD37GoQTG/YDdOgentY5RjMPhZRjKV0csrNxWTWIzVP0sKVKTdeOONePGLX1xJ3fHvl1122byPncKDCy+8EOuyJkM2Z6csJX5ypn2j0zHvt96HP9p/j6J685BM2kvtmLBMIKfatyeQQJulHbPOg7AVnRiObRSLEFMnW9uU9E1v7xjyeTvGxvoQDMaQy7Wj6ErAWc5iYm8ftp5RTZmxg75c8IudZWLWC39bSjj1MlXl9k3A7QPCY62YPtwO75kp9FqUBdaLNEouB1rdaWG5ItM3VhTQ7ZpBp6eaquK/BewZpFMBRAo+UacZiW8QfSeOkHKeKF1+7LEdcLmyyOfscDizIuDPJd04js7F1jJyKR8yXqaBHOjfeUQMs6IsuFxOIQsfXK6o8ALLpZwIdERRbLWi31eN6JizpnU+t/wj8X50eceFu3EmYcNxmxXSYUqMsuDybB7trUpNios7iZT9NaETY4hHfZiNdAib/7a2MFrUVFjAkUTcEUSgM4HYVAj5vA3+/iSSUw609c2KiEwiioDY6U8f9qNz04SwoZlFGzbhsPjc9BmbOtSCGX83Omnlry7Krd0xJIY92N6xT/Qa0fImmImilFQK/0JUFGdEY638joxk2LMj/m+xVQv/MkIhF6jpsnDQh4umfot/jJ2Bnr67lO+n2n8qRBa5mi74YsW6RqKQU8krlTckGX3TZTZVL2EuFtTemWNFMrpue+FRZiQ/FnNjasmgnMnj3hntp6nWWfTeZ3MpuxwGVjPi+A1+liME9C4ARMEoksnl6lwAiPwcP5tQ+2RWciqmx+NZtBMJ5cuXXnopnvzkJwsBASXMfG2pNnvd614npu1+5jOfEX//5Cc/KXoNqdilnJr9NdzYv+lNb8K6IBntgKyxsTGhIKM0mR9YElAjJEND+mnLFILlkIhi7GU7fPDicfs+8cldBRd6LL2ww4YhptEsyi631a14cG30HRaqKHfMAfapOxxFDAwoclxC2LHAjWLBJqS/nZvHYXU4YXHk4HKEMXByWOxm99+9Ff2nzQL2aqNBOurBxhOOwG3JCmlzGK3i9XqcU4IUCHbLU+nUGxxHNhzEaLYHPkdCKM8GQoeURcU7I3bbB8NbheHjWKIPve4J2Ow5jIxswI4dVd17Su2fd/rTijkoz6sjCyuc8FsjIo00/ng3Clkreo5LIOCfEeeEkUx81odM3AunNSXcjPlKTnpAJ8oY6K2eE56vTNYBSzyNRNYHvyuJ6VQHurwTgryIcLoVk8lOETH2qfYygVAS0XhbJSU5PtoJX18axUwZ3ZsnRWHfqzoHx+CHx5vHyJ4NcHpyglASaS9aW+IIbVFqXLmMIin3nZlA3OpHEEoUlklasXOTck44GXM62Y7C/hQ2n6IQuM+ZFNEWxydTCcffoZSaKFjscNiV1JpLjWTYcElQxOAqqNYzapTqYOOpnXJgD84bugND0c01Ume/o7rY5dKFushBnHwN8hk1stHVU+IHlfctpmvJJ2SXKaRq1FIoKJ/l5A21O+VsSlmkbSrJZHWNl4zSMgbNmKVsFozpa3+W3fpG7gAFxAzSZQWnEXHQNNOAOAxTazlEDInDSCTAzn5P3fhmoyjNycZN1buskXRZI4X/RCJR0/3fKC6++GKhwr3iiitEM+app56KP/7xjxUxwOHDh2tKD5zCS8kzf5aWN4yEbr/9djH6fd2QjJxWyR4YdqfqG4aosmBBbCHwwIMLis8WD5LNIcshPGJ7CJayBWVLGbaiHRFnBFELU092dBf7cBvdf10xYYNCUIWUSHci5E0in6dL8Ca43Rm0BLOwe6NwWfIYOEmdxPjgAHp3xMB4wVqyCNuUww8NCNWWlDHTUn3fnYMItMdhp3uv2CXlhEqNNZmS2yrUZCz4MwLY1q5ESPb2MFylGEbjHLg1JWoHfmseAecsJnMd2KJGXEQu48Hk4Q1ivhbly4wMUikvLN4SWhETs4rZyDi+rxdt3TkEBxQ5ssubgy+UgMVegssXQ4odNFEXokcCaO2NoHNQWYhbkBRqOOaOEpNWxL0DYgjYbKYDHZ4puPw5kc5iilDUaawFsUOmao/3Dhf3bW2Pi6iAn3N0jxfZYgg9myeFsEKc994pxOFHizuJmeF2xKeCcAdSsDpy6BoMUxInzmshZxW1sNZN04oUO1aA3UEPth7haSYxg1akIi44XCkUKYSgE4M6AbPzjCkRITE65PG47Vmc6H5YNFcqEmZUCUW99wspdfPjVUcAWKyw5emizR2ssyoSyAMtD0QrBEOojjSVaIjw+51qMk/9DuM5uHTZ4UJeNeDURSwOh7JolmK13dmRSL3dTCGvLJp6F+aMGvXwe+JoaH3EYJ0jknEYNFIKJZoBId13YLY+OuGwMKN0Wb6AsI4MxPEbkUEujxmDSMYoOmFqbVqXLgsIMqkvtFM4SHPURqdirvbAsssuu2zO9NjNN99c8/cvfelL4rEcsDcLwdx9991C3830mNFJXuwMmI5yp3g8uXSGEAHcNnELRv2jmHQqxbGCpQCHxSbqIGx2Y72Axo/cobIznY2VjAVaWiKiI93ePiNMIrmptMQKmDzYh02nKgV53gZc4PffvU3swGdG2sQCzeeKBS92nle1Q4na2xC3OtGtemvxfULuMJBpFfNZKPXlohyKJZBrs2FArdP4nQmkMkHk0n6xc2bEwxkp+ZID7mIJPZurg8IOHx5AuWRDx6bxSiE8PhNA7/Yx4VVGSXKm7MORv/eipT+Czn4lpcUhYOGCFT3bJpBNuDH0wCa4B6MotDjRw6I/Sgiewp+MVpwJGEmFHLTA4cI6KMhELors1h95vBW+rUXRQ0KScVhzKBX82HbyIaGCm5joRjLphaVcwuatymdt3zADf1sM04e70NI3K8ikMGWFpWhBJuGtmYCZLLsxvq9PzHuhfU/H4BSSFq+Qim9sGxdjBTIFNw5GNgi+6PErqUpGqc5cFiF3VJzbxKgf1kxJyI/lRp0eZlYWwsRTsh9KWfTyeatIB5FAXAElsojFArB0lTDprZWg+rzKgpVSpzsTJV3hnxOd6cSgBQmV0I2dQXhKfV53f0xNKM+nkln41YkX3Cgp11ntz27wtVdqMlmDXbswwjQgjqlYAZk637EC0gY1GUchj2ndws9GyqzHZ+y2rI96ymXk5hAJzOrMLSmXzs0hPtATki+bNeyzKWuOtdHCv2uB49MlySxVt30zoClIhmzPJiDqweeyh2m0JmMEH3zYmjgOPRN9OP7k43HIehAjlmE8YLtfmfeh2tazKZMNe5mYHxOzHSIFVSzaKwO1AsiLCGRspA8uX0bYsmzYNYGyi0Vuj1CQSeRzfqScFtGHQ/kwawnpuAcedxrt4OJvwQzaUNjjQK7XWZkm2eaZFfUhTygFl6sshAhUqrFO0+GNwtvK5JGC0bjiqpyPWZBPWRHsimB4eKOYOkkBgFRBjTy4BSU2B3YqRQGWnEtlK7aeqZApI4diqQzHQB6h1rhQ5Drdefjb4hiZppvxjHA15lH4JmYRdvXWKN4KRSsO3NEO6y5gPNkjUmU0woyG/dhxmpIOY63pcGQA6YIbvb3KZxVmnX7lmLq7J4SLcYq+ZPvbxICx/p1Kao0OBUmXE5moosDhee86bhRWHxdrKwZO1qTv8h2isEw5tidvhdNREoo3ukBTQUgBwvRQCNF0AL7+giAYot0xI/zGOGOMRXkiW6RvmNrvomqY7f6q8zIbc6litqi73/Z8GP8YOBGXd/5rzfWXzajF9XiVRCh0qP2hAsq6moxS+K8nE7dbSYu5PbWklM0qi2lBpN4UPPKQoi7M52pTa5yWKT6X6sqthyj8G9RTCrn6SMZGkpnDTHNS9zxFAhmDFJpwUJ6n2zInYOrtY+g7ljTq7LfZ6+xjAqJB0yB6oDGhipWsyfiWKJJpFjQFyRAc0nO0wV/SVmapIAeN2WHHttJx2Ibj8JTi03Du3mkEg3vEMKtdncqUSncwgdayRewC/f4ZHDmyEa3BHCyhsDiBG47XOPemSD1chOyYPNCFjsFJTA91oX0wihAlSi5lJsjeO7YLNVp/51hloafUtW1wBg5PQRhLRjLsTLcJua9bneLISIYRQpdvBql0O1LjrbB2xYW540DosFK4VVln/+O0XClgaqoLXV3jsNuLSFm82HiyQn6lohWRkU6U+7PwWbKVZr5QVxjhiRBaEUc2b8PEXu76SygWrcJYk6CpJxejmQkPvFuSQvHmc8SFS0IiHcCWc2j6OSN+lo2NkWwr20CRjjvgCeSRzHlF/WNAtZyZHQ9iJt4l0mUDA8p70AInb7WIVJ3LnxFzbuzuDLybU3A6C2LEskQs4oUDRZFWS0yHxOC1if296NoxIYaWSZuXx2/thu0EjxBWkGRY10pGbNh04ohIIcWzfsym21GatsJeKiBL0lDXXDZeyvWpoI5mzpekCaXGyVsdOpmzOEUNoRDTpbfUVFQpXb390kldMT+eg5WhrwaFrPKe8ngkZqaU12dPVs1rZJXFqqiac4r3SSnvuXtPrbw1HlM+x64eLzIp46ZLIzLY0O6sW7SZ6jKyhBGvoYt6KD9OG0zLZOF/Wj83Jp9H0qAfhmcp7XLWDRwzik6MRAacgBk16ktRNxcrXZPxr3CPzBOGZI6F5RiZrH89NlsVsn3465DS4Ro6EMZx7XtwSmYPQpvGEAwpi9rGjUcQj3ax5C/cwLiTbukJC2+t/p1jwqgRtjTcWyCkzWxkLGbz9BkXCrPRx/pFlEM7FC5Ks2gVyiiaOJJgqsaSrdjWtl+kd6b3dCLV60ah5MCWVsVxwBUYQ8lrwZH4AALOuCK3diTR7ppBdKQTW7dVIymmoLJ2NhqWkIp5xOhimnpywmM76zTsaZkIYeZIhxgjvPl0pZ7hchXR1jeNdNyL1r5ZTM2yV8YNhxhoXETfyUrk0YlpUdDn/JhszoHRB1rQeWICsWxINI6KY24VY+KFIwDRwrSgChbqN2wYEeOXUymP6JcJDEThsaXh6lCK+f27RjB5qBVeZJFIujC2ux/elqRovOw/YVicW89OpZZ28B8cp1zAdKENISfd4DIYm27F1nMnKi7GPN6JZBdavPwdZYFmJEN12ebAQdhKZfH95IrKgknDUdn07ZSGl+zqFwu6sngxXRYMKtGQLVgQ/RcuSp81SCaU3/E6NDtWXSBTShdgoQRag3xWOXCHbt1yuZSILqMp/NNWp6Ta22jXOdk6oTfTfHyf8vfIbAI9HX7DKMSIDIwK8TbWZAzSZST5ki5dZs9kMZ6vJVgBi7Xed0y4AHjmRRy+LD3KvPMSGZCQogayZmgsg5wrVJNJpVLzHm62VrCmSGap0mVHI62A5rqOZltxz+g5SP2hDdGuPoQ2HsL27bvh8aawdevjsLAV05LG4MmHMPTgJgQ7Y0jNdMLaFYannBE1jC2nVwvynOeSTSuCBi70gbakcAAgUYkBYHx+xo0Jay9yRQe2tikkwVqHdQNHHXejP3BIzGthgbrFHoc1Bgz2qbUL7wwSWS8S0U6hahse3iDMPOlLxamTHeqogLKfIoVB0V/SsbHaO1Qq2NCxcVr08eTSdjEdMTnK+fZu4RxNdLaFBSmygZPHffjONrh22ETDYivHEXjCSiTVyZ6djaKnI8Hx0HR5dcUxsb8NA1uHhJmlOMeZIMbv8iDYVxLO0YTXm8ZUyYYgdcM2VMYp0ypmy5MPiKgv1J5GqP0QDt23GR2bJhGe9sPdXYQjlcPY3g3Y/KRq+q6oGnrSVGdmyI/2TQmRHqSCUDSKdkhDzz7RoMn+KGtENQMtVe8SYRej/rmUURYgq0d2/KummDYrUjEu+hGknW785cnnwDJTu1jKfHtkososfq+jpvCPLC1taknGpfqJFXX3weyMspC1tFR321mVkPRFfodDIZCA2skuUcgri2w4nEIqUy+woZuBUbrs0Vh97UDUZIyUYQa+Y3zXcke9bUpCE31JeHPGkYxRI6WXLgBGHmWGhJStm7ZJlJyrky7zm5HM6k3HXO5IhgjqVDeEPxhAFl48+uhJ4mGxFPGUsx/F9mf9BUHEEH6sXSidJFj3yJaDooFx+NENireWzYKs0wavZxKDpyiNesOPbIBvkw0uX9V8kwi5YmjzziiS4kS3mMUSnJwVbs5Ej38Ms6k2OKxFlO0FYUEjiMhSgMeeRah9XKi7iLGxHuEezYilpT0GuyWH2eEudG6aFNEMwdpHLOFCataP3u3Ke9BmhSTS3sWUUgoT+7tFDcnSlkT/wKwopHPKZugs4PBkP9paophOkUDL6PRMYOIePzaeM1LTbb7v4T64SjmR2uv0TYoUUzjTjh1PUQiBzaPDw71IJvzo33BYKXtbge4t48Kgc9uZ+xGb9gIdVhQngOhYBzapM3C4dPKdRg/1CpWciC57w/B1JjBrbVcMPVUrp5l0K2ZTHULtRjKnVJmZWtr4cFIm6zTxuLIYM5Jx+tTGS+7+ZTOmmoKThX9GDkTJZYPNWcJkewdeOPW/6N5+GLHf1V5nTnU37bJUF7ZUqpY4fA4LUrpIJqn6n6klnwqy6rTMVKpKDkXV0l/5DOU6QqIJqRappHIshVweFk/9gloOp0CnPT2yBsRBkjFKrfV31ZMU6yx5g+52ZSgF6tJlccPopP54vbkcYga1DaPGT0Y9Rg2aWvPTler4T5oks77TZUTQ4JpwuBxwi8VS+cdy2YaDB7fCCyWldO9/nottZ+7F9nN3o70/CltrGm5rDAMnKakkdqYHWoBCWxLFogs2WxZThzvQ0huB36fsX5PwYhLt8E2k0bZTiS7Ys0OZba9/Cq5AVvSgpAsesfD1BcbEokiXTj/oXbYBQVdKpNhKsQC8/imMTfWit3e0IgsmIggim/SIwr7HnxapI/bOtPsS6DgpIVJow4/2IpctYcNpk8JGn+jeOiGihoGNU0jAjzgCwlU6cVsJm88bqaSaqMYaGu+H28f03YBIiQWcMeGtdtyJVRVYLBsQEZlY6FNWOL0lMSeFu/cdO/eI/6eKLqSidkT29WHbWcq5DnakxNydWNkvfNaOPLJREHb7pklYAhZsOL7aKEobmul0C8o+m5Bct5VmkZpxib9LdwIKEA7d2Y5ywIm2rUpRnHUad1GRA/NcK+Pe1KbHyhAyVQDgKtWMY2b6aNjfj38e/omQLT89fRB21O7q02q6LDFTXcR8HrvqYqce+3SuruMfnE+jIw3CopJVIFhdQJNqzUgco4YbXE6FPNPJWrlzLuutDJ2zGIw9Zixa1KX9iLxRumyO4WSdBjYxLPwnDX7W01rvW2ZNJjAh9d/HSNkx6jGKTvJzENKR9vr3K2sL/ybJPDHSZXKezFLI++YiGXeRKRtds5bNAqdqjCgRTVRPXXi4E7cf6MHtP3kKzn3+MLa8//foLiVgz8bE9EnuxDnQjOC6wSZBFv3Zce9vU0iGI40HMALb8SXkC04MJ7qRzHlUFVSm0rPDqIW7bdY6ovFuOMIJRFpDwlqGUmH2+HANmnmsV3T+syGztXUWgUAM+XKL8EVr2aUaS077RX2o/5wjQt1Gl2L287C5dMtpwyLFFY96MLW/B8WsXYwoIDhp0s3UE9rRd3IUI5O9KLocwmGaNZnNnGPTy5+MibQeCYatn+NHQujq5fAwN1J5X8XQk82fQw91IutqEaoycc5VRwCP24KOs/YJE8zZkXY43Cm0bI+jrUdZktnQGp0IiloPazJDD26EvzUppMYUMvS2VsUBU9MBJA84YNvlRSrvhteREUqzjq0x+DuUVB0l42we7U4rx8E1za5a/dOXjXcMLwWOAdCmyyoO0RYLXnrrzzEhfO+U30/pCv9udR6KvVy9zuLR2g75gIukU3udx1XHd4+3lnyowBNnO1olDqtFGzVUF+bwrHLdhlpqd1MZ2j5wo+CzI2KwoKbCacOIITFaPznRTmdmox4XgyjCPYd9jFFaKwgL0sF6jzKDAaIKyehkzeIYdHUe8bNZNmgaKNE0h7DQWTKNFv6TyaSYLbOe0DQkcyzikF8WiWGhUzDnSzJi/OlEEXBpRgISVgscGhkoEU04anLesnTJOS0hdMBSGMR1L34GNp12ADvO242TnvkIHN4U8rn2mibBbNaPMVcbvJyaqVZ/bda8yP/v6NhbmcWSuquA4qktQghAtLgjKDrjmB1pQ8/GMWGFI8w83bMoxUPo2DmqeQ8HhoY2o29jRDRYuksZ5FIuIVSQ3mpsUKSxJGsfvTuU3+U34nSxQTMlIpn4dACRhBv2TRah2Orh2OgA0B8YE6qsTIHeagUcGutHMBmGbYMdxbJdkKJy0MDk434k2zvFz8kZOPEpN1p7Ugh2KnUfesTNzLShZ9MIAh7FJSDQnhC9Op5QEm5kMXUkiNRsG6yOPNr6wvC1KIvd4MlHML6/G/ZyEbFwENmAFW3eqHBUCASj6Dyb5DQrajJsFOXO3ZpMASrJULa+qeUgOtX6Fb+SlLoAi254K70sbWpHPm1lVJVZRrkm/aUUsolq4dxmsSKkqw2kEkpkmY5WFyC3yyrnmgnEpxQWI3nKwWdFGm4KQqgtlNtt/jrvsli0ej9pb5dUUjo2V6/nXJY/oPx8NpPFdKk+feWxWzCtkyqLz+028DOLZ5ByGsid57Dpjxk1UhoU6D2caaN6r2kRMWjS9uaNiSNnQHS0/58yIKTiEkQy1gaaMc2azCpBEstSkQy/fEkyjI44q+bIkSPYMnA+oGmSE2C9Vx2tW4UF7rIdGUsBNkeZHjYC8Uj12Ip5B/bftUM8kgfPRvu7/4xN+SJmjzjQs20c6Ti7+n3Y5DpcqY1MPxpCvj2IzjZV2mwpw++Mo9xuR0/bfrHz5oNTOQO2PDpPnKyYeXKh52RHj7WI6Ue7Ye9TZsoU8g5s3lwthE8dbkd8JiRcmOXY6FzBjmBXTLgdEzPDrbBtKCAfKYuaEhHoiIvBXyQYpsom0IXsHXnYNlkQbMspQ8gIPzD5iB+Fggupgk94mdFbbXo0gGBnGl0h5VjY//M4HQGOhBHalNaQolPY97idWeRyNkw81i8aV7u2jcPXovxc58YYhhMBdG2aRGw6iHjUgeBgFrOPtKN72wQcroJwxiY43pqNo9MTAbR4UvB3ZTEc3SAUbyQZqt6iMx6MlQaEAwDrSJJEGBX6vOnqIm9jE78DDouy0Nu9CmH4skpEGra1IJoN1SgWw9O1GxSfT1mASxqpcDFfux0Pup2IgfY/1edcame/y127cM2ozZguV3VRpFedhDa95nQqkUA2V42cMhqjTq/XCjvN9XQoJLOwbeuvez5vrb8XLfEs4mpPzzHtY4olQ2dmI0KiuszIz8xvME64HI5g0iAFljd63Vy9XJoo8b5ukGR4zhutyQQW4cDcjGiaSGY+pMBoZ6nqMvzyGc7SZYBuz+l0WrgN7It760iGzXXWfH0u2FWyIWMrVHoniKiajqBtjRapmBNeGu0ne3HNW85BqDuMzaftx/lvuE80dHIRpAuzvysFX8e02GkzgonnAmIWSf8pI5UIhnUEly0LlzWPsXvbUdjmFRY2AVdSLOaU7ATagMnJLlGvSGe8KBTtQmmWtnjga0uic5OSRqIl/myKs4xLikRXBccRhBCFvadUSVVxxHTvziPwqVaO7JUZdbagvS2BaKEVM+lOUX9Jx+zo2jQNp6g3KcTDNJ8jmxRd6O0n2eBzpoVFzqbWA7Cfo3yn4XCr6PinAalUmjmdReEEsPn0A0LBNXGgA54tGST2eiru0B1qvwzbUlv6Ihjb2ycaO+lpNlNsEd5vYvkdVNJXjGCobmNqTI4kCI/5sPNEJaqjGCA6oyy0JTstgpRzQgds3jGsF9lyypP8LogOh1JHs7aUUB7SDjlj/aZ2YUvE1b4d1flYnG825GgQn1ZJV9RllH+LzKrmnHRo1qBUVhZIj8deE71KaGfHpJLq9Sk0dwqkDY5yHDmkDaKTkNuGIYMif17Xn0LYUjlkWutl0H9/sJq6lHAV8ojN05nZnc9hIlSfLjOypWFqbdZApZU2WvS54TRIoxW1JLPAdJns+bM14MJsSpiXCfOpsyxl8Z+vxd3G3//+d9FhS4JhhBSoelhWQCWpJSeS/jVwcvGwZYUPlkQyZhcLC2dBapGI2qiUrSA60Yr7//hk+Pt34uCrx7EBR7ARw9jqVnpgROd+yY6NwcNCABA+6EW2rROJgh0bQkeEdT7Re3oGR6JetHoiQrXFekKPYwbJIQ9aj5utKfpPTwfh6igiNhqE3TEtIpjRPf3YcBKbOMsiApk+3CHcodv6ZwTxyVTV7HC7GM3MhswJdIrxZ1TR9Z/KhV6xxpdk0uafxtg9IThOdqIrNCVqMoOhQ7C0qDNVylbsnd4uVF2xTAhtXmWBjkZD2LlTmdxH2XU8a0E+4RSNlXLEMsmLnmZ926cQHm1FdLIFbl8KcGTRsykiohLWZOhgfPihQXi2RDCGXgQQg6+YxPBdbZXaUkWAcLMHvi0FZAsOuOx5Ya0iNxUs/hZVj7ECJ1BaKYm2w1nK1zTsjad6sBnDyFpqd+WZZBGp2kGWgvjZ4V9Wh5eJa8llq3FhLqdVQUGpXLnsvGpB3qPr7E+rNRnF+ki+QHVR1I5lzuWU15DzjcS1qXEe8HmtiOUMFtxkFuWO+h12rmAgYS4UkA755yU1LkRSiGzompegwM0sgNG0TAOS8ZdLCAfqj2EymZjXcDNxbPbGI5lGSSaRSJiRzGqCJLBUvTIzM8quikacHD0qSc5IXQabBWVageiyCHZ1zK70s9Km0fQKzHhYmhXqurITVuThxEFsFY/d7zkRnZeUUNwWwUldDwiCIVo3pzARsQgfNTZnUrnlQwnRu9PY/MxhkRaj1xkzI5HJjXAGoxgZ2QivN4mOjkkcPry5aoezMymaH+k64GLKLG+D21kQ+f/ErB87z1d29Bz0FRn1YPZgL7acrljO8NGJKYyjBxswium9fiSdPvh6CkgVApXZNsGnKnWeI9FNYkT0kdgA2i1jcLtzGElvxPaO6hTP2UMeMfrAuykrSIyzTuxc7AtutHRFhTMBxyynY25B3luepNR4WvvCwuqG4orODTOIF72I7GkXUWJ8qkUjK4+IoXDD+1tQKihO0ZRQ05eOPTzbnzNatbqJbhR1mSfZ71O/ZCvsasWNTaUiXVa2wZ1T3ZgD2UoaJu1147LU12u+X15XNl2PhYWdPlleB9WLJKdazUj4HHbh/K303yjXzMyUGsnka++Bckm5MOOxam2ipIlOtJFMWO3ZaW2rLsxZtZ4kjiObR8Ygkimmcogb9MkY1WSc5RJKBg2dE4n6bMDkgTAOhDrqX9coksnlMGMgKMgZGGHaqE40iLLcBqm1eM7YeDevCj5oqEtnkIVAboatq2iQ2SxYM+mypYpkGL3s378fBw8qCy5HCmijKKM+GRb+2YGtJxkbexFclWm7FThLdn0Dd02tRousrpmNS9LMUDcecp6OWw4/U9jb0HWg/a+7ccqlQ4JMaIfS6x9BNNGHgX8KYyLRi2zRgaArKhbNjd2KFxh377mcHYcPbxKR0eyMH6H2JIoZGyYP9GD7OdWFPpF2Y/T+AbT2zoqUEqXN1nJRkMqO8/Yil3EIW5eCpQj7TosYskZ07kogmMlgJt+FNvcMhkRaLIGgJYK4vQ2b2qq1oHTCgSP3tAPHWzCTahfNo2x+zKcc2HrOaGWa4+HDfWJ2z6bNisiBxxDsiorPTgEChwzM7AuiVHAKx2Vp6+NibNVPaXBAuCywV6Z9xxRsniIycGNgewTYrhwL3z9CNwJrHpmIDe6WIjJ5lxg/MBA6Ap86LpnOy8WUWiynrMymRGLsgBf/rqbL4nY/3v2KT+BXn3t57aXD1Jou1WK321BI1V4hLSEXtN1SxZSMZKrP+VWn4LJOxpvNKq/vVefRE+Fqj21NM2ZO9TPLaRbWUqkafVHckDJoPAy4gJxBCszXWR/duEolQ0t/R7D+OSSzyBmMQ37kUNxwzPKsQWrNaCy00bRN8bMGdSEYiBSEGaf6sosZvWxZgBKWa5NZk1nj6TLttE0O6LnjjjvqXi9ocG1ywS2m6t/Xqg6n0tumO4t25NWOdoloJZKpfY1MrJahuFMvJ6uJk6lUt3hY3uzG5JaLkenej22te9HiCaNPNdHscY2K2S0ssnscKYTHN6IUj8DZUxLS1E2bqo2iNIOZeIwjWC1IzPjgb08inbGLBVySDqOZice70X7aBFrald4RGmQGOmLC/JImOOz6T7NJ5+4k3INl9HUpJBFwxRFL+JA65Eap2yqsbto9UyiU7SinSth0LiNIJYocjfUiXfDCk5sWA7koFU5n3MLpurU1LKKyaNiDqcf74HTnMHCS8nk55TLAQXD5Atz+NI48vBHuQBKevgQKWYcgosp5T9BQxieixcmhItr74ogVWkV9Zatqz1PMW3Dwvg4UB1vQ7lGOrVL4d1pETYwo0WqfEuaStdIn4wzk2eSER5ObsadtZ901UiyUEY/othw009SlmSIztVJga05NLWr4ZGpCuabc3tpwOxlXB2tpOtQdFcua2ve2WpUFXcsBJXWQGcG12dVWFS5UDjlXQDpQTxKTyfr7wlUuItteTxyz6tA1LWy5PFJt9eRlMPNMOFXrPcqIjN5nRxTtjdNbGQPysRvIor3pDGbFUEMXbAU78vm8GEm8nPJlwqzJrGFrGe4Q7rvvPmG9rZ22qSeZkEEkw5kSObVYW/O8mlOXo5Ql7EVbHckw7eEqs5NDJwiI6pxzczm4M8ZGoeW0Bw9MPFk8uEg+JfQYPHfcjv6Li/C6I+jyKXURDjKbivoEwXCOzMhInyj6JxNOeFvzGDzlcKU2QI+v4OA03N7qzpZpKSq0/I6EmGEy/Gi/kDYzmugcVBRbbaC9TAucAxl2jSoRjC0Hq6WIoDWGrhO5MCv574lEl5Ay5yZzKMZKaN+WwmSiS9jq9znHhP1+NmnHAY5QsBfR3a34j3HfYbNa0KeOJIhOhkRarfWUKdGzEmhX9v4bTzyC2bEAnNYikgk3Do21CosfIV7YGEZIJTUW/ofvbEFxR1CQKo+XvTLTu/3o2RaFJ6B8Nqr3ZsfURdplgZXWyvyeueJbFZKRyMUcwAhJmCnR+mvEbrPBrRlOJr7jTAnZWO137HTU1mRSYSVFZxVGa6rIQFWMldTjqUuXxatEFY0oCzobXLWIxZTFN5Gsft+5rLMmkomp0ZkWFD/kDSTMrtb6Gkk2nEYqZCAJNnASaPHYkAnW/+y42rCqxT0UDjy37mlkDMYsFw1SaETSaVAXMhIO5PMotKufNwvccsstCAaDwimeDyrAjrYxbkRZRpiRzCqjUWuZ6elpPPDAA2Lc6Pbt2yt5Uq2M2ci7rAIrFxMDclNJRn8t2fIOlF31TWou3ry66zIdYYa+CqfLDmQMUnZiJG+p0idKo8yxmR3Y/dYkNu86HRnfOHa078b29j3IT+XRtSECp0+JQoihoUH4PBFwDmY+4hRTKkf29KP/+GERpRCxqSDG9vYK00maYRJ2h7JIbTzpsJj0SPlzfsAplr1uTMHRzfOSgx+HhOtAyBVBeCaA6Xs9aD89hXChA92+ccWr7ETlWPbPbhF1AkY3LnsGdmsJMweC2LzzoBAqyLkyVgfjrhz8bUokEeqKil4dfzmFgssuppHyZ1lL2nD8COy2YiWKoZ0PiWk6EoSrq4w2zGJyXxDdJ8Xg8EaqAoSpbbBPheFoLwp3aCKR86OjRWlYLdusKKmFfxG92GjLr3xjzFp96e734bFDJ8N/0h64NHUOiUyygERUtwmx22FTbWjmqsko14ClMuBM/J5VtePXrZPZrPK8x6NVtSkXCkm/BjSuE69RfeHZ2erxMVAokDF1iOWpfqlfVGcNvC3dPidKRvWQ9vqIJcrPafC6Ja+BtY1B/YcYiefmZZpJJA3SZUUDkglkckirBdqQJ4TzzjtP1HH54FRJEgibJjs6OsQkSX2U00iPTC6XExGTKWFeQ+ky5jgPHTqExx9/XIwRJcnoX08/XiBkQDKUMGdi9XcT58nXF/6pRKuvyRAOWo/UCQIs0CYn7HYHigYpCHG8OpmrhLNcxsHEBowlNuDmoWch9Ic7sT15ANvfm8bg4CExmmBwUKnnEKWABXv/vl00WiZm/GhTh5RRpsyUGdVI7EtJl+2Ij7rFoDBJRJ0DMxhFL/owjlTZi5G/huAaKCLd2iqK/lSp+XpTouP/yEgfnKE8RuIbEHTSj20Wh25uw+anHqzMo6c/24E/tcDVURDeZSQZElA67cFg14RYfxjBRMZakU05sPlJBxRnAk8eG044gqH7N4mu/8ikD7a2IoozLsSngxg4aUhEmLJ3ehh9bPbA2L0htJ+VERJqGnhu69gP6zOVY4lmQmIqacAVE0OvZLqMEzLF90w2swM5VfDBdF6s1Iq/DF+AjZvtCEZrPegIl8MKq64mUy5ZUay2BQkEAy56YVevgxInBtEFunrBlNWxAizOa5FWp1rmNDNi4qpiTEsm2rRtSJMX5rRWiUK+gBmNuaZEbo5FO9hTn2oqzLG4zhhE6E6/QZ1mDvuXlIGSjRiOFedlNcMvLGnwvJGSzZ/LItFiqSjLmAHp6+sTD64Z0WhUEA5ru4888ghCoVCFdFi4b7RHhjCbMddIuoxf8sMPPyxmV5955pniIpgPabk4oMtSRk57c1stlfSFFgXRKV2r3iEsWZJMPc04ivz52uezSSuspTJKquuhqP/MQTLQ7XYlL+djOUDjW8Uej7uv2YK7Q0+C01fEtm17kYgHhYO03ZXH8O5+bD+7WvRPxp3IlNxw+9KiBmBj6cGZRypjR9/mWSEHpkFmKupFsQBsO1M1pbQk4T0/ieF9HegPjGIktkHYx7TbxzF9jx+DT626DtC5YOjBbg5TwHisB90BEkhJTNLc/jylZsSoZHy8G5FIC7o6lf4VItAeFXLl7efsE2m7uM2FxAE/UlE/tp6h1FXa+xU7naHpTqE6o6Gmw5ND15ZRTNp7hRIOu9RjEb0y2+Gw5RFOtqDdr5BsONOGnZ2KhNqi9sHkyg4E1ESWlYPFrICvoOjcmTW7ac+zxJ99HieCBo2ChXwZGVU4IEFn+4xqdilR1PVhlVSvlIJmYma54DRWKOaU921prS7MaSlW0KXLikUlmkgmqyxXUkcZENyQZwwiGVeo/rMRM0IlV4tw1pgMvN31kcx0co7UsIFIINhpPGdlVjdVlPjbPZPAqbU1Mle+gIh3fsIBN0rIu4xHLzNCYfTCx7Zt25Ch8EWNcripZVTj8XjE2lIoFObdPE75MmH2ySxzNKM3/2skkmFjJesvvBhYf5lrBOrRTDKntVxmAZIGeQEqrghp+yFRztil83vt+1EoUPe8BV5YkVDJh+egYLAzEwvuHLWag49OA2dXpW+trS6EWY0uMDrw4NFHTxaPLt/deOr7dyO2twX+thTa+maF6wAlrO2ckhmKIR13C7kwxwBsPUsZNcCIgLJgepr17RhFTLiX+WErZlG4p4iBs5VaBqd5stdkdqoVrlAGQ4d6EOzOwudMYCrZhcGTlYZSIpHziaiB/T7psB2e1oJwiqZj9M6dioQ6nXYiMetBeKi7MmXU5cuCPXIJS0DY+Svu0F6EuidhCZYrsmWm1jiueHyyA46+IsbQjVB0Fi72BiU34jjVN40IH3QLmxr/aRkUSlaRvrOXlQvA7i3BrvrWcZgo02WhshpzOIHRiQ2VYv7MhME1ki2jpEa8ElarDV5b7TWZjNf+rjXPTpZyTbpMSpidms7+QsFSmRuT0ghG3G6l8G6x1G7KshlJSNVjisc0i2CpCGd7fXTCSUhGMJIwl+cokM8YKYUNZNGEtaW+TjNhcF8QBV89IVlUfzgtXMkMJgzUaTmDRkxtSedYNv9ut1tkSfhglBOJRDA0NCTI55ZbbkFLS0ullkMCmStrI+XLC02zNTvWlISZOwLmLI+G2dlZ3H///eju7sauXbuO+oXNl2QYycTpiquD7C/QRzLFjHEkY2XhVrcLJTwlCxLqazA1VzAoeop/S9c+PzmhhNc+O0mqinBYrQcxfaK5qWjzQnL487eeiz997YWiI37w1AM4+1W3KBMdLRDkEuqKoa1/VkQWkSk/Zoe64PTksOEERSrMOS9sxcxbHAhuT2NkTztKvV74HAnRpNnbO1kxyOR8m8lkj5jwOf54EF2bYkgXvUIJx9HW4nzlLRi+tw2pQAc6O1RrGnWwltOVx/Zz9yKTcGH0sS5hqd+2axK9xym1F9ZgSJTJsE9M5mT6jHY5wa4wYlOt2HBc1ZU5Y7Vh6G/tsJ5SlVBn83akp13Y/pTJSvpuNNaD5IS70owplWZCAGAHRgu9SuOlvbqQifEnqhOzFvQus1KFp0EhRwVf7eIfCDhrXJiVa8AqhAMcHiGORd1Ra683xXcMdQIUWczX+pkRqYRyPcTj1UjGqhk5wJLFtDqBUwuL15gMjHpUUnMQkrurPhKx+owX8Ihu9LQ4NiMJNM9LqP75GQPXTHsshXGDJtGcQZ3G1qBvGdcbps0YlXC92rp1q1iTGOUcOHBACI4k4TAS0qbU+DskmaUwAG4mrCmS4RfC3YERGAHRe+yxxx4TzZUDAwOLnClT/aKZwuJId6+9XJMXzqaNSaaQMq7JsFaTsVaL8RJu/QREnfJIoqT2TkhMTytpm4Rqplh5PZddmC26WFvRPG93KouJzPNPHeoWj1g+ja7zxxC7eSc2n3YAO//pkernspTQtXVCdNHPHKHnWQCuE1MI2uNo4cS0NqC/bQYzKabZSsjmPThyuEf0yRQ8Djhovx88UjHIHL8/gExfK+zuMjIFp4hkYsNu+LvS2LBxb8VehiaZwcAEOjqVpdftJ3FExWC1gDONyYMdIl3m9GXg9uTQMaAIFQZPPSRscOLTLUKNNTbRgZbuuBgOl56xYssFVQn1ZLILsYgHXh6rKqEmGbodGbS3Ku+bp/RarYXZKuoymxgvfKnzh5Vzm4jRMbp+cS3kyopTgAYOuw0OnYJLrxiDKvfN5UqQFFDKK4udXTPnpKjK6MV7aa7lhOoSrp3nIz5PTol429qqC+fMtOZaRwkOA/nx1Bwbn5xBxBCYK61lQBxhXb+QhK2jviExbBQJlcvIB+ujk5a+IO1ba18zW0DGQPX2j31RoLfW+dihIexGp2JyffF6veKxYcMG8RzT9yQdeiWy0C+jHP7bUsqXv/71r+MLX/gCxsfHccopp+CrX/2qKBvMhZ///Of42Mc+JtJ9xx13HD73uc/hwgsvfGKmy4xqMgxRH330UUxOTuLJT36y2CEs5UwZ2rcTPkctySTTDiH20kuYC0njSCYZyaHgqydJJ19Tptttc5AMfdt0tRqv1yG8m302K5NjFWTYOEroor68ukhBR2rZpEWMD37wT6eLx++uejE2n/44znvrDWhricAXVI65feMMMgk3OmyzwiRyrNCN0r0ZlH1A5864cCegLJnOzKP3BuHst2E23SIcCjq9k5g6EEJoMIWeVkVCTafpx2ePg/XxMNp3Vffx+bwDfX2j8HpTwplgfF+fmIrJ+Tt0XCa6Nk9j5khZpM9YGzvySC9Cg2HkZnxw+XLCgFQ7Q4fNmNmpPCyFMtq2pRDOtMBpy2Jb/yTQD2QTNhxODCqRmH8YVjV6sTgtcKgDxfi73KQnHW48t/gH/O2xp1Xeg3Jwp4G3V8DPiZ61O2FewvGZ2u84qTozK98PZz6r35GcFS18xZTXyaSrUXVBQzLazU40rPyeXeO/JX5XnRsT00YyVm8NyUxr7G4qn8+ATAh3Vz0hRVRLHD3yBooxd5txd3tCn4Pm9WKgWHOkssh21Kf3Rg3UoGI6tUGUkDaomWQ14oqlmopps9mEMIAPLuQkFUY4JIIXvvCFIq3P9e/666/HU5/6VJGGawQ//elP8b73vQ/f+ta3cNZZZ+Hqq6/Gc57zHLEBp8OJHrfffjsuueQSfOYzn8FFF12E6667Di9+8Yvxj3/8AyeeqEpCFwHrWpcwZ7NZ3HXXXYjFYqL+Ml+CWch0TDk7yqt7PpUy7pNhz4dRTSYXL8NnMIHPqZGzsrM4F62/SXlvxCdrCSqVVG6EXLSWTPx+ZbHLxmu3fiX1ZmFxvub5nAM+zQLAnfe+O3bh0fsH8dtrzsX33vE23PLjp+GRm09E7/YxOCwFeJFGr30ChawNnZuTmE51YigyKNJjw0Nd6D4pjo6esLDNZ//OwcgW5FIOzO71IV9Qbr7ReB8GQwex5VnTCG3Iiujlsce2o1CwwaNa/DvddLlWuvpDnTEhsY6mfDj8UK+YxcMHyWfjCUyLWeBwFxAZb8WRhwdEGm1qMihGA3AsweAZYUEwQxP9wuctnfcK6xsiPulBl38C29r2ifk9BdWGvwgbHMV8lWTyVDm58LfDVYIRcDiQytQvjNHZbM2USvGarKNkdLee9oLRGGCyAVFCqtS0XJZMVH8vFNSMX87IBUpjeio8ySx10ua8SjziY9jKsLbURyJugxrJXNFJ2aDbX6DVIDrRRecSlvb693O11kcsjkQGUQMnAqOenJJFuV+dExkgUb1nZgoWnHjjFH530I6X3jiLjlunsP/RWey696l4XuL56M33YaE4VjOmxWIRqTFmXM444wzs3r0bL3nJS4Ro4M1vfrNIub3gBS8QG+eF4qqrrhKv8YY3vEGoakk2jJC+//3vG/78l7/8ZTz3uc/FBz/4QVFiuPLKK/GkJz0JX/va17DuIpmFkgJlhGRbhpsnnHDCgiWDRn0yhr0y6n3k0dVT4km7mHKs7/jPxh1igJYePnsbrNawwbhazY1qYSRj0BBaBnbfNwVcsrn6ej6HiGDyuj6Boiq5tdFuXPN8WhUq1O0tCk7kDVymi1krcpkyRvdsFA+CAoDTv/5XDLaNUsuLwacoNY9+diTSIDO8GX5PGEdua0Pw+BzauuM4FNmMzS0HYWlVPle+YMdj0zvgtmdFDUT00AijRj+OO26f2JEnkz5kUnakZu3o3DhVGfpGkoskvejfOYHoRAvG9vXBE+CcHgv6dkzA6k8j1K1M96ITgIPmnmhDQMzyTGDk7hB6T52E06G8XibrxOPXt8DVWYB3o0JslFfLSMbqK8E2rp5FDqs8ANjc9ddMKVWGkcK2KCzvdf9QtqJU1EU9VK5JaObFyCiaiKhWMS5n9QItq9My9c2Ybk+wzjQzr0nbeTRr+OysZpNjKSNurV+gJ+ZIl9kMFv6o6lagR9ZlMPrYIBLixW7knzadMTDjLJaQMmjmLLhcsKQK2P5QHHt7nSgP+JAu2eB8OIIbOjagrWjHj+4cw29SCXQl7fjRM3aKDd4XOoMimiiVtgmiKOcoIy8jh5wgBmkVc6zi/EJHkrS0tODkk08WkugbbrhBZGb++Mc/LniAGVNw9957Ly6//PLKczzWCy64QDicGIHPM/LRgpHPr3/9a6w7kpnP4DKZLhsZGRFfBCWEev+xxUYyId1ZYeGfcGmca4l4Urrf1v58KkbZaz0oCLAbFP5tGrsN9qhk9DYkorBcEvYlWiRTymIUdDpqeizSanqH7iPaYnJJ9mvoXt5p82iHJ1ZfP1KGS1cUFe7Ro9vwcNuT4IjG0PKuvdh56QQGnjmLcLoDm1oVeXNHF3fswIHwVuFqPJ7oQZd7TGR/xia7sKPvsQp5Tj7kR/iQD+3nJCopH58viXg0hI07ZsSIATaOMi2Wz9qw7UxFtty2YVY86FFGEUMk4kVqvFXMvxnd24+BE5VeGYnH/9Ihemxm4yF0tc4IMpmabsXWZ0/B7iqJ5kweZzQThJ1OmoS9aiFD5dsl5evgdyjHXgOHHWWDAnEhbrDTJ8nooh6LU7MtyVajD1Xkph6KsphmRN5H/VHN6/gD1Qt3ekp9XxYTVdDHrnoM1ecL+eoizRpOylVPHF6DRkoiYdA/k5vD0sXSUv+60waNx7ZMDkmD6MTdxnmstcioTaIn/3UK7oIFD3bakD4hhLzVhiv3lPGak44TqesbHp3BL0eAj526Af2qPc47TtqId+heT5KH3LCSaOREXvln7c/Khx6N9sn41MI/N818NNJ4zvem8EkL/n3PHkWerwfTdUY/z+fXHckcC5JkGFpyiuVpp50m8puLeT0yvx4BfbrMUnWX1SKSMC78p6N2eA1mkeeSDsOpFJaavgILiplyXa8OYS+wy0SDkrEgwON1II004rNpQAlABIoVlZPON2u6hFadck38fNaKskEPgsem+BJncnY88tN+PHJnLyw/OhODLYewvW0PdnofgtueQngmiC2qySWRmHZh7K4gvE8qCWNMpqU4uIyR344XKGqxWCwopmLmck7R30NwAFnvcaOi92Xrkw8ikXJhem8PfK0JJMOBimyZyZi2tqSwymFd5vDDg2jfPI5AMIMR9GLbM6pKsxQFCtEBeMthFDIWUCjGBZYD1o5r3wePU7kuMprdvzVfxEipHzkWznQocbGk+7YOZd0sGfGchc2Yta9RVvtyBDRTWLUlymLeXTd+OatRl2klzE57/bTMZKL6e0FNX5WclklQvl0KGvSzzCGftxhEMsFuP9S5opoPWEbKQMWV1dsX8NjTeeF9ZkkWULZZAFVQEc5Z4L1vFt/2d+Dnw7O4xV1AuNWGk/73EN7nTAoSaE+1I/GgA/n+DXjqZqX+wAjlWZs6xGOh0JKIiGzUYWSSdGQztz7KaZRk/AYzcNY61hTJ8ItlDYbFMtZfFqvEmDOSsRlHMvrpmImUMjtGX5OZnSqh1UDAkOPAKKOAK12bLiM8YIhe+3wtGSkTErMG6jKr+ntBj70mwinDjqLBYkE3Y6NIMOj1IScbADVIzWThgBcO9eZzWuyij+JQZKt4/PV3x8H/myGc/O8zKLT70R8YFos6fdqOu1CRKJNc2HGfSTvR2Vftn3E6M2IcwObNh0QNITIWEq4EZHoqxwi/Nwv38cMiVda+YQZDD2yCw51B15ZxjO4ZFP0zlc9WAg6jX8zGmdztQ+eOpDiXk8lu7OhQR0/nLBh9MIh4Xx96/KMiwrGpai9a1UjYSkVk6fWsPamVk25HMWvQeZ42SKsULCiq3mLVA9X8mbOLVFAVJxFTfyerTadp0mU+f/VnZ6bltEzNy2oIKRarXjNerxxprFyzSUf9VshroBizlErIeOvrL+NxAzPZfBHZFt+cVjF9D0Yx2usCOt1gtq0UKeDrE26c1u7GZ/52P+5u8cORseHXA5vQ5nHi/H4ljZQtFuAaYBRZFnVZ7uRTU+PIxeO4aypUKbQfy2tsPphvlCN63QqFBb9fIpFYNMnws/L4JiaqJrEE/97TQ2PcevD5hfz8uiUZOic/9NBD4gulg/JSjGCeb+FfrvVy4dH+gxt2rQBIIBFxiH4XPbJxY9WZlmTkhZmYSgLaIVFU3unknhUPtlztGpVMKDva2Iw+uVDtJK9B0Q2vp36XGZkuIqcxUqy8htrlLbvU9b5uhWwRM3uCuOlHbUDP6aJ/ZnPLPpw0dRtae9NweIvCH8zvTGBjSLW0edyLMHrEudy0SUmHOZxFeFviQqbbsXFaNIlS3da2ZQSpyXZhJyPHQtNyJhrzwWorimFl7azjuHOYmgxhYEAlsV1A9IgL44Ue+NvzYp4M60FMUeZTNuzoUNIJnJiZjyjn1u/XqLAKZaTghV2j6KqcE55yi0G6zCD1SeLQRzLQRowZTbpMbQRV/qIs6D5/9f3jGhdvWYsTL1dUIoxioUpIJQ0haVVn42qAZ+PkT/5usJ4M4tq6oQprJo+kgXzY3+UXqkctbJy42eZH3/0RtIYL2DPgQnFrQPQuveTmKL505lYUyyX8/L5J/HIqhbdu7Mb5fT5RX3jL5hZcffzxhmkplxoJ8b6hqwcf7E3hZpSEwwdluVLZ1dnZKeocSzXC3SjKYUTCB98jl8tVIpxj1XKoNlvsLBn24Zx++um48cYbhUJMHhv/ftlllxn+Djfs/Pf3vve9lef+/Oc/i+fXHcnMxfzMDZJgNm7cWLlglgILlTCL6ZiWetNLfSSTSXgN+2RScYdhJFPWkExcJQh7vlhTtGe6oaAbsZFVc/csOmvhprMtb3anpaZJU3wGA8uPQsaBOK1pdLCxxqDJ3Uu41GY7u/y+dL9qo9yau1+HTSw2ybwfD0+dhoffkYV9fwlbfpDD4PlJnNj1YPXjlYCunkkEggnhHD093QG7NQGPI4uuTYrChrLk6FRQ3KiULrMW4w5k4A0lkEu50TEwDaijCZIRL0Ye2yCEAFEEEUIMmagNqUk3dpyuDD0jyRwZ6kLqfqD/nKrvWNAVgV10VwJxC4k+jJLFgqtf8i7sw04cF61O1qwcf9GKUt6gibFskC4rWiuNlRVodwlqrU05l9VFKaOOTs5oIhmHvRrN5/PFOmdmrUNMKqEx0NSoy8ol1TTTqlxPKXc9cSSkxFIDR76AHJVookm1+u/TqRKsBxP4aNiJv4TjuLfdisxGF7rvj+Avpx4Hl3r//v3xCB5r7calJ26ofNZX7egRD7p23HPPPUItSoXUQqMCyoH1XfhTU1PYt2+feG2+roxylmJImCQPkhvXKnqc8bXLZUU4MFdaTQsSk7420ghYxL/00ktFOwd7Yyhh5mtTbUa87nWvE+eFkmXiPe95j5BM/+d//iee//zn4yc/+Yk499/5znew7khGD345vCjoesqGIl4YJJmFKjcWTzLqH5gOcdcPKCtpq7NCBWQcsaSFfYdBUV+TUjlwkDt7N7KRjNo1r4GuxaakKpL8dnsNmWTU+koiXE8cGSNrjqIHbgPlTzICeAP1u3PF8oTNqLJ/xFrzqXKyT0cfBJUtKGRs2PtAH/b2bcefD1yI/vwebPnrXdj63Cm0b1eiGvbHuN0ZBANxYf0vi/52Zw6h7jj8/izgz6K1N4zwaAuyCQ8ycQ9mpvzweXLIptyiaXPTKdU5OtPlNkTusMHXxembFqEI5HhrpzOHjS+KCJJjc2Y8ywXXAo9TPXinBXmHHe867Wp8O/ZO5XQZSJVht1WaJWvOla4RU4EFhajlKIV/bbG+embtdl9d0Z7D3SRCoep7xdQheZy5U3kPS5U8tOtqPKo2edqKQtJNZVbd51CL5YEDCcT6PbC4bCgUyygULXj/fVk8s68V39k/Luok+bwNP7F14PidfijLGjAUSaLvtA2VFCtxdl+LeOghCYaL9M6dO5ckzcXohQ82ajNikFEO1xf2ozDCkY7Kjdq68HUZeZEo6PYuj7ukiXKOJh5YqqmYF198sSDUK664QmzQTz31VKFUkwTG9VT7Gc8991zRG/PRj34UH/nIR0QPD5VlS9Ej09QkQ/sYDhjjiWd6jLlKuRtYKpKZW8JsXPgX0zHd9emnXK42LVXI22A3aEFKRI2EzUBe0+tAWTKpwaaPZMSbWWDJF1DWWXkkpnIGUxbT3KrXvZdNtanXglLhrN5qXkQqbhTy9URlLaoT4lWSK+vIL+h3IoYkUhEdy+QVJwXRdCuesGBkvBsjV+7ELVfuRMt/9GP7aYfR1zeM47bvgVt1fu7fOYKxx3qFkiw2HUQqZ4U15xReahyk1hqsRiETB7uQS/Hf7LA6iqK3Jga/6JXZ9txkRbZ8KLpROB/0h5SohgED03os/nd4pgA1JZjIeXHxKf+DX8WqEy+LcBimy4rZ+ufTOjIR54tzhTTjkcVzGuEF63zyOtG6MMcjtkotTmJWHZWjd2G22pToxOmuhkiRsGaomVPT5KmqyyylPDLCX8+C1sficM/kMLrLD0urC0mLHWf9ZRo/OXsHpmM5fHfvGK5PJ/DaFgfeeLKiLrm6YxvmwqBBPcYIvN+5UOvHoi8lWMtlfwofrJ2wA5+EQzdl/l26KfMx34ZIEqM8bi3B6Gs52jVMko9UzFLMtFRZGqbG5kqP3XzzzXXPveIVrxCP5UBTkUylHpFICINLOpkyLyhnNcgwc7EjmI9m9W9Y+FePq8COe12vZz5VgM/ArtwllEa19jTxsHGE8+h9UViU9CmSosHSBienAGp/SF0TxNTMFuVr8/udSgSjW+QjjIJIlm5HjYSZSBnUCIp5F5yOevKx0uJcnSejRWwqJ6xOXHIiYVltIFQHQqWTyk3jstprgxn1aytrGuG06cPIdAB33XWu8ruuNJ508g0Y7BoFKXvw5CHRlNnpU4QDI5wX05rE1MFuONw5dG0eRyzsQUtXVKTSJIb29sK23QJPKQkfxRNW1qzcwgWaNSFGNeP3B5GYcsJzlgv9wRHYuIlRDzY25sWvgrUjlYuaAnoVdpQMnrcbyJpp9V9QCaNyGuzVaLCspkyrQ8sUFHJKhKG9/ovFatSh3YBLS/9SSfOzmgmYiUT1onnrWwqYHN6HyWGbIBnPrWP4armIzb1dCJRa8fN7ZjHqCOGjZ20VP9/pc+Hy0zah2omxNOB9z4W6t7dX7KZXwsOLm1USAx9c8HkMjAK44FPyy82tJBzWe4yOSUZejIb0BKOHdpYVIRVqNNLkmveMZzwD6w1NRTIEO1wZwbD+YvSF8aJodDrmfNNlc0UyBXXxrPlZZwvsBgu0q2iHxVZGWdMRHY8Y12RS4TJ86j941UgmLU0u9dDk4xNyMTJIS1Ujh1p4DKYIcvyuQekFsTBrTgaGpGpdp6Rp4GTzYkklmbxauM7qnIUrlh7aw9IqsjSEn8168NiDm/DYvk2IDB2PgZMPYfu5u7H9nD3IFYro3TEqGg2ZMiNYn/F2zGLiQDs6BqfgDaYxsrsffVsm4KDw2wYkJpwYuasF7lMsCLaoY5ZtZdicJfSeGoMvmEM43YpswgVLQjkWd75e+FBXtBfngodfn2aKTRu62KGoS61JfzTxr6XqKbJrah3Snsbnq962aXWgmvh3zX3h4vA75Z0qz/V0d+IbXz+C//5xHi2t1Z/dvn0r/nFvP6KTYey79iL8ZbAfyUhYpFUSjz6KE4NBPL2rc1kNHCXBsFbAwv1qmETyPalC42PLli2iaE8lK6McEgD/XRIOG8C5+aWXIo+bzzUSeVmtVtx5553C1uUrX/kK3va2t2G9oalIRk6wZC6Qu5mlGFy2JM2Y6oWTNRjBbCk6a3aQElQgsfFP+xvRWePTnY1ZRI+H+LOQr9rgY4625iCU/zmKJdGjQlgtStrKmlXTVxpbmQQ7ZaL1qa5ozQyDyqcQjZdGkYxRftrvUtpStX2ldCiWx+CwWcUxemy22mhMTZfVzMXR3pOaJkTxkW1cVIsoFW04dN9W8bj+6xfh5Jf9CkF3C7afuwd9O0Yw9OCgiHSoEsOGGEpFKx67fbuw9U/E3Wh1KRWryAEvtj5rGnZ3CbmiAyOxfqSGiujwReHrVM5VqycMbyqJkF3RKf9+9J8gbB00KKgDwWpQtBqmy0r6/Kr6oYuJWpKxOKqp1LKGnOmJVnnfnPJaCY3DQ1eXBz7fNJJJ9oBUv5D/ua4Djz44hptuGUc+R3FKCYlECy69tB2XXgrc8PcLMPHQhzA6/RB2bVH6R/ytAZz4ru/BSuv79jax2HMRlfUL6SKsrV8sRXqHylEu1ExfcXFvFvCzch3iQw4q43lgVz5nVZGMWIeRtZ5GiPHuu+/Gy172MnzqU58SBLPeHJibjmR44bIIdbTi10qQTGAOWymj6KKcp4TZwFa8YGPrhNY6StQQrJr58BIuUZBN18hQcwYEIf5dk0opqb04JZ1eVPaBFtLz7NuYwwSwkHHCL1NiGiTDeWEMmlWdBQjWiirHpUY46aiO0CQLaUlG0xNSMzxFtWTRmQgLzI634sHbnoZbr306vK1x7Dh3D3JpF7acvg92ZwmHHxrAjnOr82I4/2Y6GYTHFYdVnRTptOVFh/+WXWOwWkuYfDiA1IYOdJQn8Qb79ytd/hkDU0cjMilb7ShpUlcSBU2kUfMaVBpqf7/GVqb6odNSREGrj2dP4KYb0+J4JQL+AUxOeHH99Xvg6LFj/+6T0L9pn9hlP/e5IZxy+nEYf/iX2NizGf2h6hC5C85+vfj/zq0nV56zGdj2syZBB2GtizDTSWyI5k6fO3pJOnPNbToapDUUXTs2b65aJjUbtIPKmMqjWo0jRZhZIfHcdtttlSiHpDMf8r3vvvuEzJgF93e9613rkmCajmSkadxSTcc8FvhaUl6o3bEz++W1lpFSd5FyMxnTNT3KBccokqGtvxwxrMXUcD1RcTSAhC/AqfZAOaUjiEqTZtVdhr0tgl/yape4Onyp0i9h4CHlNxjaJGAwQ6SYdyBtMLnQqXa8WzQNp3mSn7rj97jV+TZ6nqwU/jVqNG3vka4+VrI4YDX4rrU1kVQ4gPt+d4Z49J7wN3i93dj1lEeEnJnmmUQcfmzxjQBPYiOpAzOJVqR9LRhoOVQZT9x1YhyHI63whNL41fRL8bb09+FFBvFy/fkq2dyGdZaSrpgvPp6mj6X2M8jXELFozXfVOVjGM19Vwl/+kqoxzvzxf20Q3ev37/Zj74NPQzLeChQ7MTw8DLv9cQRKH8GTB89AKpWGs00hsd7OHvEgWlvqlVwLgdZFWNYvuMDS4omkw5297EWZT/MjF2outIxeBgcHsVZAmTJFAvyclFdz/SD58lywjkPy1YoHPAb3HGXOdF7+wAc+gPe///3rlmCajmTmg6WOZAi+nj4tRBmzdGtJqzNsCkmDHpOcTdRe6pC11XmaEUN744Z2MxWoC7ctV6y1kFGR1DZYag7HmiugpJKMtPr3OlUS0h6vAZkQkUmDc1pyw++v352mIsLNHy5NvUAbkSQihYqSreZVJYdoLFTYtGrk2UWUOcvF4FgLVTFZDbIJJ8Ye2YH9d+8QqaP+XUew6fyHceolj1Z+xtOaRzEMbO/Yi0LJjtEDrcgdsaCYtmLrc5URBA57XhhF3tB7Dn5484sAnWgqb6AYE7JmA+ttm2okZMnPomwLCmcA8fNRJzxb78Rdv9qOr3w1jJu9ecjumzMHN+J771d+7/v/+Bvyd5yHkeyjsJypvP7mDScj4P+hEAVQ0k8ZLi2WpAO5d66NxDLVLxiBcGGVaTUpkZURDqMd/c6eizIJhlEB669riWCY2qMIQPbvaMmXaTMq5Hge2DX/2GOPCTUbzwUdCVgK2L9/v3BYpvqLkuH1TDBNRzLzOdlGdv+NQhILX08q2LTF/3G1y7moLvxJ3TRDIp9x1LkwC+QY4dQ/X0gZ7GxV1ZB4jxS3/x5kw7r3kv5ppWqAIK3+oUqeK0u3+rapSH3KLTZrfO58zvqFic1/HMalh129bNKa+Ta0l6rEaLJLXff2dotVEGdZ4+hb1ogHfG5bTR2qWLQDmoJ45fm88Xjfck39w4KR3QOYiHix75IT4Xn4CLq/O4TOE+I45Y2KU4DdWkDvpjCGDrdj63OnMZ1oR6IQhCuRwS8Dz8Jrr/o/wC4D80ODIVZFNRoNWGbhs4Qxnt8A2FxIR63Y3HYAhdN3C5lzfL8fqdkuuLZPYPJmZYjU1V/qwX/cZMc2expj8Qx6tlbvg0tPfT9sgpjOrzwXCihOwY8//riIYmjLzkVvtesXbEDkQ9v8yOFcXJhJgJJ0qMZiqonCHqbh1gqkw3EwGBTmlUbrFZ+jIo0PpgDz+byQSPNcsBmSqjWeH7oiv/Od71z3BNN0JLPSkYyURBvJmP1iD64sMG7VIy2XKsFBCxJNMTaTMa7JZGOcJ1H/vMtSv0CV2Zugdk2zf4Frs89Wu+AqEY4FFo3liBb5WAZQp/61tLgR4ZJv8KN0MTZCeMJgGFzBLaZs6mFXTR8rEmaeB01PjMNuERk8Gn1qUVTTZbZSVfNkL1XFEcloFtCM7rDZXTXqKImCOryr7rPp/cAEiSnPxRI+DH1ZkeDesPdMHPdGK7a374ZnbBxbnqbIojv8M2gvzSD1QACDL/0WvH++G1l/a90RFJNW5gfh8z2IbLQNBfsACmUHLJlh3PXffgz09WJqJoYv/+Xj+98AAD56SURBVGgMew4m8bYXTcPtdolFlg/auluttemhjzxdnoXa60MhGN1nKpfFDlkO6Ws2U0Vt8yOJhMVxLrLc2UsnYJINj5ufZS0stCQYypQZuc1FMEZwOByiCZKPX/ziF4JcmB5kkyQFBZwl873vfa8hx+W1gqYjmUanYy4lafHmLXEWur2jxiCT4Dww7RjYTNoBi0F1mjPcc8I3qnZBtOnniAhYUEoUYG3hXBfls2f1RXOpcNNEJz6vMk+GsOaqkUwkrKbUDMbV0jbfCC3eANQhyRUUc05o6vkVJCNFtMCOrMb+xG2zVCIZp9WmKODUhkaJskoyRY0goahJnQV9tYaeHG7mMmh8lFGDHgVNH0j1Pa2V0+fwR5FPBITr88NTJ+LhqVPwir17cL4/hq/edQc2PT+L5/zpZgRiCbx23xnYepEDw1M+FA7eBwuiSKEThc5BFLMFdG27FZ5ufo4wSsX9SAw58MDVZ6G9RTneznYfPvU+Jc/G60vuZpmL56bmnbfUHuejVzwd84GcAstIgQuUUb6/Geus8sHjZvTCHT6jGULrKabPKDRTBENSXAjBaHHo0CG86EUvwqte9SohVSYRk2j+8Ic/iOhvPaPpSGYl02V6kiG58WJgGqK3++l4QF2rtRTis5cR1vh/JVN2+Cz1vSSzYw7DQWCpqDGBWljobwECIaeYQF/Qp6nUXwvY7ZUGS6VxExWHaEk/Doe6yBtM0QoFAxg2eP+JkfrUGmsi2YRRr42ymHucNkTUOCSjIb+K3oHuAhQh2NSUk9oz5LVbK7WissbWPsbRBAPV9ynBgZxaD9PCqMAuXp7eXKUc3I6HkUu2oOQaAIpKtNXhCeLIxHaMjqbxhttSuH9sFkl/ED2tXXjLNRxHsA033z+Ixxx+9FoVquN/+zsnKue7FUmUygfh21XGvZ96Jo7/5E3i+eduBK7+92o6y+gak1EMr7ETrtR2XCuFf74WP9X9RyEbEgxJijl/RjCNjuddDXDjxmM/6aSTKvYmWlkw6xT8d+kpxnO1VPPul4JgeCyspzRiOTMyMiI8wTh9UhIMQZdj6Se2nrHmSIY3LHdBS/l60uKBihFe8DSV+9m0W6qKa+q5HiEftdaMYA7Y6hfoqVHOk6jtkyESM8YkU4yzy5w9A3wtNzyw1w1oIrIaaTMbN+ViXdD0TuRlEZ4kw1Sg5saYGjeOAo0aDAmX1aBWk1CSOiWNMsxltVQCp4RWfs1QSPS7VNk6pQoDCJ/HAvhSSCa9tU7EovbhgNUgkhGKLWsJLz91CD5XGf/91xJKzgEUs2W0b/or/Jv4/mMoZHYjMexF4Tcd8KpDsFyuFM7LDeFrT2rFwKALJ115lxTlod0yhL2FQVTFz8CdVzy9QiayNynttMwr+tD+HvG+PuCqqopYvmLlTzkUxbz1b92QxT2an+D78Brlzp9RPAmGNZC1Au7YeW+RYLQz5vWyYNZqGO1JTzFGabKOo6QYV3ZaPNcZyqtJMDz2Rt5/bGwMF154IZ72tKfhG9/4xop/hmbAmkyXsUFsqSBfj01RvJFpY8MdYjBcPQZtPOKuG8HsQI+9/nhnJzhpsT6SKaSNT7mnpNQxZMiS1SzEWhVWSSNtTml6Zrw2VTbMaCfgQlwu+fSzclfTSAWD+TDid9wBEUHNpyfEwoI8CshohqVRCVzJzmnt6VlDUmeGOHqmURhxo5yvjjDoamvBDQ8O4oYbjuCLj5YxlIoh5/Lxi0GZU2CyNjjz96H9lDHEHw8iHe0WEugPvvgwPnGZsiP+BpNW0QxGp1rxiuuqDsl2N9CyLQXcfhj0BRjvL4vF7k1P31VJUWjnxcyHKIyeNyIbo9+rJxg9bHjTDdmjvtYFXcBZZ60dguEiS3kzDW6PNWCQpKL3FNOmGGVPDv+/3CRLgmEEw7WgUYJhDeqiiy4Sm9ZrrrlmyXzJ1hqajmSOhaWuyRAsRvLC5cUkL4Sg5syUNFphh/CCqi680YQDFoMx3MWMC/Tw1MdcLX7jdFVyOgcnfHC5bSLtZc3WkpvkXUehWs/3+pyVSCYxWRUrU34rYS1pVGfi34wb5oz9uGiLYtANKZx/C3CKNJiySCdnqsTv2ZZBeXAG2REfyhqTzp6LLbBasrj8+SfjQ9MpuNmDk7OItMFrXtODyG8zeOFpETx+eC/++b/TSNm64SxPoPM8JX3VfmqUZxylUhmfuOwZ+O53b6os3Nwf33yMyOIZ33kUf3vHsRe7hYIkoCWauYipcQh/BfG4YbL+/ZoVTBNRoECC4f21GE8xyn9JOENDQ2KjQDWdTKsttdWNjGDYXHryySc3RDCMxihTPvHEE/HDH/5wSQx91yqe0DUZ5onZUMYLlTeC9kLV2v1r02W0ddEilnQg5qyPrFzWAJIGqrPJYWOClAPJbGrvSSGu+11VXZae1aTFNAu4s1xVDMc06Sp7uVijJHY4jAvFUUOPLdKp8vO2cg5FdShXXpVhd3e68OXrQrjmmhHc7yuLaEH8rCeE9leRELI4MlOA64FHke3urViXnb3Rg7u3FLGh1Ya8xtvtsotYY3Djud97CG09QBurIp3yX6tpSqvVUreQT85rcVe8p5YDcuFfeoIh9ItcSbzPb/55o7h2KaltNoXWkSNHRMqLNvMs6C8G2oFk27Ztq1jdkHRodUMykISzGKt+STDs32GkxDWhkddiDxCL/DzWa6+9tinFDE9okjnWzbIUEmbujOg/xGIjJYncZenfVzsdU0srNh3JpDI2PP64XF6riM5YUaibpMlCt3GY71YbD4vSXkRfkFEPzw1rRcVVqb2IyEfT4KhxscrRbddXTU/lVJNFPTJxm/hFvzWKXMGCnDUonqen2SbvfnzxLXswPNOPX90awH1iVokF+awHL3pRD170ol686pdZnJyYxR/umcShYrKyLvbuu1NJSR2pjkQ+rqvK4A7OcNdg7kVae7PX1sUWg6UkhuUhGCMon53SYO64ZeOjVGitdlqGzZi8t9i/w1rKUkNvdaO36uf9LJsjF2J1w98lwXAj22gEQyEDCYbp2J/+9Kdrqnb2hCGZ5U6XMbdLczu6qzJXSrIx6pPRRjLaEgOlwrWwIKsfPyl2/n5DaXNNd78G9rxiKpkTr+8wlB8Tec3QMZfLhqw6TiCjGVD2rGf5sbOriF//Oophiho0v59gjwcL7pYYdnaHcf+RVhQdQRQzdnT6hnHnD/zoanPg1zeM4ppfpeGwxPCulwzjhBOOx/N6e/Hmf1Ze5/6DWdxtqx7k/7zUJSKVjzxtAMd/cn/1c+lqHkuT5rGuMVJYHnAh1DY+MjXFxkcSjSSdRvzEFgOqM3lPrVSDqFa5xwFn0qpfa3UjxQNHs7rhmkLCJsEwgmmEqGn0+ZKXvESc/1/+8pcrfu6bFWuSZBqNZHgDcqfCSEYW+OecjqlJdWkyOop9im5z4nHVSy1TMSccrvoUlNfpR9To2KIlZemU9RQ2fKZz1AmLv/r8BSSzdhQ1kmKfz4qvf3cCv/kNcF/Xxkqfi93ehs98pg+crnrSx/N4Xs8wbt6fx+5yBwolF4Klcdz6TSe2DlJSC/zijyO46W7gSx9ugUsdZvXiC0I4ecsMDh8exSmnnFaX8ji12yUeEtp7d67o4PgVyRrUzvAxgraW0wyQxPve796E60cX1/hIebN2HopcZPlgn8dyptWYumIUwxnzTOGttlU/73duJnk+SH4kEG1PjiQSGcHw740SDM/7y1/+ciFe+NWvfrWm5OVPOJI51k3QaE1G2okzfNcW+I1IhiTk15CMlirS7MTsPnYXfSnvRcGmbS1UfzZjHD6nZwvC7n9wkxNTrWmEw5RQ5ysks3v3KYhOZPC1347h28USrEWOc/Xhla98Bi64YBYf+lMez3v0HqQKVmxodWFy0ifSBnd9xA6PSyEIEspPbs7gwg8GEQqo80YswCue1yoe+oY/5pbZ8NdIR/nqFaYtc6bVeEyNLOQrhY+95GRc//UHjkmSEpLIpehBWppccNXdalMrr0sKQobwtfMPidqAJJzF1i709wsJhnUYEgwX+WYAIwmt1Y10kNZGfLxHqIDjusL6USMEQ+n1K1/5SvHn3/zmN0syQnk9oelIZjkiGUoJOQiNuxs+tESmfz05i9uv+RmtuuyBu8aAF9TOuomF69Ni+awbTl+9GWZ0Wrmxg5YZnHtcDLfs9iFp60JZteB/yUsHcMWVbdi9O4anjtjgH49ioi0gxvBu2eLDG1++CT17o3jzmQ5c7ynAZlPme/zgNTz2XpETpqBBekbxJqJChzs45ocvefrRi7Dc1XGmDwugJJiV3pEtRY3k5y/rwhX/O4ndheoi+pVzi3jjJ2/C39HczYo3vGkXLvjuYwv7XTU6e9Ob5j53l91qwd/esUssstrahUwlzbd2YPT6/3vJBhE5NaPFjQQJlZ+XD97jjDx4zlk74rngcTPNt1AhBUUIHDhGovnTn/7UNATbTFizJDMfzyO5w+KD0Qulskavx65e+fNyHGpIduip++Hjj3fg0Uczhk7Mbpe7zu2Yu9Fc3AJnYRafeHUSf7w9h7sOhpCzuNDlHMbfv+9Hd7sSEt336DR+8IgbN91bgK2sLOq7dgXxn8kMXvtkJybiKbR6lfc9sc8uHsTFp+kGX1ksIlLjg81t8kbiDpORCZ8n4fBGMrIj4Q3DtAF3gFwwVlp2uRQ1kheK7+p4/FLnBdXM9Rfl2JSo69kHF0YwEkz/vekYP3PRNx7C3694uqhdMLIn4TC9xWuD9RMZ5bD50Ojemuscvuh/hnH3+89cMzt4fjZe/4xsSC5cG6RVvxRSaCdgznUfcBP32te+VggP/vznP6+6SWmzwlI+WufjKoAL/NE6+vlvN954ozCaO9oiSCLijo0XAIuQc+WIuXvh7p+5WNn5z4ssVrSg736l1uLLFTFxTgaRSB5f/8MsPn94AJZSAQWnQgj2+/YC01348YeiuOf+Ifz+rhbsi2xEu2caN32jEwN9VTL4+/1xnH6CHw7HykpOSSAkHC4s8ubigkLS4Z9ZMCXB8KbatWvXinYmN1IjYcTzpE/eVHV+Rhl/fP020cinx7EJ5th1nOXFyr2/URpTKwnm/cJNRq2Zp1V3rutBNwNGUo1C+x2xdveLy5cv3SrdE3ivc0SCdh2RVjfSeYAKPq2DtLS64Tr0ute9TtR6/vKXvyy4D+iJhDVHMvz366+/Hk9/+tPnVG9wh8EdCcGL6GgpHzZ38WIiyUiCEc7MZaD3Pg+Oi2bwUMaK6POUlNrND+YRSwPPeZIN3/nTLK79RxHJ0TR+/O4AotOPiDyvMmfCKpoGbTqJbjOA55efmaTD//Mm43N0hV1pgmkkytAuaEzTUEVE40KjSHXhEcxqE87K4dl9wNU6YuACLIvlvDZ4T3BxvfhX08tShzvaBmM56nr8fEwHM0XGzeexonWSjCRgbs6++MUvCgt/bk5JyDfddFONVY6JdUAyBHOf559/vmF4zs5gEgx3H+y2PVohjx+dUkc2jXHwEi8WLXE9mrbgeE8ZyWwZPtfcC4+0vuBr8AJstsa4o4HzSKhCYqjP9Bohd7FGw6aWAwshAu3Cw10kU6FH6yhfXJrsiUM4Rou6ttP+hdcdOeb5aIQUjvX9yNeUP7eYiIlri6w3zodg9ODv/fjHP8ZnPvMZ4cfG+gt9yWgdQwPM1VDUrQWsuZrM0RRm/OK52BsV+PWQBX7u0pguoMKEqhMuttLO4ni1bjEXwfA1ZOMZd9LSXXYtQDakMpJjtCcLouy50AoHpOSTj9XqXDZa/LgxkMXm5bu5nzgEQ+jtarSd9hACeXk+ynXnZ7k6QvQkxKjnqgZsdSTBsP7aCMEQjPDpcch6Du8dXn+//e1v8fnPf15MxOTrmlgDkQwPRxbi58LNN98sdq9y3Ky2wM8GtWMt9rLAL8cuSzIi2XDXxkVW1i0k4ej9kfj7jAD485Q+rqWiH4+dKSamRUgwRooYOcNdng/++VjCgUYx392sfp4Kj/1YxeblK/iv3yhHr/BjMugvH30qTvzUX+f8nW88xVIzbnm+i/hivp+FzOChupSbJhJBI5slvsa//uu/inowU2SDg7VD50yss0hG2/VPomAHP0nhrLPOOuauVkYw2vqLBGs3nDfOB0NjucBy18J/k4TDBZYRE8mQrgHNPjhKC543ecPx2OeqV+kb26QNuxypKwl4JZr89Pl0Hjvl1avbUW3Bb18zqIzV/RMtH9aPhbuRLxwJ5lwAtxv8/MMffWqlWM5ZTLwfZbGcj9VsTJQzeLiBZA9PowTzb//2byJNzw2uSTBPgEiGMze48HFnTUUUFzjuao+16Eh5shHBHGtxk4Vy3kj8fb4XpaDcta2VGRG80aiq4Y3GSLBRibIkYFkclmokkg6/k0YIZ64drdyt8prgsfNc89jnu1gcbaesz+/PVYTWz4HRq58u+9pN+Mvs+o9w5hs9SNcBPkg+UsnIKEffg7KckYwkGG6QFkMwH/vYx/Czn/1MRDB0VVhNjIyMCMLjRE2KEmjC+YMf/ECkjZsVTUcyBHeqR8Ndd90lVFzs/+Aiz3rIsQr8MoIheJE3shAyRUNS4y6NzWu8ifi6coFtBnPCuSAlyjzGpVSQadVIfBCNng/tIq9dQLhI8NiZGjuWmMMIRgvZfEYDHO33GS+z5+RoP7MesdBaiFQy8trgdcLvTmvt8oMf/K1hi5+jHYv0KCThkWAaMarkvX3llVcKq34SDO+b1UQ4HBabaSpr3/72t4tzyNrk1q1bxaNZsSZJhpEMm8nYcEhF13wK/PJjNkowFAawFsCdDNNp8rW5U6OjAG8i7rZ5A8kO+2aZIUGpJdNM7CE5liBiMZA9Boz4+OACo3UcaGQnSXKkWlAaIK60cu9o5LFwoln7Uc5iZMVaaxd5v0jXgWd9b29dhLmQWt1yEMznPvc5fPOb3xR9MGzYXG18+MMfxm233YZbbrkFawlNSTK8+IwOi89RycUH/YiO9cUfrf4yX0hRAVVkfL+55pHIQrlcYHmByxuIi+xqWX5LcuQuTE6DXAlozwcXFP5Z5ul5PuaTp+eCxBTZcpPj0TCfha6xKGZtEs5S9a5IaxdJOJRKM5UmoxxZ55vr3M4lZebrkmC4CW10TDVf40tf+hKuuuoqUehn9NAMOP744/Gc5zxHtB389a9/RX9/P97xjnfgzW9+M5oZa4ZkSBbMrzJlxYuRBWlGMstJMNI1gLtzXmgL8WWSNxAXWd5AVJ9R9bbUyqz5SJSpuFvtjmQpHOD54Hd4NOUewZ/jYsHIkXNDVgsrkwZbO4SzXManzF5o02okBykc+KevPzivY+A1z/uV9xsjmEaEIXyNr33ta/jsZz8rCv0UxzQL3OrG7H3vex9e8YpXCDn1e97zHnzrW9/CpZdeimbFmiAZFqylpxAXe0YWXJSYPlnKAr/+omeKiaBEeTGRyNGk0cthKCjl1bxp55Ior/b3K4UUXFC4GMjzQTKWNvWsv6x279Fyk4xRJPTNpypDyF7+i4kF/Z7RzyzlZ1opZ23tIDLeN1RESi+xucw8uV5IaTsjmEYJ5jvf+Q4+8YlPiML6uedST9c8cDqd4rOxXCDx7ne/W5DNHXfcgWZFcxQNdCApSJJhFEGC4cXFAj9Jg8VDI1cAfYG/UYKRRXIqpRiiLraYP19p9FKM0Z2vRHm1bxZpwa4VDkg/KT5YyFyuUcnNArlo6/uAuFCe/7UHmirKOdpVZERaiyEk/SCyo5l5MgrWEsxiIhgW+D/+8Y+LBstmIxiCtk9cj7RgGpwD0poZTUky2noCUyaU6WntWngRMv2ihdZBeTEFfu6emJZbrjoAi9/aBVbu6OVUPq0UeKEKMDmUje/BHc9amC3O71L22zB6oWsDoxfmnUnCixUOrCzmTwhzLcJyCFkVFsPX//KXla73Y0m/5/q3hUYzc5ljzvU62ucXQzi8/7j54oMbD5kV4H3D2iw3UTxnsmetkU0V145rr71WFNb/93//F095ylPQjDjvvPOEK4kW7Flr9r6dpk2X8eTRm4o9EXoDOj4v3ZX19RdelI3KcymJ5vtyt8Bdw0qCx87PJAvlC5VGy+iLxXUe/1rp3dGqgWQunTUrIyEFP5skpJWM0Oa3IM+PYObrvXWs9zwZRbz7Aq84F7TaX+jrz+c9jKAljEY955YKzArIOi2vd14zUjjAzcl8NiX8nZ///Oe47LLL8Itf/ALPfe5z0ay4++67RYT17//+72JIGls5WPRniu/Vr341mhVNSTJcLLnQkkSM6gnc5TLKYdf3UinIuEPgLpr1F0YRqwmth5iUAh9NGr1SEuXlgBySxv+zfjRX7YuRqyRgnhteF5JwjIQDK4HqIrv4CGbu1zbGgx/5p5r+JF77MoU03/4knlNOiyV5v+KX7OufH/SmlQv9vaW6Rxj58hxwY8JNh9aiX25K5DmZS2zDUclvectb8NOf/lQYXTY7fvvb3+Lyyy+vmPpSBGCqyxqA7CKfa8EhwTCaOfvssxdNMFzcZFcwF7lms4jhzcSctCQcdvnKFBJvHt5kzEczd01J41oCI1amCRfqQMDfk4srPz8XGBn1MV+/UoTDa/DC/zq4LIvsscZEG9Vx5DkxmoiqB68jEgz/XfYfzZc0Vptk5KaQn5VpYaN7VmuDRLENNyL8rCRgbiJJwlyw3/CGNwhn5Ze85CVLcmwm1kgkw4X/aCOWpUswSUZOyGxkYeGFyGIzb0LKfJs/569IoyXhML1EkFy4q2k2gjwauMiRYEgKUtDRCKRwQEY5ckcv04zLkTaU/VqMqBlt/+xn91a61o1mtDSKRuotWmNTPrhB0RfKeQ2RYFj7okRc3jvzHR63miTDz8d7n9/3XASjBzMBMuqjauwrX/mK+NzMmDDV1Mzy3/WApiQZLhzSAHOuVBLzkxxSxcWkEf8whtYkGP4+bbrXWg2DOzk6DVBAwIWEuzWZQpK9J80KHi8Jhoscz/1SRR5yRy8JR6YZpW/WUmwi5C6a70GCadaZ9vqplzxeplUZ9THS4TViRO7zIRpJFgudaLpYkpEjHpjWJsHIKZULAaNgNllyJgyvCd43z3zmM/HiF7+46dNOaxVrimS0FjEyPcCbhz8rd6/zGbTFxZlNW1SrsI6xlmoY/PyUKOvTe/reEz4vU2pLIY1eKsj6EdWCyzngTZtm5HXC3Tsjm8U4A2vHDEiBwloCj5vkzuMm+chZ9npxydEilEYL//rfbeT7pMMzU+WNEgzxt7/9TTQyfvnLXxapMkZFv/nNb0Tq8+tf/3rDx2diHZCMlmC06TE5vY+LCcnjaP5hsgueFxQtYrjYrCVwB8roi4vB0ZyIee5kCkmOV5YkzGLoahEOvx+qyFajfsT0nNyUMIpdaNQnHX2lH9bqjhlYOOTEWCkOmauOI0n41M/eVvca8/UUWy6CkUPqGo3S2cT40pe+VAwZe+tb39o0G6/1jqYkGf0I5rkIRg+t7JULGnf73KExLcMbiKE2d9LN2AV/LHBx4yLBouVCahhaaTQfhDbqW6k0IeXhPP/NQO5SOKBNIcmoz0g4wGuP5H4sBVyzQjY0y/Hgc/mIyajPqI6jxUpKl2X9ixb3iyEYptdf9KIXCVdlypVNglk5NDXJyA7+Ri1i5I3DHC7JhxEAd3GsY6ylhUIaRdIxgCm+Rm+QhUqjlwJag1Eu0KstD58r6tNLgWUKiQTDAjGfp7y9WZy1FzqeQqaGG6njMDUlCefcr9w37/deChGEFFgwemy0/sXPT3nyRz/6USH5XU2C+cQnPiH6XLRgXZJy7PWKpiUZ7jaXooOfRMOLjDcKU0XS8VWOEp6vI/BqgQTJ+hEvxKU0itRLo2XUJ3f0S0HCfA+OeeaCtRaK5Forelnr4zXHGsZ8huI1G/hZeO3TSFaOp1gMCfN7fMffyisWxcjNCSOYRq8dpjgvvPBCfOADHxAd/asdwXziE58QTZ833HBD5TluXNazhVJTkgwvbO6mJBpN6cgiMxdnWtPIC4yvLRdX6erMxZVptWYp5vJroYMyb7SVSDFppdEkn8WSsByLzddtxv6jY0H2kchiOP+uJeFmJxwSA699bk6Wqv5FEuYY5iqMm1CXQqosHcQXQzAUaTzvec/DO9/5TuFJttoEI0nm17/+tchMPFHQlLH/v/zLv4gwmbLCF77wheImWegFwhCbUlOjIjMXTaYO+GDEJBdXFhelQzIJZ7VkwFImy7oSbzKS4HKDn5U5ez60JEz1zUKL5EzDcYHjotToTI9mqH9pGxVJMjwfVDcxtcHvRKbVmk0uzoiDCkQe+1LOEKrf7NX7qv3+dZvF+VvMOZEEs5gUGe8fpsje9KY3NQ3BSLA2ye+F69A555wj5NTzTWWuRTRlJEOCYEj5f//v/xWKEC5ULNrxQTO4Y03C5JdIJQobLGvNBo8OrUOyVgZMwpFDlJYbcm4OF7VmiAD0RXJ5Tvgg+ejPCVVKXKB5A/H8N+s46mP18HAR0Ea/+s8oazjynEjCWW25OI+JBENxCPvIlgNHK/xf+4KWmnMyl5hiLlD5yQcJplFxDjeo9CB71atehS984QtN1QP3hz/8QdSHGWFyw8L6DEUNjPrXmhhpTZOMBA+NXwT9hUg41Lhz4ZKEo18E5ALNnRSLtIvZTTFlJ4uf/D934ySb5VxIuKAzh34sifJqQebnGWHxnPD4JOEwvSa7+NeiSae2SE4FFiO6+UAvF5c29VIuvpLnQErEV2IOj55omFD9h5om09dxeK9ojSvn2njI9DAJptHonQTFFNkLXvAC0dnf7NdgJBIRG2c2iDKDsx7R1CSjBQ+TF6wkHM7dZjpAEg53zm9729vwwQ9+EE972tOWdIHWW5dIe3reyFxcl4JwpECBNxcXiWa/OZgKkwsJz4uUmXMh4fGvtQhG1jAWUySXwgF5nfB8aB0HllOZJgUi3ISttkRci2P148hUKgv8jEAWQzDMgHA88bOf/Wx885vfbPp7SIJGvxdccIFIm61HrBmS0YKHzJuZnboc2MMxqXLQFb2IeKEu1wWm7zshwfBmIeE0unPlTchCIGtHc6Vomhkkfy7QXBxYz5HSaNmf1OyyX36PjICXcsSDbBKWJLycwgE5SZQE08wqJdmPI8+J7MfhhpD3FO9b/r0RMOPBFNk//dM/4Zprrlkzm5xEIiHqMRQEcMrlesSaJBktqNR47Wtfi+c///lil3T99deLhYLRDYUDrGssJ+HIvhOmKrQzYObb6Cgtbhazg15NyB20XKBXQhq9lNCOetbPLVpKaBdXko9UNBo1Oy4EzOezyM30Kq+5tQRuSFg/5T1AaK2QFlLH4e8zRcba7Y9+9KOmJpgPfOADIpXHFBmvPYoSuMGkEq6ZItClxJomGe6ezz//fPzXf/1XxaqbO4Pf//73IsJhkY0LHBVqJByGpct1AfI0srOaF7xsdJSEw92l0fsyB80UQTN0wTcCpjioyDvaDnqppdHLcfwrvUBrhQNM07GHqxGfOemiwI0Uo+i1Bqa3qF6UPVSN1HH4s+yD4Sbnuuuua/qo+VWvepWoLfOz8rNx/fr0pz8tsjDrFWuaZOSNNlcEwBQFU2kknN/97ndix8hdBAmH0sHluiDlbl4SDndssrOeFxZvGN5cckhaoymC1YL0kuIumgvcfI9f35+0Wq7R0sOOJL/aLgRSYCIX1/kKB2QNY7WPf7EEY0SQ863jMMXGDAZdPDh0rNmiZBPrhGTmCy5w7LKlaIBzvEkwJBxGQNxNLJeSS+ahJeHwz/K9eIOtRA/MUoILALv45fjrRslhodLopYKUuDOHP9fk1dWCVjjAB/8uNyfa2hYVVCRJHv9a26BoU3zzicC0dRw+mGL63Oc+J2ovFP9QBch7utmbY5/IeMKQjBZMZd10000iwmFNhyog7ogY4VCZtlwXLBdWdpFz90qiYWpPzq3no9lvFDlmgITNBWKp0l16ubheGr1UhCNtbpiqWAxBrgS07uJcXGVti+eCRLQYFVYz1MAYwS+kh02C18e3v/1tIU9mpoJCGakw5RDDZq7HPFHxhCQZ/QJ366234uc//7kgHO6aSDi8aDnMaKmaIWUPCRcGNsrxZpBz66X9PHelVGQdbSb5ahKztMLgArFckR+JTKr3uLhK9d5iJ10yKmAPCdOYcib8WgI3JFycmUbiLUvyleel0dkqKw1GjyT5RglGngfa9TM19pOf/ETcu8xMsP5KAcparG2udzzhSUa/wN1xxx3CbYD9ONwxUhZJwqH2vtGdr5Qos4ucKrK5usgl4WinXJJ0VnsRYeRCguRxUKSwUrtF/aRLbgiM0kfzjcB4jhnBrLXcvXaeCgmSBK9NNfK6lISznKnGpSCYxYgsuFF7+ctfLq4Linu0ljPSpd1E88EkmTnAi5YzKCTh8CYh0ZBwKJecby6fCwF30AuRKGvrFUztcBHR+qmt5CLCnSObRLkwsPl1tW5kfd8Jo0Aek1TvzUUcJCYSPH9/OSOw5YKcac+aHglGv9HRpxpJvFJgstKOA3OBAhdKdBdDMNzoXHzxxeJ6pJhnLaYKn6gwSWaehMOFShIOC69MpZFwmFqbS9MvJbKL6cFgmkq7iDDNI+1tlnvXKiMwulgvZo7NcoCLjbapT9a2tKOVpU0PiYUL3FrL15NgmCLj906COVZEq20U5rnh36Uiay4Z/UpZ3fD8N9ooygj01a9+tfhM7INrJrn2Zz/7WVx++eV4z3veg6uvvnq1D6cpYZJMAzc+c7/SwJOLwNOf/nQhGiDhcKfGm/sHP/iBKEouROI7n7QPFxzpHcbduyyQL6R5bT7gDc0ueH6GZneIZVQjCUeObuBCxPNEImaKrxl29Au9zrj7l0X+hdboZN+WPC+MBFa6KVYSzGKsbrjJet3rXifk5jfeeGNTNZwy0/HKV75SXG9cA0ySMYZJMkuQyqBKjQ/m/c8991yRHuDu+rbbbls2o0JZIOeNrPVTk/0ViyEc5v6ZP19OJ9/lAqMXSmRptEiyl6nGZq5XzLWRIUkslUhBO16ZaUduSiThLEfNT1r1LIZgmAp84xvfKDZyVIM2U1GfkTTre9/4xjfwqU99SqRiTZIxhkkyS7gw3HPPPXjFK14hogzurs877zzhNsC0Gov+y7XAyf4KSTg8FrmwLlSRxVQgF+i1aFMib37KxEmObNLTOiQvlzR6KSFVcPwcJJjlkLUzqtGOKlhq4YAcN8AIstE0MTdRb33rW0W6kwTTbJudSy+9VNxbX/rSl0Tbg0kyc8MkmSUCm+PoAMudG21uuKgxumFKjYo1WtpIexumn5az2VDb0CedkaUia668vLZJcS02iRLc+XNRosCCBKMfA7Ec0uilJhg5S4gEsxIpLVnzk44DJGJ5XkjECz0vkmAWM26A39W73vUukQm4+eabl2yy51KB0mlawTBdxijTJJmjwySZJQJTTN/73vfwf/7P/6m5Mbl489/kiIJbbrlFEBHJhhHOchbUpSJLug0wlSQlwFrreS5uzP+znsEUwGpLphczapvkQvPB+UqjJRFrz8tqFMh5TDz+1ZRZ64UDvH60kvFjnRfpxr0YguExvPe97xXd/IxgjvVdroaNFY04//znP4v7mDBJ5ugwSWYFwZuWNzCbPkk4vIkoC5aEI0f9Ltd7MwUjCUdKgLmIsIbE/DcjmGZ3HTiaSKGRefbaznpZIJfSaO7oV0LyTJLj4syoggTTDDJrKRyQ52Uu/zD9yGcaVTaa2iLBfOhDH8Jvf/tbEcFww9Bs4L1LKyot4fL7433LzSXP01pTMS43TJJZJci0FruVmVajrxpvKjmigEX35UzhkHCYGqPMmjc3U0ZSGr2WmhXlqIGlmgbJ8yIXVq3tj1YavZTgAkWZOL8DknwzugjPNQdG1reY3iNJLpZgPvrRjwrnDRIM+8qaEfzsVLpp8YY3vEFsEP/t3/5NXIcmamGSTJOAu8b/9//+X2UIG3fkknBYVFxqwmEkwy5+dk0zZSd7cbirZy5e2ts0s/2KdPJdrmFdUhrN6I/fz1LNgJFg9MgaEnfB/I6bkWCOJhyQjgMEzwmvI15PC43GSWKf/OQnxSwYEgwX7LUEM112dJgk06S7Je1MHC6g0jGaAoLFEg5fn4sbFwZ9ik7a8WsXVhnhNJOfmnQi5uK8Es15rGfJWoXWhYGPRhZWpsb4HTC1ws+wFlMsJBh+Bg6rk84DUsHHa2s+wgESDBsav/Wtb4n08VqMBEySOTpMkmlyMBXxxz/+sTIThwuaVKlxJs5CFyem6JieYUGVNulHWxzlcC0SDn+P7y0JZ7UcjLkocY4KoxjWL1ZDBae3cmF6UY7gnk9TLAmGUeRadSIgeD2QYLhJoTxfq+CT8uhjCQf475QA88FGS5KtifUHk2TWEBhlUNUiZ+JwcZMRDntyjlUwlj5q27dvF1YxCwEXRkk4S7GTbwRclDiHhJ9DTlNcbSxUGs2IiATDNCTTfM0gnW6UYI4mtJhLOEDlooyOv/a1r4nZMEwPM0I3sT5hkswaBRcr7UwcFk4vuuiiykwcffFe1i+WYpa93MlLexsumJJwFjI+eCHQyqwbsVlZCRxLGs2/s1FUulmvRYLh5yNJLmSjIoUDPCcs7LPHhCk2XjvXXnutsO43sX5hksw6ABd99t/ImTgsWMuZOPRUYlGVP8MenqWuX3DhZGQjCUe6AC9lVz0Xb8pj+bkYwawFmbWRNJrngtEXU2Rr4TMYEQwjGPrZzddR3Oi8fP7znxd1GBKt7KthNP6xj32sKV0YTCwOJsmsM3DRv/322yuO0ZT48sb9yEc+gne+853L2mhJMpA2LjJ1pPVTa2TnLiW+JMlm6SFZKEiOtBwiAfOcrIQ0eqnB1BcjmMUSzI9//GN84AMfwG9+8xuxAWLqjbVGpnFJPCbWH5qSZL75zW+KBxVEBHtGrrjiCjHHxcT8wBz4a1/7Wtx555141rOeJVJrjDb4Z6bUOIxtOefb6+fV8zLT2tvMh3CkAos/u5YkvnqCYYqM9Zldu3YJktFPRJXS6GadcikJhhLlRh25+f3/7Gc/E3YxTPHSgsnEEwNNSTLsF6EShQ1ZPDzq57/whS+IBYeEY+LYYIMYaxjsnubizkWf50+OKGAT5gUXXCBSahdeeOGSjwrQgt+htlYhJ1yy+DuXXQlJUlsgX4sKLCoDSTA8/yySzzURVT/lcqUFFUcDU378DPOx6zkaeM3R8PKnP/2pqB2uJsxN7MqiKUnGCNwJkmj+5V/+ZbUPZU2APSRc3IwUWPzKmZ6QhENBgHYmDs/1cvupyV4cLrKScKSfmmwU5Q5/uZ0PlgssdHNx5udikXw+51M7oI5px+WcF7QQgqHUfdOmTQ2/Djc63PQwVcbay2rD3MSuLJqeZJiTZ0Gb1trcidO6wsTSy4LlTBx6gD3lKU8REQ7l0VzglttPTRIOyYWLKZ8jQfK7Xu2d/GLGDbB/hDWMRj6DkTR6sfWthTbs8jOQXBZDMOzxYtqWQ/w44KtZYW5in4Akw8WOzYZU5XA3ft1114m0jonlb3SUIwq4yHAIGwmHDaDLORNHO+iK0Qx39XKS41ryU5OLs9G4gaWURs9nfMNiP4Ns2G0UbLC85JJL8O1vfxv//M//3JQbBnMT+wQmGfaBsG7AoiPTOt/97nfx17/+1YxkVgi8LHj+JeH8/e9/Fw1zJBw+lnomjpTHctfMhY1RjXSMln5qknCaVY3F42Sab7GL80Kl0UwzSnfkxarvGIVRCcfvdzEuyH/729/EAL+vfOUreP3rX990BGNuYlcOTUsyerBITXULd0UmVhZyJg7Jho9bb71V9HrIEQWL3bGz/sB+CQo9jOSx0k+ND5JRM6qxpAJrsfWLhUDb5Kh3jeZjob04Ms3HJkvea42CEno2WH7xi1/Em9/85qYjGMLcxK4c1gzJPOMZzxC7qx/+8IerfShPaPByYYQhZ+LQNZfSXEk4c6mojpUiY/2FXeDzNaqUaiymUrVqrNWAjMIWI/FdCixGGk2yYgRDmxjWkRrFXXfdJa4Fzr1nX1YzEowRzE3sE4xkLr/8ctETwxuW+WHWY6THEfs8TDQHeOlwodfOxOECJUcUkDiOVqBmdLRnz56GrW6kn5pUY9Fqhq9DRddKyX/5+dks2ogf3HJiIdJoLcEsZlIrIzmKRdi5/6//+q9rhmAIcxP7BCMZypRZNORQLaqN2CfBgUAmwTQvpCGinIlz/fXXi0VXEg7Ta1rCocCAw5/YZMkC/3I4I0vCWS4/NZnma2Qi50pCK43m/5lGk4RDkQXJgVFko0o4grY/lL9/8IMfFPdqMxOMuYldWTQlyZhY+2AESrsQEg5lrCxOU6FG0mGajTtn2t5wE7HUkH5qUv5L9ZVW/rsUC6AcN8xU4XzSfM0C/bkhOTOyYT3MyDV6PmDTLzMPl112mXDmaGaCIcxN7MrCJBkTyw6mY+RMHCoFudAxunnb296Gs88+e1m7+Sn/lf0mfEgrfkY4jfabyJEJixk33AxuBCR6kjyjPhJOI9Jo9liRYLhwsw7T7ARjYuVhkswCQAM/htrvec97cPXVVy/ft7IOwQXs7W9/u6ir0azzjjvuECaJTN0wj0/Smc9MnKXqN6F4gX+XhMNd/HwWVf4eCYYOwosdmbDahp08fulGMJcTgxxTYPS9PP7444Jg2AtDZ+W16MxgYvlhksw8cffdd4uOZeb3acFikszCwD6n//zP/xRD12SBnEqxv/zlL5WZOIScifPUpz51WRswZQ1J9uKwbqG1tzEiHNYId+/eLQiG5LSWCeZofmpSGs1zwwiH0mbZGCtrXTSvpckqvyveCybBmJgLJsnMA7zJaDP/jW98Q6QEWKw2SWbhkQx3ynPNs2FtgA18ciYOe2NIOKzhUPmznA2YXFRZQ5K7eNngqN3FSyUcBQxMJa1F8HORYHj8HJs839SWlEaTWEgq7AXieWIUQz8yk2BMHA0mycwD9E3jTo6zyDl10iSZ5Sek2267TdRvSDiMOOSumQrD5WzA1O7iubDyz3w/1jCoclyrKTJJMNqRA41G9Gy0JOkzymFdiqaXb3rTm5paYWdi9WAmUY+Bn/zkJ0Li+ZnPfGZlvhETIlVFk05aknD3TNEAU2yc7Mlu+te85jWCgBh9LDXk9Er2i9A7T1rcsAeHajJ2xB85ckTULNYSwehn2jQCEi/t+ukhSMshkjAlyzwvVNuZMGEEM5I5CriYPPnJTxZ1BO5iCTOSWT2wUE/ClyMK+P2wU5sRDhe+pe6HYR/PgQMHcNpppwnvNH1HPZVZUhpNEmpGkAwZwfD4F+NqzaiFfTAcvXDttdeuyQFyJlYHJskcBUzVMBWgLQIzlcMblXlo3sBrcZjWeiEc7Uycffv2idoNazhLMROH83gYRbEWZ9TLw+9eEg4ngHLKqCQcdtc3A3iMjGDkXJ5Gzwcl4CRxRnecbrnaI7CZVeB3zhoZyZ1O4XQEoZDBRPPBJJmjgOkY7ma14PAlFk3Z1Uw7FBOrD9ZRuOBIwiH5UJ0mZ+JQSbWQBZZuBIySTj/99HmNqKZKTmtvIy1cqFTjn1ejd4THxAhmsQRDyTfPIRtOeW6bYeQC63OvetWrhCs4BSOUxPM7Z1NosxC8iSpMklkgzHTZ2piJIwmHxpXamThcLOdacOXvjoyMCIJpxHBTb+HCArkkHBLWShCOJBi+HzdCjb4n1YBMRTKSoz9ds45YIMHzHHMUCGt5JpoLJsksECbJrB2QNBiJsg+HFjaciXPmmWdWZuJwrIBcgJl+Y/c6yYF1uKXYETO1SqJhwZz/Z5pJEs5yjVMmwTBFxuMnwTQqL6ZsnyoyRi60B2rWmpNsCqUtDt28zexC88EkGRNPGMJhhCJn4lAiTSk6d+pMBzHPzxrGd77znWWRSGvHKfOh9VNjUX4pek0kwfD42TDa6GtSrv3yl79cnDMSzGqNUJgPuDlghMq0HuccmWg+mCRj4gk7E4fRDaMcug5w0eeALT6k1cpyLowUC8iOeh6PJJxGTSqZpiPBMOJYDMFQ7nzxxRdX/OZY02lm0KroD3/4gyCYZhq1YKIKk2TWED7xiU/g3//932ueo6KGRW8TjUUXbCLk4DUuVvw/R0ww9cLdMZWF7CtZzo52Egx34bL5sxGTSkkwrJlQat/o8TKSe/WrXy1SexzVwAirmUHXZ9aK6BSxXOOuTSwepth9jYFKIQ4HkzD7FRoHvdRo1MldMLvV2VjI/hcadzLCocMDB+dJwlnMAj4XGDHRaocPbhhYbCfh7N27V6S/tPY2Rt81CYa9QzQaXczx8b3obEF/NhJtMxMMifld73qXiES5MTAJprlhRjJrLJJh7w4nMZpYPFjcZlqIhXgjcMHXzsThYi8Jh+qz5Y5weHwywmEjKCMbHgMjHYoIKN8lwfDP+qFwCwGJilb9FD4wddjs5p/veMc7xLRcRjHa3hiKKZpZoPBEhUkya4xkvvCFL4ibiakR2p6wYL2ac+WfKCAZMfdP0QCJh98BCYfCgbPOOmvZm3JJOFI0wD8z8iHx8DqgI0Gj70+iolUMJ3zedNNNcxJuM2GuetkPfvADvP71r1/x4zFxdJgks4bARY4LDHdvTGuwPkPFFBvR5tM0aGJpwMWdNQsSDsdNc6HXzsRZ7hQmm4QZzZIgWMNhaksKBxbSy8LfZV2DKUOmnfr6+pb1uE08MWGSzBoGC8aDg4O46qqrRLrDxMqDtQzWyEg4TN9wl01bG6bU2Bi41B3yJBY2mDI1Rgk2U11yRAHrSVSDyV6co6WOqHB773vfK6IXPsxo2MRywSSZNQ5aa9Ak0nSJXn1wwZczcUg4VGuRcBjhcNDdYjvmGXmQYEhkJBh9iozvR0k0CYcSafa3kGz0fmokmA996EMi7WcWzk0sN0ySWcNg6ow7UNZq3v3ud6/24ZjQEQJVa3ImDkUEHPJFwuGmYKENn5JgiPnUYEh4knDop0ZiYrr1ZS97mSBARl6MYCjXNmFiOWGSzBrCBz7wAZH7Z4qMkxo//vGPi9w8jQGbXRH0RAYjB1raSMLh4v/sZz9bEM5znvOcY3bUk2D4PfN16Aq90CI/U2y0XPnYxz4mIi1p9PqWt7xFWOishoGniScOzKFlawjDw8O45JJLROH/la98pZC0cvEyCaa5wfoJTTpZO6PPFmXCjCCuvPJKMYSNjsL/8z//I2oqlC7rCYbKLxJMoyoyChGYXuMxUJX2xS9+UYgXGFHx/R955JEl/LQmTNTCjGRMmFglkDg4VZJ9OExfkYCe+cxnCmn0RRddJEQDjDbYhc+Ip1HVGomLBHf11VcLgmNPjazhsPGS9SKzv8TEcsEkGRMmmgAkgt27d1dGFDC6YBqNiz9rKdu2bWsorcXX/epXv4rPf/7zQnbN9Nhqgyk79nvRCodSfHbuM3VoYn3CTJeZWBTYp/Oa17xGpO6kOSNnmZhYGEggHI98xRVX4M477xTyZyrCqA4jMXAy5be//W2xKOtTanOBP8ff+exnP4vf//73TUEwsrGV0dTXv/711T4UEysA07vMRMOgTJbNh0y3cLfN2hDHIDPvb6IxsAZDF2TWZ9hkS2cBjoFmSo1RDv3V6DAgZ+LQedgowiHBsAOeykNKlc8+++ym+UqosuPDxBMDZrrMRMP48Ic/LOay3HLLLeZZXEJ873vfE82ctP3XEwfFH9qZOFSbMdVEwmERn4TDn/vv//5vQUh0JOCgvWYFj9dMl61vmCRjomEwvcOCNBc+jr6lkzHNCzmTxcTygkQyPj4uFmgSDs8/p0KSbOjI/OlPf1o8T6l0M8MkmfUPk2RMNAzZwf6+970Pr3jFK3D33XfjPe95D771rW8J23gTK0c4bLhkkyXdiakg+/GPfyxUac0Ok2TWP0ySMdEwKLFlMfn222+vPEfnAZINTRdNrN6Y6bUyJdIkmfUPU11momH09vaKlJkWnCR5+PBh86yu4qK9VgjGxBMDprrMRMOgsoyDrrTgREfa3pgwcTTPPTaeShw8eFDY5lDoYLpBrz+Y6TITDYNpMVqVcK4NbW7uuusuUfT/zne+sybqASZWB3R+puxdD9bxfvjDH67KMZlYPpgkY2JR+O1vf4vLL79c9Mdw1jpFAKa6zIQJExImyZgwYcKEiWWDWfg3YcKECRPLBpNkTKw7yM53/eOd73znah+aCRNPOJjqMhPrUpBADzAJeoA961nPEg2jJkyYWFmYNRkT6x7vfe97hUCB4gRzCqQJEysLM11mYl0jl8sJi5U3vvGNJsGYMLEKMEnGxLrGr3/9a0QiEbz+9a9f7UMxYeIJCZNkTKx723zOLunr61vtQ1lX4MAxCixoksr5NmzENWHCCCbJmFi3GBoawg033IA3velNq30o6wo//elPRdPtxz/+cfzjH/8QUy458mFycnK1D81EE8Is/JtYt+BUSI4fPnLkCOx2U0i5VGDkcsYZZ+BrX/ua+HupVMLGjRvxrne9SwyyM2FCCzOSMbEuwYWP44fph2USzNIKKe69915ccMEFleesVqv4uznewYQRTJIxsS7BNBlHDlBVZmLpMD09LXqQuru7a57n3zmp04QJPcwcgol1CY4d5gAvEyZMrC7MSMaECRPzRkdHB2w2GyYmJmqe5997enrMM2miDibJmDBhYkEjt08//XTceOONNfUv/v2cc84xz6SJOpgkY8LECoB1jI997GNi5o7H48HWrVtx5ZVXrsmUHuXL11xzDX70ox9h9+7dePvb345kMok3vOENq31oJpoQZk3GhIkVwOc+9zl885vfFAvzCSecgHvuuUcsyqFQCO9+97vX1Hdw8cUXY2pqCldccYUo9p966qn44x//WCcGMGGCMPtkTJhYAVx00UViEaYDgcTLXvYyEdXQW82EifUKM11mwsQK4NxzzxV1i71794q/P/DAA7j11luF5Y0JE+sZZrrMhIkVADvhY7EYdu7cKdRZrNF8+tOfxqtf/Wrz/JtY1zBJxoSJFcDPfvYzXHvttbjuuutETeb+++8Xc25o3ElXAhMm1ivMmowJEysAensxmtGOgP7Upz4l6jF79uwxvwMT6xZmTcaEiRVAKpUSHl9aMG3GHhMTJtYzzHSZCRMrgBe84AWiBjMwMCDSZffddx+uuuoq01vNxLqHmS4zYWIFEI/HRTPmr371KzF3hbWYSy65RPSasIvehIn1CpNkTJgwYcLEssGsyZgwYcKEiWWDSTImTJgwYWLZYJKMCRMmTJhYNpgkY8KECRMmlg0myZgwYcKEiWWDSTImTJgwYWLZYJKMCRMmTJhYNpgkY8KECRMmlg0myZgwYcKEiWWDSTImTJgwYWLZYJKMCRMmTJjAcuH/A2Y38Sfrj2VcAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -5814,7 +752,7 @@
    "source": [
     "fig, ax =plt.subplots(subplot_kw={\"projection\": \"3d\"})\n",
     "surf = ax.plot_surface(x0,x1,zz, cmap='jet')\n",
-    "ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or')"
+    "ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or');"
    ]
   },
   {
@@ -5853,1275 +791,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 44,
    "id": "5112f48b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>.sk-global {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "}\n",
-       "\n",
-       ".sk-global.light {\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: black;\n",
-       "  --sklearn-color-background: white;\n",
-       "  --sklearn-color-border-box: black;\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "}\n",
-       "\n",
-       ".sk-global.dark {\n",
-       "  --sklearn-color-text-on-default-background: white;\n",
-       "  --sklearn-color-background: #111;\n",
-       "  --sklearn-color-border-box: white;\n",
-       "  --sklearn-color-icon: #878787;\n",
-       "}\n",
-       "\n",
-       ".sk-global {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".sk-global pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip-path: inset(100%);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       ".sk-global div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       ".sk-global div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       ".sk-global label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label label.sk-toggleable__label,\n",
-       ".sk-global div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       ".sk-global div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       ".sk-global div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       ".sk-global div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".sk-global div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-0);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       ".sk-global a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       ".sk-global a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       ".sk-global a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-top-container.sk-global {\n",
-       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
-       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
-       "   to allow keyboard-only users to navigate the display. */\n",
-       "  overflow-x: scroll !important;\n",
-       "  max-width: 100%;\n",
-       "}\n",
-       "\n",
-       ".estimator-table {\n",
-       "    font-family: monospace;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary::marker {\n",
-       "    font-size: 0.7rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "    margin-top: 0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover td {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table :is(td, th) {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    `table td`is set in notebook with right text-align.\n",
-       "    We need to overwrite it.\n",
-       "*/\n",
-       ".estimator-table table td.param {\n",
-       "    text-align: left;\n",
-       "    position: relative;\n",
-       "    padding: 0;\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    background-color: transparent;\n",
-       "}\n",
-       "\n",
-       ".default td, .estimator-table th {\n",
-       "    color: black;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       "td.fitted-att-type {\n",
-       "    white-space: preserve nowrap;\n",
-       "}\n",
-       "\n",
-       "/*\n",
-       "    Styles for parameter documentation links\n",
-       "    We need styling for visited so jupyter doesn't overwrite it\n",
-       "*/\n",
-       "a.param-doc-link,\n",
-       "a.param-doc-link:link,\n",
-       "a.param-doc-link:visited {\n",
-       "    text-decoration: underline dashed;\n",
-       "    text-underline-offset: .3em;\n",
-       "    color: inherit;\n",
-       "    display: block;\n",
-       "    padding: .5em;\n",
-       "}\n",
-       "\n",
-       "@supports(anchor-name: --doc-link) {\n",
-       "    a.param-doc-link,\n",
-       "    a.param-doc-link:link,\n",
-       "    a.param-doc-link:visited {\n",
-       "    anchor-name: --doc-link;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
-       "a.param-doc-link::before {\n",
-       "    position: absolute;\n",
-       "    content: \"\";\n",
-       "    inset: 0;\n",
-       "}\n",
-       "\n",
-       ".param-doc-description {\n",
-       "    display: none;\n",
-       "    position: absolute;\n",
-       "    z-index: 9999;\n",
-       "    left: 0;\n",
-       "    padding: .5ex;\n",
-       "    margin-left: 1.5em;\n",
-       "    color: var(--sklearn-color-text);\n",
-       "    box-shadow: .3em .3em .4em #999;\n",
-       "    width: max-content;\n",
-       "    text-align: left;\n",
-       "    max-height: 10em;\n",
-       "    overflow-y: auto;\n",
-       "\n",
-       "    /* unfitted */\n",
-       "    background: var(--sklearn-color-unfitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       "@supports(position-area: center right) {\n",
-       "    .param-doc-description {\n",
-       "    position-area: center right;\n",
-       "    position: fixed;\n",
-       "    margin-left: 0;\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "/* Fitted state for parameter tooltips */\n",
-       ".fitted .param-doc-description {\n",
-       "    /* fitted */\n",
-       "    background: var(--sklearn-color-fitted-level-0);\n",
-       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".param-doc-link:hover .param-doc-description {\n",
-       "    display: block;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".features {\n",
-       "  font-family: monospace;\n",
-       "  cursor: pointer;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: .20em;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  font-size: inherit; /* Needed for jupyter */\n",
-       "}\n",
-       "\n",
-       ".features.fitted {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features summary {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  margin-bottom: 0;\n",
-       "  text-align: center;\n",
-       "  align-items: center;\n",
-       "  justify-content: center;\n",
-       "  gap: 0.5em;\n",
-       "  padding: .25em;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features.fitted details[open] > summary {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "  border-radius: .20em 0 0 0;\n",
-       "}\n",
-       "\n",
-       ".features details > summary .arrow::before {\n",
-       "  content: \"▸\";\n",
-       "  color: grey;\n",
-       "}\n",
-       "\n",
-       ".features details[open] > summary .arrow::before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       ".features details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features.fitted details:hover > summary {\n",
-       "  margin: 0;\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       ".features .features-container {\n",
-       "  max-width: 15em;\n",
-       "  max-height: 10em;\n",
-       "  overflow: auto;\n",
-       "  scrollbar-width: thin;\n",
-       "  padding: .25em 0.1rem;\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "  border-radius: 0 0 .5em .5em;\n",
-       "}\n",
-       "\n",
-       ".features.fitted .features-container {\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       ".features .image-container {\n",
-       "  block-size: 1em;\n",
-       "  inline-size: 1em;\n",
-       "  padding: 0;\n",
-       "  margin: 0%;\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  align-items: center;\n",
-       "}\n",
-       "\n",
-       ".features .copy-paste-icon {\n",
-       "  background-size: 1em 1em;\n",
-       "  width: 1em;\n",
-       "  height: 1em;\n",
-       "  filter: grayscale(100%) opacity(60%);\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  width: 100%;\n",
-       "  margin: 0.01em;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(odd) {\n",
-       "  background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:nth-child(even) {\n",
-       "  background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table tr:hover {\n",
-       "  background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table {\n",
-       "  table-layout: inherit;\n",
-       "}\n",
-       "\n",
-       ".features .features-container table td {\n",
-       "  text-align: left;\n",
-       "  padding: 0 0.5em;\n",
-       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "  white-space: nowrap;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       ".total_features {\n",
-       "  display: flex;\n",
-       "  justify-content: center;\n",
-       "  margin-top: 0.5em;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-5\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=100, random_state=22)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('C',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-C;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
-       "            C\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-C;\">\n",
-       "            C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-random_state;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
-       "            random_state\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-random_state;\">\n",
-       "            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">22</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('penalty',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-penalty;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
-       "            penalty\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-penalty;\">\n",
-       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing &#x27;multinomial&#x27; + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('l1_ratio',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-l1_ratio;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
-       "            l1_ratio\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-l1_ratio;\">\n",
-       "            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('dual',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-dual;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
-       "            dual\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-dual;\">\n",
-       "            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-tol;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
-       "            tol\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-tol;\">\n",
-       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0.0001</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fit_intercept',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
-       "            fit_intercept\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
-       "            fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
-       "            intercept_scaling\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
-       "            intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a &quot;synthetic&quot; feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
-       "            class_weight\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
-       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight=&#x27;balanced&#x27;*</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-solver;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
-       "            solver\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-solver;\">\n",
-       "            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_iter',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
-       "            max_iter\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
-       "            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">100</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-verbose;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
-       "            verbose\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-verbose;\">\n",
-       "            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
-       "            warm_start\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
-       "            warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
-       "            n_jobs\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
-       "            n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    \n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Fitted attributes</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                    <tbody>\n",
-       "                        <tr>\n",
-       "                        <th>Name</th>\n",
-       "                        <th>Type</th>\n",
-       "                        <th>Value</th>\n",
-       "                        </tr>\n",
-       "                        \n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-classes_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29\">\n",
-       "            classes_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-classes_;\">\n",
-       "            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[int64](3,)</td>\n",
-       "           <td>[0,1,2]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-coef_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29\">\n",
-       "            coef_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-coef_;\">\n",
-       "            coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3, 2)</td>\n",
-       "           <td>[[-9.33, 8.18],\n",
-       " [ 3.73,-4.3 ],\n",
-       " [ 5.61,-3.88]]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29\">\n",
-       "            intercept_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
-       "            intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
-       "           <td>[ 25.2 , -6.11,-19.1 ]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int\">\n",
-       "            n_features_in_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
-       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">int</td>\n",
-       "           <td>2</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "\n",
-       "       <tr class=\"default\">\n",
-       "           <td class=\"param\">\n",
-       "        <a class=\"param-doc-link\"\n",
-       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
-       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29\">\n",
-       "            n_iter_\n",
-       "            <span class=\"param-doc-description\"\n",
-       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
-       "            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>\n",
-       "        </a>\n",
-       "    </td>\n",
-       "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
-       "           <td>[59]</td>\n",
-       "\n",
-       "\n",
-       "       </tr>\n",
-       "    \n",
-       "                    </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
-       " SPDX-License-Identifier: BSD-3-Clause\n",
-       "*/\n",
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-       "/**\n",
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-       "\n",
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-       "forceTheme('sk-container-id-5');</script></body>"
-      ],
-      "text/plain": [
-       "LogisticRegression(C=100, random_state=22)"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "X = iris.data[:,0:2]\n",
     "y = iris.target\n",
@@ -7130,7 +803,7 @@
     "    C=100,\n",
     "    random_state=22\n",
     ")\n",
-    "lrmc.fit(X,y)"
+    "lrmc.fit(X,y);"
    ]
   },
   {
@@ -7254,20 +927,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 45,
    "id": "fbac3068",
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x2a3c02451d0>"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
     {
      "data": {
       "image/png": 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",
@@ -7282,7 +945,7 @@
    "source": [
     "fig, ax =plt.subplots(subplot_kw={\"projection\": \"3d\"})\n",
     "surf = ax.plot_surface(x0,x1,zz, cmap='jet')\n",
-    "ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or')"
+    "ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or');"
    ]
   },
   {