8000 DOC examples with correct notebook style · scikit-learn/scikit-learn@58f7bc0 · GitHub
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DOC examples with correct notebook style
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‎examples/cluster/plot_dict_face_patches.py‎

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###############################################################################
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# Learn the dictionary of images
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# ------------------------------
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print('Learning the dictionary... ')
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rng = np.random.RandomState(0)
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###############################################################################
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# Plot the results
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# ----------------
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plt.figure(figsize=(4.2, 4))
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for i, patch in enumerate(kmeans.cluster_centers_):
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plt.subplot(9, 9, i + 1)

‎examples/feature_selection/plot_feature_selection.py‎

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from sklearn import datasets, svm
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from sklearn.feature_selection import SelectPercentile, f_classif
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###############################################################################
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# import some data to play with
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# The iris dataset
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X = np.hstack((iris.data, E))
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y = iris.target
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###############################################################################
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plt.figure(1)
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plt.clf()
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X_indices = np.arange(X.shape[-1])
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###############################################################################
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# Univariate feature selection with F-test for feature scoring
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# We use the default selection function: the 10% most significant features
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selector = SelectPercentile(f_classif, percentile=10)
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label=r'Univariate score ($-Log(p_{value})$)', color='darkorange',
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edgecolor='black')
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###############################################################################
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# Compare to the weights of an SVM
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clf = svm.SVC(kernel='linear')
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clf.fit(X, y)

‎examples/feature_selection/plot_permutation_test_for_classification.py‎

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from sklearn import datasets
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##############################################################################
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# Loading a dataset
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iris = datasets.load_iris()
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X = iris.data
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print("Classification score %s (pvalue : %s)" % (score, pvalue))
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###############################################################################
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# View histogram of permutation scores
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plt.hist(permutation_scores, 20, label='Permutation scores',
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edgecolor='black')

‎examples/plot_kernel_ridge_regression.py‎

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rng = np.random.RandomState(0)
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#############################################################################
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# Generate sample data
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X = 5 * rng.rand(10000, 1)
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y = np.sin(X).ravel()
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X_plot = np.linspace(0, 5, 100000)[:, None]
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#############################################################################
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# Fit regression model
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train_size = 100
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svr = GridSearchCV(SVR(kernel='rbf', gamma=0.1), cv=5,
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% (X_plot.shape[0], kr_predict))
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#############################################################################
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# look at the results
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# Look at the results
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sv_ind = svr.best_estimator_.support_
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plt.scatter(X[sv_ind], y[sv_ind], c='r', s=50, label='SVR support vectors',
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zorder=2)

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