File manager - Edit - /opt/alt/python35/lib64/python3.5/site-packages/sklearn/semi_supervised/tests/test_label_propagation.py
Back
""" test the label propagation module """ import numpy as np from sklearn.utils.testing import assert_equal from sklearn.semi_supervised import label_propagation from numpy.testing import assert_array_almost_equal from numpy.testing import assert_array_equal ESTIMATORS = [ (label_propagation.LabelPropagation, {'kernel': 'rbf'}), (label_propagation.LabelPropagation, {'kernel': 'knn', 'n_neighbors': 2}), (label_propagation.LabelSpreading, {'kernel': 'rbf'}), (label_propagation.LabelSpreading, {'kernel': 'knn', 'n_neighbors': 2}) ] def test_fit_transduction(): samples = [[1., 0.], [0., 2.], [1., 3.]] labels = [0, 1, -1] for estimator, parameters in ESTIMATORS: clf = estimator(**parameters).fit(samples, labels) assert_equal(clf.transduction_[2], 1) def test_distribution(): samples = [[1., 0.], [0., 1.], [1., 1.]] labels = [0, 1, -1] for estimator, parameters in ESTIMATORS: clf = estimator(**parameters).fit(samples, labels) if parameters['kernel'] == 'knn': continue # unstable test; changes in k-NN ordering break it assert_array_almost_equal(clf.predict_proba([[1., 0.0]]), np.array([[1., 0.]]), 2) else: assert_array_almost_equal(np.asarray(clf.label_distributions_[2]), np.array([.5, .5]), 2) def test_predict(): samples = [[1., 0.], [0., 2.], [1., 3.]] labels = [0, 1, -1] for estimator, parameters in ESTIMATORS: clf = estimator(**parameters).fit(samples, labels) assert_array_equal(clf.predict([[0.5, 2.5]]), np.array([1])) def test_predict_proba(): samples = [[1., 0.], [0., 1.], [1., 2.5]] labels = [0, 1, -1] for estimator, parameters in ESTIMATORS: clf = estimator(**parameters).fit(samples, labels) assert_array_almost_equal(clf.predict_proba([[1., 1.]]), np.array([[0.5, 0.5]]))
| ver. 1.4 |
Github
|
.
| PHP 7.3.33 | Generation time: 0.05 |
proxy
|
phpinfo
|
Settings