def test_gnb_check_update_with_no_data(): """Test when the partial fit is called without any data""" # Create an empty array prev_points = 100 mean = 0.0 var = 1.0 x_empty = np.empty((0, X.shape[1])) tmean, tvar = GaussianNB._update_mean_variance(prev_points, mean, var, x_empty) assert tmean == mean assert tvar == var
def test_check_update_with_no_data(): """ Test when the partial fit is called without any data""" # Create an empty array prev_points = 100 mean = 0. var = 1. x_empty = np.empty((0, X.shape[1])) tmean, tvar = GaussianNB._update_mean_variance(prev_points, mean, var, x_empty) assert_equal(tmean, mean) assert_equal(tvar, var)
tmp.append(testRows[i]) posNo -= 1 testRows = tmp print 'test length = ', len(testRows) classes = [ 'Nominated Best Picture', 'Won Best Picture', ] clf=GaussianNB() clf.fit(features[trainRows, :])[:, favoriteCols], labels[trainRows, 0], sample_weight=None) clf._update_mean_variance(n_past, mu, var, X, sample_weight=None) clf.partial_fit(features[trainRows, :])[:, favoriteCols], labels[trainRows, 0], classes=classes, sample_weight=None) clf._partial_fit(features[trainRows, :])[:, favoriteCols], labels[trainRows, 0], classes=classes, _refit=False,sample_weight=None) clf._joint_log_likelihood(features[trainRows, :])[:, favoriteCols]) print 'accuracy = %f' %(np.mean((y_test-y_pred)==0))