def test_GM_fit_lowiterations(): X, y = data.categorical_2Dmatrix_data_big() np.random.seed(0) GM = gaussianmixture.GaussianMixture() GM.fit(X, iterations=1) assert GM.learned np.testing.assert_array_almost_equal(GM.mus[0], [2.22, 2.5], decimal=1)
def test_LVQ(): X, y = data.categorical_2Dmatrix_data_big() lvq = prototypemthods.LearningVectorQuantization() lvq.fit(X, y, n_prototypes=3) assert [0, 1] == sorted(lvq.prototypes.keys()) assert (3, 2) == lvq.prototypes[0].shape assert (3, 2) == lvq.prototypes[1].shape
def test_test_train_splitter(): X, y = data.categorical_2Dmatrix_data_big() X_train, X_test, y_train, y_test = modelselection.test_train_splitter(X, y) assert X_train.shape == (9, 2) assert X_test.shape == (2, 2) assert len(y_train) == 9 assert len(y_test) == 2
def test_Perceptron(): X, y = data.categorical_2Dmatrix_data_big() y = (y * 2) - 1 perceptron = svm.Perceptron() perceptron.fit(X, y) predictions = perceptron.predict(X) assert (predictions == y).sum() > 7
def test_KMediods(): X, y = data.categorical_2Dmatrix_data_big() km = prototypemthods.KMediods() km.fit(X) assignments = km.sample_assignments reversed_assignments = (assignments - 1) * -1 assert np.array_equal(assignments, y) or \ np.array_equal(reversed_assignments, y)
def test_LVQ_prediction(): X, y = data.categorical_2Dmatrix_data_big() lvq = prototypemthods.LearningVectorQuantization() lvq.fit(X, y, n_prototypes=3) prediction = lvq.predict(X[0]) assert prediction == y[0] prediction = lvq.predict(X[-1]) assert prediction == y[-1]
def test_DANN_prediction(): X, y = data.categorical_2Dmatrix_data_big() dann = prototypemthods.DANN() dann.fit(X, y, neighborhood_size=3) prediction = dann.predict(X[0]) assert prediction == y[0] prediction = dann.predict(X[-1]) assert prediction == y[-1]
def test_KMediods_prediction(): X, y = data.categorical_2Dmatrix_data_big() km = prototypemthods.KMediods() km.fit(X) assignments = km.sample_assignments reversed_assignments = (assignments - 1) * -1 prediction = km.predict(X[0]) np.testing.assert_array_almost_equal(prediction, [3.0, 3.0])
def test_GM_predict_probs(): X, y = data.categorical_2Dmatrix_data_big() np.random.seed(0) GM = gaussianmixture.GaussianMixture() GM.fit(X) max_class, class_probs = GM.predict(X[0], probs=True) assert max_class == 0 assert np.isclose(class_probs[0], 1) assert np.isclose(class_probs[1], 0)
def test_GM_predict(): X, y = data.categorical_2Dmatrix_data_big() np.random.seed(0) GM = gaussianmixture.GaussianMixture() GM.fit(X) pred_one = GM.predict(X[0]) pred_two = GM.predict(X[-1]) assert pred_one == y[0] assert pred_two == y[-1]
def test_SupportVectorMachine(): X, y = data.categorical_2Dmatrix_data_big() y = (y * 2) - 1 SVM = svm.SupportVectorMachine() SVM.fit(X, y) prediction = SVM.predict(X[0]) assert prediction == y[0] prediction = SVM.predict(X[-1]) assert prediction == y[-1]
def test_RegressionTree(): tree = treemethods.RegressionTree() X, y = data.categorical_2Dmatrix_data_big() tree.fit(X, y, 3) assert tree.predict(X[0]) == y[0] assert tree.predict(X[-1]) == y[-1]
def test_PrimRegression(): tree = treemethods.PrimRegression() X, y = data.categorical_2Dmatrix_data_big() tree.fit(X, y, 1) assert tree.predict(X[1]) == y[1] assert np.isclose(tree.predict(X[-1]), 0.6666, 1)
def test_DiscreteAdaBoost(): tree = treemethods.DiscreteAdaBoost() X, y = data.categorical_2Dmatrix_data_big() tree.fit(X, y, 3) assert tree.predict(X[0]) == y[0] assert tree.predict(X[-1]) == y[-1]
def test_DANN(): X, y = data.categorical_2Dmatrix_data_big() dann = prototypemthods.DANN() dann.fit(X, y) assert dann.learned
def test_GradientBoostingRegression(): tree = treemethods.GradientBoostingRegression() X, y = data.categorical_2Dmatrix_data_big() tree.fit(X, y, 3) assert np.isclose(tree.predict(X[0]), 0.3976, 1) assert np.isclose(tree.predict(X[-1]), y[-1], 1)
def test_knearestneighbor_regression(): X, y = data.categorical_2Dmatrix_data_big() knn = prototypemthods.KNearestNeighbor() knn.fit(X, y) prediction = knn.predict(X[0]) assert prediction == y[0]