def test_classify(index, expected):
query = np.atleast_2d([1, 1])
knop_test = KNOP(create_pool_classifiers())
knop_test.fit(X_dsel_ex1, y_dsel_ex1)
knop_test.DFP_mask = np.ones(knop_test.n_classifiers)
knop_test.neighbors = neighbors_ex1[index, :]
knop_test.distances = distances_ex1[index, :]
prediction = knop_test.classify_instance(query)
assert prediction == expected
def test_classify(index, expected):
query = np.atleast_2d([1, 1])
knop_test = KNOP(create_pool_classifiers())
knop_test.fit(X_dsel_ex1, y_dsel_ex1)
knop_test.DFP_mask = np.ones(knop_test.n_classifiers)
knop_test.neighbors = neighbors_ex1[index, :]
knop_test.distances = distances_ex1[index, :]
predictions = []
for clf in knop_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
predicted_label = knop_test.classify_instance(query, np.array(predictions))
assert predicted_label == expected
