def test_predict_1_returns_correct_shape(self):
layer = Layer(1)
for i in range(functions_in_layer):
classifier = NodeClassifier('extra node' + str(i))
X = np.random.randn(samples_count, feature_count)
y = np.random.randint(0, high=2, size=samples_count)
classifier.fit(X, y)
layer.nodes.append(classifier)
x_transformed = layer.predict(X)
self.assertEqual(x_transformed.shape, (samples_count, functions_in_layer))
def test_predict_1_returns_correct_shape(self):
layer = Layer(1)
for i in range(functions_in_layer):
classifier = NodeClassifier('extra node' + str(i))
X = np.random.randn(samples_count, feature_count)
y = np.random.randint(0, high=2, size=samples_count)
classifier.fit(X, y)
layer.nodes.append(classifier)
x_transformed = layer.predict(X)
self.assertEqual(x_transformed.shape,
(samples_count, functions_in_layer))
def test_predict_0_returns_same(self):
layer = Layer(0, NodeClassifier('test layer'))
X = np.random.randn(3, 3)
X2 = layer.predict(X)
self.assertEqual(X.__hash__, X2.__hash__)
def test_predict_0_returns_same(self):
layer = Layer(0, NodeClassifier('test layer'))
X = np.random.randn(3, 3)
X2 = layer.predict(X)
self.assertEqual(X.__hash__, X2.__hash__)
