def test_cmac(self):
input_train = np.reshape(np.linspace(0, 2 * np.pi, 100), (100, 1))
input_train_before = input_train.copy()
input_test = np.reshape(np.linspace(np.pi, 2 * np.pi, 50), (50, 1))
input_test_before = input_test.copy()
target_train = np.sin(input_train)
target_train_before = target_train.copy()
target_test = np.sin(input_test)
cmac = algorithms.CMAC(
quantization=100,
associative_unit_size=32,
step=0.2,
verbose=False,
)
cmac.train(input_train, target_train, epochs=100)
predicted_test = cmac.predict(input_test)
error = errors.mae(target_test, predicted_test)
self.assertEqual(round(error, 4), 0.0024)
np.testing.assert_array_equal(input_train, input_train_before)
np.testing.assert_array_equal(input_train, input_train_before)
np.testing.assert_array_equal(target_train, target_train_before)
def test_cmac(self):
input_train = np.reshape(np.linspace(0, 2 * np.pi, 100), (100, 1))
input_train_before = input_train.copy()
input_test = np.reshape(np.linspace(np.pi, 2 * np.pi, 50), (50, 1))
input_test_before = input_test.copy()
target_train = np.sin(input_train)
target_train_before = target_train.copy()
target_test = np.sin(input_test)
cmac = algorithms.CMAC(
quantization=100,
associative_unit_size=32,
step=0.2,
verbose=False,
)
cmac.train(input_train, target_train, epochs=100)
predicted_test = cmac.predict(input_test)
error = errors.mae(target_test, predicted_test)
self.assertEqual(round(error, 4), 0.0024)
np.testing.assert_array_equal(input_train, input_train_before)
np.testing.assert_array_equal(input_train, input_train_before)
np.testing.assert_array_equal(target_train, target_train_before)
def test_cmac(self):
input_train = np.reshape(np.linspace(0, 2 * np.pi, 100), (100, 1))
input_test = np.reshape(np.linspace(np.pi, 2 * np.pi, 50), (50, 1))
target_train = np.sin(input_train)
target_test = np.sin(input_test)
cmac = algorithms.CMAC(
quantization=100,
associative_unit_size=32,
step=0.2,
)
cmac.train(input_train, target_train, epochs=100)
predicted_test = cmac.predict(input_test)
error = errors.mae(target_test, predicted_test)
self.assertEqual(round(error, 4), 0.0024)
def test_cmac_multi_output(self):
input_train = np.linspace(0, 2 * np.pi, 100)
input_train = np.vstack([input_train, input_train])
input_test = np.linspace(0, 2 * np.pi, 100)
input_test = np.vstack([input_test, input_test])
target_train = np.sin(input_train)
target_test = np.sin(input_test)
cmac = algorithms.CMAC(
quantization=100,
associative_unit_size=32,
step=0.2,
)
cmac.train(input_train, target_train, epochs=100)
predicted_test = cmac.predict(input_test)
error = errors.mae(target_test, predicted_test)
self.assertEqual(round(error, 6), 0)
def test_cmac_multi_output(self):
input_train = np.linspace(0, 2 * np.pi, 100)
input_train = np.vstack([input_train, input_train])
input_test = np.linspace(0, 2 * np.pi, 100)
input_test = np.vstack([input_test, input_test])
target_train = np.sin(input_train)
target_test = np.sin(input_test)
cmac = algorithms.CMAC(
quantization=100,
associative_unit_size=32,
step=0.2,
)
cmac.train(input_train, target_train, epochs=100)
predicted_test = cmac.predict(input_test)
error = errors.mae(target_test, predicted_test)
self.assertEqual(round(error, 6), 0)
