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))
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)
predicted_test = predicted_test.reshape((len(predicted_test), 1))
error = metrics.mean_absolute_error(target_test, predicted_test)
self.assertAlmostEqual(error, 0.0024, places=4)
# Test that algorithm didn't modify data samples
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)
self.assertPickledNetwork(cmac, input_train)
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_training_exceptions(self):
cmac = algorithms.CMAC(
quantization=100,
associative_unit_size=32,
step=0.2,
)
with self.assertRaises(ValueError):
cmac.train(X_train=True, y_train=True, X_test=None, y_test=True)
def test_predict_different_inputs(self):
cmac = algorithms.CMAC()
data = np.array([[1, 2, 3]]).T
target = np.array([[1, 2, 3]]).T
cmac.train(data, target, epochs=100)
self.assertInvalidVectorPred(network=cmac,
input_vector=np.array([1, 2, 3]),
target=target,
decimal=2)
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):
X_train = np.linspace(0, 2 * np.pi, 100)
X_train = np.vstack([X_train, X_train])
X_test = np.linspace(0, 2 * np.pi, 100)
X_test = np.vstack([X_test, X_test])
y_train = np.sin(X_train)
y_test = np.sin(X_test)
cmac = algorithms.CMAC(
quantization=100,
associative_unit_size=32,
step=0.2,
)
cmac.train(X_train, y_train, X_test, y_test, epochs=100)
predicted_test = cmac.predict(X_test)
error = metrics.mean_absolute_error(y_test, predicted_test)
self.assertAlmostEqual(error, 0, places=6)
import numpy as np
import matplotlib.pyplot as plt
from neupy import algorithms, utils
utils.reproducible()
plt.style.use('ggplot')
X_train = np.reshape(np.linspace(0, 2 * np.pi, 100), (100, 1))
X_test = np.reshape(np.sort(2 * np.pi * np.random.random(50)), (50, 1))
y_train = np.sin(X_train)
y_test = np.sin(X_test)
cmac = algorithms.CMAC(
quantization=100,
associative_unit_size=10,
step=0.2,
verbose=True,
show_epoch=100,
)
cmac.train(X_train, y_train, epochs=100)
predicted_test = cmac.predict(X_test)
plt.plot(X_train, y_train)
plt.plot(X_test, predicted_test)
plt.show()
def test_train_different_inputs(self):
self.assertInvalidVectorTrain(network=algorithms.CMAC(),
input_vector=np.array([1, 2, 3]),
target=np.array([1, 2, 3]))
def test_train_different_inputs(self):
self.assertInvalidVectorTrain(algorithms.CMAC(), np.array([1, 2, 3]),
np.array([1, 2, 3]))
