def test_predict_2():
number_of_classes = 10
number_of_training_samples_to_use = 3
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()
X_train_vectorized = ((X_train.reshape(
X_train.shape[0], -1)).T)[:, 0:number_of_training_samples_to_use]
y_train = y_train[0:number_of_training_samples_to_use]
input_dimensions = X_train_vectorized.shape[0]
model = Hebbian(input_dimensions=input_dimensions,
number_of_classes=number_of_classes,
transfer_function="Linear",
seed=5)
Y_hat = model.predict(X_train_vectorized)
np.testing.assert_almost_equal(Y_hat, np.array( \
[[-4101.41409432, -3820.1709349, -1235.86331202],
[854.07167203, 4061.22006877, 434.40971256],
[-552.17756811, -1791.61373625, -2591.16737069],
[-355.4367891, -3858.75847581, 1320.1141753],
[1087.86080571, -607.49532925, -460.80234811],
[-2459.84338339, -1681.30331925, -255.00327678],
[-460.58803655, -4439.85928602, -1093.82071536],
[4066.25628304, 4814.90762933, 1955.78972208],
[564.64444411, -3117.99849963, -419.49244877],
[-2374.84426405, -2878.08764629, 2979.99404738]]), decimal=2)
model = Hebbian(input_dimensions=input_dimensions,
number_of_classes=number_of_classes,
transfer_function="Hard_limit",
seed=5)
Y_hat = model.predict(X_train_vectorized)
assert (np.allclose(Y_hat, np.array( \
[[0, 0, 0],
[1, 1, 1],
[0, 0, 0],
[0, 0, 1],
[1, 0, 0],
[0, 0, 0],
[0, 0, 0],
[1, 1, 1],
[1, 0, 0],
[0, 0, 1]]),rtol=1e-3, atol=1e-3))
model = Hebbian(input_dimensions=input_dimensions,
number_of_classes=number_of_classes,
transfer_function="Hard_limit",
seed=5)
Y_hat = model.predict(X_train_vectorized)
assert np.allclose(Y_hat, np.array( \
[[0.00000000e+000, 0.00000000e+000, 0.00000000e+000],
[1.00000000e+000, 1.00000000e+000, 1.00000000e+000],
[1.55714531e-240, 0.00000000e+000, 0.00000000e+000],
[4.32278692e-155, 0.00000000e+000, 1.00000000e+000],
[1.00000000e+000, 1.47275575e-264, 7.51766500e-201],
[0.00000000e+000, 0.00000000e+000, 1.79260263e-111],
[9.31445172e-201, 0.00000000e+000, 0.00000000e+000],
[1.00000000e+000, 1.00000000e+000, 1.00000000e+000],
[1.00000000e+000, 0.00000000e+000, 6.55759060e-183],
[0.00000000e+000, 0.00000000e+000, 1.00000000e+000]]),rtol=1e-3, atol=1e-3)
def test_predict():
input_dimensions = 2
number_of_classes = 2
model = Hebbian(input_dimensions=input_dimensions, number_of_classes=number_of_classes,
transfer_function="Hard_limit",seed=1)
X_train = np.array([[-1.43815556, 0.10089809, -1.25432937, 1.48410426],
[-1.81784194, 0.42935033, -1.2806198, 0.06527391]])
model.initialize_all_weights_to_zeros()
Y_hat = model.predict(X_train)
assert (np.array_equal(Y_hat, np.array([[0,0,0,0], [0,0,0,0]]))) or \
(np.array_equal(Y_hat, np.array([[1,1,1,1], [1,1,1,1]])))