def test_sofm_euclide_norm_distance(self):
weight = np.array([
[1.41700099, 0.52680476],
[-0.60938464, 1.56545643],
[-0.30243644, 0.13994967],
[-0.07456091, 0.54797268],
[-1.12894803, 0.32702141],
[0.92084690, 0.02683249],
]).T
input_layer = EuclideDistanceLayer(2, weight=weight)
output_layer = CompetitiveOutputLayer(6)
sn = SOFM(
input_layer > output_layer,
learning_radius=1,
features_grid=(3, 2)
)
sn.train(input_data, epochs=10)
answers = np.array([
[0., 0., 0., 1., 0., 0.],
[0., 0., 0., 1., 0., 0.],
[1., 0., 0., 0., 0., 0.],
[1., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 1., 0.],
[0., 0., 0., 0., 1., 0.],
])
for data, answer in zip(input_data, answers):
network_output = sn.predict(np.reshape(data, (2, 1)).T)
correct_result = np.reshape(answer, (6, 1)).T
self.assertTrue(np.all(network_output == correct_result))
def test_sofm(self):
input_layer = LinearLayer(2, weight=self.weight)
output_layer = CompetitiveOutputLayer(3)
sn = SOFM(
input_layer > output_layer,
learning_radius=0,
features_grid=(3, 1)
)
sn.train(input_data, epochs=100)
answers = np.array([
[0., 1., 0.],
[0., 1., 0.],
[1., 0., 0.],
[1., 0., 0.],
[0., 0., 1.],
[0., 0., 1.],
])
for data, answer in zip(input_data, answers):
network_output = sn.predict(np.reshape(data, (2, 1)).T)
correct_result = np.reshape(answer, (3, 1)).T
self.assertTrue(np.all(network_output == correct_result))
