class TestMinisom(unittest.TestCase):
def setUp(self):
self.som = MiniSom(5, 5, 1)
for i in range(5):
for j in range(5):
# checking weights normalization
assert_almost_equal(1.0, linalg.norm(self.som._weights[i, j]))
self.som._weights = zeros((5, 5, 1)) # fake weights
self.som._weights[2, 3] = 5.0
self.som._weights[1, 1] = 2.0
def test_decay_function(self):
assert self.som._decay_function(1., 2.,
3.) == 1. / (1. + 2. / (3. / 2))
def test_fast_norm(self):
assert fast_norm(array([1, 3])) == sqrt(1 + 9)
def test_check_input_len(self):
with self.assertRaises(ValueError):
self.som.train_batch([[1, 2]], 1)
with self.assertRaises(ValueError):
self.som.random_weights_init(array([[1, 2]]))
with self.assertRaises(ValueError):
self.som._check_input_len(array([[1, 2]]))
self.som._check_input_len(array([[1]]))
self.som._check_input_len([[1]])
def test_unavailable_neigh_function(self):
with self.assertRaises(ValueError):
MiniSom(5, 5, 1, neighborhood_function='boooom')
def test_gaussian(self):
bell = self.som._gaussian((2, 2), 1)
assert bell.max() == 1.0
assert bell.argmax() == 12 # unravel(12) = (2,2)
def test_mexican_hat(self):
bell = self.som._mexican_hat((2, 2), 1)
assert bell.max() == 1.0
assert bell.argmax() == 12 # unravel(12) = (2,2)
def test_bubble(self):
bubble = self.som._bubble((2, 2), 1)
assert bubble[2, 2] == 1
assert sum(sum(bubble)) == 1
def test_triangle(self):
bubble = self.som._triangle((2, 2), 1)
assert bubble[2, 2] == 1
assert sum(sum(bubble)) == 1
def test_win_map(self):
winners = self.som.win_map([[5.0], [2.0]])
assert winners[(2, 3)][0] == [5.0]
assert winners[(1, 1)][0] == [2.0]
def test_labels_map(self):
labels_map = self.som.labels_map([[5.0], [2.0]], ['a', 'b'])
assert labels_map[(2, 3)]['a'] == 1
assert labels_map[(1, 1)]['b'] == 1
with self.assertRaises(ValueError):
self.som.labels_map([[5.0]], ['a', 'b'])
def test_activation_reponse(self):
response = self.som.activation_response([[5.0], [2.0]])
assert response[2, 3] == 1
assert response[1, 1] == 1
def test_activate(self):
assert self.som.activate(5.0).argmin() == 13.0 # unravel(13) = (2,3)
def test_quantization_error(self):
assert self.som.quantization_error([[5], [2]]) == 0.0
assert self.som.quantization_error([[4], [1]]) == 1.0
def test_quantization(self):
q = self.som.quantization(array([[4], [2]]))
assert q[0] == 5.0
assert q[1] == 2.0
def test_random_seed(self):
som1 = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
som2 = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
# same initialization
assert_array_almost_equal(som1._weights, som2._weights)
data = random.rand(100, 2)
som1 = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
som1.train_random(data, 10)
som2 = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
som2.train_random(data, 10)
# same state after training
assert_array_almost_equal(som1._weights, som2._weights)
def test_train_batch(self):
som = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
data = array([[4, 2], [3, 1]])
q1 = som.quantization_error(data)
som.train_batch(data, 10)
assert q1 > som.quantization_error(data)
data = array([[1, 5], [6, 7]])
q1 = som.quantization_error(data)
som.train_batch(data, 10, verbose=True)
assert q1 > som.quantization_error(data)
def test_train_random(self):
som = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
data = array([[4, 2], [3, 1]])
q1 = som.quantization_error(data)
som.train_random(data, 10)
assert q1 > som.quantization_error(data)
data = array([[1, 5], [6, 7]])
q1 = som.quantization_error(data)
som.train_random(data, 10, verbose=True)
assert q1 > som.quantization_error(data)
def test_random_weights_init(self):
som = MiniSom(2, 2, 2, random_seed=1)
som.random_weights_init(array([[1.0, .0]]))
for w in som._weights:
assert_array_equal(w[0], array([1.0, .0]))
def test_pca_weights_init(self):
som = MiniSom(2, 2, 2)
som.pca_weights_init(array([[1., 0.], [0., 1.], [1., 0.], [0., 1.]]))
expected = array([[[0., -1.41421356], [-1.41421356, 0.]],
[[1.41421356, 0.], [0., 1.41421356]]])
assert_array_almost_equal(som._weights, expected)
def test_distance_map(self):
som = MiniSom(2, 2, 2, random_seed=1)
som._weights = array([[[1., 0.], [0., 1.]], [[1., 0.], [0., 1.]]])
assert_array_equal(som.distance_map(), array([[1., 1.], [1., 1.]]))
def test_pickling(self):
with open('som.p', 'wb') as outfile:
pickle.dump(self.som, outfile)
with open('som.p', 'rb') as infile:
pickle.load(infile)
os.remove('som.p')
