def test_provide_neighbourhood_radius(self):
map_dim = MapDimension(10, 15, 3)
total_iter_cnt = torch.tensor(100, dtype=torch.float64)
radius = SomContainer.get_neighbourhood_radius(map_dim, total_iter_cnt)
self.assertIsInstance(radius, Radius)
def test_get_som(self):
map_dim = MapDimension(10, 10, 3)
board_dir = os.path.join(os.path.dirname(__file__), "board")
train_params = TrainParams(50, 1, 10, 10, board_dir)
som = SomContainer.get_som(map_dim, .001, 100.0, (-1, 1), board_dir,
train_params)
self.assertIsInstance(som, Som)
def test_provide_map_dim(self):
rows = np.random.randint(5, 10)
cols = np.random.randint(5, 10)
features = np.random.randint(3, 5)
map_dim = SomContainer.get_map_dim(rows, cols, features)
self.assertIsInstance(map_dim, MapDimension)
self.assertEqual(map_dim.rows, rows)
self.assertEqual(map_dim.cols, cols)
self.assertEqual(map_dim.features, features)
def test_get_som_model(self):
dist = EuclideanDistance()
map_indices = torch.tensor(np.asarray((10, 10, 2)))
bmu = Bmu(dist, map_indices)
total_iter_cnt = torch.tensor(100, dtype=torch.float64)
learning_rate = LearningRate(.001, total_iter_cnt)
map_dim = MapDimension(10, 10, 3)
radius = Radius(map_dim, total_iter_cnt)
weights_updater = WeightsUpdater(map_indices)
w_range = (-1.0, 1.0)
som_model = SomContainer.get_som_model(bmu, learning_rate, map_dim,
map_indices, radius,
weights_updater, w_range)
self.assertIsInstance(som_model, SomModel)
def test_get_weights_update_provider(self):
map_indices = torch.tensor(np.zeros((10, 10, 2)))
weights_updater = SomContainer.get_weights_updater(map_indices)
self.assertIsInstance(weights_updater, WeightsUpdater)
def test_provide_learning_rate(self):
learning_rate = SomContainer.get_learning_rate(.001,
torch.tensor(100.0))
self.assertIsInstance(learning_rate, LearningRate)
def test_provide_eucd_dist(self):
eucd_dist = SomContainer.get_eucd_dist()
self.assertIsInstance(eucd_dist, EuclideanDistance)
def test_provide_bmu_with_custim_dist(self):
dist = MagicMock()
bmu = SomContainer.get_bmu(torch.tensor(np.zeros((5, 5))), dist)
self.assertIsInstance(bmu, Bmu)
self.assertEqual(bmu._distance, dist)
def test_provide_bmu_with_default_dist(self):
bmu = SomContainer.get_bmu(torch.tensor(np.zeros((5, 5))))
self.assertIsInstance(bmu, Bmu)