def compress_(path_feature_map, learning_rate):
path_feature_map = copy(path_feature_map)
feature_vectors = np.array(list(path_feature_map.values()))
vector_size = feature_vectors.shape[2]
feature_vectors = feature_vectors.reshape(-1, vector_size)
feature_vectors /= np.sum(feature_vectors)
#feature_vectors /= np.mean(feature_vectors)
#feature_vectors = np.std(feature_vectors, axis=0)
#print(np.std(feature_vectors, axis=0))
#print(np.sum(feature_vectors, axis=0))
#print(feature_vectors)
autoencoder = Autoencoder(
feature_vectors,
n_visible=feature_vectors.shape[1],
n_hidden=n_components,
learning_rate=learning_rate
)
for epoch in range(n_iter):
autoencoder.train()
for path, vector in path_feature_map.items():
v = autoencoder.get_hidden_values(vector)
path_feature_map[path] = v
error = autoencoder.negative_log_likelihood()
error = abs(error)
return path_feature_map, error
