def test_gaussian_random_matrix():
# Check some statical properties of Gaussian random matrix
# Check that the random matrix follow the proper distribution.
# Let's say that each element of a_{ij} of A is taken from
# a_ij ~ N(0.0, 1 / n_components).
#
n_components = 100
n_features = 1000
A = _gaussian_random_matrix(n_components, n_features, random_state=0)
assert_array_almost_equal(0.0, np.mean(A), 2)
assert_array_almost_equal(np.var(A, ddof=1), 1 / n_components, 1)
def generate_random_projection_weights(original_dim, projected_dim):
random_projection_matrix = None
if projected_dim and original_dim > projected_dim:
random_projection_matrix = pr._gaussian_random_matrix(
projected_dim, original_dim).T
print("A Gaussian random weight matrix was creates with shape of {}".
format(random_projection_matrix.shape))
print('Storing random projection matrix to disk...')
with open('random_projection_matrix', 'wb') as handle:
pickle.dump(random_projection_matrix,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
return random_projection_matrix
