def test_actual_results_cost_matrix(params, arr_desired):
"""Test that the actual results are the expected ones."""
arr_actual = cost_matrix(x, y, **params)
np.testing.assert_allclose(arr_actual, arr_desired, atol=1e-5, rtol=0.)
def test_parameter_check_cost_matrix(params, err_msg):
"""Test parameter validation."""
with pytest.raises(ValueError, match=re.escape(err_msg)):
cost_matrix(x, y, **params)
plt.xlabel('x', fontsize=12)
plt.ylabel('y', fontsize=12)
plt.title("{0}\nDTW(x, y) = {1:.2f}".format('itakura', dtw_itakura),
fontsize=14)
# Dynamic Time Warping: multiscale
resolution, radius = 5, 2
dtw_multiscale, path_multiscale = dtw(
x, y, dist='square', method='multiscale',
options={'resolution': resolution, 'radius': radius}, return_path=True
)
x_padded = x.reshape(-1, resolution).mean(axis=1)
y_padded = y.reshape(-1, resolution).mean(axis=1)
cost_mat_res = cost_matrix(x_padded, y_padded, dist='square', region=None)
acc_cost_mat_res = accumulated_cost_matrix(cost_mat_res)
path_res = _return_path(acc_cost_mat_res)
multiscale_region = _blurred_path_region(
n_timestamps_1, n_timestamps_2, resolution, x_padded.size, y_padded.size,
path_res,
radius=radius
)
matrix_multiscale = np.zeros((n_timestamps_1 + 1, n_timestamps_2 + 1))
for i in range(n_timestamps_1):
matrix_multiscale[i, np.arange(*multiscale_region[:, i])] = 0.5
matrix_multiscale[tuple(path_multiscale)] = 1.
plt.subplot(2, 2, 4)
plt.pcolor(timestamps_1, timestamps_2, matrix_multiscale.T,