n_batches = loc['n_batches']
if np.mod((ii-iter_offset)/int(n_batches), n_iter) == 0:
# Compute distance only every 5 iterations, as in previous case
d = loc['dict_obj']
d.wasserstein.append(emd(loc['dictionary'], d.generating_dict,
'chordal', scale=True))
d.detect_rate.append(detection_rate(loc['dictionary'],
d.generating_dict, 0.99))
d.objective_error.append(loc['current_cost'])
# reinitializing the random generator
learned_dict2 = MiniBatchMultivariateDictLearning(n_kernels=n_kernels,
batch_size=batch_size, n_iter=max_iter*n_iter,
n_nonzero_coefs=n_nonzero_coefs,
callback=callback_distance,
n_jobs=n_jobs, learning_rate=learning_rate,
kernel_init_len=kernel_init_len, verbose=1,
dict_init=dict_init, random_state=rng_global)
learned_dict2.generating_dict = list(generating_dict)
learned_dict2.wasserstein = list()
learned_dict2.detect_rate = list()
learned_dict2.objective_error = list()
learned_dict2 = learned_dict2.fit(X)
plot_univariate(array(learned_dict2.objective_error),
array(learned_dict2.detect_rate),
array(learned_dict2.wasserstein),
n_iter=1, figname='univariate-case-callback')
