def callback_distance(loc):
ii, iter_offset = loc['ii'], loc['iter_offset']
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.detection_rate.append(detectionRate(loc['dictionary'],
d.generating_dict, 0.99))
d.objective_error.append(loc['current_cost'])
# dict_init[i] /= norm(dict_init[i], 'fro')
dict_init = None
learned_dict = MiniBatchMultivariateDictLearning(n_kernels=n_kernels,
batch_size=batch_size, n_iter=n_iter,
n_nonzero_coefs=n_nonzero_coefs,
n_jobs=n_jobs, learning_rate=learning_rate,
kernel_init_len=kernel_init_len, verbose=1,
dict_init=dict_init, random_state=rng_global)
# Update learned dictionary at each iteration and compute a distance
# with the generating dictionary
for i in range(max_iter):
learned_dict = learned_dict.partial_fit(X)
# Compute the detection rate
detection_rate.append(detectionRate(learned_dict.kernels_,
generating_dict, 0.99))
# Compute the Wasserstein distance
wasserstein.append(emd(learned_dict.kernels_, generating_dict,
'chordal', scale=True))
# Get the objective error
objective_error.append(learned_dict.error_.sum())
plot_multivariate(array(objective_error), array(detection_rate),
100.-array(wasserstein), n_iter, 'multivariate-case')
# Another possibility is to rely on a callback function such as
def callback_distance(loc):
ii, iter_offset = loc['ii'], loc['iter_offset']
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