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')
hfb = zeros((n_snr, n_experiments, n_iter))
dr99 = zeros((n_snr, n_experiments, n_iter))
dr97 = zeros((n_snr, n_experiments, n_iter))
for i, s in enumerate(snr):
for e in range(n_experiments):
g, X, code = _generate_testbed(kernel_init_len,
n_nonzero_coefs, n_kernels, n_samples, n_features,
n_dims, s)
d = MiniBatchMultivariateDictLearning(n_kernels=n_kernels,
batch_size=batch_size, n_iter=n_iter,
n_nonzero_coefs=n_nonzero_coefs, callback=callback_recovery,
n_jobs=n_jobs, learning_rate=learning_rate,
kernel_init_len=kernel_init_len, verbose=1,
random_state=rng_global)
d.generating_dict = list(g)
d.wc, d.wfs, d.hc, d.hfs = list(), list(), list(), list()
d.wcpa, d.wbc, d.wg, d.wfb = list(), list(), list(), list()
d.hcpa, d.hbc, d.hg, d.hfb = list(), list(), list(), list()
d.dr99, d.dr97 = list(), list()
print ('\nExperiment', e+1, 'on', n_experiments)
d = d.fit(X)
wc[i, e, :] = array(d.wc); wfs[i, e, :] = array(d.wfs)
hc[i, e, :] = array(d.hc); hfs[i, e, :] = array(d.hfs)
wcpa[i, e, :] = array(d.wcpa); wbc[i, e, :] = array(d.wbc)
wg[i, e, :] = array(d.wg); wfb[i, e, :] = array(d.wfb)
hcpa[i, e, :] = array(d.hcpa); hbc[i, e, :] = array(d.hbc)
hg[i, e, :] = array(d.hg); hfb[i, e, :] = array(d.hfb)
dr99[i, e, :] = array(d.dr99); dr97[i, e, :] = array(d.dr97)
with open(backup_fname, "w") as f:
o = {'wc':wc, 'wfs':wfs, 'hc':hc, 'hfs':hfs, 'dr99':dr99, 'dr97':dr97,
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'])
# 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.detection_rate = list()
learned_dict2.objective_error = list()
learned_dict2 = learned_dict2.fit(X)
plot_multivariate(array(learned_dict2.objective_error),
array(learned_dict2.detection_rate),
100.-array(learned_dict2.wasserstein),
n_iter=1, figname='multivariate-case-callback')