)
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.bd, d.dr99, d.dr97 = list(), 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)
dr99[i, e, :] = array(d.dr99)
dr97[i, e, :] = array(d.dr97)
bd[i, e, :] = array(d.bd)
with open("expe_reco.pck", "w") as f:
o = {
"wc": wc,
"wfs": wfs,
"hc": hc,
"hfs": hfs,
hfs = zeros((n_snr, n_experiments, n_iter))
bd = 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.bd, d.dr99, d.dr97 = list(), 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)
dr99[i, e, :] = array(d.dr99); dr97[i, e, :] = array(d.dr97)
bd[i, e,:] = array(d.bd)
with open("expe_reco.pck", "w") as f:
o = {'wc':wc, 'wfs':wfs, 'hc':hc, 'hfs':hfs, 'bd':bd, 'dr99':dr99, 'dr97':dr97}
pickle.dump(o, f)
plot_recov(wc, wfs, hc, hfs, bd, dr99, dr97, n_iter, "univariate_recov")
