model = BSC_ET(D, H, Hprime, gamma, to_learn=['W','sigma','pi','mu'])
data = {'y':patches}
out_fname = output_path + "/data.h5"
#setting up logging/output
print_list = ('T', 'Q', 'pi', 'sigma', 'N', 'MAE', 'L')
dlog.set_handler(print_list, TextPrinter)
h5store_list = ('W', 'pi', 'sigma', 'y', 'MAE', 'N','L','Q','mu')
dlog.set_handler(h5store_list, StoreToH5, output_path +'/result.h5')
###### Initialize model #######
# Initialize (Random) model parameters
model_params = model.standard_init(data)
#model_params['mu'] = np.mean(data['y'],axis=0)
# Create and start EM annealing
em = EM(model=model, anneal=anneal)
em.data = data
em.lparams = model_params
em.run()
dlog.close(True)
pprint("Done")
W_gt = W_gt.reshape((H, D))
# Prepare model...
model = MCA_ET(D, H, Hprime, gamma)
gt_params = {"W": W_gt, "pi": 2.0 / H, "sigma": 1.00}
# Generate trainig data
my_N = N // comm.size
my_data = model.generate_data(gt_params, my_N)
dlog.append("y", my_data["y"][0:25, :])
# Initialize model parameters (to be learned)
params = {
# 'W' : W_gt,
"W": np.abs(5 + np.random.normal(size=W_gt.shape)),
"pi": 2 / H,
"sigma": 5.00,
}
# params = model.noisify_params(params, anneal)
params = comm.bcast(params)
# Create and start EM annealing
em = EM(model=model, anneal=anneal)
em.data = my_data
em.lparams = params
em.run()
dlog.close()
# print(em.lparams['W'])
if sigma_init != "estimate": lparams['sigma'] = sigma_init
#As a control, we also append the initial values
dlog.append_all( {
'W' : lparams['W'],
'pi': lparams['pi'],
'sigma': lparams['sigma'],
} )
comm.Barrier()
#==================== Create and run EM =====================================
dlog.progress("Starting EM")
em = EM(model=model, anneal=anneal)
em.data = {'y': my_y, 'y_rc': my_y_rc}
em.lparams = lparams
em.run(verbose=True)
lparams = em.lparams
#dlog.close()
comm.Barrier()
#============================================================================
# Extract final generative fields (W)
if lparams.has_key("rev_corr") and lparams['rev_corr'].max() > 1e-7:
dlog.progress("Using actual reverse correlated fields")
RF = lparams["rev_corr"]
H, D = RF.shape
if sigma_init != "estimate": lparams['sigma'] = sigma_init
#As a control, we also append the initial values
dlog.append_all({
'W': lparams['W'],
'pi': lparams['pi'],
'sigma': lparams['sigma'],
})
comm.Barrier()
#==================== Create and run EM =====================================
dlog.progress("Starting EM")
em = EM(model=model, anneal=anneal)
em.data = {'y': my_y, 'y_rc': my_y_rc}
em.lparams = lparams
em.run(verbose=True)
lparams = em.lparams
#dlog.close()
comm.Barrier()
#============================================================================
# Extract final generative fields (W)
if lparams.has_key("rev_corr") and lparams['rev_corr'].max() > 1e-7:
dlog.progress("Using actual reverse correlated fields")
RF = lparams["rev_corr"]
H, D = RF.shape
if W_learn == 'mean':
lparams = {
'W': mean_W,
'pi': pi_learn,
'sigma': sigma_learn,
'mu': mu_learn
}
elif W_learn == 'ground_truth':
lparams = {'W': W_gt, 'pi': pi_gt, 'sigma': sigma_gt, 'mu': mu_gt}
else:
lparams = {
'W': W_learn,
'pi': pi_learn,
'sigma': sigma_learn,
'mu': mu_learn
}
lparams = model.noisify_params(lparams, anneal)
# Create and start EM annealing
em = EM(model=model, anneal=anneal)
em.data = my_data
em.lparams = lparams
em.run()
pprint("Done with ET, Calculating MAE...")
MAE = check_basis(em.lparams['W'], W_gt)
if MAE == None:
MAE = np.inf
dlog.append('MAE', MAE[0])
dlog.close(True)
pprint("Done")