crit_sB0 = EvalCriterion_fast(A0, S0_GMCA, A_sB0, S_sB0)
ca_final_sB0[0, it1, it_n] = crit_sB0['ca_mean']
ca_final_sB0[1, it1, it_n] = crit_sB0['ca_med']
if numIts == 1:
print('-10*np.log10(ca_final_sB0): ')
print(-10 * np.log10(ca_final_sB0))
it1 = 0
if J == 0:
numBlock = totalSize / divisors[it1]
sizeBlock = divisors[it1]
if optDecBGMCA == 1:
time1 = time.time()
Results_sB1 = bgmca3(X,n=n_s,maxts = 7,mints=3,nmax=100,L0=1,UseP=1,verb=0,Init=0,Aposit=False,\
BlockSize= None,NoiseStd=[],IndNoise=[],Kmax=1.,AInit=None,tol=1e-6,subBlockSize=sizeBlock,\
threshOpt=2,weightFMOpt=1,SCOpt=1,alphaEstOpt=1,optA=1,alpha_exp=alpha_init,J=J,WNFactors=WNFactors,\
normOpt=normOpt)
time_results[1, it_n] = time.time() - time1
A_sB1 = Results_sB1['mixmat']
S_sB1 = Results_sB1['sources']
crit_sB1 = EvalCriterion_fast(A0, S0_GMCA, A_sB1, S_sB1)
ca_final_sB1[0, it1, it_n] = crit_sB1['ca_mean']
ca_final_sB1[1, it1, it_n] = crit_sB1['ca_med']
S_2D_sB1 = Results_sB1['images']
if numIts == 1:
print('-10*np.log10(ca_final_sB1): ')
print(-10 * np.log10(ca_final_sB1))
if optDecBGMCA2 == 1:
time1 = time.time()
Results_sB2 = bgmca3(X,n=n_s,maxts = 7,mints=3,nmax=100,L0=1,UseP=1,verb=0,Init=0,Aposit=False,\
print(title_str)
print('***********************************')
for it_n in tqdm(range(numIts)):
# Data generation
X,X0,A0,S0,N = Make_Experiment_GG(n_s=n_s,n_obs=n_obs,t_samp=totalSize,noise_level=80.0,\
dynamic=0,CondNumber=1,alpha=rho_original)
for it_1 in range(len(alpha_test)):
alpha_est = alpha_test[it_1]
print 'alpha_est'
print alpha_est
Results_sB1 = bgmca3(cp.deepcopy(X),n=n_s,maxts = 7,mints=3,nmax=100,L0=1,UseP=1,verb=0,Init=0,Aposit=False,\
BlockSize= None,NoiseStd=[],IndNoise=[],Kmax=1.,AInit=None,tol=1e-6,subBlockSize=sizeBlock,\
threshOpt=2,weightFMOpt=1,SCOpt=1,alphaEstOpt=1,optA=1,alpha_exp=alpha_est)
A_sB1 = Results_sB1['mixmat']
S_sB1 = Results_sB1['sources']
crit_sB1 = EvalCriterion_fast(A0, S0, A_sB1, S_sB1)
ca_final_sB1[0, it_1, it_n] = crit_sB1['ca_mean']
ca_final_sB1[1, it_1, it_n] = crit_sB1['ca_med']
Results_sB0 = bgmca3(cp.deepcopy(X),n=n_s,maxts = 7,mints=3,nmax=100,L0=1,UseP=1,verb=0,Init=0,Aposit=False,\
BlockSize= None,NoiseStd=[],IndNoise=[],Kmax=1.,AInit=None,tol=1e-6,subBlockSize=sizeBlock,\
threshOpt=2,weightFMOpt=1,SCOpt=1,alphaEstOpt=0,optA=1,alpha_exp=alpha_est)
A_sB0 = Results_sB0['mixmat']
S_sB0 = Results_sB0['sources']
crit_sB0 = EvalCriterion_fast(A0, S0, A_sB0, S_sB0)
ca_final_sB0[0, it_1, it_n] = crit_sB0['ca_mean']
ca_final_sB0[1, it_1, it_n] = crit_sB0['ca_med']