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']