print(ind_seq)
for j in range(0, n_of_seq):
print(j)
seq = input_data[ind_seq][j][0][0]
EXP_Param = trainGD_EXP(seq, eps)
PWL_Param = trainGD_PWL(seq, eps)
QEXP_Param = trainGD_QEXP(seq, eps)
RAY_Param = trainGD_RAY(seq, eps)
llh_GD_EXP = np.append(llh_GD_EXP, EXP_Param['final_llh'])
llh_GD_PWL = np.append(llh_GD_PWL, PWL_Param['final_llh'])
llh_GD_QEXP = np.append(llh_GD_QEXP, QEXP_Param['final_llh'])
llh_GD_RAY = np.append(llh_GD_RAY, RAY_Param['final_llh'])
llh_GD_EXP_Renorm_alpha = np.append(llh_GD_EXP_Renorm_alpha,
EXP_Param['llh_renorm_alpha'])
llh_GD_PWL_Renorm_K = np.append(llh_GD_PWL_Renorm_K,
PWL_Param['llh_renorm_K'])
llh_GD_QEXP_Renorm_a = np.append(llh_GD_QEXP_Renorm_a,
QEXP_Param['llh_renorm_alpha'])
llh_GD_RAY_Renorm_gamma = np.append(llh_GD_RAY_Renorm_gamma,
RAY_Param['llh_renorm_gamma'])
print(kernel) for j in range(n_of_seq): print(j) seq = simulated_sequences[kernel + "_" + str(i)] EXP_Param = trainGD_EXP(seq,eps,M, method, train_method, train_frac, T) PWL_Param = trainGD_PWL(seq,eps,M,method, train_method, train_frac, T) QEXP_Param = trainGD_QEXP(seq,eps,M,method, train_method, train_frac, T) RAY_Param = trainGD_RAY(seq,eps,M,method, train_method, train_frac, T) GSS_Param = trainGD_GSS(seq,eps,M,method, train_method, train_frac, T) llh_GD_EXP = np.append(llh_GD_EXP,EXP_Param['final_llh']) llh_GD_PWL = np.append(llh_GD_PWL,PWL_Param['final_llh']) llh_GD_QEXP = np.append(llh_GD_QEXP,QEXP_Param['final_llh']) llh_GD_RAY = np.append(llh_GD_RAY,RAY_Param['final_llh']) llh_GD_GSS = np.append(llh_GD_GSS, GSS_Param['final_llh']) llh_GD_EXP_Renorm_alpha = np.append(llh_GD_EXP_Renorm_alpha,EXP_Param['llh_renorm_alpha']) llh_GD_PWL_Renorm_K = np.append(llh_GD_PWL_Renorm_K,PWL_Param['llh_renorm_K']) llh_GD_QEXP_Renorm_a = np.append(llh_GD_QEXP_Renorm_a,QEXP_Param['llh_renorm_alpha']) llh_GD_RAY_Renorm_gamma = np.append(llh_GD_RAY_Renorm_gamma,RAY_Param['llh_renorm_gamma']) llh_GD_GSS_Renorm_kappa = np.append(llh_GD_GSS_Renorm_kappa, GSS_Param['llh_renorm_kappa'])