def inner_fn_test(x_t, s_tm1): prior_1_t = prior_1.fprop([x_t,s_tm1], params) prior_mu_t = prior_mu.fprop([prior_1_t], params) prior_sig_t = prior_sig.fprop([prior_1_t], params) z_t = Gaussian_sample(prior_mu_t, prior_sig_t)#in the original code it is gaussian. GMM is for the generation z_1_t = z_1.fprop([z_t], params) theta_1_t = theta_1.fprop([z_1_t, s_tm1], params) theta_mu1_t = theta_mu1.fprop([theta_1_t], params) theta_sig1_t = theta_sig1.fprop([theta_1_t], params) coeff1_t = coeff1.fprop([theta_1_t], params) y_pred1 = GMM_sampleY(theta_mu1_t, theta_sig1_t, coeff1_t) #Gaussian_sample(theta_mu_t, theta_sig_t) tupleMulti = prior_mu_t, prior_sig_t, theta_mu1_t, theta_sig1_t, coeff1_t, y_pred1 if (y_dim>1): theta_mu2_t = theta_mu2.fprop([theta_1_t], params) theta_sig2_t = theta_sig2.fprop([theta_1_t], params) coeff2_t = coeff2.fprop([theta_1_t], params) y_pred2 = GMM_sampleY(theta_mu2_t, theta_sig2_t, coeff2_t) y_pred1 = T.concatenate([y_pred1, y_pred2],axis=1) tupleMulti = tupleMulti + (theta_mu2_t, theta_sig2_t, coeff2_t, y_pred2) if (y_dim>2): theta_mu3_t = theta_mu3.fprop([theta_1_t], params) theta_sig3_t = theta_sig3.fprop([theta_1_t], params) coeff3_t = coeff3.fprop([theta_1_t], params) y_pred3 = GMM_sampleY(theta_mu3_t, theta_sig3_t, coeff3_t) y_pred1 = T.concatenate([y_pred1, y_pred3],axis=1) tupleMulti = tupleMulti + (theta_mu3_t, theta_sig3_t, coeff3_t, y_pred3) if (y_dim>3): theta_mu4_t = theta_mu4.fprop([theta_1_t], params) theta_sig4_t = theta_sig4.fprop([theta_1_t], params) coeff4_t = coeff4.fprop([theta_1_t], params) y_pred4 = GMM_sampleY(theta_mu4_t, theta_sig4_t, coeff4_t) y_pred1 = T.concatenate([y_pred1, y_pred4],axis=1) tupleMulti = tupleMulti + (theta_mu4_t, theta_sig4_t, coeff4_t, y_pred4) if (y_dim>4): theta_mu5_t = theta_mu5.fprop([theta_1_t], params) theta_sig5_t = theta_sig5.fprop([theta_1_t], params) coeff5_t = coeff5.fprop([theta_1_t], params) y_pred5 = GMM_sampleY(theta_mu5_t, theta_sig5_t, coeff5_t) y_pred1 = T.concatenate([y_pred1, y_pred5],axis=1) tupleMulti = tupleMulti + (theta_mu5_t, theta_sig5_t, coeff5_t, y_pred5) pred_1_t=y_1.fprop([y_pred1], params) #y_pred = [GMM_sampleY(theta_mu_t[i], theta_sig_t[i], coeff_t[i]) for i in range(y_dim)]#T.stack([y_pred1,y_pred2],axis = 0 ) s_t = rnn.fprop([[x_t, z_1_t, pred_1_t], [s_tm1]], params) #y_pred = dissag_pred.fprop([s_t], params) return (s_t,)+tupleMulti
def inner_fn(x_t, y_t, scheduleSamplingMask, s_tm1): phi_1_t = phi_1.fprop([x_t, s_tm1, y_t], params) phi_mu_t = phi_mu.fprop([phi_1_t], params) phi_sig_t = phi_sig.fprop([phi_1_t], params) prior_1_t = prior_1.fprop([x_t, s_tm1], params) prior_mu_t = prior_mu.fprop([prior_1_t], params) prior_sig_t = prior_sig.fprop([prior_1_t], params) z_t = Gaussian_sample( phi_mu_t, phi_sig_t ) #in the original code it is gaussian. GMM is for the generation z_1_t = z_1.fprop([z_t], params) theta_1_t = theta_1.fprop([z_1_t, s_tm1], params) theta_mu1_t = theta_mu1.fprop([theta_1_t], params) theta_sig1_t = theta_sig1.fprop([theta_1_t], params) coeff1_t = coeff1.fprop([theta_1_t], params) y_pred1 = GMM_sampleY( theta_mu1_t, theta_sig1_t, coeff1_t) #Gaussian_sample(theta_mu_t, theta_sig_t) theta_mu2_t = theta_mu2.fprop([theta_1_t], params) theta_sig2_t = theta_sig2.fprop([theta_1_t], params) coeff2_t = coeff2.fprop([theta_1_t], params) y_pred2 = GMM_sampleY(theta_mu2_t, theta_sig2_t, coeff2_t) theta_mu3_t = theta_mu3.fprop([theta_1_t], params) theta_sig3_t = theta_sig3.fprop([theta_1_t], params) coeff3_t = coeff3.fprop([theta_1_t], params) y_pred3 = GMM_sampleY(theta_mu3_t, theta_sig3_t, coeff3_t) theta_mu4_t = theta_mu4.fprop([theta_1_t], params) theta_sig4_t = theta_sig4.fprop([theta_1_t], params) coeff4_t = coeff4.fprop([theta_1_t], params) y_pred4 = GMM_sampleY(theta_mu4_t, theta_sig4_t, coeff4_t) theta_mu5_t = theta_mu5.fprop([theta_1_t], params) theta_sig5_t = theta_sig5.fprop([theta_1_t], params) coeff5_t = coeff5.fprop([theta_1_t], params) y_pred5 = GMM_sampleY(theta_mu5_t, theta_sig5_t, coeff5_t) if (scheduleSamplingMask == 1): s_t = rnn.fprop([[x_t, z_1_t, y_t], [s_tm1]], params) else: y_t_aux = y_1.fprop([ T.concatenate([y_pred1, y_pred2, y_pred3, y_pred4, y_pred5], axis=1) ], params) s_t = rnn.fprop([[x_t, z_1_t, y_t_aux], [s_tm1]], params) return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, theta_mu1_t, theta_sig1_t, coeff1_t, y_pred1, theta_mu2_t, theta_sig2_t, coeff2_t, y_pred2, theta_mu3_t, theta_sig3_t, coeff3_t, y_pred3, theta_mu4_t, theta_sig4_t, coeff4_t, y_pred4, theta_mu5_t, theta_sig5_t, coeff5_t, y_pred5
def inner_fn(x_t, s_tm1): phi_1_t = phi_1.fprop([x_t, s_tm1], params) phi_mu_t = phi_mu.fprop([phi_1_t], params) phi_sig_t = phi_sig.fprop([phi_1_t], params) prior_1_t = prior_1.fprop([s_tm1], params) prior_mu_t = prior_mu.fprop([prior_1_t], params) prior_sig_t = prior_sig.fprop([prior_1_t], params) z_t = Gaussian_sample( phi_mu_t, phi_sig_t ) #in the original code it is gaussian. GMM is for the generation z_1_t = z_1.fprop([z_t], params) theta_1_t = theta_1.fprop([z_1_t, s_tm1], params) theta_mu1_t = theta_mu1.fprop([theta_1_t], params) theta_sig1_t = theta_sig1.fprop([theta_1_t], params) coeff1_t = coeff1.fprop([theta_1_t], params) theta_mu2_t = theta_mu2.fprop([theta_1_t], params) theta_sig2_t = theta_sig2.fprop([theta_1_t], params) coeff2_t = coeff2.fprop([theta_1_t], params) theta_mu3_t = theta_mu3.fprop([theta_1_t], params) theta_sig3_t = theta_sig3.fprop([theta_1_t], params) coeff3_t = coeff3.fprop([theta_1_t], params) theta_mu4_t = theta_mu4.fprop([theta_1_t], params) theta_sig4_t = theta_sig4.fprop([theta_1_t], params) coeff4_t = coeff4.fprop([theta_1_t], params) theta_mu5_t = theta_mu5.fprop([theta_1_t], params) theta_sig5_t = theta_sig5.fprop([theta_1_t], params) coeff5_t = coeff5.fprop([theta_1_t], params) y_pred1 = GMM_sampleY( theta_mu1_t, theta_sig1_t, coeff1_t) #Gaussian_sample(theta_mu_t, theta_sig_t) y_pred2 = GMM_sampleY(theta_mu2_t, theta_sig2_t, coeff2_t) y_pred3 = GMM_sampleY(theta_mu3_t, theta_sig3_t, coeff3_t) y_pred4 = GMM_sampleY(theta_mu4_t, theta_sig4_t, coeff4_t) y_pred5 = GMM_sampleY(theta_mu5_t, theta_sig5_t, coeff5_t) #y_pred = [GMM_sampleY(theta_mu_t[i], theta_sig_t[i], coeff_t[i]) for i in range(y_dim)]#T.stack([y_pred1,y_pred2],axis = 0 ) s_t = rnn.fprop([[x_t, z_1_t], [s_tm1]], params) #y_pred = dissag_pred.fprop([s_t], params) return (s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, z_t, z_1_t, theta_1_t, theta_mu1_t, theta_sig1_t, coeff1_t, theta_mu2_t, theta_sig2_t, coeff2_t, theta_mu3_t, theta_sig3_t, coeff3_t, theta_mu4_t, theta_sig4_t, coeff4_t, theta_mu5_t, theta_sig5_t, coeff5_t, y_pred1, y_pred2, y_pred3, y_pred4, y_pred5)
def inner_fn(x_t, y_t, s_tm1): phi_1_t = phi_1.fprop([x_t, s_tm1, y_t], params) phi_mu_t = phi_mu.fprop([phi_1_t], params) phi_sig_t = phi_sig.fprop([phi_1_t], params) prior_1_t = prior_1.fprop([x_t, s_tm1], params) prior_mu_t = prior_mu.fprop([prior_1_t], params) prior_sig_t = prior_sig.fprop([prior_1_t], params) z_t = Gaussian_sample( phi_mu_t, phi_sig_t ) #in the original code it is gaussian. GMM is for the generation z_1_t = z_1.fprop([z_t], params) theta_1_t = theta_1.fprop([z_1_t, s_tm1], params) theta_mu1_t = theta_mu1.fprop([theta_1_t], params) theta_sig1_t = theta_sig1.fprop([theta_1_t], params) coeff1_t = coeff1.fprop([theta_1_t], params) y_pred1 = GMM_sampleY( theta_mu1_t, theta_sig1_t, coeff1_t) #Gaussian_sample(theta_mu_t, theta_sig_t) theta_mu2_t = theta_mu2.fprop([theta_1_t], params) theta_sig2_t = theta_sig2.fprop([theta_1_t], params) coeff2_t = coeff2.fprop([theta_1_t], params) y_pred2 = GMM_sampleY(theta_mu2_t, theta_sig2_t, coeff2_t) theta_mu3_t = theta_mu3.fprop([theta_1_t], params) theta_sig3_t = theta_sig3.fprop([theta_1_t], params) coeff3_t = coeff3.fprop([theta_1_t], params) y_pred3 = GMM_sampleY(theta_mu3_t, theta_sig3_t, coeff3_t) theta_mu4_t = theta_mu4.fprop([theta_1_t], params) theta_sig4_t = theta_sig4.fprop([theta_1_t], params) coeff4_t = coeff4.fprop([theta_1_t], params) y_pred4 = GMM_sampleY(theta_mu4_t, theta_sig4_t, coeff4_t) theta_mu5_t = theta_mu5.fprop([theta_1_t], params) theta_sig5_t = theta_sig5.fprop([theta_1_t], params) coeff5_t = coeff5.fprop([theta_1_t], params) y_pred5 = GMM_sampleY(theta_mu5_t, theta_sig5_t, coeff5_t) s_t = rnn.fprop([[x_t, z_1_t, y_t], [s_tm1]], params) return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, theta_mu1_t, theta_sig1_t, coeff1_t, y_pred1, theta_mu2_t, theta_sig2_t, coeff2_t, y_pred2, theta_mu3_t, theta_sig3_t, coeff3_t, y_pred3, theta_mu4_t, theta_sig4_t, coeff4_t, y_pred4, theta_mu5_t, theta_sig5_t, coeff5_t, y_pred5
def inner_fn(x_t, s_tm1): phi_1_t = phi_1.fprop([x_t, s_tm1], params) phi_mu_t = phi_mu.fprop([phi_1_t], params) phi_sig_t = phi_sig.fprop([phi_1_t], params) prior_1_t = prior_1.fprop([s_tm1], params) prior_mu_t = prior_mu.fprop([prior_1_t], params) prior_sig_t = prior_sig.fprop([prior_1_t], params) z_t = Gaussian_sample( phi_mu_t, phi_sig_t ) #in the original code it is gaussian. GMM is for the generation z_1_t = z_1.fprop([z_t], params) theta_1_t = theta_1.fprop([z_1_t, s_tm1], params) theta_mu1_t = theta_mu1.fprop([theta_1_t], params) theta_sig1_t = theta_sig1.fprop([theta_1_t], params) coeff1_t = coeff1.fprop([theta_1_t], params) theta_mu2_t = theta_mu2.fprop([theta_1_t], params) theta_sig2_t = theta_sig2.fprop([theta_1_t], params) coeff2_t = coeff2.fprop([theta_1_t], params) #corr_t = corr.fprop([theta_1_t], params) #binary_t = binary.fprop([theta_1_t], params) # I was missing this reshape that is done before BiGMM in the original code ''' theta_mu_in = theta_mu_t.reshape((x_t[0]*x_t[1], -1)) theta_sig_in = theta_sig_t.reshape((x_t[0]*x_t[1], -1)) coeff_in = coeff_t.reshape((x_t[0]*x_t[1], -1)) ''' y_pred1 = GMM_sampleY( theta_mu1_t, theta_sig1_t, coeff1_t) #Gaussian_sample(theta_mu_t, theta_sig_t) y_pred2 = GMM_sampleY(theta_mu2_t, theta_sig2_t, coeff2_t) #y_pred = T.stack([y_pred1,y_pred2],axis = 0 ) s_t = rnn.fprop([[x_t, z_1_t], [s_tm1]], params) #y_pred = dissag_pred.fprop([s_t], params) return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, z_t, z_1_t, theta_1_t, theta_mu1_t, theta_sig1_t, coeff1_t, theta_mu2_t, theta_sig2_t, coeff2_t, y_pred1, y_pred2
def inner_fn(x_t, y_t, scheduleSamplingMask, s_tm1): phi_1_t = phi_1.fprop([x_t, s_tm1, y_t], params) phi_mu_t = phi_mu.fprop([phi_1_t], params) phi_sig_t = phi_sig.fprop([phi_1_t], params) prior_1_t = prior_1.fprop([x_t, s_tm1], params) prior_mu_t = prior_mu.fprop([prior_1_t], params) prior_sig_t = prior_sig.fprop([prior_1_t], params) z_t = Gaussian_sample( phi_mu_t, phi_sig_t ) #in the original code it is gaussian. GMM is for the generation z_1_t = z_1.fprop([z_t], params) theta_1_t = theta_1.fprop([z_1_t, s_tm1], params) theta_mu1_t = theta_mu1.fprop([theta_1_t], params) theta_sig1_t = theta_sig1.fprop([theta_1_t], params) coeff1_t = coeff1.fprop([theta_1_t], params) ## prediction 1 y_pred = GMM_sampleY( theta_mu1_t, theta_sig1_t, coeff1_t) #Gaussian_sample(theta_mu_t, theta_sig_t) tupleMulti = phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, theta_mu1_t, theta_sig1_t, coeff1_t, y_pred if (y_dim > 1): theta_mu2_t = theta_mu2.fprop([theta_1_t], params) theta_sig2_t = theta_sig2.fprop([theta_1_t], params) coeff2_t = coeff2.fprop([theta_1_t], params) y_pred2 = GMM_sampleY(theta_mu2_t, theta_sig2_t, coeff2_t) y_pred = T.concatenate([y_pred, y_pred2], axis=1) tupleMulti = tupleMulti + (theta_mu2_t, theta_sig2_t, coeff2_t, y_pred2) if (y_dim > 2): theta_mu3_t = theta_mu3.fprop([theta_1_t], params) theta_sig3_t = theta_sig3.fprop([theta_1_t], params) coeff3_t = coeff3.fprop([theta_1_t], params) y_pred3 = GMM_sampleY(theta_mu3_t, theta_sig3_t, coeff3_t) y_pred = T.concatenate([y_pred, y_pred3], axis=1) tupleMulti = tupleMulti + (theta_mu3_t, theta_sig3_t, coeff3_t, y_pred3) if (y_dim > 3): theta_mu4_t = theta_mu4.fprop([theta_1_t], params) theta_sig4_t = theta_sig4.fprop([theta_1_t], params) coeff4_t = coeff4.fprop([theta_1_t], params) y_pred4 = GMM_sampleY(theta_mu4_t, theta_sig4_t, coeff4_t) y_pred = T.concatenate([y_pred, y_pred4], axis=1) tupleMulti = tupleMulti + (theta_mu4_t, theta_sig4_t, coeff4_t, y_pred4) #s_t = rnn.fprop([[x_t, z_1_t, y_t], [s_tm1]], params) if (scheduleSamplingMask == 1): s_t = rnn.fprop([[x_t, z_1_t, y_t], [s_tm1]], params) else: y_t_aux = y_1.fprop([y_pred], params) s_t = rnn.fprop([[x_t, z_1_t, y_t_aux], [s_tm1]], params) return (s_t, ) + tupleMulti