From 283969bfb7852515d13cc7435011dd39148a1a9e Mon Sep 17 00:00:00 2001
From: Yi Wang <37149810+yiwang12@users.noreply.github.com>
Date: Fri, 15 Nov 2024 00:08:44 +0100
Subject: [PATCH] Update pf_multiSample.py

---
 mNSF/pf_multiSample.py | 38 ++++++++++++++------------------------
 1 file changed, 14 insertions(+), 24 deletions(-)

diff --git a/mNSF/pf_multiSample.py b/mNSF/pf_multiSample.py
index f506c78..bafb08d 100644
--- a/mNSF/pf_multiSample.py
+++ b/mNSF/pf_multiSample.py
@@ -250,29 +250,19 @@ def sample_latent_GP_funcs(self, X, S=1, kernel=None, mu_z=None, Kuu_chol=None,
       mu_z = self.get_mu_z()
     if Kuu_chol is None:
       Kuu_chol = self.get_Kuu_chol(kernel=kernel, from_cache=(not chol))
-    if (not chol):
-      N = X.shape[0]
-      L = self.W.shape[1]
-      mu_x = self.beta0+tfl.matmul(self.beta, X, transpose_b=True) #LxN
-      mu_tilde = mu_x + tfl.matvec(self.alpha_x, self.delta-mu_z, transpose_a=True) #LxN
-      #a_t_Kchol = self.a_t_Kchol
-      #aKa = tf.reduce_sum(tf.square(a_t_Kchol), axis=2) #LxN
-      Sigma_tilde = self.Sigma_tilde #LxN
-    if chol:
-      alpha_x = tfl.cholesky_solve(Kuu_chol, Kuf) #LxMxN
-      N = X.shape[0]
-      L = self.W.shape[1]
-      mu_x = self.beta0+tfl.matmul(self.beta, X, transpose_b=True) #LxN
-      Kuf = kernel.matrix(self.Z, X) #LxMxN
-      Kff_diag = kernel.apply(X, X, example_ndims=1)+self.nugget #LxN
-    
-      mu_tilde = mu_x + tfl.matvec(alpha_x, self.delta-mu_z, transpose_a=True) #LxN
-      #compute the alpha(x_i)'(K_uu-Omega)alpha(x_i) term
-      a_t_Kchol = tfl.matmul(alpha_x, Kuu_chol, transpose_a=True) #LxNxM
-      aKa = tf.reduce_sum(tf.square(a_t_Kchol), axis=2) #LxN
-      a_t_Omega_tril = tfl.matmul(alpha_x, self.Omega_tril, transpose_a=True) #LxNxM
-      aOmega_a = tf.reduce_sum(tf.square(a_t_Omega_tril), axis=2) #LxN
-      Sigma_tilde = Kff_diag - aKa + aOmega_a #LxN
+    N = X.shape[0]
+    L = self.W.shape[1]
+    mu_x = self.beta0+tfl.matmul(self.beta, X, transpose_b=True) #LxN
+    Kuf = kernel.matrix(self.Z, X) #LxMxN
+    Kff_diag = kernel.apply(X, X, example_ndims=1)+self.nugget #LxN
+    alpha_x = tfl.cholesky_solve(Kuu_chol, Kuf) #LxMxN
+    mu_tilde = mu_x + tfl.matvec(alpha_x, self.delta-mu_z, transpose_a=True) #LxN
+    #compute the alpha(x_i)'(K_uu-Omega)alpha(x_i) term
+    a_t_Kchol = tfl.matmul(alpha_x, Kuu_chol, transpose_a=True) #LxNxM
+    aKa = tf.reduce_sum(tf.square(a_t_Kchol), axis=2) #LxN
+    a_t_Omega_tril = tfl.matmul(alpha_x, self.Omega_tril, transpose_a=True) #LxNxM
+    aOmega_a = tf.reduce_sum(tf.square(a_t_Omega_tril), axis=2) #LxN
+    Sigma_tilde = Kff_diag - aKa + aOmega_a #LxN
     #print(S)
     #print(L)
     #print(N)
@@ -351,7 +341,7 @@ def elbo_avg(self, X, Y, sz=1, S=1, Ntot=None, chol=True):
     #kl_terms is not affected by minibatching so use reduce_sum
     #print(1111)
     kl_term = tf.reduce_sum(self.eval_kl_term(mu_z, Kuu_chol))
-    Mu = self.sample_predictive_mean(X, sz=sz, S=S, kernel=ker, mu_z=mu_z, Kuu_chol=Kuu_chol, chol = chol)
+    Mu = self.sample_predictive_mean(X, sz=sz, S=S, kernel=ker, mu_z=mu_z, Kuu_chol=Kuu_chol)
     eloglik = likelihoods.lik_to_distr(self.lik, Mu, self.disp).log_prob(Y)
     return J*tf.reduce_mean(eloglik) - kl_term/Ntot