changed lod density for printing to account for the coordinate transf…

…ormation

linfa/maf.py

@@ -334,6 +334,4 @@ def inverse(self, u, y=None):
 
  def log_prob(self, x, y=None):
  u, sum_log_abs_det_jacobians = self.forward(x, y)
- print(torch.exp(sum_log_abs_det_jacobians))
- exit()
  return torch.sum(self.base_dist.log_prob(u) + sum_log_abs_det_jacobians, dim=1)

linfa/run_experiment.py

@@ -235,22 +235,23 @@ def train(self, nf, optimizer, iteration, log, sampling=True, t=1):
 
  # Save normalized domain samples
  np.savetxt(self.output_dir + '/' + self.name + '_samples_' + str(iteration), xkk.data.clone().cpu().numpy(), newline="\n")
- 
+
  # Save samples in the original space
  if self.transform:
  xkk_samples = self.transform.forward(xkk).data.cpu().numpy()
  np.savetxt(self.output_dir + '/' + self.name + '_params_' + str(iteration), xkk_samples, newline="\n")
+ # Conpute the sample density using normalizing flow AND account for the coordinate transformation
+ xkk_logprob = nf.log_prob(xkk).data.cpu().numpy() - self.transform.compute_log_jacob_func(xkk).data.cpu().numpy().flatten() 
  else:
  xkk_samples = xkk.data.cpu().numpy()
  np.savetxt(self.output_dir + '/' + self.name + '_params_' + str(iteration), xkk_samples, newline="\n")
+ xkk_logprob = nf.log_prob(xkk).data.cpu().numpy()
 
  # Save marginal statistics
  np.savetxt(self.output_dir + '/' + self.name + '_marginal_stats_' + str(iteration), np.concatenate((xkk_samples.mean(axis=0).reshape(-1,1),xkk_samples.std(axis=0).reshape(-1,1)),axis=1), newline="\n")
 
- # Save log density at the same samples
- # np.savetxt(self.output_dir + '/' + self.name + '_logdensity_' + str(iteration), self.model_logdensity(xkk).data.cpu().numpy(), newline="\n")
- np.savetxt(self.output_dir + '/' + self.name + '_logdensity_' + str(iteration), nf.log_prob(xkk).data.cpu().numpy(), newline="\n") 
- print('Current MC integral: ',np.exp(nf.log_prob(xkk).data.cpu().numpy()).mean())
+ # Save log density at the samples in the PHYSICAL SPACE (!)
+ np.savetxt(self.output_dir + '/' + self.name + '_logdensity_' + str(iteration), xkk_logprob, newline="\n")
 
  # Save model outputs at the samples - If a model is defined
  if self.transform: