Update tester.py

sdfvae/tester.py

@@ -84,9 +84,9 @@ class Tester(object):
         # See https://arxiv.org/pdf/1606.05908.pdf, Page 9, Section 2.2 for details.
         # The constant items in the loss function (not the coefficients) can be any number here, or even omitted        
         # due to they have no any impact on gradientis propagation during training. So do in testing.
-        # log(N(x|mu,var))
-        # = log{1/(sqrt(2*pi)*var)exp{-(x-mu)^2/(2*var^2)}} 
-        # = -0.5*{log(2*pi)+2*log(var)+[(x-mu)/exp{log(var)}]^2}
+        # log(N(x|mu,sigma^2))
+        # = log{1/(sqrt(2*pi)*sigma)exp{-(x-mu)^2/(2*sigma^2)}} 
+        # = -0.5*{log(2*pi)+2*log(sigma)+[(x-mu)/exp{log(sigma)}]^2}
         loglikelihood = -0.5 * torch.sum(torch.pow(((original_seq.float()-recon_seq_mu.float())/torch.exp(recon_seq_logvar.float())), 2) 
                                          + 2 * recon_seq_logvar.float() 
                                          + np.log(np.pi*2))
@@ -106,9 +106,9 @@ class Tester(object):
         # The constant items in the loss function (not the coefficients) can be any number here, or even omitted        
         # due to they have no any impact on gradientis propagation during training. 
         # So do in testing, because they also have no any impact on the results (all anomaly scores increase or decrease to the same extent).
-        # log(N(x|mu,var))
-        # = log{1/(sqrt(2*pi)*var)exp{-(x-mu)^2/(2*var^2)}} 
-        # = -0.5*{log(2*pi)+2*log(var)+[(x-mu)/exp{log(var)}]^2}
+        # log(N(x|mu,sigma^2))
+        # = log{1/(sqrt(2*pi)*sigma)exp{-(x-mu)^2/(2*sigma^2)}} 
+        # = -0.5*{log(2*pi)+2*log(sigma)+[(x-mu)/exp{log(sigma)}]^2}
         llh = -0.5 * torch.sum(torch.pow(((x.float()-recon_x_mu.float())/torch.exp(recon_x_logvar.float())), 2) 
               + 2 * recon_x_logvar.float() 
               + np.log(np.pi*2))