minor changes

Minor changes

README

@@ -1,4 +1,4 @@
-LowRAMP: Low rank Approximate Message Passing, a software for low rank matrix factorization based on belief propagation.
+LowRAMP: Low rank Approximate Message Passing, a MATLAB software for low rank matrix factorization based on belief propagation.
 COPYRIGHT (C) 2015 Thibault Lesieur, Florent Krzakala and Lenka Zdeborova
 Contact : [email protected]
 
@@ -21,7 +21,7 @@ USAGE : from matlab, includes the two subfolders Functions and Subroutines (for
 path(path,'./Subroutines'); path(path,'./Functions');). 
 
 Then run 
-* demo demo_LowRAMP_UV for a UV factorization of a clustering  problem
+* demo_LowRAMP_UV for a UV factorization of a clustering  problem
 * demo_PCA_and_completion for a completion problem
 * demo_LowRAMP_XX for a community detection problem
 * demo_Rank1_Submatrix for a submatrix localization problem

Subroutines/f_Rank1Binary.m

@@ -1,9 +1,9 @@
 function [MEAN,VAR,logZ] = f_Rank1Binary(A,B,rho)
-    %Rank 1 0/1 prior
-    %rho is the a priori fraction of 1
+   %Rank 1 0/1 prior
+   %rho is the a priori fraction of 1
    if (nargin <= 2)
         rho=0.5; % Use default  parameters
-    end   
+   end   
    Weight=-0.5*A+B;
    pos=find(Weight>0);
    neg=setdiff([1:size(B,1)],pos);

Subroutines/f_gauss.m

@@ -1,6 +1,11 @@
-function [MEAN,VAR,logZ] = f_gauss(A,B)
-    VAR=inv(eye(size(A))+A);
-    MEAN=B*VAR;   
-    logZ=trace(0.5*B'*B*VAR);
-    logZ=-0.5*log(det(eye(size(A))+A))*size(B,1)+logZ ;          
+function [MEAN,VAR,logZ] = f_gauss(A,B,var_gauss)
+    %Rank K Gaussian prior
+    %var_gauss is the a priori variance of the prior
+   if (nargin <= 2)
+        var_gauss=1; % Use default  parameters
+   end
+   VAR=inv((1./var_gauss)*eye(size(A))+A);
+   MEAN=B*VAR;   
+   logZ=trace(0.5*B'*B*VAR);
+   logZ=-0.5*log(det(var_gauss*eye(size(A))+A))*size(B,1)+logZ ;          
 end

demo_PCA_and_completion.m

@@ -9,8 +9,6 @@
 RANK=3;%rank
 fprintf(1,'Creating a %dx%d signal of rank %d ...\n',m,n,RANK);
 
-fprintf(1,'Creating a %dx%d signal of rank %d ...\n',m,n,RANK);
-
 U = randn(m,RANK);
 V = randn(n,RANK);