Correcting few bug & time indices

Not entirely sure of the t-1 dependence for UV factorization.

LowRAMP_UV.m

@@ -74,8 +74,8 @@
 
     u_old=zeros(m,RANK);
     v_old=zeros(n,RANK);
-    u_var=zeros(RANK,RANK);
-    v_var=zeros(RANK,RANK);
+    u_var=zeros(RANK,RANK);u_var_old=zeros(RANK,RANK);
+    v_var=zeros(RANK,RANK);v_var_old=zeros(RANK,RANK);
 
     A_u=zeros(RANK,RANK);
     B_u=zeros(m,RANK);
@@ -93,20 +93,21 @@
     disp(PR);
     old_free_nrg=-realmax('double');delta_free_nrg=0;
 
+
     while ((diff>opt.conv_criterion)&&(t<opt.nb_iter))    
         %Keep old variable
         A_u_old=A_u;        A_v_old=A_v;
         B_u_old=B_u;        B_v_old=B_v;      
 
         %AMP iteration
-        B_u_new=(S*v)/sqrt(n)-u_old*v_var/(Delta);
+        B_u_new=(S*v)/sqrt(n)-u_old*v_var_old/(Delta);
         A_u_new=v'*v/(n*Delta);
-        B_v_new=(S'*u)/sqrt(n)-v_old*(m*u_var/n)/(Delta);
+        B_v_new=(S'*u)/sqrt(n)-v_old*(m*u_var_old/n)/(Delta);
         A_v_new=u'*u/(n*Delta);
 
         %Keep old variables
-        u_old=u;
-        v_old=v;
+        u_old=u;u_var_old=u_var;
+        v_old=v;v_var_old=v_var;
 
         %Iteration with fixed damping or learner one
         pass=0;
@@ -128,8 +129,9 @@
             end
 
             [u,u_var,logu] = Fun_u(A_u,B_u);
-            [v,v_var,logv] = Fun_v(A_v,B_v);
+            [v,v_var,logv] = Fun_v(A_v,B_v);            
 
+
             %Compute the Free Entropy
             minusDKL_u=logu+0.5*m*trace(A_u*u_var)+trace(0.5*A_u*u'*u)-trace(u'*B_u);   
             minusDKL_v=logv+0.5*n*trace(A_v*v_var)+trace(0.5*A_v*v'*v)-trace(v'*B_v);   
@@ -139,10 +141,10 @@
             free_nrg=(minusDKL_u+minusDKL_v+term_u+term_v+term_uv)/n;
 
             if (t==0)  delta_free_nrg=old_free_nrg-free_nrg;old_free_nrg=free_nrg; break; end
-            if (opt.damping>0)  delta_free_nrg=old_free_nrg-free_nrg;old_free_nrg=free_nrg; break;end
+            if (opt.damping>=0)  delta_free_nrg=old_free_nrg-free_nrg;old_free_nrg=free_nrg; break;end
             %Otherwise adapative damping
             if (free_nrg>old_free_nrg)
-                delta_free_nrg=old_free_nrg-free_nrg;old_free_nrg=free_nrg;
+                delta_free_nrg=old_free_nrg-free_nrg;
                 old_free_nrg=free_nrg;
                 pass=1;
             else

LowRAMP_XX.m

@@ -54,7 +54,8 @@
             x=opt.signal+1e-4*randn(n,RANK);        
     end    
     x_old=zeros(n,RANK);
-    x_V=zeros(RANK,RANK);
+    x_V=zeros(RANK,RANK);    x_V_old=zeros(RANK,RANK);
+
 
     A=zeros(RANK,RANK);
     B=zeros(n,RANK);
@@ -75,11 +76,12 @@
         B_old=B;
 
         %AMP iteration
-        B_new=(S*x)/sqrt(n)-x_old*x_V/(Delta);
+        B_new=(S*x)/sqrt(n)-x_old*x_V_old/(Delta);
         A_new=x'*x/(n*Delta);
 
         %Keep old variables
         x_old=x;
+        x_V_old=x_V;
 
         %Iteration with fixed damping or learner one
         pass=0;
@@ -91,14 +93,14 @@
 
         while (pass~=1) 
             if (t>0)
-                A=1./((1-damp)./A_old+damp./A_new);
+                A=(1-damp)*A_old+damp*A_new;
                 B=(1-damp)*B_old+damp*B_new;
             else
                 A=A_new;
                 B=B_new;
             end
 
-            [x,x_V,logZ] = Fun_a(A,B);%Community prior                
+            [x,x_V,logZ] = Fun_a(A,B);            
 
             %Compute the Free Entropy
             minusDKL=logZ+0.5*n*trace(A*x_V)+trace(0.5*A*x'*x)-trace(x'*B)   ;  
@@ -107,7 +109,7 @@
             free_nrg=(minusDKL+term_x+term_xx)/n;
 
             if (t==0) delta_free_nrg=old_free_nrg-free_nrg;old_free_nrg=free_nrg; break;end
-            if (opt.damping>0) delta_free_nrg=old_free_nrg-free_nrg;old_free_nrg=free_nrg;break;end
+            if (opt.damping>=0) delta_free_nrg=old_free_nrg-free_nrg;old_free_nrg=free_nrg;break;end
             %Otherwise adapative damping
             if (free_nrg>old_free_nrg)
                 delta_free_nrg=old_free_nrg-free_nrg;old_free_nrg=free_nrg;

run_LowRAMP_UV.m

@@ -3,7 +3,7 @@
 Delta=1e-2;%Variance of the gaussian noise
 RANK=3;%rank
 
-fprintf(1,'Creating a signal of rank %d \n',RANK);
+fprintf(1,'Creating a %dx%d signal of rank %d \n',m,n,RANK);
 V = zeros(m,RANK);
 for i=1:m
     V(i,ceil(rand()*RANK))=1;

run_LowRAMP_XX.m

@@ -8,7 +8,7 @@
 pout = p - Delta/(RANK*sqrt(n));
 pin = p + (1-1/RANK)*Delta/sqrt(n);
 
-fprintf(1,'Creating a signal of rank %d \n',RANK);
+fprintf(1,'Creating a %dx%d signal of rank %d \n',n,n,RANK);
  X = zeros(n,RANK);
 for i=1:n
     X(i,ceil(rand()*RANK))=1;

run_PCA.m

@@ -1,9 +1,9 @@
 path(path,'./Subroutines');
-m=500;n=100;%size of the vector U and V
+m=1000;n=1000;%size of the vector U and V
 Delta=1e-4;%Variance of the gaussian noise
 RANK=3;%rank
 
-fprintf(1,'Creating a signal of rank %d \n',RANK);
+fprintf(1,'Creating a %dx%d signal of rank %d \n',m,n,RANK);
 U = randn(m,RANK);
 V = randn(n,RANK);
 
@@ -20,16 +20,11 @@
 opt.damping=-1;%adaptive damping
 opt.prior_u='Gauss';     opt.signal_u=U;       
 opt.prior_v='Gauss';     opt.signal_v=V;       
-opt.damping=0.2;
 opt.verbose_n=1;
 tic
 [ u_ample,v_ample ]  = LowRAMP_UV(S,Iinv,RANK,opt)    ;
 toc;
-disp('Squared Reconstruction error');
+disp('Squared Reconstruction error on the matrix');
 mean2((u_ample*v_ample'/sqrt(n)-Y).^2)
 
-tic
-[ u_amp,v_amp ] = LowRAMP_UV_completion(S,Iinv,ones(size(S)),RANK,opt)    ;
-toc
-disp('Squared Reconstruction error');
-mean2((u_amp*v_amp'/sqrt(n)-Y).^2)
+