fixed precrec_driver; readded paramsel_lambdaguesses calls;

Author: matteosantoro

gurls/paramsel/paramsel_hodual.m

@@ -47,31 +47,23 @@
 
 	[n,T]  = size(y(tr,:));
 
-	if strcmp(opt.kernel.type,'load')
-		d = n;
-	else
-		[n, d] = size(X(tr,:));
+	if strcmp(opt.kernel.type,'linear')
+		d = size(X(tr,:),2);
+        r = min(n,d);
+    else
+        r = n;
 	end
 
 	[Q,L] = eig(opt.kernel.K(tr,tr));
 	Q = double(Q);
 	L = double(diag(L));
 
-	% Replaced with paramsel_lambdaguesses
-	%tot = opt.nlambda;
-	%filtered = L(L > 200*eps^0.5);
-	%lmin = min(filtered)/n;
-	%lmax = max(filtered)/n;
-	%q = (lmax/lmin)^(1/tot);
-	%guesses = zeros(1,tot);
-	
 	tot = opt.nlambda;
-	guesses = paramsel_lambdaguesses(L, min(n,d), n, opt);
+	guesses = paramsel_lambdaguesses(L, r, n, opt);
 
 	ap = zeros(tot,T);
 	QtY = Q'*y(tr,:);
 	for i = 1:tot
-		%guesses(i) = lmin*(q^i);
 		%%%%%% REPLICATING CODE FROM RLS_DUAL %%%%%%%%
 		opt.rls.C = rls_eigen(Q,L,QtY,guesses(i),n);
 		%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

gurls/paramsel/paramsel_hodualr.m

@@ -50,23 +50,23 @@
 
 	[n,T]  = size(y(tr,:));
 
-	if strcmp(opt.kernel.type,'load')
-		d = n; % Here U is same as Q
-	else
-		[n, d] = size(X(tr,:));
+	if strcmp(opt.kernel.type,'linear')
+		d = size(X(tr,:),2);
+        r = min(n,d);
+    else
+        r = n;
 	end
 
 	[Q,L,U] = tygert_svd(opt.kernel.K(tr,tr),n);
 	Q = double(Q);
 	L = double(diag(L));
 
-	guesses = paramsel_lambdaguesses(L, min(n,d), n, opt);
+	guesses = paramsel_lambdaguesses(L, r, n, opt);
 	tot = opt.nlambda;
 	ap = zeros(tot,T);
 
 	QtY = Q'*y(tr,:);
 	for i = 1:tot
-		%guesses(i) = lmin*(q^i);
 		%%%%%% REPLICATING CODE FROM RLS_DUAL %%%%%%%%
 		opt.rls.C = rls_eigen(Q,L,QtY,guesses(i),n);
 		%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

gurls/paramsel/paramsel_hoprimal.m

@@ -57,17 +57,9 @@
 	QtXtY = Q'*(X(tr,:)'*y(tr,:));
 
 	guesses = paramsel_lambdaguesses(L, min(n,d), n, opt);
-	
-	% Replaced with new function paramsel_lambdaguesses
-	%filtered = L(L > 200*eps^0.5);
-	%lmin = min(filtered)/n;
-	%lmax = max(filtered)/n;
-	%q = (lmax/lmin)^(1/tot);
-	%guesses = zeros(1,tot);
-	
+		
 	ap = zeros(tot,T);
 	for i = 1:tot
-		% guesses(i) = lmin*(q^i); % replaced the computation with new function paramsel_lambdaguesses
 		opt.rls.W = rls_eigen(Q,L,QtXtY,guesses(i),n);
 		opt.pred = pred_primal(X(va,:),y(va,:),opt);
 		opt.perf = opt.hoperf(X(va,:),y(va,:),opt);

gurls/paramsel/paramsel_hoprimalr.m

@@ -57,17 +57,10 @@
 
 	QtXtY = Q'*(X(tr,:)'*y(tr,:));
 	guesses = paramsel_lambdaguesses(L, d, n, opt);
-	% Replaced with new function paramsel_lambdaguesses
-	%filtered = L(L > 200*eps^0.5);
-	%lmin = min(filtered)/n;
-	%lmax = max(filtered)/n;
-	%q = (lmax/lmin)^(1/tot);
-	%guesses = zeros(1,tot);
 
 	tot = opt.nlambda;
 	ap = zeros(tot,T);
 	for i = 1:tot
-		% guesses(i) = lmin*(q^i); % replaced the computation with new function paramsel_lambdaguesses
 		opt.rls.W = rls_eigen(Q,L,QtXtY,guesses(i),n);
 		opt.pred = pred_primal(X(va,:),y(va,:),opt);
 		opt.perf = opt.hoperf(X(va,:),y(va,:),opt);

gurls/paramsel/paramsel_loocvdual.m

@@ -35,14 +35,16 @@
 Q = double(Q);
 L = double(diag(L));
 Qty = Q'*y;
-filtered = L(L > 200*eps^0.5);
-lmin = min(filtered)/n;
-lmax = max(filtered)/n;
-q = (lmax/lmin)^(1/tot);
-guesses = zeros(1,tot);
+
+if strcmp(opt.kernel.type,'linear')
+    d = size(X,2);
+    r = min(n,d);
+else
+    r = n;
+end
+guesses = paramsel_lambdaguesses(L, r, n, opt);
+
 for i = 1:tot
-	guesses(i) = lmin*(q^i);
-	%C = rls_dual(K,y,guesses(i));
 	C = rls_eigen(Q,L,Qty,guesses(i),n);
 	Z = GInverseDiagonal(Q,L,guesses(i));
 	opt.pred = zeros(n,T);

gurls/paramsel/paramsel_loocvprimal.m

@@ -29,7 +29,6 @@
 K = X'*X;
 [n,T]  = size(y);
 d = size(X,2);
-tot = opt.nlambda;
 [Q,L] = eig(K);
 L = double(diag(L));
 

gurls/paramsel/paramsel_siglam.m

@@ -58,7 +58,7 @@
 	opt.sigmamax = eps;
 end	
 q = (opt.sigmamax/opt.sigmamin)^(1/(opt.nsigma-1));
-LOOSQE = zeros(opt.nsigma,opt.nlambda,T);
+PERF = zeros(opt.nsigma,opt.nlambda,T);
 sigmas = zeros(1,opt.nsigma);
 
 for i = 1:opt.nsigma

gurls/utils/precrec_driver.m

@@ -23,8 +23,8 @@
 
 % compute average precision
 ap=0;
-for t=0:0.1:1
-    p=max(prec(rec>=t));
+for t=0.0:0.1:1.0
+    p=max(prec(rec>t | abs(rec - t) < eps));
     if isempty(p)
         p=0;
     end