fix linRegPred knRegPred

chapter02/logGauss.m

@@ -7,21 +7,13 @@
 % Output:
 %   y: 1 x n probability density in logrithm scale y=log p(x)
 % Written by Mo Chen (sth4nth@gmail.com).
-[d,k] = size(mu);
-if all(size(sigma)==d) && k==1   % one mu and one dxd sigma
-    X = bsxfun(@minus,X,mu);
-    [R,p]= chol(sigma);
-    if p ~= 0
-        error('ERROR: sigma is not PD.');
-    end
-    Q = R'\X;
-    q = dot(Q,Q,1);  % quadratic term (M distance)
-    c = d*log(2*pi)+2*sum(log(diag(R)));   % normalization constant
-    y = -0.5*(c+q);
-elseif size(sigma,1)==1 && size(sigma,2)==size(mu,2) % k mu and (k or one) scalar sigma
-    X2 = repmat(dot(X,X,1)',1,k);
-    D = bsxfun(@plus,X2-2*X'*mu,dot(mu,mu,1));
-    q = bsxfun(@times,D,1./sigma);  % M distance
-    c = d*(log(2*pi)+2*log(sigma));          % normalization constant
-    y = -0.5*bsxfun(@plus,q,c);
-end
+d = size(X,1);
+X = X-mu;
+[U,p]= chol(sigma);
+if p ~= 0
+    error('ERROR: sigma is not PD.');
+end
+Q = U'\X;
+q = dot(Q,Q,1);  % quadratic term (M distance)
+c = d*log(2*pi)+2*sum(log(diag(U)));   % normalization constant
+y = -(c+q)/2;

chapter03/linRegPred.m

@@ -26,7 +26,6 @@
 end
 
 if nargin == 3 && nargout == 3
-    p = exp(logGauss(t,y,sigma));
-%     p = exp(-0.5*(((t-y)./sigma).^2+log(2*pi))-log(sigma));
+    p = exp(-0.5*(((t-y)./sigma).^2+log(2*pi))-log(sigma));
 end
 

chapter06/knRegPred.m

@@ -25,5 +25,5 @@
 end
 
 if nargin == 3 && nargout == 3
-    p = exp(logGauss(t,y,sigma));
+    p = exp(-0.5*(((t-y)./sigma).^2+log(2*pi))-log(sigma));
 end