change API of logitBin related functions to support rvm functions in …

…chapter07

chapter04/binPlot.m

@@ -4,7 +4,6 @@ function binPlot(model, X, t)
 %   t: 1xn label
 assert(size(X,1) == 2);
 w = model.w;
-w0 = model.w0;
 xi = min(X,[],2);
 xa = max(X,[],2);
 [x1,x2] = meshgrid(linspace(xi(1),xa(1)), linspace(xi(2),xa(2)));
@@ -20,6 +19,6 @@ function binPlot(model, X, t)
     idc = t==i;
     scatter(X(1,idc),X(2,idc),36,color(mod(i-1,m)+1));
 end
-y = w0+w(1)*x1+w(2)*x2;
+y = w(1)*x1+w(2)*x2+w(3);
 contour(x1,x2,y,[-0 0]);
 hold off;

chapter04/demo.m

@@ -6,13 +6,14 @@
 [X,t] = kmeansRnd(2,k,n);
 [model, llh] = logitBin(X,t-1,0);
 plot(llh);
-binPlot(model,X,t)
+y = logitBinPred(model,X)+1;
+binPlot(model,X,y)
 pause
 %%
-clear
-k = 3;
-n = 1000;
-[X,t] = kmeansRnd(2,k,n);
-[model, llh] = logitMn(X,t);
-y = logitMnPred(model,X);
-spread(X,y)
+% clear
+% k = 3;
+% n = 1000;
+% [X,t] = kmeansRnd(2,k,n);
+% [model, llh] = logitMn(X,t);
+% y = logitMnPred(model,X);
+% plotClass(X,y)

chapter04/logitBin.m

@@ -1,4 +1,4 @@
-function [model, llh] = logitBin(X, t, lambda)
+function [model, llh] = logitBin(X, t, lambda, w)
 % Logistic regression for binary classification optimized by Newton-Raphson
 % method.
 %   X: dxn data matrix
@@ -43,5 +43,4 @@
     if incr < tol; break; end
 end
 llh = llh(2:iter);
-model.w = w(1:(end-1));
-model.w0 = w(end);
+model.w = w;

chapter04/logitBinPred.m

@@ -3,8 +3,8 @@
 %   model: trained model structure
 %   X: d x n testing data
 % Written by Mo Chen ([email protected]).
+X = [X;ones(1,size(X,2))];
 w = model.w;
-w0 = model.w0;
-p = exp(-log1pexp(w'*X+w0)); 
+p = exp(-log1pexp(w'*X)); 
 y = (p>0.5)+0;