chapter04 is done for now

chapter04/TODO.txt

@@ -1,3 +1,2 @@
-binPlot
-multiPlot
-demo
+multiPlot: plot multclass decison boundary
+

chapter04/binPlot.m

@@ -2,11 +2,24 @@ function binPlot(model, X, t)
 % Plot binary classification result for 2d data
 %   X: 2xn data matrix
 %   t: 1xn label
+assert(size(X,1) == 2);
 w = model.w;
 w0 = model.w0;
-figure;
-spread(X,t);
-y = w'*X+w0;
+xi = min(X,[],2);
+xa = max(X,[],2);
+[x1,x2] = meshgrid(linspace(xi(1),xa(1)), linspace(xi(2),xa(2)));
+
+color = 'brgmcyk';
+m = length(color);
+figure(gcf);
+axis equal
+clf;
 hold on;
-contour(X(1,:),X(2,:),y,1);
-hold off;
+view(2);
+for i = 1:max(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;
+contour(x1,x2,y,[-0 0]);
+hold off;

chapter04/demo.m

@@ -1,39 +1,17 @@
-
+% 
 clear; close all;
 k = 2;
 n = 1000;
 [X,t] = kmeansRnd(2,k,n);
-
-[x1,x2] = meshgrid(linspace(min(X(1,:)),max(X(1,:)),n), linspace(min(X(2,:)),max(X(2,:)),n));
 [model, llh] = logitReg(X,t-1,0);
-[y,p] = logitPred(model,X);
-
-w = model.w;
-w0 = model.w0;
 plot(llh);
-figure;
-spread(X,t);
-
-y = w(1)*x1+w(2)*x2+w0;
-
-hold on;
-contour(x1,x2,y,1);
-hold off;
+binPlot(model,X,t)
+pause
 %%
-% clear; close all;
-% k = 3;
-% n = 200;
-% [X,t] = rndKCluster(2,k,n);
-% 
-% [x1,x2] = meshgrid(linspace(min(X(1,:)),max(X(1,:)),n), linspace(min(X(2,:)),max(X(2,:)),n));
-% [model, llh] = mnReg(X,t, 1e-4,2);
-% plot(llh);
-% figure;
-% spread(X,t);
-% 
-% W = model.W;
-% % y = w(1)*x1+w(2)*x2+w(3);
-% 
-% hold on;
-% contour(x1,x2,t,1);
-% hold off;
+clear
+k = 3;
+n = 1000;
+[X,t] = kmeansRnd(2,k,n);
+[model, llh] = mnReg(X,t);
+y = mnPred(model,X);
+spread(X,y)

chapter06/knCenterize.m

@@ -2,7 +2,7 @@
 % Centerize the data in the kernel space
 %   kn: kernel function
 %   X: dxn data matrix of which the center is computed
-%   Xt(option): dxn test data to be centerized by the center of X
+%   Xt(optional): dxn test data to be centerized by the center of X
 % Written by Mo Chen ([email protected]).
 K = kn(X,X);
 mK = mean(K);

chapter06/knKmeans.m

@@ -0,0 +1,25 @@
+function [label, energy, model] = knKmeans(X, k, kn)
+% Perform kernel k-means clustering.
+%   K: nxn kernel matrix
+%   k: number of cluster
+% Reference: Kernel Methods for Pattern Analysis
+% by John Shawe-Taylor, Nello Cristianini
+% Written by Mo Chen ([email protected]).
+K = kn(X,X);
+n = size(X,2);
+label = ceil(k*rand(1,n));
+last = 0;
+while any(label ~= last)
+    E = sparse(label,1:n,1,k,n,n);
+    E = bsxfun(@times,E,1./sum(E,2));
+    T = E*K;
+    Z = repmat(diag(T*E'),1,n)-2*T;
+    last = label;
+    [val, label] = min(Z,[],1);
+end
+energy = sum(val)+trace(K);
+if nargout == 3
+    model.X = X;
+    model.kn = kn;
+    model.label = label;
+end

chapter06/knKmeansPred.m

@@ -0,0 +1,7 @@
+function [ output_args ] = knKmeansPred( input_args )
+%KNKMEANSPRED Summary of this function goes here
+%   Detailed explanation goes here
+
+
+end
+

chapter06/knRegPred.m

@@ -0,0 +1,26 @@
+function [y, sigma, p] = knRegPred(model, x, t)
+% Prediction for kernel regression model
+% Written by Mo Chen ([email protected]).
+kn = model.kn;
+a = model.a;
+X = model.X;
+tbar = model.tbar;
+y = a'*knCenterize(kn,X,x)+tbar;
+if nargin == 3
+    sigma = sqrt(1/beta+dot(X,X,1));   % 3.59
+    p = exp(((t-y).^2/sigma2+log(2*pi*sigma2))/(-2));
+end
+
+% if nargout > 1
+%     beta = model.beta;
+%     if isfield(model,'V')   % V*V'=inv(S) 3.54
+%         U = model.V'*bsxfun(@minus,X,model.xbar);
+%         sigma = sqrt(1/beta+dot(U,U,1));   % 3.59
+%     else
+%         sigma = sqrt(1/beta);
+%     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
+% end