nb not done

chapter08/demo.m

@@ -0,0 +1,10 @@
+% demo for ch08
+
+%% Naive Bayes with Gauss
+d = 2;
+k = 3;
+n = 1000;
+[X, t] = kmeansRnd(d,k,n);
+plotClass(X,t);
+
+model = nbGauss(X,t);

chapter08/nbGauss.m

@@ -0,0 +1,21 @@
+function model = nbGauss(X, t)
+% Naive bayes classifier with indepenet Gauss
+% Input:
+%   X: d x n data matrix
+%   t: 1 x n label (1~k)
+% Output:
+%   model: trained model structure
+% Written by Mo Chen ([email protected]).
+n = size(X,2);
+k = max(t);
+E = sparse(t,1:n,1,k,n,n);
+nk = sum(E,2);
+a = nk/n;
+z = spdiags(1./nk,0,k,k);
+mu = X*E'*z;  
+mm = bsxfun(@times,X*E',1./nk');
+sigma = sqdist(mu,X)*z;
+
+model.mu = mu;
+model.sigma = sigma;
+model.a = a;

chapter08/nbGaussPred.m

@@ -0,0 +1,14 @@
+function [y, R] = nbGaussPred(model, X)
+
+
+mu = model.mu;
+sigma = model.sigma;
+a = model.a;
+
+
+lambda = 1./sigma;
+ml = mu.*lambda;
+q = bsxfun(@plus,X'.^2*lambda-2*X'*ml,dot(mu,ml,1)); % M distance
+c = d*log(2*pi)+2*sum(log(sigma),1); % normalization constant
+R = -0.5*bsxfun(@plus,q,c);
+y = max(R,[],1);

chapter09/demo.m

@@ -1,71 +1,76 @@
 % demos for ch09
 
 %% Empirical Bayesian linear regression via EM
-close all; clear;
-d = 5;
-n = 200;
-[x,t] = linRnd(d,n);
-[model,llh] = linRegEm(x,t);
-plot(llh);
-
-%% RVM classification via EM
-clear; close all
-k = 2;
-d = 2;
-n = 1000;
-[X,t] = kmeansRnd(d,k,n);
-[x1,x2] = meshgrid(linspace(min(X(1,:)),max(X(1,:)),n), linspace(min(X(2,:)),max(X(2,:)),n));
-
-[model, llh] = rvmBinEm(X,t-1);
-plot(llh);
-y = rvmBinPred(model,X)+1;
-figure;
-binPlot(model,X,y);
+% close all; clear;
+% d = 5;
+% n = 200;
+% [x,t] = linRnd(d,n);
+% [model,llh] = linRegEm(x,t);
+% plot(llh);
+% 
+% %% RVM classification via EM
+% clear; close all
+% k = 2;
+% d = 2;
+% n = 1000;
+% [X,t] = kmeansRnd(d,k,n);
+% [x1,x2] = meshgrid(linspace(min(X(1,:)),max(X(1,:)),n), linspace(min(X(2,:)),max(X(2,:)),n));
+% 
+% [model, llh] = rvmBinEm(X,t-1);
+% plot(llh);
+% y = rvmBinPred(model,X)+1;
+% figure;
+% binPlot(model,X,y);
 %% kmeans 
 close all; clear;
-d = 2;
-k = 3;
-n = 500;
+d = 20;
+k = 6;
+n = 5000;
 [X,label] = kmeansRnd(d,k,n);
-y = kmeans(X,k);
+tic;
+y = kmeans_(X,k);
+toc
+tic
+y = kmeans(X',k);
+toc
 % y = kmedoids(X,k);
-plotClass(X,label);
-figure;
-plotClass(X,y);
+% plotClass(X,label);
+% figure;
+% plotClass(X,y);
 
 %% Gausssian Mixture via EM
-close all; clear;
-d = 2;
-k = 3;
-n = 1000;
-[X,label] = mixGaussRnd(d,k,n);
-plotClass(X,label);
-
-m = floor(n/2);
-X1 = X(:,1:m);
-X2 = X(:,(m+1):end);
-% train
-[z1,model,llh] = mixGaussEm(X1,k);
-figure;
-plot(llh);
-figure;
-plotClass(X1,z1);
-% predict
-z2 = mixGaussPred(X2,model);
-figure;
-plotClass(X2,z2);
-%% Gauss mixture initialized by kmeans
-close all; clear;
-d = 2;
-k = 3;
-n = 500;
-[X,label] = mixGaussRnd(d,k,n);
-init = kmeans(X,k);
-[z,model,llh] = mixGaussEm(X,init);
-plotClass(X,label);
-figure;
-plotClass(X,init);
-figure;
-plotClass(X,z);
-figure;
-plot(llh);
+% close all; clear;
+% d = 2;
+% k = 3;
+% n = 1000;
+% [X,label] = mixGaussRnd(d,k,n);
+% plotClass(X,label);
+% 
+% m = floor(n/2);
+% X1 = X(:,1:m);
+% X2 = X(:,(m+1):end);
+% % train
+% [z1,model,llh] = mixGaussEm(X1,k);
+% figure;
+% plot(llh);
+% figure;
+% plotClass(X1,z1);
+% % predict
+% z2 = mixGaussPred(X2,model);
+% figure;
+% plotClass(X2,z2);
+% %% Gauss mixture initialized by kmeans
+% close all; clear;
+% d = 2;
+% k = 3;
+% n = 500;
+% [X,label] = mixGaussRnd(d,k,n);
+% init = kmeans(X,k);
+% [z,model,llh] = mixGaussEm(X,init);
+% plotClass(X,label);
+% figure;
+% plotClass(X,init);
+% figure;
+% plotClass(X,z);
+% figure;
+% plot(llh);