removed knRegPlot

chapter03/demo.m

@@ -15,7 +15,7 @@
 plot(llh);
 [y,sigma] = linRegPred(model,x,t);
 figure
-plotCurveBar( x, y, sigma );
+plotCurveBar(x,y,sigma);
 hold on;
 plot(x,t,'o');
 hold off;
@@ -24,7 +24,7 @@
 plot(llh);
 [y,sigma] = linRegPred(model,x,t);
 figure
-plotCurveBar( x, y, sigma );
+plotCurveBar(x,y,sigma);
 hold on;
 plot(x,t,'o');
 hold off;

chapter06/demo.m

@@ -7,71 +7,74 @@
 x = linspace(0,2*pi,n);   % test data
 t = sin(x)+rand(1,n)/2;
 model = knReg(x,t,1e-4,@knGauss);
-knRegPlot(model,x,t);
-
+[y,s] = knRegPred(model,x);
+plotCurveBar(x,y,s);
+hold on;
+plot(x,t,'o');
+hold off;
 %% Kernel regression with linear kernel is EQUIVALENT to linear regression
-lambda = 1e-4;
-model_kn = knReg(x,t,lambda,@knLin);
-model_lin = linReg(x,t,lambda);
-
-idx = 1:2:n;
-xt = x(:,idx);
-tt = t(idx);
-
-[y_kn, sigma_kn,p_kn] = knRegPred(model_kn,xt,tt);
-[y_lin, sigma_lin,p_lin] = linRegPred(model_lin,xt,tt);
-
-maxdiff(y_kn,y_lin)
-maxdiff(sigma_kn,sigma_lin)
-maxdiff(p_kn,p_lin)
-%% Kernel kmeans with linear kernel is EQUIVALENT to kmeans
-clear; close all;
-d = 2;
-k = 3;
-n = 500;
-[X,y] = kmeansRnd(d,k,n);
-init = ceil(k*rand(1,n));
-[y_kn,en_kn,model_kn] = knKmeans(X,init,@knLin);
-[y_lin,en_lin,model_lin] = kmeans(X,init);
-
-idx = 1:2:n;
-Xt = X(:,idx);
-
-[t_kn,ent_kn] = knKmeansPred(model_kn, Xt);
-[t_lin,ent_lin] = kmeansPred(model_lin, Xt);
-
-maxdiff(y_kn,y_lin)
-maxdiff(en_kn,en_lin)
-
-maxdiff(t_kn,t_lin)
-maxdiff(ent_kn,ent_lin)
-%% Kernel PCA with linear kernel is EQUIVALENT TO PCA
-clear; close all;
-d = 10;
-q = 2;
-n = 500;
-X = randn(d,n);
-
-
-model_kn = knPca(X,q,@knLin);
-idx = 1:2:n;
-Xt = X(:,idx);
-
-Y_kn = knPcaPred(model_kn,Xt);
-
-[U,L,mu,mse] = pca(X,q);
-Y_lin = U'*bsxfun(@minus,Xt,mu);   % projection
-
-
-R = Y_lin/Y_kn;    % the results are equivalent up to a rotation.
-maxdiff(R*R', eye(q))
-
-%% demo for knCenter
-clear; close all;
-kn = @knGauss;
-X=rand(2,100);
-X1=rand(2,10);
-X2=rand(2,5);
-
-maxdiff(knCenter(kn,X,X1),diag(knCenter(kn,X,X1,X1)))
-maxdiff(knCenter(kn,X),knCenter(kn,X,X,X))
+% lambda = 1e-4;
+% model_kn = knReg(x,t,lambda,@knLin);
+% model_lin = linReg(x,t,lambda);
+% 
+% idx = 1:2:n;
+% xt = x(:,idx);
+% tt = t(idx);
+% 
+% [y_kn, sigma_kn,p_kn] = knRegPred(model_kn,xt,tt);
+% [y_lin, sigma_lin,p_lin] = linRegPred(model_lin,xt,tt);
+% 
+% maxdiff(y_kn,y_lin)
+% maxdiff(sigma_kn,sigma_lin)
+% maxdiff(p_kn,p_lin)
+% %% Kernel kmeans with linear kernel is EQUIVALENT to kmeans
+% clear; close all;
+% d = 2;
+% k = 3;
+% n = 500;
+% [X,y] = kmeansRnd(d,k,n);
+% init = ceil(k*rand(1,n));
+% [y_kn,en_kn,model_kn] = knKmeans(X,init,@knLin);
+% [y_lin,en_lin,model_lin] = kmeans(X,init);
+% 
+% idx = 1:2:n;
+% Xt = X(:,idx);
+% 
+% [t_kn,ent_kn] = knKmeansPred(model_kn, Xt);
+% [t_lin,ent_lin] = kmeansPred(model_lin, Xt);
+% 
+% maxdiff(y_kn,y_lin)
+% maxdiff(en_kn,en_lin)
+% 
+% maxdiff(t_kn,t_lin)
+% maxdiff(ent_kn,ent_lin)
+% %% Kernel PCA with linear kernel is EQUIVALENT TO PCA
+% clear; close all;
+% d = 10;
+% q = 2;
+% n = 500;
+% X = randn(d,n);
+% 
+% 
+% model_kn = knPca(X,q,@knLin);
+% idx = 1:2:n;
+% Xt = X(:,idx);
+% 
+% Y_kn = knPcaPred(model_kn,Xt);
+% 
+% [U,L,mu,mse] = pca(X,q);
+% Y_lin = U'*bsxfun(@minus,Xt,mu);   % projection
+% 
+% 
+% R = Y_lin/Y_kn;    % the results are equivalent up to a rotation.
+% maxdiff(R*R', eye(q))
+% 
+% %% demo for knCenter
+% clear; close all;
+% kn = @knGauss;
+% X=rand(2,100);
+% X1=rand(2,10);
+% X2=rand(2,5);
+% 
+% maxdiff(knCenter(kn,X,X1),diag(knCenter(kn,X,X1,X1)))
+% maxdiff(knCenter(kn,X),knCenter(kn,X,X,X))

common/plotCurveBar.m

@@ -14,4 +14,5 @@ function plotCurveBar( x, y, sigma )
 hold on;
 plot(x,y,'r-');
 hold off
+axis([x(1),x(end),-inf,inf])