adaboost.zip_Adaboost分类_分类器_多个弱分类器_组合分类器_组合成强分类器

function [X, z, mu] = kmeansRnd(d, k, n) % Generate samples from a Gaussian mixture distribution with common variances (kmeans model). % Input: % d: dimension of data % k: number of components % n: number of data % Output: % X: d x n data matrix % z: 1 x n response variable % mu: d x k centers of clusters % Written by Mo Chen (sth4nth@gmail.com). alpha = 1; beta = nthroot(k,d); % in volume x^d there is k points: x^d=k X = randn(d,n); w = dirichletRnd(alpha,ones(1,k)/k); z = discreteRnd(w,n); E = full(sparse(z,1:n,1,k,n,n)); mu = randn(d,k)*beta; X = X+mu*E; function x = dirichletRnd(a, m) % Generate samples from a Dirichlet distribution. % Input: % a: k dimensional vector % m: k dimensional mean vector % Outpet: % x: generated sample x~Dir(a,m) % Written by Mo Chen (sth4nth@gmail.com). if nargin == 2 a = a*m; end x = gamrnd(a,1); x = x/sum(x); function x = discreteRnd(p, n) % Generate samples from a discrete distribution (multinomial). % Input: % p: k dimensional probability vector % n: number of samples % Ouput: % x: k x n generated samples x~Mul(p) % Written by Mo Chen (sth4nth@gmail.com). if nargin == 1 n = 1; end r = rand(1,n); p = cumsum(p(:)); [~,x] = histc(r,[0;p/p(end)]);