finished naive bayes gauss training

Author: sth4nth

chapter08/nbGauss.m

@@ -1,5 +1,6 @@
 function model = nbGauss(X, t)
-% Naive bayes classifier with indepenet Gauss
+% Naive bayes classifier with indepenet Gaussian, each dimension of data is
+% assumed from a 1d Gaussian distribution with independent mean and variance.
 % Input:
 %   X: d x n data matrix
 %   t: 1 x n label (1~k)
@@ -9,13 +10,12 @@
 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;
+nk = full(sum(E,2));
+w = nk/n;
+R = E'*spdiags(1./nk,0,k,k);
+mu = X*R;  
+var = X.^2*R-mu.^2;
 
-model.mu = mu;
-model.sigma = sigma;
-model.a = a;
+model.mu = mu;      % d x k means 
+model.var = var;  % d x k variances
+model.w = w;