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注册: 2011-11-21
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专业: 计算机软件

[求助] matlab求助

请问帮我解释一下这个程序的意思吧，希望能详细解释一下每句的意思！
function [res mi]=forward_sel_class(X,Y,k)
% Forward feature selection using the MI criterion.
% X is a dataset (n x d) with n samples and d features and Y is the class
% vector with classes denoted 1, 2 ... c.
[s1,s2]=size(X);
res = [];
remainingset = [1:size(X,2)];
for j=1:size(X,2)
j;
currscore=[];
for i=1:length(remainingset)
tempset = [res remainingset(i)];
tempscore=miv_opt(X(:,tempset),Y,k);
currscore(i,

=tempscore;
end
[winnerscore, winner] = max(currscore);
mi(i)=winnerscore;
res = [res remainingset(winner)];
remainingset(winner) = [];
end
function res = miv(X,Y,k)
if(size(Y,1) > size(Y,2))
Y = Y';
end
warning off
% Estimate mutual information between X ad Y with Y a discrete random variable
% and X a real random vector
% k must be smaller than the minimum number of points in a class.

load digammaMAT;
Y = 1 + Y - min(Y);
[N,d] = size(X);             % Sample size and dimension
C    = full(ind2vec(Y))'; % Matrix of binary class description
[N, L]  = size(C);             % Sample size and number of classes
cN    = sum(C);             % sample size of each class
for l=1:L, digammacN(l) = digammaMAT(cN(l));end
distmat = pairwisedistances(X,2);
epsilonallclasses = zeros(N,1);
epsilonsameclass = zeros(N,1);
for j = 1:N
% get vector of all distances
dist = distmat(j,

;
% sort this vector
[sorteddistances, distanceindices] = sort(dist);

% find the distance to the k th neighbour
epsilonallclasses(j) = 2*sorteddistances(k+1); % the first neighbour is the point itself
% find the distance to the k th neighbour in the same class
whichclass = Y(j);
mask = C(:,whichclass);
mask = mask(distanceindices);
sorteddistancesinsameclass = sorteddistances(find(mask));
epsilonsameclass(j) = 2*sorteddistancesinsameclass(k+1);
end
res = digammaMAT(N) - 1/N * sum(cN.*digammacN)+ d/N * sum((log(epsilonallclasses))-(log(epsilonsameclass)));
res=res/log(2);
% ajout
% if (isnan(res)==1)|(res==-Inf)|(res<0)
%    res=0;
% end
% if j is specified, output is the distance from all points in X to j
% otherwise, output is a full squared distance matrix with all distances
% between all points
function [res] = pairwisedistances(X, pp,j)
[n,p] = size(X);
if nargin < 3
%outClass = class(X);
Y = zeros(1,n*(n-1)./2, 'single');
k = 1;
for i = 1:n-1
      dsq = zeros(n-i,1,'single');
      for q = 1:p
         dsq = dsq + abs(X(i,q) - X((i+1):n,q)).^pp;
      end
      Y(k