inputselectexhaustive.m

一个关于数据聚类和模式识别的程序,在生物化学,化学中因该都可以用到.希望对大家有用,谢谢支持

function [bestSelectedInput, bestRecogRate, allSelectedInput, allRecogRate, elapsedTime] = inputSelectExhaustive(DS, inputNum, classifier, param, plotOpt)
% inputSelectExhaustive: Input selection via sequential forward selection using leave-one-out
%	Usage: [bestSelectedInput, allSelectedInput, allRecogRate, elapsedTime] = inputSelectExhaustive(DS, inputNum, classifier, param, plotOpt)
%
%	Input:
%		DS: design set
%		inputNum: up to inputNum inputs are selected
%		classifier: classifier for input selection
%		param: parameters for classifier
%		plotOpt: 0 for not plotting (default: 1)
%	Output:
%		bestSelectedInput: overall selected input index
%		bestRecogRate: recognition rate based on the final selected input
%		allSelectedInput: all selected input during the process 
%		allRecogRate: all recognition rate 
%		elapseTime: elapsed time

%	Roger Jang, 19971227, 20041102

if nargin<1, selfdemo; return; end
[dim, dataNum]=size(DS.input);
if nargin<2, inputNum=dim; end
if nargin<3, classifier='knnrLoo'; end
if nargin<4, param=1; end
if nargin<5, plotOpt=1; end

DS.output=classConvert(DS.output);	% Convert the output to be intergers from 1 to classNum
inputName=DS.inputName;

t0=clock;
% Construct all input indices for possible models
allSelectedInput={};
for i=1:inputNum
	x=combine(1:dim, i);
	thisInputIndex=mat2cell(x, ones(1,size(x,1)), size(x,2));
	allSelectedInput={allSelectedInput{:}, thisInputIndex{:}};
end

modelNum=length(allSelectedInput);
fprintf('\nConstruct %d KNN models, each with up to %d inputs selected from %d candidates...\n', modelNum, inputNum, dim);
for i=1:modelNum
	DS2=DS;
	DS2.input=DS.input(allSelectedInput{i}, :);
    	allRecogRate(i) = feval(classifier, DS2, param);
    	fprintf('modelIndex %d/%d: %s --> Recognition rate = %f%%\n', i, modelNum, inputNameList(allSelectedInput{i}, inputName), allRecogRate(i)*100);
end

[bestRecogRate, b] = max(allRecogRate);
bestSelectedInput = allSelectedInput{b};
fprintf('\nOverall max recognition rate = %.1f%%.\n', bestRecogRate*100);
fprintf('Overall selected inputs: %s\n', inputNameList(bestSelectedInput, inputName));
elapsedTime=etime(clock, t0);

if plotOpt
	inputSelectPlot(allRecogRate*100, allSelectedInput, inputName, mfilename);
end

% ====== Subfunction
function out = combine(obj, n)
%	out = combine(obj, n) returns combinations of obj with n distinct
%	elements.
%	For instance: combine([1 2 3 4 5], 2) or combine('abcde', 3).

%	Roger Jang, Sept-21-1996

if n>length(obj)
	out=[];
	return;
end
if n==1
	out = obj(:);
	return;
end
if n==length(obj)
	out = obj(:)';
	return;
end

out = [];
for i = 1:length(obj)-1,
	first = obj(i);
	tail = obj(i+1:end);
	tail_combinat = combine(tail, n-1);
	loop_out = [first*ones(size(tail_combinat,1), 1), tail_combinat]; 
	out = [out; loop_out];
end

% ====== Self demo
function selfdemo
DS=prData('random6');
feval(mfilename, DS);