ls_vc.m

最新的模式识别分类工具箱,希望对朋友们有用!

% Learns classifier and classifies test set
%  using the least-squares algorithm
%
%  If there are 2 classes, there is no problem (except that the labels are
%     converted to 0 and 1).
%  If there are more than 2 classes, pairwise classification is made,
%  followed by majority vote.
%
% Inputs:
% Usage
%      [trainError, testError, estTrainLabels, estTestLabels] = ...
%           LVQ1_VC(trainFeatures, trainLabels,Nmu ,testFeatures, testLabels)
% where
%
% Inputs:
% 	trainFeatures	- the training set vectors, one vector per column
%	trainLabels    - the labels of the above
%       Nmu             - Number of centroids
%       testFeatures   - test set, one column per vector
%       testLabels     - labels  for test set%
% Outputs
%	trainError     - the error rate on the training set (one entry per
%	                   class + total error)
%	testError      - the error rate on the test set (one entry per class
%	                  + total error)
%       estTrainLabels - the labels produced by the algorithm for the
%                          training samples
%       estTestLabels - the labels produced by the algorithm for the
%                          test samples

function [trainError, testError,estTrainLabels, estTestLabels] = ...
    LS_VC(trainFeatures, trainLabels, weights, testFeatures, testLabels)

[Nclasses, classes]  = find_classes([trainLabels(:);testLabels(:)]);
%  Number of classes in labels

hm = findobj('Tag', 'Messages'); 
fprintf('Least Squares Algorithm\n');
if (isempty(hm)==0)
  s = sprintf('Least Squares Algorithm\n');
  set(hm,'String',s);
  refresh;
  pause(.1);
end

if (Nclasses > 2)
  [trainError, testError] = classifierWrapper(trainFeatures, trainLabels, ...
      'LS_VCcore', weights, testFeatures, testLabels);
else
  if (max(classes) > 1 | min(classes) <0)
    ind0tr = find(trainLabels == min(classes));
    ind0te = find(testLabels  == min(classes));
    ind1tr = find(trainLabels == max(classes));
    ind1te = find(testLabels  == max(classes));
    trainLabels(ind0tr) = 0;
    trainLabels(ind1tr) = 1;
    testLabels(ind0te)  = 0;
    testLabels(ind1te)  = 1;
  end

  [trainError, testError] = LS_VCcore(trainFeatures, trainLabels, weights, testFeatures, testLabels);
end