perceptron_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 perceptron learning 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] = ...
%           Perceptron_VC(trainFeatures, trainLabels,algParam ,testFeatures, testLabels)
% where
%
% Inputs:
% 	trainFeatures  - the training set vectors, one vector per column
%	trainLabels    - the labels of the above
%       algParam       - algorithm parameters
%	                  Number of iterations, or weights vector, or [weights, number of iterations]
%       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] = ...
    Perceptron_VC(trainFeatures, trainLabels,algoParams,testFeatures, testLabels)

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

if (Nclasses > 2)
  [trainError, testError] = classifierWrapper(trainFeatures, trainLabels, ...
      'Perceptron_VCcore', algoParams, 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] = Perceptron_VCcore(trainFeatures, trainLabels, algoParams, testFeatures, testLabels);
end

trainError = reshape(trainError,length(trainError),1);
testError  = reshape(testError,length(testError),1);