train_test_multiple_label.m

一个matlab的工具包,里面包括一些分类器 例如 KNN KMEAN SVM NETLAB 等等有很多.

% Input pararmeter: 
% D: data array, including the feature data and output class

function run = train_test_multiple_label(X, Y, trainindex, testindex, classifier)

global preprocess;

% The statistics of dataset
num_class = length(preprocess.ClassSet);
actual_num_class = length(preprocess.OrgClassSet);
class_set = preprocess.ClassSet;

for j = 1:actual_num_class
  X_train = X(trainindex, :);
  Y_train = Y(trainindex, j);
  X_test = X(testindex, :);
  Y_test = Y(testindex, j);

  [Y_compute, Y_prob] = Classify(classifier, X_train, Y_train, X_test, Y_test, num_class);
  run.Y_compute = Y_compute; run.Y_prob = Y_prob; run.Y_test = Y_test;  % Bugs for this version
  
  % Aggregate the predictions in a shot
  if (preprocess.ShotAvailable == 1), [Y_compute, Y_prob, Y_test] = AggregatePredByShot(Y_compute, Y_prob, Y_test, testindex, preprocess.ClassSet); end;  

  [run_class.yy(j), run_class.yn(j), run_class.ny(j), run_class.nn(j), run_class.prec(j), run_class.rec(j), run_class.F1(j),...
   run_class.err(j)] = CalculatePerformance(Y_compute, Y_test, class_set);
end  

run.Macro_Prec = sum(run_class.prec) / actual_num_class;
run.Macro_Rec = sum(run_class.rec) / actual_num_class;
run.Macro_F1 = NormalizeRatio(2 * run.Macro_Prec * run.Macro_Rec, run.Macro_Prec + run.Macro_Rec);
run.Micro_Prec = NormalizeRatio(sum(run_class.yy), sum(run_class.yy) + sum(run_class.ny)); 
run.Micro_Rec = NormalizeRatio(sum(run_class.yy), sum(run_class.yy) + sum(run_class.yn));  
run.Micro_F1 = NormalizeRatio(2 * run.Micro_Prec * run.Micro_Rec, run.Micro_Prec + run.Micro_Rec);
run.Err = sum(run_class.err) / actual_num_class;
fprintf('Micro_Prec:%f, Micro_Rec:%f, Micro_F1:%f, Err:%f\n', run.Micro_Prec, run.Micro_Rec, run.Micro_F1, run.Err);