dualgeneralfeaturestest2.m

a function inside machine learning

%A script to improve the efficiency of dualGeneralFeatures 
clear;
rand('state',22); 

numExamples = 2000;
numFeatures = 500;
numLabels = 20; 
tol = 10^-5;

X = rand(numExamples, numFeatures);
Y = sign(rand(numExamples, numLabels)-0.5);

X = centerData(X);
X = normalise(X);

d = data; 
d = addDataField(d, 'X', X, 'examples'); 
d = addDataField(d, 'Y', Y, 'labels'); 

[trainData, testData] = splitData2(d, 2/3); 

T = 10; 

params.dualFeatureDirection = 'dualMaxCovariance';
params.iterations = T;
params.X.kernel = getDefaultLinearKernel;
params.normalise = 1;

[subspaceInfo, trainInfo] = dualGeneralFeaturesTrain(trainData, params);
[testInfo, projectionInfo] = dualGeneralFeaturesProject(trainData, testData, subspaceInfo, params);