dualpca2test.m.svn-base

a function inside machine learning

% A script to test dualPCA2 code 
rand('state',0); 

numTrainExamples = 1000; 
numTestExamples = 1000; 
numFeatures = 20; 

trainX = rand(numTrainExamples, numFeatures);
trainY = sign(rand(numTrainExamples, 1)-0.5);
testX = rand(numTestExamples, numFeatures); 
testY = sign(rand(numTestExamples, 1)-0.5);

[trainX, testX] = centerData(trainX, testX); 
[trainX, testX] = normalise(trainX, testX); 

T = 10; 

params.iterations = T; 
params.kernelFunctionName = 'linearKernel'; 
params.kernelParams = 0; 
params.chunkSize = 100; 

params2.iterations = T; 
params2.projectionDirection = {'maxVariance'}; 

[newTrainX, newTestX, subspaceInfo] = dualPCA2(trainX, trainY, testX, params); 
[newTrainX2, newTestX2, subspaceInfo2] = primalGeneralFeaturesSubspace(trainX, trainY, testX, params2); 

%Note that some directions are flipped in KPCA and PCA, so 
norm(abs(newTrainX) - abs(newTrainX2))
norm(abs(newTestX) - abs(newTestX2))