dualgreedypcatest2.m.svn-base

a function inside machine learning

% A script to see if greedy KPCA is identicle to maxVar KPLS 
clear; 
rand('state',21); 

tol = 10^-7; 
numExamples = 15; 
numFeatures = 10; 
X = rand(numExamples, numFeatures); 
X = normalise(X); 

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

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

T = 5;
params.iterations = T; 
params.X.kernel = getDefaultLinearKernel; 

[subspaceInfo, trainInfo] = dualGreedyPCATrain(trainData, params); 
[testInfo, projectionInfo] = dualGreedyPCAProject(trainData, testData, subspaceInfo, params); 

gKPLSParams.iterations = T; 
gKPLSParams.doubleDeflation = 0; 
gKPLSParams.dualFeatureDirection = 'dualMaxSparseVariance'; 
gKPLSParams.X.kernel = getDefaultLinearKernel; 
gKPLSParams.Y.name = '';  %Ignore Y 
gKPLSParams.normalise = 0;

[subspaceInfo2, trainInfo2] = dualGeneralFeaturesTrain(trainData, gKPLSParams);
[testInfo2, projectionInfo2] = dualGeneralFeaturesProject(trainData, testData, subspaceInfo2, gKPLSParams); 


newTrainX = getDataFieldValue(trainInfo.data, 'X'); 
newTrainX2 = getDataFieldValue(trainInfo2.data, 'X');

norm(getDataFieldValue(trainInfo.data, 'X') - getDataFieldValue(trainInfo2.data, 'X'));