dualgreedypcaproject.m.svn-base

a function inside machine learning

function [testInfo, projectionInfo] = dualGreedyPCAProject(trainData, testData, subspaceInfo, params)
%Find the projections for a greedy PCA on a test set 

if (nargin ~= 4)
    fprintf('%s\n', help(sprintf('%s', mfilename)));
    error('Incorrect number of inputs - see above usage instructions.');
end

[nameX, nameY] = getSpaceNames(params); 
[numTestExamples, numFeatures] = getDataFieldSize(testData, nameX);

%Store parameters
T = params.iterations; 
kernelFunction = char(params.X.kernel.name); 
kernelParams = params.X.kernel.params; 

%Now compute projections on the test data 
testK = feval(kernelFunction, getDataFieldValue(testData, nameX), getDataFieldValue(trainData, nameX), kernelParams);
predictedX = zeros(numTestExamples, T); 

%Is this just the same as predictedX = testK*subspaceInfo.b ? 
for i=1:T
	predictedX(:, i) = testK*subspaceInfo.b(:, i); 
    testK = testK - testK*subspaceInfo.b(:, i)*subspaceInfo.tau(:, i)'; 
end 

testInfo.data = data; 
testInfo.data = addDataField(testInfo.data, 'X', predictedX, 'examples'); 

projectionInfo = struct;