dualsparsedoublefeatures.m.svn-base

a function inside machine learning

function  [newTrainX, newTrainY, newTestX, newTestY, subspaceInfo] = dualSparseDoubleFeatures(trainX, trainY, testX, testY, params)
%A function to compute CCA-type features using the PLS deflation 
%Deprecated: Use dualSparseGeneralFeaturesTrain instead  

if (nargin ~= 5)
    fprintf('%s\n', help(sprintf('%s', mfilename)));
    error('Incorrect number of inputs - see above usage instructions.');
end

if size(trainX, 2) ~= size(testX, 2) | size(trainY, 2) ~= size(testY, 2)
    error('Train and test sets must have same number of features\n'); 
end 

trainData = data; 
trainData = addDataField(trainData, 'X', trainX, 'examples'); 
trainData = addDataField(trainData, 'Y', trainY, 'examples'); 

testData = data; 
testData = addDataField(testData, 'X', testX, 'examples'); 
testData = addDataField(testData, 'Y', testY, 'examples'); 

[subspaceInfo, trainInfo] = dualSparseDoubleFeaturesTrain(trainData, params);
[testInfo, projectionInfo] = dualSparseDoubleFeaturesProject(trainData, testData, subspaceInfo, params);

newTrainX = getDataFieldValue(trainInfo.data, 'X'); 
newTrainY = getDataFieldValue(trainInfo.data, 'Y'); 
newTestX = getDataFieldValue(testInfo.data, 'X'); 
newTestY = getDataFieldValue(testInfo.data, 'Y'); 

subspaceInfo.projectionInfo = projectionInfo; 
subspaceInfo.XInfo = rmfield(subspaceInfo.X, 'Q'); 
subspaceInfo.YInfo = rmfield(subspaceInfo.Y, 'Q');