C++ (Cpp) TreeBoosterType::loadFromMatlab Examples

Example #1

File: SQBTrees.cpp Project: kundajelab/multitaskBoosting

void mexFunctionTest(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
    if (nrhs < 2 || nrhs > 3)
    {
        mexPrintf("Usage: [score] = SQBTreesPredict( model, feats, [maxIters] )\n");
        mexPrintf("\tfeats must be of type SINGLE\n");
        mexPrintf("\tmaxIters is optional and limits the amount of weak learners evaluated for classification.");
        mexErrMsgTxt("Incorrect input format.\n");
    }

    if (nlhs != 1)
        mexErrMsgTxt("Two output args expected");

#define mFeats (prhs[1])
#define mModel (prhs[0])
#define mMaxIters (prhs[2])

    MatlabInputMatrix<FeatsType> pFeats( mFeats, 0, 0, "feats" );

    //const unsigned maxIters = pMaxIters.data()[0];

    TreeBoosterType TB;

    // load model
    TB.loadFromMatlab( mModel );

    unsigned maxIters = TB.numWeakLearners();
    if (nrhs >= 3)
    {
        MatlabInputMatrix<unsigned int> pMaxIters( mMaxIters, 1, 1, "maxiters" );
        unsigned inputMaxIters = pMaxIters.data()[0];

        if (inputMaxIters <= 0)
            mexErrMsgTxt("maxIters must be higher than zero.");

        if (inputMaxIters > maxIters)
            mexPrintf("-- WARNING: maxIters is greater than the number of weaklearners used!\n");
        else
        {
            maxIters = inputMaxIters;
            mexPrintf("Limiting number of weak learners to %d\n", (int)maxIters);
        }
    }

    // for now just copy the values
    gFeatArrayType feats = Eigen::Map< const gFeatArrayType >( pFeats.data(), pFeats.rows(), pFeats.cols() );

    TreeBoosterType::ResponseArrayType newScores;
    TB.predict( TreeBoosterType::SampleListType(feats),
                TreeBoosterType::FeatureValueObjectType(feats),
                newScores,
                maxIters );

    MatlabOutputMatrix<double>   outMatrix( &plhs[0], feats.rows(), 1 );
    for (unsigned i=0; i < feats.rows(); i++)
        outMatrix.data()[i] = newScores.coeff(i);
}