Latest snapshot failed to build with VS 2013 Express

[XiaoboGu]

The major problem is regarding to array size in the GRecommander.cpp such as

    double features[m_pUsers->cols() - m_intrinsicDims + 1];

Error 14 error C2057: expected constant expression d:\amber\wrokspace\waffles\waffles-code-0f8e1ebd0992c6f12b4fc1f8438a9acc7aaee049\src\gclasses\grecommender.cpp 1396 1 GClasses

[XiaoboGu]

the following version works, just a work around

double GHybridNonlinearPCA::predict(size_t item, size_t user)
{
//The user and item are reversed (the item is the user and user is the item)
//If the user is new
if(user >= m_pUsers->rows() || item >= m_items)
return 0.0;

//If the item has not yet been rated but has item features
if(m_itemSet.find(user) == m_itemSet.end())
{
    //find closest instances
    double* pVec = m_pUsers->row(user);

    //double features[m_pUsers->cols() - m_intrinsicDims + 1];

    double* features = new double(m_pUsers->cols() - m_intrinsicDims + 1);

    features[0] = 0;
    for(size_t i = m_intrinsicDims; i < m_pUsers->cols(); i++)
    {
        features[i-m_intrinsicDims+1] = pVec[i] / 0.01;
    }
    //size_t neighbors[m_numNeighbors];
    size_t* neighbors = new size_t[m_numNeighbors];

    //double distances[m_numNeighbors];
    double* distances = new double[m_numNeighbors];

    m_neighbors->neighbors(neighbors, distances, features);

    //sort the neighbors based on distance
    m_neighbors->sortNeighbors(m_numNeighbors, neighbors, distances);

/// Commentted out portion is for returning a weighted average instead of a weighted mode
// double sum =0;
// double denom = 0;

    size_t counts [11];
    GIndexVec::setAll(counts, 0, 11);
    size_t i = 0;
    while(distances[i] == 0 && i < m_numNeighbors)
    {
        size_t neighbor = m_itemMap[neighbors[i]];
        if(m_pRatingCount[neighbor] > 50)
        {
            double prediction = round(((m_pMaxs[item] - m_pMins[item]) * m_pModel->forwardPropSingleOutput(m_pUsers->row(neighbor), item) + m_pMins[item])* 2.0) / 2.0;

// double prediction = (m_pMaxs[item] - m_pMins[item]) * m_pModel->forwardPropSingleOutput(m_pUsers->row(neighbor), item) + m_pMins[item];
if(prediction > m_pMaxs[item])
prediction = m_pMaxs[item];
if(prediction < m_pMins[item])
prediction = m_pMins[item];
counts[(size_t) (prediction * 2)] += m_pRatingCount[neighbor];
}
i++;
/
if(m_pRatingCount[neighbor] > 50)
{
sum+=prediction * m_pRatingCount[neighbor];
denom+=m_pRatingCount[neighbor];
}
/
}
// return sum / denom;

    delete features;
    delete neighbors;
    delete distances;


    size_t predMode = 0;
    for(size_t i = 1; i < 11; i++)
    {
        if(counts[i] > counts[predMode])
            predMode = i;
    }
    return predMode / 2.0;
}

return (m_pMaxs[item] - m_pMins[item]) * m_pModel->forwardPropSingleOutput(m_pUsers->row(user), item) + m_pMins[item];

}

[Mike Gashler]

Thank you! I have integrated these fixes into the tree. (I modified it slightly to use GTEMPBUF, which uses alloca, because it is faster than allocating on the heap.)