hoeffding_tree.cpp

#include <sicstus/sicstus.h>
/* ex_glue.h is generated by splfr from the foreign/[2,3] facts.
   Always include the glue header in your foreign resource code.
*/
#include "hoeffding_tree_glue.h"
#include <mlpack/methods/hoeffding_trees/hoeffding_tree_model.hpp>
#include <mlpack/core.hpp>

// including helper functions for converting between arma structures and arrays
#include "../../helper_files/helper.hpp"

// some of the most used namespaces
using namespace arma;
using namespace mlpack;
using namespace std;
using namespace mlpack::tree;

// Global Variable of the HoeffdingTreeModel object so it can be accessed from all functions
HoeffdingTreeModel hoeffdingTreeObj;

// TODO: 
// input:   const TreeType & 	            type = GINI_HOEFFDING,
//          const arma::mat & 	            dataset,
//          const data::DatasetInfo & 	    datasetInfo,
//          const arma::Row< size_t > & 	labels,
//          const size_t 	                numClasses,
//          const bool 	                    batchTraining,
//          const double 	                successProbability,
//          const size_t 	                maxSamples,
//          const size_t 	                checkInterval,
//          const size_t 	                minSamples,
//          const size_t 	                bins,
//          const size_t 	                observationsBeforeBinning
// output: 
// description: 
void initAndBuildModel(char const *treeType,
                        float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
                        float *labelsArr, SP_integer labelsArrSize,
                        SP_integer numClasses, SP_integer batchTraining, double successProbability, SP_integer maxSamples, SP_integer checkInterval, SP_integer minSamples, SP_integer bins, SP_integer observationsBeforeBinning)
{
    // convert the Prolog arrays to arma::mat
    mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);

    // convert the Prolog arrays to arma::rowvec
    Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);

    if (strcmp(treeType, "gini-hoeffding") == 0)
        hoeffdingTreeObj = HoeffdingTreeModel(HoeffdingTreeModel::GINI_HOEFFDING);
    else if (strcmp(treeType, "gini-binary") == 0)
        hoeffdingTreeObj = HoeffdingTreeModel(HoeffdingTreeModel::GINI_BINARY);
    else if (strcmp(treeType, "info-hoeffding") == 0)
        hoeffdingTreeObj = HoeffdingTreeModel(HoeffdingTreeModel::INFO_HOEFFDING);
    else if (strcmp(treeType, "info-binary") == 0)
        hoeffdingTreeObj = HoeffdingTreeModel(HoeffdingTreeModel::INFO_BINARY);
    else
        cout << "wrong treeType input" << endl;
    
    hoeffdingTreeObj.BuildModel(data, data::DatasetInfo(data.n_rows), labelsVector, numClasses, (batchTraining == 1), successProbability, maxSamples, checkInterval, minSamples, bins, observationsBeforeBinning);
}

// TODO: 
// input:   const arma::mat & 	dataset,
//          arma::Row< size_t > & 	predictions <-,
//          arma::rowvec & 	probabilities <-
// output: 
// description: 
void classify(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
                float **predictArr, SP_integer *predictArrSize,
                float **probsArr, SP_integer *probsArrSize)
{
    // convert the Prolog arrays to arma::mat
    mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);

    // create the ReturnVector
    Row< size_t > predictReturnVector;
    
    // create the ReturnVector
    rowvec probsReturnVector;


    hoeffdingTreeObj.Classify(data, predictReturnVector, probsReturnVector);

    
    // return the Vector
    returnVectorInformation(predictReturnVector, predictArr, predictArrSize);

    // return the Vector
    returnVectorInformation(probsReturnVector, probsArr, probsArrSize);
}

// TODO: 
// input:   const arma::mat & 	dataset,
//          const arma::Row< size_t > & 	labels,
//          const bool 	batchTraining
// output: 
// description: 
void train(float *dataMatArr, SP_integer dataMatSize, SP_integer dataMatRowNum,
            float *labelsArr, SP_integer labelsArrSize,
            SP_integer batchTraining)
{
    // convert the Prolog arrays to arma::mat
    mat data = convertArrayToMat(dataMatArr, dataMatSize, dataMatRowNum);
    
    // convert the Prolog arrays to arma::rowvec
    Row< size_t > labelsVector = convertArrayToVec(labelsArr, labelsArrSize);

    hoeffdingTreeObj.Train(data, labelsVector, (batchTraining == 1));
}