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//
// abstractrandomforest.cpp
// Mothur
//
// Created by Sarah Westcott on 10/1/12.
// Copyright (c) 2012 Schloss Lab. All rights reserved.
//
#include "abstractrandomforest.hpp"
/***********************************************************************/
AbstractRandomForest::AbstractRandomForest(const std::vector < std::vector<int> > dataSet,
const int numDecisionTrees,
const string treeSplitCriterion = "informationGain")
: dataSet(dataSet),
numDecisionTrees(numDecisionTrees),
numSamples((int)dataSet.size()),
numFeatures((int)(dataSet[0].size() - 1)),
globalDiscardedFeatureIndices(getGlobalDiscardedFeatureIndices()),
globalVariableImportanceList(numFeatures, 0),
treeSplitCriterion(treeSplitCriterion) {
m = MothurOut::getInstance();
// TODO: double check if the implemenatation of 'globalOutOfBagEstimates' is correct
}
/***********************************************************************/
vector<int> AbstractRandomForest::getGlobalDiscardedFeatureIndices() {
try {
vector<int> globalDiscardedFeatureIndices;
// calculate feature vectors
vector< vector<int> > featureVectors(numFeatures, vector<int>(numSamples, 0));
for (int i = 0; i < numSamples; i++) {
if (m->control_pressed) { return globalDiscardedFeatureIndices; }
for (int j = 0; j < numFeatures; j++) { featureVectors[j][i] = dataSet[i][j]; }
}
for (int i = 0; i < featureVectors.size(); i++) {
if (m->control_pressed) { return globalDiscardedFeatureIndices; }
double standardDeviation = m->getStandardDeviation(featureVectors[i]);
if (standardDeviation <= 0){ globalDiscardedFeatureIndices.push_back(i); }
}
if (m->debug) {
m->mothurOut("number of global discarded features: " + toString(globalDiscardedFeatureIndices.size())+ "\n");
m->mothurOut("total features: " + toString(featureVectors.size())+ "\n");
}
return globalDiscardedFeatureIndices;
}
catch(exception& e) {
m->errorOut(e, "AbstractRandomForest", "getGlobalDiscardedFeatureIndices");
exit(1);
}
}
/***********************************************************************/
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