add pathdump
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@@ -179,6 +179,25 @@ namespace xgboost{
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boosters[i]->DumpModel( fo );
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}
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}
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/*!
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* \brief Dump path of all trees
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* \param fo text file
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* \param data input data
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*/
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inline void DumpPath( FILE *fo, const FMatrixS &data ){
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for( size_t i = 0; i < data.NumRow(); ++ i ){
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for( size_t j = 0; j < boosters.size(); ++ j ){
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if( j != 0 ) fprintf( fo, "\t" );
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std::vector<int> path;
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boosters[j]->PredPath( path, data[i] );
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fprintf( fo, "%d", path[0] );
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for( size_t k = 1; k < path.size(); ++ k ){
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fprintf( fo, ",%d", path[k] );
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}
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}
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fprintf( fo, "\n" );
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}
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}
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public:
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/*!
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* \brief do gradient boost training for one step, using the information given
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@@ -195,7 +214,7 @@ namespace xgboost{
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const std::vector<unsigned> &root_index ) {
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booster::IBooster *bst = this->GetUpdateBooster();
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bst->DoBoost( grad, hess, feats, root_index );
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}
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}
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/*!
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* \brief predict values for given sparse feature vector
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* NOTE: in tree implementation, this is not threadsafe
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@@ -204,7 +223,7 @@ namespace xgboost{
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* \param rid root id of current instance, default = 0
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* \return prediction
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*/
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virtual float Predict( const booster::FMatrixS::Line &feat, int buffer_index = -1, unsigned rid = 0 ){
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virtual float Predict( const booster::FMatrixS::Line &feat, int buffer_index = -1, unsigned rid = 0 ){
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size_t istart = 0;
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float psum = 0.0f;
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