Lines Matching refs:params
878 params = _params;
879 if( params.boost_type != DISCRETE && params.boost_type != REAL &&
880 params.boost_type != LOGIT && params.boost_type != GENTLE )
883 params.weak_count = MAX( params.weak_count, 1 );
884 params.weight_trim_rate = MAX( params.weight_trim_rate, 0. );
885 params.weight_trim_rate = MIN( params.weight_trim_rate, 1. );
886 if( params.weight_trim_rate < FLT_EPSILON )
887 params.weight_trim_rate = 1.f;
889 if( params.boost_type == DISCRETE &&
890 params.split_criteria != GINI && params.split_criteria != MISCLASS )
891 params.split_criteria = MISCLASS;
892 if( params.boost_type == REAL &&
893 params.split_criteria != GINI && params.split_criteria != MISCLASS )
894 params.split_criteria = GINI;
895 if( (params.boost_type == LOGIT || params.boost_type == GENTLE) &&
896 params.split_criteria != SQERR )
897 params.split_criteria = SQERR;
946 for( i = 0; i < params.weak_count; i++ )
1027 if( params.boost_type == LOGIT )
1041 else if( params.boost_type == GENTLE )
1080 if( params.boost_type == DISCRETE )
1114 else if( params.boost_type == REAL )
1132 else if( params.boost_type == LOGIT )
1184 assert( params.boost_type == GENTLE );
1225 if( params.weight_trim_rate <= 0. || params.weight_trim_rate >= 1. )
1235 sum = 1. - params.weight_trim_rate;
1450 params.boost_type == DISCRETE ? "DiscreteAdaboost" :
1451 params.boost_type == REAL ? "RealAdaboost" :
1452 params.boost_type == LOGIT ? "LogitBoost" :
1453 params.boost_type == GENTLE ? "GentleAdaboost" : 0;
1456 params.split_criteria == DEFAULT ? "Default" :
1457 params.split_criteria == GINI ? "Gini" :
1458 params.boost_type == MISCLASS ? "Misclassification" :
1459 params.boost_type == SQERR ? "SquaredErr" : 0;
1464 cvWriteInt( fs, "boosting_type", params.boost_type );
1469 cvWriteInt( fs, "splitting_criteria", params.split_criteria );
1471 cvWriteInt( fs, "ntrees", params.weak_count );
1472 cvWriteReal( fs, "weight_trimming_rate", params.weight_trim_rate );
1495 params.max_depth = data->params.max_depth;
1496 params.min_sample_count = data->params.min_sample_count;
1497 params.max_categories = data->params.max_categories;
1498 params.priors = data->params.priors;
1499 params.regression_accuracy = data->params.regression_accuracy;
1500 params.use_surrogates = data->params.use_surrogates;
1509 params.boost_type = strcmp( boost_type_str, "DiscreteAdaboost" ) == 0 ? DISCRETE :
1515 params.boost_type = cvReadInt( temp, -1 );
1517 if( params.boost_type < DISCRETE || params.boost_type > GENTLE )
1524 params.split_criteria = strcmp( split_crit_str, "Default" ) == 0 ? DEFAULT :
1530 params.split_criteria = cvReadInt( temp, -1 );
1532 if( params.split_criteria < DEFAULT || params.boost_type > SQERR )
1535 params.weak_count = cvReadIntByName( fs, fnode, "ntrees" );
1536 params.weight_trim_rate = cvReadRealByName( fs, fnode, "weight_trimming_rate", 0. );
1568 if( ntrees != params.weak_count )
1647 return params;
