Lines Matching refs:params
87 params = _params;
89 if( params.max_categories < 2 )
90 CV_ERROR( CV_StsOutOfRange, "params.max_categories should be >= 2" );
91 params.max_categories = MIN( params.max_categories, 15 );
93 if( params.max_depth < 0 )
94 CV_ERROR( CV_StsOutOfRange, "params.max_depth should be >= 0" );
95 params.max_depth = MIN( params.max_depth, 25 );
97 params.min_sample_count = MAX(params.min_sample_count,1);
99 if( params.cv_folds < 0 )
101 "params.cv_folds should be =0 (the tree is not pruned) "
104 if( params.cv_folds == 1 )
105 params.cv_folds = 0;
107 if( params.regression_accuracy < 0 )
108 CV_ERROR( CV_StsOutOfRange, "params.regression_accuracy should be >= 0" );
250 cv_n = params.cv_folds;
286 if( sample_count < cv_n*MAX(params.min_sample_count,10) )
508 have_priors = is_classifier && params.priors;
516 double val = have_priors ? params.priors[i] : 1.;
824 if( params.cv_folds > 0 && cv_heap )
826 int cv_n = params.cv_folds;
1017 cvWriteInt( fs, "use_surrogates", params.use_surrogates ? 1 : 0 );
1021 cvWriteInt( fs, "max_categories", params.max_categories );
1025 cvWriteReal( fs, "regression_accuracy", params.regression_accuracy );
1028 cvWriteInt( fs, "max_depth", params.max_depth );
1029 cvWriteInt( fs, "min_sample_count", params.min_sample_count );
1030 cvWriteInt( fs, "cross_validation_folds", params.cv_folds );
1032 if( params.cv_folds > 1 )
1034 cvWriteInt( fs, "use_1se_rule", params
1035 cvWriteInt( fs, "truncate_pruned_tree", params.truncate_pruned_tree ? 1 : 0 );
1084 params.use_surrogates = cvReadIntByName( fs, tparams_node, "use_surrogates", 1 ) != 0;
1088 params.max_categories = cvReadIntByName( fs, tparams_node, "max_categories" );
1092 params.regression_accuracy =
1096 params.max_depth = cvReadIntByName( fs, tparams_node, "max_depth" );
1097 params.min_sample_count = cvReadIntByName( fs, tparams_node, "min_sample_count" );
1098 params.cv_folds = cvReadIntByName( fs, tparams_node, "cross_validation_folds" );
1100 if( params.cv_folds > 1 )
1102 params.use_1se_rule = cvReadIntByName( fs, tparams_node, "use_1se_rule" ) != 0;
1103 params.truncate_pruned_tree =
1320 if( data->params.cv_folds > 0 )
1343 if( node->sample_count <= data->params.min_sample_count ||
1344 node->depth >= data->params.max_depth )
1360 if( sqrt(node->node_risk)/n < data->params.regression_accuracy )
1379 if( data->params.use_surrogates )
1792 if( mi > data->params.max_categories )
1794 mi = MIN(data->params.max_categories, n);
2256 int i, j, k, n = node->sample_count, cv_n = data->params.cv_folds;
2443 if( nz && data->params.use_surrogates )
2546 split_input_data = node->depth + 1 < data->params.max_depth &&
2547 (node->left->sample_count > data->params.min_sample_count ||
2548 node->right->sample_count > data->params.min_sample_count);
2651 int ti, j, tree_count = 0, cv_n = data->params.cv_folds, n = root->sample_count;
2653 bool use_1se = data->params.use_1se_rule != 0 && data->is_classifier;
2727 free_prune_data(data->params.truncate_pruned_tree != 0);
