"atomic bomb" commit. Reorganized OpenCV directory structure
This commit is contained in:
Vadim Pisarevsky
728
modules/ml/src/estimate.cpp
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728
modules/ml/src/estimate.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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#if 0
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ML_IMPL int
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icvCmpIntegers (const void* a, const void* b) {return *(const int*)a - *(const int*)b;}
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/****************************************************************************************\
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* Cross-validation algorithms realizations *
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\****************************************************************************************/
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// Return pointer to trainIdx. Function DOES NOT FILL this matrix!
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ML_IMPL
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const CvMat* cvCrossValGetTrainIdxMatrix (const CvStatModel* estimateModel)
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{
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CvMat* result = NULL;
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CV_FUNCNAME ("cvCrossValGetTrainIdxMatrix");
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__BEGIN__
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if (!CV_IS_CROSSVAL(estimateModel))
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{
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CV_ERROR (CV_StsBadArg, "Pointer point to not CvCrossValidationModel");
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}
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result = ((CvCrossValidationModel*)estimateModel)->sampleIdxTrain;
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__END__
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return result;
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} // End of cvCrossValGetTrainIdxMatrix
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/****************************************************************************************/
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// Return pointer to checkIdx. Function DOES NOT FILL this matrix!
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ML_IMPL
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const CvMat* cvCrossValGetCheckIdxMatrix (const CvStatModel* estimateModel)
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{
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CvMat* result = NULL;
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CV_FUNCNAME ("cvCrossValGetCheckIdxMatrix");
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__BEGIN__
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if (!CV_IS_CROSSVAL (estimateModel))
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{
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CV_ERROR (CV_StsBadArg, "Pointer point to not CvCrossValidationModel");
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}
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result = ((CvCrossValidationModel*)estimateModel)->sampleIdxEval;
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__END__
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return result;
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} // End of cvCrossValGetCheckIdxMatrix
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/****************************************************************************************/
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// Create new Idx-matrix for next classifiers training and return code of result.
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// Result is 0 if function can't make next step (error input or folds are finished),
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// it is 1 if all was correct, and it is 2 if current fold wasn't' checked.
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ML_IMPL
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int cvCrossValNextStep (CvStatModel* estimateModel)
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{
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int result = 0;
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CV_FUNCNAME ("cvCrossValGetNextTrainIdx");
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__BEGIN__
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CvCrossValidationModel* crVal = (CvCrossValidationModel*) estimateModel;
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int k, fold;
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if (!CV_IS_CROSSVAL (estimateModel))
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{
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CV_ERROR (CV_StsBadArg, "Pointer point to not CvCrossValidationModel");
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}
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fold = ++crVal->current_fold;
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if (fold >= crVal->folds_all)
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{
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if (fold == crVal->folds_all)
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EXIT;
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else
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{
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CV_ERROR (CV_StsInternal, "All iterations has end long ago");
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}
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}
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k = crVal->folds[fold + 1] - crVal->folds[fold];
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crVal->sampleIdxTrain->data.i = crVal->sampleIdxAll + crVal->folds[fold + 1];
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crVal->sampleIdxTrain->cols = crVal->samples_all - k;
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crVal->sampleIdxEval->data.i = crVal->sampleIdxAll + crVal->folds[fold];
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crVal->sampleIdxEval->cols = k;
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if (crVal->is_checked)
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{
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crVal->is_checked = 0;
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result = 1;
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}
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else
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{
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result = 2;
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}
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__END__
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return result;
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}
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/****************************************************************************************/
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// Do checking part of loop of cross-validations metod.
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ML_IMPL
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void cvCrossValCheckClassifier (CvStatModel* estimateModel,
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const CvStatModel* model,
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const CvMat* trainData,
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int sample_t_flag,
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const CvMat* trainClasses)
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{
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CV_FUNCNAME ("cvCrossValCheckClassifier ");
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__BEGIN__
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CvCrossValidationModel* crVal = (CvCrossValidationModel*) estimateModel;
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int i, j, k;
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int* data;
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float* responses_fl;
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int step;
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float* responses_result;
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int* responses_i;
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double te, te1;
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double sum_c, sum_p, sum_pp, sum_cp, sum_cc, sq_err;
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// Check input data to correct values.
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if (!CV_IS_CROSSVAL (estimateModel))
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{
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CV_ERROR (CV_StsBadArg,"First parameter point to not CvCrossValidationModel");
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}
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if (!CV_IS_STAT_MODEL (model))
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{
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CV_ERROR (CV_StsBadArg, "Second parameter point to not CvStatModel");
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}
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if (!CV_IS_MAT (trainData))
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{
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CV_ERROR (CV_StsBadArg, "Third parameter point to not CvMat");
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}
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if (!CV_IS_MAT (trainClasses))
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{
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CV_ERROR (CV_StsBadArg, "Fifth parameter point to not CvMat");
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}
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if (crVal->is_checked)
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{
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CV_ERROR (CV_StsInternal, "This iterations already was checked");
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}
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// Initialize.
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k = crVal->sampleIdxEval->cols;
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data = crVal->sampleIdxEval->data.i;
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// Eval tested feature vectors.
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CV_CALL (cvStatModelMultiPredict (model, trainData, sample_t_flag,
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crVal->predict_results, NULL, crVal->sampleIdxEval));
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// Count number if correct results.
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responses_result = crVal->predict_results->data.fl;
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if (crVal->is_regression)
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{
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sum_c = sum_p = sum_pp = sum_cp = sum_cc = sq_err = 0;
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if (CV_MAT_TYPE (trainClasses->type) == CV_32FC1)
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{
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responses_fl = trainClasses->data.fl;
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step = trainClasses->rows == 1 ? 1 : trainClasses->step / sizeof(float);
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for (i = 0; i < k; i++)
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{
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te = responses_result[*data];
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te1 = responses_fl[*data * step];
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sum_c += te1;
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sum_p += te;
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sum_cc += te1 * te1;
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sum_pp += te * te;
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sum_cp += te1 * te;
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te -= te1;
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sq_err += te * te;
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data++;
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}
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}
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else
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{
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responses_i = trainClasses->data.i;
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step = trainClasses->rows == 1 ? 1 : trainClasses->step / sizeof(int);
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for (i = 0; i < k; i++)
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{
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te = responses_result[*data];
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te1 = responses_i[*data * step];
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sum_c += te1;
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sum_p += te;
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sum_cc += te1 * te1;
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sum_pp += te * te;
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sum_cp += te1 * te;
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te -= te1;
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sq_err += te * te;
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data++;
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}
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}
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// Fixing new internal values of accuracy.
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crVal->sum_correct += sum_c;
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crVal->sum_predict += sum_p;
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crVal->sum_cc += sum_cc;
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crVal->sum_pp += sum_pp;
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crVal->sum_cp += sum_cp;
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crVal->sq_error += sq_err;
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}
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else
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{
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if (CV_MAT_TYPE (trainClasses->type) == CV_32FC1)
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{
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responses_fl = trainClasses->data.fl;
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step = trainClasses->rows == 1 ? 1 : trainClasses->step / sizeof(float);
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for (i = 0, j = 0; i < k; i++)
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{
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if (cvRound (responses_result[*data]) == cvRound (responses_fl[*data * step]))
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j++;
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data++;
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}
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}
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else
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{
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responses_i = trainClasses->data.i;
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step = trainClasses->rows == 1 ? 1 : trainClasses->step / sizeof(int);
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for (i = 0, j = 0; i < k; i++)
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{
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if (cvRound (responses_result[*data]) == responses_i[*data * step])
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j++;
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data++;
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}
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}
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// Fixing new internal values of accuracy.
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crVal->correct_results += j;
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}
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// Fixing that this fold already checked.
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crVal->all_results += k;
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crVal->is_checked = 1;
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__END__
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} // End of cvCrossValCheckClassifier
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/****************************************************************************************/
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// Return current accuracy.
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ML_IMPL
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float cvCrossValGetResult (const CvStatModel* estimateModel,
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float* correlation)
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{
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float result = 0;
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CV_FUNCNAME ("cvCrossValGetResult");
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__BEGIN__
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double te, te1;
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CvCrossValidationModel* crVal = (CvCrossValidationModel*)estimateModel;
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if (!CV_IS_CROSSVAL (estimateModel))
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{
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CV_ERROR (CV_StsBadArg, "Pointer point to not CvCrossValidationModel");
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}
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if (crVal->all_results)
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{
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if (crVal->is_regression)
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{
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result = ((float)crVal->sq_error) / crVal->all_results;
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if (correlation)
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{
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te = crVal->all_results * crVal->sum_cp -
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crVal->sum_correct * crVal->sum_predict;
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te *= te;
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te1 = (crVal->all_results * crVal->sum_cc -
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crVal->sum_correct * crVal->sum_correct) *
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(crVal->all_results * crVal->sum_pp -
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crVal->sum_predict * crVal->sum_predict);
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*correlation = (float)(te / te1);
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}
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}
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else
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{
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result = ((float)crVal->correct_results) / crVal->all_results;
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}
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}
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__END__
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return result;
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}
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/****************************************************************************************/
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// Reset cross-validation EstimateModel to state the same as it was immidiatly after
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// its creating.
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ML_IMPL
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void cvCrossValReset (CvStatModel* estimateModel)
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{
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CV_FUNCNAME ("cvCrossValReset");
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__BEGIN__
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CvCrossValidationModel* crVal = (CvCrossValidationModel*)estimateModel;
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if (!CV_IS_CROSSVAL (estimateModel))
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{
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CV_ERROR (CV_StsBadArg, "Pointer point to not CvCrossValidationModel");
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}
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crVal->current_fold = -1;
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crVal->is_checked = 1;
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crVal->all_results = 0;
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crVal->correct_results = 0;
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crVal->sq_error = 0;
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crVal->sum_correct = 0;
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crVal->sum_predict = 0;
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crVal->sum_cc = 0;
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crVal->sum_pp = 0;
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crVal->sum_cp = 0;
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__END__
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}
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/****************************************************************************************/
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// This function is standart CvStatModel field to release cross-validation EstimateModel.
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ML_IMPL
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void cvReleaseCrossValidationModel (CvStatModel** model)
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{
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CvCrossValidationModel* pModel;
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CV_FUNCNAME ("cvReleaseCrossValidationModel");
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__BEGIN__
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if (!model)
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{
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CV_ERROR (CV_StsNullPtr, "");
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}
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pModel = (CvCrossValidationModel*)*model;
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if (!pModel)
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{
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return;
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}
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if (!CV_IS_CROSSVAL (pModel))
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{
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CV_ERROR (CV_StsBadArg, "");
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}
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cvFree (&pModel->sampleIdxAll);
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cvFree (&pModel->folds);
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cvReleaseMat (&pModel->sampleIdxEval);
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cvReleaseMat (&pModel->sampleIdxTrain);
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cvReleaseMat (&pModel->predict_results);
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cvFree (model);
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__END__
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} // End of cvReleaseCrossValidationModel.
|
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|
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/****************************************************************************************/
|
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// This function create cross-validation EstimateModel.
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ML_IMPL CvStatModel*
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cvCreateCrossValidationEstimateModel(
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int samples_all,
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const CvStatModelParams* estimateParams,
|
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const CvMat* sampleIdx)
|
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{
|
||||
CvStatModel* model = NULL;
|
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CvCrossValidationModel* crVal = NULL;
|
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CV_FUNCNAME ("cvCreateCrossValidationEstimateModel");
|
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__BEGIN__
|
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int k_fold = 10;
|
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|
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int i, j, k, s_len;
|
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int samples_selected;
|
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CvRNG rng;
|
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CvRNG* prng;
|
||||
int* res_s_data;
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int* te_s_data;
|
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int* folds;
|
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|
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rng = cvRNG(cvGetTickCount());
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cvRandInt (&rng); cvRandInt (&rng); cvRandInt (&rng); cvRandInt (&rng);
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// Check input parameters.
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if (estimateParams)
|
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k_fold = ((CvCrossValidationParams*)estimateParams)->k_fold;
|
||||
if (!k_fold)
|
||||
{
|
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CV_ERROR (CV_StsBadArg, "Error in parameters of cross-validation (k_fold == 0)!");
|
||||
}
|
||||
if (samples_all <= 0)
|
||||
{
|
||||
CV_ERROR (CV_StsBadArg, "<samples_all> should be positive!");
|
||||
}
|
||||
|
||||
// Alloc memory and fill standart StatModel's fields.
|
||||
CV_CALL (crVal = (CvCrossValidationModel*)cvCreateStatModel (
|
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CV_STAT_MODEL_MAGIC_VAL | CV_CROSSVAL_MAGIC_VAL,
|
||||
sizeof(CvCrossValidationModel),
|
||||
cvReleaseCrossValidationModel,
|
||||
NULL, NULL));
|
||||
crVal->current_fold = -1;
|
||||
crVal->folds_all = k_fold;
|
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if (estimateParams && ((CvCrossValidationParams*)estimateParams)->is_regression)
|
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crVal->is_regression = 1;
|
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else
|
||||
crVal->is_regression = 0;
|
||||
if (estimateParams && ((CvCrossValidationParams*)estimateParams)->rng)
|
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prng = ((CvCrossValidationParams*)estimateParams)->rng;
|
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else
|
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prng = &rng;
|
||||
|
||||
// Check and preprocess sample indices.
|
||||
if (sampleIdx)
|
||||
{
|
||||
int s_step;
|
||||
int s_type = 0;
|
||||
|
||||
if (!CV_IS_MAT (sampleIdx))
|
||||
CV_ERROR (CV_StsBadArg, "Invalid sampleIdx array");
|
||||
|
||||
if (sampleIdx->rows != 1 && sampleIdx->cols != 1)
|
||||
CV_ERROR (CV_StsBadSize, "sampleIdx array must be 1-dimensional");
|
||||
|
||||
s_len = sampleIdx->rows + sampleIdx->cols - 1;
|
||||
s_step = sampleIdx->rows == 1 ?
|
||||
1 : sampleIdx->step / CV_ELEM_SIZE(sampleIdx->type);
|
||||
|
||||
s_type = CV_MAT_TYPE (sampleIdx->type);
|
||||
|
||||
switch (s_type)
|
||||
{
|
||||
case CV_8UC1:
|
||||
case CV_8SC1:
|
||||
{
|
||||
uchar* s_data = sampleIdx->data.ptr;
|
||||
|
||||
// sampleIdx is array of 1's and 0's -
|
||||
// i.e. it is a mask of the selected samples
|
||||
if( s_len != samples_all )
|
||||
CV_ERROR (CV_StsUnmatchedSizes,
|
||||
"Sample mask should contain as many elements as the total number of samples");
|
||||
|
||||
samples_selected = 0;
|
||||
for (i = 0; i < s_len; i++)
|
||||
samples_selected += s_data[i * s_step] != 0;
|
||||
|
||||
if (samples_selected == 0)
|
||||
CV_ERROR (CV_StsOutOfRange, "No samples is selected!");
|
||||
}
|
||||
s_len = samples_selected;
|
||||
break;
|
||||
case CV_32SC1:
|
||||
if (s_len > samples_all)
|
||||
CV_ERROR (CV_StsOutOfRange,
|
||||
"sampleIdx array may not contain more elements than the total number of samples");
|
||||
samples_selected = s_len;
|
||||
break;
|
||||
default:
|
||||
CV_ERROR (CV_StsUnsupportedFormat, "Unsupported sampleIdx array data type "
|
||||
"(it should be 8uC1, 8sC1 or 32sC1)");
|
||||
}
|
||||
|
||||
// Alloc additional memory for internal Idx and fill it.
|
||||
/*!!*/ CV_CALL (res_s_data = crVal->sampleIdxAll =
|
||||
(int*)cvAlloc (2 * s_len * sizeof(int)));
|
||||
|
||||
if (s_type < CV_32SC1)
|
||||
{
|
||||
uchar* s_data = sampleIdx->data.ptr;
|
||||
for (i = 0; i < s_len; i++)
|
||||
if (s_data[i * s_step])
|
||||
{
|
||||
*res_s_data++ = i;
|
||||
}
|
||||
res_s_data = crVal->sampleIdxAll;
|
||||
}
|
||||
else
|
||||
{
|
||||
int* s_data = sampleIdx->data.i;
|
||||
int out_of_order = 0;
|
||||
|
||||
for (i = 0; i < s_len; i++)
|
||||
{
|
||||
res_s_data[i] = s_data[i * s_step];
|
||||
if (i > 0 && res_s_data[i] < res_s_data[i - 1])
|
||||
out_of_order = 1;
|
||||
}
|
||||
|
||||
if (out_of_order)
|
||||
qsort (res_s_data, s_len, sizeof(res_s_data[0]), icvCmpIntegers);
|
||||
|
||||
if (res_s_data[0] < 0 ||
|
||||
res_s_data[s_len - 1] >= samples_all)
|
||||
CV_ERROR (CV_StsBadArg, "There are out-of-range sample indices");
|
||||
for (i = 1; i < s_len; i++)
|
||||
if (res_s_data[i] <= res_s_data[i - 1])
|
||||
CV_ERROR (CV_StsBadArg, "There are duplicated");
|
||||
}
|
||||
}
|
||||
else // if (sampleIdx)
|
||||
{
|
||||
// Alloc additional memory for internal Idx and fill it.
|
||||
s_len = samples_all;
|
||||
CV_CALL (res_s_data = crVal->sampleIdxAll = (int*)cvAlloc (2 * s_len * sizeof(int)));
|
||||
for (i = 0; i < s_len; i++)
|
||||
{
|
||||
*res_s_data++ = i;
|
||||
}
|
||||
res_s_data = crVal->sampleIdxAll;
|
||||
} // if (sampleIdx) ... else
|
||||
|
||||
// Resort internal Idx.
|
||||
te_s_data = res_s_data + s_len;
|
||||
for (i = s_len; i > 1; i--)
|
||||
{
|
||||
j = cvRandInt (prng) % i;
|
||||
k = *(--te_s_data);
|
||||
*te_s_data = res_s_data[j];
|
||||
res_s_data[j] = k;
|
||||
}
|
||||
|
||||
// Duplicate resorted internal Idx.
|
||||
// It will be used to simplify operation of getting trainIdx.
|
||||
te_s_data = res_s_data + s_len;
|
||||
for (i = 0; i < s_len; i++)
|
||||
{
|
||||
*te_s_data++ = *res_s_data++;
|
||||
}
|
||||
|
||||
// Cut sampleIdxAll to parts.
|
||||
if (k_fold > 0)
|
||||
{
|
||||
if (k_fold > s_len)
|
||||
{
|
||||
CV_ERROR (CV_StsBadArg,
|
||||
"Error in parameters of cross-validation ('k_fold' > #samples)!");
|
||||
}
|
||||
folds = crVal->folds = (int*) cvAlloc ((k_fold + 1) * sizeof (int));
|
||||
*folds++ = 0;
|
||||
for (i = 1; i < k_fold; i++)
|
||||
{
|
||||
*folds++ = cvRound (i * s_len * 1. / k_fold);
|
||||
}
|
||||
*folds = s_len;
|
||||
folds = crVal->folds;
|
||||
|
||||
crVal->max_fold_size = (s_len - 1) / k_fold + 1;
|
||||
}
|
||||
else
|
||||
{
|
||||
k = -k_fold;
|
||||
crVal->max_fold_size = k;
|
||||
if (k >= s_len)
|
||||
{
|
||||
CV_ERROR (CV_StsBadArg,
|
||||
"Error in parameters of cross-validation (-'k_fold' > #samples)!");
|
||||
}
|
||||
crVal->folds_all = k = (s_len - 1) / k + 1;
|
||||
|
||||
folds = crVal->folds = (int*) cvAlloc ((k + 1) * sizeof (int));
|
||||
for (i = 0; i < k; i++)
|
||||
{
|
||||
*folds++ = -i * k_fold;
|
||||
}
|
||||
*folds = s_len;
|
||||
folds = crVal->folds;
|
||||
}
|
||||
|
||||
// Prepare other internal fields to working.
|
||||
CV_CALL (crVal->predict_results = cvCreateMat (1, samples_all, CV_32FC1));
|
||||
CV_CALL (crVal->sampleIdxEval = cvCreateMatHeader (1, 1, CV_32SC1));
|
||||
CV_CALL (crVal->sampleIdxTrain = cvCreateMatHeader (1, 1, CV_32SC1));
|
||||
crVal->sampleIdxEval->cols = 0;
|
||||
crVal->sampleIdxTrain->cols = 0;
|
||||
crVal->samples_all = s_len;
|
||||
crVal->is_checked = 1;
|
||||
|
||||
crVal->getTrainIdxMat = cvCrossValGetTrainIdxMatrix;
|
||||
crVal->getCheckIdxMat = cvCrossValGetCheckIdxMatrix;
|
||||
crVal->nextStep = cvCrossValNextStep;
|
||||
crVal->check = cvCrossValCheckClassifier;
|
||||
crVal->getResult = cvCrossValGetResult;
|
||||
crVal->reset = cvCrossValReset;
|
||||
|
||||
model = (CvStatModel*)crVal;
|
||||
|
||||
__END__
|
||||
|
||||
if (!model)
|
||||
{
|
||||
cvReleaseCrossValidationModel ((CvStatModel**)&crVal);
|
||||
}
|
||||
|
||||
return model;
|
||||
} // End of cvCreateCrossValidationEstimateModel
|
||||
|
||||
|
||||
/****************************************************************************************\
|
||||
* Extended interface with backcalls for models *
|
||||
\****************************************************************************************/
|
||||
ML_IMPL float
|
||||
cvCrossValidation (const CvMat* trueData,
|
||||
int tflag,
|
||||
const CvMat* trueClasses,
|
||||
CvStatModel* (*createClassifier) (const CvMat*,
|
||||
int,
|
||||
const CvMat*,
|
||||
const CvClassifierTrainParams*,
|
||||
const CvMat*,
|
||||
const CvMat*,
|
||||
const CvMat*,
|
||||
const CvMat*),
|
||||
const CvClassifierTrainParams* estimateParams,
|
||||
const CvClassifierTrainParams* trainParams,
|
||||
const CvMat* compIdx,
|
||||
const CvMat* sampleIdx,
|
||||
CvStatModel** pCrValModel,
|
||||
const CvMat* typeMask,
|
||||
const CvMat* missedMeasurementMask)
|
||||
{
|
||||
CvCrossValidationModel* crVal = NULL;
|
||||
float result = 0;
|
||||
CvStatModel* pClassifier = NULL;
|
||||
|
||||
CV_FUNCNAME ("cvCrossValidation");
|
||||
__BEGIN__
|
||||
|
||||
const CvMat* trainDataIdx;
|
||||
int samples_all;
|
||||
|
||||
// checking input data
|
||||
if ((createClassifier) == NULL)
|
||||
{
|
||||
CV_ERROR (CV_StsNullPtr, "Null pointer to functiion which create classifier");
|
||||
}
|
||||
if (pCrValModel && *pCrValModel && !CV_IS_CROSSVAL(*pCrValModel))
|
||||
{
|
||||
CV_ERROR (CV_StsBadArg,
|
||||
"<pCrValModel> point to not cross-validation model");
|
||||
}
|
||||
|
||||
// initialization
|
||||
if (pCrValModel && *pCrValModel)
|
||||
{
|
||||
crVal = (CvCrossValidationModel*)*pCrValModel;
|
||||
crVal->reset ((CvStatModel*)crVal);
|
||||
}
|
||||
else
|
||||
{
|
||||
samples_all = ((tflag) ? trueData->rows : trueData->cols);
|
||||
CV_CALL (crVal = (CvCrossValidationModel*)
|
||||
cvCreateCrossValidationEstimateModel (samples_all, estimateParams, sampleIdx));
|
||||
}
|
||||
|
||||
CV_CALL (trainDataIdx = crVal->getTrainIdxMat ((CvStatModel*)crVal));
|
||||
|
||||
// operation loop
|
||||
for (; crVal->nextStep((CvStatModel*)crVal) != 0; )
|
||||
{
|
||||
CV_CALL (pClassifier = createClassifier (trueData, tflag, trueClasses,
|
||||
trainParams, compIdx, trainDataIdx, typeMask, missedMeasurementMask));
|
||||
CV_CALL (crVal->check ((CvStatModel*)crVal, pClassifier,
|
||||
trueData, tflag, trueClasses));
|
||||
|
||||
pClassifier->release (&pClassifier);
|
||||
}
|
||||
|
||||
// Get result and fill output field.
|
||||
CV_CALL (result = crVal->getResult ((CvStatModel*)crVal, 0));
|
||||
|
||||
if (pCrValModel && !*pCrValModel)
|
||||
*pCrValModel = (CvStatModel*)crVal;
|
||||
|
||||
__END__
|
||||
|
||||
// Free all memory that should be freed.
|
||||
if (pClassifier)
|
||||
pClassifier->release (&pClassifier);
|
||||
if (crVal && (!pCrValModel || !*pCrValModel))
|
||||
crVal->release ((CvStatModel**)&crVal);
|
||||
|
||||
return result;
|
||||
} // End of cvCrossValidation
|
||||
|
||||
#endif
|
||||
|
||||
/* End of file */
|
||||
Reference in New Issue
Block a user