add part of opencv
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Tang1705
401
Lib/opencv/sources/modules/ml/src/precomp.hpp
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401
Lib/opencv/sources/modules/ml/src/precomp.hpp
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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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#ifndef __OPENCV_ML_PRECOMP_HPP__
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#define __OPENCV_ML_PRECOMP_HPP__
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#include "opencv2/core.hpp"
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#include "opencv2/ml.hpp"
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#include "opencv2/core/core_c.h"
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#include "opencv2/core/utility.hpp"
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#include "opencv2/core/private.hpp"
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#include <assert.h>
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#include <float.h>
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#include <limits.h>
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#include <math.h>
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#include <stdlib.h>
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#include <stdio.h>
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#include <string.h>
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#include <time.h>
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#include <vector>
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/****************************************************************************************\
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* Main struct definitions *
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\****************************************************************************************/
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/* log(2*PI) */
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#define CV_LOG2PI (1.8378770664093454835606594728112)
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namespace cv
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{
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namespace ml
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{
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using std::vector;
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#define CV_DTREE_CAT_DIR(idx,subset) \
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(2*((subset[(idx)>>5]&(1 << ((idx) & 31)))==0)-1)
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template<typename _Tp> struct cmp_lt_idx
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{
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cmp_lt_idx(const _Tp* _arr) : arr(_arr) {}
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bool operator ()(int a, int b) const { return arr[a] < arr[b]; }
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const _Tp* arr;
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};
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template<typename _Tp> struct cmp_lt_ptr
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{
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cmp_lt_ptr() {}
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bool operator ()(const _Tp* a, const _Tp* b) const { return *a < *b; }
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};
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static inline void setRangeVector(std::vector<int>& vec, int n)
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{
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vec.resize(n);
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for( int i = 0; i < n; i++ )
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vec[i] = i;
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}
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static inline void writeTermCrit(FileStorage& fs, const TermCriteria& termCrit)
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{
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if( (termCrit.type & TermCriteria::EPS) != 0 )
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fs << "epsilon" << termCrit.epsilon;
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if( (termCrit.type & TermCriteria::COUNT) != 0 )
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fs << "iterations" << termCrit.maxCount;
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}
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static inline TermCriteria readTermCrit(const FileNode& fn)
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{
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TermCriteria termCrit;
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double epsilon = (double)fn["epsilon"];
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if( epsilon > 0 )
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{
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termCrit.type |= TermCriteria::EPS;
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termCrit.epsilon = epsilon;
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}
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int iters = (int)fn["iterations"];
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if( iters > 0 )
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{
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termCrit.type |= TermCriteria::COUNT;
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termCrit.maxCount = iters;
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}
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return termCrit;
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}
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struct TreeParams
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{
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TreeParams();
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TreeParams( int maxDepth, int minSampleCount,
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double regressionAccuracy, bool useSurrogates,
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int maxCategories, int CVFolds,
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bool use1SERule, bool truncatePrunedTree,
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const Mat& priors );
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inline void setMaxCategories(int val)
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{
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if( val < 2 )
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CV_Error( CV_StsOutOfRange, "max_categories should be >= 2" );
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maxCategories = std::min(val, 15 );
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}
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inline void setMaxDepth(int val)
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{
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if( val < 0 )
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CV_Error( CV_StsOutOfRange, "max_depth should be >= 0" );
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maxDepth = std::min( val, 25 );
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}
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inline void setMinSampleCount(int val)
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{
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minSampleCount = std::max(val, 1);
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}
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inline void setCVFolds(int val)
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{
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if( val < 0 )
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CV_Error( CV_StsOutOfRange,
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"params.CVFolds should be =0 (the tree is not pruned) "
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"or n>0 (tree is pruned using n-fold cross-validation)" );
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if(val > 1)
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CV_Error( CV_StsNotImplemented,
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"tree pruning using cross-validation is not implemented."
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"Set CVFolds to 1");
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if( val == 1 )
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val = 0;
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CVFolds = val;
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}
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inline void setRegressionAccuracy(float val)
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{
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if( val < 0 )
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CV_Error( CV_StsOutOfRange, "params.regression_accuracy should be >= 0" );
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regressionAccuracy = val;
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}
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inline int getMaxCategories() const { return maxCategories; }
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inline int getMaxDepth() const { return maxDepth; }
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inline int getMinSampleCount() const { return minSampleCount; }
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inline int getCVFolds() const { return CVFolds; }
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inline float getRegressionAccuracy() const { return regressionAccuracy; }
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inline bool getUseSurrogates() const { return useSurrogates; }
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inline void setUseSurrogates(bool val) { useSurrogates = val; }
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inline bool getUse1SERule() const { return use1SERule; }
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inline void setUse1SERule(bool val) { use1SERule = val; }
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inline bool getTruncatePrunedTree() const { return truncatePrunedTree; }
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inline void setTruncatePrunedTree(bool val) { truncatePrunedTree = val; }
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inline cv::Mat getPriors() const { return priors; }
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inline void setPriors(const cv::Mat& val) { priors = val; }
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public:
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bool useSurrogates;
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bool use1SERule;
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bool truncatePrunedTree;
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Mat priors;
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protected:
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int maxCategories;
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int maxDepth;
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int minSampleCount;
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int CVFolds;
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float regressionAccuracy;
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};
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struct RTreeParams
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{
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RTreeParams();
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RTreeParams(bool calcVarImportance, int nactiveVars, TermCriteria termCrit );
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bool calcVarImportance;
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int nactiveVars;
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TermCriteria termCrit;
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};
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struct BoostTreeParams
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{
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BoostTreeParams();
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BoostTreeParams(int boostType, int weakCount, double weightTrimRate);
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int boostType;
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int weakCount;
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double weightTrimRate;
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};
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class DTreesImpl : public DTrees
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{
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public:
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struct WNode
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{
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WNode()
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{
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class_idx = sample_count = depth = complexity = 0;
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parent = left = right = split = defaultDir = -1;
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Tn = INT_MAX;
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value = maxlr = alpha = node_risk = tree_risk = tree_error = 0.;
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}
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int class_idx;
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double Tn;
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double value;
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int parent;
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int left;
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int right;
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int defaultDir;
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int split;
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int sample_count;
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int depth;
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double maxlr;
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// global pruning data
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int complexity;
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double alpha;
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double node_risk, tree_risk, tree_error;
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};
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struct WSplit
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{
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WSplit()
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{
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varIdx = next = 0;
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inversed = false;
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quality = c = 0.f;
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subsetOfs = -1;
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}
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int varIdx;
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bool inversed;
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float quality;
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int next;
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float c;
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int subsetOfs;
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};
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struct WorkData
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{
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WorkData(const Ptr<TrainData>& _data);
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Ptr<TrainData> data;
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vector<WNode> wnodes;
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vector<WSplit> wsplits;
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vector<int> wsubsets;
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vector<double> cv_Tn;
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vector<double> cv_node_risk;
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vector<double> cv_node_error;
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vector<int> cv_labels;
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vector<double> sample_weights;
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vector<int> cat_responses;
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vector<double> ord_responses;
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vector<int> sidx;
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int maxSubsetSize;
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};
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inline int getMaxCategories() const CV_OVERRIDE { return params.getMaxCategories(); }
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inline void setMaxCategories(int val) CV_OVERRIDE { params.setMaxCategories(val); }
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inline int getMaxDepth() const CV_OVERRIDE { return params.getMaxDepth(); }
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inline void setMaxDepth(int val) CV_OVERRIDE { params.setMaxDepth(val); }
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inline int getMinSampleCount() const CV_OVERRIDE { return params.getMinSampleCount(); }
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inline void setMinSampleCount(int val) CV_OVERRIDE { params.setMinSampleCount(val); }
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inline int getCVFolds() const CV_OVERRIDE { return params.getCVFolds(); }
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inline void setCVFolds(int val) CV_OVERRIDE { params.setCVFolds(val); }
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inline bool getUseSurrogates() const CV_OVERRIDE { return params.getUseSurrogates(); }
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inline void setUseSurrogates(bool val) CV_OVERRIDE { params.setUseSurrogates(val); }
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inline bool getUse1SERule() const CV_OVERRIDE { return params.getUse1SERule(); }
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inline void setUse1SERule(bool val) CV_OVERRIDE { params.setUse1SERule(val); }
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inline bool getTruncatePrunedTree() const CV_OVERRIDE { return params.getTruncatePrunedTree(); }
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inline void setTruncatePrunedTree(bool val) CV_OVERRIDE { params.setTruncatePrunedTree(val); }
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inline float getRegressionAccuracy() const CV_OVERRIDE { return params.getRegressionAccuracy(); }
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inline void setRegressionAccuracy(float val) CV_OVERRIDE { params.setRegressionAccuracy(val); }
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inline cv::Mat getPriors() const CV_OVERRIDE { return params.getPriors(); }
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inline void setPriors(const cv::Mat& val) CV_OVERRIDE { params.setPriors(val); }
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DTreesImpl();
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virtual ~DTreesImpl() CV_OVERRIDE;
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virtual void clear() CV_OVERRIDE;
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String getDefaultName() const CV_OVERRIDE { return "opencv_ml_dtree"; }
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bool isTrained() const CV_OVERRIDE { return !roots.empty(); }
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bool isClassifier() const CV_OVERRIDE { return _isClassifier; }
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int getVarCount() const CV_OVERRIDE { return varType.empty() ? 0 : (int)(varType.size() - 1); }
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int getCatCount(int vi) const { return catOfs[vi][1] - catOfs[vi][0]; }
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int getSubsetSize(int vi) const { return (getCatCount(vi) + 31)/32; }
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virtual void setDParams(const TreeParams& _params);
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virtual void startTraining( const Ptr<TrainData>& trainData, int flags );
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virtual void endTraining();
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virtual void initCompVarIdx();
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virtual bool train( const Ptr<TrainData>& trainData, int flags ) CV_OVERRIDE;
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virtual int addTree( const vector<int>& sidx );
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virtual int addNodeAndTrySplit( int parent, const vector<int>& sidx );
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virtual const vector<int>& getActiveVars();
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virtual int findBestSplit( const vector<int>& _sidx );
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virtual void calcValue( int nidx, const vector<int>& _sidx );
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virtual WSplit findSplitOrdClass( int vi, const vector<int>& _sidx, double initQuality );
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// simple k-means, slightly modified to take into account the "weight" (L1-norm) of each vector.
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virtual void clusterCategories( const double* vectors, int n, int m, double* csums, int k, int* labels );
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virtual WSplit findSplitCatClass( int vi, const vector<int>& _sidx, double initQuality, int* subset );
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virtual WSplit findSplitOrdReg( int vi, const vector<int>& _sidx, double initQuality );
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virtual WSplit findSplitCatReg( int vi, const vector<int>& _sidx, double initQuality, int* subset );
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virtual int calcDir( int splitidx, const vector<int>& _sidx, vector<int>& _sleft, vector<int>& _sright );
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virtual int pruneCV( int root );
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virtual double updateTreeRNC( int root, double T, int fold );
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virtual bool cutTree( int root, double T, int fold, double min_alpha );
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virtual float predictTrees( const Range& range, const Mat& sample, int flags ) const;
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virtual float predict( InputArray inputs, OutputArray outputs, int flags ) const CV_OVERRIDE;
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virtual void writeTrainingParams( FileStorage& fs ) const;
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virtual void writeParams( FileStorage& fs ) const;
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virtual void writeSplit( FileStorage& fs, int splitidx ) const;
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virtual void writeNode( FileStorage& fs, int nidx, int depth ) const;
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virtual void writeTree( FileStorage& fs, int root ) const;
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virtual void write( FileStorage& fs ) const CV_OVERRIDE;
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virtual void readParams( const FileNode& fn );
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virtual int readSplit( const FileNode& fn );
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virtual int readNode( const FileNode& fn );
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virtual int readTree( const FileNode& fn );
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virtual void read( const FileNode& fn ) CV_OVERRIDE;
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virtual const std::vector<int>& getRoots() const CV_OVERRIDE { return roots; }
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virtual const std::vector<Node>& getNodes() const CV_OVERRIDE { return nodes; }
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virtual const std::vector<Split>& getSplits() const CV_OVERRIDE { return splits; }
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virtual const std::vector<int>& getSubsets() const CV_OVERRIDE { return subsets; }
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TreeParams params;
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vector<int> varIdx;
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vector<int> compVarIdx;
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vector<uchar> varType;
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vector<Vec2i> catOfs;
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vector<int> catMap;
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vector<int> roots;
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vector<Node> nodes;
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vector<Split> splits;
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vector<int> subsets;
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vector<int> classLabels;
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vector<float> missingSubst;
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vector<int> varMapping;
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bool _isClassifier;
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Ptr<WorkData> w;
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};
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template <typename T>
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static inline void readVectorOrMat(const FileNode & node, std::vector<T> & v)
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{
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if (node.type() == FileNode::MAP)
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{
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Mat m;
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node >> m;
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m.copyTo(v);
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}
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else if (node.type() == FileNode::SEQ)
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{
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node >> v;
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}
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}
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}}
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#endif /* __OPENCV_ML_PRECOMP_HPP__ */
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