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initial commit; ml has been refactored; it compiles and the tests run well; some other modules, apps and samples do not compile; to be fixed

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This commit is contained in:
Vadim Pisarevsky
2014-07-29 23:54:23 +04:00
parent dce1824a91
commit ba3783d205
25 changed files with 8320 additions and 21792 deletions
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512
modules/ml/src/precomp.hpp
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@@ -38,8 +38,8 @@
//
//M*/
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#ifndef __OPENCV_ML_PRECOMP_HPP__
#define __OPENCV_ML_PRECOMP_HPP__
#include "opencv2/core.hpp"
#include "opencv2/ml.hpp"
@@ -56,321 +56,217 @@
#include <stdio.h>
#include <string.h>
#include <time.h>
#define ML_IMPL CV_IMPL
#define __BEGIN__ __CV_BEGIN__
#define __END__ __CV_END__
#define EXIT __CV_EXIT__
#define CV_MAT_ELEM_FLAG( mat, type, comp, vect, tflag ) \
(( tflag == CV_ROW_SAMPLE ) \
? (CV_MAT_ELEM( mat, type, comp, vect )) \
: (CV_MAT_ELEM( mat, type, vect, comp )))
/* Convert matrix to vector */
#define ICV_MAT2VEC( mat, vdata, vstep, num ) \
if( MIN( (mat).rows, (mat).cols ) != 1 ) \
CV_ERROR( CV_StsBadArg, "" ); \
(vdata) = ((mat).data.ptr); \
if( (mat).rows == 1 ) \
{ \
(vstep) = CV_ELEM_SIZE( (mat).type ); \
(num) = (mat).cols; \
} \
else \
{ \
(vstep) = (mat).step; \
(num) = (mat).rows; \
}
/* get raw data */
#define ICV_RAWDATA( mat, flags, rdata, sstep, cstep, m, n ) \
(rdata) = (mat).data.ptr; \
if( CV_IS_ROW_SAMPLE( flags ) ) \
{ \
(sstep) = (mat).step; \
(cstep) = CV_ELEM_SIZE( (mat).type ); \
(m) = (mat).rows; \
(n) = (mat).cols; \
} \
else \
{ \
(cstep) = (mat).step; \
(sstep) = CV_ELEM_SIZE( (mat).type ); \
(n) = (mat).rows; \
(m) = (mat).cols; \
}
#define ICV_IS_MAT_OF_TYPE( mat, mat_type) \
(CV_IS_MAT( mat ) && CV_MAT_TYPE( mat->type ) == (mat_type) && \
(mat)->cols > 0 && (mat)->rows > 0)
/*
uchar* data; int sstep, cstep; - trainData->data
uchar* classes; int clstep; int ncl;- trainClasses
uchar* tmask; int tmstep; int ntm; - typeMask
uchar* missed;int msstep, mcstep; -missedMeasurements...
int mm, mn; == m,n == size,dim
uchar* sidx;int sistep; - sampleIdx
uchar* cidx;int cistep; - compIdx
int k, l; == n,m == dim,size (length of cidx, sidx)
int m, n; == size,dim
*/
#define ICV_DECLARE_TRAIN_ARGS() \
uchar* data; \
int sstep, cstep; \
uchar* classes; \
int clstep; \
int ncl; \
uchar* tmask; \
int tmstep; \
int ntm; \
uchar* missed; \
int msstep, mcstep; \
int mm, mn; \
uchar* sidx; \
int sistep; \
uchar* cidx; \
int cistep; \
int k, l; \
int m, n; \
\
data = classes = tmask = missed = sidx = cidx = NULL; \
sstep = cstep = clstep = ncl = tmstep = ntm = msstep = mcstep = mm = mn = 0; \
sistep = cistep = k = l = m = n = 0;
#define ICV_TRAIN_DATA_REQUIRED( param, flags ) \
if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) ) \
{ \
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
} \
else \
{ \
ICV_RAWDATA( *(param), (flags), data, sstep, cstep, m, n ); \
k = n; \
l = m; \
}
#define ICV_TRAIN_CLASSES_REQUIRED( param ) \
if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) ) \
{ \
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
} \
else \
{ \
ICV_MAT2VEC( *(param), classes, clstep, ncl ); \
if( m != ncl ) \
{ \
CV_ERROR( CV_StsBadArg, "Unmatched sizes" ); \
} \
}
#define ICV_ARG_NULL( param ) \
if( (param) != NULL ) \
{ \
CV_ERROR( CV_StsBadArg, #param " parameter must be NULL" ); \
}
#define ICV_MISSED_MEASUREMENTS_OPTIONAL( param, flags ) \
if( param ) \
{ \
if( !ICV_IS_MAT_OF_TYPE( param, CV_8UC1 ) ) \
{ \
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
} \
else \
{ \
ICV_RAWDATA( *(param), (flags), missed, msstep, mcstep, mm, mn ); \
if( mm != m || mn != n ) \
{ \
CV_ERROR( CV_StsBadArg, "Unmatched sizes" ); \
} \
} \
}
#define ICV_COMP_IDX_OPTIONAL( param ) \
if( param ) \
{ \
if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) ) \
{ \
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
} \
else \
{ \
ICV_MAT2VEC( *(param), cidx, cistep, k ); \
if( k > n ) \
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
} \
}
#define ICV_SAMPLE_IDX_OPTIONAL( param ) \
if( param ) \
{ \
if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) ) \
{ \
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
} \
else \
{ \
ICV_MAT2VEC( *sampleIdx, sidx, sistep, l ); \
if( l > m ) \
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
} \
}
/****************************************************************************************/
#define ICV_CONVERT_FLOAT_ARRAY_TO_MATRICE( array, matrice ) \
{ \
CvMat a, b; \
int dims = (matrice)->cols; \
int nsamples = (matrice)->rows; \
int type = CV_MAT_TYPE((matrice)->type); \
int i, offset = dims; \
\
CV_ASSERT( type == CV_32FC1 || type == CV_64FC1 ); \
offset *= ((type == CV_32FC1) ? sizeof(float) : sizeof(double));\
\
b = cvMat( 1, dims, CV_32FC1 ); \
cvGetRow( matrice, &a, 0 ); \
for( i = 0; i < nsamples; i++, a.data.ptr += offset ) \
{ \
b.data.fl = (float*)array[i]; \
CV_CALL( cvConvert( &b, &a ) ); \
} \
}
#include <vector>
/****************************************************************************************\
* Auxiliary functions declarations *
\****************************************************************************************/
* Main struct definitions *
\****************************************************************************************/
/* Generates a set of classes centers in quantity <num_of_clusters> that are generated as
uniform random vectors in parallelepiped, where <data> is concentrated. Vectors in
<data> should have horizontal orientation. If <centers> != NULL, the function doesn't
allocate any memory and stores generated centers in <centers>, returns <centers>.
If <centers> == NULL, the function allocates memory and creates the matrice. Centers
are supposed to be oriented horizontally. */
CvMat* icvGenerateRandomClusterCenters( int seed,
const CvMat* data,
int num_of_clusters,
CvMat* centers CV_DEFAULT(0));
/* Fills the <labels> using <probs> by choosing the maximal probability. Outliers are
fixed by <oulier_tresh> and have cluster label (-1). Function also controls that there
weren't "empty" clusters by filling empty clusters with the maximal probability vector.
If probs_sums != NULL, filles it with the sums of probabilities for each sample (it is
useful for normalizing probabilities' matrice of FCM) */
void icvFindClusterLabels( const CvMat* probs, float outlier_thresh, float r,
const CvMat* labels );
typedef struct CvSparseVecElem32f
{
int idx;
float val;
}
CvSparseVecElem32f;
/* Prepare training data and related parameters */
#define CV_TRAIN_STATMODEL_DEFRAGMENT_TRAIN_DATA 1
#define CV_TRAIN_STATMODEL_SAMPLES_AS_ROWS 2
#define CV_TRAIN_STATMODEL_SAMPLES_AS_COLUMNS 4
#define CV_TRAIN_STATMODEL_CATEGORICAL_RESPONSE 8
#define CV_TRAIN_STATMODEL_ORDERED_RESPONSE 16
#define CV_TRAIN_STATMODEL_RESPONSES_ON_OUTPUT 32
#define CV_TRAIN_STATMODEL_ALWAYS_COPY_TRAIN_DATA 64
#define CV_TRAIN_STATMODEL_SPARSE_AS_SPARSE 128
int
cvPrepareTrainData( const char* /*funcname*/,
const CvMat* train_data, int tflag,
const CvMat* responses, int response_type,
const CvMat* var_idx,
const CvMat* sample_idx,
bool always_copy_data,
const float*** out_train_samples,
int* _sample_count,
int* _var_count,
int* _var_all,
CvMat** out_responses,
CvMat** out_response_map,
CvMat** out_var_idx,
CvMat** out_sample_idx=0 );
void
cvSortSamplesByClasses( const float** samples, const CvMat* classes,
int* class_ranges, const uchar** mask CV_DEFAULT(0) );
void
cvCombineResponseMaps (CvMat* _responses,
const CvMat* old_response_map,
CvMat* new_response_map,
CvMat** out_response_map);
void
cvPreparePredictData( const CvArr* sample, int dims_all, const CvMat* comp_idx,
int class_count, const CvMat* prob, float** row_sample,
int as_sparse CV_DEFAULT(0) );
/* copies clustering [or batch "predict"] results
(labels and/or centers and/or probs) back to the output arrays */
void
cvWritebackLabels( const CvMat* labels, CvMat* dst_labels,
const CvMat* centers, CvMat* dst_centers,
const CvMat* probs, CvMat* dst_probs,
const CvMat* sample_idx, int samples_all,
const CvMat* comp_idx, int dims_all );
#define cvWritebackResponses cvWritebackLabels
#define XML_FIELD_NAME "_name"
CvFileNode* icvFileNodeGetChild(CvFileNode* father, const char* name);
CvFileNode* icvFileNodeGetChildArrayElem(CvFileNode* father, const char* name,int index);
CvFileNode* icvFileNodeGetNext(CvFileNode* n, const char* name);
void cvCheckTrainData( const CvMat* train_data, int tflag,
const CvMat* missing_mask,
int* var_all, int* sample_all );
CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, bool check_for_duplicates=false );
CvMat* cvPreprocessVarType( const CvMat* type_mask, const CvMat* var_idx,
int var_all, int* response_type );
CvMat* cvPreprocessOrderedResponses( const CvMat* responses,
const CvMat* sample_idx, int sample_all );
CvMat* cvPreprocessCategoricalResponses( const CvMat* responses,
const CvMat* sample_idx, int sample_all,
CvMat** out_response_map, CvMat** class_counts=0 );
const float** cvGetTrainSamples( const CvMat* train_data, int tflag,
const CvMat* var_idx, const CvMat* sample_idx,
int* _var_count, int* _sample_count,
bool always_copy_data=false );
/* log(2*PI) */
#define CV_LOG2PI (1.8378770664093454835606594728112)
namespace cv
{
struct DTreeBestSplitFinder
namespace ml
{
using std::vector;
#define CV_DTREE_CAT_DIR(idx,subset) \
(2*((subset[(idx)>>5]&(1 << ((idx) & 31)))==0)-1)
template<typename _Tp> struct cmp_lt_idx
{
DTreeBestSplitFinder(){ splitSize = 0, tree = 0; node = 0; }
DTreeBestSplitFinder( CvDTree* _tree, CvDTreeNode* _node);
DTreeBestSplitFinder( const DTreeBestSplitFinder& finder, Split );
virtual ~DTreeBestSplitFinder() {}
virtual void operator()(const BlockedRange& range);
void join( DTreeBestSplitFinder& rhs );
Ptr<CvDTreeSplit> bestSplit;
Ptr<CvDTreeSplit> split;
int splitSize;
CvDTree* tree;
CvDTreeNode* node;
cmp_lt_idx(const _Tp* _arr) : arr(_arr) {}
bool operator ()(int a, int b) const { return arr[a] < arr[b]; }
const _Tp* arr;
};
struct ForestTreeBestSplitFinder : DTreeBestSplitFinder
template<typename _Tp> struct cmp_lt_ptr
{
ForestTreeBestSplitFinder() : DTreeBestSplitFinder() {}
ForestTreeBestSplitFinder( CvForestTree* _tree, CvDTreeNode* _node );
ForestTreeBestSplitFinder( const ForestTreeBestSplitFinder& finder, Split );
virtual void operator()(const BlockedRange& range);
cmp_lt_ptr() {}
bool operator ()(const _Tp* a, const _Tp* b) const { return *a < *b; }
};
}
#endif /* __ML_H__ */
static inline void setRangeVector(std::vector<int>& vec, int n)
{
vec.resize(n);
for( int i = 0; i < n; i++ )
vec[i] = i;
}
static inline void writeTermCrit(FileStorage& fs, const TermCriteria& termCrit)
{
if( (termCrit.type & TermCriteria::EPS) != 0 )
fs << "epsilon" << termCrit.epsilon;
if( (termCrit.type & TermCriteria::COUNT) != 0 )
fs << "iterations" << termCrit.maxCount;
}
static inline TermCriteria readTermCrit(const FileNode& fn)
{
TermCriteria termCrit;
double epsilon = (double)fn["epsilon"];
if( epsilon > 0 )
{
termCrit.type |= TermCriteria::EPS;
termCrit.epsilon = epsilon;
}
int iters = (double)fn["iterations"];
if( iters > 0 )
{
termCrit.type |= TermCriteria::COUNT;
termCrit.maxCount = iters;
}
return termCrit;
}
class DTreesImpl : public DTrees
{
public:
struct WNode
{
WNode()
{
class_idx = sample_count = depth = complexity = 0;
parent = left = right = split = defaultDir = -1;
Tn = INT_MAX;
value = maxlr = alpha = node_risk = tree_risk = tree_error = 0.;
}
int class_idx;
int Tn;
double value;
int parent;
int left;
int right;
int defaultDir;
int split;
int sample_count;
int depth;
double maxlr;
// global pruning data
int complexity;
double alpha;
double node_risk, tree_risk, tree_error;
};
struct WSplit
{
WSplit()
{
varIdx = inversed = next = 0;
quality = c = 0.f;
subsetOfs = -1;
}
int varIdx;
int inversed;
float quality;
int next;
float c;
int subsetOfs;
};
struct WorkData
{
WorkData(const Ptr<TrainData>& _data);
Ptr<TrainData> data;
vector<WNode> wnodes;
vector<WSplit> wsplits;
vector<int> wsubsets;
vector<int> cv_Tn;
vector<double> cv_node_risk;
vector<double> cv_node_error;
vector<int> cv_labels;
vector<double> sample_weights;
vector<int> cat_responses;
vector<double> ord_responses;
vector<int> sidx;
int maxSubsetSize;
};
DTreesImpl();
virtual ~DTreesImpl();
virtual void clear();
String getDefaultModelName() const { return "opencv_ml_dtree"; }
bool isTrained() const { return !roots.empty(); }
bool isClassifier() const { return _isClassifier; }
int getVarCount() const { return varType.empty() ? 0 : (int)(varType.size() - 1); }
int getCatCount(int vi) const { return catOfs[vi][1] - catOfs[vi][0]; }
int getSubsetSize(int vi) const { return (getCatCount(vi) + 31)/32; }
virtual void setDParams(const Params& _params);
virtual Params getDParams() const;
virtual void startTraining( const Ptr<TrainData>& trainData, int flags );
virtual void endTraining();
virtual void initCompVarIdx();
virtual bool train( const Ptr<TrainData>& trainData, int flags );
virtual int addTree( const vector<int>& sidx );
virtual int addNodeAndTrySplit( int parent, const vector<int>& sidx );
virtual const vector<int>& getActiveVars();
virtual int findBestSplit( const vector<int>& _sidx );
virtual void calcValue( int nidx, const vector<int>& _sidx );
virtual WSplit findSplitOrdClass( int vi, const vector<int>& _sidx, double initQuality );
// simple k-means, slightly modified to take into account the "weight" (L1-norm) of each vector.
virtual void clusterCategories( const double* vectors, int n, int m, double* csums, int k, int* labels );
virtual WSplit findSplitCatClass( int vi, const vector<int>& _sidx, double initQuality, int* subset );
virtual WSplit findSplitOrdReg( int vi, const vector<int>& _sidx, double initQuality );
virtual WSplit findSplitCatReg( int vi, const vector<int>& _sidx, double initQuality, int* subset );
virtual int calcDir( int splitidx, const vector<int>& _sidx, vector<int>& _sleft, vector<int>& _sright );
virtual int pruneCV( int root );
virtual double updateTreeRNC( int root, double T, int fold );
virtual bool cutTree( int root, double T, int fold, double min_alpha );
virtual float predictTrees( const Range& range, const Mat& sample, int flags ) const;
virtual float predict( InputArray inputs, OutputArray outputs, int flags ) const;
virtual void writeTrainingParams( FileStorage& fs ) const;
virtual void writeParams( FileStorage& fs ) const;
virtual void writeSplit( FileStorage& fs, int splitidx ) const;
virtual void writeNode( FileStorage& fs, int nidx, int depth ) const;
virtual void writeTree( FileStorage& fs, int root ) const;
virtual void write( FileStorage& fs ) const;
virtual void readParams( const FileNode& fn );
virtual int readSplit( const FileNode& fn );
virtual int readNode( const FileNode& fn );
virtual int readTree( const FileNode& fn );
virtual void read( const FileNode& fn );
virtual const std::vector<int>& getRoots() const { return roots; }
virtual const std::vector<Node>& getNodes() const { return nodes; }
virtual const std::vector<Split>& getSplits() const { return splits; }
virtual const std::vector<int>& getSubsets() const { return subsets; }
Params params0, params;
vector<int> varIdx;
vector<int> compVarIdx;
vector<uchar> varType;
vector<Vec2i> catOfs;
vector<int> catMap;
vector<int> roots;
vector<Node> nodes;
vector<Split> splits;
vector<int> subsets;
vector<int> classLabels;
vector<float> missingSubst;
bool _isClassifier;
Ptr<WorkData> w;
};
}}
#endif /* __OPENCV_ML_PRECOMP_HPP__ */