generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Updated ml module interfaces and documentation

Browse Source
This commit is contained in:
Maksim Shabunin
2015-02-11 13:24:14 +03:00
parent da383e65e2
commit 79e8f0680c
32 changed files with 1403 additions and 1528 deletions
Download Patch File Download Diff File Expand all files Collapse all files

236
modules/ml/src/svm.cpp
View File

@@ -103,54 +103,60 @@ static void checkParamGrid(const ParamGrid& pg)
}
// SVM training parameters
SVM::Params::Params()
struct SvmParams
{
svmType = SVM::C_SVC;
kernelType = SVM::RBF;
degree = 0;
gamma = 1;
coef0 = 0;
C = 1;
nu = 0;
p = 0;
termCrit = TermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 1000, FLT_EPSILON );
}
int svmType;
int kernelType;
double gamma;
double coef0;
double degree;
double C;
double nu;
double p;
Mat classWeights;
TermCriteria termCrit;
SvmParams()
{
svmType = SVM::C_SVC;
kernelType = SVM::RBF;
degree = 0;
gamma = 1;
coef0 = 0;
C = 1;
nu = 0;
p = 0;
termCrit = TermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 1000, FLT_EPSILON );
}
SVM::Params::Params( int _svmType, int _kernelType,
double _degree, double _gamma, double _coef0,
double _Con, double _nu, double _p,
const Mat& _classWeights, TermCriteria _termCrit )
{
svmType = _svmType;
kernelType = _kernelType;
degree = _degree;
gamma = _gamma;
coef0 = _coef0;
C = _Con;
nu = _nu;
p = _p;
classWeights = _classWeights;
termCrit = _termCrit;
}
SvmParams( int _svmType, int _kernelType,
double _degree, double _gamma, double _coef0,
double _Con, double _nu, double _p,
const Mat& _classWeights, TermCriteria _termCrit )
{
svmType = _svmType;
kernelType = _kernelType;
degree = _degree;
gamma = _gamma;
coef0 = _coef0;
C = _Con;
nu = _nu;
p = _p;
classWeights = _classWeights;
termCrit = _termCrit;
}
};
/////////////////////////////////////// SVM kernel ///////////////////////////////////////
class SVMKernelImpl : public SVM::Kernel
{
public:
SVMKernelImpl()
{
}
SVMKernelImpl( const SVM::Params& _params )
SVMKernelImpl( const SvmParams& _params = SvmParams() )
{
params = _params;
}
virtual ~SVMKernelImpl()
{
}
int getType() const
{
return params.kernelType;
@@ -327,7 +333,7 @@ public:
}
}
SVM::Params params;
SvmParams params;
};
@@ -1185,7 +1191,7 @@ public:
int cache_size;
int max_cache_size;
Mat samples;
SVM::Params params;
SvmParams params;
vector<KernelRow> lru_cache;
int lru_first;
int lru_last;
@@ -1215,6 +1221,7 @@ public:
SVMImpl()
{
clear();
checkParams();
}
~SVMImpl()
@@ -1235,33 +1242,69 @@ public:
return sv;
}
void setParams( const Params& _params, const Ptr<Kernel>& _kernel )
CV_IMPL_PROPERTY(int, Type, params.svmType)
CV_IMPL_PROPERTY(double, Gamma, params.gamma)
CV_IMPL_PROPERTY(double, Coef0, params.coef0)
CV_IMPL_PROPERTY(double, Degree, params.degree)
CV_IMPL_PROPERTY(double, C, params.C)
CV_IMPL_PROPERTY(double, Nu, params.nu)
CV_IMPL_PROPERTY(double, P, params.p)
CV_IMPL_PROPERTY_S(cv::Mat, ClassWeights, params.classWeights)
CV_IMPL_PROPERTY_S(cv::TermCriteria, TermCriteria, params.termCrit)
int getKernelType() const
{
params = _params;
return params.kernelType;
}
void setKernel(int kernelType)
{
params.kernelType = kernelType;
if (kernelType != CUSTOM)
kernel = makePtr<SVMKernelImpl>(params);
}
void setCustomKernel(const Ptr<Kernel> &_kernel)
{
params.kernelType = CUSTOM;
kernel = _kernel;
}
void checkParams()
{
int kernelType = params.kernelType;
if (kernelType != CUSTOM)
{
if( kernelType != LINEAR && kernelType != POLY &&
kernelType != SIGMOID && kernelType != RBF &&
kernelType != INTER && kernelType != CHI2)
CV_Error( CV_StsBadArg, "Unknown/unsupported kernel type" );
if( kernelType == LINEAR )
params.gamma = 1;
else if( params.gamma <= 0 )
CV_Error( CV_StsOutOfRange, "gamma parameter of the kernel must be positive" );
if( kernelType != SIGMOID && kernelType != POLY )
params.coef0 = 0;
else if( params.coef0 < 0 )
CV_Error( CV_StsOutOfRange, "The kernel parameter <coef0> must be positive or zero" );
if( kernelType != POLY )
params.degree = 0;
else if( params.degree <= 0 )
CV_Error( CV_StsOutOfRange, "The kernel parameter <degree> must be positive" );
kernel = makePtr<SVMKernelImpl>(params);
}
else
{
if (!kernel)
CV_Error( CV_StsBadArg, "Custom kernel is not set" );
}
int svmType = params.svmType;
if( kernelType != LINEAR && kernelType != POLY &&
kernelType != SIGMOID && kernelType != RBF &&
kernelType != INTER && kernelType != CHI2)
CV_Error( CV_StsBadArg, "Unknown/unsupported kernel type" );
if( kernelType == LINEAR )
params.gamma = 1;
else if( params.gamma <= 0 )
CV_Error( CV_StsOutOfRange, "gamma parameter of the kernel must be positive" );
if( kernelType != SIGMOID && kernelType != POLY )
params.coef0 = 0;
else if( params.coef0 < 0 )
CV_Error( CV_StsOutOfRange, "The kernel parameter <coef0> must be positive or zero" );
if( kernelType != POLY )
params.degree = 0;
else if( params.degree <= 0 )
CV_Error( CV_StsOutOfRange, "The kernel parameter <degree> must be positive" );
if( svmType != C_SVC && svmType != NU_SVC &&
svmType != ONE_CLASS && svmType != EPS_SVR &&
svmType != NU_SVR )
@@ -1285,28 +1328,18 @@ public:
if( svmType != C_SVC )
params.classWeights.release();
termCrit = params.termCrit;
if( !(termCrit.type & TermCriteria::EPS) )
termCrit.epsilon = DBL_EPSILON;
termCrit.epsilon = std::max(termCrit.epsilon, DBL_EPSILON);
if( !(termCrit.type & TermCriteria::COUNT) )
termCrit.maxCount = INT_MAX;
termCrit.maxCount = std::max(termCrit.maxCount, 1);
if( _kernel )
kernel = _kernel;
else
kernel = makePtr<SVMKernelImpl>(params);
if( !(params.termCrit.type & TermCriteria::EPS) )
params.termCrit.epsilon = DBL_EPSILON;
params.termCrit.epsilon = std::max(params.termCrit.epsilon, DBL_EPSILON);
if( !(params.termCrit.type & TermCriteria::COUNT) )
params.termCrit.maxCount = INT_MAX;
params.termCrit.maxCount = std::max(params.termCrit.maxCount, 1);
}
Params getParams() const
void setParams( const SvmParams& _params)
{
return params;
}
Ptr<Kernel> getKernel() const
{
return kernel;
params = _params;
checkParams();
}
int getSVCount(int i) const
@@ -1335,9 +1368,9 @@ public:
_responses.convertTo(_yf, CV_32F);
bool ok =
svmType == ONE_CLASS ? Solver::solve_one_class( _samples, params.nu, kernel, _alpha, sinfo, termCrit ) :
svmType == EPS_SVR ? Solver::solve_eps_svr( _samples, _yf, params.p, params.C, kernel, _alpha, sinfo, termCrit ) :
svmType == NU_SVR ? Solver::solve_nu_svr( _samples, _yf, params.nu, params.C, kernel, _alpha, sinfo, termCrit ) : false;
svmType == ONE_CLASS ? Solver::solve_one_class( _samples, params.nu, kernel, _alpha, sinfo, params.termCrit ) :
svmType == EPS_SVR ? Solver::solve_eps_svr( _samples, _yf, params.p, params.C, kernel, _alpha, sinfo, params.termCrit ) :
svmType == NU_SVR ? Solver::solve_nu_svr( _samples, _yf, params.nu, params.C, kernel, _alpha, sinfo, params.termCrit ) : false;
if( !ok )
return false;
@@ -1397,7 +1430,7 @@ public:
//check that while cross-validation there were the samples from all the classes
if( class_ranges[class_count] <= 0 )
CV_Error( CV_StsBadArg, "While cross-validation one or more of the classes have "
"been fell out of the sample. Try to enlarge <CvSVMParams::k_fold>" );
"been fell out of the sample. Try to enlarge <Params::k_fold>" );
if( svmType == NU_SVC )
{
@@ -1448,10 +1481,10 @@ public:
DecisionFunc df;
bool ok = params.svmType == C_SVC ?
Solver::solve_c_svc( temp_samples, temp_y, Cp, Cn,
kernel, _alpha, sinfo, termCrit ) :
kernel, _alpha, sinfo, params.termCrit ) :
params.svmType == NU_SVC ?
Solver::solve_nu_svc( temp_samples, temp_y, params.nu,
kernel, _alpha, sinfo, termCrit ) :
kernel, _alpha, sinfo, params.termCrit ) :
false;
if( !ok )
return false;
@@ -1557,6 +1590,8 @@ public:
{
clear();
checkParams();
int svmType = params.svmType;
Mat samples = data->getTrainSamples();
Mat responses;
@@ -1586,6 +1621,8 @@ public:
ParamGrid nu_grid, ParamGrid coef_grid, ParamGrid degree_grid,
bool balanced )
{
checkParams();
int svmType = params.svmType;
RNG rng((uint64)-1);
@@ -1708,7 +1745,7 @@ public:
int test_sample_count = (sample_count + k_fold/2)/k_fold;
int train_sample_count = sample_count - test_sample_count;
Params best_params = params;
SvmParams best_params = params;
double min_error = FLT_MAX;
int rtype = responses.type();
@@ -1729,7 +1766,7 @@ public:
FOR_IN_GRID(degree, degree_grid)
{
// make sure we updated the kernel and other parameters
setParams(params, Ptr<Kernel>() );
setParams(params);
double error = 0;
for( k = 0; k < k_fold; k++ )
@@ -1919,7 +1956,9 @@ public:
kernelType == LINEAR ? "LINEAR" :
kernelType == POLY ? "POLY" :
kernelType == RBF ? "RBF" :
kernelType == SIGMOID ? "SIGMOID" : format("Unknown_%d", kernelType);
kernelType == SIGMOID ? "SIGMOID" :
kernelType == CHI2 ? "CHI2" :
kernelType == INTER ? "INTER" : format("Unknown_%d", kernelType);
fs << "svmType" << svm_type_str;
@@ -2036,7 +2075,7 @@ public:
void read_params( const FileNode& fn )
{
Params _params;
SvmParams _params;
// check for old naming
String svm_type_str = (String)(fn["svm_type"].empty() ? fn["svmType"] : fn["svm_type"]);
@@ -2059,10 +2098,12 @@ public:
kernel_type_str == "LINEAR" ? LINEAR :
kernel_type_str == "POLY" ? POLY :
kernel_type_str == "RBF" ? RBF :
kernel_type_str == "SIGMOID" ? SIGMOID : -1;
kernel_type_str == "SIGMOID" ? SIGMOID :
kernel_type_str == "CHI2" ? CHI2 :
kernel_type_str == "INTER" ? INTER : CUSTOM;
if( kernelType < 0 )
CV_Error( CV_StsParseError, "Missing of invalid SVM kernel type" );
if( kernelType == CUSTOM )
CV_Error( CV_StsParseError, "Invalid SVM kernel type (or custom kernel)" );
_params.svmType = svmType;
_params.kernelType = kernelType;
@@ -2086,7 +2127,7 @@ public:
else
_params.termCrit = TermCriteria( TermCriteria::EPS + TermCriteria::COUNT, 1000, FLT_EPSILON );
setParams( _params, Ptr<Kernel>() );
setParams( _params );
}
void read( const FileNode& fn )
@@ -2154,8 +2195,7 @@ public:
optimize_linear_svm();
}
Params params;
TermCriteria termCrit;
SvmParams params;
Mat class_labels;
int var_count;
Mat sv;
@@ -2167,11 +2207,9 @@ public:
};
Ptr<SVM> SVM::create(const Params& params, const Ptr<SVM::Kernel>& kernel)
Ptr<SVM> SVM::create()
{
Ptr<SVMImpl> p = makePtr<SVMImpl>();
p->setParams(params, kernel);
return p;
return makePtr<SVMImpl>();
}
}