potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-31 00:03:03 +04:00
Code Packages 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
modules/ml/test/test_mltests2.cpp
+52 -37
View File
@@ -73,30 +73,14 @@ int str_to_svm_kernel_type( String& str )
return -1;
}
Ptr<SVM> svm_train_auto( Ptr<TrainData> _data, SVM::Params _params,
int k_fold, ParamGrid C_grid, ParamGrid gamma_grid,
ParamGrid p_grid, ParamGrid nu_grid, ParamGrid coef_grid,
ParamGrid degree_grid )
{
Mat _train_data = _data->getSamples();
Mat _responses = _data->getResponses();
Mat _var_idx = _data->getVarIdx();
Mat _sample_idx = _data->getTrainSampleIdx();
Ptr<SVM> svm = SVM::create(_params);
if( svm->trainAuto( _data, k_fold, C_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid ) )
return svm;
return Ptr<SVM>();
}
// 4. em
// 5. ann
int str_to_ann_train_method( String& str )
{
if( !str.compare("BACKPROP") )
return ANN_MLP::Params::BACKPROP;
return ANN_MLP::BACKPROP;
if( !str.compare("RPROP") )
return ANN_MLP::Params::RPROP;
return ANN_MLP::RPROP;
CV_Error( CV_StsBadArg, "incorrect ann train method string" );
return -1;
}
@@ -343,16 +327,16 @@ int CV_MLBaseTest::train( int testCaseIdx )
String svm_type_str, kernel_type_str;
modelParamsNode["svm_type"] >> svm_type_str;
modelParamsNode["kernel_type"] >> kernel_type_str;
SVM::Params params;
params.svmType = str_to_svm_type( svm_type_str );
params.kernelType = str_to_svm_kernel_type( kernel_type_str );
modelParamsNode["degree"] >> params.degree;
modelParamsNode["gamma"] >> params.gamma;
modelParamsNode["coef0"] >> params.coef0;
modelParamsNode["C"] >> params.C;
modelParamsNode["nu"] >> params.nu;
modelParamsNode["p"] >> params.p;
model = SVM::create(params);
Ptr<SVM> m = SVM::create();
m->setType(str_to_svm_type( svm_type_str ));
m->setKernel(str_to_svm_kernel_type( kernel_type_str ));
m->setDegree(modelParamsNode["degree"]);
m->setGamma(modelParamsNode["gamma"]);
m->setCoef0(modelParamsNode["coef0"]);
m->setC(modelParamsNode["C"]);
m->setNu(modelParamsNode["nu"]);
m->setP(modelParamsNode["p"]);
model = m;
}
else if( modelName == CV_EM )
{
@@ -371,9 +355,13 @@ int CV_MLBaseTest::train( int testCaseIdx )
data->getVarIdx(), data->getTrainSampleIdx());
int layer_sz[] = { data->getNAllVars(), 100, 100, (int)cls_map.size() };
Mat layer_sizes( 1, (int)(sizeof(layer_sz)/sizeof(layer_sz[0])), CV_32S, layer_sz );
model = ANN_MLP::create(ANN_MLP::Params(layer_sizes, ANN_MLP::SIGMOID_SYM, 0, 0,
TermCriteria(TermCriteria::COUNT,300,0.01),
str_to_ann_train_method(train_method_str), param1, param2));
Ptr<ANN_MLP> m = ANN_MLP::create();
m->setLayerSizes(layer_sizes);
m->setActivationFunction(ANN_MLP::SIGMOID_SYM, 0, 0);
m->setTermCriteria(TermCriteria(TermCriteria::COUNT,300,0.01));
m->setTrainMethod(str_to_ann_train_method(train_method_str), param1, param2);
model = m;
}
else if( modelName == CV_DTREE )
{
@@ -386,8 +374,18 @@ int CV_MLBaseTest::train( int testCaseIdx )
modelParamsNode["max_categories"] >> MAX_CATEGORIES;
modelParamsNode["cv_folds"] >> CV_FOLDS;
modelParamsNode["is_pruned"] >> IS_PRUNED;
model = DTrees::create(DTrees::Params(MAX_DEPTH, MIN_SAMPLE_COUNT, REG_ACCURACY, USE_SURROGATE,
MAX_CATEGORIES, CV_FOLDS, false, IS_PRUNED, Mat() ));
Ptr<DTrees> m = DTrees::create();
m->setMaxDepth(MAX_DEPTH);
m->setMinSampleCount(MIN_SAMPLE_COUNT);
m->setRegressionAccuracy(REG_ACCURACY);
m->setUseSurrogates(USE_SURROGATE);
m->setMaxCategories(MAX_CATEGORIES);
m->setCVFolds(CV_FOLDS);
m->setUse1SERule(false);
m->setTruncatePrunedTree(IS_PRUNED);
m->setPriors(Mat());
model = m;
}
else if( modelName == CV_BOOST )
{
@@ -401,7 +399,15 @@ int CV_MLBaseTest::train( int testCaseIdx )
modelParamsNode["weight_trim_rate"] >> WEIGHT_TRIM_RATE;
modelParamsNode["max_depth"] >> MAX_DEPTH;
//modelParamsNode["use_surrogate"] >> USE_SURROGATE;
model = Boost::create( Boost::Params(BOOST_TYPE, WEAK_COUNT, WEIGHT_TRIM_RATE, MAX_DEPTH, USE_SURROGATE, Mat()) );
Ptr<Boost> m = Boost::create();
m->setBoostType(BOOST_TYPE);
m->setWeakCount(WEAK_COUNT);
m->setWeightTrimRate(WEIGHT_TRIM_RATE);
m->setMaxDepth(MAX_DEPTH);
m->setUseSurrogates(USE_SURROGATE);
m->setPriors(Mat());
model = m;
}
else if( modelName == CV_RTREES )
{
@@ -416,9 +422,18 @@ int CV_MLBaseTest::train( int testCaseIdx )
modelParamsNode["is_pruned"] >> IS_PRUNED;
modelParamsNode["nactive_vars"] >> NACTIVE_VARS;
modelParamsNode["max_trees_num"] >> MAX_TREES_NUM;
model = RTrees::create(RTrees::Params( MAX_DEPTH, MIN_SAMPLE_COUNT, REG_ACCURACY,
USE_SURROGATE, MAX_CATEGORIES, Mat(), true, // (calc_var_importance == true) <=> RF processes variable importance
NACTIVE_VARS, TermCriteria(TermCriteria::COUNT, MAX_TREES_NUM, OOB_EPS)));
Ptr<RTrees> m = RTrees::create();
m->setMaxDepth(MAX_DEPTH);
m->setMinSampleCount(MIN_SAMPLE_COUNT);
m->setRegressionAccuracy(REG_ACCURACY);
m->setUseSurrogates(USE_SURROGATE);
m->setMaxCategories(MAX_CATEGORIES);
m->setPriors(Mat());
m->setCalculateVarImportance(true);
m->setActiveVarCount(NACTIVE_VARS);
m->setTermCriteria(TermCriteria(TermCriteria::COUNT, MAX_TREES_NUM, OOB_EPS));
model = m;
}
if( !model.empty() )