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Updated ml module interfaces and documentation

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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
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118
samples/cpp/points_classifier.cpp
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@@ -102,7 +102,7 @@ static void predict_and_paint(const Ptr<StatModel>& model, Mat& dst)
static void find_decision_boundary_NBC()
{
// learn classifier
Ptr<NormalBayesClassifier> normalBayesClassifier = StatModel::train<NormalBayesClassifier>(prepare_train_data(), NormalBayesClassifier::Params());
Ptr<NormalBayesClassifier> normalBayesClassifier = StatModel::train<NormalBayesClassifier>(prepare_train_data());
predict_and_paint(normalBayesClassifier, imgDst);
}
@@ -112,15 +112,29 @@ static void find_decision_boundary_NBC()
#if _KNN_
static void find_decision_boundary_KNN( int K )
{
Ptr<KNearest> knn = StatModel::train<KNearest>(prepare_train_data(), KNearest::Params(K, true));
Ptr<KNearest> knn = KNearest::create();
knn->setDefaultK(K);
knn->setIsClassifier(true);
knn->train(prepare_train_data());
predict_and_paint(knn, imgDst);
}
#endif
#if _SVM_
static void find_decision_boundary_SVM( SVM::Params params )
static void find_decision_boundary_SVM( double C )
{
Ptr<SVM> svm = StatModel::train<SVM>(prepare_train_data(), params);
Ptr<SVM> svm = SVM::create();
svm->setType(SVM::C_SVC);
svm->setKernel(SVM::POLY); //SVM::LINEAR;
svm->setDegree(0.5);
svm->setGamma(1);
svm->setCoef0(1);
svm->setNu(0.5);
svm->setP(0);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1000, 0.01));
svm->setC(C);
svm->train(prepare_train_data());
predict_and_paint(svm, imgDst);
Mat sv = svm->getSupportVectors();
@@ -135,16 +149,14 @@ static void find_decision_boundary_SVM( SVM::Params params )
#if _DT_
static void find_decision_boundary_DT()
{
DTrees::Params params;
params.maxDepth = 8;
params.minSampleCount = 2;
params.useSurrogates = false;
params.CVFolds = 0; // the number of cross-validation folds
params.use1SERule = false;
params.truncatePrunedTree = false;
Ptr<DTrees> dtree = StatModel::train<DTrees>(prepare_train_data(), params);
Ptr<DTrees> dtree = DTrees::create();
dtree->setMaxDepth(8);
dtree->setMinSampleCount(2);
dtree->setUseSurrogates(false);
dtree->setCVFolds(0); // the number of cross-validation folds
dtree->setUse1SERule(false);
dtree->setTruncatePrunedTree(false);
dtree->train(prepare_train_data());
predict_and_paint(dtree, imgDst);
}
#endif
@@ -152,15 +164,14 @@ static void find_decision_boundary_DT()
#if _BT_
static void find_decision_boundary_BT()
{
Boost::Params params( Boost::DISCRETE, // boost_type
100, // weak_count
0.95, // weight_trim_rate
2, // max_depth
false, //use_surrogates
Mat() // priors
);
Ptr<Boost> boost = StatModel::train<Boost>(prepare_train_data(), params);
Ptr<Boost> boost = Boost::create();
boost->setBoostType(Boost::DISCRETE);
boost->setWeakCount(100);
boost->setWeightTrimRate(0.95);
boost->setMaxDepth(2);
boost->setUseSurrogates(false);
boost->setPriors(Mat());
boost->train(prepare_train_data());
predict_and_paint(boost, imgDst);
}
@@ -185,18 +196,17 @@ static void find_decision_boundary_GBT()
#if _RF_
static void find_decision_boundary_RF()
{
RTrees::Params params( 4, // max_depth,
2, // min_sample_count,
0.f, // regression_accuracy,
false, // use_surrogates,
16, // max_categories,
Mat(), // priors,
false, // calc_var_importance,
1, // nactive_vars,
TermCriteria(TermCriteria::MAX_ITER, 5, 0) // max_num_of_trees_in_the_forest,
);
Ptr<RTrees> rtrees = StatModel::train<RTrees>(prepare_train_data(), params);
Ptr<RTrees> rtrees = RTrees::create();
rtrees->setMaxDepth(4);
rtrees->setMinSampleCount(2);
rtrees->setRegressionAccuracy(0.f);
rtrees->setUseSurrogates(false);
rtrees->setMaxCategories(16);
rtrees->setPriors(Mat());
rtrees->setCalculateVarImportance(false);
rtrees->setActiveVarCount(1);
rtrees->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 5, 0));
rtrees->train(prepare_train_data());
predict_and_paint(rtrees, imgDst);
}
@@ -205,9 +215,6 @@ static void find_decision_boundary_RF()
#if _ANN_
static void find_decision_boundary_ANN( const Mat& layer_sizes )
{
ANN_MLP::Params params(layer_sizes, ANN_MLP::SIGMOID_SYM, 1, 1, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 300, FLT_EPSILON),
ANN_MLP::Params::BACKPROP, 0.001);
Mat trainClasses = Mat::zeros( (int)trainedPoints.size(), (int)classColors.size(), CV_32FC1 );
for( int i = 0; i < trainClasses.rows; i++ )
{
@@ -217,7 +224,12 @@ static void find_decision_boundary_ANN( const Mat& layer_sizes )
Mat samples = prepare_train_samples(trainedPoints);
Ptr<TrainData> tdata = TrainData::create(samples, ROW_SAMPLE, trainClasses);
Ptr<ANN_MLP> ann = StatModel::train<ANN_MLP>(tdata, params);
Ptr<ANN_MLP> ann = ANN_MLP::create();
ann->setLayerSizes(layer_sizes);
ann->setActivationFunction(ANN_MLP::SIGMOID_SYM, 1, 1);
ann->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 300, FLT_EPSILON));
ann->setTrainMethod(ANN_MLP::BACKPROP, 0.001);
ann->train(tdata);
predict_and_paint(ann, imgDst);
}
#endif
@@ -247,8 +259,11 @@ static void find_decision_boundary_EM()
// learn models
if( !modelSamples.empty() )
{
em_models[i] = EM::train(modelSamples, noArray(), noArray(), noArray(),
EM::Params(componentCount, EM::COV_MAT_DIAGONAL));
Ptr<EM> em = EM::create();
em->setClustersNumber(componentCount);
em->setCovarianceMatrixType(EM::COV_MAT_DIAGONAL);
em->trainEM(modelSamples, noArray(), noArray(), noArray());
em_models[i] = em;
}
}
@@ -332,33 +347,20 @@ int main()
imshow( "NormalBayesClassifier", imgDst );
#endif
#if _KNN_
int K = 3;
find_decision_boundary_KNN( K );
find_decision_boundary_KNN( 3 );
imshow( "kNN", imgDst );
K = 15;
find_decision_boundary_KNN( K );
find_decision_boundary_KNN( 15 );
imshow( "kNN2", imgDst );
#endif
#if _SVM_
//(1)-(2)separable and not sets
SVM::Params params;
params.svmType = SVM::C_SVC;
params.kernelType = SVM::POLY; //CvSVM::LINEAR;
params.degree = 0.5;
params.gamma = 1;
params.coef0 = 1;
params.C = 1;
params.nu = 0.5;
params.p = 0;
params.termCrit = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1000, 0.01);
find_decision_boundary_SVM( params );
find_decision_boundary_SVM( 1 );
imshow( "classificationSVM1", imgDst );
params.C = 10;
find_decision_boundary_SVM( params );
find_decision_boundary_SVM( 10 );
imshow( "classificationSVM2", imgDst );
#endif