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made everything compile and even run somehow

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This commit is contained in:
Vadim Pisarevsky
2014-08-03 01:41:09 +04:00
parent 10b60f8d16
commit c20ff6ce19
31 changed files with 11910 additions and 9061 deletions
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40
samples/cpp/points_classifier.cpp
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@@ -102,8 +102,7 @@ static void predict_and_paint(const Ptr<StatModel>& model, Mat& dst)
static void find_decision_boundary_NBC()
{
// learn classifier
Ptr<NormalBayesClassifier> normalBayesClassifier = NormalBayesClassifier::create();
normalBayesClassifier->train(prepare_train_data());
Ptr<NormalBayesClassifier> normalBayesClassifier = StatModel::train<NormalBayesClassifier>(prepare_train_data(), NormalBayesClassifier::Params());
predict_and_paint(normalBayesClassifier, imgDst);
}
@@ -113,10 +112,7 @@ static void find_decision_boundary_NBC()
#if _KNN_
static void find_decision_boundary_KNN( int K )
{
Ptr<KNearest> knn = KNearest::create(true);
knn->setDefaultK(K);
knn->train(prepare_train_data());
Ptr<KNearest> knn = StatModel::train<KNearest>(prepare_train_data(), KNearest::Params(K, true));
predict_and_paint(knn, imgDst);
}
#endif
@@ -124,9 +120,7 @@ static void find_decision_boundary_KNN( int K )
#if _SVM_
static void find_decision_boundary_SVM( SVM::Params params )
{
Ptr<SVM> svm = SVM::create(params);
svm->train(prepare_train_data());
Ptr<SVM> svm = StatModel::train<SVM>(prepare_train_data(), params);
predict_and_paint(svm, imgDst);
Mat sv = svm->getSupportVectors();
@@ -149,8 +143,7 @@ static void find_decision_boundary_DT()
params.use1SERule = false;
params.truncatePrunedTree = false;
Ptr<DTrees> dtree = DTrees::create(params);
dtree->train(prepare_train_data());
Ptr<DTrees> dtree = StatModel::train<DTrees>(prepare_train_data(), params);
predict_and_paint(dtree, imgDst);
}
@@ -167,8 +160,7 @@ static void find_decision_boundary_BT()
Mat() // priors
);
Ptr<Boost> boost = Boost::create(params);
boost->train(prepare_train_data());
Ptr<Boost> boost = StatModel::train<Boost>(prepare_train_data(), params);
predict_and_paint(boost, imgDst);
}
@@ -185,8 +177,7 @@ static void find_decision_boundary_GBT()
false // use_surrogates )
);
Ptr<GBTrees> gbtrees = GBTrees::create(params);
gbtrees->train(prepare_train_data());
Ptr<GBTrees> gbtrees = StatModel::train<GBTrees>(prepare_train_data(), params);
predict_and_paint(gbtrees, imgDst);
}
#endif
@@ -205,8 +196,7 @@ static void find_decision_boundary_RF()
TermCriteria(TermCriteria::MAX_ITER, 5, 0) // max_num_of_trees_in_the_forest,
);
Ptr<RTrees> rtrees = RTrees::create(params);
rtrees->train(prepare_train_data());
Ptr<RTrees> rtrees = StatModel::train<RTrees>(prepare_train_data(), params);
predict_and_paint(rtrees, imgDst);
}
@@ -215,9 +205,8 @@ static void find_decision_boundary_RF()
#if _ANN_
static void find_decision_boundary_ANN( const Mat& layer_sizes )
{
ANN_MLP::Params params(TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 300, FLT_EPSILON),
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);
Ptr<ANN_MLP> ann = ANN_MLP::create(layer_sizes, params, ANN_MLP::SIGMOID_SYM, 1, 1 );
Mat trainClasses = Mat::zeros( trainedPoints.size(), classColors.size(), CV_32FC1 );
for( int i = 0; i < trainClasses.rows; i++ )
@@ -228,7 +217,7 @@ 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);
ann->train(tdata);
Ptr<ANN_MLP> ann = StatModel::train<ANN_MLP>(tdata, params);
predict_and_paint(ann, imgDst);
}
#endif
@@ -340,18 +329,15 @@ int main()
img.copyTo( imgDst );
#if _NBC_
find_decision_boundary_NBC();
namedWindow( "NormalBayesClassifier", WINDOW_AUTOSIZE );
imshow( "NormalBayesClassifier", imgDst );
#endif
#if _KNN_
int K = 3;
find_decision_boundary_KNN( K );
namedWindow( "kNN", WINDOW_AUTOSIZE );
imshow( "kNN", imgDst );
K = 15;
find_decision_boundary_KNN( K );
namedWindow( "kNN2", WINDOW_AUTOSIZE );
imshow( "kNN2", imgDst );
#endif
@@ -369,36 +355,30 @@ int main()
params.termCrit = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1000, 0.01);
find_decision_boundary_SVM( params );
namedWindow( "classificationSVM1", WINDOW_AUTOSIZE );
imshow( "classificationSVM1", imgDst );
params.C = 10;
find_decision_boundary_SVM( params );
namedWindow( "classificationSVM2", WINDOW_AUTOSIZE );
imshow( "classificationSVM2", imgDst );
#endif
#if _DT_
find_decision_boundary_DT();
namedWindow( "DT", WINDOW_AUTOSIZE );
imshow( "DT", imgDst );
#endif
#if _BT_
find_decision_boundary_BT();
namedWindow( "BT", WINDOW_AUTOSIZE );
imshow( "BT", imgDst);
#endif
#if _GBT_
find_decision_boundary_GBT();
namedWindow( "GBT", WINDOW_AUTOSIZE );
imshow( "GBT", imgDst);
#endif
#if _RF_
find_decision_boundary_RF();
namedWindow( "RF", WINDOW_AUTOSIZE );
imshow( "RF", imgDst);
#endif
@@ -408,13 +388,11 @@ int main()
layer_sizes1.at<int>(1) = 5;
layer_sizes1.at<int>(2) = classColors.size();
find_decision_boundary_ANN( layer_sizes1 );
namedWindow( "ANN", WINDOW_AUTOSIZE );
imshow( "ANN", imgDst );
#endif
#if _EM_
find_decision_boundary_EM();
namedWindow( "EM", WINDOW_AUTOSIZE );
imshow( "EM", imgDst );
#endif
}