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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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29
samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp
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@@ -39,6 +39,7 @@ int main()
// Set up the linearly separable part of the training data
int nLinearSamples = (int) (FRAC_LINEAR_SEP * NTRAINING_SAMPLES);
//! [setup1]
// Generate random points for the class 1
Mat trainClass = trainData.rowRange(0, nLinearSamples);
// The x coordinate of the points is in [0, 0.4)
@@ -56,9 +57,10 @@ int main()
// The y coordinate of the points is in [0, 1)
c = trainClass.colRange(1,2);
rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
//! [setup1]
//------------------ Set up the non-linearly separable part of the training data ---------------
//! [setup2]
// Generate random points for the classes 1 and 2
trainClass = trainData.rowRange( nLinearSamples, 2*NTRAINING_SAMPLES-nLinearSamples);
// The x coordinate of the points is in [0.4, 0.6)
@@ -67,24 +69,28 @@ int main()
// The y coordinate of the points is in [0, 1)
c = trainClass.colRange(1,2);
rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));
//! [setup2]
//------------------------- Set up the labels for the classes ---------------------------------
labels.rowRange( 0, NTRAINING_SAMPLES).setTo(1); // Class 1
labels.rowRange(NTRAINING_SAMPLES, 2*NTRAINING_SAMPLES).setTo(2); // Class 2
//------------------------ 2. Set up the support vector machines parameters --------------------
SVM::Params params;
params.svmType = SVM::C_SVC;
params.C = 0.1;
params.kernelType = SVM::LINEAR;
params.termCrit = TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6);
//------------------------ 3. Train the svm ----------------------------------------------------
cout << "Starting training process" << endl;
Ptr<SVM> svm = StatModel::train<SVM>(trainData, ROW_SAMPLE, labels, params);
//! [init]
Ptr<SVM> svm = SVM::create();
svm->setType(SVM::C_SVC);
svm->setC(0.1);
svm->setKernel(SVM::LINEAR);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6));
//! [init]
//! [train]
svm->train(trainData, ROW_SAMPLE, labels);
//! [train]
cout << "Finished training process" << endl;
//------------------------ 4. Show the decision regions ----------------------------------------
//! [show]
Vec3b green(0,100,0), blue (100,0,0);
for (int i = 0; i < I.rows; ++i)
for (int j = 0; j < I.cols; ++j)
@@ -95,8 +101,10 @@ int main()
if (response == 1) I.at<Vec3b>(j, i) = green;
else if (response == 2) I.at<Vec3b>(j, i) = blue;
}
//! [show]
//----------------------- 5. Show the training data --------------------------------------------
//! [show_data]
int thick = -1;
int lineType = 8;
float px, py;
@@ -114,8 +122,10 @@ int main()
py = trainData.at<float>(i,1);
circle(I, Point( (int) px, (int) py ), 3, Scalar(255, 0, 0), thick, lineType);
}
//! [show_data]
//------------------------- 6. Show support vectors --------------------------------------------
//! [show_vectors]
thick = 2;
lineType = 8;
Mat sv = svm->getSupportVectors();
@@ -125,6 +135,7 @@ int main()
const float* v = sv.ptr<float>(i);
circle( I, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thick, lineType);
}
//! [show_vectors]
imwrite("result.png", I); // save the Image
imshow("SVM for Non-Linear Training Data", I); // show it to the user