支持向量机的介绍(3)

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Mat trainData(2*NTRAINING_SAMPLES, 2, CV_32FC1); Mat labels (2*NTRAINING_SAMPLES, 1, CV_32FC1);

RNG rng(100); // Random value generation class

// Set up the linearly separable part of the training data intnLinearSamples= (int) (FRAC_LINEAR_SEP * NTRAINING_SAMPLES);

// Generate random points for the class 1

Mat trainClass=trainData.rowRange(0, nLinearSamples); // The x coordinate of the points is in [0, 0.4) Mat c =trainClass.colRange(0, 1);

rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(0.4* WIDTH)); // The y coordinate of the points is in [0, 1) c =trainClass.colRange(1,2);

rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));

// Generate random points for the class 2

trainClass=trainData.rowRange(2*NTRAINING_SAMPLES-nLinearSamples, 2*NTRAINING_SAMPLES);

// The x coordinate of the points is in [0.6, 1] c =trainClass.colRange(0 , 1);

rng.fill(c, RNG::UNIFORM, Scalar(0.6*WIDTH), Scalar(WIDTH)); // The y coordinate of the points is in [0, 1) c =trainClass.colRange(1,2);

rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));

//------------------ Set up the non-linearly separable part of the training data ---------------

// 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)

c =trainClass.colRange(0,1);

rng.fill(c, RNG::UNIFORM, Scalar(0.4*WIDTH), Scalar(0.6*WIDTH)); // The y coordinate of the points is in [0, 1) c =trainClass.colRange(1,2);

rng.fill(c, RNG::UNIFORM, Scalar(1), Scalar(HEIGHT));

//------------------------- Set up the labels for the classes --------------------------------- labels.rowRange( 0,

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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 --------------------

CvSVMParamsparams;

params.svm_type= SVM::C_SVC; params.C=0.1;

params.kernel_type= SVM::LINEAR;

params.term_crit=TermCriteria(CV_TERMCRIT_ITER, (int)1e7, 1e-6);

//------------------------ 3. Train the svm ---------------------------------------------------- cout<<\<

svm.train(trainData, labels, Mat(), Mat(), params); cout<<\<

//------------------------ 4. Show the decision regions ----------------------------------------

Vec3b green(0,100,0), blue (100,0,0); for (int i =0; i

Mat sampleMat= (Mat_(1,2) << i, j); float response =svm.predict(sampleMat);

if (response ==1) I.at(j, i) = green; elseif (response ==2) I.at(j, i) = blue; }

//----------------------- 5. Show the training data --------------------------------------------

int thick =-1; intlineType=8; floatpx, py; // Class 1

for (int i =0; i < NTRAINING_SAMPLES; ++i) {

px= trainData.at(i,0); py= trainData.at(i,1);

circle(I, Point( (int) px, (int) py ), 3, Scalar(0, 255, 0), thick, lineType);

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}

// Class 2

for (int i = NTRAINING_SAMPLES; i <2*NTRAINING_SAMPLES; ++i) {

px= trainData.at(i,0); py= trainData.at(i,1);

circle(I, Point( (int) px, (int) py ), 3, Scalar(255, 0, 0), thick, lineType); }

//------------------------- 6. Show support vectors --------------------------------------------

thick =2; lineType=8;

int x =svm.get_support_vector_count();

for (int i =0; i < x; ++i) {

constfloat* v =svm.get_support_vector(i); circle( I, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thick, lineType); }

imwrite(\, I); // save the Image imshow(\, I); // show it to the user

waitKey(0); }

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解释

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