int main()
{
srand(time(0));
SVMClassifier svm;
//Do a simple test of loading a pre-made model, testing 2 vectors, and saving it
double testVectorPos[] = {.7,1,1,-.32,-.105,-1,1,-.4198,-1,-.2258,1,-1};
double testVectorNeg[] = {.58,-1,.3333,-.6,1,-1,1,.358779,-1,-.484,-1,1};
unsigned int testVectorSize = 12;
svm.loadModel("svm.model");
double probability;
probability = svm.classifyVector(testVectorPos,testVectorSize);
cout << "Test VectorPos Probability is " << probability << endl;
probability = svm.classifyVector(testVectorNeg,testVectorSize);
cout << "Test VectorNeg Probability is " << probability << endl;
svm.saveModel("svmNew1.model");
//Create a new model that exists of a simple cluster in the second quadrant of a 2d grid being positive, and the rest being negative (x,y)
testVectorSize = 2;
unsigned int numSamples = 1000;
double **trainVec = new double*[numSamples];
bool *trainPolarities = new bool[numSamples];
for (unsigned int vecIter = 0; vecIter < numSamples; vecIter++)
{
trainVec[vecIter] = new double[2];
//Pos
if (vecIter <= (numSamples - 1)/2)
{
trainVec[vecIter][0] = -1*rand()/(double)RAND_MAX;
trainVec[vecIter][1] = rand()/(double)RAND_MAX;
trainPolarities[vecIter] = true;
}
//Neg
else
{
trainVec[vecIter][0] = rand()/(double)RAND_MAX;
trainVec[vecIter][1] = -1*rand()/(double)RAND_MAX;
trainPolarities[vecIter] = false;
}
}
svm.trainModel(trainVec, trainPolarities, numSamples, testVectorSize);
svm.saveModel("svmNew2.model");
for (unsigned int testVecIter = 0; testVecIter < numSamples; testVecIter++)
{
probability = svm.classifyVector(trainVec[testVecIter],testVectorSize);
cout << "Test Probability is " << probability << endl;
}
delete [] trainPolarities;
for (unsigned int vecIter = 0; vecIter < numSamples; vecIter++)
delete [] trainVec[vecIter];
delete [] trainVec;
return 0;
}
