I want to classify a object as positive or negative by using SVM, I wrote the following codes. Is the code is right or any suggestions ?

edit

I have 37 images in my database for each postive and negative for training. I have extracted HOG features of each images and labelled as 'Positive' and 'negative'. its saved into 'Hogfeat' Matrix. Is this code need to be improved or any errors? Now i need to load this features into SVM .(I'm just the learning SVM).I have gone through many links , but its not useful for my codes.Please help me to solve this.

int main()

{

HOGDescriptor hog;
vector<Point> locs;





for (size_t i = 1; i <= 37; ++i)
{

    ostringstream os;
    os << "C:/Users/Sam/Desktop/Images/" << "Content\\" << setw(2) << setfill('0') << i << ".JPG";
    cout << os.str();
    Mat img = imread(os.str(), IMREAD_GRAYSCALE);

    if (!img.data)
    {
        break;
    }
    else
    {

        // obtain feature vector:
        vector<float> featureVector;
        hog.compute(img, featureVector, Size(32, 32), Size(0, 0));


        //HOG features computed for img are stored in featureVector vector to make it into a matrix

        Mat Hogfeat(featureVector.size(), 1, CV_32FC1); //label 1

        for (int i = 0; i < featureVector.size(); i++)
        {
            Hogfeat.at<float>(i, 0) = featureVector.at(i);
        }



        //HOG features are stored in Hogfeat matrix
        cout << Hogfeat;
        cout << "Postive Images";
        system("PAUSE");
    }
}
for (size_t i = 1; i <= 37; ++i)
{

    ostringstream os;
    os << "C:/Users/Sam/Desktop/Images/" << "No humans\\" << setw(2) << setfill('0') << i << ".JPG";
    cout << os.str();
    Mat img = imread(os.str(), IMREAD_GRAYSCALE);

    if (!img.data)
    {
        break;
    }
    else
    {

        // obtain feature vector:
        vector<float> featureVector;
        hog.compute(img, featureVector, Size(32, 32), Size(0, 0));


        //HOG features computed for img are stored in featureVector vector to make it into a matrix

        Mat Hogfeat(featureVector.size(), -1, CV_32FC1); //label -1

        for (int i = 0; i < featureVector.size(); i++)
        {
            Hogfeat.at<float>(i, 0) = featureVector.at(i);
        }



        //HOG features are stored in Hogfeat matrix
        cout << Hogfeat;
        cout << "Negative Images";
    }

}





return 0;

}