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hi everyone, anyway i use to this way to configure the SVM. If anyone has better idea, any comment, any suggestion or whatever thing about following code, plz comment it here. i use only 30 samples for one character and 16 features for one sample, i know thats not enough at all. but to get the clear idea about SVM, i use this way.

    float labels[1080][1] = {1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,  1.0,
                         2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,  2.0,
                         3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,  3.0,
                         4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,  4.0,
                         5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,  5.0,
                         6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,  6.0,
                         7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,  7.0,
                         8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,  8.0,
                         9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,  9.0,
                        10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0,                     
                        11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0,
                        12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0,
                        13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0, 13.0,
                        14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0, 14.0,
                        15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0, 15.0,
                        16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0,
                        17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0, 17.0,
                        18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0, 18.0,
                        19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0, 19.0,
                        20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0,
                        21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0, 21.0,
                        22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0,
                        23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0,
                        24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0,
                        25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0,
                        26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0, 26.0,
                        27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0, 27.0,
                        28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0, 28.0,
                        29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0,
                        30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0, 30.0,
                        31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0, 31.0,
                        32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0, 32.0,
                        33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0, 33.0,
                        34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0, 34.0,
                        35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0, 35.0,
                        36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0, 36.0};

Mat matlabesls(1080,1, CV_32FC1, labels);
Mat mattrainingDataMat(1080, 16, CV_32FC1, ifarr_readtrainingdata);
//CvSVMParams params;
params.svm_type    = CvSVM::C_SVC;
params.kernel_type = CvSVM::RBF;
params.term_crit   = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
//CvSVM SVM;
SVM.train(mattrainingDataMat,matlabesls,Mat(),Mat(),params);