static
    int build_knearest_classifier( char* data_filename,
        char* filename_to_save, char* filename_to_load )
    {
        CvMat* data = 0;
        CvMat* responses = 0;
        CvMat* sample_idx = 0;

        int ok = read_num_class_data( data_filename, 16, &data, &responses );
        int nsamples_all = 0, ntrain_samples = 0;
        int i = 0;
        double train_hr = 0, test_hr = 0;
        CvKNearest knn;
        CvMat* var_importance = 0;

        if( !ok )
        {
            printf( "Could not read the database %s\n", data_filename );
            return -1;
        }

        printf( "The database %s is loaded.\n", data_filename );
        nsamples_all = data->rows;
        //半分を学習に使用
        ntrain_samples = (int)(nsamples_all*0.5);

        // Create or load Random Trees classifier
        if( filename_to_load )
        {
            // load classifier from the specified file
            knn.load( filename_to_load );
            ntrain_samples = 0;
            //KNNかのチェック(未確認)
            if( knn.get_var_count() == 0 )
            {
                printf( "Could not read the classifier %s\n", filename_to_load );
                return -1;
            }
            printf( "The classifier %s is loaded.\n", data_filename );
        }
        else
        {
            // create classifier by using <data> and <responses>
            printf( "Training the classifier ...");

            // 2. create sample_idx
            sample_idx = cvCreateMat( 1, nsamples_all, CV_8UC1 );
            {
                CvMat mat;
                cvGetCols( sample_idx, &mat, 0, ntrain_samples );
                cvSet( &mat, cvRealScalar(1) );

                cvGetCols( sample_idx, &mat, ntrain_samples, nsamples_all );
                cvSetZero( &mat );
            }

            // 3. train classifier
            //32個までfind_nearest()の際に問合せ可能とする
            //パラメータは
            //http://opencv.jp/document/opencvref_ml.html#decl_CvKNearest_train
            //を参照
            //responsesはカテゴリ変数としている
            knn.train(data,responses,sample_idx,false, 32, false);
            printf( "\n");
        }

        // compute prediction error on train and test data
        for( i = 0; i < nsamples_all; i++ )
        {
            double r;
            CvMat sample;
            cvGetRow( data, &sample, i );
            //テストを行なう
            //返り値は近い10個(ここでは10：さっきの32より小さければ良い)で投票を行なった結果
            //近傍のベクトルの座標や距離が欲しい場合はCvMatサンプル数x個数のサイズで受け取る。詳細は
            //http://opencv.jp/document/opencvref_ml.html#decl_CvKNearest_find_nearest
            //を参照
            r = knn.find_nearest( &sample, 10);
              //結果の比較
            printf("predict: %c, responses: %c, %s\n", (unsigned char)r, (unsigned char)responses->data.fl[i],
              fabs((double)r - responses->data.fl[i]) <= FLT_EPSILON?"Good!":"Bad!");
            r = fabs((double)r - responses->data.fl[i]) <= FLT_EPSILON ? 1 : 0;
            if( i < ntrain_samples )
                train_hr += r;
            else
                test_hr += r;
        }

        test_hr /= (double)(nsamples_all-ntrain_samples);
        train_hr /= (double)ntrain_samples;
        printf( "Recognition rate: train = %.1f%%, test = %.1f%%\n",
                train_hr*100., test_hr*100. );

        // Save KNN classifier to file if needed
        if( filename_to_save )
           knn.save( filename_to_save );

        cvReleaseMat( &sample_idx );
        cvReleaseMat( &data );
        cvReleaseMat( &responses );

        return 0;
    }