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for opencv machinelearning you will have to convert your features to float, and flatten them int o a single row per feature. then you stack your features into a single huge Mat, and build a response/labels Mat with one number (the label/id) per feature row:

      [data]         [labels]
      feature1        1
      feature2        1
      feature3        2
      feature4        2
      feature5        1
      ...

the layout will be either ROW_SAMPLE (most commonly used) or COL_SAMPLE

here's some code to try:

import org.opencv.core.*;
import org.opencv.imgproc.*;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.ml.*;
import org.opencv.utils.*;
import java.util.*;


class SimpleSample {
    static{ System.loadLibrary(Core.NATIVE_LIBRARY_NAME); }

    public static void main(String[] args) {
        // samples/data/digits.png, have a look at it.
        Mat digits = Imgcodecs.imread("digits.png", 0);
        // setup train/test data:
        Mat trainData = new Mat(),
            testData = new Mat();
        List<Integer> trainLabs = new ArrayList<Integer>(),
            testLabs = new ArrayList<Integer>();
        // 10 digits a 5 rows:
        for (int r=0; r<50; r++) { 
            // 100 digits per row:
            for (int c=0; c<100; c++) {
                // crop out 1 digit:
                Mat num = digits.submat(new Rect(c*20,r*20,20,20));
                // we need float data for knn:
                num.convertTo(num, CvType.CV_32F);
                // 50/50 train/test split:
                if (c % 2 == 0) {
                    // for opencv ml, each feature has to be a single row:
                    trainData.push_back(num.reshape(1,1));
                    // add a label for that feature (the digit number):
                    trainLabs.add(r/5);
                } else {
                    testData.push_back(num.reshape(1,1));
                    testLabs.add(r/5);
                }
            }                
        }

        // make a Mat of the train labels, and train knn:
        KNearest knn = KNearest.create();
        knn.train(trainData, Ml.ROW_SAMPLE, Converters.vector_int_to_Mat(trainLabs));
        // now test predictions:
        for (int i=0; i<testData.rows(); i++)
        {
            Mat one_feature = testData.row(i);
            int testLabel = testLabs.get(i);

            Mat res = new Mat();
            float p = knn.findNearest(one_feature, 1, res);
            System.out.println(testLabel + " " + p + " " + res.dump());
        }

        //// hmm, the 'real world' test case probably looks more like this:
        //// make sure, you follow the very same preprocessing steps used in the train phase:
        //  Mat one_feature = Imgcodecs.imread("one_digit.png", 0);
        //  Mat feature; one_feature.convertTo(feature, CvTypes.CV_32F);
        //  Imgproc.resize(feature, feature, Size(20,20));
        //  int predicted = knn.findNearest(feature.reshape(1,1), 1);
    }
}