How to use libVMR in Java

/**
 * Example for libVMR
 * @author Yury V. Reshetov
 * @version 1.0
 */

import libvmr.*;

public class Example {

        public static void main(String[] args) {
                // Create an array of data for the task - XOR
                // Columns in the beginning - the input data (v0 and v1)
                // The last column - the output values
                double[][] data = {
                        {0, 0, 0},
                        {0, 1, 1},
                        {1, 0, 1},
                        {1, 1, 0},
                };

                // Triple copy of the data
                // For big data reproduce copies are not necessary
                double[][] patterns = new double[data.length * 3][data[0].length];
                for (int i = 0; i < 3; i++) {
                        for (int j = 0; j < data.length; j++) {
                                for (int n = 0; n < data[0].length; n++) {
                                        patterns[i*3 + j][n] = data[j][n];
                                }
                        }
                }

                // Create an instance of the class VMR
                VMR vmr = new VMR();

                //Try to train
                if (vmr.train(patterns)) {
                        // Let's see the result
                        double[][] testdata = {
                                        {0, 0},
                                        {0, 1},
                                        {1, 0},
                                        {1, 1},
                                }; 
                        for (int i = 0; i < testdata.length; i++) {
                                double result = (vmr.getDecision(testdata[i]) + 1d) / 2d;
                                System.out.println("v0 = " + testdata[i][0] + ", v1 = " + testdata[i][1] + ", result = " + result);
                        }

                        System.out.println();
                        System.out.println("Sensitivity of generalization abiliy: " + vmr.getSensitivity() + "%");
                        System.out.println("Specificity of generalization ability: " + vmr.getSpecificity() + "%");
                        System.out.println("Generalization abiliy: " + vmr.getGeneralizationAbility() + "%");
                        System.out.println();
                        System.out.println("Formula:");
                        System.out.println();
                        System.out.println(vmr.getFormula());
                } else {
                        System.out.println("Bad data");
                }
        }

}