Jumping Sumo Training Code

drone.ega

[HEADER]
[HEADER:DATASOURCE]
rawFile=FILE_RAW
sourceFile=
sourceFormat=
sourceHeaders=f
[SETUP]
[SETUP:CONFIG]
allowedClasses=integer,string
csvFormat=decpnt|comma
inputHeaders=f
maxClassCount=50
[SETUP:FILENAMES]
FILE_RANDOMIZE=drone_random.csv
FILE_TRAIN=drone_train.csv
FILE_RAW=drone.csv
FILE_TRAINSET=drone_train.egb
FILE_CLUSTER=drone_cluster.csv
FILE_OUTPUT=drone_output.csv
FILE_EVAL=drone_eval.csv
FILE_ML=drone_train.eg
FILE_NORMALIZE=drone_norm.csv
FILE_EVAL_NORM=drone_eval_norm.csv
[DATA]
[DATA:CONFIG]
goal=regression
[DATA:STATS]
"name","isclass","iscomplete","isint","isreal","amax","amin","mean","sdev","source"
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[DATA:CLASSES]
[NORMALIZE]
[NORMALIZE:CONFIG]
missingValues=DiscardMissing
sourceFile=FILE_TRAIN
targetFile=FILE_NORMALIZE
[NORMALIZE:RANGE]
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"px_751","input",0,"pass",1,0
"px_752","input",0,"pass",1,0
"px_753","input",0,"pass",1,0
"px_754","input",0,"pass",1,0
"px_755","input",0,"pass",1,0
"px_756","input",0,"pass",1,0
"px_757","input",0,"pass",1,0
"px_758","input",0,"pass",1,0
"px_759","input",0,"pass",1,0
"px_760","input",0,"pass",1,0
"px_761","input",0,"pass",1,0
"px_762","input",0,"pass",1,0
"px_763","input",0,"pass",1,0
"px_764","input",0,"pass",1,0
"px_765","input",0,"pass",1,0
"px_766","input",0,"pass",1,0
"px_767","input",0,"pass",1,0
"px_768","input",0,"pass",1,0
"n_turn","output",0,"range",1,0
"n_forward","output",0,"range",1,0
[PROCESS]
[PROCESS:CONFIG]
backwardSize=
forwardSize=
sourceFile=
targetFile=
[PROCESS:FIELDS]
"name","command"
[RANDOMIZE]
[RANDOMIZE:CONFIG]
sourceFile=FILE_RAW
targetFile=FILE_RANDOMIZE
[CLUSTER]
[CLUSTER:CONFIG]
clusters=2
sourceFile=FILE_EVAL
targetFile=FILE_CLUSTER
type=kmeans
[BALANCE]
[BALANCE:CONFIG]
balanceField=
countPer=
sourceFile=
targetFile=
[CODE]
[CODE:CONFIG]
embedData=f
targetFile=FILE_CODE
targetLanguage=NOGENERATION
[SEGREGATE]
[SEGREGATE:CONFIG]
sourceFile=FILE_RANDOMIZE
[SEGREGATE:FILES]
"file","percent"
"FILE_TRAIN",75
"FILE_EVAL",25
[GENERATE]
[GENERATE:CONFIG]
sourceFile=FILE_NORMALIZE
targetFile=FILE_TRAINSET
[ML]
[ML:CONFIG]
architecture=?:B->TANH->500:B->TANH->100:B->LINEAR->?
evalFile=FILE_EVAL
machineLearningFile=FILE_ML
outputFile=FILE_OUTPUT
query=
trainingFile=FILE_TRAINSET
type=feedforward
[ML:TRAIN]
arguments=
cross=
targetError=0.01
type=rprop
[ML:OPCODES]
"code","count"
[TASKS]
[TASKS:task-cluster]
cluster
[TASKS:task-code]
code
[TASKS:task-create]
create
[TASKS:task-evaluate]
evaluate
[TASKS:task-evaluate-raw]
set ML.CONFIG.evalFile="FILE_EVAL_NORM"
set NORMALIZE.CONFIG.sourceFile="FILE_EVAL"
set NORMALIZE.CONFIG.targetFile="FILE_EVAL_NORM"
normalize
evaluate-raw
[TASKS:task-full]
randomize
segregate
normalize
generate
create
train
evaluate
[TASKS:task-generate]
randomize
segregate
normalize
generate
[TASKS:task-train]
train

Generator.java

import org.encog.Encog;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.imgcodecs.Imgcodecs;

import javax.imageio.ImageIO;
import java.awt.image.BufferedImage;
import java.awt.image.DataBufferByte;
import java.io.File;
import java.io.FileWriter;
import java.io.IOException;
import java.text.NumberFormat;

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

    private static final String RESULT_FILE = "drone.csv";

    public static void main(String[] args) {
      generateCSVFromFolder(args[0], args[1]);
    }

    private static void generateCSVFromFolder(String runFolder, String outputDir) {
        File rundir = new File(runFolder);
        File[] runFiles = rundir.listFiles();

        int count = 0;
        double length = runFiles.length;
        double percentage = 0;
        NumberFormat percentFormat = NumberFormat.getPercentInstance();
        percentFormat.setMaximumFractionDigits(1);

        try (FileWriter writer = new FileWriter(new File(outputDir, RESULT_FILE))) {
            for (int i = 0; i < runFiles.length; i++) {
                MotionData data = getData(runFiles[i]);

                StringBuilder sb = new StringBuilder(2000);
                sb.append(data.getPrevTurnSpeed()).append(',');
                sb.append(data.getPrevForwardSpeed()).append(',');

                byte[] image = data.getImage();
                for (int j = 0; j < image.length; j++) {
                    sb.append(image[j]).append(',');
                }

                sb.append(data.getTurnSpeed()).append(',');
                sb.append(data.getForwardSpeed());

                if (i != (runFiles.length - 1)) {
                    sb.append('\n');
                }

                writer.write(sb.toString());

                count++;
                percentage = count / length;
                System.out.println("Progress: " + percentFormat.format(percentage));
            }
        } catch (IOException ex) {
            ex.printStackTrace();
        }
    }

    private static MotionData getData(File dataFile) {
        MotionData result = new MotionData();
        Mat img = Imgcodecs.imread(dataFile.getAbsolutePath(), Imgcodecs.CV_LOAD_IMAGE_GRAYSCALE);
        byte[] buffer = new byte[img.cols() * img.rows()];
        img.get(0, 0, buffer);
        result.setImage(buffer);

        return result;
    }
}

InvalidConverter.java

import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.Size;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;

import java.io.File;
import java.nio.file.Paths;

public class InvalidConverter {
    private static final String FOLDER = "<path_to_invalid_files>";
    private static final String OUT_DIR = "<out_dir>";

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

    public static void main(String[] args) {
        File imgFolder = new File(FOLDER);
        File[] runFiles = imgFolder.listFiles();

        int index = 0;
        for (File imageFile : runFiles) {
            String fileName = Paths.get(OUT_DIR, String.format("junk_%d_0_0_0_0.png", index)).toString();
            Mat img = Imgcodecs.imread(imageFile.getAbsolutePath());
            Imgproc.cvtColor(img, img, Imgproc.COLOR_BGR2GRAY);
            Imgproc.resize(img, img, new Size(32, 24));
            Imgcodecs.imwrite(fileName, img);

            index++;
        }
    }
}

MotionData.java

/**
 * Stores data related to the motion of the Jumping Sumo
 */
public class MotionData {
    private byte prevTurnSpeed;
    private byte prevForwardSpeed;
    private byte turnSpeed;
    private byte forwardSpeed;
    private byte[] image;

    public byte getPrevTurnSpeed() {
        return prevTurnSpeed;
    }

    public void setPrevTurnSpeed(byte prevTurnSpeed) {
        this.prevTurnSpeed = prevTurnSpeed;
    }

    public byte getPrevForwardSpeed() {
        return prevForwardSpeed;
    }

    public void setPrevForwardSpeed(byte prevForwardSpeed) {
        this.prevForwardSpeed = prevForwardSpeed;
    }

    public byte getTurnSpeed() {
        return turnSpeed;
    }

    public void setTurnSpeed(byte turnSpeed) {
        this.turnSpeed = turnSpeed;
    }

    public byte getForwardSpeed() {
        return forwardSpeed;
    }

    public void setForwardSpeed(byte forwardSpeed) {
        this.forwardSpeed = forwardSpeed;
    }

    public byte[] getImage() {
        return image;
    }

    public void setImage(byte[] image) {
        this.image = image;
    }

    @Override
    public String toString() {
        return String.format("%d_%d_%d_%d", prevTurnSpeed, prevForwardSpeed, turnSpeed, forwardSpeed);
    }
}