first prototype learner backup

feedbackloop.learner_backup/.gitignore

+/build/
+logs/

feedbackloop.learner_backup/build.gradle

+repositories {
+    mavenCentral()
+}
+
+sourceCompatibility = 1.8
+
+apply plugin: 'java'
+apply plugin: 'application'
+
+dependencies {
+    compile project(':eraser-base')
+    compile group: 'com.fasterxml.jackson.core', name: 'jackson-databind', version: '2.9.8'
+    compile group: 'org.apache.logging.log4j', name: 'log4j-api', version: '2.11.2'
+    compile group: 'org.apache.logging.log4j', name: 'log4j-core', version: '2.11.2'
+    testCompile group: 'junit', name: 'junit', version: '4.12'
+    testCompile group: 'org.hamcrest', name: 'hamcrest-junit', version: '2.0.0.0'
+    compile group: 'org.encog', name: 'encog-core', version: '3.4'
+}
+
+run {
+    mainClassName = 'de.tudresden.inf.st.eraser.feedbackloop.learner_backup.Main'
+    standardInput = System.in
+    if (project.hasProperty("appArgs")) {
+        args Eval.me(appArgs)
+    }
+}
+
+sourceSets {
+    main {
+        java {
+            srcDir 'src/main/java'
+        }
+    }
+}

feedbackloop.learner_backup/out/production/resources/log4j2.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<Configuration>
+    <Appenders>
+        <Console name="Console">
+            <PatternLayout pattern="%highlight{%d{HH:mm:ss.SSS} %-5level} %c{1.} - %msg%n"/>
+        </Console>
+        <RollingFile name="RollingFile" fileName="logs/eraser.log"
+                    filePattern="logs/eraser-%i.log">
+            <PatternLayout pattern="%d{HH:mm:ss.SSS} %-5level %logger{36} - %msg%n"/>
+            <Policies>
+                <OnStartupTriggeringPolicy/>
+            </Policies>
+            <DefaultRolloverStrategy max="20"/>
+        </RollingFile>
+    </Appenders>
+    <Loggers>
+        <Root level="debug">
+            <AppenderRef ref="Console"/>
+            <AppenderRef ref="RollingFile"/>
+        </Root>
+    </Loggers>
+</Configuration>

feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Learner.java

+package de.tudresden.inf.st.eraser.feedbackloop.learner_backup;
+
+import java.io.File;
+import java.net.MalformedURLException;
+import java.net.URL;
+import java.util.Arrays;
+
+import com.sun.javafx.tools.packager.Log;
+import org.encog.ConsoleStatusReportable;
+import org.encog.Encog;
+import org.encog.bot.BotUtil;
+import org.encog.ml.MLInput;
+import org.encog.ml.MLRegression;
+import org.encog.ml.data.MLData;
+import org.encog.ml.data.versatile.NormalizationHelper;
+import org.encog.ml.data.versatile.VersatileMLDataSet;
+import org.encog.ml.data.versatile.columns.ColumnDefinition;
+import org.encog.ml.data.versatile.columns.ColumnType;
+import org.encog.ml.data.versatile.sources.CSVDataSource;
+import org.encog.ml.data.versatile.sources.VersatileDataSource;
+import org.encog.ml.factory.MLMethodFactory;
+import org.encog.ml.model.EncogModel;
+import org.encog.neural.networks.BasicNetwork;
+import org.encog.util.csv.CSVFormat;
+import org.encog.util.csv.ReadCSV;
+import org.encog.util.simple.EncogUtility;
+import static org.encog.persist.EncogDirectoryPersistence.*;
+
+public class Learner {
+
+    /**
+     * intial train
+     * */
+    private String csv_url_activity;
+    private String csv_url_perference;
+    private String save_activity_model_file = "datasets/backup/activity_model.eg";
+    private String save_perference_model_file = "datasets/backup/preference_model.eg";
+    private File csv_file;
+    private VersatileDataSource souce;
+    private VersatileMLDataSet data;
+    private EncogModel model;
+    private NormalizationHelper activity_helper;
+    private NormalizationHelper preference_helper;
+    private MLRegression best_method;
+    private String[] new_data;
+    private String activity_result;
+    private String preference_result;
+
+    private void activityDataAnalyser(String activity_csv_url){
+        this.csv_url_activity = activity_csv_url;
+        this.csv_file = new File(csv_url_activity);
+        souce = new CSVDataSource(csv_file,false,CSVFormat.DECIMAL_POINT);
+        data = new VersatileMLDataSet(souce);
+        data.defineSourceColumn("monat", 0, ColumnType.continuous);
+        data.defineSourceColumn("day", 1, ColumnType.continuous);
+        data.defineSourceColumn("hour", 2, ColumnType.continuous);
+        data.defineSourceColumn("minute", 3, ColumnType.continuous);
+        ColumnDefinition outputColumn = data.defineSourceColumn("labels", 4, ColumnType.continuous);
+        data.defineSingleOutputOthersInput(outputColumn);
+        data.analyze();
+        System.out.println("get data ");
+        model = new EncogModel(data);
+        model.selectMethod(data, MLMethodFactory.TYPE_FEEDFORWARD);
+        data.normalize();
+        activity_helper = data.getNormHelper();
+        System.out.println(activity_helper.toString());
+    }
+
+    private void perferenceDataAnalyser(String perference_csv_url){
+        this.csv_url_perference = perference_csv_url;
+        this.csv_file = new File(this.csv_url_perference);
+        souce = new CSVDataSource(csv_file,false,CSVFormat.DECIMAL_POINT);
+        data = new VersatileMLDataSet(souce);
+        data.defineSourceColumn("activity", 0, ColumnType.continuous);
+        data.defineSourceColumn("brightness", 1, ColumnType.continuous);
+        data.defineSourceColumn("time", 2, ColumnType.continuous);
+        data.defineSourceColumn("minute", 3, ColumnType.continuous);
+        ColumnDefinition outputColumn = data.defineSourceColumn("labels", 4, ColumnType.continuous);
+        data.defineSingleOutputOthersInput(outputColumn);
+        data.analyze();
+        model = new EncogModel(data);
+        model.selectMethod(data, MLMethodFactory.TYPE_FEEDFORWARD);
+        //model.setReport(new ConsoleStatusReportable());
+        data.normalize();
+        preference_helper = data.getNormHelper();
+        System.out.println(activity_helper.toString());
+    }
+
+    void train(String activity_url,String perference_url){
+        activity_train(activity_url);
+        Log.info("activity training finished");
+        preference_train(perference_url);
+        Log.info("preference training finished");
+
+    }
+    private void activity_train(String activity_csv_url){
+        activityDataAnalyser(activity_csv_url);
+        model.holdBackValidation(0.3, true, 1001);
+        model.selectTrainingType(data);
+        best_method = (MLRegression)model.crossvalidate(5, true);
+        System.out.println(best_method);
+        saveEncogModel(save_activity_model_file);
+    }
+    private void preference_train(String perfence_csv_url){
+        perferenceDataAnalyser(perfence_csv_url);
+        model.holdBackValidation(0.3, true, 1001);
+        model.selectTrainingType(data);
+        best_method = (MLRegression)model.crossvalidate(5, true);
+        System.out.println(best_method);
+        saveEncogModel(save_perference_model_file);
+    }
+
+    String[] predictor(String[] new_data){
+        this.new_data = new_data;
+        activityDataAnalyser("datasets/backup/activity_data.csv");
+        perferenceDataAnalyser("datasets/backup/preference_data.csv");
+        String[] result = new String[2];
+        result[0] = activity_predictor();
+        result[1] = perference_predictor();
+        Encog.getInstance().shutdown();
+        return result;
+    }
+
+    private String activity_predictor(){
+        BasicNetwork activity_method = (BasicNetwork) loadObject(new File(save_activity_model_file));
+        MLData input = activity_helper.allocateInputVector();
+        String[] activity_new_data = new String[4];
+        activity_new_data[0] = new_data[0];
+        activity_new_data[1] = new_data[1];
+        activity_new_data[2] = new_data[2];
+        activity_new_data[3] = new_data[3];
+        activity_helper.normalizeInputVector(activity_new_data,input.getData(),false);
+        MLData output = activity_method.compute(input);
+        System.out.println("input:"+input);
+        System.out.println("output"+output);
+        activity_result = activity_helper.denormalizeOutputVectorToString(output)[0];
+        System.out.println("output activity"+ activity_result);
+        return activity_result;
+    }
+
+    private String perference_predictor(){
+        BasicNetwork preference_method = (BasicNetwork)loadObject(new File(save_perference_model_file));
+        MLData input = preference_helper.allocateInputVector();
+        String[] perference_new_data = new String[4];
+        perference_new_data[0] = activity_result;
+        perference_new_data[1] = new_data[4];
+        perference_new_data[2] = new_data[5];
+        perference_new_data[3] = new_data[6];
+        preference_helper.normalizeInputVector(perference_new_data, input.getData(),false);
+        MLData output = preference_method.compute(input);
+        preference_result = preference_helper.denormalizeOutputVectorToString(output)[0];
+        return preference_result;
+    }
+
+    private void saveEncogModel(String model_file_url){
+        saveObject(new File(model_file_url), this.best_method);
+    }
+}
+
+

feedbackloop.learner_backup/src/main/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/Main.java

+package de.tudresden.inf.st.eraser.feedbackloop.learner_backup;
+
+import de.tudresden.inf.st.eraser.jastadd.model.*;
+
+public class Main {
+
+  public static void main(String[] args) {
+    /**
+     * new data from KB
+     * */
+    String[] new_data = new String[7];
+    new_data[0]="7";
+    new_data[1]="20";
+    new_data[2]="12";
+    new_data[3]="13";
+    new_data[4]="7";
+    new_data[5]="25";
+    new_data[6]="12";
+    /**
+     * learner.train(activity_csv_url, preference_data_url)
+     * learner.predictor get the result from predictor for new data
+     * */
+    Learner learner=new Learner();
+    //learner.train("datasets/activity_data.csv", "datasets/preference_data.csv");
+    String[] result = learner.predictor(new_data);
+    System.out.println("activity is:" + result[0]);
+    System.out.println("perference is: "+ result[1]);
+  }
+}

feedbackloop.learner_backup/src/main/resources/log4j2.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<Configuration>
+    <Appenders>
+        <Console name="Console">
+            <PatternLayout pattern="%highlight{%d{HH:mm:ss.SSS} %-5level} %c{1.} - %msg%n"/>
+        </Console>
+        <RollingFile name="RollingFile" fileName="logs/eraser.log"
+                    filePattern="logs/eraser-%i.log">
+            <PatternLayout pattern="%d{HH:mm:ss.SSS} %-5level %logger{36} - %msg%n"/>
+            <Policies>
+                <OnStartupTriggeringPolicy/>
+            </Policies>
+            <DefaultRolloverStrategy max="20"/>
+        </RollingFile>
+    </Appenders>
+    <Loggers>
+        <Root level="debug">
+            <AppenderRef ref="Console"/>
+            <AppenderRef ref="RollingFile"/>
+        </Root>
+    </Loggers>
+</Configuration>

feedbackloop.learner_backup/src/test/java/de/tudresden/inf/st/eraser/feedbackloop.learner_backup/ATest.java

+package de.tudresden.inf.st.eraser.feedbackloop.learner_backup;
+
+import de.tudresden.inf.st.eraser.jastadd.model.*;
+import org.junit.Test;
+
+import java.util.Set;
+
+import static org.hamcrest.Matchers.*;
+import static org.junit.Assert.fail;
+
+/**
+ * TODO: Add description.
+ *
+ * @author rschoene - Initial contribution
+ */
+public class ATest {
+
+  @Test
+  public void test1() {
+    fail();
+  }
+}