Remove unused modules.

org.openhab.action.machinelearn/src/main/java/org/openhab/action/machinelearn/internal/MachineLearnActivator.java

-/**
- * Copyright (c) 2010-2016 by the respective copyright holders.
- *
- * All rights reserved. This program and the accompanying materials
- * are made available under the terms of the Eclipse Public License v1.0
- * which accompanies this distribution, and is available at
- * http://www.eclipse.org/legal/epl-v10.html
- */
-package org.openhab.action.machinelearn.internal;
-
-import org.osgi.framework.BundleActivator;
-import org.osgi.framework.BundleContext;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
-
-/**
- * Extension of the default OSGi bundle activator
- *
- * @author Kai Kreuzer
- * @since 1.3.0
- */
-public final class MachineLearnActivator implements BundleActivator {
-
-    private static Logger logger = LoggerFactory.getLogger(MachineLearnActivator.class);
-
-    private static BundleContext context;
-
-    /**
-     * Called whenever the OSGi framework starts our bundle
-     */
-    @Override
-    public void start(BundleContext bc) throws Exception {
-        context = bc;
-        logger.debug("Machine Learning action has been started.");
-    }
-
-    /**
-     * Called whenever the OSGi framework stops our bundle
-     */
-    @Override
-    public void stop(BundleContext bc) throws Exception {
-        context = null;
-        logger.debug("Machine Learning action has been stopped.");
-    }
-
-    /**
-     * Returns the bundle context of this bundle
-     *
-     * @return the bundle context
-     */
-    public static BundleContext getContext() {
-        return context;
-    }
-
-}

org.openhab.action.machinelearn/src/main/resources/readme.txt

-Bundle resources go in here!
\ No newline at end of file

org.openlicht.action.reinforcementlearning/.gitignore

-build/
-/bin/
-logs/

org.openlicht.action.reinforcementlearning/META-INF/MANIFEST.MF

-Manifest-Version: 1.0
-Private-Package: org.openhab.action.helloworld.internal
-Ignore-Package: org.openhab.action.helloworld.internal
-Bundle-License: http://www.eclipse.org/legal/epl-v10.html
-Bundle-Name: openLicht Reinforcement Learning Action
-Bundle-SymbolicName: org.openlicht.action.reinforcementlearning
-Bundle-Version: 1.9.0.qualifier
-Bundle-Activator: org.openlicht.action.reinforcementlearning.internal.MainActivator
-Bundle-ManifestVersion: 2
-Bundle-Description: This is the Hello World action of the open Home Aut
- omation Bus (openHAB)
-Import-Package: org.apache.commons.lang,
- org.eclipse.jdt.annotation,
- org.openhab.core.library.types,
- org.openhab.core.scriptengine.action,
- org.osgi.framework,
- org.osgi.service.cm,
- org.osgi.service.component,
- org.slf4j
-Bundle-DocURL: http://www.openhab.org
-Bundle-RequiredExecutionEnvironment: JavaSE-1.8
-Service-Component: OSGI-INF/action.xml
-Bundle-ClassPath: .,
- lib/encog-core-3.4.jar
-Bundle-ActivationPolicy: lazy
-Automatic-Module-Name: org.openlicht.action.reinforcementlearning
-Require-Bundle: org.eclipse.smarthome.core

org.openlicht.action.reinforcementlearning/OSGI-INF/action.xml

-<?xml version="1.0" encoding="UTF-8"?>
-<!--
-
-    Copyright (c) 2010-2016 by the respective copyright holders.
-
-    All rights reserved. This program and the accompanying materials
-    are made available under the terms of the Eclipse Public License v1.0
-    which accompanies this distribution, and is available at
-    http://www.eclipse.org/legal/epl-v10.html
-
--->
-<scr:component xmlns:scr="http://www.osgi.org/xmlns/scr/v1.1.0" activate="activate" deactivate="deactivate" immediate="true" name="org.openlicht.action.reinforcementlearning.action">
-	<implementation class="org.openlicht.action.reinforcementlearning.internal.MainActionService" />
-
-	<service>
-		<provide interface="org.openhab.core.scriptengine.action.ActionService" />
-		<provide interface="org.osgi.service.cm.ManagedService" />
-	</service>
-
-	<property name="service.pid" type="String" value="org.openhab.helloworld" />
-	
-</scr:component>

org.openlicht.action.reinforcementlearning/build.gradle

-dependencies {
-    compile files('lib/encog-core-3.4.jar')
-    compile project(':stub.org.openhab.core.scriptengine.action')
-    compile group: 'org.eclipse.jdt', name: 'org.eclipse.jdt.annotation', version: '2.2.200'
-    compile group: 'org.osgi', name: 'org.osgi.framework', version: '1.9.0'
-    compile group: 'org.osgi', name: 'org.osgi.service.cm', version: '1.6.0'
-    compile group: 'org.osgi', name: 'org.osgi.service.component', version: '1.4.0'
-    compile group: 'org.slf4j', name: 'slf4j-api', version: '1.7.25'
-}
-
-sourceSets {
-    main {
-        java {
-            srcDir 'src/main/java'
-        }
-    }
-}

org.openlicht.action.reinforcementlearning/src/main/java/org/openlicht/action/reinforcementlearning/internal/Main.java

-
-/**
- * Copyright (c) 2010-2016 by the respective copyright holders.
- *
- * All rights reserved. This program and the accompanying materials
- * are made available under the terms of the Eclipse Public License v1.0
- * which accompanies this distribution, and is available at
- * http://www.eclipse.org/legal/epl-v10.html
- */
-package org.openlicht.action.reinforcementlearning.internal;
-
-import java.io.BufferedReader;
-import java.io.BufferedWriter;
-import java.io.FileReader;
-import java.io.FileWriter;
-import java.io.IOException;
-import java.util.HashMap;
-
-import org.openhab.core.scriptengine.action.ActionDoc;
-import org.openhab.core.scriptengine.action.ParamDoc;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
-
-/**
- * This class provides static methods that can be used in automation rules
- * for using Reinforcement Learning methods and conect to OpenHab.
- *
- * @author David Morales Rodríguez
- *
- *
- */
-public class Main {
-
-    private static int N_LAMPS = 3;
-    private static int N_VALUES = 3;
-    private static ReinforcementLearningAlgorithm rl_algorithm = null;
-    private static Logger logger = LoggerFactory.getLogger(Main.class);
-
-    // method to change string to double. Used to get naturalLight (home I/O uses "," for float numbers)
-    private static double changeStringToDouble(String string_number) {
-        double number;
-        int endIndex = string_number.indexOf(",");
-        if (endIndex > 0) {
-            number = Double.parseDouble(string_number.substring(0, endIndex));
-            if (endIndex < string_number.length() - 1) {
-                String decimal_string = string_number.substring(endIndex + 1, string_number.length());
-                double decimal = Double.parseDouble(decimal_string);
-                decimal = decimal / (Math.pow(10, decimal_string.length()));
-                number = number + decimal;
-
-            }
-
-        } else {
-            number = Double.parseDouble(string_number);
-        }
-        return number;
-
-    }
-
-    // Method to init the model
-    @ActionDoc(text = "Test Method for building the model")
-    public static synchronized void initModel() throws IOException {
-
-        logger.warn("Initializing model.");
-
-        if (rl_algorithm == null) {
-            trainAndSave();
-
-        } else {
-            logger.warn("model was already initialized.");
-
-        }
-
-    }
-
-    // Method to init the model loading it
-    @ActionDoc(text = "Test Method for building the model")
-    public static synchronized void initModel(@ParamDoc(name = "f_nnRgb") String f_nnRgb,
-            @ParamDoc(name = "f_nnBright") String f_nnBright) throws IOException {
-
-        logger.warn("Initializing model.");
-
-        if (rl_algorithm == null) {
-            rl_algorithm = new ReinforcementLearningAlgorithm(N_LAMPS, N_VALUES, f_nnRgb, f_nnBright);
-
-        } else {
-            logger.warn("model was already initialized.");
-
-        }
-    }
-
-    // Method to save the model in the following files
-    // file for nn rgb: "\\openhab-2.3.0\\datasets\\RGB_NN"
-    // file for nn bright: "\\openhab-2.3.0\\datasets\\Bright_NN"
-    @ActionDoc(text = "Test Method for saving the model")
-    public static synchronized void saveModel() throws IOException {
-        logger.warn("saving model.");
-        rl_algorithm.saveModel();
-        logger.warn("model was saved");
-
-    }
-
-    // Method to get the plan for the current state(activity, natural light)
-    @ActionDoc(text = "Test Method for getting the plan", returns = "plan")
-    public static synchronized HashMap<String, Double> planBright(@ParamDoc(name = "activity") int activity,
-            @ParamDoc(name = "natural_light") String natural_light) throws IOException {
-
-        if (rl_algorithm == null) {
-            initModel();
-        }
-
-        // natural light from 0 to 10 in HOME IO, from 0 to 100 for us
-        double light = changeStringToDouble(natural_light) * 10;
-        State state = new State(activity, light);
-
-        logger.warn("getting plan for activity: " + Integer.toString(activity) + " natural light: "
-                + Double.toString(light));
-
-        // get plan using RL
-        double aux_plan = rl_algorithm.planBright(state);
-
-        // construct hashMap to return
-        HashMap<String, Double> plan = new HashMap<String, Double>();
-
-        plan.put("lamp" + Integer.toString(activity), aux_plan);
-
-        return plan;
-    }
-
-    // training method, input: activity, natural light, ideal_value
-    @ActionDoc(text = "Test Method for training")
-    public static synchronized void trainBright(@ParamDoc(name = "activity") int activity,
-            @ParamDoc(name = "natural_light") String natural_light, @ParamDoc(name = "ideal_light") double ideal_light)
-            throws IOException {
-
-        if (rl_algorithm == null) {
-            initModel();
-        }
-
-        logger.warn("training algorithm");
-        double ideal = ideal_light;
-        // natural light from 0 to 10 in HOME IO, from 0 to 100 for us
-        State state = new State(activity, changeStringToDouble(natural_light) * 10);
-        rl_algorithm.trainAlgorithmBright(state, ideal);
-
-    }
-
-    // method for training the model using datasets and then save the model
-    private static void trainAndSave() {
-
-        rl_algorithm = new ReinforcementLearningAlgorithm(N_LAMPS, N_VALUES);
-
-        String dataset = "\\openhab-2.3.0\\datasets\\dataset.txt";
-        String output = "\\openhab-2.3.0\\datasets\\output.txt";
-
-        String line;
-        // read dataset to train our model
-        try {
-            FileReader f = new FileReader(dataset);
-            BufferedReader b = new BufferedReader(f);
-
-            logger.warn("reading dataset.");
-            FileWriter f_output = new FileWriter(output);
-            BufferedWriter bw = new BufferedWriter(f_output);
-
-            // we don´t need the first line (header)
-            line = b.readLine();
-
-            // read data
-            double lamps_values[] = new double[N_VALUES];
-            // double[] planRGB;
-            double planBright;
-            int contador = 0;
-            while ((line = b.readLine()) != null) {
-                ++contador;
-                int index = line.indexOf(" ");
-                int activity = Integer.parseInt((line.substring(0, index)));
-                index = index + 1;
-                int n_index = line.indexOf(" ", index);
-                double natural_light = Double.parseDouble(line.substring(index, n_index));
-                double ideal_bright;
-                index = n_index;
-                State state = new State(activity, natural_light);
-
-                index = index + 2;
-                int last_index = index;
-
-                for (int j = 0; j < N_VALUES; j++) {
-
-                    if (j == 2) {
-                        index = line.indexOf("]", index);
-                    } else {
-                        index = line.indexOf(",", index);
-                    }
-
-                    lamps_values[j] = Integer.parseInt((line.substring(last_index, index)));
-                    index = index + 2;
-                    last_index = index;
-                }
-
-                index = line.length();
-                ideal_bright = Double.parseDouble(line.substring(last_index, index));
-                // before training
-
-                logger.warn("Before Training");
-                // planRGB = rl_algorithm.planRGB(state);
-                planBright = rl_algorithm.planBright(state);
-                logger.warn("natural light " + natural_light + " activity: " + activity + " light plan: "
-                        + Double.toString(planBright));
-                logger.warn("user values: " + Double.toString(ideal_bright));
-
-                // training the algorithm
-                // rl_algorithm.trainAlgorithmRGB(state, lamps_values);
-                rl_algorithm.trainAlgorithmBright(state, ideal_bright);
-                logger.warn("After Training");
-                planBright = rl_algorithm.planBright(state);
-                logger.warn("natural light " + natural_light + " activity: " + activity + " light plan: "
-                        + Double.toString(planBright));
-                logger.warn("user values: " + Double.toString(ideal_bright));
-
-            }
-            bw.close();
-            f_output.close();
-            b.close();
-            f.close();
-            ;
-
-        } catch (Exception e) {
-            logger.error("Exception occurred during execution: {}", e.getMessage(), e);
-        }
-        rl_algorithm.saveModel();
-
-    }
-
-    /*
-     * @ActionDoc(text = "Test Method for training", returns = "algorithm trained")
-     * public static synchronized void trainRGB(@ParamDoc(name = "activity") int activity,
-     *
-     * @ParamDoc(name = "bright") String bright, @ParamDoc(name = "configuration") HSBType configuration)
-     * throws IOException {
-     * double[] users_lamps_values = new double[3];
-     *
-     * Color color = new Color(configuration.getRGB());
-     * users_lamps_values[0] = color.getRed();
-     * users_lamps_values[1] = color.getGreen();
-     * users_lamps_values[2] = color.getBlue();
-     *
-     * logger.warn("lamps values given by user: {} {} {}", Double.toString(users_lamps_values[0]),
-     * Double.toString(users_lamps_values[1]), Double.toString(users_lamps_values[2]));
-     * if (rl_algorithm == null) {
-     * initModel();
-     * }
-     * logger.warn("training algorithm");
-     *
-     * State state = new State(activity, Double.parseDouble(bright)*10);
-     * rl_algorithm.trainAlgorithmRGB(state, users_lamps_values);
-     *
-     * }
-     *
-     *
-     *
-     * @ActionDoc(text = "Test Method for getting the plan", returns = "plan")
-     * public static synchronized HashMap<String, HSBType> planRGB() throws IOException {
-     *
-     * if (rl_algorithm == null) {
-     * initModel();
-     * }
-     * logger.warn("getting plan");
-     * HashMap<String, HSBType> plan = new HashMap<String, HSBType>();
-     *
-     * double[] lamps_values = rl_algorithm.planRGB(state);
-     *
-     * for (int i = 0; i < N_LAMPS; i++) {
-     * int red = (int) Math.round(lamps_values[i][0]);
-     * int green = (int) Math.round(lamps_values[i][1]);
-     * int blue = (int) Math.round(lamps_values[i][2]);
-     * logger.warn("lamps values given by plan function: {} {} {}", red, green, blue);
-     * HSBType configuration = HSBType.fromRGB(red, green, blue);
-     * configuration.toRGB();
-     * plan.put("lamp" + Integer.toString(i), configuration);
-     *
-     * }
-     * return plan;
-     *
-     * }
-     */
-
-}

org.openlicht.action.reinforcementlearning/src/main/java/org/openlicht/action/reinforcementlearning/internal/MainActionService.java

-/**
- * Copyright (c) 2010-2016 by the respective copyright holders.
- *
- * All rights reserved. This program and the accompanying materials
- * are made available under the terms of the Eclipse Public License v1.0
- * which accompanies this distribution, and is available at
- * http://www.eclipse.org/legal/epl-v10.html
- */
-package org.openlicht.action.reinforcementlearning.internal;
-
-import java.util.Dictionary;
-
-import org.openhab.core.scriptengine.action.ActionService;
-import org.osgi.service.cm.ConfigurationException;
-import org.osgi.service.cm.ManagedService;
-
-/**
- * This class registers an OSGi service for the Main action.
- *
- * @author David Morales Rodríguez
- */
-public class MainActionService implements ActionService, ManagedService {
-
-    /**
-     * Indicates whether this action is properly configured which means all
-     * necessary configurations are set. This flag can be checked by the
-     * action methods before executing code.
-     */
-    /* default */ static boolean isProperlyConfigured = false;
-
-    public MainActionService() {
-    }
-
-    public void activate() {
-    }
-
-    public void deactivate() {
-        // deallocate Resources here that are no longer needed and
-        // should be reset when activating this binding again
-    }
-
-    @Override
-    public String getActionClassName() {
-        return Main.class.getCanonicalName();
-    }
-
-    @Override
-    public Class<?> getActionClass() {
-        return Main.class;
-    }
-
-    @Override
-    @SuppressWarnings("rawtypes")
-    public void updated(Dictionary config) throws ConfigurationException {
-
-    }
-
-}

org.openlicht.action.reinforcementlearning/src/main/java/org/openlicht/action/reinforcementlearning/internal/MainActivator.java

-/**
- * Copyright (c) 2010-2016 by the respective copyright holders.
- *
- * All rights reserved. This program and the accompanying materials
- * are made available under the terms of the Eclipse Public License v1.0
- * which accompanies this distribution, and is available at
- * http://www.eclipse.org/legal/epl-v10.html
- */
-package org.openlicht.action.reinforcementlearning.internal;
-
-import org.osgi.framework.BundleActivator;
-import org.osgi.framework.BundleContext;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
-
-/**
- * Extension of the default OSGi bundle activator
- *
- * @author David Morales Rodríguez
- */
-public final class MainActivator implements BundleActivator {
-
-    private static Logger logger = LoggerFactory.getLogger(MainActivator.class);
-
-    private static BundleContext context;
-
-    /**
-     * Called whenever the OSGi framework starts our bundle
-     */
-    @Override
-    public void start(BundleContext bc) throws Exception {
-        context = bc;
-        logger.debug("Main action has been started.");
-    }
-
-    /**
-     * Called whenever the OSGi framework stops our bundle
-     */
-    @Override
-    public void stop(BundleContext bc) throws Exception {
-        context = null;
-        logger.debug("Main action has been stopped.");
-    }
-
-    /**
-     * Returns the bundle context of this bundle
-     *
-     * @return the bundle context
-     */
-    public static BundleContext getContext() {
-        return context;
-    }
-
-}

org.openlicht.action.reinforcementlearning/src/main/java/org/openlicht/action/reinforcementlearning/internal/Model.java

-package org.openlicht.action.reinforcementlearning.internal;
-
-import java.util.Random;
-
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
-
-//abstract class Model
-public abstract class Model {
-    protected int N_VALUES;
-    protected int N_LAMPS;
-    protected Random rnd = new Random();
-    protected static Logger logger = LoggerFactory.getLogger(Model.class);
-
-    public Model(int N_LAMPS, int N_VALUES) {
-        this.N_LAMPS = N_LAMPS;
-        this.N_VALUES = N_VALUES;
-        rnd.setSeed(System.currentTimeMillis());
-    }
-
-    // method to validate lamps values (RGB)
-    protected double[] validateLampsValues(double[] l_values) {
-        for (int i = 0; i < this.N_VALUES; ++i) {
-            if (l_values[i] > 255) {
-                l_values[i] = 255;
-            }
-
-            if (l_values[i] < 0) {
-                l_values[i] = 0;
-            }
-
-        }
-        return l_values;
-    }
-
-    // method to validate bright value
-    protected double validateBrightValue(double bright_value) {
-
-        if (bright_value > 100) {
-            bright_value = 100;
-        }
-
-        if (bright_value < 0) {
-            bright_value = 0;
-        }
-
-        return bright_value;
-    }
-
-    // training the model for the current state using the values given by the user
-    public abstract void trainBright(State state, double bright);
-
-    // training the model for the current state using the values given by the user
-    public abstract void trainRGB(State state, double[] user_lamps_values);
-
-    // get output (lams_values (RGB)) using exploitation
-    public abstract double[] getPlanRGB(State state);
-
-    // get output (lams_values (Bright)) using exploitation
-    public abstract double getPlanBright(State state);
-
-    // method to save the model
-    public abstract void saveModel();
-
-}

org.openlicht.action.reinforcementlearning/src/main/java/org/openlicht/action/reinforcementlearning/internal/MultilayerPerceptron_Model.java

-package org.openlicht.action.reinforcementlearning.internal;
-
-import java.io.File;
-
-import org.encog.engine.network.activation.ActivationSigmoid;
-import org.encog.ml.data.MLDataSet;
-import org.encog.ml.data.basic.BasicMLDataSet;
-import org.encog.ml.train.MLTrain;
-import org.encog.neural.networks.BasicNetwork;
-import org.encog.neural.networks.layers.BasicLayer;
-import org.encog.neural.networks.training.propagation.back.Backpropagation;
-import org.encog.persist.EncogDirectoryPersistence;
-
-//This class implement a Rl model using two Neural networks build using encog
-public class MultilayerPerceptron_Model extends Model {
-
-    // first NN to get rgb configuration
-    private BasicNetwork RGB_network;
-    // second NN to get bright configuration
-    private BasicNetwork Bright_network;
-
-    // constructor
-    public MultilayerPerceptron_Model(int N_LAMPS, int N_VALUES) {
-
-        super(N_LAMPS, N_VALUES);
-
-        // NN to predict RGB values
-        this.RGB_network = new BasicNetwork();
-
-        // no activation function,true->bias neurons, input layer has 2 neurons (activity, natural_light)
-        this.RGB_network.addLayer(new BasicLayer(null, true, 2));
-
-        // Sigmoid activation function,true->bias neurons, hidden layer has 6 neurons
-        this.RGB_network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 6));
-
-        // Sigmoid activation function,false->bias neurons, output layer has 3 neurons (RGB values)
-        this.RGB_network.addLayer(new BasicLayer(new ActivationSigmoid(), false, 3));
-        this.RGB_network.getStructure().finalizeStructure();
-        this.RGB_network.reset();
-
-        // NN to predict bright
-        this.Bright_network = new BasicNetwork();
-
-        // no activation function,true->bias neurons, input layer has 2 neurons (activity, natural_light)
-        this.Bright_network.addLayer(new BasicLayer(null, true, 2));
-
-        // Sigmoid activation function,true->bias neurons, hidden layer has 10 neurons
-        this.Bright_network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 10));
-
-        // Sigmoid activation function,false->bias neurons, output layer has 1 neurons (Bright value)
-        this.Bright_network.addLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
-        this.Bright_network.getStructure().finalizeStructure();
-        this.Bright_network.reset();
-
-    }
-
-    // constructor with files to initialize NNs
-    public MultilayerPerceptron_Model(int N_LAMPS, int N_VALUES, String file_NNrgb, String file_NNbright) {
-
-        super(N_LAMPS, N_VALUES);
-        logger.warn("trying to load nn");
-        this.RGB_network = (BasicNetwork) EncogDirectoryPersistence.loadObject(new File(file_NNrgb));
-        this.Bright_network = (BasicNetwork) EncogDirectoryPersistence.loadObject(new File(file_NNbright));
-        logger.warn("nn initialized");
-
-    }
-
-    // method to save the model
-    @Override
-    public void saveModel() {
-
-        // save model
-        EncogDirectoryPersistence.saveObject(new File("\\openhab-2.3.0\\datasets\\RGB_NN"), this.RGB_network);
-
-        EncogDirectoryPersistence.saveObject(new File("\\openhab-2.3.0\\datasets\\Bright_NN"), this.Bright_network);
-
-    }
-
-    /////////////////////////////////////// aux
-    /////////////////////////////////////// functions////////////////////////////////////////////////////////////////
-
-    // function to normalize values. NN get values for a sigmoid function
-    private double normalizeActivityPlanRGB(int activity) {
-        return (activity - 2) * 1;
-
-    }
-
-    // function to normalize values. NN get values for a sigmoid function
-    private double normalizeNatural_lightPlanRGB(double natural_light) {
-        return ((natural_light - 50) / 8);
-    }
-
-    // function to normalize values. NN get values for a sigmoid function
-    private double normalizeActivityPlanBright(int activity) {
-        return (activity - 2) * 1;
-
-    }
-
-    // function to normalize values. NN get values for a sigmoid function
-    private double normalizeNatural_lightPlanBright(double natural_light) {
-        return ((natural_light - 50) / 8);
-    }
-
-    ///////////////////////////////// train methods/////////////////////////////////////////////////////////////////
-
-    // train model for bright prediction
-    @Override
-    public void trainBright(State state, double ideal_bright) {
-        double[][] IDEAL = new double[1][1];
-        // scale for the NN
-        IDEAL[0][0] = ideal_bright / 100;
-
-        // normalize values to use sigmod functions
-        double activity_lamp = this.normalizeActivityPlanBright(state.getActivity());
-        double natural_light = this.normalizeNatural_lightPlanBright(state.getNatural_light());
-
-        double[][] INPUT = new double[1][2];
-        INPUT[0][0] = activity_lamp;
-        INPUT[0][1] = natural_light;
-
-        // training using backpropagation
-        MLDataSet trainingSet = new BasicMLDataSet(INPUT, IDEAL);
-        // learning rate of 3.5 and a momentum of 0.3
-        MLTrain train = new Backpropagation(this.Bright_network, trainingSet, 3.5, 0.3);
-        int epoch = 1;
-        do {
-            train.iteration();
-            // System.out.println("Epoch #" + epoch + "Error:" + train.getError());
-            epoch++;
-        } while (train.getError() > 0.005);
-
-        train.finishTraining();
-
-        // test the neural network
-        // System.out.println("Bright Neural Network Results:");
-        /*
-         * for (MLDataPair pair : trainingSet) {
-         * final MLData output = this.Bright_network.compute(pair.getInput());
-         * System.out.println(
-         * "INPUT= [" + pair.getInput().getData(0) + ", " + pair.getInput().getData(1) + "], actual= ["
-         * + output.getData(0) * 100 + "] ,ideal= [" + pair.getIdeal().getData(0) * 100 + "]");
-         * }
-         */
-    }
-
-    @Override
-    public void trainRGB(State state, double[] user_lamps_values) {
-
-        double[][] IDEAL = new double[1][3];
-
-        // from RGB to %R %G %B
-        for (int i = 0; i < this.N_VALUES; i++) {
-            IDEAL[0][i] = user_lamps_values[i] / 255;
-        }
-
-        double activity_lamp = this.normalizeActivityPlanRGB(state.getActivity());
-        double natural_light = this.normalizeNatural_lightPlanRGB(state.getNatural_light());
-        double[][] INPUT = new double[1][2];
-        INPUT[0][0] = activity_lamp;
-        INPUT[0][1] = natural_light;
-
-        // training using backpropagation
-        MLDataSet trainingSet = new BasicMLDataSet(INPUT, IDEAL);
-        // learning rate of 3.5 and a momentum of 0.3
-        MLTrain train = new Backpropagation(this.RGB_network, trainingSet, 3.5, 0.3);
-        int epoch = 1;
-        do {
-            train.iteration();
-            System.out.println("Epoch #" + epoch + "Error:" + train.getError());
-            epoch++;
-        } while (train.getError() > 0.005);
-
-        train.finishTraining();
-
-        // test the neural network
-        /*
-         * System.out.println("RGB Neural Network Results:");
-         * for (MLDataPair pair : trainingSet) {
-         * final MLData output = this.RGB_network.compute(pair.getInput());
-         * System.out.println("INPUT= [" + pair.getInput().getData(0) + ", " + pair.getInput().getData(1)
-         * + "], actual= [" + output.getData(0) * 255 + ", " + output.getData(1) * 255 + ", "
-         * + output.getData(2) * 255 + "] ,ideal= [" + pair.getIdeal().getData(0) * 255 + ", "
-         * + pair.getIdeal().getData(1) * 255 + ", " + pair.getIdeal().getData(2) * 255 + "]");
-         * }
-         */
-    }
-
-    ///////////////////////////////////////////// exploitation
-    ///////////////////////////////////////////// methods///////////////////////////////////////////////////////////////////
-
-    // exploitation method for RGB values
-    @Override
-    public double[] getPlanRGB(State state) {
-
-        double[] output = new double[this.N_VALUES];
-
-        // input must be an array, network.compute(input,output)...
-        double[] input = new double[2];
-        input[0] = this.normalizeActivityPlanRGB(state.getActivity());
-        input[1] = this.normalizeNatural_lightPlanBright(state.getNatural_light());
-
-        // get output
-        this.RGB_network.compute(input, output);
-
-        // to RGB
-        for (int i = 0; i < this.N_VALUES; i++) {
-            output[i] = output[i] * 255;
-        }
-
-        return output;
-
-    }
-
-    // exploitation method for Bright values
-    @Override
-    public double getPlanBright(State state) {
-
-        double aux[] = new double[1];
-        double output;
-
-        // input must be an array, network.compute(input,output)...
-        double[] input = new double[2];
-        input[0] = this.normalizeActivityPlanBright(state.getActivity());
-        input[1] = this.normalizeNatural_lightPlanBright(state.getNatural_light());
-
-        // get output
-        this.Bright_network.compute(input, aux);
-
-        // to bright
-        output = validateBrightValue(100 * aux[0]);
-
-        return output;
-
-    }
-
-}

org.openlicht.action.reinforcementlearning/src/main/java/org/openlicht/action/reinforcementlearning/internal/RLAgent.java

-package org.openlicht.action.reinforcementlearning.internal;
-
-import java.util.Random;
-
-/*This class defines a RL agent*/
-public class RLAgent {
-    private Random rnd = new Random();
-
-    // constant to do exploration
-    private static final double EPSILON = 0;
-
-    // variables that define our environment
-    // n_lamps: n° lamps
-    // n_values: n° values for each lamp
-    private int N_LAMPS;
-    private int N_VALUES;
-
-    private MultilayerPerceptron_Model model;
-
-    // method to save the model
-    public void saveModel() {
-        this.model.saveModel();
-    }
-
-    // contructor withoud initialized NN files
-    public RLAgent(int n_lamps, int n_values) {
-        rnd.setSeed(System.currentTimeMillis());
-        this.N_LAMPS = n_lamps;
-        this.N_VALUES = n_values;
-        model = new MultilayerPerceptron_Model(this.N_LAMPS, this.N_VALUES);
-    }
-
-    // constructor with initialized NN files
-    public RLAgent(int n_lamps, int n_values, String file_NNrgb, String file_NNbright) {
-        rnd.setSeed(System.currentTimeMillis());
-        this.N_LAMPS = n_lamps;
-        this.N_VALUES = n_values;
-        model = new MultilayerPerceptron_Model(this.N_LAMPS, this.N_VALUES, file_NNrgb, file_NNbright);
-    }
-
-    // method call from reinforcementLearning class to train the agent
-    // last_state: last state given
-    // ideal bright values
-    public void trainingAlgorithmBright(State state, double ideal_bright) {
-
-        // TRAIN THE SYSTEM
-        model.trainBright(state, ideal_bright);
-    }
-
-    // method call from reinforcementLearning class to train the agent
-    // last_state: last state given
-    // lamps_values: lamps values the algorithm gave back
-    public void trainingAlgorithmRGB(State state, double[] user_lamps_values) {
-
-        // TRAIN THE SYSTEM
-        model.trainRGB(state, user_lamps_values);
-    }
-
-    // method call from reinforcementLearning class to get a configuration for the lamps (RGB)
-    public double[] getPlanRGB(State state) {
-
-        if (decide_if_exploration()) {// do exploration
-            // not implemented
-            return null;
-        } else {// do exploitation
-            return model.getPlanRGB(state);
-        }
-
-    }
-
-    // method call from reinforcementLearning class to get a configuration for the lamps (RGB)
-    public double getPlanBright(State state) {
-
-        if (decide_if_exploration()) {// do exploration
-            // not implemented
-            return -1;
-        } else {// do exploitation
-
-            return model.getPlanBright(state);
-        }
-
-    }
-
-    // method to decide if we do exploration
-    private boolean decide_if_exploration() {
-
-        boolean exploration = false;
-        if (rnd.nextFloat() < EPSILON) {
-            exploration = true;
-        }
-
-        return exploration;
-
-    }
-
-}

org.openlicht.action.reinforcementlearning/src/main/java/org/openlicht/action/reinforcementlearning/internal/ReinforcementLearningAlgorithm.java

-package org.openlicht.action.reinforcementlearning.internal;
-
-/*This class implement a reinforcement learning algorithm */
-public class ReinforcementLearningAlgorithm {
-
-    // reinforcement learning agent
-    private RLAgent agent;
-    private int N_LAMPS;
-    private int N_VALUES;
-
-    // constructor without initialized NN files
-    public ReinforcementLearningAlgorithm(int n_lamps, int n_values) {
-
-        this.N_LAMPS = n_lamps;
-        this.N_VALUES = n_values;
-
-        agent = new RLAgent(N_LAMPS, N_VALUES);
-    }
-
-    // constructor with initialized NN files
-    public ReinforcementLearningAlgorithm(int n_lamps, int n_values, String file_NNrgb, String file_NNbright) {
-
-        this.N_LAMPS = n_lamps;
-        this.N_VALUES = n_values;
-
-        agent = new RLAgent(N_LAMPS, N_VALUES, file_NNrgb, file_NNbright);
-    }
-
-    // we get the feedback from the user and we train our algorithm with it
-    public void trainAlgorithmRGB(State state, double[] users_lamps_values) {
-        this.agent.trainingAlgorithmRGB(state, users_lamps_values);
-    }
-
-    // we get the feedback from the user and we train our algorithm with it
-    public void trainAlgorithmBright(State state, double ideal_bright) {
-        this.agent.trainingAlgorithmBright(state, ideal_bright);
-    }
-
-    // get plan (RGB)
-    public double[] planRGB(State state) {
-        return this.agent.getPlanRGB(state);
-
-    }
-
-    // get plan (Bright)
-    public double planBright(State state) {
-        return this.agent.getPlanBright(state);
-
-    }
-
-    // method to save the model
-    public void saveModel() {
-        this.agent.saveModel();
-    }
-}
\ No newline at end of file

org.openlicht.action.reinforcementlearning/src/main/java/org/openlicht/action/reinforcementlearning/internal/State.java

-package org.openlicht.action.reinforcementlearning.internal;
-
-//class to define the state(activity, natural light).
-public class State {
-    // user´s activity
-    private int activity;
-    private double natural_light;
-
-    public State(int activity, double natural_light) {
-        this.natural_light = natural_light;
-        this.activity = activity;
-
-    }
-
-    public int getActivity() {
-        return this.activity;
-
-    }
-
-    public double getNatural_light() {
-        return this.natural_light;
-    }
-}
\ No newline at end of file

org.openlicht.action.reinforcementlearning/src/main/resources/readme.txt

-Bundle resources go in here!
\ No newline at end of file

settings.gradle

@@ -5,19 +5,13 @@ include 'openhab-mock'
 include 'integration'
 include ':benchmark'
 include ':commons.color'
-include ':skywriter-hue-integration'
-include ':org.openhab.action.machinelearn'
-include ':org.openlicht.action.reinforcementlearning'
-include ':stub.org.openhab.core.scriptengine.action'
 include ':feedbackloop.analyze'
 include ':feedbackloop.plan'
 include ':feedbackloop.execute'
 include ':feedbackloop.api'
 include ':feedbackloop.main'
-include ':ml_test'
 include ':feedbackloop.monitor'
 include ':feedbackloop.learner'
-include ':influx_test'
 include ':eraser.spark'
 include ':eraser.starter'
 include ':feedbackloop.learner_backup'

stub.org.openhab.core.scriptengine.action/.gitignore

-/build/
-/bin/
-logs/

stub.org.openhab.core.scriptengine.action/build.gradle

-sourceSets {
-    main {
-        java {
-            srcDir 'src/main/java'
-        }
-    }
-}

stub.org.openhab.core.scriptengine.action/src/main/java/org/openhab/core/scriptengine/action/ActionDoc.java

-package org.openhab.core.scriptengine.action;
-
-import java.lang.annotation.*;
-
-/**
- * Stub to make projects build using ActionDoc.
- *
- * @author rschoene - Initial contribution
- */
-@Target(ElementType.METHOD)
-@Inherited
-@Retention(RetentionPolicy.RUNTIME)
-public @interface ActionDoc {
-  String text();
-
-  String returns() default "";
-}