praktikum-holons/src/exampleAlgorithms/PSOAlgotihm.java

package exampleAlgorithms;

import java.awt.BorderLayout;
import java.awt.Component;
import java.awt.Dimension;
import java.awt.FlowLayout;
import java.awt.Font;
import java.io.BufferedWriter;
import java.io.File;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.OutputStreamWriter;
import java.math.RoundingMode;
import java.text.NumberFormat;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Locale;
import java.util.stream.Collectors;

import javax.swing.BorderFactory;
import javax.swing.JButton;
import javax.swing.JCheckBox;
import javax.swing.JFileChooser;
import javax.swing.JFormattedTextField;
import javax.swing.JFrame;
import javax.swing.JLabel;
import javax.swing.JPanel;
import javax.swing.JScrollPane;
import javax.swing.JSplitPane;
import javax.swing.JTextArea;
import javax.swing.filechooser.FileNameExtensionFilter;
import javax.swing.text.NumberFormatter;

import api.Algorithm;
import classes.AbstractCpsObject;
import classes.CpsUpperNode;
import classes.HolonElement;
import classes.HolonObject;
import classes.HolonSwitch;

import ui.controller.Control;
import ui.model.Model;
import ui.model.DecoratedHolonObject.HolonObjectState;
import ui.model.DecoratedNetwork;
import ui.model.DecoratedState;

public class PSOAlgotihm implements Algorithm {
	//Parameter for Algo with default Values:
	private int swarmSize = 20; 
	private int maxIterations = 100; 
	private double limit = 0.3; 
	private double dependency = 2.07; 
	private int rounds = 20; 
	private boolean append = false;
	
	//Parameter defined by Algo
	private HashMap<Integer, AccessWrapper> access;
	private List<Boolean> initialState;
	private double c1, c2, w;
	private RunDataBase db;
	
	//Parameter for Plotting (Default Directory in Constructor)
	private JFileChooser fileChooser = new JFileChooser();

	
	
	//Gui Part:
	private Control  control;
	private JTextArea textArea;
	private JPanel content = new JPanel();
	public static void main(String[] args)
	{
	      JFrame newFrame = new JFrame("exampleWindow");
	      PSOAlgotihm instance = new PSOAlgotihm();
	      newFrame.setContentPane(instance.getAlgorithmPanel());
	      newFrame.pack();
	      newFrame.setVisible(true);
	      newFrame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
	}
	public PSOAlgotihm() {
		content.setLayout(new BorderLayout());
	
		textArea = new JTextArea();
		textArea.setEditable(false);
		JScrollPane scrollPane = new JScrollPane(textArea);
		JSplitPane splitPane = new JSplitPane(JSplitPane.VERTICAL_SPLIT,
				createOptionPanel() , scrollPane);
		splitPane.setResizeWeight(0.0);
		content.add(splitPane, BorderLayout.CENTER);
		content.setPreferredSize(new Dimension(800,800));	
		//Default Directory
		fileChooser.setCurrentDirectory(new File(System.getProperty("user.dir")));
		fileChooser.setSelectedFile(new File("plott.txt"));
	}
	public JPanel createOptionPanel() {
		JPanel optionPanel = new JPanel(new BorderLayout());
		JScrollPane scrollPane = new JScrollPane(createParameterPanel());
		scrollPane.setBorder(BorderFactory.createTitledBorder("Parameter"));
		optionPanel.add(scrollPane,  BorderLayout.CENTER);
		optionPanel.add(createButtonPanel(), BorderLayout.PAGE_END);
		return optionPanel;
	}
	
	private Component createParameterPanel() {
		JPanel parameterPanel = new JPanel(null);
		parameterPanel.setPreferredSize(new Dimension(510,300));
		
		JLabel info = new JLabel("Tune the variables of the PSO algorithm in order to reach better results.");
		info.setBounds(10, 10, 480, 15);
		parameterPanel.add(info);
		
		JLabel swarmSizeLabel = new JLabel("Swarm Size:");
		swarmSizeLabel.setBounds(20, 60, 100, 20);
		parameterPanel.add(swarmSizeLabel);
		
		JLabel showDiagnosticsLabel = new JLabel("Append Plott on existing File:");
		showDiagnosticsLabel.setBounds(200, 60, 170, 20);
		parameterPanel.add(showDiagnosticsLabel);
		
		JLabel cautionLabel = new JLabel(
				"Caution: High values in the fields of 'Swarm Size' and 'Max. Iteration' may take some time to calculate.");
		cautionLabel.setFont(new Font("Serif", Font.ITALIC, 12));
		cautionLabel.setBounds(10, 210, 500, 15);
		parameterPanel.add(cautionLabel);
		
		JLabel maxIterLabel = new JLabel("Max. Iterations:");
		maxIterLabel.setBounds(20, 85, 100, 20);
		parameterPanel.add(maxIterLabel);
		
		JLabel limitLabel = new JLabel("Limit:");
		limitLabel.setBounds(20, 110, 100, 20);
		parameterPanel.add(limitLabel);
		
		JLabel dependecyLabel = new JLabel("Dependency:");
		dependecyLabel.setBounds(20, 135, 100, 20);
		parameterPanel.add(dependecyLabel);
			
		JLabel roundsLabel = new JLabel("Round:");
		roundsLabel.setBounds(20, 160, 100, 20);
		parameterPanel.add(roundsLabel);
		
		
		JCheckBox diagnosticsCheckBox = new JCheckBox();
		diagnosticsCheckBox.setSelected(false);
		diagnosticsCheckBox.setBounds(370, 60, 25, 20);
		diagnosticsCheckBox.addPropertyChangeListener(propertyChange -> append = diagnosticsCheckBox.isSelected());
		parameterPanel.add(diagnosticsCheckBox);
		
		//Integer formatter
		NumberFormat format = NumberFormat.getIntegerInstance();
		format.setGroupingUsed(false);
		format.setParseIntegerOnly(true);
		NumberFormatter integerFormatter = new NumberFormatter(format);
		integerFormatter.setMinimum(0);
		integerFormatter.setCommitsOnValidEdit(true);
		
		
		JFormattedTextField swarmSizeTextField = new  JFormattedTextField(integerFormatter);
		swarmSizeTextField.setValue(swarmSize);
		swarmSizeTextField.setToolTipText("Only positive Integer.");
		swarmSizeTextField.addPropertyChangeListener(propertyChange -> swarmSize = Integer.parseInt(swarmSizeTextField.getValue().toString()));
		swarmSizeTextField.setBounds(125, 60, 50, 20);
		parameterPanel.add(swarmSizeTextField);
		
		JFormattedTextField maxIterTextField = new JFormattedTextField(integerFormatter);
		maxIterTextField.setValue(maxIterations);
		maxIterTextField.setToolTipText("Only positive Integer.");
		maxIterTextField.addPropertyChangeListener(propertyChange -> maxIterations = Integer.parseInt(maxIterTextField.getValue().toString()));
		maxIterTextField.setBounds(125, 85, 50, 20);
		parameterPanel.add(maxIterTextField);

		//Double Format:
		NumberFormat doubleFormat = NumberFormat.getNumberInstance(Locale.US);
		doubleFormat.setMinimumFractionDigits(1);
		doubleFormat.setMaximumFractionDigits(3);
		doubleFormat.setRoundingMode(RoundingMode.HALF_UP);

		//Limit Formatter:
		NumberFormatter limitFormatter = new NumberFormatter(doubleFormat);
		limitFormatter.setMinimum(0.0);
		limitFormatter.setMaximum(1.0);
		
		JFormattedTextField limitTextField = new JFormattedTextField(limitFormatter);
		limitTextField.setValue(limit);
		limitTextField.setToolTipText("Only Double in range [0.0, 1.0] with DecimalSeperator Point('.').");
		limitTextField.addPropertyChangeListener(propertyChange -> limit = Double.parseDouble(limitTextField.getValue().toString()));
		limitTextField.setBounds(125, 110, 50, 20);
		parameterPanel.add(limitTextField);
		
		//Limit Formatter:
		NumberFormatter dependencyFormatter = new NumberFormatter(doubleFormat);
		dependencyFormatter.setMinimum(2.001);
		dependencyFormatter.setMaximum(2.4);
		
		
		JFormattedTextField dependencyTextField = new JFormattedTextField(dependencyFormatter);
		dependencyTextField.setValue(dependency);
		dependencyTextField.setToolTipText("Only Double in range [2.001, 2.4] with DecimalSeperator Point('.').");
		dependencyTextField.addPropertyChangeListener(propertyChange -> dependency = Double.parseDouble(dependencyTextField.getValue().toString()));
		dependencyTextField.setBounds(125, 135, 50, 20);
		parameterPanel.add(dependencyTextField);
		
		NumberFormatter roundsFormatter = new NumberFormatter(format);
		roundsFormatter.setMinimum(1);
		roundsFormatter.setCommitsOnValidEdit(true);
		
		JFormattedTextField roundsTextField = new JFormattedTextField(roundsFormatter);
		roundsTextField.setValue(rounds);
		roundsTextField.setToolTipText("Amount of rounds to be runed with the same starting ");
		roundsTextField.addPropertyChangeListener(propertyChange -> rounds = Integer.parseInt((roundsTextField.getValue().toString())));
		roundsTextField.setBounds(125, 160, 50, 20);
		parameterPanel.add(roundsTextField);
		return parameterPanel;
	}
	public JPanel createButtonPanel() {
		JPanel buttonPanel = new JPanel(new FlowLayout(FlowLayout.RIGHT));
		JButton clearButton =  new JButton("Clear Console");
		clearButton.addActionListener(actionEvent -> clear());
		buttonPanel.add(clearButton);
		JButton folderButton =  new JButton("Change Plott-File");
		folderButton.addActionListener(actionEvent -> setSaveFile());
		buttonPanel.add(folderButton);
		JButton plottButton =  new JButton("Plott");
		plottButton.addActionListener(actionEvent -> plott());
		buttonPanel.add(plottButton);
		JButton resetButton =  new JButton("Reset");
		resetButton.setToolTipText("Resets the State to before the Algorithm has runed.");
		resetButton.addActionListener(actionEvent -> reset());
		buttonPanel.add(resetButton);
		JButton runButton =  new JButton("Run");
		runButton.addActionListener(actionEvent -> executePsoAlgoWithCurrentParameters());
		buttonPanel.add(runButton);
		return buttonPanel;
	}
	
	private void setSaveFile() {
		fileChooser.setFileFilter(new FileNameExtensionFilter("File", "txt"));
		fileChooser.setFileSelectionMode(JFileChooser.FILES_ONLY);
		int result = fileChooser.showSaveDialog(content);
		if(result == JFileChooser.APPROVE_OPTION) {
			println("Set save File to:" + fileChooser.getSelectedFile().getAbsolutePath());
		}
		
	}
	private void plott() {
		if(db!=null) {
			println("Plott..");
			db.initFileStream();
		}else {
			println("No run inistialized.");
		}
	}
	private void reset() {
		if(initialState != null) {
			println("Resetting..");
			resetState();
			updateVisual();
		}else {
			println("No run inistialized.");
		}
	}
	private void printParameter() {
		println("SwarmSize:" + swarmSize + ", MaxIter:" + maxIterations + ", Limit:" + limit + ", Dependency:" + dependency + ", Rounds:" + rounds +", DependentParameter: w:"+ w + ", c1:" + c1 + ", c2:" + c2 );
	}
	@Override
	public JPanel getAlgorithmPanel() {
		return content;
	}
	@Override
	public void setController(Control control) {
		this.control = control;
		
	}
	private void clear() {
		textArea.setText("");
	}
	private void print(String message) {
		textArea.append(message);
	}
	private void println(String message) {
		textArea.append(message  + "\n");
	}
	
	
	
	
	
	
	
	
	//Algo Part:
	/**
	 * The Execution of the Algo its initialize the missing parameter and execute single Algo runs successively.
	 */
	private void executePsoAlgoWithCurrentParameters() {
		initDependentParameter();
		printParameter();
		Best runBest = new Best();
		runBest.value = Double.MAX_VALUE;
		db = new RunDataBase();
		for(int r = 0; r < rounds; r++)
		{		
          
		  List<Double> runList = db.insertNewRun();
		  Best lastRunBest = executePSOoneTime(runList);
		  resetState();
		  if(lastRunBest.value < runBest.value) runBest = lastRunBest;
		}
		println("AlgoResult:" + runBest.value);
		//println("[" + lastRunBest.position.stream().map(Object::toString).collect(Collectors.joining(", ")) + "]");
		setState(runBest.position);
		updateVisual();
	}
	/**
	 * Calculate w, c1, c2
	 */
	private void initDependentParameter() {
		w = 1.0 / (dependency - 1 + Math.sqrt(dependency * dependency - 2 * dependency));
		c1 = c2 = dependency * w;
	}
	/**
	 *  <p>Algo from Paper:</p><font size="3"><pre>
	 *  
	 *  Begin
	 *  	t = 0; {t: generation index}
	 *  	initialize particles x<sub>p,i,j</sub>(t);
	 *  	evaluation x<sub>p,i,j</sub>(t);
	 *  	while (termination condition &ne; true) do
	 *  		v<sub>i,j</sub>(t) = update v<sub>i,j</sub>(t); {by Eq. (6)}
	 *  		x<sub>g,i,j</sub>(t) = update x<sub>g,i,j</sub>(t); {by Eq. (7)}
	 *  		x<sub>g,i,j</sub>(t) = mutation x<sub>g,i,j</sub>(t); {by Eq. (11)}
	 *  		x<sub>p,i,j</sub>(t) = decode x<sub>g,i,j</sub>(t); {by Eqs. (8) and (9)}
	 *  		evaluate x<sub>p,i,j</sub>(t);
	 *  		t = t + 1;
	 *  	end while
	 *  End</pre></font>
	 *  <p>with:</p><font size="3">
	 *  
	 *  x<sub>g,i,j</sub>: genotype ->genetic information -> in continuous space<br>
	 *  x<sub>p,i,j</sub>: phenotype -> observable characteristics-> in binary space<br>
	 *  X<sub>g,max</sub>: is the Maximum here set to 4.<br>
	 *  Eq. (6):v<sub>i,j</sub>(t + 1) = wv<sub>i,j</sub>+c<sub>1</sub>R<sub>1</sub>(P<sub>best,i,j</sub>-x<sub>p,i,j</sub>(t))+c<sub>2</sub>R<sub>2</sub>(g<sub>best,i,j</sub>-x<sub>p,i,j</sub>(t))<br>
	 *  Eq. (7):x<sub>g,i,j</sub>(t + 1) = x<sub>g,i,j</sub>(t) + v<sub>i,j</sub>(t + 1)<br>
	 *  Eq. (11):<b>if(</b>rand()&lt;r<sub>mu</sub><b>)then</b> x<sub>g,i,j</sub>(t + 1) = -x<sub>g,i,j</sub>(t + 1)<br>
	 *  Eq. (8):x<sub>p,i,j</sub>(t + 1) = <b>(</b>rand() &lt; S(x<sub>g,i,j</sub>(t + 1))<b>) ?</b> 1 <b>:</b> 0<br>
	 *  Eq. (9) Sigmoid:S(x<sub>g,i,j</sub>(t + 1)) := 1/(1 + e<sup>-x<sub>g,i,j</sub>(t + 1)</sup>)<br></font>
	 *  <p>Parameter:</p>
	 *  w inertia, calculated from phi(Variable:{@link #dependency})<br>
	 *  c1:	influence, calculated from phi(Variable:{@link #dependency}) <br>
	 *  c2:	influence, calculated from phi(Variable:{@link #dependency})<br>
	 *  r<sub>mu</sub>: probability that the proposed operation is conducted defined by limit(Variable:{@link #limit})<br>
	 *  
	 *  
	 */
	private Best executePSOoneTime(List<Double> runList) {
		Best globalBest = new Best();
		globalBest.position = extractPositionAndAccess(control.getModel());
		globalBest.value = evaluatePosition(globalBest.position);
		print("Start Value:" + globalBest.value);
		int dimensions = globalBest.position.size();
		List<Particle> swarm= initializeParticles(dimensions);
		runList.add(globalBest.value);
		evaluation(globalBest, swarm);
		runList.add(globalBest.value);
		for (int iteration = 0; iteration < maxIterations ; iteration++) {
			for (int particleNumber = 0; particleNumber < swarmSize; particleNumber++) {
				Particle particle = swarm.get(particleNumber);				
				for(int index = 0; index < dimensions; index++) {
					updateVelocity(particle, index, globalBest);
					updateGenotype(particle, index);
					mutation(particle, index);
					decode(particle, index);
				}
			}
			evaluation(globalBest, swarm);
			runList.add(globalBest.value);
		}
		println(" End Value:" + globalBest.value);
		return globalBest;
	}
	/**
	 * 	Eq. (6):v<sub>i,j</sub>(t + 1) = wv<sub>i,j</sub>+c<sub>1</sub>R<sub>1</sub>(P<sub>best,i,j</sub>-x<sub>p,i,j</sub>(t))+c<sub>2</sub>R<sub>2</sub>(g<sub>best,i,j</sub>-x<sub>p,i,j</sub>(t))<br>
	 * @param particle
	 * @param index
	 * @param globalBest
	 */
	private void updateVelocity(Particle particle, int index, Best globalBest) {
		double r1 = Random.nextDouble();
		double r2 =	Random.nextDouble();
		double posValue = particle.xPhenotype.get(index)?1.0:0.0;
		particle.velocity.set(index, clamp(w*particle.velocity.get(index) + c1*r1*((particle.localBest.position.get(index)?1.0:0.0)  - posValue) + c2*r2*((globalBest.position.get(index)?1.0:0.0)- posValue)) );
	}
	/**
	 * Eq. (7):x<sub>g,i,j</sub>(t + 1) = x<sub>g,i,j</sub>(t) + v<sub>i,j</sub>(t + 1)<br>
	 * @param particle
	 * @param index
	 */
	private void updateGenotype(Particle particle, int index) {
		particle.xGenotype.set(index, clamp(particle.xGenotype.get(index) + particle.velocity.get(index)));
	}
	/**
	 * Eq. (11):<b>if(</b>rand()&lt;r<sub>mu</sub><b>)then</b> x<sub>g,i,j</sub>(t + 1) = -x<sub>g,i,j</sub>(t + 1)<br>
	 * @param particle
	 * @param index
	 */
	private void mutation(Particle particle, int index) {
		if(Random.nextDouble() < limit) particle.xGenotype.set(index, -particle.xGenotype.get(index));
	}
	/**
	 * Eq. (8):x<sub>p,i,j</sub>(t + 1) = <b>(</b>rand() &lt; S(x<sub>g,i,j</sub>(t + 1))<b>) ?</b> 1 <b>:</b> 0<br>
	 * @param particle
	 * @param index
	 */
	private void decode(Particle particle, int index) {
		particle.xPhenotype.set(index, Random.nextDouble() < Sigmoid(particle.xGenotype.get(index)));
	}
	/**
	 * Eq. (9) Sigmoid:S(x<sub>g,i,j</sub>(t + 1)) := 1/(1 + e<sup>-x<sub>g,i,j</sub>(t + 1)</sup>)<br></font>
	 * @param value
	 * @return
	 */
	private double Sigmoid(double value) {
		return 1.0 / (1.0 + Math.exp(-value));
	}

	/**
	 * To clamp X<sub>g,j,i</sub> and v<sub>i,j</sub> in Range [-X<sub>g,max</sub>|+X<sub>g,max</sub>] with {X<sub>g,max</sub>= 4}
	 * @param value
	 * @return
	 */
	private double clamp(double value) {
		return Math.max(-4.0, Math.min(4.0, value));
	}
	/**
	 * 
	 * @param j maximum index of position in the particle
	 * @return
	 */
	private List<Particle> initializeParticles(int j) {
		List<Particle> swarm = new ArrayList<Particle>();
		//Create The Particle
		for (int particleNumber = 0; particleNumber < swarmSize; particleNumber++){
			//Create a Random position
			List<Boolean> aRandomPosition = new ArrayList<Boolean>();
			for (int index = 0; index < j; index++){
				aRandomPosition.add(Random.nextBoolean());
			}
			swarm.add(new Particle(aRandomPosition));
		}
		return swarm;
	}
	/**
	 * Evaluate each particle and update the global Best position;
	 * @param globalBest
	 * @param swarm
	 */
	private void evaluation(Best globalBest, List<Particle> swarm) {
		for(Particle p: swarm) {
			double localEvaluationValue = evaluatePosition(p.xPhenotype);
			p.checkNewEvaluationValue(localEvaluationValue);
			if(localEvaluationValue < globalBest.value) {
				globalBest.value = localEvaluationValue;
				globalBest.position = p.localBest.position;
			}
		}
	}
	/**
	 * Evaluate a position.
	 * @param position
	 * @return
	 */
	private double evaluatePosition(List<Boolean> position) {
		setState(position);
		control.calculateStateForCurrentTimeStep();
		DecoratedState actualstate = control.getSimManager().getActualDecorState();		
		return getFitnessValueForState(actualstate);
	}
	/**
	 * Calculate the Fitness(Penelty) Value for a state (alias the calculated Position).
	 * TODO: Make me better Rolf.
	 * @param state
	 * @return
	 */
	private double getFitnessValueForState(DecoratedState state) {
		double fitness = 0.0;
		double nw_fitness =0.0;
		double object_fitness = 0.0;
		
		// calculate network_fitness
		for(DecoratedNetwork net : state.getNetworkList()) {
			float production = net.getSupplierList().stream().map(supplier -> supplier.getEnergyToSupplyNetwork()).reduce(0.0f, (a, b) -> a + b);
			float consumption = net.getConsumerList().stream().map(con -> con.getEnergyNeededFromNetwork()).reduce(0.0f, (a, b) -> a + b);
			nw_fitness += Math.abs(production - consumption); //Energy is now everywhere positive
		}
		
		// calculate object_fitness
		for(DecoratedNetwork net : state.getNetworkList()) {
			object_fitness += net.getConsumerList().stream().map(con -> holonObjectSupplyPenaltyFunction(con.getSupplyBarPercentage()) + inactiveHolonElementPenalty(con.getModel())).reduce(0.0, (a, b) -> (a + b));
			//warum war das im network fitness und nicht hier im Object fitness??
			object_fitness += net.getPassivNoEnergyList().stream().map(con -> 1000.0).reduce(0.0, (a, b) -> (a + b));
			object_fitness += net.getSupplierList().stream().map(sup -> inactiveHolonElementPenalty(sup.getModel())).reduce(0.0, (a, b) -> (a + b));
			object_fitness += net.getConsumerSelfSuppliedList().stream().map(con -> inactiveHolonElementPenalty(con.getModel())).reduce(0.0, (a, b) -> (a + b));
		}
		fitness = nw_fitness + object_fitness;
		return fitness;
	}

	
	/**
	 * Untouched:
	 * Function that returns the fitness depending on the number of elements deactivated in a single holon object
	 * @param obj Holon Object that contains Holon Elements
	 * @return fitness value for that object depending on the number of deactivated holon elements
	 */
	private double inactiveHolonElementPenalty(HolonObject obj) {
		float result = 0;
		int activeElements = obj.getNumberOfActiveElements();
		int maxElements = obj.getElements().size();
		
		if(activeElements == maxElements)
			result =0;
		else result = (float) Math.pow((maxElements -activeElements),2)*100;
		
		
	return result;
		
	}
	/**
	 * Untouched:
	 * Calculates a penalty value based on the HOs current supply percentage
	 * @param supplyPercentage
	 * @return
	 */
	private double holonObjectSupplyPenaltyFunction(float supplyPercentage) {
		float result = 0;
		if(supplyPercentage == 1)
			return result;
		else if(supplyPercentage < 1 && supplyPercentage >= 0.25) // undersupplied inbetween 25% and 100%
			result = (float) Math.pow(1/supplyPercentage, 2);
		else if (supplyPercentage < 0.25) //undersupplied with less than 25%
			result = (float) Math.pow(1/supplyPercentage,2);
		else if (supplyPercentage < 1.25)  //Oversupplied less than 25%
			result = (float) Math.pow(supplyPercentage,3) ;
		else result = (float) Math.pow(supplyPercentage,4); //Oversupplied more than 25%
		
		
		if(Float.isInfinite(result) || Float.isNaN(result))
			result = 1000;
	
		return result;
	}
	/**
	 * If you want to get in touch with a reliable state? Working function not in use currently.
	 * @param state
	 * @return
	 */
	private double StateToDouble(HolonObjectState state) {
		switch (state) {
		case NOT_SUPPLIED:
			return 10.0;
		case NO_ENERGY:
			return 15.0;
		case OVER_SUPPLIED:
			return 5.0;
		case PARTIALLY_SUPPLIED:
			return 3.0;
		case PRODUCER:
			return 2.0;
		case SUPPLIED:
			return 0;
		default:
			return 0;
		}
	}
	

	/**
	 * Method to get the current Position alias a ListOf Booleans for aktive settings on the Objects on the Canvas.
	 * Also initialize the Access Hashmap to swap faster positions.
	 * @param model
	 * @return
	 */
	private List<Boolean> extractPositionAndAccess(Model model) {
		initialState = new ArrayList<Boolean>(); 
		access= new HashMap<Integer, AccessWrapper>();
		rollOutNodes(model.getObjectsOnCanvas(), initialState, model.getCurIteration());
		return initialState;
	}
	/**
	 * Method to extract the Informations recursively out of the Model.
	 * @param nodes
	 * @param positionToInit
	 * @param timeStep
	 */
	private void rollOutNodes(List<AbstractCpsObject> nodes, List<Boolean> positionToInit, int timeStep) {
		for(AbstractCpsObject aCps : nodes) {
			if (aCps instanceof HolonObject) {
				for (HolonElement hE : ((HolonObject) aCps).getElements()) {
					positionToInit.add(hE.isActive());
					access.put(positionToInit.size() - 1 , new AccessWrapper(hE));
				}
			}
			else if (aCps instanceof HolonSwitch) {
				HolonSwitch sw = (HolonSwitch) aCps;
				positionToInit.add(sw.getState(timeStep));
				access.put(positionToInit.size() - 1 , new AccessWrapper(sw));
			}
			else if(aCps instanceof CpsUpperNode) {
				rollOutNodes(((CpsUpperNode)aCps).getNodes(), positionToInit ,timeStep );
			}
		}
	}
	/**
	 * To let the User See the current state without touching the Canvas.
	 */
	private void updateVisual() {
		control.calculateStateForCurrentTimeStep();
		control.updateCanvas();
	}
	/**
	 * Sets the Model back to its original State before the LAST run.
	 */
	private void resetState() {
		setState(initialState);
	}
	
	/**
	 * Sets the State out of the given position for calculation or to show the user.
	 * @param position
	 */
	private void setState(List<Boolean> position) {
		for(int i = 0;i<position.size();i++) {
			access.get(i).setState(position.get(i));
		}
	}
	
	/**
	 * A Database for all Global Best(G<sub>Best</sub>) Values in a execution of a the Algo. For Easy Printing.
	 */
	private class RunDataBase {
		List<List<Double>> allRuns;
		RunDataBase(){
			allRuns = new ArrayList<List<Double>>();
		}
		
		
		/**
		 * Initialize The Stream before you can write to a File.
		 */
		public void initFileStream() {
			File file = fileChooser.getSelectedFile();
			try {
				file.createNewFile();
				BufferedWriter out = new BufferedWriter(new OutputStreamWriter(
					    new FileOutputStream(file, append), "UTF-8"));
				printToStream(out);
				out.close();
			} catch (IOException e) {
				println(e.getMessage());
			}
		}
		
		/**
		 * 
		 * TODO: Rolf Change this method to suit your Python script respectively. 
		 * A run have maxIterations + 2 values. As described: First is the InitialState Value, 
		 * Second is The best Value after the swarm is Initialized not have moved jet, and then comes the Iterations that described 
		 * each step of movement from the swarm.
		 */
		public void printToStream(BufferedWriter out) throws IOException {
			allRuns.forEach(run -> {
				try {
					out.write( run.stream().map(Object::toString).collect(Collectors.joining(", ")));
					out.newLine();
				} catch (IOException e) {
					println(e.getMessage());
				}
			} );
			out.write("AverageRun:");
			out.newLine();
			out.write(calculateAverageRun().stream().map(Object::toString).collect(Collectors.joining(", ")));
			out.newLine();
		}
		private List<Double> calculateAverageRun(){
			int amountOfRuns = allRuns.size();
			List<Double> newAverageRun = new ArrayList<Double>();
			for(int iteration = 0; iteration < maxIterations + 2; iteration++) {
				final int  currentIter = iteration;
				double sum = 0.0;
				sum = allRuns.stream().map(run -> run.get(currentIter)).reduce(0.0, (a, b) -> a + b);
				newAverageRun.add(sum / amountOfRuns);
			}
			return newAverageRun;
		}
		public List<Double> insertNewRun(){
			List<Double> newRun = new ArrayList<Double>();
			allRuns.add(newRun);
			return newRun;
		}
	}
	
	
	/**
	 * To give the Local Best of a Partice(P<sub>Best</sub>) or the Global Best(G<sub>Best</sub>) a Wrapper to have Position And Evaluation Value in one Place.
	 */
	private class Best{
		public double value;
		public  List<Boolean> position;
		public Best(){
		}
	}
	/**
	 * A Wrapper Class for Access HolonElement and HolonSwitch in one Element and not have to split the List.
	 */
	private class AccessWrapper {
		public static final int HOLONELEMENT = 0;
		public static final int SWITCH = 1;
		private int type;
		private HolonSwitch hSwitch;
		private HolonElement hElement;
		public AccessWrapper(HolonSwitch hSwitch){
			type = SWITCH;
			this.hSwitch = hSwitch;
		}
		public AccessWrapper(HolonElement hElement){
			type = HOLONELEMENT;
			this.hElement = hElement;
		}
		public void setState(boolean state) {
			if(type == HOLONELEMENT) {
				hElement.setActive(state);
			}else{//is switch
				hSwitch.setManualMode(true);
				hSwitch.setManualState(state);
			}
				
		}
		public boolean getState(int timeStep) {
			return (type == HOLONELEMENT)?hElement.isActive():hSwitch.getState(timeStep);
		}
	}
	/**
	 * Class to represent a Particle.
	 */
	private class Particle{
		/**
		 * The velocity of a particle.
		 */
		public List<Double> velocity;
		/**
		 * The positions genotype.
		 */
		public List<Double> xGenotype;
		/**
		 * The positions phenotype. Alias the current position.
		 */
		public List<Boolean> xPhenotype;
		
		public Best localBest;
		
		Particle(List<Boolean> position){
			this.xPhenotype = position;
			//Init velocity, xGenotype with 0.0 values.
			this.velocity = position.stream().map(bool -> 0.0).collect(Collectors.toList());
			this.xGenotype = position.stream().map(bool -> 0.0).collect(Collectors.toList());
			localBest = new Best();
			localBest.value = Double.MAX_VALUE;
		}
		public void checkNewEvaluationValue(double newEvaluationValue) {
			if(newEvaluationValue < localBest.value) {
				localBest.value = newEvaluationValue;
				localBest.position = xPhenotype.stream().map(bool -> bool).collect(Collectors.toList());
			}
		}
		public String toString() {
			return "Particle with xPhenotype(Position), xGenotype, velocity:[" 
					+ listToString(xPhenotype) + "],[" + listToString(xGenotype) + "],[" 
					+ listToString(velocity) + "]";
		}
		private <Type> String listToString(List<Type> list) {
			return list.stream().map(Object::toString).collect(Collectors.joining(", "));
		}
		
	}
	
	/**
	* To create Random and maybe switch the random generation in the future.
	*/
	private static class Random{
			/**
			 * True or false
			 * @return the random boolean.
			 */
			public static boolean nextBoolean(){
				return (Math.random() < 0.5);
			}
			/**
			 * Between 0.0 and 1.0
			 * @return the random double.
			 */
			public static double nextDouble(){
				return Math.random();
			}
		}
	
}