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Code cleaned up.
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@RuedigerLunde
RuedigerLunde committed Nov 5, 2016
1 parent ef18bb8 commit 46a08cf
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Showing 10 changed files with 1,337 additions and 1,355 deletions.
19 changes: 5 additions & 14 deletions aima-gui/src/main/java/aima/gui/fx/demo/search/games/EightPuzzleApp.java
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@@ -1,45 +1,36 @@
package aima.gui.fx.demo.search.games;

import java.util.ArrayList;
import java.util.List;
import java.util.Random;

import aima.core.agent.Action;
import aima.core.environment.eightpuzzle.BidirectionalEightPuzzleProblem;
import aima.core.environment.eightpuzzle.EightPuzzleBoard;
import aima.core.environment.eightpuzzle.ManhattanHeuristicFunction;
import aima.core.environment.eightpuzzle.MisplacedTilleHeuristicFunction;
import aima.core.environment.nqueens.AttackingPairsHeuristic;
import aima.core.environment.nqueens.NQueensBoard;
import aima.core.environment.nqueens.NQueensBoard.Config;
import aima.core.search.framework.Metrics;
import aima.core.search.framework.Search;
import aima.core.search.framework.SearchForActions;
import aima.core.search.framework.problem.Problem;
import aima.core.search.framework.qsearch.BidirectionalSearch;
import aima.core.search.framework.qsearch.GraphSearch;
import aima.core.search.framework.qsearch.TreeSearch;
import aima.core.search.informed.AStarSearch;
import aima.core.search.informed.GreedyBestFirstSearch;
import aima.core.search.local.SimulatedAnnealingSearch;
import aima.core.search.uninformed.BreadthFirstSearch;
import aima.core.search.uninformed.DepthFirstSearch;
import aima.core.search.uninformed.DepthLimitedSearch;
import aima.core.search.uninformed.IterativeDeepeningSearch;
import aima.core.util.CancelableThread;
import aima.gui.fx.demo.search.NQueensSearchProg;
import aima.gui.fx.framework.IntegrableApplication;
import aima.gui.fx.framework.Parameter;
import aima.gui.fx.framework.SimulationPaneBuilder;
import aima.gui.fx.framework.SimulationPaneCtrl;
import aima.gui.fx.views.EightPuzzleViewCtrl;
import aima.gui.fx.views.NQueensViewCtrl;
import javafx.application.Platform;
import javafx.scene.layout.BorderPane;
import javafx.scene.layout.Pane;
import javafx.scene.layout.StackPane;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.Random;


/**
* Integrable application which demonstrates how different search strategies
Expand Down Expand Up @@ -125,7 +116,7 @@ public Pane createRootPane() {

protected Parameter[] createParameters() {
Parameter p1 = new Parameter(PARAM_INIT_CONF, "Three Moves", "Medium", "Extreme", "Random");
Parameter p2 = new Parameter(PARAM_STRATEGY, (String[]) SEARCH_NAMES.toArray(new String[] {}));
Parameter p2 = new Parameter(PARAM_STRATEGY, (Object[]) SEARCH_NAMES.toArray(new String[] {}));
return new Parameter[] {p1, p2};
}

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270 changes: 136 additions & 134 deletions aima-gui/src/main/java/aima/gui/fx/demo/search/local/GeneticMaximumFinderApp.java
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@@ -1,134 +1,136 @@
package aima.gui.fx.demo.search.local;

import java.util.Collection;
import java.util.Optional;
import java.util.function.Function;

import aima.core.search.local.Individual;
import aima.gui.fx.framework.IntegrableApplication;
import aima.gui.fx.framework.Parameter;
import aima.gui.fx.framework.SimulationPaneBuilder;
import aima.gui.fx.framework.SimulationPaneCtrl;
import aima.gui.fx.views.FunctionPlotterCtrl;
import javafx.application.Platform;
import javafx.scene.canvas.Canvas;
import javafx.scene.layout.BorderPane;
import javafx.scene.layout.Pane;
import javafx.scene.paint.Color;
import javafx.scene.paint.Paint;


/**
* Demonstrates, how the genetic algorithm can be used, to find maximums in
* mathematical functions. Different parameter settings can be tried out and
* progress shown for each iteration.
*
* @author Ruediger Lunde
*/
public class GeneticMaximumFinderApp extends IntegrableApplication {

public static void main(String[] args) {
launch(args);
}

public final static String PARAM_FUNC_SELECT = "funcSelect";
public final static String PARAM_MUT_PROB = "mutProb";
public final static String PARAM_POPULATION = "population";
public final static String PARAM_MAX_ITER = "maxIter";

protected FunctionPlotterCtrl funcPlotterCtrl;
private SimulationPaneCtrl simPaneCtrl;
private GeneticMaximumFinderProg experiment;

@Override
public String getTitle() {
return "Genetic Maxium Finder App";
}

/**
* Defines state view, parameters, and call-back functions and calls the
* simulation pane builder to create layout and controller objects.
*/
@Override
public Pane createRootPane() {
BorderPane root = new BorderPane();

Canvas canvas = new Canvas();
funcPlotterCtrl = new FunctionPlotterCtrl(canvas);
funcPlotterCtrl.setLimits(Functions.minX, Functions.maxX, Functions.minY, Functions.maxY);
Parameter[] params = createParameters();

SimulationPaneBuilder builder = new SimulationPaneBuilder();
builder.defineParameters(params);
builder.defineStateView(canvas);
builder.defineInitMethod(this::initialize);
builder.defineSimMethod(this::simulate);
simPaneCtrl = builder.getResultFor(root);

return root;
}

protected Parameter[] createParameters() {
Parameter p1 = new Parameter(PARAM_FUNC_SELECT);
p1.setValues(Functions.f1, Functions.f2, Functions.f3);
p1.setValueNames("f1", "f2", "f3");
Parameter p2 = new Parameter(PARAM_MUT_PROB, 0.0, 0.2, 0.5, 1.0);
p2.setDefaultValueIndex(1);
Parameter p3 = new Parameter(PARAM_POPULATION, 2, 10, 20, 100);
p3.setDefaultValueIndex(2);
Parameter p4 = new Parameter(PARAM_MAX_ITER, 100, 200, 400);
p4.setDefaultValueIndex(0);
return new Parameter[] {p1, p2, p3, p4};
}

/** Displays the selected function on the state view. */
@Override
public void initialize() {
funcPlotterCtrl.setFunction((Function<Double, Double>) simPaneCtrl.getParamValue(PARAM_FUNC_SELECT));
}

@Override
public void finalize() {
simPaneCtrl.cancelSimulation();
}

/** Starts the experiment. */
public void simulate() {
experiment = new GeneticMaximumFinderProg();
experiment.setFunction((Function<Double, Double>) simPaneCtrl.getParamValue(PARAM_FUNC_SELECT));
experiment.setMutationProb(simPaneCtrl.getParamAsDouble(PARAM_MUT_PROB));
experiment.setPopulationSize(simPaneCtrl.getParamAsInt(PARAM_POPULATION));
experiment.setMaxIterations(simPaneCtrl.getParamAsInt(PARAM_MAX_ITER));
experiment.startExperiment(this::updateStateView);
}

/**
* Caution: While the background thread should be slowed down, updates of
* the GUI have to be done in the GUI thread!
*/
private void updateStateView(int itCount, Collection<Individual<Double>> gen) {
Platform.runLater(() -> updateStateViewLater(itCount, gen));
simPaneCtrl.waitAfterStep();
}

/**
* Must be called by the GUI thread!
*/
private void updateStateViewLater(int itCount, Collection<Individual<Double>> gen) {
funcPlotterCtrl.update();
if (gen != null) {
for (Individual<Double> ind : gen) {
Optional<Paint> fill = Optional.empty();
if (ind.getDescendants() > 0)
fill = Optional.of(Color.rgb(Math.max(255 - ind.getDescendants() * 20, 0), 0, 0));
else
fill = Optional.of(Color.RED.brighter());
double x = ind.getRepresentation().get(0);
funcPlotterCtrl.setMarker(x, fill);
}
simPaneCtrl.setStatus(experiment.getIterationInfo(itCount, gen));
} else {
simPaneCtrl.setStatus("");
}
}
}
package aima.gui.fx.demo.search.local;

import java.util.Collection;
import java.util.Optional;
import java.util.function.Function;

import aima.core.search.local.Individual;
import aima.gui.fx.framework.IntegrableApplication;
import aima.gui.fx.framework.Parameter;
import aima.gui.fx.framework.SimulationPaneBuilder;
import aima.gui.fx.framework.SimulationPaneCtrl;
import aima.gui.fx.views.FunctionPlotterCtrl;
import javafx.application.Platform;
import javafx.scene.canvas.Canvas;
import javafx.scene.layout.BorderPane;
import javafx.scene.layout.Pane;
import javafx.scene.paint.Color;
import javafx.scene.paint.Paint;


/**
* Demonstrates, how the genetic algorithm can be used, to find maximums in
* mathematical functions. Different parameter settings can be tried out and
* progress shown for each iteration.
*
* @author Ruediger Lunde
*/
public class GeneticMaximumFinderApp extends IntegrableApplication {

public static void main(String[] args) {
launch(args);
}

public final static String PARAM_FUNC_SELECT = "funcSelect";
public final static String PARAM_MUT_PROB = "mutProb";
public final static String PARAM_POPULATION = "population";
public final static String PARAM_MAX_ITER = "maxIter";

protected FunctionPlotterCtrl funcPlotterCtrl;
private SimulationPaneCtrl simPaneCtrl;
private GeneticMaximumFinderProg experiment;

@Override
public String getTitle() {
return "Genetic Maxium Finder App";
}

/**
* Defines state view, parameters, and call-back functions and calls the
* simulation pane builder to create layout and controller objects.
*/
@Override
public Pane createRootPane() {
BorderPane root = new BorderPane();

Canvas canvas = new Canvas();
funcPlotterCtrl = new FunctionPlotterCtrl(canvas);
funcPlotterCtrl.setLimits(Functions.minX, Functions.maxX, Functions.minY, Functions.maxY);
Parameter[] params = createParameters();

SimulationPaneBuilder builder = new SimulationPaneBuilder();
builder.defineParameters(params);
builder.defineStateView(canvas);
builder.defineInitMethod(this::initialize);
builder.defineSimMethod(this::simulate);
simPaneCtrl = builder.getResultFor(root);

return root;
}

protected Parameter[] createParameters() {
Parameter p1 = new Parameter(PARAM_FUNC_SELECT);
p1.setValues(Functions.f1, Functions.f2, Functions.f3);
p1.setValueNames("f1", "f2", "f3");
Parameter p2 = new Parameter(PARAM_MUT_PROB, 0.0, 0.2, 0.5, 1.0);
p2.setDefaultValueIndex(1);
Parameter p3 = new Parameter(PARAM_POPULATION, 2, 10, 20, 100);
p3.setDefaultValueIndex(2);
Parameter p4 = new Parameter(PARAM_MAX_ITER, 100, 200, 400);
p4.setDefaultValueIndex(0);
return new Parameter[] {p1, p2, p3, p4};
}

/** Displays the selected function on the state view. */
@SuppressWarnings("unchecked")
@Override
public void initialize() {
funcPlotterCtrl.setFunction((Function<Double, Double>) simPaneCtrl.getParamValue(PARAM_FUNC_SELECT));
}

@Override
public void finalize() {
simPaneCtrl.cancelSimulation();
}

/** Starts the experiment. */
@SuppressWarnings("unchecked")
public void simulate() {
experiment = new GeneticMaximumFinderProg();
experiment.setFunction((Function<Double, Double>) simPaneCtrl.getParamValue(PARAM_FUNC_SELECT));
experiment.setMutationProb(simPaneCtrl.getParamAsDouble(PARAM_MUT_PROB));
experiment.setPopulationSize(simPaneCtrl.getParamAsInt(PARAM_POPULATION));
experiment.setMaxIterations(simPaneCtrl.getParamAsInt(PARAM_MAX_ITER));
experiment.startExperiment(this::updateStateView);
}

/**
* Caution: While the background thread should be slowed down, updates of
* the GUI have to be done in the GUI thread!
*/
private void updateStateView(int itCount, Collection<Individual<Double>> gen) {
Platform.runLater(() -> updateStateViewLater(itCount, gen));
simPaneCtrl.waitAfterStep();
}

/**
* Must be called by the GUI thread!
*/
private void updateStateViewLater(int itCount, Collection<Individual<Double>> gen) {
funcPlotterCtrl.update();
if (gen != null) {
for (Individual<Double> ind : gen) {
Optional<Paint> fill = Optional.empty();
if (ind.getDescendants() > 0)
fill = Optional.of(Color.rgb(Math.max(255 - ind.getDescendants() * 20, 0), 0, 0));
else
fill = Optional.of(Color.RED.brighter());
double x = ind.getRepresentation().get(0);
funcPlotterCtrl.setMarker(x, fill);
}
simPaneCtrl.setStatus(experiment.getIterationInfo(itCount, gen));
} else {
simPaneCtrl.setStatus("");
}
}
}

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