diff --git a/.gitlab/issue_templates/Bug.md b/.gitlab/issue_templates/Bug.md
index d2774f03eb010ba6fb5925ad0f16478c40a42e29..c61a027fa5e51dd3dd03908ae3effd74d975e884 100644
--- a/.gitlab/issue_templates/Bug.md
+++ b/.gitlab/issue_templates/Bug.md
@@ -8,13 +8,6 @@ Steps to reproduce
(How one can reproduce the issue - this is very important)
-Example Project
-
-(If possible, please create an example project here on GitLab.com that exhibits the problematic behaviour, and link to it here in the bug report)
-
-(If you are using an older version of GitLab, this will also determine whether the bug has been fixed in a more recent version)
-
-
What is the current bug behavior?
(What actually happens)
@@ -35,7 +28,5 @@ Possible fixes
(If you can, link to the line of code that might be responsible for the problem)
-/label ~bug ~reproduced ~needs-investigation
-/cc @project-manager
-/assign @qa-tester
+/label ~bug ~needs-investigation
diff --git a/.gitlab/issue_templates/Task.md b/.gitlab/issue_templates/Task.md
new file mode 100644
index 0000000000000000000000000000000000000000..d5f427351bbcb83e00f144eaad595ad3466a7915
--- /dev/null
+++ b/.gitlab/issue_templates/Task.md
@@ -0,0 +1,11 @@
+Summary
+
+(Summarize the bug encountered concisely)
+
+Associated classes
+
+(Classes, which need to be modified, if any)
+
+
+/label ~bug ~reproduced ~needs-investigation
+
diff --git a/Doxyfile b/Doxyfile
index 5818beceff6cfc24770e55ce00b4e639049f49c0..bc77ff124a4e9ebf597b2d286af9747988923ebd 100644
--- a/Doxyfile
+++ b/Doxyfile
@@ -32,7 +32,7 @@ DOXYFILE_ENCODING = UTF-8
# title of most generated pages and in a few other places.
# The default value is: My Project.
-PROJECT_NAME = "4Neuro"
+PROJECT_NAME = "lib4neuro"
# The PROJECT_NUMBER tag can be used to enter a project or revision number. This
# could be handy for archiving the generated documentation or if some version
@@ -51,7 +51,7 @@ PROJECT_BRIEF = "Massivelly-parallel neural networks library"
# pixels and the maximum width should not exceed 200 pixels. Doxygen will copy
# the logo to the output directory.
-PROJECT_LOGO = "img/4neuro_logo_small.png"
+PROJECT_LOGO = "img/lib4neuro_logo.png"
# The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path
# into which the generated documentation will be written. If a relative path is
diff --git a/img/4neuro_logo_small.png b/img/4neuro_logo_small.png
deleted file mode 100644
index 01a1d4491b309bff787c0f3a69fc3f09bfc647d9..0000000000000000000000000000000000000000
Binary files a/img/4neuro_logo_small.png and /dev/null differ
diff --git a/img/lib4neuro_logo.png b/img/lib4neuro_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..306b8e759c771a94669f73e062095a7eabaaf48a
Binary files /dev/null and b/img/lib4neuro_logo.png differ
diff --git a/java/src/BPNet.java b/java/src/BPNet.java
deleted file mode 100644
index 81f1c98881a89eb95be9da10ad985e4fe5577bf2..0000000000000000000000000000000000000000
--- a/java/src/BPNet.java
+++ /dev/null
@@ -1,462 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-import java.util.ArrayList;
-import java.util.Arrays;
-
-public class BPNet extends MultiLayeredNet {
-
- protected float error;
-
-
- public float getError() {
- return error;
- }
-
-
- public void setError(float error) {
- this.error = error;
- }
-
-
- protected float tolerance;
- protected String neuronType;
- protected float learnCoeff;
-
-
-
- public BPNet( float tolerance, int nrOfLayers, int nrOfInputs, ArrayList<Integer> nrOfNeuronsPerLayer, float slope, float learnCoeff ) {
- super(nrOfInputs, nrOfLayers, nrOfNeuronsPerLayer);
- this.neuronType = "SigmoidalNeuron";
- this.tolerance = tolerance;
- this.learnCoeff = learnCoeff;
-
- for (int i = 0; i < nrOfLayers; i++) {
- for (int j = 0; j < nrOfNeuronsPerLayer.get(i); j++) {
- this.neuronLayers.get(i).add(new SigmoidalNeuron(slope));
- }
- }
- for (int i = 0; i < nrOfLayers; i++) {
- this.interconnectionsLayers.add(new InterconnectionsBP(this.learnCoeff));
- }
- for (Neuron neuronIn : this.inputNeuronLayer) {
- for (Neuron neuronFirstLevel : this.neuronLayers.get(0)) {
- this.interconnectionsLayers.get(0).addConnection(new Connection(neuronIn, neuronFirstLevel, (float) (Math.random())));
- }
- }
- for (int i = 1; i < nrOfLayers; i++) {
- for (Neuron neuronIn : this.neuronLayers.get(i-1)) {
- for (Neuron neuronOut : this.neuronLayers.get(i)) {
- this.interconnectionsLayers.get(i).addConnection(new Connection(neuronIn, neuronOut, (float) (Math.random())));
- }
-
- }
- }
-
-
- }
-
-
- public float getTolerance() {
- return tolerance;
- }
-
- public void setTolerance(float tolerance) {
- this.tolerance = tolerance;
- }
-
-
- @Override
- public String getNeuronType() {
- return this.neuronType;
- }
-
- @Override
- public int learn(String trainingSet) {
- boolean learned = false;
- int iter = 0;
- ArrayList<String> trainingElements = new ArrayList<String>(Arrays.asList(trainingSet.split("\n")));
- while(!learned) {
- learned = true;
- this.error = 0;
- for (int i = 0; i < trainingElements.size(); i++) {
- learned &= learnStep(trainingElements.get(i));
- }
- iter++;
-// System.out.println(iter);
- }
- return iter;
-// System.out.println("Learned in " + iter + " whole training set iterations.");
- }
-
- public boolean learnStep(String trainingElement) {
- // Run training Element
- String[] splitedTrainingElement = trainingElement.split(" ");
- StringBuffer inputString = new StringBuffer();
- for (int i = 0; i < this.nrOfInputs; i++) { //Input values
- inputString.append(splitedTrainingElement[i]);
- inputString.append(" ");
- }
- ArrayList<Float> expectedValues = new ArrayList<Float>();
- for (int i = this.nrOfInputs; i < splitedTrainingElement.length; i++) { //Expected values
- expectedValues.add(Float.parseFloat(splitedTrainingElement[i]));
- }
- this.run(inputString.substring(0, inputString.length() - 1));
- // Calculate error
- float error = 0;
- for (int i = 0; i < expectedValues.size(); i++) {
- float y = this.neuronLayers.get(this.nrOfLayers-1).get(i).getState(); //output of ith neuron
- float o = expectedValues.get(i);
- error += (float)( 0.5 * Math.pow((y-o), 2));
- }
- if (this.error < error) {
- this.error = error;
- }
- if (error > this.tolerance) { //Error is too high -> modify weights
- // Calculate deltas
- for (int i = this.nrOfLayers - 1; i >= 0; i -= 1) {
- for (Neuron n : this.neuronLayers.get(i)) {
- SigmoidalNeuron neuron = (SigmoidalNeuron)n;
- if (i == this.nrOfLayers - 1) { //Top layer
- float y = neuron.getState();
- float o = expectedValues.get(this.neuronLayers.get(i).indexOf(neuron));
- float delta = y - o;
- neuron.setError(delta);
- } else { //Other layers
- ArrayList<Connection> connectionsToUpperLayerFromNeuron = new ArrayList<Connection>();
- // Find all connections, that have "neuron" as input
- for (Connection c : this.interconnectionsLayers.get(i+1).getConnections()) {
- if (c.getInputNeuron().equals(neuron))
- connectionsToUpperLayerFromNeuron.add(c);
- }
- float delta = 0;
- for (Connection c : connectionsToUpperLayerFromNeuron) {
- float deltaUpper = ((SigmoidalNeuron)c.getOutputNeuron()).getError();
- float lambdaUpper = ((SigmoidalNeuron)c.getOutputNeuron()).getSlope();
- float yUpper = c.getOutputNeuron().getState();
- float w = c.getWeight();
- delta += deltaUpper*lambdaUpper*yUpper*(1-yUpper)*w;
- }
- neuron.setError(delta);
- }
- }
- }
- // Adjust weights
- for (Interconnections interconnectionsLayer : this.interconnectionsLayers) {
- interconnectionsLayer.adjustWeights();
- }
- return false;
- } else {
- return true;
- }
-
-
- }
-
- public String getOutput() {
- StringBuffer output = new StringBuffer();
- ArrayList<Neuron> outputLayer = this.neuronLayers.get(this.nrOfLayers-1);
- for (int i = 0; i < outputLayer.size(); i++) {
- output.append(String.valueOf(outputLayer.get(i).getState()));
- output.append(" ");
- }
-
- return output.toString();
- }
-
-
-
-
- public void changeSlopeTo(float slope) {
- for (ArrayList<Neuron> neuronLayer : this.neuronLayers) {
- for (Neuron neuron : neuronLayer) {
- ((SigmoidalNeuron)neuron).setSlope(slope);
- }
- }
- }
-
-
- public void changeLearnCoeffTo(float learnCoeff) {
- for (Interconnections layer : interconnectionsLayers) {
- ((InterconnectionsBP)layer).setLearningRate(learnCoeff);
- }
-
- }
-
-
- public void resetWeights() {
- for (Interconnections layer : interconnectionsLayers) {
- for (Connection connection : layer.getConnections()) {
- connection.setWeight((float)Math.random());
- }
- }
-
- }
-
- public void addNeuron(int layerIndex, float slope) {
- SigmoidalNeuron newNeuron = new SigmoidalNeuron(slope);
- neuronLayers.get(layerIndex).add(newNeuron);
- if ((layerIndex < nrOfLayers) && (layerIndex >= 0)) {
- Interconnections inputConnectionLayer = this.interconnectionsLayers.get(layerIndex);
- if (layerIndex == 0) {
- ArrayList<InputLayerPseudoNeuron> inputNeurons = this.inputNeuronLayer;
- for (Neuron inputNeuron : inputNeurons) {
- inputConnectionLayer.addConnection(new Connection(inputNeuron, newNeuron, (float)Math.random()));
- }
- } else {
- ArrayList<Neuron> inputNeurons = this.neuronLayers.get(layerIndex - 1);
- for (Neuron inputNeuron : inputNeurons) {
- inputConnectionLayer.addConnection(new Connection(inputNeuron, newNeuron, (float)Math.random()));
- }
- }
-
- if (layerIndex < nrOfLayers - 1) {
- Interconnections outputConnectionLayer = this.interconnectionsLayers.get(layerIndex + 1);
- ArrayList<Neuron> outputNeurons = this.neuronLayers.get(layerIndex + 1);
- for (Neuron outputNeuron : outputNeurons) {
- outputConnectionLayer.addConnection(new Connection(newNeuron, outputNeuron, (float)Math.random()));
- }
- }
- this.nrOfNeuronsPerLayer.set(layerIndex, this.nrOfNeuronsPerLayer.get(layerIndex) + 1 );
-
-
- } else {
-
- throw new InvalidLayerNumberException();
-
- }
- }
-
- public void removeNeuron(int layerIndex) {
- int nrOfNeuronsInThisLayer = this.nrOfNeuronsPerLayer.get(layerIndex);
- if ((layerIndex < nrOfLayers) && (layerIndex >= 0)) {
- if (nrOfNeuronsInThisLayer == 1) {
-
- removeNeuronLayer(layerIndex);
-
- } else {
- Neuron removedNeuron = this.neuronLayers.get(layerIndex).get(nrOfNeuronsInThisLayer - 1);
- Interconnections inputConnectionLayer = this.interconnectionsLayers.get(layerIndex);
- ArrayList<Connection> removedConnections = new ArrayList<Connection>();
- for (Connection connection : inputConnectionLayer.getConnections()) {
- if (connection.getOutputNeuron().equals(removedNeuron)) {
- removedConnections.add(connection);
- }
- }
- for (Connection connection : removedConnections) {
- inputConnectionLayer.getConnections().remove(connection);
- }
- removedConnections = new ArrayList<Connection>();
- if (layerIndex < nrOfLayers - 1) {
- Interconnections outputConnectionLayer = this.interconnectionsLayers.get(layerIndex + 1);
- for (Connection connection : outputConnectionLayer.getConnections()) {
- if (connection.getInputNeuron().equals(removedNeuron)) {
- removedConnections.add(connection);
- }
- }
- for (Connection connection : removedConnections) {
- outputConnectionLayer.getConnections().remove(connection);
- }
- }
-
- this.neuronLayers.get(layerIndex).remove(removedNeuron);
- this.nrOfNeuronsPerLayer.set(layerIndex, this.nrOfNeuronsPerLayer.get(layerIndex) - 1 );
- }
-
- } else {
- throw new InvalidLayerNumberException();
- }
- }
-
- public void addNeuronLayer(int nrOfNeurons, int layerIndex, float slope) {
- if ((layerIndex < nrOfLayers + 1) && (layerIndex >= 0) && (nrOfNeurons > 0)) {
-
- this.nrOfLayers++;
- this.nrOfNeuronsPerLayer.add(layerIndex, nrOfNeurons);
- // new layer creation
- ArrayList<Neuron> newNeuronLayer = new ArrayList<Neuron>();
- for (int i = 0; i < nrOfNeurons; i++) {
- newNeuronLayer.add(new SigmoidalNeuron(slope));
- }
- // old connections removal
- if (layerIndex < nrOfLayers - 1) { // only if inner layer is added
- this.interconnectionsLayers.remove(layerIndex);
- }
- // new layer adding
- this.neuronLayers.add(layerIndex, newNeuronLayer);
- // new connections creation
- // input
- Interconnections inputConnLayer = new InterconnectionsBP(learnCoeff);
- if (layerIndex == 0) {
- ArrayList<InputLayerPseudoNeuron> inputNeurons = this.inputNeuronLayer;
- ArrayList<Neuron> outputNeurons = newNeuronLayer; //Layers already shifted
- for (Neuron inputNeuron : inputNeurons) {
- for (Neuron outputNeuron : outputNeurons) {
- inputConnLayer.addConnection(new Connection(inputNeuron, outputNeuron, (float)Math.random()));
- }
- }
- } else {
- ArrayList<Neuron> inputNeurons = this.neuronLayers.get(layerIndex - 1);
- ArrayList<Neuron> outputNeurons = newNeuronLayer; //Layers already shifted, this is new layer
- for (Neuron inputNeuron : inputNeurons) {
- for (Neuron outputNeuron : outputNeurons) {
- inputConnLayer.addConnection(new Connection(inputNeuron, outputNeuron, (float)Math.random()));
- }
- }
- }
- this.interconnectionsLayers.add(layerIndex, inputConnLayer);
- // output
- Interconnections outputConnLayer = new InterconnectionsBP(learnCoeff);
- if (layerIndex < nrOfLayers - 1) {
- ArrayList<Neuron> inputNeurons = newNeuronLayer;
- ArrayList<Neuron> outputNeurons = this.neuronLayers.get(layerIndex + 1); //Layers already shifted
- for (Neuron inputNeuron : inputNeurons) {
- for (Neuron outputNeuron : outputNeurons) {
- outputConnLayer.addConnection(new Connection(inputNeuron, outputNeuron, (float)Math.random()));
- }
- }
- this.interconnectionsLayers.add(layerIndex + 1, outputConnLayer);
- }
-
-
- } else {
- throw new InvalidLayerNumberException();
- }
-
- }
-
-
-
- public void removeNeuronLayer(int layerIndex) {
- if ((layerIndex < nrOfLayers ) && (layerIndex >= 0) && (nrOfLayers > 1)) {
- // delete output connections
- if (layerIndex < nrOfLayers - 1) {
- this.interconnectionsLayers.remove(layerIndex + 1);
- }
- // delete input connections
- this.interconnectionsLayers.remove(layerIndex);
- // delete neurons on layer
- this.neuronLayers.remove(layerIndex);
- this.nrOfNeuronsPerLayer.remove(layerIndex);
- this.nrOfLayers--;
- // create new connections
- if (layerIndex < nrOfLayers + 1) {
- Interconnections connLayer = new InterconnectionsBP(learnCoeff);
- if (layerIndex == 0) {
- ArrayList<InputLayerPseudoNeuron> inputNeurons = this.inputNeuronLayer;
- ArrayList<Neuron> outputNeurons = this.neuronLayers.get(0);
- for (Neuron inputNeuron : inputNeurons) {
- for (Neuron outputNeuron : outputNeurons) {
- connLayer.addConnection(new Connection(inputNeuron, outputNeuron, (float)Math.random()));
- }
- }
- } else {
- ArrayList<Neuron> inputNeurons = this.neuronLayers.get(layerIndex - 1);
- ArrayList<Neuron> outputNeurons = this.neuronLayers.get(layerIndex);
- for (Neuron inputNeuron : inputNeurons) {
- for (Neuron outputNeuron : outputNeurons) {
- connLayer.addConnection(new Connection(inputNeuron, outputNeuron, (float)Math.random()));
- }
- }
- }
- this.interconnectionsLayers.add(layerIndex, connLayer);
-
- }
-
- } else {
- throw new InvalidLayerNumberException();
- }
- }
-
- @Override
- public String toString() {
- return getNeuronMap();
- }
-
- public String getNeuronMap() {
- StringBuffer map = new StringBuffer();
- for (int i = 0; i < nrOfLayers; i++) {
- map.append(String.valueOf(nrOfNeuronsPerLayer.get(i)));
- map.append(" ");
- }
- map.deleteCharAt(map.length() - 1);
- return map.toString();
- }
-
- public static void main(String[] args) {
- ArrayList<Integer> nrOfNeuronsPerLayer = new ArrayList<Integer>();
- nrOfNeuronsPerLayer.add(10);
- nrOfNeuronsPerLayer.add(7);
- nrOfNeuronsPerLayer.add(2);
- BPNet net = new BPNet( (float)0.01, 3, 5, nrOfNeuronsPerLayer, (float)1.8, (float)0.7); // bigger slope = better resolution
-
- String trainingSet = "0.4 0.5 1 0.5 1 0 1\n0 0 0 0 0 1 1\n0.1 0.2 0.3 0.4 0.5 0 0\n1 0 1 0 1 1 0\n0.2 0.4 0 0 0.9 0 1";
- net.learn(trainingSet);
- net.run("0.4 0.5 1 0.5 1"); //expected 0 1
- System.out.println(net.getOutput());
- net.run("0 0 0 0 0"); // 1 1
- System.out.println(net.getOutput());
- net.run("0.1 0.2 0.3 0.4 0.5"); // 0 0
- System.out.println(net.getOutput());
- net.run("1 0 1 0 1"); // 1 0
- System.out.println(net.getOutput());
- net.run("0.2 0.4 0 0 0.9"); // 0 1
- System.out.println(net.getOutput());
-
- System.out.println("Not trained elements:");
- net.run("0.9 0.1 0.9 0.1 0.9"); // expected 1 0
- System.out.println(net.getOutput());
- net.run("0.01 0.01 0.01 0.01 0.01"); // expected 1 1
- System.out.println(net.getOutput());
- net.run("0.15 0.15 0.35 0.35 0.5"); // 0 0
- System.out.println(net.getOutput());
-
- System.out.println(net.getNeuronMap());
- net.addNeuron(0, 1.8f);
- System.out.println(net.getNeuronMap());
- net.addNeuron(1, 1.8f);
- System.out.println(net.getNeuronMap());
- net.addNeuron(2, 1.8f);
- System.out.println(net.getNeuronMap());
- net.removeNeuron(0);
- System.out.println(net.getNeuronMap());
- net.removeNeuron(1);
- System.out.println(net.getNeuronMap());
- net.removeNeuron(2);
- System.out.println(net.getNeuronMap());
-
- net.addNeuronLayer(5, 0, 1.8f);
- System.out.println(net.getNeuronMap());
- net.addNeuronLayer(5, 2, 1.8f);
- System.out.println(net.getNeuronMap());
- net.addNeuronLayer(5, 5, 1.8f);
- System.out.println(net.getNeuronMap());
-
- net.removeNeuronLayer(5);
- System.out.println(net.getNeuronMap());
- net.removeNeuronLayer(2);
- System.out.println(net.getNeuronMap());
- net.removeNeuronLayer(0);
- System.out.println(net.getNeuronMap());
-
- net.learn(trainingSet);
- net.run("0.4 0.5 1 0.5 1"); //expected 0 1
- System.out.println(net.getOutput());
- net.run("0 0 0 0 0"); // 1 1
- System.out.println(net.getOutput());
- net.run("0.1 0.2 0.3 0.4 0.5"); // 0 0
- System.out.println(net.getOutput());
- net.run("1 0 1 0 1"); // 1 0
- System.out.println(net.getOutput());
- net.run("0.2 0.4 0 0 0.9"); // 0 1
- System.out.println(net.getOutput());
-
- System.out.println("Not trained elements:");
- net.run("0.9 0.1 0.9 0.1 0.9"); // expected 1 0
- System.out.println(net.getOutput());
- net.run("0.01 0.01 0.01 0.01 0.01"); // expected 1 1
- System.out.println(net.getOutput());
- net.run("0.15 0.15 0.35 0.35 0.5"); // 0 0
- System.out.println(net.getOutput());
- }
-
-}
diff --git a/java/src/BinaryNeuron.java b/java/src/BinaryNeuron.java
deleted file mode 100644
index 25a0b26fdfdf4b36904d110960c55d4af6e284a0..0000000000000000000000000000000000000000
--- a/java/src/BinaryNeuron.java
+++ /dev/null
@@ -1,15 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-public class BinaryNeuron extends Neuron {
-
- @Override
- public void transfer() {
- if (this.getPotential() > this.getThreshold()) {
- this.setState(1);
- } else {
- this.setState(0);
- }
-
- }
-
-}
diff --git a/java/src/CarDriverClient.java b/java/src/CarDriverClient.java
deleted file mode 100644
index bd5a0b9fa5b5895a8a6055e1c7b96be59c2f3c92..0000000000000000000000000000000000000000
--- a/java/src/CarDriverClient.java
+++ /dev/null
@@ -1,528 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-import java.io.BufferedReader;
-import java.io.BufferedWriter;
-import java.io.File;
-import java.io.FileReader;
-import java.io.InputStreamReader;
-import java.io.OutputStreamWriter;
-import java.io.IOException;
-import java.io.StreamTokenizer;
-import java.net.ConnectException;
-import java.net.Socket;
-import java.util.ArrayList;
-import java.util.List;
-import java.util.Random;
-
-import javax.swing.JOptionPane;
-
-
-/**
- * Jednoduchy ukazkovy klient.
- * Pripoji se k zavodnimu serveru a ridi auto.
- *
- */
-public class CarDriverClient {
-
- private Socket socket; // spojeni
- private BufferedReader in; // cteni se serveru
- private BufferedWriter out; // zapis na server
- private BPNet neuralNetwork;
-
- /**
- * Pripoji se k zavodu.
- *
- * @param host zavodni server
- * @param port port serveru
- * @param raceName nazev zavodu, do nehoz se chce klient pripojit
- * @param driverName jmeno ridice
- * @throws java.lang.IOException problem s pripojenim
- */
- public CarDriverClient(String host, int port, String raceName, String driverName, String carType, BPNet neuralNetwork) throws IOException {
- // add neural net
- this.neuralNetwork = neuralNetwork;
-
- // connect to server
- socket = new Socket(host, port);
- out = new BufferedWriter(new OutputStreamWriter(socket.getOutputStream(), "UTF-8"));
- in = new BufferedReader(new InputStreamReader(socket.getInputStream(), "UTF-8"));
-
- // connect to race
- out.write("driver\n"); // protocol specification
- out.write("race:" + raceName + "\n"); // race name
- out.write("driver:" + driverName + "\n"); // driver name
- out.write("color:0000FF\n"); // car color
- if(carType != null){
- out.write("car:" + carType + "\n"); // car type
- }
- out.write("\n");
- out.flush();
-
- // precteni a kontrola dopovedi serveru
- String line = in.readLine();
- if (!line.equals("ok")) {
- // pokud se pripojeni nepodari, je oznamena chyba a vyvolana vyjimka
- System.err.println("Chyba: " + line);
- throw new ConnectException(line);
- }
- in.readLine(); // precteni prazdneho radku
- }
-
- public static List<String> listRaces(String host, int port) throws IOException {
- // pripojeni k serveru
- Socket socket = new Socket(host, port);
- BufferedWriter out = new BufferedWriter(new OutputStreamWriter(socket.getOutputStream(), "UTF-8"));
- BufferedReader in = new BufferedReader(new InputStreamReader(socket.getInputStream(), "UTF-8"));
-
- // pripojeni k zavodu
- out.write("racelist\n"); // specifikace protokolu
- out.write("\n");
- out.flush();
-
- // precteni a kontrola dopovedi serveru
- String line = in.readLine();
- if (!line.equals("ok")) {
- // pokud se pripojeni nepodari, je oznamena chyba a vyvolana vyjimka
- System.err.println("Chyba: " + line);
- throw new ConnectException(line);
- }
- line = in.readLine(); // precteni prazdneho radku
- List<String> racelist = new ArrayList<String>();
- line = in.readLine();
- System.out.println("Races:");
- while(line != null && !"".equals(line)){
- racelist.add(line);
- System.out.println(line);
- line = in.readLine();
- }
- return racelist;
- }
- public static List<String> listCars(String host, int port, String raceName) throws IOException {
- // pripojeni k serveru
- Socket socket = new Socket(host, port);
- BufferedWriter out = new BufferedWriter(new OutputStreamWriter(socket.getOutputStream(), "UTF-8"));
- BufferedReader in = new BufferedReader(new InputStreamReader(socket.getInputStream(), "UTF-8"));
-
- // pripojeni k zavodu
- out.write("carlist\n"); // specifikace protokolu
- out.write("race:" + raceName + "\n");
- out.write("\n");
- out.flush();
-
- // precteni a kontrola dopovedi serveru
- String line = in.readLine();
- if (!line.equals("ok")) {
- // pokud se pripojeni nepodari, je oznamena chyba a vyvolana vyjimka
- System.err.println("Chyba: " + line);
- throw new ConnectException(line);
- }
- line = in.readLine(); // precteni prazdneho radku
- List<String> carList = new ArrayList<String>();
- line = in.readLine();
- System.out.println("cars:");
- while(line != null && !"".equals(line)){
- carList.add(line);
- System.out.println(line);
- line = in.readLine();
- }
- return carList;
- }
-
- /**
- * Beh zavodu. Cte data ze serveru. Spousti rizeni auta.
- * Ukonci se po ukonceni zavodu.
- *
- * @throws java.io.IOException problem ve spojeni k serveru
- */
- public void run() throws IOException {
- while (true) { // smycka do konce zavodu
- String line = in.readLine();
-// System.out.println(line);
- if (line.equals("round")) { // dalsi kolo v zavode
- round();
- } else if (line.equals("finish")) { // konec zavodu konci smucku
- break;
- } else {
- System.err.println("Chyba se serveru: " + line);
- }
- }
- }
-
- /**
- * Resi jedno posunuti auta. Precte pozici auta od servru,
- * vypocte nastaveni rizeni, ktere na server.
- *
- * @throws java.io.IOException problem ve spojeni k serveru
- */
- public void round() throws IOException {
- float angle = 0; // uhel k care <0,1>
- float speed = 0; // rychlost auta <0,1>
- float distance0 = 0; // vzdalenost od cary <0,1>
- float distance4 = 0; // vzdalenost od cary za 4m<0,1>
- float distance8 = 0; // vzdalenost od cary za 8m<0,1>
- float distance16 = 0; // vzdalenost od cary za 16m<0,1>
- float distance32 = 0; // vzdalenost od cary za 32m<0,1>
- float friction = 0;
- float skid = 0;
- float checkpoint = 0;
- float sensorFrontLeft = 0;
- float sensorFrontMiddleLeft = 0;
- float sensorFrontMiddleRight = 0;
- float sensorFrontRight = 0;
- float sensorFrontRightCorner1 = 0;
- float sensorFrontRightCorner2 = 0;
- float sensorRight1 = 0;
- float sensorRight2 = 0;
- float sensorRearRightCorner2 = 0;
- float sensorRearRightCorner1 = 0;
- float sensorRearRight = 0;
- float sensorRearLeft = 0;
- float sensorRearLeftCorner1 = 0;
- float sensorRearLeftCorner2 = 0;
- float sensorLeft1 = 0;
- float sensorLeft2 = 0;
- float sensorFrontLeftCorner1 = 0;
- float sensorFrontLeftCorner2 = 0;
-
- // cteni dat ze serveru
- String line = in.readLine();
-// System.out.println(line);
- while (line.length() > 0) {
- String[] data = line.split(":", 2);
- String key = data[0];
- String value = data[1];
- if (key.equals("angle")) {
- angle = Float.parseFloat(value);
- } else if (key.equals("speed")) {
- speed = Float.parseFloat(value);
- } else if (key.equals("distance0")) {
- distance0 = Float.parseFloat(value);
- } else if (key.equals("distance4")) {
- distance4 = Float.parseFloat(value);
- } else if (key.equals("distance8")) {
- distance8 = Float.parseFloat(value);
- } else if (key.equals("distance16")) {
- distance16 = Float.parseFloat(value);
- } else if (key.equals("distance32")) {
- distance32 = Float.parseFloat(value);
- } else if (key.equals("friction")) {
- friction = Float.parseFloat(value);
- } else if (key.equals("skid")) {
- skid = Float.parseFloat(value);
- } else if (key.equals("checkpoint")) {
- checkpoint = Float.parseFloat(value);
- } else if (key.equals("sensorFrontLeft")) {
- sensorFrontLeft = Float.parseFloat(value);
- } else if (key.equals("sensorFrontMiddleLeft")) {
- sensorFrontMiddleLeft = Float.parseFloat(value);
- } else if (key.equals("sensorFrontMiddleRight")) {
- sensorFrontMiddleRight = Float.parseFloat(value);
- } else if (key.equals("sensorFrontRight")) {
- sensorFrontRight = Float.parseFloat(value);
- } else if (key.equals("sensorFrontRightCorner1")) {
- sensorFrontRightCorner1 = Float.parseFloat(value);
- } else if (key.equals("sensorFrontRightCorner2")) {
- sensorFrontRightCorner2 = Float.parseFloat(value);
- } else if (key.equals("sensorRight1")) {
- sensorRight1 = Float.parseFloat(value);
- } else if (key.equals("sensorRight2")) {
- sensorRight2 = Float.parseFloat(value);
- } else if (key.equals("sensorRearRightCorner2")) {
- sensorRearRightCorner2 = Float.parseFloat(value);
- } else if (key.equals("sensorRearRightCorner1")) {
- sensorRearRightCorner1 = Float.parseFloat(value);
- } else if (key.equals("sensorRearRight")) {
- sensorRearRight = Float.parseFloat(value);
- } else if (key.equals("sensorRearLeft")) {
- sensorRearLeft = Float.parseFloat(value);
- } else if (key.equals("sensorRearLeftCorner1")) {
- sensorRearLeftCorner1 = Float.parseFloat(value);
- } else if (key.equals("sensorRearLeftCorner2")) {
- sensorRearLeftCorner2 = Float.parseFloat(value);
- } else if (key.equals("sensorLeft1")) {
- sensorLeft1 = Float.parseFloat(value);
- } else if (key.equals("sensorLeft2")) {
- sensorLeft2 = Float.parseFloat(value);
- } else if (key.equals("sensorFrontLeftCorner1")) {
- sensorFrontLeftCorner1 = Float.parseFloat(value);
- } else if (key.equals("sensorFrontLeftCorner2")) {
- sensorFrontLeftCorner2 = Float.parseFloat(value);
- } else {
- System.err.println("Chyba se serveru: " + line);
- }
- line = in.readLine();
-// System.out.println(line);
- }
-
- // vypocet nastaveni rizeni, ktery je mozno zmenit za jiny algoritmus
- float acc; // zrychleni auta <0,1>
- float wheel; // otoceni volantem (kolama) <0,1>
-
- StringBuffer neuralNetInput = new StringBuffer();
-
-
-// float angle = 0; // uhel k care <0,1>
-// float speed = 0; // rychlost auta <0,1>
-// float distance0 = 0; // vzdalenost od cary <0,1>
-// float distance4 = 0; // vzdalenost od cary za 4m<0,1>
-// float distance8 = 0; // vzdalenost od cary za 8m<0,1>
-// float distance16 = 0; // vzdalenost od cary za 16m<0,1>
-// float distance32 = 0; // vzdalenost od cary za 32m<0,1>
-// float friction = 0;
-// float skid = 0;
-// float checkpoint = 0;
-// float sensorFrontLeft = 0;
-// float sensorFrontMiddleLeft = 0;
-// float sensorFrontMiddleRight = 0;
-// float sensorFrontRight = 0;
-// float sensorFrontRightCorner1 = 0;
-// float sensorFrontRightCorner2 = 0;
-// float sensorRight1 = 0;
-// float sensorRight2 = 0;
-// float sensorRearRightCorner2 = 0;
-// float sensorRearRightCorner1 = 0;
-// float sensorRearRight = 0;
-// float sensorRearLeft = 0;
-// float sensorRearLeftCorner1 = 0;
-// float sensorRearLeftCorner2 = 0;
-// float sensorLeft1 = 0;
-// float sensorLeft2 = 0;
-// float sensorFrontLeftCorner1 = 0;
-// float sensorFrontLeftCorner2 = 0;
-
-
-
-// neuralNetInput.append(String.valueOf(angle));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(speed));
-// neuralNetInput.append(" ");
- neuralNetInput.append("0.5 0.5 ");
- neuralNetInput.append(String.valueOf(distance0));
- neuralNetInput.append(" ");
- neuralNetInput.append(String.valueOf(distance4));
- neuralNetInput.append(" ");
- neuralNetInput.append(String.valueOf(distance8));
- neuralNetInput.append(" ");
- neuralNetInput.append(String.valueOf(distance16));
- neuralNetInput.append(" ");
- neuralNetInput.append(String.valueOf(distance32));
- neuralNetInput.append(" ");
- neuralNetInput.append("1 1 0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0");
-// neuralNetInput.append(String.valueOf(friction));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(skid));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(checkpoint));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorFrontLeft));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorFrontMiddleLeft));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorFrontMiddleRight));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorFrontRight));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorFrontRightCorner1));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorFrontRightCorner2));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorRight1));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorRight2));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorRearRightCorner2));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorRearRightCorner1));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorRearRight));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorRearLeft));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorRearLeftCorner1));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorRearLeftCorner2));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorLeft1));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorLeft2));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorFrontLeftCorner1));
-// neuralNetInput.append(" ");
-// neuralNetInput.append(String.valueOf(sensorFrontLeftCorner2));
-
- neuralNetwork.run(neuralNetInput.toString());
-
- String output = neuralNetwork.getOutput();
- String[] outputArray = output.split(" ");
-
- wheel = Float.parseFloat(outputArray[0]);
- acc = Float.parseFloat(outputArray[1]);
-
-
-
-
- // odpoved serveru
- out.write("ok\n");
- out.write("acc:" + acc + "\n");
- out.write("wheel:" + wheel + "\n");
- out.write("\n");
- out.flush();
- }
-
- /**
- * Funkce, ktera vytvari a spousti klienta.
- *
- * @param args pole argumentu: server port nazev_zavodu jmeno_ridice
- * @throws java.io.IOException problem ve spojeni k serveru, zavodu
- */
- public static void main(String[] args) throws IOException {
-// String host = "java.cs.vsb.cz";
-// int port = 9460;
- String host = "localhost";
-// int port = 9461; // test
- int port = 9460; // normal
- String raceName = "Zavod";
- String driverName = "basic_client";
- String carType = null;
- if (args.length < 4) {
- // kontrola argumentu programu
- System.err.println("argumenty: server port nazev_zavodu jmeno_ridice [typ_auta]");
- List<String> raceList = CarDriverClient.listRaces(host, port);
- raceName = raceList.get(new Random().nextInt(raceList.size()));
- List<String> carList = CarDriverClient.listCars(host, port, raceName);
- carType = carList.get(0);
- driverName += "_" + carType;
-// host = JOptionPane.showInputDialog("Host:", host);
-// port = Integer.parseInt(JOptionPane.showInputDialog("Port:", Integer.toString(port)));
-// raceName = JOptionPane.showInputDialog("Race name:", raceName);
-// driverName = JOptionPane.showInputDialog("Driver name:", driverName);
- } else {
- // nacteni parametu
- host = args[0];
- port = Integer.parseInt(args[1]);
- raceName = args[2];
- driverName = args[3];
- if(args.length > 4){
- carType = args[4];
- }
- }
- // vytvoreni neuronove site
- ArrayList<Integer> nrOfNeuronsPerLayer = new ArrayList<Integer>();
- //nrOfNeuronsPerLayer.add(20);
-// nrOfNeuronsPerLayer.add(15);
-// nrOfNeuronsPerLayer.add(10);
-// nrOfNeuronsPerLayer.add(2);
- nrOfNeuronsPerLayer.add(3);
- nrOfNeuronsPerLayer.add(3);
- nrOfNeuronsPerLayer.add(2);
- BPNet neuralNet = new BPNet(0.1f, 3, 28, nrOfNeuronsPerLayer, 1.4f, 0.4f);
-
- FileReader fr = new FileReader(new File("C:\\Users\\Martin\\Desktop\\NSProjekty\\testTrainingSet5.txt"));
- StreamTokenizer tokenizer = new StreamTokenizer(fr);
- /*for (int i = 0; i < 6; i++ )
- tokenizer.nextToken();
- */
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
-
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- int nrOfLayers = (int)tokenizer.nval;
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- int nrOfInputs = (int)tokenizer.nval;
- tokenizer.nextToken();
- tokenizer.nextToken();
- tokenizer.nextToken();
- tokenizer.nextToken();
- ArrayList<float[]> inputRanges = new ArrayList<float[]>();
- ArrayList<String> inputNames = new ArrayList<String>();
- for (int i = 0; i < nrOfInputs; i++) {
- String inputName = tokenizer.sval;
- inputNames.add(inputName);
- tokenizer.nextToken();
- float[] dims = new float[2];
- dims[0] = (float)tokenizer.nval;
- tokenizer.nextToken();
- dims[1] = (float)tokenizer.nval;
- inputRanges.add(dims);
- tokenizer.nextToken();
- }
- /*for (int i = 0; i < 3; i++ )
- tokenizer.nextToken();*/
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- nrOfNeuronsPerLayer = new ArrayList<Integer>();
- int nrOfOutputs = 0;
- for (int i = 0; i < nrOfLayers; i++) {
- nrOfNeuronsPerLayer.add((int)tokenizer.nval);
- if (i == nrOfLayers - 1) {
- nrOfOutputs = (int)tokenizer.nval;
- }
- tokenizer.nextToken();
- }
- for (int i = 0; i < 3; i++ )
- tokenizer.nextToken();
- ArrayList<String> outputNames = new ArrayList<String>();
- for (int i = 0; i < nrOfOutputs; i++) {
- outputNames.add(tokenizer.sval);
- tokenizer.nextToken();
- }
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- float learnCoeff = (float)tokenizer.nval;
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- float inertiaCoeff = (float)tokenizer.nval;
- /*for (int i = 0; i < 7; i++ )
- tokenizer.nextToken();*/
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- int nrOfTrainingElements = (int)tokenizer.nval;
- /*for (int i = 0; i < 4; i++ )
- tokenizer.nextToken();*/
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- StringBuffer sb = new StringBuffer();
- for (int i = 0; i < nrOfTrainingElements; i++) {
- for (int j = 0; j < nrOfInputs; j++) {
- sb.append(String.valueOf(tokenizer.nval/(inputRanges.get(j)[1]-inputRanges.get(j)[0]) - inputRanges.get(j)[0]/(inputRanges.get(j)[1]-inputRanges.get(j)[0])));
- sb.append(" ");
- tokenizer.nextToken();
- }
- for (int j = 0; j < nrOfOutputs; j++) {
- sb.append(String.valueOf(tokenizer.nval));
- sb.append(" ");
- tokenizer.nextToken();
- }
- sb.deleteCharAt(sb.length() - 1);
- sb.append("\n");
- }
- String trainingData = sb.toString();
- sb = new StringBuffer();
- /*for (int i = 0; i < 5; i++ )
- tokenizer.nextToken();*/
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- int nrOfTestElements = (int)tokenizer.nval;
- String testData;
- /*tokenizer.nextToken();*/
- if (nrOfTestElements > 0) {
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- for (int i = 0; i < nrOfTestElements; i++) {
- for (int j = 0; j < nrOfInputs; j++) {
- sb.append(String.valueOf(String.valueOf(tokenizer.nval/(inputRanges.get(j)[1]-inputRanges.get(j)[0]) - inputRanges.get(j)[0]/(inputRanges.get(j)[1]-inputRanges.get(j)[0]))));
- sb.append(" ");
- tokenizer.nextToken();
- }
- sb.deleteCharAt(sb.lastIndexOf(" "));
- sb.append("\n");
- }
- testData = sb.substring(0,sb.lastIndexOf("\n"));
- } else {
- testData = "";
- }
- fr.close();
-
- String trainingSet = trainingData;
- System.out.println("Learning started.");
- ;
- System.out.println("Net learned in " + neuralNet.learn(trainingSet) + " iterations");
-
- // vytvoreni klienta
- CarDriverClient driver = new CarDriverClient(host, port, raceName, driverName, carType, neuralNet);
- // spusteni
- driver.run();
- }
-}
diff --git a/java/src/Connection.java b/java/src/Connection.java
deleted file mode 100644
index d8c451c787cc81045320f8ddc7fa5ac986fd4314..0000000000000000000000000000000000000000
--- a/java/src/Connection.java
+++ /dev/null
@@ -1,52 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-public class Connection {
-
- private Neuron inputNeuron;
- private Neuron outputNeuron;
- private float weight;
-
- public Connection(Neuron inputNeuron, Neuron outputNeuron, float weight) {
- this.setInputNeuron(inputNeuron);
- this.setOutputNeuron(outputNeuron);
- this.setWeight(weight);
- }
-
- protected Neuron getInputNeuron() {
- return inputNeuron;
- }
-
- protected void setInputNeuron(Neuron inputNeuron) {
- this.inputNeuron = inputNeuron;
- }
-
- protected Neuron getOutputNeuron() {
- return outputNeuron;
- }
-
- protected void setOutputNeuron(Neuron outputNeuron) {
- this.outputNeuron = outputNeuron;
- }
-
- public float getWeight() {
- return weight;
- }
-
- public void setWeight(float weight) {
- this.weight = weight;
- }
-
- public void adjustWeight(float value) {
- this.weight += value;
- }
-
- public void passSignal() {
- outputNeuron.adjustPotential(inputNeuron.getState()*this.getWeight());
- }
-
- @Override
- public String toString() {
- return "Weight: " + this.getWeight();
- }
-
-}
diff --git a/java/src/InputLayerPseudoNeuron.java b/java/src/InputLayerPseudoNeuron.java
deleted file mode 100644
index d8489247c360c56a17e826eca0536b63ad7f209e..0000000000000000000000000000000000000000
--- a/java/src/InputLayerPseudoNeuron.java
+++ /dev/null
@@ -1,14 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-public class InputLayerPseudoNeuron extends Neuron {
-
- public InputLayerPseudoNeuron() {
- super();
- }
-
- @Override
- public void transfer() {
- this.setState(this.getPotential());
- }
-
-}
diff --git a/java/src/Interconnections.java b/java/src/Interconnections.java
deleted file mode 100644
index 8732fb18c3413be7cc31217c22570fb950b25fe9..0000000000000000000000000000000000000000
--- a/java/src/Interconnections.java
+++ /dev/null
@@ -1,54 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-import java.util.ArrayList;
-
-public abstract class Interconnections {
-
- protected ArrayList<Connection> connections;
-
- public ArrayList<Connection> getConnections() {
- return connections;
- }
-
- public Interconnections() {
- this.connections = new ArrayList<Connection>();
- }
-
- public void addConnection(Connection connection) {
- this.connections.add(connection);
- }
-
-// public void passSignal() {
-// for (Connection c : this.connections) {
-//
-// Neuron n = c.getOutputNeuron();
-// n.initialize();
-// for (Connection cn : this.connections) {
-// if (cn.getOutputNeuron().equals(n)) {
-// cn.passSignal();
-// }
-// }
-// n.transfer();
-// }
-// }
-
- public void passSignal() { // Faster version
- ArrayList<Neuron> processedNeurons = new ArrayList<Neuron>();
- for (Connection c : this.connections) {
-
- Neuron n = c.getOutputNeuron();
- if (!processedNeurons.contains(n)) {
- processedNeurons.add(n);
- n.initialize();
- for (Connection cn : this.connections) {
- if (cn.getOutputNeuron().equals(n)) {
- cn.passSignal();
- }
- }
- n.transfer();
- }
- }
- }
-
- public abstract void adjustWeights();
-}
diff --git a/java/src/InterconnectionsBP.java b/java/src/InterconnectionsBP.java
deleted file mode 100644
index fc29d41da6161dd3c75f8c464014704c9a311c24..0000000000000000000000000000000000000000
--- a/java/src/InterconnectionsBP.java
+++ /dev/null
@@ -1,25 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-public class InterconnectionsBP extends InterconnectionsMultiLayer {
-
- public InterconnectionsBP(float learningRate) {
- super(learningRate);
- }
-
- public void setLearningRate(float learningRate) {
- this.learningRate = learningRate;
- }
-
- @Override
- public void adjustWeights() { // backPropagation - set new weights !after! all deltas are calculated
- for (Connection connection : this.connections) {
- float delta = ((SigmoidalNeuron)connection.getOutputNeuron()).getError();
- float lambda = ((SigmoidalNeuron)connection.getOutputNeuron()).getSlope();
- float y = connection.getOutputNeuron().getState();
- float x = connection.getInputNeuron().getState();
- float errorDerivative = delta*lambda*y*(1-y)*x;
- connection.adjustWeight(-learningRate*errorDerivative);
- }
- }
-
-}
diff --git a/java/src/InterconnectionsMultiLayer.java b/java/src/InterconnectionsMultiLayer.java
deleted file mode 100644
index 5785bdfda73453a4d2e56fdc5f6a9fc265190249..0000000000000000000000000000000000000000
--- a/java/src/InterconnectionsMultiLayer.java
+++ /dev/null
@@ -1,11 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-public abstract class InterconnectionsMultiLayer extends Interconnections {
-
- protected float learningRate; //eta
-
- public InterconnectionsMultiLayer(float learningRate) {
- this.learningRate = learningRate;
- }
-
-}
diff --git a/java/src/InvalidInputNumberException.java b/java/src/InvalidInputNumberException.java
deleted file mode 100644
index 3582a092522588d48d59d8994ceccef69548bab7..0000000000000000000000000000000000000000
--- a/java/src/InvalidInputNumberException.java
+++ /dev/null
@@ -1,14 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-public class InvalidInputNumberException extends RuntimeException {
-
- /**
- *
- */
- private static final long serialVersionUID = -6282750644609100469L;
-
- public InvalidInputNumberException() {
- super("Number of input values does not correspond with network input size");
- }
-
-}
diff --git a/java/src/InvalidLayerNumberException.java b/java/src/InvalidLayerNumberException.java
deleted file mode 100644
index 5e77f8e09054c7de2f508cd3a026b1653b7757ed..0000000000000000000000000000000000000000
--- a/java/src/InvalidLayerNumberException.java
+++ /dev/null
@@ -1,14 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-public class InvalidLayerNumberException extends RuntimeException {
-
-
- /**
- *
- */
- private static final long serialVersionUID = 1366940285989358521L;
-
- public InvalidLayerNumberException() {
- super("Number of layer does not correspond with network");
- }
-}
diff --git a/java/src/InvalidNeuronTypeException.java b/java/src/InvalidNeuronTypeException.java
deleted file mode 100644
index a983ab477d08c053e1aa455d2c8e48d8c23eb5f3..0000000000000000000000000000000000000000
--- a/java/src/InvalidNeuronTypeException.java
+++ /dev/null
@@ -1,14 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-public class InvalidNeuronTypeException extends RuntimeException {
-
-
- /**
- *
- */
- private static final long serialVersionUID = 5354372081840990196L;
-
- public InvalidNeuronTypeException() {
- super("Wrong Neuron type");
- }
-}
diff --git a/java/src/MultiLayeredNet.java b/java/src/MultiLayeredNet.java
deleted file mode 100644
index 164170bd774ab09b86f911acd5cf445f29e09c7a..0000000000000000000000000000000000000000
--- a/java/src/MultiLayeredNet.java
+++ /dev/null
@@ -1,58 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-import java.util.ArrayList;
-
-public abstract class MultiLayeredNet extends NeuralNet {
-
- protected ArrayList<ArrayList<Neuron>> neuronLayers;
- protected ArrayList<InputLayerPseudoNeuron> inputNeuronLayer;
- protected int nrOfInputs;
- protected int nrOfLayers;
- protected ArrayList<Integer> nrOfNeuronsPerLayer;
-
- public MultiLayeredNet(int nrOfInputs, int nrOfLayers, ArrayList<Integer> nrOfNeuronsPerLayer) {
- super();
- this.nrOfInputs = nrOfInputs;
- this.nrOfLayers = nrOfLayers;
- this.nrOfNeuronsPerLayer = nrOfNeuronsPerLayer;
- neuronLayers = new ArrayList<ArrayList<Neuron>>(nrOfLayers);
- inputNeuronLayer = new ArrayList<InputLayerPseudoNeuron>(nrOfInputs);
- for (int i = 0; i < nrOfLayers; i++) {
- neuronLayers.add(new ArrayList<Neuron>(nrOfNeuronsPerLayer.get(i)));
- }
- for (int i = 0; i < nrOfInputs; i++) {
- inputNeuronLayer.add(new InputLayerPseudoNeuron());
- }
- }
-
- public MultiLayeredNet() {
- this(0,0,null);
- }
-
- public int getNrOfInputs() {
- return nrOfInputs;
- }
-
- public int getNrOfLayers() {
- return nrOfLayers;
- }
-
- @Override
- public void run(String input) {
- String[] inputValues = input.split(" ");
- if (inputValues.length != nrOfInputs)
- throw new InvalidInputNumberException();
- for (int i = 0; i < nrOfInputs; i++) {
- InputLayerPseudoNeuron in = this.inputNeuronLayer.get(i);
- in.initialize();
- in.adjustPotential(Float.parseFloat(inputValues[i]));
- in.transfer();
- }
-
- for (int i = 0; i < nrOfLayers; i++) {
- Interconnections interconnectionsLayer = interconnectionsLayers.get(i);
- interconnectionsLayer.passSignal();
- }
- }
-
-}
diff --git a/java/src/NeuralNet.java b/java/src/NeuralNet.java
deleted file mode 100644
index 9a666d7f2931f5273f084bb7eee011b26c94853c..0000000000000000000000000000000000000000
--- a/java/src/NeuralNet.java
+++ /dev/null
@@ -1,20 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-import java.util.ArrayList;
-
-public abstract class NeuralNet {
-
- protected ArrayList<Interconnections> interconnectionsLayers;
-
- public NeuralNet(ArrayList<Interconnections> interconnectionsLayers) {
- this.interconnectionsLayers = interconnectionsLayers;
- }
-
- public NeuralNet() {
- this(new ArrayList<Interconnections>());
- }
-
- public abstract String getNeuronType();
- public abstract int learn(String trainingSet);
- public abstract void run(String input);
-}
diff --git a/java/src/Neuron.java b/java/src/Neuron.java
deleted file mode 100644
index 69260b322b7d949ad01866f9b0b087175a4dcb04..0000000000000000000000000000000000000000
--- a/java/src/Neuron.java
+++ /dev/null
@@ -1,59 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-public abstract class Neuron {
-
- private float potential; // inner potential
- private float state; // excitation state
- private float threshold; // threshold of excitation
-
-
- public Neuron() {
- this(0, 0, 0);
- }
-
- public Neuron(float potential, float state, float threshold) {
- this.setPotential(potential);
- this.setState(state);
- this.setThreshold(threshold);
- }
-
- public void initialize() {
- this.setPotential(0);
- this.setState(0);
- }
-
- public float getThreshold() {
- return threshold;
- }
-
- public void setThreshold(float threshold) {
- this.threshold = threshold;
- }
-
- public float getState() {
- return state;
- }
-
- protected void setState(float state) {
- this.state = state;
- }
-
- protected float getPotential() {
- return this.potential;
- }
-
- private void setPotential(float potential) {
- this.potential = potential;
- }
-
- public void adjustPotential(float value) {
- this.potential += value;
- }
-
- @Override
- public String toString() {
- return "Pot.: " + this.potential + ", State: " + this.state + ", Thr.: " + this.threshold;
- }
-
- public abstract void transfer();
-}
diff --git a/java/src/Projekt1GUI.java b/java/src/Projekt1GUI.java
deleted file mode 100644
index e5c5945300ca9a713d299a4d9c7453747f1aee9d..0000000000000000000000000000000000000000
--- a/java/src/Projekt1GUI.java
+++ /dev/null
@@ -1,581 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-import java.awt.Color;
-import java.awt.EventQueue;
-import java.awt.Rectangle;
-import javax.swing.JFileChooser;
-import javax.swing.JFrame;
-import javax.swing.JMenuBar;
-import javax.swing.JMenu;
-import javax.swing.JMenuItem;
-import javax.swing.JOptionPane;
-import javax.swing.JTable;
-
-import java.awt.event.ActionListener;
-import java.awt.event.ActionEvent;
-import java.awt.event.WindowEvent;
-import java.io.File;
-import java.io.FileNotFoundException;
-import java.io.FileReader;
-import java.io.IOException;
-import java.io.StreamTokenizer;
-import java.util.ArrayList;
-
-import javax.swing.JButton;
-import javax.swing.JScrollPane;
-import javax.swing.JLabel;
-import javax.swing.event.ListSelectionEvent;
-import javax.swing.event.ListSelectionListener;
-import javax.swing.filechooser.FileFilter;
-
-import org.jfree.chart.ChartFactory;
-import org.jfree.chart.ChartPanel;
-import org.jfree.chart.JFreeChart;
-import org.jfree.chart.axis.NumberAxis;
-import org.jfree.chart.plot.XYPlot;
-import org.jfree.chart.renderer.xy.XYLineAndShapeRenderer;
-import org.jfree.chart.renderer.xy.XYSplineRenderer;
-import org.jfree.data.xy.XYSeries;
-import org.jfree.data.xy.XYSeriesCollection;
-import org.jfree.ui.RectangleInsets;
-import org.jfree.util.ShapeUtilities;
-
-public class Projekt1GUI {
-
- private JFrame frmPerceptron;
- private SinglePerceptronNeuralNet neuralNet;
- private File dataFile;
- private String trainingData;
- private String testData;
- private int nrOfInputs;
- private ArrayList<float[]> inputRanges;
- private float learnCoeff;
- private int nrOfTrainingElements;
- private int nrOfTestElements;
- private String trainingOutput;
- private int nrOfTrainingIterations;
-
- //Swing components
- private JButton btnLearn;
- private JTable tableLearn;
- private JTable tableTest;
- private JTable tableTrainingProcess;
- private JScrollPane scrollPaneLearn;
- private JScrollPane scrollPaneTest;
- private JScrollPane scrollPaneTrainingProcess;
- private JButton buttonBackward;
- private JButton buttonForward;
- private JButton btnTestData;
-
- //Chart components
- private XYSeriesCollection dataset;
- private ChartPanel pnlChart;
- private XYLineAndShapeRenderer renderer;
-
- /**
- * Launch the application.
- */
- public static void main(String[] args) {
- EventQueue.invokeLater(new Runnable() {
- public void run() {
- try {
- Projekt1GUI window = new Projekt1GUI();
- window.frmPerceptron.setVisible(true);
- } catch (Exception e) {
- e.printStackTrace();
- }
- }
- });
- }
-
- /**
- * Create the application.
- */
- public Projekt1GUI() {
- initialize();
-
- }
-
- /**
- * Initialize the contents of the frame.
- */
- private void initialize() {
- frmPerceptron = new JFrame();
- frmPerceptron.setTitle("Perceptron");
- frmPerceptron.setBounds(100, 100, 652, 498);
- frmPerceptron.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
- frmPerceptron.getContentPane().setLayout(null);
-
-
-
- btnLearn = new JButton("Learn");
- btnLearn.addActionListener(new ActionListener() {
-
-
- public void actionPerformed(ActionEvent e) {
- btnLearn.setEnabled(false);
- btnTestData.setEnabled(true);
- neuralNet.learn(trainingData);
- trainingOutput = neuralNet.getTrainingOutput();
-
- //Show training process table
- String[] columnNames = new String[nrOfInputs + 1];
- for (int i = 0; i < nrOfInputs; i++) {
- columnNames[i] = "w" + String.valueOf(i+1);
- }
- columnNames[nrOfInputs] = "Threshold";
- String[] rows = trainingOutput.split("\n");
- nrOfTrainingIterations = rows.length;
- Float[][] fDataTable = new Float[nrOfTrainingIterations][nrOfInputs + 1];
- for (int i = 0; i < nrOfTrainingIterations; i++) {
- String[] cells = rows[i].split(" ");
- for (int j = 0; j < nrOfInputs + 1; j++) {
- fDataTable[i][j] = Float.valueOf(cells[j]);
- }
- }
- tableTrainingProcess = new JTable( fDataTable, columnNames);
- tableTrainingProcess.getSelectionModel().addListSelectionListener(new ListSelectionListener(){
- public void valueChanged(ListSelectionEvent event) {
-
- if (tableTrainingProcess.getSelectedRow() == 0) {
- buttonForward.setEnabled(true);
- buttonBackward.setEnabled(false);
- }
- else if (tableTrainingProcess.getSelectedRow() == tableTrainingProcess.getRowCount()-1) {
- buttonBackward.setEnabled(true);
- buttonForward.setEnabled(false);
- }
- else {
- buttonBackward.setEnabled(true);
- buttonForward.setEnabled(true);
- }
-
- //redraw chart in 2D
- if ((nrOfInputs == 2) && (dataset != null)) {
- float xMin = inputRanges.get(0)[0];
- float xMax = inputRanges.get(0)[1];
- float yMin = inputRanges.get(1)[0];
- float yMax = inputRanges.get(1)[1];
-
- int selectedRow = tableTrainingProcess.getSelectedRow();
- float w0 = -(float)tableTrainingProcess.getModel().getValueAt(selectedRow, 2);
- float w1 = (float)tableTrainingProcess.getModel().getValueAt(selectedRow, 0);
- float w2 = (float)tableTrainingProcess.getModel().getValueAt(selectedRow, 1);
- float step = (float)0.01;
-
- boolean containSeries = false;
- String key = "Line";
- for (Object obj : dataset.getSeries()) {
- if (obj instanceof XYSeries) {
- XYSeries xys = (XYSeries)obj;
- if (xys.getKey().equals(key)) {
- containSeries = true;
- }
- }
- }
- if (!containSeries) {
- XYSeries series = new XYSeries(key);
- dataset.addSeries(series);
- }
- for (Object obj : dataset.getSeries()) {
- if (obj instanceof XYSeries) {
- XYSeries xys = (XYSeries)obj;
- if (xys.getKey().equals(key)) {
- int index = dataset.getSeries().indexOf(xys);
- xys.clear();
- for (float x = xMin; x < xMax; x += step) {
- float y = -w1/w2 * x - w0/w2;
- if ( (yMin <= y) && (y <= yMax)) {
- xys.add(x, y);
- }
- }
- renderer.setSeriesPaint(index, Color.RED);
- }
- }
- }
- }
- }
- });
- scrollPaneTrainingProcess.setViewportView(tableTrainingProcess);
- tableTrainingProcess.setRowSelectionInterval(0, 0);
- if (nrOfTrainingIterations > 1)
- buttonForward.setEnabled(true);
-
- // in 2D case draw graph
- if (nrOfInputs == 2) {
- //Create a chart
- XYSeries series = new XYSeries("Line");
- float xMin = 0;//inputRanges.get(0)[0];
- float xMax = 1;//inputRanges.get(0)[1];
- float yMin = 0;//inputRanges.get(1)[0];
- float yMax = 1;//inputRanges.get(1)[1];
-
- int selectedRow = tableTrainingProcess.getSelectedRow();
- float w0 = -(float)tableTrainingProcess.getModel().getValueAt(selectedRow, 2);
- float w1 = (float)tableTrainingProcess.getModel().getValueAt(selectedRow, 0);
- float w2 = (float)tableTrainingProcess.getModel().getValueAt(selectedRow, 1);
- float step = (float)0.01;
- for (float x = xMin; x < xMax; x += step) {
- float y = -w1/w2 * x - w0/w2;
- if ( (yMin <= y) && (y <= yMax)) {
- series.add(x, y);
- }
- }
-
- XYSeries seriesLearnNeg = new XYSeries("LN");
- XYSeries seriesLearnPoz = new XYSeries("LP");
- String[] trainingRows = trainingData.split("\n");
- for (int i = 0; i < nrOfTrainingElements; i++) {
- String[] trainingElement = trainingRows[i].split(" ");
- if (Float.valueOf(trainingElement[2]) == 1) {
- seriesLearnPoz.add(Float.valueOf(trainingElement[0]), Float.valueOf(trainingElement[1]));
- } else {
- seriesLearnNeg.add(Float.valueOf(trainingElement[0]), Float.valueOf(trainingElement[1]));
- }
- }
-
-
- dataset = new XYSeriesCollection();
- dataset.addSeries(series);
- dataset.addSeries(seriesLearnPoz);
- dataset.addSeries(seriesLearnNeg);
-
- //Create chart with name , axis names and dataset
- JFreeChart chart = ChartFactory.createXYLineChart("", "x1", "x2", dataset);
- if ((pnlChart != null) && (pnlChart.getParent() == frmPerceptron.getContentPane()))
- frmPerceptron.getContentPane().remove(pnlChart);
-
- //Change plot properties
-
- XYPlot plot = (XYPlot) chart.getPlot();
- plot.setBackgroundPaint(Color.white);
- plot.setAxisOffset(new RectangleInsets(0, 0, 0, 0));
- plot.setDomainGridlinesVisible(false);
- plot.setDomainGridlinePaint(Color.lightGray);
- plot.setRangeGridlinePaint(Color.white);
- //Set axes range
- //x
- NumberAxis domain = (NumberAxis) plot.getDomainAxis();
- domain.setRange(xMin, xMax);
- //y
- NumberAxis yRange = (NumberAxis) plot.getRangeAxis();
- yRange.setRange(yMin, yMax);
-
- //Set renderer
-
- renderer = new XYSplineRenderer();
- renderer.setSeriesShapesVisible(0, false);
- renderer.setSeriesShapesVisible(1, true);
- renderer.setSeriesShape(1, ShapeUtilities.createUpTriangle(4));
- renderer.setSeriesShapesVisible(2, true);
- renderer.setSeriesShape(2, ShapeUtilities.createDownTriangle(4));
- renderer.setSeriesPaint(0, Color.RED);
- renderer.setSeriesPaint(1, Color.BLUE);
- renderer.setSeriesPaint(2, Color.BLUE);
- renderer.setSeriesLinesVisible(0, true);
- renderer.setSeriesLinesVisible(1, false);
- renderer.setSeriesLinesVisible(2, false);
- plot.setRenderer(renderer);
- pnlChart = new ChartPanel(chart);
- pnlChart.setBounds(309, 267, 273, 150);
- pnlChart.setDomainZoomable(false);
- pnlChart.setRangeZoomable(false);
- pnlChart.getChart().removeLegend();
- frmPerceptron.getContentPane().add(pnlChart);
- frmPerceptron.repaint();
- } else {
- if (pnlChart != null) {
- frmPerceptron.getContentPane().remove(pnlChart);
- frmPerceptron.repaint();
- }
- }
- }
- });
- btnLearn.setEnabled(false);
- btnLearn.setBounds(10, 188, 89, 23);
- frmPerceptron.getContentPane().add(btnLearn);
-
- btnTestData = new JButton("Test data");
- btnTestData.addActionListener(new ActionListener() {
- public void actionPerformed(ActionEvent e) {
- btnTestData.setEnabled(false);
- String[] columnNames = new String[nrOfInputs + 1];
- for (int i = 0; i < nrOfInputs; i++) {
- columnNames[i] = "x" + String.valueOf(i+1);
- }
- columnNames[nrOfInputs] = "y";
- Float[][] fDataTable = new Float[nrOfTestElements][nrOfInputs + 1];
- String[] rows = testData.split("\n");
- for (int i = 0; i < nrOfTestElements; i++) {
- String[] cells = rows[i].split(" ");
- for (int j = 0; j < nrOfInputs; j++) {
- fDataTable[i][j] = Float.valueOf(cells[j]);
- }
- neuralNet.run(rows[i]);
- String y = neuralNet.getOutput();
- fDataTable[i][nrOfInputs] = Float.valueOf(y);
- }
- tableTest = new JTable( fDataTable, columnNames);
- scrollPaneTest.setViewportView(tableTest);
- // in 2D case redraw graph
- if (nrOfInputs == 2) {
- XYSeries seriesTestNeg = new XYSeries("TN");
- XYSeries seriesTestPoz = new XYSeries("TP");
- String[] testRows = testData.split("\n");
- for (int i = 0; i < nrOfTestElements; i++) {
- String[] testElement = testRows[i].split(" ");
- neuralNet.run(testRows[i]);
- String y = neuralNet.getOutput();
- if (Float.valueOf(y) == 1) {
- seriesTestPoz.add(Float.valueOf(testElement[0]), Float.valueOf(testElement[1]));
- } else {
- seriesTestNeg.add(Float.valueOf(testElement[0]), Float.valueOf(testElement[1]));
- }
- }
- dataset.addSeries(seriesTestPoz);
- dataset.addSeries(seriesTestNeg);
-
- renderer.setSeriesShapesVisible(3, true);
- renderer.setSeriesShape(3, ShapeUtilities.createUpTriangle(6));
- renderer.setSeriesShapesVisible(4, true);
- renderer.setSeriesShape(4, ShapeUtilities.createDownTriangle(6));
- renderer.setSeriesPaint(3, Color.GREEN);
- renderer.setSeriesPaint(4, Color.GREEN);
- renderer.setSeriesLinesVisible(3, false);
- renderer.setSeriesLinesVisible(4, false);
-
- }
- }
- });
- btnTestData.setEnabled(false);
- btnTestData.setBounds(10, 222, 89, 23);
- frmPerceptron.getContentPane().add(btnTestData);
-
- scrollPaneLearn = new JScrollPane();
- scrollPaneLearn.setBounds(10, 25, 283, 156);
- frmPerceptron.getContentPane().add(scrollPaneLearn);
-
- scrollPaneTest = new JScrollPane();
- scrollPaneTest.setBounds(10, 267, 283, 160);
- frmPerceptron.getContentPane().add(scrollPaneTest);
-
- JLabel lblNewLabel = new JLabel("Training data");
- lblNewLabel.setBounds(10, 11, 116, 14);
- frmPerceptron.getContentPane().add(lblNewLabel);
-
- JLabel lblTestData = new JLabel("Test data");
- lblTestData.setBounds(10, 252, 103, 14);
- frmPerceptron.getContentPane().add(lblTestData);
-
- scrollPaneTrainingProcess = new JScrollPane();
- scrollPaneTrainingProcess.setBounds(303, 25, 283, 156);
- frmPerceptron.getContentPane().add(scrollPaneTrainingProcess);
-
- JLabel lblTrainingProcess = new JLabel("Training process");
- lblTrainingProcess.setBounds(303, 11, 97, 14);
- frmPerceptron.getContentPane().add(lblTrainingProcess);
-
- buttonBackward = new JButton("<<");
- buttonBackward.setEnabled(false);
- buttonBackward.addActionListener(new ActionListener() {
- public void actionPerformed(ActionEvent arg0) {
- int row = tableTrainingProcess.getSelectedRow();
- int tableRows = tableTrainingProcess.getRowCount();
- if (row == tableRows - 1) {
- buttonForward.setEnabled(true);
- }
- if (row == 1) {
- buttonBackward.setEnabled(false);
- }
- tableTrainingProcess.setRowSelectionInterval(row-1, row-1);
- Rectangle rect = tableTrainingProcess.getCellRect(row-1, 0, true);
- tableTrainingProcess.scrollRectToVisible(rect);
- }
- });
- buttonBackward.setBounds(348, 188, 89, 23);
- frmPerceptron.getContentPane().add(buttonBackward);
-
- buttonForward = new JButton(">>");
- buttonForward.addActionListener(new ActionListener() {
- public void actionPerformed(ActionEvent e) {
- int row = tableTrainingProcess.getSelectedRow();
- int tableRows = tableTrainingProcess.getRowCount();
- if (row == 0) {
- buttonBackward.setEnabled(true);
- }
- if (row == tableRows - 2) {
- buttonForward.setEnabled(false);
- }
- tableTrainingProcess.setRowSelectionInterval(row+1, row+1);
- Rectangle rect = tableTrainingProcess.getCellRect(row+1, 0, true);
- tableTrainingProcess.scrollRectToVisible(rect);
-
-
- }
- });
- buttonForward.setEnabled(false);
- buttonForward.setBounds(447, 188, 89, 23);
- frmPerceptron.getContentPane().add(buttonForward);
-
- JLabel lbldView = new JLabel("2D View");
- lbldView.setBounds(312, 226, 46, 14);
- frmPerceptron.getContentPane().add(lbldView);
-
- JMenuBar menuBar = new JMenuBar();
- frmPerceptron.setJMenuBar(menuBar);
-
- JMenu mnFile = new JMenu("File");
- menuBar.add(mnFile);
-
- JMenuItem mntmLoadData = new JMenuItem("Load data");
- mntmLoadData.addActionListener(new ActionListener() {
-
-
- public void actionPerformed(ActionEvent e) {
- JFileChooser fc = new JFileChooser();
- fc.setDialogType(JFileChooser.OPEN_DIALOG);
- FileFilter filter = new FileFilter() {
-
- @Override
- public String getDescription() {
- // TODO Auto-generated method stub
- return "Txt files";
- }
-
- @Override
- public boolean accept(File f) {
- // TODO Auto-generated method stub
- return (f.getName().endsWith(".txt") || f.isDirectory());
- }
- };
- fc.setFileFilter(filter);
-
-
-
- if (fc.showOpenDialog(frmPerceptron) == JFileChooser.APPROVE_OPTION) {
- dataFile = fc.getSelectedFile();
- FileReader fr;
- try {
- //Parse data file
- fr = new FileReader(dataFile);
- StreamTokenizer tokenizer = new StreamTokenizer(fr);
- /*for (int i = 0; i < 6; i++ )
- tokenizer.nextToken();
- */
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- nrOfInputs = (int)tokenizer.nval;
- /*tokenizer.nextToken();
- tokenizer.nextToken();
- tokenizer.nextToken();
- tokenizer.nextToken();*/
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- inputRanges = new ArrayList<float[]>();
- for (int i = 0; i < nrOfInputs; i++) {
- float[] dims = new float[2];
- dims[0] = (float)tokenizer.nval;
- tokenizer.nextToken();
- dims[1] = (float)tokenizer.nval;
- inputRanges.add(dims);
- tokenizer.nextToken();
- tokenizer.nextToken();
- }
- /*for (int i = 0; i < 3; i++ )
- tokenizer.nextToken();*/
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- learnCoeff = (float)tokenizer.nval;
- /*for (int i = 0; i < 7; i++ )
- tokenizer.nextToken();*/
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- nrOfTrainingElements = (int)tokenizer.nval;
- /*for (int i = 0; i < 4; i++ )
- tokenizer.nextToken();*/
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- StringBuffer sb = new StringBuffer();
- for (int i = 0; i < nrOfTrainingElements; i++) {
- for (int j = 0; j < nrOfInputs; j++) {
- sb.append(String.valueOf(tokenizer.nval/(inputRanges.get(j)[1]-inputRanges.get(j)[0]) - inputRanges.get(j)[0]/(inputRanges.get(j)[1]-inputRanges.get(j)[0])));
- sb.append(" ");
- tokenizer.nextToken();
- }
- sb.append(String.valueOf(tokenizer.nval));
- sb.append("\n");
- tokenizer.nextToken();
- }
- trainingData = sb.toString();
- sb = new StringBuffer();
- /*for (int i = 0; i < 5; i++ )
- tokenizer.nextToken();*/
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- nrOfTestElements = (int)tokenizer.nval;
- /*tokenizer.nextToken();*/
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- for (int i = 0; i < nrOfTestElements; i++) {
- for (int j = 0; j < nrOfInputs; j++) {
- sb.append(String.valueOf(String.valueOf(tokenizer.nval/(inputRanges.get(j)[1]-inputRanges.get(j)[0]) - inputRanges.get(j)[0]/(inputRanges.get(j)[1]-inputRanges.get(j)[0]))));
- sb.append(" ");
- tokenizer.nextToken();
- }
- sb.deleteCharAt(sb.lastIndexOf(" "));
- sb.append("\n");
- }
-
- testData = sb.substring(0,sb.lastIndexOf("\n"));
- fr.close();
- neuralNet = new SinglePerceptronNeuralNet(new BinaryNeuron(), nrOfInputs, learnCoeff);
- btnLearn.setEnabled(true);
- //Show learn table
- String[] columnNames = new String[nrOfInputs + 1];
- for (int i = 0; i < nrOfInputs; i++) {
- columnNames[i] = "x" + String.valueOf(i+1);
- }
- columnNames[nrOfInputs] = "y";
- Float[][] fDataTable = new Float[nrOfTrainingElements][nrOfInputs + 1];
- String[] rows = trainingData.split("\n");
- for (int i = 0; i < nrOfTrainingElements; i++) {
- String[] cells = rows[i].split(" ");
- for (int j = 0; j < nrOfInputs + 1; j++) {
- fDataTable[i][j] = Float.valueOf(cells[j]);
- }
- }
- tableLearn = new JTable( fDataTable, columnNames);
- scrollPaneLearn.setViewportView(tableLearn);
- //Show test table
- columnNames = new String[nrOfInputs];
- for (int i = 0; i < nrOfInputs; i++) {
- columnNames[i] = "x" + String.valueOf(i+1);
- }
- fDataTable = new Float[nrOfTestElements][nrOfInputs];
- rows = testData.split("\n");
- for (int i = 0; i < nrOfTestElements; i++) {
- String[] cells = rows[i].split(" ");
- for (int j = 0; j < nrOfInputs; j++) {
- fDataTable[i][j] = Float.valueOf(cells[j]);
- }
- }
- tableTest = new JTable( fDataTable, columnNames);
- scrollPaneTest.setViewportView(tableTest);
-
- } catch (FileNotFoundException e1) {
- e1.printStackTrace();
- JOptionPane.showMessageDialog(null, "Error: File not found");
- } catch (IOException e1) {
- // TODO Auto-generated catch block
- e1.printStackTrace();
- }
-
-
- }
-
- }
- });
- mnFile.add(mntmLoadData);
-
- JMenuItem mntmExit = new JMenuItem("Exit");
- mntmExit.addActionListener(new ActionListener() {
- public void actionPerformed(ActionEvent arg0) {
- frmPerceptron.dispatchEvent(new WindowEvent(frmPerceptron, WindowEvent.WINDOW_CLOSING));
- }
- });
- mnFile.add(mntmExit);
- }
-}
diff --git a/java/src/Projekt2GUI.java b/java/src/Projekt2GUI.java
deleted file mode 100644
index 1b4406f177ec50e2148f271a690aa8c53cb2d5e5..0000000000000000000000000000000000000000
--- a/java/src/Projekt2GUI.java
+++ /dev/null
@@ -1,784 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-import java.awt.Color;
-import java.awt.EventQueue;
-
-import javax.swing.JFileChooser;
-import javax.swing.JFrame;
-import javax.swing.JMenuBar;
-import javax.swing.JMenu;
-import javax.swing.JMenuItem;
-import javax.swing.JOptionPane;
-import javax.swing.JTable;
-import javax.swing.ScrollPaneConstants;
-
-import java.awt.event.ActionListener;
-import java.awt.event.ActionEvent;
-import java.awt.event.WindowEvent;
-import java.io.BufferedWriter;
-import java.io.File;
-import java.io.FileNotFoundException;
-import java.io.FileReader;
-import java.io.FileWriter;
-import java.io.IOException;
-import java.io.StreamTokenizer;
-import java.text.DecimalFormat;
-import java.util.ArrayList;
-import java.util.Arrays;
-
-import javax.swing.JButton;
-import javax.swing.JScrollPane;
-import javax.swing.JLabel;
-import javax.swing.filechooser.FileFilter;
-import javax.swing.JSpinner;
-import javax.swing.JTextField;
-import javax.swing.SpinnerNumberModel;
-import javax.swing.event.ChangeListener;
-import javax.swing.event.ChangeEvent;
-import javax.swing.JPanel;
-
-import com.thoughtworks.xstream.XStream;
-
-import java.awt.GridLayout;
-
-public class Projekt2GUI {
-
- private JFrame frmBPnet;
- private BPNet neuralNet;
- private File dataFile;
- private String trainingData;
- private String testData;
-
- private int nrOfInputs;
- private int nrOfOutputs;
- private int nrOfLayers;
- private float maxError;
- private float slope;
- private float inertiaCoeff;
- private ArrayList<Integer> nrOfNeuronsPerLayer;
- private ArrayList<String> inputNames;
- private ArrayList<String> outputNames;
-
-
- private ArrayList<float[]> inputRanges;
- private float learnCoeff;
-
-
- private int nrOfTrainingElements;
- private int nrOfTestElements;
-
- //Swing components
- private JButton btnLearn;
- private JTable tableLearn;
- private JTable tableTest;
- private JScrollPane scrollPaneLearn;
- private JScrollPane scrollPaneTest;
- private JButton btnTestData;
- private JButton btnDoSpecifiedLearn;
- private JSpinner spinnerLearnSteps;
- private JTextField textFieldIterations;
- private JLabel lblLearned;
- private JLabel lblLearnCoeff;
- private JLabel lblSlopeLambda;
- private JSpinner spinnerLearnCoeff;
- private JSpinner spinnerSlope;
- private JLabel lblMaxError;
- private JSpinner spinnerError;
- private JLabel lblCurentError;
- private JTextField textFieldCurrentError;
- private JTextField textFieldTestElement;
- private JTextField textFieldTestOutput;
- private JButton btnTestElement;
- private JButton btnResetWeights;
- private JPanel panelTopology;
- private JButton btnAddLayer;
- private JSpinner spinnerLayer;
- private JSpinner spinnerLayerNeurons;
- private JMenuItem mntmSaveNeuralNet;
-
-
- /**
- * Launch the application.
- */
- public static void main(String[] args) {
- EventQueue.invokeLater(new Runnable() {
- public void run() {
- try {
- Projekt2GUI window = new Projekt2GUI();
- window.frmBPnet.setVisible(true);
- } catch (Exception e) {
- e.printStackTrace();
- }
- }
- });
- }
-
- /**
- * Create the application.
- */
- public Projekt2GUI() {
- initialize();
- }
-
-
- private void changeAfterLearn() {
- btnLearn.setEnabled(false);
- btnTestData.setEnabled(true);
- btnDoSpecifiedLearn.setEnabled(false);
- lblLearned.setText("Learned");
- lblLearned.setForeground(Color.GREEN);
- spinnerLearnSteps.setEnabled(false);
- spinnerError.setEnabled(false);
- spinnerLearnCoeff.setEnabled(false);
- spinnerSlope.setEnabled(false);
- textFieldCurrentError.setText(String.valueOf(neuralNet.getError()));
- btnTestElement.setEnabled(true);
- textFieldTestElement.setEnabled(true);
- textFieldTestOutput.setEnabled(true);
- btnResetWeights.setEnabled(false);
- btnAddLayer.setEnabled(false);
- spinnerLayer.setEnabled(false);
- spinnerLayerNeurons.setEnabled(false);
- frmBPnet.getContentPane().remove(panelTopology);
- frmBPnet.revalidate();
- frmBPnet.repaint();
- mntmSaveNeuralNet.setEnabled(true);
- }
-
- /**
- * Initialize the contents of the frame.
- */
- private void initialize() {
- //Default values
- slope = (float)1.1;
- maxError = (float)0.1;
-
-
- frmBPnet = new JFrame();
- frmBPnet.setTitle("Backpropagation network");
- frmBPnet.setBounds(100, 100, 778, 562);
- frmBPnet.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
- frmBPnet.getContentPane().setLayout(null);
-
-
-
- btnLearn = new JButton("Quick Learn");
- btnLearn.addActionListener(new ActionListener() {
-
-
- public void actionPerformed(ActionEvent e) {
-
- int iterations = neuralNet.learn(trainingData);
- changeAfterLearn();
- textFieldIterations.setText(String.valueOf(iterations));
- //JOptionPane.showMessageDialog(null, "Neural Net learned in " + iterations + " iterations.");
-
- }
- });
- btnLearn.setEnabled(false);
- btnLearn.setBounds(10, 188, 174, 23);
- frmBPnet.getContentPane().add(btnLearn);
-
- btnTestData = new JButton("Test data");
- btnTestData.addActionListener(new ActionListener() {
- public void actionPerformed(ActionEvent e) {
- btnTestData.setEnabled(false);
-
-
- String[] columnNames = new String[nrOfInputs + nrOfOutputs];
- for (int i = 0; i < nrOfInputs; i++) {
- columnNames[i] = inputNames.get(i);
-
- }
- for (int i = 0; i < nrOfOutputs; i++) {
- columnNames[nrOfInputs + i] = outputNames.get(i);
- }
- Float[][] fDataTable = new Float[nrOfTestElements][nrOfInputs + nrOfOutputs];
- String[] rows = testData.split("\n");
- for (int i = 0; i < nrOfTestElements; i++) {
- neuralNet.run(rows[i]);
- String output = neuralNet.getOutput();
- String[] cells = (rows[i] + " " + output).split(" ");
- for (int j = 0; j < nrOfInputs + nrOfOutputs; j++) {
- fDataTable[i][j] = Float.valueOf(cells[j]);
- }
- }
- tableTest = new JTable( fDataTable, columnNames);
- tableTest.setAutoResizeMode(JTable.AUTO_RESIZE_OFF);
- scrollPaneTest.setHorizontalScrollBarPolicy(ScrollPaneConstants.HORIZONTAL_SCROLLBAR_ALWAYS);
- scrollPaneTest.setViewportView(tableTest);
-
-
- }
- });
- btnTestData.setEnabled(false);
- btnTestData.setBounds(10, 222, 174, 23);
- frmBPnet.getContentPane().add(btnTestData);
-
- scrollPaneLearn = new JScrollPane();
- scrollPaneLearn.setBounds(10, 25, 368, 156);
- frmBPnet.getContentPane().add(scrollPaneLearn);
-
- scrollPaneTest = new JScrollPane();
- scrollPaneTest.setBounds(10, 267, 368, 160);
- frmBPnet.getContentPane().add(scrollPaneTest);
-
- JLabel lblNewLabel = new JLabel("Training data");
- lblNewLabel.setBounds(10, 11, 116, 14);
- frmBPnet.getContentPane().add(lblNewLabel);
-
- JLabel lblTestData = new JLabel("Test data");
- lblTestData.setBounds(10, 252, 103, 14);
- frmBPnet.getContentPane().add(lblTestData);
-
- JLabel lblTrainingProcess = new JLabel("Training modification");
- lblTrainingProcess.setBounds(386, 11, 132, 14);
- frmBPnet.getContentPane().add(lblTrainingProcess);
-
- btnDoSpecifiedLearn = new JButton("Do specified learn steps");
- btnDoSpecifiedLearn.addActionListener(new ActionListener() {
- public void actionPerformed(ActionEvent e) {
- boolean learned = false;
- int iter = 0;
- int maxIterations = (int)spinnerLearnSteps.getModel().getValue();
- ArrayList<String> trainingElements = new ArrayList<String>(Arrays.asList(trainingData.split("\n")));
-// float maxError = 0;
- while(!learned && (iter < maxIterations)) {
- neuralNet.setError(0);
- learned = true;
- for (int i = 0; i < trainingElements.size(); i++) {
- learned &= neuralNet.learnStep(trainingElements.get(i));
-// if (neuralNet.getError() > maxError) {
-// maxError = neuralNet.getError();
-// }
- }
- iter++;
- textFieldCurrentError.setText(String.valueOf(neuralNet.getError()));
-// System.out.println(iter);
- }
- if (learned) {
- changeAfterLearn();
- }
- int currentIter = Integer.parseInt(textFieldIterations.getText());
- textFieldIterations.setText(String.valueOf(currentIter + iter));
-
-
- }
- });
- btnDoSpecifiedLearn.setEnabled(false);
- btnDoSpecifiedLearn.setBounds(194, 188, 184, 23);
- frmBPnet.getContentPane().add(btnDoSpecifiedLearn);
-
- spinnerLearnSteps = new JSpinner();
- spinnerLearnSteps.setModel(new SpinnerNumberModel(1, 1, 100000, 1));
- spinnerLearnSteps.setEnabled(false);
- spinnerLearnSteps.setBounds(194, 223, 88, 20);
- frmBPnet.getContentPane().add(spinnerLearnSteps);
-
- textFieldIterations = new JTextField();
- textFieldIterations.setEnabled(false);
- textFieldIterations.setText("0");
- textFieldIterations.setBounds(292, 223, 86, 20);
- frmBPnet.getContentPane().add(textFieldIterations);
- textFieldIterations.setColumns(10);
-
- lblLearned = new JLabel("Not Learned");
- lblLearned.setForeground(Color.RED);
- lblLearned.setBackground(Color.LIGHT_GRAY);
- lblLearned.setBounds(514, 143, 74, 14);
- frmBPnet.getContentPane().add(lblLearned);
-
- lblLearnCoeff = new JLabel("Learn coeff");
- lblLearnCoeff.setBounds(388, 39, 79, 14);
- frmBPnet.getContentPane().add(lblLearnCoeff);
-
- lblSlopeLambda = new JLabel("Slope - lambda");
- lblSlopeLambda.setBounds(388, 64, 89, 14);
- frmBPnet.getContentPane().add(lblSlopeLambda);
-
-
-
- spinnerLearnCoeff = new JSpinner();
- spinnerLearnCoeff.addChangeListener(new ChangeListener() {
- public void stateChanged(ChangeEvent e) {
- learnCoeff = (float)spinnerLearnCoeff.getModel().getValue();
- if (neuralNet != null)
- neuralNet.changeLearnCoeffTo(learnCoeff);
- }
- });
- spinnerLearnCoeff.setEnabled(false);
- spinnerLearnCoeff.setModel(new SpinnerNumberModel(new Float(0.5), new Float(0.05), new Float(1), new Float(0.05)));
- JSpinner.NumberEditor editor = (JSpinner.NumberEditor)spinnerLearnCoeff.getEditor();
- DecimalFormat format = editor.getFormat();
- format.setMinimumFractionDigits(5);
- spinnerLearnCoeff.setBounds(499, 36, 74, 20);
- frmBPnet.getContentPane().add(spinnerLearnCoeff);
-
- slope = (float)1.1;
- spinnerSlope = new JSpinner();
- spinnerSlope.addChangeListener(new ChangeListener() {
- public void stateChanged(ChangeEvent e) {
- slope = (float)spinnerSlope.getModel().getValue();
- if (neuralNet != null)
- neuralNet.changeSlopeTo(slope);
- }
- });
- spinnerSlope.setEnabled(false);
- spinnerSlope.setBounds(499, 61, 74, 20);
- spinnerSlope.setModel(new SpinnerNumberModel(new Float(slope), new Float(0.05), null, new Float(0.05)));
- editor = (JSpinner.NumberEditor)spinnerSlope.getEditor();
- format = editor.getFormat();
- format.setMinimumFractionDigits(5);
- frmBPnet.getContentPane().add(spinnerSlope);
-
- lblMaxError = new JLabel("Max error");
- lblMaxError.setBounds(388, 89, 67, 14);
- frmBPnet.getContentPane().add(lblMaxError);
-
-
- maxError = (float)0.1;
- spinnerError = new JSpinner();
- spinnerError.addChangeListener(new ChangeListener() {
- public void stateChanged(ChangeEvent e) {
- maxError = (float)spinnerError.getModel().getValue();
- if (neuralNet != null)
- neuralNet.setTolerance(maxError);
- }
- });
- spinnerError.setEnabled(false);
- spinnerError.setBounds(499, 86, 74, 20);
- spinnerError.setModel(new SpinnerNumberModel(new Float(maxError), new Float(0.00001), new Float(100), new Float(0.00001)));
- editor = (JSpinner.NumberEditor)spinnerError.getEditor();
- format = editor.getFormat();
- format.setMinimumFractionDigits(5);
- frmBPnet.getContentPane().add(spinnerError);
-
- lblCurentError = new JLabel("Current Error");
- lblCurentError.setBounds(388, 115, 79, 14);
- frmBPnet.getContentPane().add(lblCurentError);
-
- textFieldCurrentError = new JTextField();
- textFieldCurrentError.setEnabled(false);
- textFieldCurrentError.setBounds(487, 112, 86, 20);
- frmBPnet.getContentPane().add(textFieldCurrentError);
- textFieldCurrentError.setColumns(10);
-
- JLabel lblChangeNetworkTopology = new JLabel("Change network topology");
- lblChangeNetworkTopology.setBounds(389, 168, 184, 14);
- frmBPnet.getContentPane().add(lblChangeNetworkTopology);
-
- textFieldTestElement = new JTextField();
- textFieldTestElement.setEnabled(false);
- textFieldTestElement.setBounds(10, 438, 272, 20);
- frmBPnet.getContentPane().add(textFieldTestElement);
- textFieldTestElement.setColumns(10);
-
- btnTestElement = new JButton("Run input");
- btnTestElement.addActionListener(new ActionListener() {
- public void actionPerformed(ActionEvent e) {
- String input = textFieldTestElement.getText();
- try {
- neuralNet.run(input);
- }
- catch(InvalidInputNumberException exception) {
- JOptionPane.showMessageDialog(null, "Invalid Input");
- }
- finally {
- String output = neuralNet.getOutput();
- textFieldTestOutput.setText(output);
- }
- }
- });
- btnTestElement.setEnabled(false);
- btnTestElement.setBounds(289, 437, 89, 23);
- frmBPnet.getContentPane().add(btnTestElement);
-
- textFieldTestOutput = new JTextField();
- textFieldTestOutput.setEnabled(false);
- textFieldTestOutput.setBounds(49, 471, 329, 20);
- frmBPnet.getContentPane().add(textFieldTestOutput);
- textFieldTestOutput.setColumns(10);
-
- JLabel lblOutput = new JLabel("Output");
- lblOutput.setBounds(10, 474, 46, 14);
- frmBPnet.getContentPane().add(lblOutput);
-
- btnResetWeights = new JButton("Reset weights");
- btnResetWeights.setEnabled(false);
- btnResetWeights.addActionListener(new ActionListener() {
- public void actionPerformed(ActionEvent e) {
- neuralNet.resetWeights();
- }
- });
- btnResetWeights.setBounds(388, 138, 116, 23);
- frmBPnet.getContentPane().add(btnResetWeights);
-
- panelTopology = new JPanel();
- panelTopology.setBounds(386, 213, 366, 214);
- frmBPnet.getContentPane().add(panelTopology);
-
- btnAddLayer = new JButton("Add layer");
- btnAddLayer.setEnabled(false);
- btnAddLayer.setBounds(386, 188, 89, 23);
- frmBPnet.getContentPane().add(btnAddLayer);
-
- spinnerLayer = new JSpinner();
- spinnerLayer.setEnabled(false);
- spinnerLayer.setBounds(499, 189, 40, 20);
- frmBPnet.getContentPane().add(spinnerLayer);
-
- spinnerLayerNeurons = new JSpinner();
- spinnerLayerNeurons.setEnabled(false);
- spinnerLayerNeurons.setBounds(571, 189, 40, 20);
- frmBPnet.getContentPane().add(spinnerLayerNeurons);
-
- JLabel lblTo = new JLabel("to");
- lblTo.setBounds(483, 192, 46, 14);
- frmBPnet.getContentPane().add(lblTo);
-
- JLabel lblWith = new JLabel("with");
- lblWith.setBounds(542, 192, 46, 14);
- frmBPnet.getContentPane().add(lblWith);
-
- JLabel lblNeurons = new JLabel("neurons");
- lblNeurons.setBounds(621, 192, 74, 14);
- frmBPnet.getContentPane().add(lblNeurons);
-
-
-
- JMenuBar menuBar = new JMenuBar();
- frmBPnet.setJMenuBar(menuBar);
-
-
- JMenu mnFile = new JMenu("File");
- menuBar.add(mnFile);
-
- JMenuItem mntmLoadData = new JMenuItem("Load data");
- mntmLoadData.addActionListener(new ActionListener() {
-
-
- public void actionPerformed(ActionEvent e) {
- JFileChooser fc = new JFileChooser();
- fc.setDialogType(JFileChooser.OPEN_DIALOG);
- FileFilter filter = new FileFilter() {
-
- @Override
- public String getDescription() {
- return "Txt files";
- }
-
- @Override
- public boolean accept(File f) {
- return (f.getName().endsWith(".txt") || f.isDirectory());
- }
- };
- fc.setFileFilter(filter);
-
-
-
- if (fc.showOpenDialog(frmBPnet) == JFileChooser.APPROVE_OPTION) {
- dataFile = fc.getSelectedFile();
- FileReader fr;
- try {
-
- spinnerLearnSteps.setEnabled(true);
- spinnerError.setEnabled(true);
- spinnerLearnCoeff.setEnabled(true);
- spinnerSlope.setEnabled(true);
- spinnerLearnCoeff.setValue(new Float((float)spinnerLearnCoeff.getValue()));
- spinnerSlope.setValue(spinnerSlope.getValue());
- spinnerError.setValue(spinnerError.getValue());
-
- //Parse data file
-
-
-
- fr = new FileReader(dataFile);
- StreamTokenizer tokenizer = new StreamTokenizer(fr);
- /*for (int i = 0; i < 6; i++ )
- tokenizer.nextToken();
- */
- while(true) {
- tokenizer.nextToken();
- if ((tokenizer.nextToken() == StreamTokenizer.TT_WORD) && tokenizer.sval.equals("vrstev")) {
- tokenizer.nextToken();
- break;
- }
- }
- nrOfLayers = (int)tokenizer.nval;
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- nrOfInputs = (int)tokenizer.nval;
- tokenizer.nextToken();
- tokenizer.nextToken();
- tokenizer.nextToken();
- tokenizer.nextToken();
- inputRanges = new ArrayList<float[]>();
- inputNames = new ArrayList<String>();
- for (int i = 0; i < nrOfInputs; i++) {
- String inputName = tokenizer.sval;
- inputNames.add(inputName);
- tokenizer.nextToken();
- float[] dims = new float[2];
- dims[0] = (float)tokenizer.nval;
- tokenizer.nextToken();
- dims[1] = (float)tokenizer.nval;
- inputRanges.add(dims);
- tokenizer.nextToken();
- }
- /*for (int i = 0; i < 3; i++ )
- tokenizer.nextToken();*/
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- nrOfNeuronsPerLayer = new ArrayList<Integer>();
- for (int i = 0; i < nrOfLayers; i++) {
- nrOfNeuronsPerLayer.add((int)tokenizer.nval);
- if (i == nrOfLayers - 1) {
- nrOfOutputs = (int)tokenizer.nval;
- }
- tokenizer.nextToken();
- }
- for (int i = 0; i < 3; i++ )
- tokenizer.nextToken();
- outputNames = new ArrayList<String>();
- for (int i = 0; i < nrOfOutputs; i++) {
- outputNames.add(tokenizer.sval);
- tokenizer.nextToken();
- }
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- learnCoeff = (float)tokenizer.nval;
- spinnerLearnCoeff.getModel().setValue(new Float(learnCoeff));
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- inertiaCoeff = (float)tokenizer.nval;
- /*for (int i = 0; i < 7; i++ )
- tokenizer.nextToken();*/
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- nrOfTrainingElements = (int)tokenizer.nval;
- /*for (int i = 0; i < 4; i++ )
- tokenizer.nextToken();*/
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- StringBuffer sb = new StringBuffer();
- for (int i = 0; i < nrOfTrainingElements; i++) {
- for (int j = 0; j < nrOfInputs; j++) {
- sb.append(String.valueOf(tokenizer.nval/(inputRanges.get(j)[1]-inputRanges.get(j)[0]) - inputRanges.get(j)[0]/(inputRanges.get(j)[1]-inputRanges.get(j)[0])));
- sb.append(" ");
- tokenizer.nextToken();
- }
- for (int j = 0; j < nrOfOutputs; j++) {
- sb.append(String.valueOf(tokenizer.nval));
- sb.append(" ");
- tokenizer.nextToken();
- }
- sb.deleteCharAt(sb.length() - 1);
- sb.append("\n");
- }
- trainingData = sb.toString();
- sb = new StringBuffer();
- /*for (int i = 0; i < 5; i++ )
- tokenizer.nextToken();*/
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- nrOfTestElements = (int)tokenizer.nval;
- /*tokenizer.nextToken();*/
- if (nrOfTestElements > 0) {
- while(tokenizer.nextToken() != StreamTokenizer.TT_NUMBER) {}
- for (int i = 0; i < nrOfTestElements; i++) {
- for (int j = 0; j < nrOfInputs; j++) {
- sb.append(String.valueOf(String.valueOf(tokenizer.nval/(inputRanges.get(j)[1]-inputRanges.get(j)[0]) - inputRanges.get(j)[0]/(inputRanges.get(j)[1]-inputRanges.get(j)[0]))));
- sb.append(" ");
- tokenizer.nextToken();
- }
- sb.deleteCharAt(sb.lastIndexOf(" "));
- sb.append("\n");
- }
- testData = sb.substring(0,sb.lastIndexOf("\n"));
- } else {
- testData = "";
- }
- fr.close();
-
-
-
- neuralNet = new BPNet(maxError, nrOfLayers, nrOfInputs, nrOfNeuronsPerLayer, slope, learnCoeff);
- spinnerError.getModel().setValue(maxError);
- btnLearn.setEnabled(true);
- //Show learn table
- String[] columnNames = new String[nrOfInputs + nrOfOutputs];
- for (int i = 0; i < nrOfInputs; i++) {
- columnNames[i] = inputNames.get(i);
-
- }
- for (int i = 0; i < nrOfOutputs; i++) {
- columnNames[nrOfInputs + i] = outputNames.get(i);
- }
- Float[][] fDataTable = new Float[nrOfTrainingElements][nrOfInputs + nrOfOutputs];
- String[] rows = trainingData.split("\n");
- for (int i = 0; i < nrOfTrainingElements; i++) {
- String[] cells = rows[i].split(" ");
- for (int j = 0; j < nrOfInputs + nrOfOutputs; j++) {
- fDataTable[i][j] = Float.valueOf(cells[j]);
- }
- }
- tableLearn = new JTable( fDataTable, columnNames);
- tableLearn.setAutoResizeMode(JTable.AUTO_RESIZE_OFF);
- scrollPaneLearn.setHorizontalScrollBarPolicy(ScrollPaneConstants.HORIZONTAL_SCROLLBAR_ALWAYS);
- scrollPaneLearn.setViewportView(tableLearn);
- //Show test table
- columnNames = new String[nrOfInputs];
- for (int i = 0; i < nrOfInputs; i++) {
- columnNames[i] = inputNames.get(i);
- }
- fDataTable = new Float[nrOfTestElements][nrOfInputs];
- rows = testData.split("\n");
- for (int i = 0; i < nrOfTestElements; i++) {
- String[] cells = rows[i].split(" ");
- for (int j = 0; j < nrOfInputs; j++) {
- fDataTable[i][j] = Float.valueOf(cells[j]);
- }
- }
- tableTest = new JTable( fDataTable, columnNames);
- tableTest.setAutoResizeMode(JTable.AUTO_RESIZE_OFF);
- scrollPaneTest.setViewportView(tableTest);
- btnResetWeights.setEnabled(true);
- btnDoSpecifiedLearn.setEnabled(true);
- btnAddLayer.setEnabled(true);
- spinnerLayer.setEnabled(true);
- spinnerLayer.setModel(new SpinnerNumberModel(0, 0, neuralNet.getNrOfLayers(), 1));
- spinnerLayerNeurons.setEnabled(true);
- spinnerLayerNeurons.setModel(new SpinnerNumberModel(1, 1, null, 1));
-
- btnAddLayer.addActionListener(new ActionListener() {
- public void actionPerformed(ActionEvent e) {
- neuralNet.addNeuronLayer((Integer)spinnerLayerNeurons.getValue(), (Integer)spinnerLayer.getValue(), slope);
- refreshPanelTopology();
- }
- });
- refreshPanelTopology();
-
-
- } catch (FileNotFoundException e1) {
- e1.printStackTrace();
- JOptionPane.showMessageDialog(null, "Error: File not found");
- } catch (IOException e1) {
- e1.printStackTrace();
- JOptionPane.showMessageDialog(null, "IOException");
- }
-
-
- }
-
- }
-
- private void refreshPanelTopology() {
- panelTopology.setLayout(new GridLayout(neuralNet.getNrOfLayers() + 1, 4));
- panelTopology.removeAll();
- nrOfLayers = neuralNet.getNrOfLayers();
- String map = neuralNet.getNeuronMap();
- String[] layers = map.split(" ");
- spinnerLayer.setModel(new SpinnerNumberModel(0, 0, neuralNet.getNrOfLayers() - 1, 1));
- spinnerLayerNeurons.setModel(new SpinnerNumberModel(1, 1, null, 1));
- for (int i = 0; i < nrOfLayers; i++) {
- JLabel label = new JLabel(layers[nrOfLayers - 1 - i]);
- panelTopology.add(label);
- if (i > 0) {
- JButton btn1 = new JButton("Rmv neuron");
- JButton btn2 = new JButton("Rmv layer");
- JButton btn3 = new JButton("Add neuron");
- btn1.setName(String.valueOf(nrOfLayers - 1 - i));
- btn2.setName(String.valueOf(nrOfLayers - 1 - i));
- btn3.setName(String.valueOf(nrOfLayers - 1 - i));
- btn1.addActionListener(new ActionListener() {
-
- @Override
- public void actionPerformed(ActionEvent e) {
- String name = ((JButton)e.getSource()).getName();
- neuralNet.removeNeuron(Integer.parseInt(name));
- refreshPanelTopology();
- }
- });
- btn2.addActionListener(new ActionListener() {
-
- @Override
- public void actionPerformed(ActionEvent e) {
- String name = ((JButton)e.getSource()).getName();
- neuralNet.removeNeuronLayer(Integer.parseInt(name));
- refreshPanelTopology();
- }
- });
- btn3.addActionListener(new ActionListener() {
-
- @Override
- public void actionPerformed(ActionEvent e) {
- String name = ((JButton)e.getSource()).getName();
- neuralNet.addNeuron(Integer.parseInt(name), slope);
- refreshPanelTopology();
-
- }
- });
- panelTopology.add(btn1);
- panelTopology.add(btn2);
- panelTopology.add(btn3);
- } else {
- panelTopology.add(new JLabel(" "));
- panelTopology.add(new JLabel(" "));
- panelTopology.add(new JLabel(" "));
- }
- }
- panelTopology.add(new JLabel("Inputs"));
- panelTopology.add(new JLabel(String.valueOf(neuralNet.getNrOfInputs())));
- panelTopology.add(new JLabel(" "));
- panelTopology.add(new JLabel(" "));
- frmBPnet.revalidate();
- }
- });
- mnFile.add(mntmLoadData);
-
- JMenuItem mntmExit = new JMenuItem("Exit");
- mntmExit.addActionListener(new ActionListener() {
- public void actionPerformed(ActionEvent arg0) {
- frmBPnet.dispatchEvent(new WindowEvent(frmBPnet, WindowEvent.WINDOW_CLOSING));
- }
- });
-
- mntmSaveNeuralNet = new JMenuItem("Save Neural Net");
- mntmSaveNeuralNet.addActionListener(new ActionListener() {
- public void actionPerformed(ActionEvent e) {
- try {
- File address = null;
- JFileChooser fc = new JFileChooser();
- FileFilter filter = new FileFilter() {
-
- @Override
- public String getDescription() {
- return "Xml files";
- }
-
- @Override
- public boolean accept(File f) {
- return (f.getName().endsWith(".xml") || f.isDirectory());
- }
- };
- fc.setFileFilter(filter);
- fc.setCurrentDirectory(new java.io.File("."));
- //fc.setFileSelectionMode(JFileChooser.SAVE_DIALOG);
- if (fc.showSaveDialog(frmBPnet) == JFileChooser.APPROVE_OPTION) {
- address = fc.getSelectedFile();
- XStream xstream = new XStream();
- String xml = xstream.toXML(neuralNet);
- BufferedWriter out = new BufferedWriter(new FileWriter(address));
- out.write(xml);
- out.close();
-// BPNet testNet = (BPNet)xstream.fromXML(xml);
-
- JOptionPane.showMessageDialog(null, "Hotovo");
- }
- }
- catch (Exception ex) {
- ex.printStackTrace();
- JOptionPane.showMessageDialog(null, ex.getMessage());
- }
-
- }
- });
- mntmSaveNeuralNet.setEnabled(false);
- mnFile.add(mntmSaveNeuralNet);
- mnFile.add(mntmExit);
- }
-}
diff --git a/java/src/SigmoidalNeuron.java b/java/src/SigmoidalNeuron.java
deleted file mode 100644
index 706eea0f33efc56898645794d3acb358c2664ca0..0000000000000000000000000000000000000000
--- a/java/src/SigmoidalNeuron.java
+++ /dev/null
@@ -1,53 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-public class SigmoidalNeuron extends Neuron {
-
- private float error; //delta
- private float slope; //lambda
-
-
-
-
- public void setSlope(float slope) {
- this.slope = slope;
- }
-
-
- public SigmoidalNeuron(float slope) {
- this.slope = slope;
- this.error = 0;
- }
-
-
- @Override
- public void transfer() {
- float z = this.getPotential();
- float y = (float) (1.0/(1.0 + Math.exp(-slope*z)));
- this.setState(y);
- }
-
-
- public float getSlope() {
- return slope;
- }
-
- public float getError() {
- return error;
- }
-
-
- public void setError(float error) {
- this.error = error;
- }
-
-
- public static void main(String args[]) {
- SigmoidalNeuron neuron = new SigmoidalNeuron((float)0.5);
- for (int i = -10; i <= 10; i++) {
- neuron.initialize();
- neuron.adjustPotential(i);
- neuron.transfer();
- System.out.println(neuron.getState());
- }
- }
-}
diff --git a/java/src/SinglePerceptronNeuralNet.java b/java/src/SinglePerceptronNeuralNet.java
deleted file mode 100644
index e741be2594a37de90b5b60c0c6a40bd2c1fbfbca..0000000000000000000000000000000000000000
--- a/java/src/SinglePerceptronNeuralNet.java
+++ /dev/null
@@ -1,147 +0,0 @@
-package cz.vsb.mro0010.neuralnetworks;
-
-import java.util.ArrayList;
-import java.util.Arrays;
-
-public class SinglePerceptronNeuralNet extends NeuralNet {
-
- private Neuron neuron;
- private int nrOfInputs;
- private ArrayList<Connection> connections;
- private ArrayList<InputLayerPseudoNeuron> input;
- private String trainingOutput;
- private float learnCoef;
-
- public SinglePerceptronNeuralNet(Neuron neuron, int nrOfInputs, float learnCoef) {
- super();
- this.neuron = neuron;
- this.nrOfInputs = nrOfInputs;
- this.input = new ArrayList<InputLayerPseudoNeuron>();
- this.connections = new ArrayList<Connection>();
- for (int i = 0; i < this.nrOfInputs; i++) {
- InputLayerPseudoNeuron inputNeuron = new InputLayerPseudoNeuron();
- this.input.add(inputNeuron);
- this.connections.add(new Connection(inputNeuron, neuron, (float)Math.random()));
- }
- this.setTrainingOutput(" ");
- this.learnCoef = learnCoef;
- }
-
- @Override
- public String getNeuronType() {
- return neuron.getClass().getSimpleName();
- }
-
- @Override
- public int learn(String trainingSet) {
- ArrayList<String> trainingElements = new ArrayList<String>(Arrays.asList(trainingSet.split("\n")));
- boolean learned = false;
- int iterations = 0;
- StringBuffer trainingProgress = new StringBuffer();
- for (Connection c : connections) {
- trainingProgress.append(String.valueOf(c.getWeight()));
- trainingProgress.append(" ");
- }
- trainingProgress.append(String.valueOf(-neuron.getThreshold()));
- trainingProgress.append("\n");
- while (!learned) {
- iterations++;
- learned = true;
- for (String element : trainingElements) {
- String[] sa = element.split(" ");
- String expectedOutput = sa[sa.length - 1];
- StringBuffer sb = new StringBuffer();
- for (int i = 0; i < sa.length - 1; i++) {
- sb.append(sa[i]);
- sb.append(" ");
- }
- this.run(sb.toString());
-
- if (Float.parseFloat(expectedOutput) != Float.parseFloat(this.getOutput())) {
- learned = false;
- float eo = Float.parseFloat(expectedOutput);
- float ro = Float.parseFloat(this.getOutput());
- neuron.setThreshold(neuron.getThreshold() + learnCoef*-(eo-ro)*1); // w_0 = -threshold
- for (Connection c : connections) {
- c.adjustWeight(learnCoef*(eo-ro)*c.getInputNeuron().getState());
- }
- for (Connection c : connections) {
- trainingProgress.append(String.valueOf(c.getWeight()));
- trainingProgress.append(" ");
- }
- trainingProgress.append(String.valueOf(neuron.getThreshold()));
- trainingProgress.append("\n");
- }
- }
- }
- //System.out.println("Learned! in " + (iterations-1) + " iterations");
- this.setTrainingOutput(trainingProgress.toString());
- return iterations;
- }
-
- @Override
- public void run(String inputString) {
- String[] input = inputString.split(" ");
- for (int i = 0; i < input.length; i++) {
- InputLayerPseudoNeuron in = this.input.get(i);
- in.initialize();
- in.adjustPotential(Float.parseFloat(input[i]));
- in.transfer();
- }
- neuron.initialize();
- for (Connection c : connections) {
- c.passSignal();
- }
- neuron.transfer();
-
- }
-
- public String getOutput() {
- String output = String.valueOf(neuron.getState());
- return output;
- }
-
- public String getTrainingOutput() {
- return trainingOutput;
- }
-
- private void setTrainingOutput(String trainingOutput) {
- this.trainingOutput = trainingOutput;
- }
-
- /*public static void main(String[] args) {
- SinglePerceptronNeuralNet net = new SinglePerceptronNeuralNet(new BinaryNeuron(), 2, (float)0.7);
- net.neuron.setThreshold((float) Math.random());
-// String learnSet = "1 0.5 0\n0.4 0.8 1\n0.1 0.1 0\n0.6 0.9 1\n0.8 0.7 0\n0.4 1.0 1";
-// net.learn(learnSet);
-// net.run("0.7 0.9");
-// System.out.println(net.getOutput());
-// net.run("0.9 0.7");
-// System.out.println(net.getOutput());
-// net.run("0.2 0.2");
-// System.out.println(net.getOutput());
-// net.run("0.1 1.0");
-// System.out.println(net.getOutput());
-// net.run("1.0 0.1");
-// System.out.println(net.getOutput());
- String learnSet = "0.7 0.3 0\n0.2 0.6 1\n0.3 0.4 1\n0.9 0.8 0\n0.1 0.2 1\n0.5 0.6 1";
- net.learn(learnSet);
- net.run("0.7 0.9");
- System.out.println(net.getOutput());
- net.run("0.9 0.7");
- System.out.println(net.getOutput());
- net.run("0.2 0.2");
- System.out.println(net.getOutput());
- net.run("0.1 1.0");
- System.out.println(net.getOutput());
- net.run("1.0 0.1");
- System.out.println(net.getOutput());
- net.run("0.6 0.5");
- System.out.println(net.getOutput());
- net.run("0.5 0.6");
- System.out.println(net.getOutput());
- }*/
-
-
-
-}
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 192861a2391ca16138d560532233c7d6dcf7910b..83b069287ac4371f57a579a43cfeb83d24ad6662 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -84,5 +84,8 @@ target_link_libraries(particle_swarm_test boost_unit_test 4neuro)
add_executable(particle_test tests/Particle_test.cpp)
target_link_libraries(particle_test boost_unit_test 4neuro)
+add_executable(NeuralNetworkSum_test tests/NeuralNetworkSum_test.cpp)
+target_link_libraries(NeuralNetworkSum_test boost_unit_test 4neuro)
+
diff --git a/src/LearningMethods/GradientDescent.cpp b/src/LearningMethods/GradientDescent.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..ef5c12fc4164437dfdca429faac18fcfe1a13874
--- /dev/null
+++ b/src/LearningMethods/GradientDescent.cpp
@@ -0,0 +1,8 @@
+/**
+ * DESCRIPTION OF THE FILE
+ *
+ * @author Michal KravÄenko
+ * @date 30.7.18 -
+ */
+
+#include "GradientDescent.h"
diff --git a/src/LearningMethods/GradientDescent.h b/src/LearningMethods/GradientDescent.h
new file mode 100644
index 0000000000000000000000000000000000000000..91f72c53c242b375b39de4c8f9f038b98cfdb50f
--- /dev/null
+++ b/src/LearningMethods/GradientDescent.h
@@ -0,0 +1,17 @@
+/**
+ * DESCRIPTION OF THE FILE
+ *
+ * @author Michal KravÄenko
+ * @date 30.7.18 -
+ */
+
+#ifndef INC_4NEURO_GRADIENTDESCENT_H
+#define INC_4NEURO_GRADIENTDESCENT_H
+
+
+class GradientDescent {
+
+};
+
+
+#endif //INC_4NEURO_GRADIENTDESCENT_H
diff --git a/src/LearningMethods/ParticleSwarm.cpp b/src/LearningMethods/ParticleSwarm.cpp
index 20c79490e3f7fcf25a60224a1bf0d961bb318101..8d9702c22964db8e7df5cee6a5e2a5ade5997ee4 100644
--- a/src/LearningMethods/ParticleSwarm.cpp
+++ b/src/LearningMethods/ParticleSwarm.cpp
@@ -203,9 +203,11 @@ void ParticleSwarm::optimize( double gamma, double epsilon, double delta) {
double max_velocity;
double max_vel_step = 0;
double prev_max_vel_step;
+ double euclidean_dist;
while( outer_it < this->iter_max ) {
max_velocity = 0;
+ euclidean_dist = 0;
//////////////////////////////////////////////////
// Clustering algorithm - termination condition //
@@ -237,7 +239,12 @@ void ParticleSwarm::optimize( double gamma, double epsilon, double delta) {
}
/* Looking for nearby particles */
- printf("iter: %d, pi: %d, euclidean dist: %f\n", outer_it, pi, this->get_euclidean_distance(particle->get_coordinate(), coords, this->func_dim));
+ //printf("iter: %d, pi: %d, euclidean dist: %f\n", outer_it, pi, this->get_euclidean_distance(particle->get_coordinate(), coords, this->func_dim));
+
+ // TODO - only in verbose mode
+ // only for info purposes
+ euclidean_dist += this->get_euclidean_distance(particle->get_coordinate(), coords, this->func_dim);
+
if(this->get_euclidean_distance(particle->get_coordinate(), coords, this->func_dim) < epsilon) {
cluster.insert(particle);
}
@@ -248,22 +255,33 @@ void ParticleSwarm::optimize( double gamma, double epsilon, double delta) {
max_vel_step = max_velocity - prev_max_velocity;
/* Check if the particles are near to each other AND the maximal velocity is less than 'gamma' */
-// printf("cluster size: %ld, n particles: %d, %f\n", cluster.size(), this->n_particles, double(cluster.size())/this->n_particles);
- printf("cluster: %f\n", double(cluster.size())/this->n_particles);
- printf("real gamma: %f\n", std::abs(prev_max_vel_step/max_vel_step));
if(double(cluster.size())/this->n_particles > delta && std::abs(prev_max_vel_step/max_vel_step) > gamma) {
break;
}
+ //TODO only in verbose mode
+ euclidean_dist /= this->n_particles*5.;
+ printf("Iteration %d, avg euclidean distance: %f\n", outer_it, euclidean_dist);
+
outer_it++;
// this->w *= 0.99;
}
- printf("Found optimum in %6d iterations: %10.8f at coordinate: ", outer_it, optimal_value);
- for(unsigned int i = 0; i < this->func_dim - 1; ++i){
- printf("%10.8f, ", p_min_glob[i]);
+ if(outer_it < this->iter_max) {
+ /* Convergence reached */
+
+ printf("Found optimum in %d iterations: %10.8f at coordinates: \n", outer_it, optimal_value);
+ for (unsigned int i = 0; i <= this->func_dim - 1; ++i) {
+ printf("%10.8f \n", p_min_glob[i]);
+ }
+ } else {
+ /* Maximal number of iterations reached */
+
+ printf("Max number of iterations reached (%d)! Found value %10.8f at coordinates: \n", outer_it, optimal_value);
+ for (unsigned int i = 0; i <= this->func_dim - 1; ++i) {
+ printf("\t%10.8f \n", p_min_glob[i]);
+ }
}
- printf("%10.8f\n", p_min_glob[this->func_dim - 1]);
delete [] p_min_glob;
delete [] coords;
diff --git a/src/tests/ConnectionWeight_test.cpp b/src/tests/ConnectionWeight_test.cpp
index d7c5dd062c72ef851d92dd3d4be619b3f75c72ec..c54060fb634b0e92227a7c1617908f8e92a2ac50 100644
--- a/src/tests/ConnectionWeight_test.cpp
+++ b/src/tests/ConnectionWeight_test.cpp
@@ -84,6 +84,7 @@ BOOST_AUTO_TEST_SUITE(ConnectionWeight_test)
std::vector<double> w_array= {2,3,4,5,6} ;
double w_array2[5] = {1,2,3,4,5};
ConnectionWeight conn(5, &w_array );
+ conn.eval();
int para[5]={0,1,2,3,4};
conn.SetParamIndices(para);
conn.set_weights(w_array2);
diff --git a/src/tests/Connection_test.cpp b/src/tests/Connection_test.cpp
index d0e069083896037720bc26c81d17d12f037a5423..027c696c88e37e52175937f74bf15fd80dcc55a3 100644
--- a/src/tests/Connection_test.cpp
+++ b/src/tests/Connection_test.cpp
@@ -25,15 +25,12 @@ BOOST_AUTO_TEST_SUITE(Connection_test)
*/
BOOST_AUTO_TEST_CASE(Connection_construction__test) {
- Neuron* neuron1 = new NeuronLinear(2,3);
- Neuron* neuron2 = new NeuronLinear(4,5);
- std::vector<double> w_array= {2,3,4,5,6} ;
+ Neuron *neuron1 = new NeuronLinear(2, 3);
+ Neuron *neuron2 = new NeuronLinear(4, 5);
+ std::vector<double> w_array = {2, 3, 4, 5, 6};
- ConnectionWeight* conn = new ConnectionWeight(2, &w_array );
- int para[2]={0,1};
- conn->SetParamIndices(para);
+ ConnectionWeight *conn = new ConnectionWeight(2, &w_array);
Connection connection(neuron1, neuron2, conn);
-
//Test of correct input neuron
BOOST_CHECK_EQUAL(neuron1, connection.get_neuron_in());
//Test of correct output neuron
@@ -45,19 +42,18 @@ BOOST_AUTO_TEST_SUITE(Connection_test)
*/
BOOST_AUTO_TEST_CASE(Connection_pass_signal_test) {
- Neuron* neuron1 = new NeuronLinear(2,3);
- Neuron* neuron2 = new NeuronLinear(4,5);
- std::vector<double> w_array= {2,3,4,5,6} ;
- std::function<double(double *, int*, int)> f= [](double * weight_array, int * index_array, int n_params){
+ Neuron *neuron1 = new NeuronLinear(2, 3);
+ Neuron *neuron2 = new NeuronLinear(4, 5);
+ std::vector<double> w_array = {2, 3, 4, 5, 6};
+ std::function<double(double *, int *, int)> f = [](double *weight_array, int *index_array, int n_params) {
double a = weight_array[0];
double b = weight_array[1];
return (a + 1.5 * b);
};
- ConnectionWeight* conn = new ConnectionWeight(2, &w_array );
- int para[2]={0,1};
- conn->SetParamIndices(para);
+ ConnectionWeight *conn = new ConnectionWeight(2, &w_array);
+
Connection connection(neuron1, neuron2, conn);
neuron1->activate();
diff --git a/src/tests/Graph_Elemetary_Functions_Function_Cosine_test.cpp b/src/tests/Graph_Elemetary_Functions_Function_Cosine_test.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..32f70c8211dc3df5156cd0025bc165313eada768
--- /dev/null
+++ b/src/tests/Graph_Elemetary_Functions_Function_Cosine_test.cpp
@@ -0,0 +1,25 @@
+/**
+ * DESCRIPTION OF THE CLASS
+ *
+ * @author David Vojtek
+ * @date 2018
+ */
+
+#define BOOST_TEST_NO_MAIN
+
+#include <boost/test/unit_test.hpp>
+#include "../Graph/ElementaryFunctions/FunctionCosine.h"
+
+/**
+ * Boost testing suite for testing FunctionCosine.h
+ * doesn't test inherited methods
+ */
+BOOST_AUTO_TEST_SUITE(FunctionCosine_test)
+
+ BOOST_AUTO_TEST_CASE(FunctionCosine_Construction_Test)
+ {
+ FunctionCosine functionCosine(1, 2);
+ // BOOST_CHECK_NO_THROW();
+ }
+
+BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file
diff --git a/src/tests/NeuralNetworkSum_test.cpp b/src/tests/NeuralNetworkSum_test.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..889f984cbfbbe9fd2159fea25397a1d6291576dd
--- /dev/null
+++ b/src/tests/NeuralNetworkSum_test.cpp
@@ -0,0 +1,82 @@
+/**
+ * DESCRIPTION OF THE CLASS
+ *
+ * @author David Vojtek
+ * @date 2018
+ */
+
+#define BOOST_TEST_NO_MAIN
+
+#include <boost/test/unit_test.hpp>
+#include "../Network/NeuralNetworkSum.h"
+#include "../Neuron/NeuronLinear.h"
+
+/**
+ * Boost testing suite for testing NeuralNetworkSum.h
+ */
+BOOST_AUTO_TEST_SUITE(NeuralNetworkSum_test)
+
+ /**
+ * Test of creating new instance of NeuralNetworkSum
+ */
+ BOOST_AUTO_TEST_CASE(NeuralNetworkSum_constuction_test) {
+ //Test of none exception raise when creating new instance of NeuralNewtwork
+ BOOST_CHECK_NO_THROW(NeuralNetworkSum networkSum);
+ }
+
+ BOOST_AUTO_TEST_CASE(NeuralNetworkSum_add_network_test) {
+ NeuralNetwork network;
+ NeuralNetworkSum networkSum;
+
+ BOOST_CHECK_NO_THROW(networkSum.add_network(&network, 5));
+ }
+
+ BOOST_AUTO_TEST_CASE(NeuralNetworkSum_eval_single_weights_test) {
+ Neuron *n1 = new NeuronLinear(1, 1);
+ Neuron *n2 = new NeuronLinear(2, 2);
+ NeuralNetwork network;
+ network.add_neuron(n1);
+ network.add_neuron(n2);
+
+ network.add_connection_simple(0, 1, -1, 2.5);
+
+ std::vector<size_t> output_neuron_indices(1);
+ output_neuron_indices[0] = (size_t) 1;
+ network.specify_output_neurons(output_neuron_indices);
+
+ std::vector<size_t> input_neuron_indices(1);
+ input_neuron_indices[0] = (size_t) 0;
+ network.specify_input_neurons(input_neuron_indices);
+
+ std::vector<double> input;
+ input.push_back(1);
+ std::vector<double> output;
+ output.push_back(1);
+
+ double weights = 5;
+ NeuralNetworkSum networkSum;
+ networkSum.add_network(&network, 2);
+
+ networkSum.eval_single(input, output, &weights);
+ BOOST_CHECK_EQUAL(44, output.at(0));
+ }
+
+ BOOST_AUTO_TEST_CASE(NeuralNetworkSum_get_weights_test) {
+ NeuralNetworkSum networkSum;
+ BOOST_CHECK_EQUAL(0, networkSum.get_n_weights());
+
+ Neuron *n1 = new NeuronLinear(1, 1);
+ Neuron *n2 = new NeuronLinear(2, 2);
+ NeuralNetwork network;
+ network.add_neuron(n1);
+ network.add_neuron(n2);
+
+ network.add_connection_simple(0, 1, -1, 2.5);
+
+ networkSum.add_network(&network, 2);
+
+ BOOST_CHECK_EQUAL(1, networkSum.get_n_weights());
+ }
+
+
+BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file
diff --git a/src/tests/NeuralNetwork_test.cpp b/src/tests/NeuralNetwork_test.cpp
index 0a6c0585c6f465f8e79791ca0c88cab505f3a03e..95e6ebe4c73e78bc89227733ba47ccf022faa45c 100644
--- a/src/tests/NeuralNetwork_test.cpp
+++ b/src/tests/NeuralNetwork_test.cpp
@@ -208,9 +208,9 @@ BOOST_AUTO_TEST_SUITE(NeuralNetwork_test)
output.push_back(1);
double weights = 5;
- network.get_n_weights();
network.eval_single(input, output, &weights);
BOOST_CHECK_EQUAL(22, output.at(0));
}
+
BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file
diff --git a/src/tests/NeuronLogistic_test.cpp b/src/tests/NeuronLogistic_test.cpp
index 1baaef5082e4e41128e2e06c31d3420d435d733f..7d3e9fe4bcca31c993e99951b8943089a80a7786 100644
--- a/src/tests/NeuronLogistic_test.cpp
+++ b/src/tests/NeuronLogistic_test.cpp
@@ -34,7 +34,7 @@ BOOST_AUTO_TEST_SUITE(neuronLogistic_test)
NeuronLogistic neuron(3.0, 2.0);
neuron.activate();
//Test of correct state after activate neuron
- BOOST_CHECK_CLOSE(0.0022492134466, neuron.get_state(), 0.00001);
+ BOOST_CHECK_CLOSE(0.0016937944, neuron.get_state(), 0.00001);
}
/**
@@ -44,10 +44,10 @@ BOOST_AUTO_TEST_SUITE(neuronLogistic_test)
NeuronLogistic neuron(3.0, 2.0);
//Test of correct output of activation_function_get_derivative method
- BOOST_CHECK_CLOSE(0.0042850850699, neuron.activation_function_eval_derivative(), 0.00001);
+ BOOST_CHECK_CLOSE(0.00447566759338, neuron.activation_function_eval_derivative(), 0.00001);
//Tests of correct outputs of activation_function_get_partial_derivative method
- BOOST_CHECK_CLOSE(-0.0068569236644, neuron.activation_function_eval_partial_derivative(0), 0.00001);
- BOOST_CHECK_CLOSE(-0.0042850850699, neuron.activation_function_eval_partial_derivative(1), 0.00001);
+ BOOST_CHECK_CLOSE(-0.0036025788498, neuron.activation_function_eval_partial_derivative(0), 0.00001);
+ BOOST_CHECK_CLOSE(-0.0044756675933, neuron.activation_function_eval_partial_derivative(1), 0.00001);
BOOST_CHECK_EQUAL(0.0, neuron.activation_function_eval_partial_derivative(10000));
}
diff --git a/src/tests/Particle_test.cpp b/src/tests/Particle_test.cpp
index 5412c453cbbd0c995ecdd84af437005293efc7d4..243fc92e0cf7f6b932b6601c224486f24687bfbd 100644
--- a/src/tests/Particle_test.cpp
+++ b/src/tests/Particle_test.cpp
@@ -16,13 +16,10 @@
* Boost testing suite for testing ParticleSwarm.h
* TODO
*/
+ BOOST_AUTO_TEST_SUITE(Particle_test)
-
-
-BOOST_AUTO_TEST_SUITE(Particle_test)
-
- BOOST_AUTO_TEST_CASE(Particle_construction_test){
- double domain_bound[5] = {1,2,3,4,5};
+ BOOST_AUTO_TEST_CASE(Particle_construction_test) {
+ double domain_bound[5] = {1, 2, 3, 4, 5};
Neuron *n1 = new NeuronLinear(1, 1);
Neuron *n2 = new NeuronLinear(2, 2);
NeuralNetwork network;
@@ -53,11 +50,11 @@ BOOST_AUTO_TEST_SUITE(Particle_test)
ErrorFunction *error = new MSE(&network, &dataSet);
Particle particle(error, &domain_bound[0]);
BOOST_CHECK_NO_THROW(Particle particle(error, &domain_bound[0]));
- // particle.get_coordinate();
-}
+ // particle.get_coordinate();
+ }
- BOOST_AUTO_TEST_CASE(Particle_get_coordinate_test){
- double domain_bound[5] = {1,2,3,4,5};
+ BOOST_AUTO_TEST_CASE(Particle_get_coordinate_test) {
+ double domain_bound[5] = {1, 2, 3, 4, 5};
Neuron *n1 = new NeuronLinear(1, 1);
Neuron *n2 = new NeuronLinear(2, 2);
NeuralNetwork network;
@@ -93,75 +90,81 @@ BOOST_AUTO_TEST_SUITE(Particle_test)
}
//Random
- /* BOOST_AUTO_TEST_CASE(particle_change_coordiante_test) {
- double domain_bound[5] = {1,2,3,4,5};
- Neuron *n1 = new NeuronLinear(1, 1);
- Neuron *n2 = new NeuronLinear(2, 2);
- NeuralNetwork network;
- std::vector<std::pair<std::vector<double>, std::vector<double>>> data_vec;
- std::vector<double> inp, out;
-
- for (int i = 0; i < 1; i++) {
- inp.push_back(i);
- out.push_back(i + 4);
- }
-
- data_vec.emplace_back(std::make_pair(inp, out));
- network.add_neuron(n1);
- network.add_neuron(n2);
- network.add_connection_simple(0, 1, 0, 2.5);
- network.randomize_weights();
-
- std::vector<size_t> net_input_neurons_indices(1);
- std::vector<size_t> net_output_neurons_indices(1);
- net_input_neurons_indices[0] = 0;
-
- net_output_neurons_indices[0] = 1;
-
- network.specify_input_neurons(net_input_neurons_indices);
- network.specify_output_neurons(net_output_neurons_indices);
-
- DataSet dataSet(&data_vec);
- ErrorFunction *error = new MSE(&network, &dataSet);
- Particle particle(error, &domain_bound[0]);
- particle.change_coordinate(1.0, 2.0, 2.0, &domain_bound[1], 1);
- BOOST_CHECK_EQUAL(1.32664, *particle.get_coordinate());
- }
-
- BOOST_AUTO_TEST_CASE(particle_optimal_value_test){
- double domain_bound[5] = {1,2,3,4,5};
- Neuron *n1 = new NeuronLinear(1, 1);
- Neuron *n2 = new NeuronLinear(2, 2);
- NeuralNetwork network;
- std::vector<std::pair<std::vector<double>, std::vector<double>>> data_vec;
- std::vector<double> inp, out;
-
- for (int i = 0; i < 1; i++) {
- inp.push_back(i);
- out.push_back(i + 4);
- }
-
- data_vec.emplace_back(std::make_pair(inp, out));
- network.add_neuron(n1);
- network.add_neuron(n2);
- network.add_connection_simple(0, 1, 0, 2.5);
- network.randomize_weights();
-
- std::vector<size_t> net_input_neurons_indices(1);
- std::vector<size_t> net_output_neurons_indices(1);
- net_input_neurons_indices[0] = 0;
-
- net_output_neurons_indices[0] = 1;
-
- network.specify_input_neurons(net_input_neurons_indices);
- network.specify_output_neurons(net_output_neurons_indices);
-
- DataSet dataSet(&data_vec);
- ErrorFunction *error = new MSE(&network, &dataSet);
- Particle particle(error, &domain_bound[0]);
- BOOST_CHECK_CLOSE(1.789708839, particle.get_optimal_value(), 0.00001 );
-
- }*/
+ //TODO
+ /*
+ BOOST_AUTO_TEST_CASE(particle_change_coordiante_test) {
+ double domain_bound[5] = {1,2,3,4,5};
+ Neuron *n1 = new NeuronLinear(1, 1);
+ Neuron *n2 = new NeuronLinear(2, 2);
+ NeuralNetwork network;
+ std::vector<std::pair<std::vector<double>, std::vector<double>>> data_vec;
+ std::vector<double> inp, out;
+
+ for (int i = 0; i < 1; i++) {
+ inp.push_back(i);
+ out.push_back(i + 4);
+ }
+
+ data_vec.emplace_back(std::make_pair(inp, out));
+ network.add_neuron(n1);
+ network.add_neuron(n2);
+ network.add_connection_simple(0, 1, 0, 2.5);
+ network.randomize_weights();
+
+ std::vector<size_t> net_input_neurons_indices(1);
+ std::vector<size_t> net_output_neurons_indices(1);
+ net_input_neurons_indices[0] = 0;
+
+ net_output_neurons_indices[0] = 1;
+
+ network.specify_input_neurons(net_input_neurons_indices);
+ network.specify_output_neurons(net_output_neurons_indices);
+
+
+
+ DataSet dataSet(&data_vec);
+ ErrorFunction *error = new MSE(&network, &dataSet);
+ Particle particle(error, &domain_bound[0]);
+ particle.change_coordinate(1.0, 2.0, 2.0, &domain_bound[1], 1);
+
+
+ BOOST_CHECK_EQUAL(1.32664, *particle.get_coordinate());
+ }
+
+ BOOST_AUTO_TEST_CASE(particle_optimal_value_test){
+ double domain_bound[5] = {1,2,3,4,5};
+ Neuron *n1 = new NeuronLinear(1, 1);
+ Neuron *n2 = new NeuronLinear(2, 2);
+ NeuralNetwork network;
+ std::vector<std::pair<std::vector<double>, std::vector<double>>> data_vec;
+ std::vector<double> inp, out;
+
+ for (int i = 0; i < 1; i++) {
+ inp.push_back(i);
+ out.push_back(i + 4);
+ }
+
+ data_vec.emplace_back(std::make_pair(inp, out));
+ network.add_neuron(n1);
+ network.add_neuron(n2);
+ network.add_connection_simple(0, 1, 0, 2.5);
+ network.randomize_weights();
+
+ std::vector<size_t> net_input_neurons_indices(1);
+ std::vector<size_t> net_output_neurons_indices(1);
+ net_input_neurons_indices[0] = 0;
+
+ net_output_neurons_indices[0] = 1;
+
+ network.specify_input_neurons(net_input_neurons_indices);
+ network.specify_output_neurons(net_output_neurons_indices);
+
+ DataSet dataSet(&data_vec);
+ ErrorFunction *error = new MSE(&network, &dataSet);
+ Particle particle(error, &domain_bound[0]);
+ BOOST_CHECK_CLOSE(1.789708839, particle.get_optimal_value(), 0.00001 );
+ }
+ */
BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file