-1
我有一個程序模擬神經網絡，完成後將通過NEAT算法演變。ConcurrentModificationException當通過連接迭代時
神經網絡的工作原理是負載神經元，通過連接進行連接。演化程序的一部分是跨越神經網絡，基本上從每個神經元和連接中取出隨機的神經元並將它們放在一起。但是，如果連接2個不存在或禁用的神經元需要連接，則會被禁用。如果神經元具有少於1個現有或啓用的輸入/輸出連接，它將被禁用。但是，當迭代神經元的連接時，我得到ConcurrentModificationException。
我對此略有困惑，我認爲這是我的一個誤解。代碼有什麼問題？
錯誤：
Exception in thread "AWT-EventQueue-0" java.util.ConcurrentModificationException
at java.util.ArrayList$Itr.checkForComodification(Unknown Source)
at java.util.ArrayList$Itr.next(Unknown Source)
at GUIDisplay$Handler.actionPerformed(GUIDisplay.java:610)
at javax.swing.AbstractButton.fireActionPerformed(Unknown Source)
at javax.swing.AbstractButton$Handler.actionPerformed(Unknown Source)
at javax.swing.DefaultButtonModel.fireActionPerformed(Unknown Source)
at javax.swing.DefaultButtonModel.setPressed(Unknown Source)
at javax.swing.plaf.basic.BasicButtonListener.mouseReleased(Unknown Source)
at java.awt.Component.processMouseEvent(Unknown Source)
at javax.swing.JComponent.processMouseEvent(Unknown Source)
at java.awt.Component.processEvent(Unknown Source)
at java.awt.Container.processEvent(Unknown Source)
at java.awt.Component.dispatchEventImpl(Unknown Source)
at java.awt.Container.dispatchEventImpl(Unknown Source)
at java.awt.Component.dispatchEvent(Unknown Source)
at java.awt.LightweightDispatcher.retargetMouseEvent(Unknown Source)
at java.awt.LightweightDispatcher.processMouseEvent(Unknown Source)
at java.awt.LightweightDispatcher.dispatchEvent(Unknown Source)
at java.awt.Container.dispatchEventImpl(Unknown Source)
at java.awt.Window.dispatchEventImpl(Unknown Source)
at java.awt.Component.dispatchEvent(Unknown Source)
at java.awt.EventQueue.dispatchEventImpl(Unknown Source)
at java.awt.EventQueue.access$300(Unknown Source)
at java.awt.EventQueue$3.run(Unknown Source)
at java.awt.EventQueue$3.run(Unknown Source)
at java.security.AccessController.doPrivileged(Native Method)
at java.security.ProtectionDomain$1.doIntersectionPrivilege(Unknown Source)
at java.security.ProtectionDomain$1.doIntersectionPrivilege(Unknown Source)
at java.awt.EventQueue$4.run(Unknown Source)
at java.awt.EventQueue$4.run(Unknown Source)
at java.security.AccessController.doPrivileged(Native Method)
at java.security.ProtectionDomain$1.doIntersectionPrivilege(Unknown Source)
at java.awt.EventQueue.dispatchEvent(Unknown Source)
at java.awt.EventDispatchThread.pumpOneEventForFilters(Unknown Source)
at java.awt.EventDispatchThread.pumpEventsForFilter(Unknown Source)
at java.awt.EventDispatchThread.pumpEventsForHierarchy(Unknown Source)
at java.awt.EventDispatchThread.pumpEvents(Unknown Source)
at java.awt.EventDispatchThread.pumpEvents(Unknown Source)
at java.awt.EventDispatchThread.run(Unknown Source)
GUIDisplay類（組織和顯示神經網絡）：
import java.awt.Color;
import java.awt.Graphics;
import java.awt.Point;
import java.awt.Rectangle;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import java.awt.event.MouseEvent;
import java.awt.event.MouseListener;
import java.awt.event.MouseMotionListener;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.Map;
import java.util.Random;
import java.util.Set;
import javax.swing.JButton;
import javax.swing.JFrame;
import javax.swing.JOptionPane;
import javax.swing.JPanel;
import javax.swing.JTextField;
public class GUIDisplay extends JPanel {
JButton save = new JButton("Save neural network");
JButton cross = new JButton("Cross 2 neural networks");
JButton newNN = new JButton("Create new neural network");
JButton load = new JButton("Load neural network");
NeuralRegister nr = new NeuralRegister();
int lastX = 0;
boolean drag = false;
InputNeuron inputA = new InputNeuron(nr),
inputB = new InputNeuron(nr);
OutputNeuron outputA = new OutputNeuron(nr),
outputB = new OutputNeuron(nr);
//ArrayList<Neuron> hidden = new ArrayList<Neuron>();
//ArrayList<Connection> connections = new ArrayList<Connection>();
ArrayList<JTextField> inputs = new ArrayList<JTextField>();
Map<String,NeuralRegister> species = new HashMap<String,NeuralRegister>();
Neuron startConnection;
Neuron endConnection;
JFrame j;
public GUIDisplay (final JFrame j) {
this.j = j;
setLayout(null);
save.setLocation(10, 30);
save.setSize(newNN.getPreferredSize());
cross.setLocation(10, 55);
cross.setSize(newNN.getPreferredSize());
newNN.setLocation(10, 80);
newNN.setSize(newNN.getPreferredSize());
load.setLocation(10, 105);
load.setSize(newNN.getPreferredSize());
add(save);
add(cross);
add(newNN);
add(load);
for (Neuron n : nr.getNeurons()) {
if (n instanceof InputNeuron) {
JTextField text = new JTextField(Double.toString(n.getOutput()));
text.setSize(100, 25);
inputs.add(text);
}
}
/*hidden.add(new Neuron(nr));
hidden.add(new Neuron(nr));
Connection inA = new Connection(inputA, hidden.get(0), nr);
Connection inB = new Connection(inputB, hidden.get(1), nr);
Connection outA = new Connection(hidden.get(hidden.size() - 1), outputA, nr);
Connection outB = new Connection(hidden.get(hidden.size() - 2), outputB, nr);
for (int i = 0; i < hidden.size(); i ++) {
if (i < hidden.size() - 1) {
Connection newC = new Connection(hidden.get(i), hidden.get(i + 1), nr);
}
}*/
calculateNeuronLocations();
inputA.setOutput(20);
inputB.setOutput(-20);
//inputA.calculateOutput();
//inputB.calculateOutput();
Handler h = new Handler();
addMouseListener(h);
addMouseMotionListener(h);
save.addActionListener(h);
cross.addActionListener(h);
newNN.addActionListener(h);
load.addActionListener(h);
}
public void calculateNeuronLocations() {
int x = 100;
int y = 250;
int xGap = 100;
int yGap = 40;
for (Neuron n : nr.getNeurons()) {
if (n instanceof InputNeuron) {
n.setLocation(new Point(x,y));
y += yGap;
}
}
y = 250;
x += xGap;
for (Neuron n : nr.getNeurons()) {
if (!(n instanceof InputNeuron) && !(n instanceof OutputNeuron)) {
n.setLocation(new Point(x,y));
y += yGap;
if (y >= 200) {
y = 100;
x += xGap;
}
}
}
y = 250;
x = j.getWidth() - 100;
for (Neuron n : nr.getNeurons()) {
if (n instanceof OutputNeuron) {
n.setLocation(new Point(x,y));
y += yGap;
}
}
lastX = x;
}
@Override
public void paintComponent (Graphics g) {
super.paintComponent(g);
g.setColor(Color.BLACK);
g.drawString("Connections: " + nr.getConnections().size(), 10, 10);
g.drawString("Neurons: " + nr.getNeurons().size(), 10, 20);
g.setColor(Color.BLUE);
g.drawString("Input", 100, 230);
g.drawString("Output", lastX, 230);
for (Connection c : nr.getConnections()) {
if (c.isEnabled()) {
g.setColor(Color.GREEN);
} else {
g.setColor(Color.MAGENTA);
}
g.drawLine((int)c.getA().getLocation().getX(),
(int)c.getA().getLocation().getY(),
(int)c.getB().getLocation().getX(),
(int)c.getB().getLocation().getY());
}
for (Neuron n : nr.getNeurons()) {
if (n instanceof InputNeuron || n instanceof OutputNeuron) {
g.setColor(new Color(150,0,0));
g.drawRect((int)n.getLocation().getX() - 2,
(int)n.getLocation().getY() - 2,
4,
4);
} else {
if (n.isEnabled()) {
g.setColor(Color.RED);
} else {
g.setColor(Color.BLUE);
}
}
g.drawRect((int)n.getLocation().getX() - 5,
(int)n.getLocation().getY() - 5,
10,
10);
g.setColor(Color.BLACK);
g.drawString(n.getId() + " (" + n.getOutput() + ")",
(int)n.getLocation().getX() - 5,
(int)n.getLocation().getY() - 5);
}
int cx = 100;
int nx = 100;
int y = j.getHeight() - 350;
int width = 100, height = 100;
for (Gene ge : nr.getGenome()) {
boolean neuron = true;
ArrayList<String> text = new ArrayList<String>();
int yAddon = 15;
int yStart = 15;
text.add("ID: " + ge.getId());
text.add("Enabled: " + ge.isEnabled());
if (ge instanceof Neuron) {
g.drawRect(nx, y, width, height);
Neuron ne = (Neuron) ge;
String nType = "Node: ";
if (ne instanceof InputNeuron) {
nType += "input";
} else if (ne instanceof OutputNeuron) {
nType += "output";
} else {
nType += "hidden";
}
text.add(nType);
nx += width;
} else if (ge instanceof Connection) {
g.drawRect(cx, y + 150, width, height);
yStart += 150;
Connection ce = (Connection) ge;
text.add("Neurons: " + ce.getA().getId() + " -> " + ce.getB().getId());
text.add("Weight: " + ce.getWeight());
cx += width;
neuron = false;
}
for (String s : text) {
int x = (neuron) ? nx : cx;
g.drawString(s, x + 5 - 100, y + yStart + yAddon);
yStart += yAddon;
}
}
if (drag) {
g.setColor(new Color(0,150,0));;
if (startConnection != null) g.drawLine((int)startConnection.getLocation().getX(),
(int)startConnection.getLocation().getY(),
(int)getMousePosition().getX(),
(int)getMousePosition().getY());
}
Set<String> keys = species.keySet();
int x = j.getWidth() - 100;
int yT = 30;
int yGap = 15;
for (String key : keys) {
g.drawString(key, x, yT);
yT += yGap;
}
}
private class Handler implements ActionListener, MouseListener, MouseMotionListener {
@Override
public void mouseClicked(MouseEvent arg0) {
Neuron toAdd = new Neuron(nr);
toAdd.setLocation(getMousePosition());
//hidden.add(toAdd);
repaint();
j.repaint();
}
@Override
public void mouseEntered(MouseEvent arg0) {
// TODO Auto-generated method stub
}
@Override
public void mouseExited(MouseEvent arg0) {
// TODO Auto-generated method stub
}
@Override
public void mousePressed(MouseEvent arg0) {
for (Neuron n : nr.getNeurons()) {
Rectangle m = new Rectangle((int)getMousePosition().getX(),
(int)getMousePosition().getY(),
1,1);
Rectangle r = new Rectangle();
r.setLocation((int)n.getLocation().getX() - 5, (int)n.getLocation().getY() - 5);
r.setSize(10, 10);
if (m.intersects(r)) {
startConnection = n;
}
}
}
@Override
public void mouseReleased(MouseEvent arg0) {
if (startConnection != null) {
if (drag) {
boolean found = false;
for (Neuron n : nr.getNeurons()) {
if (n != startConnection) {
Rectangle m = new Rectangle((int)getMousePosition().getX(),
(int)getMousePosition().getY(),
1,1);
Rectangle r = new Rectangle();
r.setLocation((int)n.getLocation().getX() - 5, (int)n.getLocation().getY() - 5);
r.setSize(10, 10);
if (m.intersects(r)) {
endConnection = n;
found = true;
}
}
}
if (found) {
Connection c = new Connection(startConnection,endConnection,nr);
c.transferOutput(c.getA().getOutput());
}
repaint();
j.repaint();
}
}
drag = false;
startConnection = null;
}
@Override
public void actionPerformed(ActionEvent e) {
if (e.getSource() == save) {
String name = JOptionPane.showInputDialog(null, "Enter desired name for network", "Save", JOptionPane.INFORMATION_MESSAGE);
species.put(name, nr);
repaint();
} else if (e.getSource() == newNN) {
nr = new NeuralRegister();
InputNeuron inputA = new InputNeuron(nr),
inputB = new InputNeuron(nr);
OutputNeuron outputA = new OutputNeuron(nr),
outputB = new OutputNeuron(nr);
inputA.setOutput(20);
inputB.setOutput(-20);
calculateNeuronLocations();
repaint();
} else if (e.getSource() == load) {
load();
} else if (e.getSource() == cross) {
String nr1Str = JOptionPane.showInputDialog(null,"Enter name of first neural network", "Cross", JOptionPane.INFORMATION_MESSAGE);
String nr2Str = JOptionPane.showInputDialog(null,"Enter name of second neural network", "Cross", JOptionPane.INFORMATION_MESSAGE);
NeuralRegister nr1 = species.get(nr1Str);
NeuralRegister nr2 = species.get(nr2Str);
NeuralRegister nrNew = new NeuralRegister();
NeuralRegister nrAdded = new NeuralRegister();
ArrayList<Neuron> nDone = new ArrayList<Neuron>();
ArrayList<Connection> cDone = new ArrayList<Connection>();
/*NeuralRegister biggestNeurons = (nr1.getNeurons().size() > nr2.getNeurons().size()) ? nr1 : nr2;
NeuralRegister smallestNeurons = (biggestNeurons == nr1) ? nr2 : nr1;
NeuralRegister biggestConnections = (nr1.getConnections().size() > nr2.getConnections().size()) ? nr1 : nr2;
NeuralRegister smallestConnections = (biggestConnections == nr1) ? nr2 : nr1;
*/
for (Neuron n : nr1.getNeurons()) {
nrAdded.registerNeuron(n);
}
for (Neuron n : nr2.getNeurons()) {
nrAdded.registerNeuron(n);
}
for (Connection c : nr1.getConnections()) {
nrAdded.registerConnection(c);
}
for (Connection c : nr2.getConnections()) {
nrAdded.registerConnection(c);
}
for (Neuron g1 : nrAdded.getNeurons()) {
if (!nDone.contains(g1)) {
Neuron match = null;
ArrayList<Neuron> search = (nr1.getNeurons().contains(g1)) ? nr2.getNeurons() : nr1.getNeurons();
for (Neuron g2 : search) {
if (g1.getId() == g2.getId()) {
match = g2;
//System.out.println("Match Found " + g1.getId());
nDone.add(g2);
}
}
Random r = new Random();
Neuron select = null;
if (match != null) {
select = (r.nextInt(2) == 1) ? g1 : match;
//System.out.println("Selected " + ((select == g1) ? "NR1" : "NR2"));
} else {
select = (r.nextInt(2) == 1) ? g1 : null;
}
nDone.add(g1);
if (select != null) nrNew.registerNeuron(select);
}
}
for (Connection c : nrAdded.getConnections()) {
if (!cDone.contains(c)) {
Connection match = null;
ArrayList<Connection> search = (nr1.getConnections().contains(c)) ? nr2.getConnections() : nr1.getConnections();
for (Connection g2 : search) {
if (c.getId() == g2.getId()) {
match = g2;
//System.out.println("Match Found " + g1.getId());
cDone.add(g2);
}
}
Random r = new Random();
Connection select = null;
if (match != null) {
select = (r.nextInt(2) == 1) ? c : match;
//System.out.println("Selected " + ((select == g1) ? "NR1" : "NR2"));
} else {
select = (r.nextInt(2) == 1) ? c : null;
}
cDone.add(c);
if (select != null) {
int complete = 0;
for (Neuron n : nrNew.getNeurons()) {
if (select.getA().getId() == n.getId() && n.isEnabled()) {
complete ++;
select.setA(n);
} else if (select.getB().getId() == n.getId() && n.isEnabled()) {
complete ++;
select.setB(n);
}
}
if (complete == 2) {
//select.setEnabled(true);
nrNew.registerConnection(select);
} else {
select.setEnabled(false);
nrNew.registerConnection(select);
}
}
}
}
for (Neuron n : nrNew.getNeurons()) {
for (Connection c : n.getInputs()) {
for (Connection c2 : nrNew.getConnections()) {
if (c.getId() != c2.getId() || !c2.isEnabled()) {
n.getInputs().remove(c);
}
}
}
//CONCURRENT MODIFICATION EXCEPTION
for (Connection c : n.getOutputs()) {
for (Connection c2 : nrNew.getConnections()) {
if (c.getId() != c2.getId() || !c2.isEnabled()) {
n.getOutputs().remove(c);
}
}
}
if (n.getInputs().size() < 1 || n.getOutputs().size() < 1) {
if (!(n instanceof InputNeuron) &&
!(n instanceof OutputNeuron))
n.setEnabled(false);
}
}
load(nrNew);
}
}
@Override
public void mouseDragged(MouseEvent arg0) {
drag = true;
repaint();
}
@Override
public void mouseMoved(MouseEvent arg0) {
// TODO Auto-generated method stub
}
}
public void load() {
String search = JOptionPane.showInputDialog(null,"Enter name of neural network to load", "Load", JOptionPane.INFORMATION_MESSAGE);
load(species.get(search));
repaint();
}
public void load (NeuralRegister nrLoad) {
//String search = JOptionPane.showInputDialog(null,"Enter name of neural network to load", "Load", JOptionPane.INFORMATION_MESSAGE);
nr = nrLoad;
nr.refresh();
repaint();
}
}
Neuron類：
import java.awt.Point;
import java.util.ArrayList;
public class Neuron extends Gene {
double threshold = 1;
ArrayList<Connection> inputs = new ArrayList<Connection>();
ArrayList<Connection> outputs = new ArrayList<Connection>();
double output;
Point location = new Point(0,0);
public Neuron (NeuralRegister nr) {
id = nr.registerNeuron(this);
}
public void calculateOutput() {
double sumOfInputs = 0;
//System.out.println("Neuron " + id + " evaluation\n----------");
for (Connection c : inputs) {
sumOfInputs += c.getWeight() * c.getValue();
}
//System.out.println("Sum of inputs = " + sumOfInputs);
sumOfInputs += -1 * threshold;
//System.out.println("Sum of inputs - threshold = " + sumOfInputs);
output = 1/(1 + Math.pow(Math.E, -sumOfInputs));
//System.out.println("Raw output = " + output);
output = (output == 0.5) ? 0.5 : Math.round(output);
//System.out.println("Output = " + output + "\n----------------");
for (Connection c : outputs) {
if (c.isEnabled()) c.transferOutput(output);
}
}
public void addInput (Connection c) {
inputs.add(c);
}
public void addOutput (Connection c) {
outputs.add(c);
}
public double getOutput() {
return output;
}
public void setOutput(double output) {
this.output = output;
for (Connection c : outputs) {
c.transferOutput(output);
}
}
public double getThreshold() {
return threshold;
}
public void setThreshold(double threshold) {
this.threshold = threshold;
}
public Point getLocation() {
return location;
}
public void setLocation(Point location) {
this.location = location;
}
public ArrayList<Connection> getInputs() {
return inputs;
}
public void setInputs(ArrayList<Connection> inputs) {
this.inputs = inputs;
}
public ArrayList<Connection> getOutputs() {
return outputs;
}
public void setOutputs(ArrayList<Connection> outputs) {
this.outputs = outputs;
}
}
Connection類：
public class Connection extends Gene {
Neuron a;
Neuron b;
double weight = 1;
double value = 0;
public Connection (Neuron start, Neuron end, NeuralRegister nr) {
id = nr.registerConnection(this);
a = start;
b = end;
a.addOutput(this);
b.addInput(this);
System.out.println("New connection made between " + a.getId() + " and " + b.getId());
}
public void transferOutput (double output) {
value = output;
b.calculateOutput();
}
public void setWeight (double w) {
weight = w;
}
public double getWeight() {
return weight;
}
public double getValue() {
return value;
}
public void setValue(double value) {
this.value = value;
}
public Neuron getA() {
return a;
}
public void setA(Neuron a) {
this.a = a;
}
public Neuron getB() {
return b;
}
public void setB(Neuron b) {
this.b = b;
}
}
http://stackoverflow.com/questions/223918/iterating-through-a-list-avoiding-concurrentmodificationexception-when-removing – PbxMan
問題是文本的牆壁和代碼與壞標題牆。我試圖改進標題，使其更具體，試圖打破文字的牆壁，使其更容易閱讀。你需要提出更具體，更易於閱讀的問題。嘗試閱讀[this]（http://stackoverflow.com/help/how-to-ask）並查看優秀標題的例子並修復它。請刪除代碼牆並創建一個[MVCE]（http://stackoverflow.com/help/mcve），並只發布特定於您遇到的錯誤的代碼。 – Ram