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| package com.company.creature;
import com.company.neuronalnetwork.*;
import java.util.ArrayList;
public class C1 implements Comparable<C1>{
private double x,y;
public boolean isDead = false;
private int Strength, Speed;
private double dist;
private InputNeuron distX,distY;
private ArrayList<Neuron> InList = new ArrayList<>();
private ArrayList<Neuron> OutList = new ArrayList<>();
private OutputNeuron moveX = new OutputNeuron(),moveY = new OutputNeuron();
private ArrayList<MiddleNeuron> tabMid;
private ArrayList<Synapse> tabSyn = new ArrayList<Synapse>();
private Wall Wall1 = new Wall(10,10,20,11);
private Wall Wall2 = new Wall(10,10,11,20);
private Wall Wall3 = new Wall(19,10,20,20);
private ArrayList<Wall> Walls = new ArrayList<Wall>();
public C1 (int FoodX, int FoodY,ArrayList<Wall> walls){
Walls = walls;
x= 100; y= 100;InList.add(new InputNeuron(FoodX-x)); InList.add(new InputNeuron(FoodY-y));
for(int i = 0;i!=32;i++)InList.add(new InputNeuron(0));
this.see();
OutList.add(new OutputNeuron()); OutList.add(new OutputNeuron());
for (int z = 0;z!=InList.size();z++) {
for(int y = 0;y!= OutList.size();y++){
double T = Math.random()*100;
if ((int)T < 10)tabSyn.add(new Synapse(Math.random()*0.25-0.125,z,y));
}
}
dist = Math.sqrt((InList.get(0).getValue())*(InList.get(0).getValue())+(InList.get(1).getValue())*(InList.get(1).getValue()));
}
public void move(){
for (int z =0;z!=InList.size();z++) {
for (Synapse s: tabSyn) {
if (s.getInput()==z){
OutList.get(s.getOutput()).setValue(InList.get(z).getValue()*s.getValue());
}
}
}
InList.get(0).setValue(20-x); InList.get(1).setValue(20-y); dist = Math.sqrt((InList.get(0).getValue())*(InList.get(0).getValue())+(InList.get(1).getValue())*(InList.get(1).getValue()));
this.see();
this.x = x+OutList.get(0).getValue();this.y=y+OutList.get(1).getValue();
for (Wall w:Walls) {
if (this.x >= w.getX1() && this.x <= w.getX2() && this.y >= w.getY1() && this.y <= w.getY2()) {
this.isDead = true;
break;
}
}
}
public double getX(){
return x;
}
public double getY(){
return y;
}
public ArrayList<Synapse> getTabSyn(){
return tabSyn;
}
@Override
public int compareTo(C1 c1) {
return (int)(this.dist - c1.dist);
}
private void see(){
for(int z=0;z!=32;z++){
double X = 20*Math.sin(z*2*Math.PI/32);
double Y = 20*Math.cos(z*2*Math.PI/32);
boolean hasHit = false;
if(X>0&&Y>0){
for (Wall w:Walls) {
if (this.x<w.getX2()&&this.y<w.getY2()){
if((this.x+X>w.getX1()&&this.y+Y>w.getY1()&&this.y+Y<w.getY2())||(this.x+X>w.getX1()&&this.x+X<w.getX2()&&this.y+Y>w.getY1())){
InList.get(z+2).setValue(-1); hasHit = true;
}
}
}
}
if(X<0&&Y>0){
for (Wall w:Walls) {
if (this.x>w.getX1()&&this.y<w.getY2()){
if((this.x+X<w.getX2()&&this.y+Y>w.getY1()&&this.y+Y<w.getY2())||(this.x+X>w.getX1()&&this.x+X<w.getX2()&&this.y+Y>w.getY1())){
InList.get(z+2).setValue(-1); hasHit = true;
}
}
}
}
if(X>0&&Y<0){
for (Wall w:Walls) {
if (this.x<w.getX2()&&this.y>w.getY1()){
if((this.x+X>w.getX1()&&this.y+Y>w.getY1()&&this.y+Y<w.getY2())||(this.x+X>w.getX1()&&this.x+X<w.getX2()&&this.y+Y<w.getY2())){
InList.get(z+2).setValue(-1); hasHit = true; }
}
}
}
if(X<0&&Y<0){
for (Wall w:Walls) {
if (this.x>w.getX1()&&this.y>w.getY1()){
if((this.x+X<w.getX2()&&this.y+Y>w.getY1()&&this.y+Y<w.getY2())||(this.x+X>w.getX1()&&this.x+X<w.getX2()&&this.y+Y<w.getY2())){
InList.get(z+2).setValue(-1); hasHit = true; }
}
}
}
if(!hasHit)InList.get(z+2).setValue(1);
}
}
public void mutate(){
ArrayList<Synapse> buffer = tabSyn;
ArrayList<Neuron> IN = InList,OUT = OutList;
int a=0;
for (Neuron in:InList) {
int b=0;
for (Neuron out:OutList) {
for (Synapse s:buffer) {
if(s.getInput()!=a&&s.getOutput()!=b){
if(Math.random()>0.9){
tabSyn.add(new Synapse(Math.random()*0.25-0.125,a,b));
}else if(Math.random()>0.99){
this.tabMid.add(new MiddleNeuron());
OUT.add(tabMid.get(tabMid.size()-1));
IN.add(tabMid.get(tabMid.size()-1));
}
}else{
if (Math.random()>0.95){
s.setValue(s.getValue() + (Math.random()*0.25-0.125));
}
else if (Math.random()>0.99){
tabSyn.remove(s);
}
}
}
}
}
this.InList = IN;
this.OutList = OUT;
this.isDead = false;
}
} |
