Bingo Game, class interface errors? - class

Can anyone tell me why I am getting these errors? And if so, how do i fix them?
Bingo.java:176: ']' expected
private static void makeCard(int[][] card, int[picks])
^
Bingo.java:176: ')' expected
private static void makeCard(int[][] card, int[picks])
^
Bingo.java:176: illegal start of type
private static void makeCard(int[][] card, int[picks])
^
Bingo.java:176: <identifier> expected
private static void makeCard(int[][] card, int[picks])
^
Bingo.java:177: ';' expected
{
^
Bingo.java:178: illegal start of type
System.out.println("Current Number Picks: \n");
^
Bingo.java:178: ';' expected
System.out.println("Current Number Picks: \n");
^
Bingo.java:178: invalid method declaration; return type required
System.out.println("Current Number Picks: \n");
^
Bingo.java:178: illegal start of type
System.out.println("Current Number Picks: \n");
^
Bingo.java:181: illegal start of type
for (int i=0; i<count; i++){
^
Bingo.java:181: ')' expected
for (int i=0; i<count; i++){
^
Bingo.java:181: illegal start of type
for (int i=0; i<count; i++){
^
Bingo.java:181: <identifier> expected
for (int i=0; i<count; i++){
^
Bingo.java:181: ';' expected
for (int i=0; i<count; i++){
^
Bingo.java:181: > expected
for (int i=0; i<count; i++){
^
Bingo.java:181: '(' expected
for (int i=0; i<count; i++){
^
Bingo.java:189: class, interface, or enum expected
private static void announceWin(int winFound, int numPicks)
^
Bingo.java:192: class, interface, or enum expected
}
^
Bingo.java:196: class, interface, or enum expected
for (int i = 0; i < numPicks; i++){
^
Bingo.java:196: class, interface, or enum expected
for (int i = 0; i < numPicks; i++){
^
Bingo.java:198: class, interface, or enum expected
return true;}
^
Bingo.java:202: class, interface, or enum expected
}
^
22 errors
Here is the code:
import java.util.*;
import java.io.*;
import java.util.Arrays;
public class Bingo
{
public static final int ROWS = 5;
public static final int COLS = 5;
public static final int VERTICAL = 1;
public static final int DIAGONAL = 2;
public static final int HORIZONTAL = 3;
public static int winFound;
public static int currPick = 0;
public static int randomPick;
public static int WinFound;
public static void main(String[] args)
{
int Totcards;
int[][] card = new int[ROWS][COLS];
fillCard (card);
printCard(card);
playGame(card);
printCard(card);
}
private static void fillCard (int[][] card)
{
// FileReader fileIn = new FileReader("Bingo.in");
// Bufferreader in = new Bufferreader(fileIn);
try {
Scanner scan = new Scanner(new File("bingo.in"));
for (int i=0; i<card.length; i++){
for (int j=0; j<card[0].length; j++){
card[i][j] = scan.nextInt();
}
}
} catch(FileNotFoundException fnfe) {
System.out.println(fnfe.getMessage());
}
}
private static void printCard (int[][] card)
{
System.out.println("\n\tYOUR BINGO CARD : ");
System.out.println("\n\tB I N G O");
System.out.println("\t----------------------");
for (int i=0; i<card.length; i++){
for (int j=0; j<card[0].length; j++){
System.out.print("\t" + card[i][j]);
}
System.out.print("\n");
}
}
private static void playGame (int[][] card)
{
int numPicks = 0;
while (true)
{
markCard (card); // Generate a random num & zero-it out
winFound = checkForWin(card); // Look for zero sums
numPicks++;
if (winFound != 0)
{
announceWin (winFound, numPicks);
return;
}
}
}
private static void markCard (int[][] card)
{
int randomPick = (int) (Math.random() * 74) + 1;
for (int j = 0; j < ROWS; j++){
for (int k = 0; k < COLS; k++){
if (card[j][k]==randomPick)
card[j][k] = 0;}
System.out.print(" " + randomPick);
}
}
private static int checkForWin(int[][] card)
{
int sum=0;
for (int i = 0; i < ROWS; i++)
{
sum = 0;
for (int j = 0; j < COLS; j++)
sum += card[i][j];
if (sum == 0)
return HORIZONTAL;
}
for (int j = 0; j < COLS; j++)
{
sum = 0;
for (int i = 0; i < ROWS; i++)
sum += card[i][j];
if (sum == 0)
return VERTICAL;
}
sum = 0;
for (int i = 0; i < ROWS; i++)
sum += card[i][ROWS-i-1];
if (sum == 0)
return DIAGONAL;
sum = 0;
for (int i = 0; i < ROWS; i++)
sum += card[i][i];
if (sum == 0)
return DIAGONAL;
return WinFound;
}
private static void makeCard(int[][] card, int[picks])
{
System.out.println("Current Number Picks: \n");
int count = 100;
int currPick = 0;
for (int i=0; i<count; i++){
currPick = (int)(Math.random() * 74) + 1;
System.out.print(" " + currPick + "\n");
picks[i] = currPick;
System.out.print("i: " + i);
}
}
private static void announceWin(int winFound, int numPicks)
{
System.out.println("winFound: " + winFound + "numpicks: " + numPicks);
}
private static boolean duplicate (int currPick, int[picks], int numPicks)
{
for (int i = 0; i < numPicks; i++){
if (picks[i] == currPick){
return true;}
}
return false;
}
}

You're accepting an int array parameter incorrectly.
You have int [picks] where it should be int[] picks
import java.util.*;
import java.io.*;
import java.util.Arrays;
public class Bingo
{
public static final int ROWS = 5;
public static final int COLS = 5;
public static final int VERTICAL = 1;
public static final int DIAGONAL = 2;
public static final int HORIZONTAL = 3;
public static int winFound;
public static int currPick = 0;
public static int randomPick;
public static int WinFound;
public static void main(String[] args)
{
int Totcards;
int[][] card = new int[ROWS][COLS];
fillCard (card);
printCard(card);
playGame(card);
printCard(card);
}
private static void fillCard (int[][] card)
{
// FileReader fileIn = new FileReader("Bingo.in");
// Bufferreader in = new Bufferreader(fileIn);
try {
Scanner scan = new Scanner(new File("bingo.in"));
for (int i=0; i<card.length; i++){
for (int j=0; j<card[0].length; j++){
card[i][j] = scan.nextInt();
}
}
} catch(FileNotFoundException fnfe) {
System.out.println(fnfe.getMessage());
}
}
private static void printCard (int[][] card)
{
System.out.println("\n\tYOUR BINGO CARD : ");
System.out.println("\n\tB I N G O");
System.out.println("\t----------------------");
for (int i=0; i<card.length; i++){
for (int j=0; j<card[0].length; j++){
System.out.print("\t" + card[i][j]);
}
System.out.print("\n");
}
}
private static void playGame (int[][] card)
{
int numPicks = 0;
while (true)
{
markCard (card); // Generate a random num & zero-it out
winFound = checkForWin(card); // Look for zero sums
numPicks++;
if (winFound != 0)
{
announceWin (winFound, numPicks);
return;
}
}
}
private static void markCard (int[][] card)
{
int randomPick = (int) (Math.random() * 74) + 1;
for (int j = 0; j < ROWS; j++){
for (int k = 0; k < COLS; k++){
if (card[j][k]==randomPick)
card[j][k] = 0;}
System.out.print(" " + randomPick);
}
}
private static int checkForWin(int[][] card)
{
int sum=0;
for (int i = 0; i < ROWS; i++)
{
sum = 0;
for (int j = 0; j < COLS; j++)
sum += card[i][j];
if (sum == 0)
return HORIZONTAL;
}
for (int j = 0; j < COLS; j++)
{
sum = 0;
for (int i = 0; i < ROWS; i++)
sum += card[i][j];
if (sum == 0)
return VERTICAL;
}
sum = 0;
for (int i = 0; i < ROWS; i++)
sum += card[i][ROWS-i-1];
if (sum == 0)
return DIAGONAL;
sum = 0;
for (int i = 0; i < ROWS; i++)
sum += card[i][i];
if (sum == 0)
return DIAGONAL;
return WinFound;
}
private static void makeCard(int[][] card, int[] picks)
{
System.out.println("Current Number Picks: \n");
int count = 100;
int currPick = 0;
for (int i=0; i<count; i++){
currPick = (int)(Math.random() * 74) + 1;
System.out.print(" " + currPick + "\n");
picks[i] = currPick;
System.out.print("i: " + i);
}
}
private static void announceWin(int winFound, int numPicks)
{
System.out.println("winFound: " + winFound + "numpicks: " + numPicks);
}
private static boolean duplicate (int currPick, int[] picks, int numPicks)
{
for (int i = 0; i < numPicks; i++){
if (picks[i] == currPick){
return true;}
}
return false;
}
}
Compiles for me

Related

Sort an array A using Quick Sort. Using reccursion

#include<iostream>
using namespace std;
void quickSort(int input[], int start, int end)
{
// your code goes here
}
void quickSort(int input[], int size)
{
quickSort(input, 0, size - 1);
}
*/
void swap(int* a,int* b){
int temp=*a;
*a=*b;
*b=temp;
}
int count(int input[],int start,int end ){
static int c=0;
if(start==end)
return c;
if(input[start]>input[end])
c++;
return count(input,start,end-1);
}
int partionArray(int input[],int start,int end ){
int c=count(input,start,end);
int pi=c+start;
swap(&input[start],&input[pi]);
int i=start;
int j=end;
while(i<pi&&j>pi)
{
if(input[i]<input[pi])
{
i++;
}
else if(input[j]>=input[pi])
{
j--;
}
else
{
swap(&input[i],&input[j]);
i++;
j--;
}
}
return pi;
}
void qs(int input[],int start, int end){
if(start>=end)
return;
int pi=partionArray(input,start,end);
qs(input,start,pi-1);
qs(input,pi+1,end);
}
void quickSort(int input[], int size) {
qs(input,0,size-1);
}
int main(){
int n;
cin >> n;
int *input = new int[n];
for(int i = 0; i < n; i++) {
cin >> input[i];
}
quickSort(input, n);
for(int i = 0; i < n; i++) {
cout << input[i] << " ";
}
delete [] input;
}
Sort an array A using Quick Sort. Using reccursion is the question.
Input format :
Line 1 : Integer n i.e. Array size
Line 2 : Array elements (separated by space)
Output format :
Array elements in increasing order (separated by space)
Constraints :
1 <= n <= 10^3
What did i do wrong in this code pls can any one explain?Is every thing right with this code?

Convert DEC to IP dart

I get a int32 : -1407942911 that I need to convert to a ip
I used this function:
_setIp(int _ip){
var _strData = StringBuffer();
for (int i = 0; i<4; i++){
_strData.write(_ip &0xff );
if (i < 3) {
_strData.write(".");
}
_ip = _ip >> 8;
}
return _strData.toString();
}
but I get the ip backwards: 1.127.20.172 instead of 172.20.127.1
I was trying to replicate this java code
public String longToIp(long ip) {
StringBuilder result = new StringBuilder(15);
for (int i = 0; i < 4; i++) {
result.insert(0,Long.toString(ip & 0xff));
if (i < 3) {
sb.insert(0,'.');
}
ip = ip >> 8;
}
return result.toString();
}
Update
I solved the error
static setIp(int _ip){
var _strData = StringBuffer();
for (int i = 0; i<4; i++){
_strData.write(_ip >> 24 &0xff );
if (i < 3) {
_strData.write(".");
}
_ip = _ip << 8;
}
return _strData.toString();
}
You can do something like this:
import 'dart:io';
import 'dart:typed_data';
void main() {
print(getIpFromInt32Value(-1407942911)); // 172.20.127.1
}
String getIpFromInt32Value(int value) => InternetAddress.fromRawAddress(
(ByteData(4)..setInt32(0, value)).buffer.asUint8List())
.address;
This solved the error:
static setIp(int _ip){
var _strData = StringBuffer();
for (int i = 0; i<4; i++){
_strData.write(_ip >> 24 &0xff );
if (i < 3) {
_strData.write(".");
}
_ip = _ip << 8;
}
return _strData.toString();
}

Unity Destroy() doesn't delete script

Noob mistake I was only referencing the parents brain from the child's instead of copying it directly. It's a neural network that can grow in size (or shrink) through genetics. I was going to post it but it doesn't fit on this without me having to type a larger ratio of words to code oh look it works now
public class Animal1 : MonoBehaviour
{
public class Neuron1
{
public int[] children;
public float[] weights;
public float bias;
public float value;
public Neuron1()
{
children = new int[10];
for (int i = 0; i < 10; i++)
{
children[i] = -1;
}
weights = new float[10];
for (int i = 0; i < 10; i++)
{
weights[i] = 1;
}
bias = 0;
value = 0;
}
}
float sightRange = 2;
Vector2 left = new Vector2(-.4f,.6f);
Vector2 right = new Vector2(.4f, .6f);
RaycastHit2D hitLeft;
RaycastHit2D hitForward;
RaycastHit2D hitRight;
public LayerMask animalLayerMask;
[System.NonSerialized]
public int food = 0;
int foodCounter = 0;
int foodForReproduction = 3;
float noFoodCounter = 0;
int lifeWithoutFood = 20;
int maxBrainSize = 100;
[System.NonSerialized]
public Neuron1[] brain1 = new Neuron1[100]; //first three and last three are inputs and outputs and are never mutated
int childrenMaxAmount = 5;
float biasInitRange = 2;
float weightInitRange = 2;
int outputSize = 2;
int inputSize = 3;
GameObject manager;
[System.NonSerialized]
public bool firstGen = false;
[System.NonSerialized]
public GameObject parent1;
[System.NonSerialized]
public bool directCopy = false;
float maxTravelRange = 5;
Color color;
float colorMutationRate = .1f;
public GameObject animal1Prefab1;
bool debugBool = false;
void Start()
{
manager = GameObject.Find("Manager Object");
if (firstGen == true)
{
InitializeBrain(3, 2, 20);
firstGen = false;
transform.GetComponent<SpriteRenderer>().color = new Color(Random.Range(0f, 1f), Random.Range(0f, 1f), Random.Range(0f, 1f), 1);
}
else
{
if (parent1.GetComponent<Animal1>().brain1[0].bias == 1234)
{
Debug.Log("parent passing brain after deletion");
}
Mutate(parent1, 5, 1f, .5f);
transform.GetComponent<SpriteRenderer>().color = color;
}
}
void Update()
{
if (brain1[0].bias == 1234)
{
GetComponent<SpriteRenderer>().color = Color.black;
}
//teleportation
if (transform.position.x > maxTravelRange)
{
Vector2 newPosition = new Vector2(-maxTravelRange, transform.position.y);
transform.position = newPosition;
}
if (transform.position.x < -maxTravelRange)
{
Vector2 newPosition = new Vector2(maxTravelRange, transform.position.y);
transform.position = newPosition;
}
if (transform.position.y > maxTravelRange)
{
Vector2 newPosition = new Vector2(transform.position.x, -maxTravelRange);
transform.position = newPosition;
}
if (transform.position.y < -maxTravelRange)
{
Vector2 newPosition = new Vector2(transform.position.x, maxTravelRange);
transform.position = newPosition;
}
//input
hitLeft = Physics2D.Raycast(transform.position, transform.TransformDirection(left), sightRange, ~animalLayerMask); //left
hitForward = Physics2D.Raycast(transform.position, transform.TransformDirection(Vector2.up), sightRange, ~animalLayerMask); //forward
hitRight = Physics2D.Raycast(transform.position, transform.TransformDirection(right), sightRange, ~animalLayerMask); //right
if (hitLeft)
{
brain1[0].value = 1;
}
if (hitForward)
{
brain1[1].value = 1;
}
if (hitRight)
{
brain1[2].value = 1;
}
//output
Think();
// one output for speed, another for turning
int rotDir = 1;
if (brain1[maxBrainSize - 1].value < 0)
{
rotDir = -1;
}
Vector3 eulerRotation = new Vector3(0, 0, (45 * rotDir + (transform.rotation.eulerAngles.z % 360)) % 360);
transform.rotation = Quaternion.RotateTowards(transform.rotation, Quaternion.Euler(eulerRotation), Mathf.Abs(brain1[maxBrainSize - 1].value) * 25 * Time.deltaTime);
if (brain1[maxBrainSize - 2].value > 0) // so they can't go backwards
{
transform.Translate(Vector2.up * (brain1[maxBrainSize - 2].value * .1f) * Time.deltaTime);
}
//reset outputs
brain1[maxBrainSize - 2].value = 0;
brain1[maxBrainSize - 1].value = 0;
// reset inputs
brain1[0].value = 0;
brain1[1].value = 0;
brain1[2].value = 0;
noFoodCounter += Time.deltaTime;
if (food > 0)
{
foodCounter++;
food = 0;
noFoodCounter = 0;
}
if (foodCounter > foodForReproduction)
{
foodCounter = 0;
Reproduce();
}
if (noFoodCounter > lifeWithoutFood/2)
{
foodCounter = 0;
}
if (noFoodCounter > lifeWithoutFood)
{
Destroy(gameObject);
}
}
void InitializeBrain(int inputs, int outputs, int sizeExcludingInputsAndOutputs)
{
for (int i = 0; i < sizeExcludingInputsAndOutputs + inputs; i++)
{
brain1[i] = new Neuron1();
}
for (int i = maxBrainSize - outputs; i < maxBrainSize; i++) // outputs
{
brain1[i] = new Neuron1();
}
// all neurons in between the inputs and outputs
for (int i = inputs; i < sizeExcludingInputsAndOutputs + inputs; i++)
{
brain1[i].bias = Random.Range(-biasInitRange, biasInitRange);
for (short j = 0; j < childrenMaxAmount -1; j++) // -1 so there is always at least one free space for a child
{
int rnd1 = Random.Range(inputs, sizeExcludingInputsAndOutputs + inputs);
bool alreadyContains = true;
int errorCounter = 0;
while (alreadyContains) // making sure none are children of themselves, and that it doesn't already have this child
{
alreadyContains = false;
for (short h = 0; h < childrenMaxAmount; h++)
{
if (brain1[i].children[h] == rnd1) // if it already has this child
{
alreadyContains = true;
rnd1 = Random.Range(inputs, sizeExcludingInputsAndOutputs + inputs);
while (rnd1 == i) // and its not itself
{
rnd1 = Random.Range(inputs, sizeExcludingInputsAndOutputs + inputs);
}
break;
}
}
errorCounter++;
if (errorCounter > 100) // so we don't get stuck in small network sizes
{
goto outside2;
}
}
brain1[i].children[j] = rnd1;
brain1[i].weights[j] = Random.Range(-weightInitRange, weightInitRange);
if (Random.Range(1, 10) > 8)
{
break; // so not every neuron has the same amount of children
}
}
outside2:
{
}
}
// assign children of inputs
for (int i = 0; i < inputs; i++)
{
for (int j = 0; j < childrenMaxAmount; j++) // assigning all max children (sometimes) to inputs, their amounts of children will never change
{
int rnd1 = Random.Range(inputs, sizeExcludingInputsAndOutputs + inputs);
bool alreadyContains = true;
int errorCounter = 0;
while (alreadyContains)
{
alreadyContains = false;
for (short h = 0; h < childrenMaxAmount; h++)
{
if (brain1[i].children[h] == rnd1)
{
alreadyContains = true;
rnd1 = Random.Range(inputs, sizeExcludingInputsAndOutputs + inputs);
}
}
errorCounter++;
if (errorCounter > 1000) // so we don't get stuck in small network sizes
{
goto outside1;
}
}
brain1[i].children[j] = rnd1;
//brain1[i].weights[j] = 100; // input weight
}
outside1:
{
}
}
RemoveDeadNeurons();
// assign parents of outputs
int brainEnd = maxBrainSize - outputs;
for (int i = brainEnd; i < maxBrainSize; i++)
{
for (short j = 0; j < childrenMaxAmount; j++) // same amount of parents as inputs have children
{
int rnd1 = Random.Range(inputs, brainEnd);
while (brain1[rnd1] == null)
{
rnd1 = Random.Range(inputs, brainEnd);
}
bool alreadyContains = true;
int errorCounter = 0;
while (alreadyContains) //making sure we dont already have this parent
{
alreadyContains = false;
for (short h = 0; h < childrenMaxAmount; h++)
{
if (brain1[rnd1].children[h] == i)
{
alreadyContains = true;
rnd1 = Random.Range(inputs, brainEnd);
while (brain1[rnd1] == null)
{
rnd1 = Random.Range(inputs, brainEnd);
}
}
}
errorCounter++;
if (errorCounter > 1000) // so we don't get stuck
{
goto outside3;
}
}
for (int h = 0; h < childrenMaxAmount; h++) //adding to next available child space of the parent
{
if (brain1[rnd1].children[h] == -1)
{
brain1[rnd1].children[h] = i;
brain1[rnd1].weights[h] = Random.Range(-weightInitRange, weightInitRange);
break;
}
}
}
outside3:
{
}
}
}
void Think()
{
int[] activatedChildrenIndexes = new int[500];
int[] workingArray = new int[500];
int workingArrayCounter = 0;
short layerCounter = 0;
for (int i = 0; i < 500; i++)
{
activatedChildrenIndexes[i] = -1;
}
//initializing inputs as first activated children layer to be calculated
activatedChildrenIndexes[0] = 0;
activatedChildrenIndexes[1] = 1;
activatedChildrenIndexes[2] = 2;
int thoughtDepth = 6; //keep this as low as possible, will change to make it a percent of the amount of neurons that already exist
for (int x = 0; x < thoughtDepth; x++)
{
for (int i = 0; i < 500; i++) // cycle through activated children and add their activated children to a list
{
int currentNodeIndex = activatedChildrenIndexes[i];
if (currentNodeIndex == -1)
{
layerCounter++;
break;
}
//activation function
if (brain1[currentNodeIndex].value <= 0)
{
brain1[currentNodeIndex].value = 0;
}
else
{
brain1[currentNodeIndex].value = 1;
}
for (int j = 0; j < childrenMaxAmount; j++)
{
if (brain1[currentNodeIndex].children[j] == -1)
{
break;
}
brain1[brain1[currentNodeIndex].children[j]].value
+= (brain1[currentNodeIndex].value * brain1[currentNodeIndex].weights[j]) + brain1[currentNodeIndex].bias;
bool activatedChildIsInNextLayer = false;
for (int h = 0; h < 500; h++)
{
if (brain1[currentNodeIndex].children[j] == activatedChildrenIndexes[h])
{
activatedChildIsInNextLayer = true;
}
}
if (brain1[brain1[currentNodeIndex].children[j]].value > 0
|| layerCounter == 0
|| brain1[currentNodeIndex].children[j] > maxBrainSize - outputSize)
{
if (!activatedChildIsInNextLayer)
{
workingArray[workingArrayCounter] = brain1[currentNodeIndex].children[j];
if (workingArrayCounter < 499)//to prevent out of bounds indexing
{
workingArrayCounter++;
}
}
}
if (brain1[brain1[currentNodeIndex].children[j]].value < 0 && brain1[currentNodeIndex].children[j] < maxBrainSize - outputSize)
{
brain1[brain1[currentNodeIndex].children[j]].value = 0; //resetting negative nodes that didn't get added to next layer
}
}
brain1[currentNodeIndex].value = 0; // resetting value after passing to children
}
for (int i = workingArrayCounter; i < 500; i++)
{
workingArray[i] = -1;
}
workingArrayCounter = 0;
for (int i = 0; i < 500; i++)
{
activatedChildrenIndexes[i] = workingArray[i];
}
}
}
void Mutate(GameObject parent, float mutationRate, float weightsMutate, float biasMutate)
{
color = parent.GetComponent<SpriteRenderer>().color;
for (int i = 0; i < maxBrainSize; i++) // initialized to its parent's brain
{
if (parent.GetComponent<Animal1>().brain1[i] != null)
{
brain1[i] = new Neuron1();
brain1[i].bias = parent.GetComponent<Animal1>().brain1[i].bias;
for (int j = 0; j < childrenMaxAmount; j++)
{
brain1[i].children[j] = parent.GetComponent<Animal1>().brain1[i].children[j];
brain1[i].weights[j] = parent.GetComponent<Animal1>().brain1[i].weights[j];
}
}
}
for (int i = inputSize; i < maxBrainSize - outputSize; i++)
{
if (brain1[i] != null)
{
if (Random.Range(0, 100) < mutationRate) // it will be mutated
{
switch (Random.Range(1, 5))
{
case 1: //change one of its weights
int weightCounter = 0;
for (int k = 0; k < childrenMaxAmount; k++) // counting its weights
{
if (brain1[i].children[k] != -1)
{
weightCounter++;
}
else
{
break;
}
}
brain1[i].weights[Random.Range(0, weightCounter)] += Random.Range(-weightsMutate, weightsMutate);
color.r += Random.Range(-colorMutationRate, colorMutationRate);
color.g += Random.Range(-colorMutationRate, colorMutationRate);
color.b += Random.Range(-colorMutationRate, colorMutationRate);
break;
case 2: //change its bias
brain1[i].bias += Random.Range(-biasMutate, biasMutate);
color.r += Random.Range(-colorMutationRate, colorMutationRate);
color.g += Random.Range(-colorMutationRate, colorMutationRate);
color.b += Random.Range(-colorMutationRate, colorMutationRate);
break;
case 3: //break a connection/ remove neuron
int childrenCounter = 0;
for (int k = 0; k < childrenMaxAmount; k++) // counting its children
{
if (brain1[i].children[k] != -1)
{
childrenCounter++;
}
else
{
break;
}
}
int rndChild = Random.Range(0, childrenCounter);
for (int s = rndChild; s < childrenMaxAmount - 1; s++) // shift children list down starting from rndChild and to the second to last element
{
brain1[i].children[s] = brain1[i].children[s + 1];
brain1[i].weights[s] = brain1[i].weights[s + 1];
}
brain1[i].children[childrenMaxAmount - 1] = -1; // removing the last child after shifting the list down one
brain1[i].weights[childrenMaxAmount - 1] = 0;
RemoveDeadNeurons();
color.r += Random.Range(-colorMutationRate, colorMutationRate);
color.g += Random.Range(-colorMutationRate, colorMutationRate);
color.b += Random.Range(-colorMutationRate, colorMutationRate);
break;
case 4: //add a new child if possible
int childrenCounter2 = 0;
for (int k = 0; k < childrenMaxAmount; k++) // counting its children
{
if (brain1[i].children[k] != -1)
{
childrenCounter2++;
}
else
{
break;
}
}
if (childrenCounter2 < childrenMaxAmount) // if we have room for another child
{
int newChild = Random.Range(inputSize, maxBrainSize);
while (brain1[newChild] == null)
{
newChild = Random.Range(inputSize, maxBrainSize);
}
brain1[i].children[childrenCounter2] = newChild;
brain1[i].weights[childrenCounter2] = Random.Range(-weightInitRange, weightInitRange);
color.r += Random.Range(-colorMutationRate, colorMutationRate);
color.g += Random.Range(-colorMutationRate, colorMutationRate);
color.b += Random.Range(-colorMutationRate, colorMutationRate);
}
break;
}
}
}
}
if (Random.Range(0, 100) < 10) // create a new neuron if space is available for one
{
for (int i = inputSize; i < maxBrainSize - outputSize; i++)
{
if (brain1[i] == null) // the first available neuron
{
brain1[i] = new Neuron1();
int rndParent = Random.Range(0, maxBrainSize - outputSize);
int childrenCounter3 = 0;
if (brain1[rndParent] != null)
{
for (int k = 0; k < childrenMaxAmount; k++) // counting its children
{
if (brain1[rndParent].children[k] != -1)
{
childrenCounter3++;
}
else
{
break;
}
}
}
while (brain1[rndParent] == null || childrenCounter3 == childrenMaxAmount) // making sure it is both an existing neuron and that it has room for another child
{
childrenCounter3 = 0;
rndParent = Random.Range(0, maxBrainSize - outputSize);
if (brain1[rndParent] != null)
{
for (int k = 0; k < childrenMaxAmount; k++) // counting its children
{
if (brain1[rndParent].children[k] != -1)
{
childrenCounter3++;
}
else
{
break;
}
}
}
}
brain1[rndParent].children[childrenCounter3] = i; //giving the neuron a parent
brain1[rndParent].weights[childrenCounter3] = Random.Range(-weightInitRange, weightInitRange);
int rndChild = Random.Range(inputSize, maxBrainSize);
while(brain1[rndChild] == null)
{
rndChild = Random.Range(inputSize, maxBrainSize);
}
brain1[i].children[0] = rndChild; // giving the new neuron a child, a weight, and its bias
brain1[i].weights[0] = Random.Range(-weightInitRange, weightInitRange);
brain1[i].bias = Random.Range(-biasInitRange, biasInitRange);
color.r += Random.Range(-colorMutationRate, colorMutationRate);
color.g += Random.Range(-colorMutationRate, colorMutationRate);
color.b += Random.Range(-colorMutationRate, colorMutationRate);
break;
}
}
}
}
void RemoveDeadNeurons()
{
bool allGood = false;
while (!allGood)
{
for (int i = inputSize; i < maxBrainSize - outputSize; i++) // for every hidden layer neuron
{
if (brain1[i] != null) // if it's a neuron
{
if (brain1[i].children[0] == -1) // if it has no children
{
brain1[i] = null; // deleting neuron
for (int h = 0; h < maxBrainSize - outputSize; h++) //remove from other neurons children list, including from the input layer
{
if (brain1[h] != null)
{
for (int g = 0; g < childrenMaxAmount; g++)
{
if (brain1[h].children[g] == i) // if brain[h] has the removed neuron as a child
{
for (int s = g; s < childrenMaxAmount - 1; s++) // shift children list down
{
brain1[h].children[s] = brain1[h].children[s + 1];
brain1[h].weights[s] = brain1[h].weights[s + 1];
}
brain1[h].children[childrenMaxAmount - 1] = -1; // removing the last child in the list after shifting it down one
brain1[h].weights[childrenMaxAmount - 1] = 0;
}
}
}
}
goto outsideRDNLoop; // break out of for loop and recheck the brain
}
bool parentless = true;
for (int h = 0; h < maxBrainSize - outputSize; h++) // check if it is parentless
{
if (brain1[h] != null)
{
for (int g = 0; g < childrenMaxAmount; g++)
{
if (brain1[h].children[g] == i) // if it is a child of brain[h]
{
parentless = false;
}
}
}
}
if (parentless)
{
brain1[i] = null; //deleting neuron
goto outsideRDNLoop;
}
}
if (i == (maxBrainSize - outputSize) - 1) // if we reached the end of the for loop and this line, we know the brain was allGood
{
allGood = true;
}
}
outsideRDNLoop:
{
}
}
}
void Reproduce()
{
GameObject offspring = Instantiate(animal1Prefab1, new Vector2(transform.position.x + 1, transform.position.y + 1), Quaternion.identity);
offspring.GetComponent<Animal1>().parent1 = gameObject;
offspring.name = "animal1";
}
void OnDrawGizmosSelected()
{
Gizmos.color = Color.red;
Gizmos.DrawRay(transform.position, transform.TransformDirection(left) * sightRange);
Gizmos.DrawRay(transform.position, transform.TransformDirection(Vector2.up) * sightRange);
Gizmos.DrawRay(transform.position, transform.TransformDirection(right) * sightRange);
}
}
I've not run your code, just looked at it, but this looks suspicious if you are having problems with the contents of brain1 being overwritten elsewhere:
void Mutate(GameObject parent, float mutationRate, float weightsMutate, float biasMutate)
{
color = parent.GetComponent<SpriteRenderer>().color;
// This line here is suspicious:
brain1 = parent.GetComponent<Animal1>().brain1; // initialized to its parent's brain
What you are doing here is replacing the reference to the child's brain with the parent's - so if you are starting with just one parent initially, every single entity will be sharing the same brain.
I think perhaps you meant to deep copy the contents from parent's brain into the child's brain? What your assignment here is doing is just taking the reference to the parent's brain and using it as the child brain as well.
Example Deep copy:
Updated Neuron1 class:
public class Neuron1
{
public int[] children;
public float[] weights;
public float bias;
public float value;
public Neuron1()
{
children = new int[10];
for (int i = 0; i < 10; i++)
{
children[i] = -1; // to stop looping when reaching -1
}
weights = new float[10];
for (int i = 0; i < 10; i++)
{
weights[i] = 1;
}
bias = 0;
value = 0;
}
// Added clone function:
public Neuron1 Clone()
{
Neuron1 clone = new Neuron1();
for (int i = 0; i < clone.children.Length; i++)
{
clone.children[i] = this.children[i];
}
for (int i = 0; i < clone.weights.Length; i++)
{
clone.weights[i] = this.weights[i];
}
clone.bias = this.bias;
clone.value = this.value;
return clone;
}
}
Updated start of Mutate function.
void Mutate(GameObject parent, float mutationRate, float weightsMutate, float biasMutate)
{
color = parent.GetComponent<SpriteRenderer>().color;
Neuron1[] parentBrain = parent.GetComponent<Animal1>().brain1; // initialized to its parent's brain
for (int i = 0; i < brain1.Length; ++i)
{
brain1[i] = parentBrain[i].Clone();
}
Well, I have a demo scene here. In it I created an empty game object. It has a script attached to it (CSharp.cs), like this:
In my Update method I wrote
And as soon as I start the game, the game object including the script is deleted.
But if I replace transform.gameObject wit just this then only the script disappears and the game object remains.
DestoryImmediate() is not recommended to be used, instead use Destory()
Destroy() will set the object to null at the end of the frame and whereas DestroyImmediate() function immediately set object reference to null.
DestoryImmediate can also delete prefabs/scenes/art in your project outside of playmode which is another reason you should not use it.

XOR Neural Network not converging

I'm having a problem with getting my XOR neural network to converge. It has two inputs, 2 nodes in the hidden layer, and one output node. I think it has something to do with my back propagation algorithm but I have tried to figure out where in it the problem occurs but I can't. I have also looked extensively over all the algorithms and they appear to be all correct.
import java.io.File;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.PrintWriter;
import java.util.ArrayList;
import java.util.Random;
public class NeuralNetwork {
public static class Perceptron {
public ArrayList<Perceptron> inputs;
public ArrayList<Double> inputWeight;
public double output;
public double error;
private double bias = 1;
private double biasWeight;
public boolean activationOn = false;
//sets up non input layers
public Perceptron(ArrayList<Perceptron> in) {
inputWeight = new ArrayList<Double>(in.size());
inputs = in;
initWeight(in.size());
}
//basic constructor
public Perceptron() { }
//generate random weights
private void initWeight(int size) {
Random generator = new Random();
for(int i=0; i<size; i++)
inputWeight.add(i, ((generator.nextDouble())));
biasWeight = (generator.nextDouble());
}
//calculate output based on current outputs of last layer
public double calculateOutput() {
double num = 0;
num = bias*biasWeight;
for(int i=0; i<inputs.size(); i++)
num += inputs.get(i).output * inputWeight.get(i);
output = num;
if(activationOn)
output = sigmoid(output);
else
output = threshold(output);
return output;
}
//methods used for learning
//calculate output error
public double calcOutputError(double expected){
error = output * (1 - output) * (expected - output);
return error;
}
//calculate node blame
public void blame(double outError, double outWeight) {
error = output * (1 - output) * outWeight * outError;
}
//adjust weights
public void adjustWeight() {
double alpha = .5;
double newWeight = 0;
for(int i=0; i<inputs.size(); i++) {
newWeight = inputWeight.get(i) + alpha * inputs.get(i).output * error;
inputWeight.set(i, newWeight);
}
//adjust bias weight
newWeight = biasWeight + alpha * bias * error;
biasWeight = newWeight;
//System.out.println("Weight " + biasWeight);
}
//returns the sigmoid of x
private double sigmoid(double x) {
return (1 / ( 1 + Math.pow(Math.E, -x)));
}
//returns threshold of x
private double threshold(double x) {
if(x>=0.5)
return 1;
else
return 0;
}
}
//teaches a neural network XOR
public static void teachXOR(ArrayList<Perceptron> inputs, ArrayList<Perceptron> hidden, Perceptron output) {
int examples[][] = { {0,0,0},
{1,1,0},
{0,1,1},
{1,0,1} };
boolean examplesFix[] = {false, false, false, false};
int layerSize = 2;
boolean learned = false;
boolean fixed;
int limit = 50000;
while(!learned && limit > 0) {
learned = true;
limit--;
//turn on using activation function
for(int i=0; i<2; i++)
hidden.get(i).activationOn = true;
output.activationOn = true;
for(int i=0; i<4; i++) {
examplesFix[i] = false;
//set up inputs
for(int j=0; j<layerSize; j++)
inputs.get(j).output = examples[i][j];
//calculate outputs for hidden layer
for(int j=0; j<layerSize; j++)
hidden.get(j).calculateOutput();
//calculate final output
double outValue = output.calculateOutput();
System.out.println("Check output " + examples[i][0] + "," + examples[i][1] + " = " + outValue);
if(((outValue < .5 && examples[i][2] == 1) || (outValue > .5 && examples[i][2] == 0))) {
learned = false;
examplesFix[i] = true;
}
}
//turn on using activation function
for(int i=0; i<2; i++)
hidden.get(i).activationOn = true;
output.activationOn = true;
//teach the nodes that are incorrect
if(!learned && limit >= 0) {
for(int i=0; i<4; i++) {
if(examplesFix[i]) {
fixed = false;
while(!fixed) {
//System.out.println("Adjusting weight: " + examples[i][0] + "," + examples[i][1] + " --> " + examples[i][2]);
for(int j=0; j<layerSize; j++)
inputs.get(j).output = examples[i][j];
//calculate outputs for hidden layer
for(int j=0; j<layerSize; j++)
hidden.get(j).calculateOutput();
//calculate final output
double outValue = output.calculateOutput();
if((outValue >= .5 && examples[i][2] == 1) || (outValue < .5 && examples[i][2] == 0)) {
fixed = true;
}
else {
double outError = output.calcOutputError(examples[i][2]);
//blame the hidden layer nodes
for(int j=0; j<layerSize; j++)
hidden.get(j).blame(outError, output.inputWeight.get(j));
//adjust weights
for(int j=0; j<layerSize; j++)
hidden.get(j).adjustWeight();
output.adjustWeight();
}
}
}
}
}
}
//if(limit <= 0)
// System.out.println("Did not converge");//, error: " + output.error);
//System.out.println("Done");
}
//runs tests for XOR, not complete
public static void runXOR(ArrayList<Perceptron> inputs, ArrayList<Perceptron> hidden, Perceptron output) throws IOException {
//create new file
PrintWriter writer;
File file = new File("Test.csv");
if(file.exists())
file.delete();
file.createNewFile();
writer = new PrintWriter(file);
ArrayList<String> positive = new ArrayList<String>();
ArrayList<String> negative = new ArrayList<String>();
//turn off using activation function
for(int i=0; i<2; i++)
hidden.get(i).activationOn = false;
output.activationOn = false;
//tests 10,000 points
for(int i=0; i<=100; i++) {
for(int j=0; j<=100; j++) {
inputs.get(0).output = (double)i/100;
inputs.get(1).output = (double)j/100;
//calculate outputs for hidden layer
for(int k=0; k<2; k++)
hidden.get(k).calculateOutput();
//calculate final output
double outValue = output.calculateOutput();
//keep track of positive and negative results
if(outValue >= .5) {
positive.add((double)i/100 + "," + (double)j/100 + "," + outValue);
//writer.println((double)i/100 + "," + (double)j/100 + ",1");
}
else if(outValue < .5) {
negative.add((double)i/100 + "," + (double)j/100 + "," + outValue);
//writer.println((double)i/100 + "," + (double)j/100 + ",0");
}
}
}
//write out to file
writer.println("X,Y,Positive,X,Y,Negative");
int i = 0;
while(i<positive.size() && i<negative.size()) {
writer.println(positive.get(i) + "," + negative.get(i));
i++;
}
while(i<positive.size()) {
writer.println(positive.get(i));
i++;
}
while(i<negative.size()) {
writer.println(",,," + negative.get(i));
i++;
}
writer.close();
}
//used for testing
public static void main(String[] args) throws IOException {
int layerSize = 2;
ArrayList<Perceptron> inputLayer;
ArrayList<Perceptron> hiddenLayer;
Perceptron outputLayer;
//XOR neural network
inputLayer = new ArrayList<Perceptron>(layerSize);
hiddenLayer = new ArrayList<Perceptron>(layerSize);
//for(Perceptron per : inputLayer)
// per = new Perceptron();
for(int i=0; i<layerSize; i++)
inputLayer.add(new Perceptron());
for(int i=0; i<layerSize; i++)
hiddenLayer.add(new Perceptron(inputLayer));
outputLayer = new Perceptron(hiddenLayer);
teachXOR(inputLayer, hiddenLayer, outputLayer);
runXOR(inputLayer, hiddenLayer, outputLayer);
}
}
First, your code has very peculiar structure and will be hard to debug. I would consider writing it from scratch, with more clear structure, less internal fields, and more actual functions returning values.
One major error (possibly not the only one) is your distinction between output and learnOutput in hidden layer. When you calculate activation of the output layer you actually use "output" field, while you should use learnOutput (which is the only one actually using sigmoid activation).
Furthermore - if you correctly restructure your code you could create unit test for numerical gradient testing, and this is what you should always do when working with neural networks/other gradient trained machines. In this case it would show you that your gradient is incorrect.

Creating a Linked list with Structs - C++

I was writing a program which could read an input file and store the read data in nodes linked by a "link list". However, I was getting a few errors:
In constructor List::List(), no match for 'operator =' in *((List*)this)->List::list[0] = 0
In constructor Polynomial::Polynomial(): no match for 'operator =' in *((Polynomial*)this)->Polynomial::poly = (operator new(400u), (<statement>), ...)
I have a feeling where I do: I try to access a certain node through an array is where I go wrong, however, I can't figure it out much.
Here is the code:
#include <iostream>
#include <fstream>
using namespace std;
enum result{success, failure};
struct Node
{
double coefficient;
int power;
Node();
Node(double coef, int pwr);
};
struct List
{
Node *list[100];
//Default constructor
List();
};
Node::Node()
{
coefficient = 0;
power = 0;
}
List::List()
{
*list[0] = NULL;
}
Node::Node(double coef, int pwr)
{
coefficient = coef;
power = pwr;
}
class Polynomial
{
public:
Polynomial();
result multiply(Polynomial &p, Polynomial &q);
result add(Polynomial p, Polynomial &q);
void initialize(ifstream &file);
void simplify(Polynomial &var);
void print_poly();
~Polynomial();
private:
List *poly; //Store the pointer links in an array
Node first_node;
int val;
};
Polynomial::Polynomial()
{
*poly = new List();
}
Polynomial::void initialize(ifstream &file)
{
int y[20];
double x[20];
int i = 0, j = 0;
//Read from the file
file >> x[j];
file >> y[j];
first_node(x[j], y[j++]); //Create the first node with coef, and pwr
*poly->list[i] = &first_node; //Link to the fist node
//Creat a linked list
while(y[j] != 0)
{
file >> x[j];
file >> y[j];
*poly->list[++i] = new Node(x[j], y[j++]);
}
val = i+1; //Keeps track of the number of nodes
}
Polynomail::result multiply(Polynomial &p, Polynomial &q)
{
int i, j, k = 0;
for(i = 0; i < p.val; i++)
{
for(j = 0; j < q.val; j++)
{
*poly->list[k] = new Node(0, 0);
*poly->list[k].coefficient = (p.poly->list[i].coefficient)*(q.poly->list[j].coefficient);
*poly->list[k++].power = (p.poly->list[i].power)+(q.poly->list[j].power);
}
}
val = k+1; //Store the nunber of nodes
return success;
}
Polynomial::void simplify(Polynomial &var)
{
int i, j, k = 0;
//Create a copy of the polynomial
for(j = 0; j < var.val; j++)
{
*poly->list[j] = new Node(0, 0);
*poly->list[j].coefficient = var.poly->list[j].coefficient;
*poly->list[j].power = var.poly->list[j].power;
}
//Iterate through the nodes to find entries which have the same power and add them, otherwise do nothing
for(k = 0; k < var.val; k++)
{
for(i = k; i < var.val;)
{
if(*poly->list[k].power == var.poly->list[++i].power)
{
if(*poly->list.power[0] == 0)
{
NULL;
}
else
{
*poly->list[k].coefficient = *poly->list[k].coefficient + var.poly->list[i].ceofficient;
var.poly->list[i] = Node(0, 0);
}
}
}
}
}
Polynomial::void print_pol()
{
int i = 0;
for(i = 0; i < temp.val; i++)
{
cout << "Coefficient: " << temp.poly->list[i].coefficient << ", and " << "Power: " << temp.poly->list[i].power << endl;
}
}
The problem is a wrong dereference. Line 34 should probably be
list[0] = NULL; // remove the *
You try to assign the value NULL to a variable of the type Node, but you probably mean a pointer to Node.
The very same is true in line 63.
In addition, line 66 sould probably b:
void Polynomial::initialize(ifstream &file) // start with return type