can you write code for neuralnetwork for this code : 

Here is the code:

import java.util.Arrays;
import java.util.Random;

public class NeuralDot {

private static final int VISION_RANGE = 5;
private static final int GRID_SIZE = 100;
private static final int NUM_INPUTS = (2VISION_RANGE + 1) * (2VISION_RANGE + 1) + 2;
private static final int NUM_HIDDEN = 16;
private static final int NUM_OUTPUTS = 3;
private static final double LEARNING_RATE = 0.1;
private static final int NUM_EPISODES = 10000;

private int x;
private int y;
private boolean[][] grid;
private Random random;
private NeuralNetwork neuralNetwork;

public NeuralDot() {
x = GRID_SIZE / 2;
y = GRID_SIZE / 2;
grid = new boolean[GRID_SIZE][GRID_SIZE];
random = new Random();
neuralNetwork = new NeuralNetwork(NUM_INPUTS, NUM_HIDDEN, NUM_OUTPUTS, LEARNING_RATE);
}

public void run() {
for (int episode = 0; episode < NUM_EPISODES; episode++) {
// Reset the grid and place a food dot randomly
resetGrid();
placeFoodDot();

// Play the episode until the dot eats the food dot
while (!grid[x][y]) {
// Get the nearest food dot
int[] foodDot = getNearestFoodDot();

// Compute the inputs to the neural network
double[] inputs = new double[NUM_INPUTS];
int index = 0;
for (int i = x - VISION_RANGE; i <= x + VISION_RANGE; i++) {
for (int j = y - VISION_RANGE; j <= y + VISION_RANGE; j++) {
if (i < 0 || i >= GRID_SIZE || j < 0 || j >= GRID_SIZE) {
inputs[index++] = 0;
} else if (i == x && j == y) {
inputs[index++] = 1;
} else {
inputs[index++] = grid[i][j] ? -1 : 0;
}
}
}
inputs[index++] = foodDot[0] - x;
inputs[index++] = foodDot[1] - y;

// Compute the outputs of the neural network
double[] outputs = neuralNetwork.feedForward(inputs);

// Choose the action with the highest probability
double maxProbability = Double.NEGATIVE_INFINITY;
int action = 0;
for (int i = 0; i < NUM_OUTPUTS; i++) {
if (outputs[i] > maxProbability) {
maxProbability = outputs[i];
action = i;
}
}

// Take the chosen action
switch (action) {
case 0:
moveLeft();
break;
case 1:
moveRight();
break;
case 2:
moveForward();
break;
}

// Compute the reward signal
double reward;
if (grid[x][y]) {
reward = 10;
} else {
reward = -0.1;
}

// Train the neural network on the input-output pair
double[] targetOutputs = new double[NUM_OUTPUTS];
targetOutputs[action] = 1;
neuralNetwork.train(inputs, targetOutputs, reward);
}
}
}

private void resetGrid() {
for (int i = 0; i < GRID_SIZE; i++) {
Arrays.fill(grid[i], false);
}
}

private void placeFoodDot() {
int foodX = random.nextInt(GRID_SIZE);
int foodY = random.nextInt(GRID_SIZE);
grid[foodX][foodY] = true;
}

private int[] getNearestFoodDot() {
int nearestX = -1;
int nearestY = -1;
double nearestDistance = Double.POSITIVE_INFINITY;
for (int i = 0; i < GRID_SIZE; i++) {
for (int j = 0; j < GRID_SIZE; j++) {
if (grid[i][j]) {
double distance = Math.sqrt((i - x)(i - x) + (j - y)(j - y));
if (distance < nearestDistance) {
nearestX = i;
nearestY = j;
nearestDistance = distance;
}
}
}
}
return new int[] {nearestX, nearestY};
}

private void moveLeft() {
if (x > 0) {
x–;
}
}

private void moveRight() {
if (x < GRID_SIZE - 1) {
x++;
}
}

private void moveForward() {
if (y < GRID_SIZE - 1) {
y++;
}
}

public static void main(String[] args) {
NeuralDot dot = new NeuralDot();
dot.run();
}

}