can u learn this code : import java.awt.Color;
import java.awt.Graphics;
import javax.swing.JFrame;
import javax.swing.JPanel;

public class NeuralDotGUI extends JPanel {

private NeuralDot neuralDot;
private int cellWidth;
private int cellHeight;

public NeuralDotGUI(NeuralDot neuralDot, int cellWidth, int cellHeight) {
this.neuralDot = neuralDot;
this.cellWidth = cellWidth;
this.cellHeight = cellHeight;
}

public void paintComponent(Graphics g) {
super.paintComponent(g);

// Draw grid cells
for (int i = 0; i < NeuralDot.GRID_SIZE; i++) {
for (int j = 0; j < NeuralDot.GRID_SIZE; j++) {
int x = i * cellWidth;
int y = j * cellHeight;
if (neuralDot.grid[i][j]) {
g.setColor(Color.GREEN);
} else {
g.setColor(Color.WHITE);
}
g.fillRect(x, y, cellWidth, cellHeight);
}
}

// Draw dot
g.setColor(Color.RED);
g.fillOval(neuralDot.x * cellWidth, neuralDot.y * cellHeight, cellWidth, cellHeight);
}

public static void main(String[] args) throws InterruptedException {
NeuralDot dot = new NeuralDot();
NeuralDotGUI gui = new NeuralDotGUI(dot, 10, 10);

JFrame frame = new JFrame();
frame.setTitle("Neural Dot");
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
frame.add(gui);
frame.pack();
frame.setLocationRelativeTo(null);
frame.setVisible(true);

while (true) {
gui.repaint();
dot.run();
Thread.sleep(250);
}
}
}


next file : import java.util.Random;
public class NeuralNetwork {

    private int numInputs;
    private int numHidden;
    private int numOutputs;
    private double[][] inputHiddenWeights;
    private double[][] hiddenOutputWeights;
    private double[] hiddenBiases;
    private double[] outputBiases;
    private double learningRate;
    
    public NeuralNetwork(int numInputs, int numHidden, int numOutputs, double learningRate) {
    this.numInputs = numInputs;
    this.numHidden = numHidden;
    this.numOutputs = numOutputs;
    this.learningRate = learningRate;
    
    // Initialize weights and biases with random values between -1 and 1
    inputHiddenWeights = new double[numInputs][numHidden];
    hiddenOutputWeights = new double[numHidden][numOutputs];
    hiddenBiases = new double[numHidden];
    outputBiases = new double[numOutputs];
    Random random = new Random();
    for (int i = 0; i < numInputs; i++) {
    for (int j = 0; j < numHidden; j++) {
    inputHiddenWeights[i][j] = random.nextDouble() * 2 - 1;
    }
    }
    for (int i = 0; i < numHidden; i++) {
    for (int j = 0; j < numOutputs; j++) {
    hiddenOutputWeights[i][j] = random.nextDouble() * 2 - 1;
    }
    hiddenBiases[i] = random.nextDouble() * 2 - 1;
    }
    for (int i = 0; i < numOutputs; i++) {
    outputBiases[i] = random.nextDouble() * 2 - 1;
    }
    }
    
    public double[] feedForward(double[] inputs) {
        // Compute hidden layer activations
        double[] hiddenLayer = new double[numHidden];
        for (int i = 0; i < numHidden; i++) {
        double activation = 0;
        for (int j = 0; j < numInputs; j++) {
        activation += inputs[j] * inputHiddenWeights[j][i];
        }
        hiddenLayer[i] = sigmoid(activation + hiddenBiases[i]);
        }
        
        // Compute output layer activations
        double[] outputs = new double[numOutputs];
        for (int i = 0; i < numOutputs; i++) {
        double activation = 0;
        for (int j = 0; j < numHidden; j++) {
        activation += hiddenLayer[j] * hiddenOutputWeights[j][i];
        }
        outputs[i] = sigmoid(activation + outputBiases[i]);
        }
        
        return outputs;
        }
        


    
        public void train(double[] inputs, double[] targetOutputs, double reward) {
            // Compute feedforward outputs and backpropagate errors
            double[] hiddenLayer = new double[numHidden];
            for (int i = 0; i < numHidden; i++) {
            double activation = 0;
            for (int j = 0; j < numInputs; j++) {
            activation += inputs[j] * inputHiddenWeights[j][i];
            }
            hiddenLayer[i] = sigmoid(activation + hiddenBiases[i]);
            }
            double[] outputs = feedForward(inputs);
            double[] outputErrors = new double[numOutputs];
            for (int i = 0; i < numOutputs; i++) {
            outputErrors[i] = (targetOutputs[i] - outputs[i]) * sigmoidDerivative(outputs[i]);
            }
            double[] hiddenErrors = new double[numHidden];
            for (int i = 0; i < numHidden; i++) {
            double error = 0;
            for (int j = 0; j < numOutputs; j++) {
            error += outputErrors[j] * hiddenOutputWeights[i][j];
            }
            hiddenErrors[i] = error * sigmoidDerivative(hiddenLayer[i]);
            }
            // Update weights and biases using errors and learning rate
            for (int i = 0; i < numInputs; i++) {
            for (int j = 0; j < numHidden; j++) {
            inputHiddenWeights[i][j] += learningRate * inputs[i] * hiddenErrors[j];
            }
            }
            for (int i = 0; i < numHidden; i++) {
            for (int j = 0; j < numOutputs; j++) {
            hiddenOutputWeights[i][j] += learningRate * hiddenLayer[i] * outputErrors[j];
            }
            hiddenBiases[i] += learningRate * hiddenErrors[i];
            }
            for (int i = 0; i < numOutputs; i++) {
            outputBiases[i] += learningRate * outputErrors[i] * reward;
            }
            }
            
    
    private double sigmoid(double x) {
    return 1 / (1 + Math.exp(-x));
    }
    
    private double sigmoidDerivative(double x) {
    return x * (1 - x);
    }
    }
     

next file : import java.util.Arrays;
import java.util.Random;

public class NeuralDot {

private static final int VISION_RANGE = 5;
public static final int GRID_SIZE = 100;
private static final int NUM_INPUTS = (2*VISION_RANGE + 1) * (2*VISION_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;

public int x;
public int y;
public 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();
    
    // Initialize variables for tracking dot movement and reward
    int lastX = x;
    int lastY = y;
    int timeWithoutFood = 0;
    double reward;
    
    // 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
    if (grid[x][y]) {
    reward = 10;
    timeWithoutFood = 0;
    } else {
    reward = -0.1;
    if (x == lastX && y == lastY) {
    timeWithoutFood++;
    reward -= 0.01 * timeWithoutFood;
    } else {
    timeWithoutFood = 0;
    }
    }
    
    // Train the neural network on the input-output pair
    double[] targetOutputs = new double[NUM_OUTPUTS];
    targetOutputs[action] = 1;
    neuralNetwork.train(inputs, targetOutputs, reward);
    
    // Update last movement variables
    lastX = x;
    lastY = y;
    }
    }
    }

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() {
    int newX = x - 1;
    if (newX < 0) {
    newX = 0;
    }
    
    grid[x][y] = false;
    x = newX;
    grid[x][y] = true;
    }
    
    private void moveRight() {
    int newX = x + 1;
    if (newX >= GRID_SIZE) {
    newX = GRID_SIZE - 1;
    }
    
    grid[x][y] = false;
    x = newX;
    grid[x][y] = true;
    }

private void moveForward() {
    int newY = y + 1;
    if (newY >= GRID_SIZE) {
    newY = GRID_SIZE - 1;
    }
    
    grid[x][y] = false;
    y = newY;
    grid[x][y] = true;
    }

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

}