using System;
using System.Collections.Generic;
using System.Linq;
using Random = UnityEngine.Random;

public sealed class NeuralNetwork : IComparable<NeuralNetwork> {

    public float Fitness { get; set; }
    private readonly int[] _layers;
    private readonly float[][] _neurons;
    private float[][][] _weights;

    public NeuralNetwork(int[] layers) {
        _layers = layers.ToArray();
        _neurons = _layers.Select(layer => new float[layer]).ToArray();
        InitWeights();
    }

    public NeuralNetwork(NeuralNetwork network) {
        _layers = network._layers.ToArray();
        _neurons = _layers.Select(layer => new float[layer]).ToArray();
        InitWeights();
        CopyWeights(network._weights);
    }

    private void CopyWeights(float[][][] weights) {
        for (int i = 0; i < _weights.Length; i++) {
            for (int j = 0; j < _weights[i].Length; j++) {
                for (int k = 0; k < _weights[i][j].Length; k++) {
                    _weights[i][j][k] = weights[i][j][k];
                }
            }
        }
    }

    private void InitWeights() {
        var weights = new List<float[][]>();

        for (int i = 1; i < _layers.Length; i++) {
            var layerWeights = new List<float[]>();
            int previousNeurons = _layers[i - 1];

            for (int j = 0; j < _neurons[i].Length; j++) {
                var neuronWeights = new float[previousNeurons];

                for (int k = 0; k < previousNeurons; k++) {
                    neuronWeights[k] = Random.Range(-0.5f, 0.5f);
                }
                    
                layerWeights.Add(neuronWeights);
            }
                
            weights.Add(layerWeights.ToArray());
        }

        _weights = weights.ToArray();
    }

    public float[] FeedForward(float[] inputs) {
        for (int i = 0; i < inputs.Length; i++) {
            _neurons[0][i] = inputs[i];
        }

        for (int i = 1; i < _layers.Length; i++) {
            for (int j = 0; j < _neurons[i].Length; j++) {
                var value = 0.25f;
                    
                for (int k = 0; k < _neurons[i - 1].Length; k++) {
                    value += _weights[i - 1][j][k] * _neurons[i - 1][k];
                }

                _neurons[i][j] = MathF.Tanh(value);
            }
        }

        return _neurons[^1];
    }

    public void Mutate() {
        for (int i = 0; i < _weights.Length; i++) {
            for (int j = 0; j < _weights[i].Length; j++) {
                for (int k = 0; k < _weights[i][j].Length; k++) {
                    var weight = _weights[i][j][k];
                    var random = Random.Range(0.0f, 1000.0f);

                    if (random <= 2.0f) weight *= -1.0f;
                    else if (random <= 4.0f) weight = Random.Range(-0.5f, 0.5f);
                    else if (random <= 6.0f) weight *= Random.Range(0.0f, 1.0f) + 1.0f;
                    else if (random <= 8.0f) weight *= Random.Range(0.0f, 1.0f);

                    _weights[i][j][k] = weight;
                }
            }
        }
    }

    public int CompareTo(NeuralNetwork other) {
        if (ReferenceEquals(this, other)) return 0;
        if (ReferenceEquals(null, other)) return 1;
        return Fitness.CompareTo(other.Fitness);
    }
}