using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;
using Genesis.Library;

namespace Genesis
{
    public partial class Form1 : Form
    {
        private int[] n0 = new int[] {
            0,0,1,1,0,0,
            0,1,0,0,1,0,
            0,1,0,0,1,0,
            0,1,0,0,1,0,
            0,1,0,0,1,0,
            0,0,1,1,0,0
        };
        private int[] n1 = new int[] {
            0,0,0,1,0,0,
            0,0,1,1,0,0,
            0,0,0,1,0,0,
            0,0,0,1,0,0,
            0,0,0,1,0,0,
            0,0,1,1,1,0
        };
        private int[] n2 = new int[] {
            0,0,1,1,0,0,
            0,1,0,0,1,0,
            0,0,0,0,1,0,
            0,0,0,1,0,0,
            0,0,1,0,0,0,
            0,1,1,1,1,0
        };
        private int[] n3 = new int[] {
            0,0,1,1,0,0,
            0,1,0,0,1,0,
            0,0,0,1,0,0,
            0,0,0,1,0,0,
            0,1,0,0,1,0,
            0,0,1,1,0,0
        };
        private int[] n4 = new int[] {
            0,0,0,0,1,0,
            0,0,0,1,1,0,
            0,0,1,0,1,0,
            0,1,1,1,1,1,
            0,0,0,0,1,0,
            0,0,0,0,1,0
        };
        private int[] n5 = new int[] {
            0,1,1,1,1,0,
            0,1,0,0,0,0,
            0,1,1,1,0,0,
            0,0,0,0,1,0,
            0,0,0,0,1,0,
            0,1,1,1,0,0
        };
        private int[] n6 = new int[] {
            0,0,1,1,1,0,
            0,1,0,0,0,0,
            0,1,1,1,0,0,
            0,1,0,0,1,0,
            0,1,0,0,1,0,
            0,0,1,1,0,0
        };
        private int[] n7 = new int[] {
            0,1,1,1,1,0,
            0,0,0,0,1,0,
            0,0,0,1,0,0,
            0,0,0,1,0,0,
            0,0,1,0,0,0,
            0,0,1,0,0,0
        };
        private int[] n8 = new int[] {
            0,0,1,1,0,0,
            0,1,0,0,1,0,
            0,1,1,1,1,0,
            0,1,0,0,1,0,
            0,1,0,0,1,0,
            0,0,1,1,0,0
        };
        private int[] n9 = new int[] {
            0,0,1,1,0,0,
            0,1,0,0,1,0,
            0,1,0,0,1,0,
            0,0,1,1,1,0,
            0,0,0,0,1,0,
            0,1,1,1,0,0
        };

        private class Symbol
        {
            public int[] Pattern { get; set; }
            public int Value { get; set; }
            public Symbol(int[] pattern, int value)
            {
                Pattern = pattern;
                Value = value;
            }
        }

        private List<Symbol> symbols;

        private class Bot
        {
            public FeedForward Network { get; set; }
            public float Sureness { get; set; }
            public float Successes { get; set; }
            public float Fitness { get; set; }
        }

        private List<Bot> generation { get; set; }
        private int generationsize = 100;
        private int[] layers = new int[] { 36, 50, 15, 10 };

        public Form1()
        {
            InitializeComponent();
            symbols = new List<Symbol>();
            symbols.Add(new Symbol(n0, 0));
            symbols.Add(new Symbol(n1, 1));
            symbols.Add(new Symbol(n2, 2));
            symbols.Add(new Symbol(n3, 3));
            symbols.Add(new Symbol(n4, 4));
            symbols.Add(new Symbol(n5, 5));
            symbols.Add(new Symbol(n6, 6));
            symbols.Add(new Symbol(n7, 7));
            symbols.Add(new Symbol(n8, 8));
            symbols.Add(new Symbol(n9, 9));
            generation = new List<Bot>();
            for (int i = 0; i < generationsize; i++) {
                Bot bot = new Bot();
                bot.Network = FeedForward.Random(layers);
                generation.Add(bot);
            }
        }

        private void Form1_Load(object sender, EventArgs e)
        {
            Random random = new Random(Guid.NewGuid().GetHashCode());
            bool searching = true;
            while (searching) {
                // Test each
                foreach (Bot bot in generation) {
                    bot.Sureness = 0;
                    bot.Successes = 0;
                    bot.Fitness = 0;
                    for (int i = 0; i < symbols.Count; i++) {
                        for (int j = 0; j < 36; j++) {
                            bot.Network.Layers[0][j].Input[0].Value = (float)symbols[i].Pattern[j];
                        }
                        bot.Network.Schedule();
                        int neuron = 0;
                        float score = 0;
                        for (int j = 0; j < bot.Network.Layers[3].Count; j++) {
                            if (bot.Network.Layers[3][j].Output > score) {
                                neuron = j;
                                score = bot.Network.Layers[3][j].Output;
                            }
                        }
                        if (neuron == symbols[i].Value) {
                            float sum = 0f;
                            for (int j = 0; j < symbols.Count; j++) {
                                float output = bot.Network.Layers[3][j].Output > 0 ? bot.Network.Layers[3][j].Output : 0;
                                sum += output;
                            }
                            bot.Sureness += bot.Network.Layers[3][neuron].Output / sum;
                            bot.Successes++;
                        }
                    }
                    bot.Fitness = bot.Successes * 4 + bot.Sureness;
                }
                // Sort by fitness
                var queryFitness = from bot in generation
                                   orderby bot.Fitness descending
                                   select bot;
                List<Bot> botsorted = queryFitness.ToList<Bot>();
                List<Bot> newgeneration = new List<Bot>();
                for (int i = 0; i < generationsize; i++) {
                    Bot child = new Bot();
                    Bot mate1 = botsorted[random.Next(0, (int)(generationsize * 0.1f))];
                    Bot mate2 = botsorted[random.Next(0, (int)(generationsize * 0.1f))];
                    string newgenome = FeedForward.Crossover(mate1.Network, mate2.Network);
                    for (int j = 0; j < 10; j++) {
                        newgenome = FeedForward.Mutate(newgenome);
                    }
                    child.Fitness = 0;
                    child.Network = new FeedForward(layers, newgenome);
                    newgeneration.Add(child);
                }
                generation = newgeneration;
                Console.WriteLine(botsorted[0].Successes + " | " + botsorted[0].Fitness);
            }
        }
    }
}