using System;
using System.Collections.Concurrent;
using System.Collections.Generic;
using System.Collections.Specialized;
using System.Linq;
using System.Net;
using System.Security;
using System.Security.AccessControl;
using System.Text;
using HeuristicLab.Common;

namespace HeuristicLab.Problems.GrammaticalOptimization {
  public class SymbolicRegressionPoly10Problem : IProblem {
    //    private const string grammarString = @"
    //    G(E):
    //    E -> V | V+E | V-E | V*E | (E)
    //    V -> a .. j 
    //    ";
    private const string grammarString = @"
    G(E):
    E -> a | b | c | d | e | f | g | h | i | j | a+E | b+E | c+E | d+E | e+E | f+E | g+E | h+E | i+E | j+E | a*E | b*E | c*E | d*E | e*E | f*E | g*E | h*E | i*E | j*E 
    ";


    private readonly IGrammar grammar;

    private readonly int N;
    private readonly double[][] x;
    private readonly double[] y;

    public SymbolicRegressionPoly10Problem() {
      this.grammar = new Grammar(grammarString);

      this.N = 500;
      this.x = new double[N][];
      this.y = new double[N];

      GenerateData();
    }

    private void GenerateData() {
      // generate data with fixed seed to make sure that data is always the same
      var rand = new Random(31415);
      for (int i = 0; i < N; i++) {
        x[i] = new double[10];
        for (int j = 0; j < 10; j++) {
          x[i][j] = rand.NextDouble() * 2 - 1;
        }
        // poly-10 no noise
        /* a*b + c*d + e*f + a*g*i + c*f*j */
        y[i] = x[i][0] * x[i][1] +
               x[i][2] * x[i][3] +
               x[i][4] * x[i][5] +
               x[i][0] * x[i][6] * x[i][8] +
               x[i][2] * x[i][5] * x[i][9];
      }
    }

    public double BestKnownQuality(int maxLen) {
      // for now only an upper bound is returned, ideally we have an R² of 1.0
      // the optimal R² can only be reached for sentences of at least 23 symbols
      return 1.0;
    }

    public IGrammar Grammar {
      get { return grammar; }
    }

    public double Evaluate(string sentence) {
      var interpreter = new ExpressionInterpreter();
      return HeuristicLab.Common.Extensions.RSq(y, Enumerable.Range(0, N).Select(i => interpreter.Interpret(sentence, x[i])).ToArray());
    }



    // most-recently-used caching (with limited capacity) for canonical representations
    MostRecentlyUsedCache<string, string> canonicalPhraseCache = new MostRecentlyUsedCache<string, string>(100000);
    // right now only + and * is supported
    public string CanonicalRepresentation(string phrase) {
      string canonicalPhrase;
      if (!canonicalPhraseCache.TryGetValue(phrase, out canonicalPhrase)) {
        var terms = phrase.Split('+');
        var canonicalTerms = new SortedSet<string>();
        // only the last term might contain a NT-symbol. make sure this term is added at the end
        for (int i = 0; i < terms.Length - 1; i++) {
          canonicalTerms.Add(CanonicalTerm(terms[i]));
        }

        var sb = new StringBuilder(phrase.Length);
        foreach (var t in canonicalTerms)
          sb.Append(t).Append('+');

        sb.Append(CanonicalTerm(terms[terms.Length - 1]));
        canonicalPhrase = sb.ToString();
        canonicalPhraseCache.Add(phrase, canonicalPhrase);
      }
      return canonicalPhrase;
    }

    // cache the canonical form of terms for performance reasons
    private Dictionary<string, string> canonicalTermDictionary = new Dictionary<string, string>();
    private string CanonicalTerm(string term) {
      string canonicalTerm;
      if (!canonicalTermDictionary.TryGetValue(term, out canonicalTerm)) {
        // add
        var chars = term.ToCharArray();
        Array.Sort(chars);
        var sb = new StringBuilder(chars.Length);
        // we want to have the up-case characters last
        for (int i = chars.Length - 1; i > 0; i--) {
          if (chars[i] != '*') {
            sb.Append(chars[i]);
            if (chars[i - 1] != '*') sb.Append('*');
          }
        }
        if (chars[0] != '*') sb.Append(chars[0]); // last term
        canonicalTerm = sb.ToString();
        canonicalTermDictionary.Add(term, canonicalTerm);
      }
      return canonicalTerm;
    }

    // splits the phrase into terms and creates (sparse) term-occurrance features
    public IEnumerable<Feature> GetFeatures(string phrase) {
      var canonicalTerms = new HashSet<string>();
      foreach (string t in phrase.Split('+')) {
        canonicalTerms.Add(CanonicalTerm(t));
      }
      return canonicalTerms.Select(entry => new Feature(entry, 1.0)).Concat(new Feature[] { new Feature(CanonicalRepresentation(phrase), 1.0) });
    }

  }
}