using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Algorithms.MonteCarloTreeSearch.Base;
using HeuristicLab.Problems.GrammaticalOptimization;

namespace HeuristicLab.Algorithms.MonteCarloTreeSearch.Simulation
{
    public class RandomSimulation : ISimulation
    {
        private IProblem problem;
        private IGrammar grammar;
        private Random random;
        private int maxLen;

        public RandomSimulation(IProblem problem, Random random, int maxLen)
        {
            this.problem = problem;
            this.grammar = problem.Grammar;
            this.random = random;
            this.maxLen = maxLen;
        }

        public double Simulate(TreeNode node)
        {
            Sequence sequence = new Sequence(node.phrase);
            List<int> ntPositions = new List<int>();
            while (!sequence.IsTerminal)
            {
                // select one random nt-symbol
                ntPositions.Clear();
                for (int i = 0; i < sequence.Length; i++)
                {
                    if (grammar.IsNonTerminal(sequence[i]))
                    {
                        ntPositions.Add(i);
                    }
                }
                int ntSymbolIndex = ntPositions[random.Next(ntPositions.Count)];
                char ntSymbol = sequence[ntSymbolIndex];

                // select one random alternative for nt-symbol
                IEnumerable<Sequence> alternatives = grammar.GetAlternatives(ntSymbol);
                Sequence alternative = alternatives.ElementAt(random.Next(alternatives.Count()));
                if (sequence.Length + alternative.Length - 1 <= maxLen)
                {
                    sequence.ReplaceAt(ntSymbolIndex, 1, alternative);
                }
            }
            return problem.Evaluate(sequence.ToString());
        }
    }
}