#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using HeuristicLab.Data;
using HeuristicLab.Encodings.IntegerVectorEncoding;
using HeuristicLab.Problems.GeneralizedQuadraticAssignment;
using HeuristicLab.Random;
using Microsoft.VisualStudio.TestTools.UnitTesting;

namespace UnitTests {
  /// <summary>
  ///This is a test class for GQAPNMoveEvaluatorTest and is intended
  ///to contain all GQAPNMoveEvaluatorTest Unit Tests
  ///</summary>
  [TestClass()]
  public class GQAPNMoveEvaluatorTest {
    private const int Equipments = 10, Locations = 5;
    private static DoubleMatrix symmetricWeights, asymmetricWeights, nonZeroDiagonalWeights;
    private static DoubleMatrix symmetricDistances, asymmetricDistances, nonZeroDiagonalDistances;
    private static DoubleMatrix installationCosts;
    private static DoubleArray demands, capacities;
    private static DoubleValue transportationCosts, overbookedCapacityPenalty;
    private static IntegerVector assignment;
    private static MersenneTwister random;

    private TestContext testContextInstance;

    /// <summary>
    ///Gets or sets the test context which provides
    ///information about and functionality for the current test run.
    ///</summary>
    public TestContext TestContext {
      get { return testContextInstance; }
      set { testContextInstance = value; }
    }

    #region Additional test attributes
    [ClassInitialize()]
    public static void MyClassInitialize(TestContext testContext) {
      random = new MersenneTwister();
      symmetricDistances = new DoubleMatrix(Locations, Locations);
      symmetricWeights = new DoubleMatrix(Equipments, Equipments);
      asymmetricDistances = new DoubleMatrix(Locations, Locations);
      asymmetricWeights = new DoubleMatrix(Equipments, Equipments);
      nonZeroDiagonalDistances = new DoubleMatrix(Locations, Locations);
      nonZeroDiagonalWeights = new DoubleMatrix(Equipments, Equipments);
      for (int i = 0; i < Equipments - 1; i++) {
        for (int j = i + 1; j < Equipments; j++) {
          symmetricWeights[i, j] = random.Next(Equipments * 100);
          symmetricWeights[j, i] = symmetricWeights[i, j];
          asymmetricWeights[i, j] = random.Next(Equipments * 100);
          asymmetricWeights[j, i] = random.Next(Equipments * 100);
          nonZeroDiagonalWeights[i, j] = random.Next(Equipments * 100);
          nonZeroDiagonalWeights[j, i] = random.Next(Equipments * 100);
        }
        nonZeroDiagonalWeights[i, i] = random.Next(Equipments * 100);
      }
      for (int i = 0; i < Locations - 1; i++) {
        for (int j = i + 1; j < Locations; j++) {
          symmetricDistances[i, j] = random.Next(Locations * 100);
          symmetricDistances[j, i] = symmetricDistances[i, j];
          asymmetricDistances[i, j] = random.Next(Locations * 100);
          asymmetricDistances[j, i] = random.Next(Locations * 100);
          nonZeroDiagonalDistances[i, j] = random.Next(Locations * 100);
          nonZeroDiagonalDistances[j, i] = random.Next(Locations * 100);
        }
        nonZeroDiagonalDistances[i, i] = random.Next(Locations * 100);
      }
      installationCosts = new DoubleMatrix(Equipments, Locations);
      for (int i = 0; i < Equipments; i++) {
        for (int j = 0; j < Locations; j++) {
          installationCosts[i, j] = random.Next(0, 10);
        }
      }
      demands = new DoubleArray(Equipments);
      for (int i = 0; i < Equipments; i++) {
        demands[i] = random.Next(1, 10);
      }
      capacities = new DoubleArray(Locations);
      for (int j = 0; j < Locations; j++) {
        capacities[j] = random.Next(1, 10) * ((double)Equipments / (double)Locations) * 1.5;
      }
      int index = random.Next(Locations);
      if (nonZeroDiagonalDistances[index, index] == 0)
        nonZeroDiagonalDistances[index, index] = random.Next(1, Equipments * 100);
      index = random.Next(Equipments);
      if (nonZeroDiagonalWeights[index, index] == 0)
        nonZeroDiagonalWeights[index, index] = random.Next(1, Equipments * 100);

      transportationCosts = new DoubleValue(random.NextDouble() * 10);
      overbookedCapacityPenalty = new DoubleValue(1000 * random.NextDouble() + 100);
      assignment = new IntegerVector(Equipments, random, 0, Locations);
    }
    #endregion


    /// <summary>
    ///A test for Evaluate
    ///</summary>
    [TestMethod()]
    public void EvaluateTest() {
      for (int i = 0; i < 500; i++) {
        NMove currentMove = StochasticNMoveSingleMoveGenerator.GenerateUpToN(random, assignment, 3, capacities);
        IntegerVector prevAssignment = (IntegerVector)assignment.Clone();
        NMoveMaker.Apply(assignment, currentMove);
        double before = GQAPEvaluator.Evaluate(prevAssignment, symmetricWeights, symmetricDistances, installationCosts, demands, capacities, transportationCosts, overbookedCapacityPenalty);
        double after = GQAPEvaluator.Evaluate(assignment, symmetricWeights, symmetricDistances, installationCosts, demands, capacities, transportationCosts, overbookedCapacityPenalty);
        double moveDiff = GQAPNMoveEvaluator.Evaluate(currentMove, prevAssignment, symmetricWeights, symmetricDistances, installationCosts, demands, capacities, transportationCosts, overbookedCapacityPenalty);
        Assert.IsTrue(Math.Abs(moveDiff - (after - before)) < 1e-07, "Failed on symmetric matrices: " + Environment.NewLine
          + "Quality changed from " + before + " to " + after + " (" + (after - before).ToString() + "), but move quality change was " + moveDiff + ".");

        before = GQAPEvaluator.Evaluate(prevAssignment, asymmetricWeights, asymmetricDistances, installationCosts, demands, capacities, transportationCosts, overbookedCapacityPenalty);
        after = GQAPEvaluator.Evaluate(assignment, asymmetricWeights, asymmetricDistances, installationCosts, demands, capacities, transportationCosts, overbookedCapacityPenalty);
        moveDiff = GQAPNMoveEvaluator.Evaluate(currentMove, prevAssignment, asymmetricWeights, asymmetricDistances, installationCosts, demands, capacities, transportationCosts, overbookedCapacityPenalty);
        Assert.IsTrue(Math.Abs(moveDiff - (after - before)) < 1e-07, "Failed on asymmetric matrices: " + Environment.NewLine
          + "Quality changed from " + before + " to " + after + " (" + (after - before).ToString() + "), but move quality change was " + moveDiff + ".");

        before = GQAPEvaluator.Evaluate(prevAssignment, nonZeroDiagonalWeights, nonZeroDiagonalDistances, installationCosts, demands, capacities, transportationCosts, overbookedCapacityPenalty);
        after = GQAPEvaluator.Evaluate(assignment, nonZeroDiagonalWeights, nonZeroDiagonalDistances, installationCosts, demands, capacities, transportationCosts, overbookedCapacityPenalty);
        moveDiff = GQAPNMoveEvaluator.Evaluate(currentMove, prevAssignment, nonZeroDiagonalWeights, nonZeroDiagonalDistances, installationCosts, demands, capacities, transportationCosts, overbookedCapacityPenalty);
        Assert.IsTrue(Math.Abs(moveDiff - (after - before)) < 1e-07, "Failed on non-zero diagonal matrices: " + Environment.NewLine
          + "Quality changed from " + before + " to " + after + " (" + (after - before).ToString() + "), but move quality change was " + moveDiff + ".");
      }
    }
  }
}