public static Bitmap AWGNNoise(Bitmap bmp, int intensity) { Bitmap img = new Bitmap(bmp.Width, bmp.Height); var normal = new NormalDistribution(mean: 0, stdDev: 1); double[] noise = normal.Generate(img.Width*img.Height); int l = 0; for (int i = 0; i < bmp.Height; i++) { for(int j = 0; j < bmp.Width; j++) { Color c = bmp.GetPixel(j, i); int c1 = c.R + ((int)Math.Ceiling(noise[l]) * intensity);//(int)noise + intensity;//(int)Math.Sqrt(noise() * 255) + intensity; int c2 = c.G + ((int)Math.Ceiling(noise[l]) * intensity);//(int)Math.Sqrt(noise() * 255) + intensity; int c3 = c.B + ((int)Math.Ceiling(noise[l]) * intensity);//(int)Math.Sqrt(noise() * 255) + intensity; c1 = c1 > 255 ? 255 : c1; c1 = c1 < 0 ? 0 : c1; c2 = c2 > 255 ? 255 : c2; c2 = c2 < 0 ? 0 : c2; c3 = c3 > 255 ? 255 : c3; c3 = c3 < 0 ? 0 : c3; Color newC = Color.FromArgb(c1, c2, c3); img.SetPixel(j, i, newC); l++; } } return img; }
static void Main(string[] args) { Process _process = Process.GetCurrentProcess(); bool _takearguments = false; // How many arguments have been stored in the game. if (args.Length > 0) { Console.WriteLine("Arguments found."); _takearguments = true; HandleArguments(args); } string HostName = "localhost"; if (!_takearguments) { Console.WriteLine("Host name:"); HostName = Console.ReadLine(); Console.Clear(); Console.WriteLine("How many games do you wish to simulate?"); string _count = Console.ReadLine(); int _result = 0; // Determine that the value that has been inputted is // in fact valid. while (!int.TryParse(_count, out _result)) { Console.Clear(); Console.WriteLine("Please try again: "); _count = Console.ReadLine(); } // Set the new count of games that we want to simulate. gamesToPlay = _result; Console.Clear(); string _consoleoutput = ""; while (_consoleoutput != "n" && _consoleoutput != "y") { // Determine if we want to log output to be silence while we do this Console.WriteLine("Silence output?"); _consoleoutput = Console.ReadLine(); } if (_consoleoutput == "n") { m_RemainQuiet = false; } else if (_consoleoutput == "y") { m_RemainQuiet = true; } } //RunGamesParallel(); //return; // Get some strange invocation error here. // tryLoadController(_agentName); gs = new GameState(125); gs.GameOver += new EventHandler(GameOverHandler); gs.StartPlay(); BasePacman controller = new LucPacScripted(); Console.WriteLine("Choose an AI agent to control Pacman:"); Console.WriteLine(" 1 - LucPacScripted"); Console.WriteLine(" 2 - LucPac (MCTS)"); Console.WriteLine(" 3 - MMLocPac (Evolved Neural Network) from .nn file"); Console.WriteLine(" 5 - SimRandom"); int Selection = int.Parse(Console.ReadKey().KeyChar.ToString()); switch (Selection) { case 1: controller = new LucPacScripted(); break; case 2: controller = new LucPac(); break; case 3: controller = new MMPac.MMLocPac("NeuralNetworkLocPac.nn"); break; default: controller = new RandomPac(); break; } var GR = new GameRunner(); var Base = new double[9] { 3.0, 2.8, 2.8, 2.8, 2.8, 1.5, 1.5, 1.5, 1.5 }; var Params = new double[9] { 0.07, 0.01, 0.02, -0.16, 0.06, -0.05, 0, 0.06, -0.09 }; //var Params = new double[9] { -0.17, 0.01, 0.02, -0.16, 0.06, -0.05, 0, 0.06, -0.09 }; // Params = Params.Add(Base); string TestAgent = "PacmanAI.UncertainAgent,PacmanAI"; var GRR = GR.RunGamesOnline(HostName, gamesToPlay, controller.GetType().Name //TestAgent , new Random().Next(), //null new List <double>(Params) ); var NewScores = new List <double>(); NewScores.AddRange(GRR.scores); for (int i = 0; i < NewScores.Count; i++) { NewScores[i] += 9000; } NewScores.AddRange(GRR.scores); var ZeroScores = new List <double>(); for (int i = 0; i < 100; i++) { ZeroScores.Add(0); } Console.WriteLine("Done - " + GRR.scores.Average() + " " + GRR.gamesPlayed); Console.WriteLine("Scores over 1600: " + GRR.scores.Where(s => s >= 1600).Count()); Console.WriteLine("Done (Altered) - " + NewScores.Average() + " " + GRR.gamesPlayed); Console.WriteLine("Scores over 1500 (Altered): " + NewScores.Where(s => s >= 1500).Count()); /*Console.WriteLine("Evaluation score via distribution evaluation: " + new DistributionWeightEvaluation(null).CalculateFitnessScore(GRR.scores, 5000, 1)); * Console.WriteLine("Evaluation score via average evaluation: " + new AccurateThresholdEvaluation(null).CalculateFitnessScore(GRR.scores, 5000, 1)); * * * Console.WriteLine("Evaluation score via distribution evaluation (All zeroes): " + new DistributionWeightEvaluation(null).CalculateFitnessScore(ZeroScores, 1500, 1)); * Console.WriteLine("Evaluation score via average evaluation (All zeroes): " + new AccurateThresholdEvaluation(null).CalculateFitnessScore(ZeroScores, 1500, 1)); * * Console.WriteLine("Evaluation score via distribution evaluation (Altered scores): " + new DistributionWeightEvaluation(null).CalculateFitnessScore(NewScores, 5000, 1)); * Console.WriteLine("Evaluation score via average evaluation (Altered scores): " + new AccurateThresholdEvaluation(null).CalculateFitnessScore(NewScores, 5000, 1)); * * var gaussianDist = new Accord.Statistics.Distributions.Univariate.NormalDistribution(2000, 10); * * Console.WriteLine("Evaluation score via distribution evaluation (Gaussian scores): " + new DistributionWeightEvaluation(null).CalculateFitnessScore(gaussianDist.Generate(100).ToList(), 2000, 1)); * Console.WriteLine("Evaluation score via average evaluation (Gaussian scores): " + new AccurateThresholdEvaluation(null).CalculateFitnessScore(gaussianDist.Generate(100).ToList(), 2000, 1)); */ Accord.Statistics.Distributions.Univariate.EmpiricalDistribution rdb = new Accord.Statistics.Distributions.Univariate.EmpiricalDistribution(GRR.scores.ToArray(), 25); Accord.Controls.DataSeriesBox.Show("Pacman score distribution", rdb.ProbabilityDensityFunction, new Accord.DoubleRange(-2000, 12000)); Accord.Statistics.Distributions.Univariate.EmpiricalDistribution rdb2 = new Accord.Statistics.Distributions.Univariate.EmpiricalDistribution(NewScores.ToArray()); Accord.Controls.DataSeriesBox.Show("Pacman score distribution (Altered)", rdb2.ProbabilityDensityFunction, new Accord.DoubleRange(-2000, 12000)); double[] coef = { 4, 1 }; var skewNormal = new Mixture <NormalDistribution>(coef, new NormalDistribution(2000, 1500), new NormalDistribution(7000, 1500));// new SkewNormalDistribution(4500, 3000, 7.2); Accord.Controls.DataSeriesBox.Show("Skew Normal", skewNormal.ProbabilityDensityFunction, new Accord.DoubleRange(-2000, 12000)); Console.ReadKey(); return; // DEFINE CONTROLLER // //BasePacman controller = new MMMCTSCode.MMMCTS(); //BasePacman controller = new RandomPac(); //BasePacman controller = new LucPacScripted(); //BasePacman controller = new LucPac(); //BasePacman controller = new MMPac.MMPac("NeuralNetwork.nn"); //BasePacman controller = new MMPac.MMPac(Weights); //BasePacman controller = new MMPac.MMLocPac("NeuralNetworkLocPac.nn"); // Turn off the logging if (controller.GetType() == typeof(LucPac) && m_RemainQuiet) { LucPac.REMAIN_QUIET = true; } if (controller.GetType() == typeof(LucPacScripted) && m_RemainQuiet) { LucPacScripted.REMAIN_QUIET = true; } //BasePacman controller = new SmartDijkstraPac(); gs.Controller = controller; Stopwatch watch = new Stopwatch(); int percentage = -1; int lastUpdate = 0; watch.Start(); while (gamesPlayed < gamesToPlay) { int newPercentage = (int)Math.Floor(((float)gamesPlayed / gamesToPlay) * 100); if (newPercentage != percentage || gamesPlayed - lastUpdate >= 100) { lastUpdate = gamesPlayed; percentage = newPercentage; Console.Clear(); Console.WriteLine("Simulating ... " + percentage + "% (" + gamesPlayed + " : " + gamesToPlay + ")"); Console.WriteLine(" - Elapsed: " + (watch.ElapsedMilliseconds / 1000.0) + "ms"); Console.WriteLine(" - Current best: " + highestScore); Console.WriteLine(" - Current worst: " + lowestScore); if (gamesPlayed > 0) { Console.WriteLine(" - Current avg.: " + (totalScore / gamesPlayed)); } } // update gamestate Direction direction = controller.Think(gs); gs.Pacman.SetDirection(direction); // update game gs.Update(); ms += GameState.MSPF; } watch.Stop(); // shut down controller controller.SimulationFinished(); // output results Console.Clear(); long seconds = ms / 1000; Console.WriteLine("Games played: " + gamesPlayed); Console.WriteLine("Avg. score: " + (totalScore / gamesPlayed)); Console.WriteLine("Highest score: " + highestScore + " points"); Console.WriteLine("Lowest score: " + lowestScore + " points"); Console.WriteLine("Max Pills Eaten: " + maxPillsEaten); Console.WriteLine("Min Pills Eaten: " + minPillsEaten); Console.WriteLine("Average Pills Eaten: " + pillsEatenTotal / gamesPlayed); Console.WriteLine("Max Ghosts Eaten: " + maxGhostsEaten); Console.WriteLine("Min Ghosts Eaten: " + minGhostsEaten); Console.WriteLine("Average Ghosts Eaten: " + totalGhostsEaten / gamesPlayed); Console.WriteLine("Longest game: " + ((float)longestGame / 1000.0f) + " seconds"); Console.WriteLine("Total simulated time: " + (seconds / 60 / 60 / 24) + "d " + ((seconds / 60 / 60) % 24) + "h " + ((seconds / 60) % 60) + "m " + (seconds % 60) + "s"); Console.WriteLine("Avg. simulated time pr. game: " + ((float)ms / 1000.0f / gamesPlayed) + " seconds"); Console.WriteLine("Simulation took: " + (watch.ElapsedMilliseconds / 1000.0f) + " seconds"); Console.WriteLine("Speed: " + (ms / watch.ElapsedMilliseconds) + " (" + ((ms / watch.ElapsedMilliseconds) / 60) + "m " + ((ms / watch.ElapsedMilliseconds) % 60) + " s) simulated seconds pr. second"); Console.WriteLine("For a total of: " + gamesPlayed / (watch.ElapsedMilliseconds / 1000.0f) + " games pr. second"); Console.WriteLine(); //Calculate standard deviation double mean = totalScore / gamesPlayed; double totalsqdif = 0; foreach (var val in scores) { totalsqdif += (val - mean) * (val - mean); } double variance = totalsqdif / gamesPlayed; double stddev = Math.Sqrt(variance); Console.WriteLine("Standard deviation of: " + stddev); Console.WriteLine("Standard deviation of (Accord): " + scores.ToArray().StandardDeviation()); //Generates a distribution from existing data Accord.Statistics.Distributions.Univariate.EmpiricalDistribution db = new Accord.Statistics.Distributions.Univariate.EmpiricalDistribution(scores.ToArray()); //Calculates standard deviation Console.WriteLine("Standard deviation of (Accord 2): " + db.StandardDeviation); double[] sample = //{ 1000, 960, 1000, 960, 1000, 600, 100, 1000, 1500}; { 2000, 2500, 2100, 9000, 1900, 2000, 150, 2100 }; //{ 60000, 70000, 80000, 90000, 40000, 100000, 200000, 15000, 500000, 44444 }; //scores.Take(20).ToArray(); double[] sample2 = scores.Take(100).ToArray(); //Shapiro Wilk test to see if distribution is normal var swT = new Accord.Statistics.Testing.ShapiroWilkTest(scores.ToArray()); Console.WriteLine("Shapiro Wilk Test on all scores: Statistic - " + swT.Statistic + " , PValue - " + swT.PValue + " , Significant - " + swT.Significant); var normalDist = new Accord.Statistics.Distributions.Univariate.NormalDistribution(950, 1200); var swT2 = new Accord.Statistics.Testing.ShapiroWilkTest(normalDist.Generate(1000)); Console.WriteLine("Shapiro Wilk Test on normal dist: Statistic - " + swT2.Statistic + " , PValue - " + swT2.PValue + " , Significant - " + swT2.Significant); //Accord.Statistics.Testing.KolmogorovSmirnovTest ks = new Accord.Statistics.Testing.KolmogorovSmirnovTest(sample, db); //Console.WriteLine("KS Test: Statistic - " + ks.Statistic + " , PValue - " + ks.PValue + " , Significant - " + ks.Significant); //Probability that the given scores were sampled from the previous distribution Accord.Statistics.Testing.ZTest ts = new Accord.Statistics.Testing.ZTest(sample, totalScore / gamesPlayed); /*sample.Average(), * db.StandardDeviation, * sample.Length, * totalScore / gamesPlayed); */ Console.WriteLine("Z Test: Statistic - " + ts.Statistic + " , PValue - " + ts.PValue + " , Significant - " + ts.Significant); Accord.Statistics.Testing.ZTest ts2 = new Accord.Statistics.Testing.ZTest(sample2, totalScore / gamesPlayed); /*sample2.Average(), * //Accord.Statistics.Tools.StandardDeviation(sample2.ToArray()), * db.StandardDeviation, * sample2.Length, * totalScore / gamesPlayed); */ Console.WriteLine("Z Test 2: Statistic - " + ts2.Statistic + " , PValue - " + ts2.PValue + " , Significant - " + ts2.Significant); //% of values that are between given ranges Console.WriteLine("Distribution function 0 - 1000: " + db.DistributionFunction(0, 1000)); Console.WriteLine("Distribution function 1000 - 11000: " + db.DistributionFunction(1000, 11000)); Console.WriteLine("Distribution function 0 - 500: " + db.DistributionFunction(0, 500)); Console.WriteLine("Distribution function 1500 - 11000: " + db.DistributionFunction(1500, 11000)); //MannWhitneyWilcoxon test on whether 2 samples are from the same distribution - high P value = likely same distribution Accord.Statistics.Testing.MannWhitneyWilcoxonTest mwTest = new Accord.Statistics.Testing.MannWhitneyWilcoxonTest(scores.ToArray(), sample2); Console.WriteLine("MWW Test: Statistic - " + mwTest.Statistic + " , PValue - " + mwTest.PValue + " , Significant - " + mwTest.Significant); Accord.Statistics.Testing.MannWhitneyWilcoxonTest mwTest2 = new Accord.Statistics.Testing.MannWhitneyWilcoxonTest(normalDist.Generate(1000), scores.ToArray()); Console.WriteLine("MWW Test 2 (actual scores versus normal dist): Statistic - " + mwTest2.Statistic + " , PValue - " + mwTest2.PValue + " , Significant - " + mwTest2.Significant); //Accord.Controls.HistogramBox.Show(scores.ToArray()); //Guess what distribution this is var analysis = new Accord.Statistics.Analysis.DistributionAnalysis(scores.ToArray()); // Compute the analysis analysis.Compute(); // Get the most likely distribution (first) var mostLikely = analysis.GoodnessOfFit[0]; var result = mostLikely.Distribution.ToString(); Console.WriteLine(result); //Plots the distributions Accord.Controls.DataSeriesBox.Show("Pacman score distribution", db.ProbabilityDensityFunction, new Accord.DoubleRange(-2000, highestScore)); Accord.Controls.DataSeriesBox.Show("Normal distribution", normalDist.ProbabilityDensityFunction, new Accord.DoubleRange(-2000, highestScore)); Accord.Controls.DataSeriesBox.Show("Gamma distribution", mostLikely.Distribution.ProbabilityFunction, new Accord.DoubleRange(-2000, highestScore)); //Calculate some CDF related malarkey //top 20 scores - 1 empirical //next 20 scores - 2nd empirical //calculate cdf of both //calculate cdf of cumulative int games1 = 80; int games2 = 20; Accord.Statistics.Distributions.Univariate.EmpiricalDistribution edb = new Accord.Statistics.Distributions.Univariate.EmpiricalDistribution(scores.GetRange(0, games1).ToArray()); Accord.Statistics.Distributions.Univariate.EmpiricalDistribution edb2 = new Accord.Statistics.Distributions.Univariate.EmpiricalDistribution(scores.GetRange(games1, games2).ToArray()); Accord.Statistics.Distributions.Univariate.EmpiricalDistribution edbC = new Accord.Statistics.Distributions.Univariate.EmpiricalDistribution(scores.GetRange(0, games1 + games2).ToArray()); var cdf1 = edb.DistributionFunction(800); var cdf2 = edb2.DistributionFunction(800); var cdfC = edbC.DistributionFunction(800); Console.WriteLine("CDF1 = " + cdf1 + ", CDF2 = " + cdf2 + ", Guess = " + (cdf1 * games1 + cdf2 * games2) / (games1 + games2) + ", Actual = " + cdfC); //Convolution var ScoresA = scores.GetRange(0, games1).ToArray(); var ScoresB = scores.GetRange(games1, games1).ToArray(); //var Convolution = ScoresA.Convolve(ScoresB); double[] Convolution = new double[games1]; Accord.Math.Transforms.FourierTransform2.Convolve(ScoresA, ScoresB, Convolution); Console.ReadLine(); }