Beispiel #1
0
        static ZergCharacter()
        {
            GOAL_PRIORITIES = new Dictionary<State, byte>(){
                {State.AttackGoal, 16},
                {State.FeedGoal, 8},
                {State.RetreatGoal, 1},
                {State.ServeGoal, 2}
            };

            var goals = new Stack<State>();

            var goalEnums = (State[])Enum.GetValues(typeof(State));
            foreach (var enumValue in goalEnums)
            {
                for (int i = 0; i < GOAL_PRIORITIES[enumValue]; i++)
                {
                    goals.Push(enumValue);
                }
            }

            int count = goals.Count;
            RND = new DiscreteUniform(0, count - 1);
            GOALS_LOOKUP_TABLE = new State[count];

            var rnd = new ContinuousUniform(0.0, 1.0);

            while (goals.Any())
            {
                int idx = (int)Math.Round(rnd.Sample() * (--count), 0);
                GOALS_LOOKUP_TABLE[idx] = goals.Pop();
            }
        }
Beispiel #2
0
        private void CreateUniformFromString(string distributionString)
        {
            var regex = new Regex(@"Uniform\(\s*(\d+)\s*,\s*(\d+)\s*\)", RegexOptions.IgnoreCase);

            var match = regex.Match(distributionString);

            var min = int.Parse(match.Groups[1].Value);
            var max = int.Parse(match.Groups[2].Value);

            _discreteUniform = new DiscreteUniform(min, max);
        }
        public void SetupDistributions()
        {
            dists = new IDistribution[8];

            dists[0] = new Beta(1.0, 1.0);
            dists[1] = new ContinuousUniform(0.0, 1.0);
            dists[2] = new Gamma(1.0, 1.0);
            dists[3] = new Normal(0.0, 1.0);
            dists[4] = new Bernoulli(0.6);
            dists[5] = new Weibull(1.0, 1.0);
            dists[6] = new DiscreteUniform(1, 10);
            dists[7] = new LogNormal(1.0, 1.0);
        }
        /// <summary>
        /// Run example
        /// </summary>
        /// <a href="http://en.wikipedia.org/wiki/Discrete_uniform">DiscreteUniform distribution</a>
        public void Run()
        {
            // 1. Initialize the new instance of the DiscreteUniform distribution class with parameters LowerBound = 2, UpperBound = 10
            var discreteUniform = new DiscreteUniform(2, 10);
            Console.WriteLine(@"1. Initialize the new instance of the DiscreteUniform distribution class with parameters LowerBound = {0}, UpperBound = {1}", discreteUniform.LowerBound, discreteUniform.UpperBound);
            Console.WriteLine();

            // 2. Distributuion properties:
            Console.WriteLine(@"2. {0} distributuion properties:", discreteUniform);

            // Cumulative distribution function
            Console.WriteLine(@"{0} - Сumulative distribution at location '3'", discreteUniform.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));

            // Probability density
            Console.WriteLine(@"{0} - Probability mass at location '3'", discreteUniform.Probability(3).ToString(" #0.00000;-#0.00000"));

            // Log probability density
            Console.WriteLine(@"{0} - Log probability mass at location '3'", discreteUniform.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));

            // Entropy
            Console.WriteLine(@"{0} - Entropy", discreteUniform.Entropy.ToString(" #0.00000;-#0.00000"));

            // Largest element in the domain
            Console.WriteLine(@"{0} - Largest element in the domain", discreteUniform.Maximum.ToString(" #0.00000;-#0.00000"));

            // Smallest element in the domain
            Console.WriteLine(@"{0} - Smallest element in the domain", discreteUniform.Minimum.ToString(" #0.00000;-#0.00000"));

            // Mean
            Console.WriteLine(@"{0} - Mean", discreteUniform.Mean.ToString(" #0.00000;-#0.00000"));

            // Median
            Console.WriteLine(@"{0} - Median", discreteUniform.Median.ToString(" #0.00000;-#0.00000"));

            // Mode
            Console.WriteLine(@"{0} - Mode", discreteUniform.Mode.ToString(" #0.00000;-#0.00000"));

            // Variance
            Console.WriteLine(@"{0} - Variance", discreteUniform.Variance.ToString(" #0.00000;-#0.00000"));

            // Standard deviation
            Console.WriteLine(@"{0} - Standard deviation", discreteUniform.StdDev.ToString(" #0.00000;-#0.00000"));

            // Skewness
            Console.WriteLine(@"{0} - Skewness", discreteUniform.Skewness.ToString(" #0.00000;-#0.00000"));
            Console.WriteLine();

            // 3. Generate 10 samples of the DiscreteUniform distribution
            Console.WriteLine(@"3. Generate 10 samples of the DiscreteUniform distribution");
            for (var i = 0; i < 10; i++)
            {
                Console.Write(discreteUniform.Sample().ToString("N05") + @" ");
            }

            Console.WriteLine();
            Console.WriteLine();

            // 4. Generate 100000 samples of the DiscreteUniform(2, 10) distribution and display histogram
            Console.WriteLine(@"4. Generate 100000 samples of the DiscreteUniform(2, 10) distribution and display histogram");
            var data = new double[100000];
            for (var i = 0; i < data.Length; i++)
            {
                data[i] = discreteUniform.Sample();
            }

            ConsoleHelper.DisplayHistogram(data);
            Console.WriteLine();

            // 5. Generate 100000 samples of the DiscreteUniform(-10, 10) distribution and display histogram
            Console.WriteLine(@"5. Generate 100000 samples of the DiscreteUniform(-10, 10) distribution and display histogram");
            discreteUniform.LowerBound = -10;
            discreteUniform.UpperBound = 10;
            for (var i = 0; i < data.Length; i++)
            {
                data[i] = discreteUniform.Sample();
            }

            ConsoleHelper.DisplayHistogram(data);
            Console.WriteLine();

            // 6. Generate 100000 samples of the DiscreteUniform(0, 40) distribution and display histogram
            Console.WriteLine(@"6. Generate 100000 samples of the DiscreteUniform(0, 40) distribution and display histogram");
            discreteUniform.LowerBound = 0;
            discreteUniform.UpperBound = 40;
            for (var i = 0; i < data.Length; i++)
            {
                data[i] = discreteUniform.Sample();
            }

            ConsoleHelper.DisplayHistogram(data);
        }
 public void ValidateEntropy(int l, int u, double e)
 {
     var du = new DiscreteUniform(l, u);
     AssertHelpers.AlmostEqualRelative(e, du.Entropy, 14);
 }
 public void CanCreateDiscreteUniform(int l, int u)
 {
     var du = new DiscreteUniform(l, u);
     Assert.AreEqual(l, du.LowerBound);
     Assert.AreEqual(u, du.UpperBound);
 }
 public void ValidateToString()
 {
     var b = new DiscreteUniform(0, 10);
     Assert.AreEqual("DiscreteUniform(Lower = 0, Upper = 10)", b.ToString());
 }
 public void CanSampleSequence()
 {
     var n = new DiscreteUniform(0, 10);
     var ied = n.Samples();
     GC.KeepAlive(ied.Take(5).ToArray());
 }
 public void ValidateCumulativeDistribution(int l, int u, double x, double cdf)
 {
     var b = new DiscreteUniform(l, u);
     Assert.AreEqual(cdf, b.CumulativeDistribution(x));
 }
 public void CanSample()
 {
     var n = new DiscreteUniform(0, 10);
     n.Sample();
 }
 public void ValidateProbabilityLn(int l, int u, int x, double dln)
 {
     var b = new DiscreteUniform(l, u);
     Assert.AreEqual(dln, b.ProbabilityLn(x));
 }
 public void ValidateMaximum()
 {
     var b = new DiscreteUniform(-10, 10);
     Assert.AreEqual(10, b.Maximum);
 }
 public void ValidateMean(int l, int u, int m)
 {
     var du = new DiscreteUniform(l, u);
     Assert.AreEqual(m, du.Mean);
 }
 public void ValidateSkewness(int l, int u)
 {
     var du = new DiscreteUniform(l, u);
     Assert.AreEqual(0.0, du.Skewness);
 }