public void ValidateDensity(double lambda, double x)
        {
            var n = new Exponential(lambda);

            if (x >= 0)
            {
                Assert.AreEqual <double>(lambda * Math.Exp(-lambda * x), n.Density(x));
            }
            else
            {
                Assert.AreEqual <double>(0.0, n.Density(lambda));
            }
        }
示例#2
0
        public void ValidateDensity(
            [Values(0.0, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.0, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.0, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.0, 0.1, 1.0, 10.0, Double.PositiveInfinity)] double lambda,
            [Values(0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 1.0, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double x)
        {
            var n = new Exponential(lambda);

            if (x >= 0)
            {
                Assert.AreEqual(lambda * Math.Exp(-lambda * x), n.Density(x));
            }
            else
            {
                Assert.AreEqual(0.0, n.Density(lambda));
            }
        }
示例#3
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        private void LeaveLift(Person p)
        {
            if (p.Departure == 0)
            {
                p.ExitingLiftTimeGoingUp = Context.TimePeriod;
                p.TotalTimeInLift        = Context.TimePeriod - p.EnteringLiftTimeGoingUp;
                p.Departure             = p.Destination;
                p.Destination           = 0;
                p.TimeBeforeGoingActive = (long)meanWorkTime * (long)expo.Density(rand.NextDouble());

                personsGenerator.PersonsPool.Add(p);
            }
            else
            {
                p.ExitingLiftTimeGoingDown = Context.TimePeriod;
                p.TotalTimeInLift         += Context.TimePeriod - p.EnteringLiftTimeGoingDown;
                ProcessedPersons.Add(p);


                ProcessedCount++;
            }

            PersonsInLift.Remove(p);


            CurrentLoad--;
        }
        /// <summary>
        /// Run example
        /// </summary>
        /// <a href="http://en.wikipedia.org/wiki/Exponential_distribution">Exponential distribution</a>
        public void Run()
        {
            // 1. Initialize the new instance of the Exponential distribution class with parameter Lambda = 1.
            var exponential = new Exponential(1);

            Console.WriteLine(@"1. Initialize the new instance of the Exponential distribution class with parameter Lambda = {0}", exponential.Rate);
            Console.WriteLine();

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

            // Cumulative distribution function
            Console.WriteLine(@"{0} - Сumulative distribution at location '0.3'", exponential.CumulativeDistribution(0.3).ToString(" #0.00000;-#0.00000"));

            // Probability density
            Console.WriteLine(@"{0} - Probability density at location '0.3'", exponential.Density(0.3).ToString(" #0.00000;-#0.00000"));

            // Log probability density
            Console.WriteLine(@"{0} - Log probability density at location '0.3'", exponential.DensityLn(0.3).ToString(" #0.00000;-#0.00000"));

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

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

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

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

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

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

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

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

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

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

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

            // 4. Generate 100000 samples of the Exponential(1) distribution and display histogram
            Console.WriteLine(@"4. Generate 100000 samples of the Exponential(1) distribution and display histogram");
            var data = new double[100000];

            for (var i = 0; i < data.Length; i++)
            {
                data[i] = exponential.Sample();
            }

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

            // 5. Generate 100000 samples of the Exponential(9) distribution and display histogram
            Console.WriteLine(@"5. Generate 100000 samples of the Exponential(9) distribution and display histogram");
            exponential.Rate = 9;
            for (var i = 0; i < data.Length; i++)
            {
                data[i] = exponential.Sample();
            }

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

            // 6. Generate 100000 samples of the Exponential(0.01) distribution and display histogram
            Console.WriteLine(@"6. Generate 100000 samples of the Exponential(0.01) distribution and display histogram");
            exponential.Rate = 0.01;
            for (var i = 0; i < data.Length; i++)
            {
                data[i] = exponential.Sample();
            }

            ConsoleHelper.DisplayHistogram(data);
        }
 public void ValidateDensity(double lambda, double x)
 {
     var n = new Exponential(lambda);
     if (x >= 0)
     {
         Assert.AreEqual(lambda * Math.Exp(-lambda * x), n.Density(x));
     }
     else
     {
         Assert.AreEqual(0.0, n.Density(lambda));
     }
 }
示例#6
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 public double ProbabilityDensity(Position1 sample)
 {
     return(Domain.Includes(sample) ? distribution.Density(sample - Domain.Entry) : 0);
 }
 public void ValidateDensity(
     [Values(0.0, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.0, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.0, 0.1, 1.0, 10.0, Double.PositiveInfinity, 0.0, 0.1, 1.0, 10.0, Double.PositiveInfinity)] double lambda, 
     [Values(0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.1, 0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 1.0, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity, Double.PositiveInfinity)] double x)
 {
     var n = new Exponential(lambda);
     if (x >= 0)
     {
         Assert.AreEqual(lambda * Math.Exp(-lambda * x), n.Density(x));
     }
     else
     {
         Assert.AreEqual(0.0, n.Density(lambda));
     }
 }