public void CanSample()
{
var d = new Zipf(0.7, 5);
var s = d.Sample();
Assert.Between(s, 0, 5);
}
public void ValidateCumulativeDistribution(double s, int n, int x)
{
var d = new Zipf(s, n);
var cdf = SpecialFunctions.GeneralHarmonic(x, s) / SpecialFunctions.GeneralHarmonic(n, s);
AssertHelpers.AlmostEqualRelative(cdf, d.CumulativeDistribution(x), 14);
}
//private static bool TEST_ACTIVE = true;
//private static void TestRequestQueue()
//{
// RequestQueue<string> strQueue = new RequestQueue<string>(8);
// long beginTicks = DateTime.Now.Ticks;
// TEST_ACTIVE = true;
// for (int i = 0; i < 8; i++)
// {
// Task.Factory.StartNew(TestEnqueue, strQueue);
// }
// Task.Factory.StartNew(TestDequeue, strQueue);
// while (DateTime.Now.Ticks - beginTicks < 1 * 10000000) ;
// TEST_ACTIVE = false;
//}
//private static Action<object> TestEnqueue = (object obj) =>
//{
// RequestQueue<string> strQueue = obj as RequestQueue<string>;
// Random rand = new Random();
// while (TEST_ACTIVE)
// {
// int pk = rand.Next(0, 8);
// strQueue.Enqueue("123", pk);
// }
//};
//private static Action<object> TestDequeue = (object obj) =>
//{
// RequestQueue<string> strQueue = obj as RequestQueue<string>;
// Random rand = new Random();
// string value = null;
// while (TEST_ACTIVE)
// {
// int pk = rand.Next(0, 8);
// if (strQueue.TryDequeue(out value))
// {
// Debug.Assert(value != null);
// }
// }
//};
private static void ZipfTest()
{
Dictionary <int, int> dict = new Dictionary <int, int>();
Zipf zipf = new Zipf(100000, 0.8, false);
for (int i = 0; i < 200000; i++)
{
int key = zipf.Next();
if (dict.ContainsKey(key))
{
dict[key]++;
}
else
{
dict[key] = 1;
}
}
List <KeyValuePair <int, int> > myList = dict.ToList();
myList.Sort(
delegate(KeyValuePair <int, int> pair1,
KeyValuePair <int, int> pair2)
{
return(pair2.Value.CompareTo(pair1.Value));
}
);
for (int i = 0; i < 5; i++)
{
Console.WriteLine(myList[i].Key + " " + myList[i].Value);
}
}
public void CanCreateZipf(double s, int n)
{
var d = new Zipf(s, n);
Assert.AreEqual(s, d.S);
Assert.AreEqual(n, d.N);
}
public void ValidateToString()
{
System.Threading.Thread.CurrentThread.CurrentCulture = System.Globalization.CultureInfo.InvariantCulture;
var d = new Zipf(1.0, 5);
Assert.AreEqual("Zipf(S = 1, N = 5)", d.ToString());
}
public void ValidateCumulativeDistribution([Values(0.1, 1)] double s, [Values(1, 20, 50)] int n, [Values(2, 15)] int x)
{
var d = new Zipf(s, n);
var cdf = SpecialFunctions.GeneralHarmonic(x, s) / SpecialFunctions.GeneralHarmonic(n, s);
AssertHelpers.AlmostEqual(cdf, d.CumulativeDistribution(x), 14);
}
public void CanSample()
{
var d = new Zipf(0.7, 5);
var s = d.Sample();
Assert.LessOrEqual(s, 5);
Assert.GreaterOrEqual(s, 0);
}
public void CanCreateZipf([Values(0.1, 1)] double s, [Values(1, 20, 50)] int n)
{
var d = new Zipf(s, n);
Assert.AreEqual(s, d.S);
Assert.AreEqual(n, d.N);
}
private static void zipf_cdf_test()
//****************************************************************************80
//
// Purpose:
//
// ZIPF_CDF_TEST tests ZIPF_CDF, ZIPF_CDF_INV, ZIPF_PDF.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 09 March 2016
//
// Author:
//
// John Burkardt
//
{
int x;
Console.WriteLine("");
Console.WriteLine("ZIPF_CDF_TEST");
Console.WriteLine(" ZIPF_CDF evaluates the Zipf CDF;");
Console.WriteLine(" ZIPF_CDF_INV inverts the Zipf CDF;");
Console.WriteLine(" ZIPF_PDF evaluates the Zipf PDF;");
double a = 2.0E+00;
Console.WriteLine("");
Console.WriteLine(" PDF parameter A = " + a + "");
if (!Zipf.zipf_check(a))
{
Console.WriteLine("");
Console.WriteLine("ZIPF_CDF_TEST - Fatal error!");
Console.WriteLine(" The parameters are not legal.");
return;
}
Console.WriteLine("");
Console.WriteLine(" X PDF CDF CDF_INV()");
Console.WriteLine("");
for (x = 1; x <= 20; x++)
{
double pdf = Zipf.zipf_pdf(x, a);
double cdf = Zipf.zipf_cdf(x, a);
int x2 = Zipf.zipf_cdf_inv(a, cdf);
Console.WriteLine(" "
+ x.ToString(CultureInfo.InvariantCulture).PadLeft(12) + " "
+ pdf.ToString(CultureInfo.InvariantCulture).PadLeft(12) + " "
+ cdf.ToString(CultureInfo.InvariantCulture).PadLeft(12) + " "
+ x2.ToString(CultureInfo.InvariantCulture).PadLeft(12) + "");
}
}
static void Main(string[] args)
{
ThreadPool.SetMaxThreads(maxThreads, maxThreads);
Console.WriteLine("Generating input distribution...");
samples = new int[sampleCount];
Zipf.Samples(samples, s, n);
int[] threadCount = Enumerable.Range(1, maxThreads).ToArray();
// Desired output:
// Class 1 2 3 4 5
// Classic 5 6 7 7 8
// Concurrent 5 6 7 7 8
DataTable resultTable = new DataTable();
resultTable.Clear();
resultTable.Columns.Add("Class");
foreach (var tc in threadCount)
{
resultTable.Columns.Add(tc.ToString());
}
DataRow concurrentLru = resultTable.NewRow();
DataRow classicLru = resultTable.NewRow();
concurrentLru["Class"] = "concurrentLru";
classicLru["Class"] = "classicLru";
foreach (int tc in threadCount)
{
const int warmup = 3;
const int runs = 6;
double[] results = new double[warmup + runs];
for (int i = 0; i < warmup + runs; i++)
{
results[i] = MeasureThroughput(new ConcurrentLru <int, int>(tc, capacity, EqualityComparer <int> .Default), tc);
}
double avg = AverageLast(results, runs) / 1000000;
Console.WriteLine($"ConcurrLru ({tc}) {avg} million ops/sec");
concurrentLru[tc.ToString()] = avg.ToString();
for (int i = 0; i < warmup + runs; i++)
{
results[i] = MeasureThroughput(new ClassicLru <int, int>(tc, capacity, EqualityComparer <int> .Default), tc);
}
avg = AverageLast(results, runs) / 1000000;
Console.WriteLine($"ClassicLru ({tc}) {avg} million ops/sec");
classicLru[tc.ToString()] = avg.ToString();
}
resultTable.Rows.Add(concurrentLru);
resultTable.Rows.Add(classicLru);
ExportCsv(resultTable);
Console.WriteLine("Done.");
}
public void CanSample()
{
var d = new Zipf(0.7, 5);
var s = d.Sample();
Assert.LessOrEqual(s, 5);
Assert.GreaterOrEqual(s, 0);
}
public void ValidateSkewness(double s, int n)
{
var d = new Zipf(s, n);
if (s > 4)
{
Assert.AreEqual(((SpecialFunctions.GeneralHarmonic(n, s - 3) * Math.Pow(SpecialFunctions.GeneralHarmonic(n, s), 2)) - (SpecialFunctions.GeneralHarmonic(n, s - 1) * ((3 * SpecialFunctions.GeneralHarmonic(n, s - 2) * SpecialFunctions.GeneralHarmonic(n, s)) - Math.Pow(SpecialFunctions.GeneralHarmonic(n, s - 1), 2)))) / Math.Pow((SpecialFunctions.GeneralHarmonic(n, s - 2) * SpecialFunctions.GeneralHarmonic(n, s)) - Math.Pow(SpecialFunctions.GeneralHarmonic(n, s - 1), 2), 1.5), d.Skewness);
}
}
public void ValidateEntropy(
[Values(0.1, 0.1, 0.1, 1.0, 1.0, 1.0)] double s,
[Values(1, 20, 50, 1, 20, 50)] int n,
[Values(0.0, 2.9924075515295949, 3.9078245132371388, 0.0, 2.5279968533953743, 3.1971263138845916)] double e)
{
var d = new Zipf(s, n);
AssertHelpers.AlmostEqual(e, d.Entropy, 15);
}
public void Setup()
{
_newCache = new MassTransitCache <Guid, Item, CacheValue <Item> >(new UsageCachePolicy <Item>(), new CacheOptions {
Capacity = 1000
});
_samples = new int[SampleCount];
Zipf.Samples(_samples, S, N);
}
public void Setup()
{
_massTransitCache = new MassTransitCache <Guid, Item, ITimeToLiveCacheValue <Item> >(new TimeToLiveCachePolicy <Item>(TimeSpan.FromSeconds(30)),
new CacheOptions {
Capacity = 1000
});
_samples = new int[SampleCount];
Zipf.Samples(_samples, S, N);
}
public void CanSampleSequence()
{
var d = new Zipf(0.7, 5);
var ied = d.Samples();
var e = ied.Take(1000).ToArray();
foreach (var i in e)
{
Assert.LessOrEqual(i, 5);
Assert.GreaterOrEqual(i, 0);
}
}
public void CanSampleSequence()
{
var d = new Zipf(0.7, 5);
var ied = d.Samples();
var e = ied.Take(1000).ToArray();
foreach (var i in e)
{
Assert.Between(i, 0, 5);
}
}
public void CanSampleSequence()
{
var d = new Zipf(0.7, 5);
var ied = d.Samples();
var e = ied.Take(1000).ToArray();
foreach (var i in e)
{
Assert.LessOrEqual(i, 5);
Assert.GreaterOrEqual(i, 0);
}
}
//****************************************************************************80
public static double planck_sample(double a, double b, ref int seed)
//****************************************************************************80
//
// Purpose:
//
// PLANCK_SAMPLE samples the Planck PDF.
//
// Licensing:
//
// This code is distributed under the GNU LGPL license.
//
// Modified:
//
// 26 October 2004
//
// Author:
//
// John Burkardt
//
// Reference:
//
// Luc Devroye,
// Non-Uniform Random Variate Generation,
// Springer Verlag, 1986, pages 552.
//
// Parameters:
//
// Input, double A, B, the parameters of the PDF.
// 0.0 < A,
// 0.0 < B.
//
// Input/output, int &SEED, a seed for the random number generator.
//
// Output, double PLANCK_SAMPLE, a sample of the PDF.
//
{
const double a2 = 0.0;
const double b2 = 1.0;
double c2 = b + 1.0;
double g = Gamma.gamma_sample(a2, b2, c2, ref seed);
int z = Zipf.zipf_sample(c2, ref seed);
double x = g / (a * z);
return(x);
}
public MongoWorker(MongoClient c, POCTestOptions t, POCTestResults r, int id)
{
logger = LogManager.GetLogger($"MongoWorker {id}");
mongoClient = c;
//Ping
c.GetDatabase("admin").RunCommand <BsonDocument>(new BsonDocument("ping", 1));
testOpts = t;
testResults = r;
workerID = id;
var db = mongoClient.GetDatabase(testOpts.namespaces[0]);
maxCollections = testOpts.numcollections;
String baseCollectionName = testOpts.namespaces[1];
if (maxCollections > 1)
{
colls = new List <IMongoCollection <BsonDocument> >();
lastCollection = 0;
for (int i = 0; i < maxCollections; i++)
{
String str = baseCollectionName + i;
colls.Add(db.GetCollection <BsonDocument>(str));
}
}
else
{
coll = db.GetCollection <BsonDocument>(baseCollectionName);
}
// id
sequence = getHighestID();
ReviewShards();
rng = new Random();
if (testOpts.zipfsize > 0)
{
zipfian = true;
zipf = new Zipf(0.99, testOpts.zipfsize);
}
if (!string.IsNullOrWhiteSpace(testOpts.workflow))
{
workflow = testOpts.workflow;
workflowed = true;
keyStack = new List <BsonDocument>();
}
}
public YCSBDataGenerator(
int recordCount,
double readWritePerc = 0.5,
Distribution dist = Distribution.Uniform,
double theta = 0.5)
{
this.recordCount = recordCount;
this.dist = dist;
this.readWritePerc = readWritePerc;
this.readWriteRandBound = (int)(this.readWritePerc * RAND_UPPER_BOUND);
if (this.dist == Distribution.Zipf)
{
// Console.WriteLine("theta = {0}", theta);
this.zipf = new Zipf(recordCount, theta);
}
else
{
this.uniform = new Uniform(recordCount);
}
this.operationDist = new Uniform(RAND_UPPER_BOUND);
}
public void ValidateProbabilityLn([Values(0.1, 1)] double s, [Values(1, 20, 50)] int n, [Values(1, 15)] int x)
{
var d = new Zipf(s, n);
Assert.AreEqual(Math.Log(d.Probability(x)), d.ProbabilityLn(x));
}
public void ValidateMinimum()
{
var d = new Zipf(1.0, 5);
Assert.AreEqual(1, d.Minimum);
}
public void ValidateProbability([Values(0.1, 1)] double s, [Values(1, 20, 50)] int n, [Values(1, 15)] int x)
{
var d = new Zipf(s, n);
Assert.AreEqual((1.0 / Math.Pow(x, s)) / SpecialFunctions.GeneralHarmonic(n, s), d.Probability(x));
}
public void SetNFails([Values(-1, 0)] int n)
{
var d = new Zipf(1.0, 5);
Assert.Throws <ArgumentOutOfRangeException>(() => d.N = n);
}
public void ValidateMode([Values(0.1, 1)] double s, [Values(1, 20, 50)] int n)
{
var d = new Zipf(s, n);
Assert.AreEqual(1, d.Mode);
}
public void SetSFails([Values(Double.NaN, -1.0, Double.NegativeInfinity)] double s)
{
var d = new Zipf(1.0, 5);
Assert.Throws<ArgumentOutOfRangeException>(() => d.S = s);
}
public void CanCreateZipf([Values(0.1, 1)] double s, [Values(1, 20, 50)] int n)
{
var d = new Zipf(s, n);
Assert.AreEqual(s, d.S);
Assert.AreEqual(n, d.N);
}
public void ValidateSkewness(double s, int n)
{
var d = new Zipf(s, n);
if (s > 4)
{
Assert.AreEqual(((SpecialFunctions.GeneralHarmonic(n, s - 3) * Math.Pow(SpecialFunctions.GeneralHarmonic(n, s), 2)) - (SpecialFunctions.GeneralHarmonic(n, s - 1) * ((3 * SpecialFunctions.GeneralHarmonic(n, s - 2) * SpecialFunctions.GeneralHarmonic(n, s)) - Math.Pow(SpecialFunctions.GeneralHarmonic(n, s - 1), 2)))) / Math.Pow((SpecialFunctions.GeneralHarmonic(n, s - 2) * SpecialFunctions.GeneralHarmonic(n, s)) - Math.Pow(SpecialFunctions.GeneralHarmonic(n, s - 1), 2), 1.5), d.Skewness);
}
}
public void ValidateToString()
{
var d = new Zipf(1.0, 5);
Assert.AreEqual("Zipf(S = 1, N = 5)", d.ToString());
}
public void ValidateEntropy(double s, int n, double e)
{
var d = new Zipf(s, n);
AssertHelpers.AlmostEqualRelative(e, d.Entropy, 15);
}
public void SetNFails(int n)
{
var d = new Zipf(1.0, 5);
Assert.Throws<ArgumentOutOfRangeException>(() => d.N = n);
}
public void SetSFails(double s)
{
var d = new Zipf(1.0, 5);
Assert.Throws<ArgumentOutOfRangeException>(() => d.S = s);
}
public void ValidateMode([Values(0.1, 1)] double s, [Values(1, 20, 50)] int n)
{
var d = new Zipf(s, n);
Assert.AreEqual(1, d.Mode);
}
public void ValidateEntropy(double s, int n, double e)
{
var d = new Zipf(s, n);
AssertHelpers.AlmostEqualRelative(e, d.Entropy, 15);
}
public void ValidateEntropy(
[Values(0.1, 0.1, 0.1, 1.0, 1.0, 1.0)] double s,
[Values(1, 20, 50, 1, 20, 50)] int n,
[Values(0.0, 2.9924075515295949, 3.9078245132371388, 0.0, 2.5279968533953743, 3.1971263138845916)] double e)
{
var d = new Zipf(s, n);
AssertHelpers.AlmostEqual(e, d.Entropy, 15);
}
public void ValidateCumulativeDistribution([Values(0.1, 1)] double s, [Values(1, 20, 50)] int n, [Values(2, 15)] int x)
{
var d = new Zipf(s, n);
var cdf = SpecialFunctions.GeneralHarmonic(x, s) / SpecialFunctions.GeneralHarmonic(n, s);
AssertHelpers.AlmostEqual(cdf, d.CumulativeDistribution(x), 14);
}
public void SetSFails(double s)
{
var d = new Zipf(1.0, 5);
Assert.That(() => d.S = s, Throws.ArgumentException);
}
public void ValidateMinimum()
{
var d = new Zipf(1.0, 5);
Assert.AreEqual(1, d.Minimum);
}
public void SetSFails([Values(Double.NaN, -1.0, Double.NegativeInfinity)] double s)
{
var d = new Zipf(1.0, 5);
Assert.Throws <ArgumentOutOfRangeException>(() => d.S = s);
}
public void ValidateMaximum(double s, int n)
{
var d = new Zipf(s, n);
Assert.AreEqual(n, d.Maximum);
}
public void SetNFails(int n)
{
var d = new Zipf(1.0, 5);
Assert.That(() => d.N = n, Throws.ArgumentException);
}
public void ValidateMaximum([Values(0.1, 1)] double s, [Values(1, 20, 50)] int n)
{
var d = new Zipf(s, n);
Assert.AreEqual(n, d.Maximum);
}
public void ValidateMedianThrowsNotSupportedException()
{
var d = new Zipf(1.0, 5);
Assert.Throws <NotSupportedException>(() => { var m = d.Median; });
}
public void ValidateCumulativeDistribution(double s, int n, int x)
{
var d = new Zipf(s, n);
var cdf = SpecialFunctions.GeneralHarmonic(x, s) / SpecialFunctions.GeneralHarmonic(n, s);
AssertHelpers.AlmostEqualRelative(cdf, d.CumulativeDistribution(x), 14);
}
public void ValidateMaximum([Values(0.1, 1)] double s, [Values(1, 20, 50)] int n)
{
var d = new Zipf(s, n);
Assert.AreEqual(n, d.Maximum);
}
public void CanCreateZipf(double s, int n)
{
var d = new Zipf(s, n);
Assert.AreEqual(s, d.S);
Assert.AreEqual(n, d.N);
}
public void ValidateProbabilityLn([Values(0.1, 1)] double s, [Values(1, 20, 50)] int n, [Values(1, 15)] int x)
{
var d = new Zipf(s, n);
Assert.AreEqual(Math.Log(d.Probability(x)), d.ProbabilityLn(x));
}
public void ValidateToString()
{
var d = new Zipf(1.0, 5);
Assert.AreEqual("Zipf(S = 1, N = 5)", d.ToString());
}
public void ValidateProbability(double s, int n, int x)
{
var d = new Zipf(s, n);
Assert.AreEqual((1.0 / Math.Pow(x, s)) / SpecialFunctions.GeneralHarmonic(n, s), d.Probability(x));
}
public void ValidateProbability([Values(0.1, 1)] double s, [Values(1, 20, 50)] int n, [Values(1, 15)] int x)
{
var d = new Zipf(s, n);
Assert.AreEqual((1.0 / Math.Pow(x, s)) / SpecialFunctions.GeneralHarmonic(n, s), d.Probability(x));
}
public void ValidateMode(double s, int n)
{
var d = new Zipf(s, n);
Assert.AreEqual(1, d.Mode);
}
public void ValidateProbabilityLn(double s, int n, int x)
{
var d = new Zipf(s, n);
Assert.AreEqual(Math.Log(d.Probability(x)), d.ProbabilityLn(x));
}
public override void ExecuteExample()
{
// <a href="http://en.wikipedia.org/wiki/Binomial_distribution">Binomial distribution</a>
MathDisplay.WriteLine("<b>Binomial distribution</b>");
// 1. Initialize the new instance of the Binomial distribution class with parameters P = 0.2, N = 20
var binomial = new Binomial(0.2, 20);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Binomial distribution class with parameters P = {0}, N = {1}", binomial.P, binomial.N);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", binomial);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", binomial.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", binomial.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", binomial.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", binomial.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", binomial.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", binomial.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", binomial.Mean.ToString(" #0.00000;-#0.00000"));
// Median
MathDisplay.WriteLine(@"{0} - Median", binomial.Median.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", binomial.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", binomial.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", binomial.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", binomial.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Binomial distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Binomial distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(binomial.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Bernoulli_distribution">Bernoulli distribution</a>
MathDisplay.WriteLine("<b>Bernoulli distribution</b>");
// 1. Initialize the new instance of the Bernoulli distribution class with parameter P = 0.2
var bernoulli = new Bernoulli(0.2);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Bernoulli distribution class with parameter P = {0}", bernoulli.P);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", bernoulli);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", bernoulli.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", bernoulli.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", bernoulli.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", bernoulli.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", bernoulli.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", bernoulli.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", bernoulli.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", bernoulli.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", bernoulli.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", bernoulli.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", bernoulli.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Bernoulli distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Bernoulli distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(bernoulli.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Categorical_distribution">Categorical distribution</a>
MathDisplay.WriteLine("<b>Categorical distribution</b>");
// 1. Initialize the new instance of the Categorical distribution class with parameters P = (0.1, 0.2, 0.25, 0.45)
var binomialC = new Categorical(new[] { 0.1, 0.2, 0.25, 0.45 });
MathDisplay.WriteLine(@"1. Initialize the new instance of the Categorical distribution class with parameters P = (0.1, 0.2, 0.25, 0.45)");
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", binomialC);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", binomialC.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", binomialC.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", binomialC.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", binomialC.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", binomialC.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", binomialC.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", binomialC.Mean.ToString(" #0.00000;-#0.00000"));
// Median
MathDisplay.WriteLine(@"{0} - Median", binomialC.Median.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", binomialC.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", binomialC.StdDev.ToString(" #0.00000;-#0.00000"));
// 3. Generate 10 samples of the Categorical distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Categorical distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(binomialC.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Conway%E2%80%93Maxwell%E2%80%93Poisson_distribution">ConwayMaxwellPoisson distribution</a>
MathDisplay.WriteLine("<b>Conway Maxwell Poisson distribution</b>");
// 1. Initialize the new instance of the ConwayMaxwellPoisson distribution class with parameters Lambda = 2, Nu = 1
var conwayMaxwellPoisson = new ConwayMaxwellPoisson(2, 1);
MathDisplay.WriteLine(@"1. Initialize the new instance of the ConwayMaxwellPoisson distribution class with parameters Lambda = {0}, Nu = {1}", conwayMaxwellPoisson.Lambda, conwayMaxwellPoisson.Nu);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", conwayMaxwellPoisson);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", conwayMaxwellPoisson.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", conwayMaxwellPoisson.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", conwayMaxwellPoisson.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", conwayMaxwellPoisson.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", conwayMaxwellPoisson.Mean.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", conwayMaxwellPoisson.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", conwayMaxwellPoisson.StdDev.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the ConwayMaxwellPoisson distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the ConwayMaxwellPoisson distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(conwayMaxwellPoisson.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Discrete_uniform">DiscreteUniform distribution</a>
MathDisplay.WriteLine("<b>Discrete Uniform distribution</b>");
// 1. Initialize the new instance of the DiscreteUniform distribution class with parameters LowerBound = 2, UpperBound = 10
var discreteUniform = new DiscreteUniform(2, 10);
MathDisplay.WriteLine(@"1. Initialize the new instance of the DiscreteUniform distribution class with parameters LowerBound = {0}, UpperBound = {1}", discreteUniform.LowerBound, discreteUniform.UpperBound);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", discreteUniform);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", discreteUniform.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", discreteUniform.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", discreteUniform.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", discreteUniform.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", discreteUniform.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", discreteUniform.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", discreteUniform.Mean.ToString(" #0.00000;-#0.00000"));
// Median
MathDisplay.WriteLine(@"{0} - Median", discreteUniform.Median.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", discreteUniform.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", discreteUniform.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", discreteUniform.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", discreteUniform.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the DiscreteUniform distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the DiscreteUniform distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(discreteUniform.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Geometric_distribution">Geometric distribution</a>
MathDisplay.WriteLine("<b>Geometric distribution</b>");
// 1. Initialize the new instance of the Geometric distribution class with parameter P = 0.2
var geometric = new Geometric(0.2);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Geometric distribution class with parameter P = {0}", geometric.P);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", geometric);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", geometric.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", geometric.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", geometric.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", geometric.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", geometric.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", geometric.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", geometric.Mean.ToString(" #0.00000;-#0.00000"));
// Median
MathDisplay.WriteLine(@"{0} - Median", geometric.Median.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", geometric.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", geometric.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", geometric.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", geometric.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Geometric distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Geometric distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(geometric.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Hypergeometric_distribution">Hypergeometric distribution</a>
MathDisplay.WriteLine("<b>Hypergeometric distribution</b>");
// 1. Initialize the new instance of the Hypergeometric distribution class with parameters PopulationSize = 10, M = 2, N = 8
var hypergeometric = new Hypergeometric(30, 15, 10);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Hypergeometric distribution class with parameters Population = {0}, Success = {1}, Draws = {2}", hypergeometric.Population, hypergeometric.Success, hypergeometric.Draws);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", hypergeometric);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", hypergeometric.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", hypergeometric.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", hypergeometric.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", hypergeometric.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", hypergeometric.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", hypergeometric.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", hypergeometric.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", hypergeometric.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", hypergeometric.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", hypergeometric.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Hypergeometric distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Hypergeometric distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(hypergeometric.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Negative_binomial">NegativeBinomial distribution</a>
MathDisplay.WriteLine("<b>Negative Binomial distribution</b>");
// 1. Initialize the new instance of the NegativeBinomial distribution class with parameters P = 0.2, R = 20
var negativeBinomial = new NegativeBinomial(20, 0.2);
MathDisplay.WriteLine(@"1. Initialize the new instance of the NegativeBinomial distribution class with parameters P = {0}, N = {1}", negativeBinomial.P, negativeBinomial.R);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", negativeBinomial);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", negativeBinomial.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", negativeBinomial.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", negativeBinomial.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", negativeBinomial.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", negativeBinomial.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", negativeBinomial.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", negativeBinomial.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", negativeBinomial.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", negativeBinomial.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", negativeBinomial.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the NegativeBinomial distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the NegativeBinomial distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(negativeBinomial.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Poisson_distribution">Poisson distribution</a>
MathDisplay.WriteLine("<b>Poisson distribution</b>");
// 1. Initialize the new instance of the Poisson distribution class with parameter Lambda = 1
var poisson = new Poisson(1);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Poisson distribution class with parameter Lambda = {0}", poisson.Lambda);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", poisson);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", poisson.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", poisson.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", poisson.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", poisson.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", poisson.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", poisson.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", poisson.Mean.ToString(" #0.00000;-#0.00000"));
// Median
MathDisplay.WriteLine(@"{0} - Median", poisson.Median.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", poisson.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", poisson.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", poisson.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", poisson.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Poisson distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Poisson distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(poisson.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
// <a href="http://en.wikipedia.org/wiki/Zipf_distribution">Zipf distribution</a>
MathDisplay.WriteLine("<b>Zipf distribution</b>");
// 1. Initialize the new instance of the Zipf distribution class with parameters S = 5, N = 10
var zipf = new Zipf(5, 10);
MathDisplay.WriteLine(@"1. Initialize the new instance of the Zipf distribution class with parameters S = {0}, N = {1}", zipf.S, zipf.N);
MathDisplay.WriteLine();
// 2. Distributuion properties:
MathDisplay.WriteLine(@"2. {0} distributuion properties:", zipf);
// Cumulative distribution function
MathDisplay.WriteLine(@"{0} - Сumulative distribution at location '3'", zipf.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
MathDisplay.WriteLine(@"{0} - Probability mass at location '3'", zipf.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
MathDisplay.WriteLine(@"{0} - Log probability mass at location '3'", zipf.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
MathDisplay.WriteLine(@"{0} - Entropy", zipf.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
MathDisplay.WriteLine(@"{0} - Largest element in the domain", zipf.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
MathDisplay.WriteLine(@"{0} - Smallest element in the domain", zipf.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
MathDisplay.WriteLine(@"{0} - Mean", zipf.Mean.ToString(" #0.00000;-#0.00000"));
// Mode
MathDisplay.WriteLine(@"{0} - Mode", zipf.Mode.ToString(" #0.00000;-#0.00000"));
// Variance
MathDisplay.WriteLine(@"{0} - Variance", zipf.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
MathDisplay.WriteLine(@"{0} - Standard deviation", zipf.StdDev.ToString(" #0.00000;-#0.00000"));
// Skewness
MathDisplay.WriteLine(@"{0} - Skewness", zipf.Skewness.ToString(" #0.00000;-#0.00000"));
MathDisplay.WriteLine();
// 3. Generate 10 samples of the Zipf distribution
MathDisplay.WriteLine(@"3. Generate 10 samples of the Zipf distribution");
for (var i = 0; i < 10; i++)
{
MathDisplay.Write(zipf.Sample().ToString("N05") + @" ");
}
MathDisplay.FlushBuffer();
MathDisplay.WriteLine();
MathDisplay.WriteLine();
}
public void ValidateMedianThrowsNotSupportedException()
{
var d = new Zipf(1.0, 5);
Assert.Throws<NotSupportedException>(() => { var m = d.Median; });
}