public void CanCreateCategoricalFromHistogram()
{
double[] smallDataset = { 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5 };
Histogram hist = new Histogram(smallDataset, 10, 0.0, 10.0);
var m = new Categorical(hist);
for (int i = 0; i <= m.Maximum; i++)
{
AssertEx.AreEqual<double>(1.0/10.0, m.P[i]);
}
}
/// <summary>
/// Samples one multinomial distributed random variable.
/// </summary>
/// <param name="rnd">The random number generator to use.</param>
/// <param name="p">An array of nonnegative ratios: this array does not need to be normalized
/// as this is often impossible using floating point arithmetic.</param>
/// <param name="n">The number of trials.</param>
/// <returns>the counts for each of the different possible values.</returns>
public static int[] Sample(System.Random rnd, double[] p, int n)
{
if (Control.CheckDistributionParameters && !IsValidParameterSet(p, n))
{
throw new ArgumentOutOfRangeException(Resources.InvalidDistributionParameters);
}
// The cumulative density of p.
var cp = Categorical.ProbabilityMassToCumulativeDistribution(p);
// The variable that stores the counts.
var ret = new int[p.Length];
for (var i = 0; i < n; i++)
{
ret[Categorical.SampleUnchecked(rnd, cp)]++;
}
return(ret);
}
/// <summary>
/// Samples a multinomially distributed random variable.
/// </summary>
/// <param name="rnd">The random number generator to use.</param>
/// <param name="p">An array of nonnegative ratios: this array does not need to be normalized
/// as this is often impossible using floating point arithmetic.</param>
/// <param name="n">The number of variables needed.</param>
/// <returns>a sequence of counts for each of the different possible values.</returns>
public static IEnumerable <int[]> Samples(Random rnd, double[] p, int n)
{
if (Control.CheckDistributionParameters && !IsValidParameterSet(p, n))
{
throw new ArgumentOutOfRangeException(Resources.InvalidDistributionParameters);
}
// The cumulative density of p.
var cp = Categorical.UnnormalizedCdf(p);
while (true)
{
// The variable that stores the counts.
var ret = new int[p.Length];
for (var i = 0; i < n; i++)
{
ret[Categorical.DoSample(rnd, cp)]++;
}
yield return(ret);
}
}
public void CanSample()
{
var n = new Categorical(_largeP);
n.Sample();
}
public void ValidateToString()
{
var b = new Categorical(_smallP);
Assert.AreEqual("Categorical(Dimension = 3)", b.ToString());
}
/// <summary>
/// Run example
/// </summary>
/// <a href="http://en.wikipedia.org/wiki/Categorical_distribution">Categorical distribution</a>
public void Run()
{
// 1. Initialize the new instance of the Categorical distribution class with parameters P = (0.1, 0.2, 0.25, 0.45)
var binomial = new Categorical(new[] { 0.1, 0.2, 0.25, 0.45 });
Console.WriteLine(@"1. Initialize the new instance of the Categorical distribution class with parameters P = (0.1, 0.2, 0.25, 0.45)");
Console.WriteLine();
// 2. Distributuion properties:
Console.WriteLine(@"2. {0} distributuion properties:", binomial);
// Cumulative distribution function
Console.WriteLine(@"{0} - Сumulative distribution at location '3'", binomial.CumulativeDistribution(3).ToString(" #0.00000;-#0.00000"));
// Probability density
Console.WriteLine(@"{0} - Probability mass at location '3'", binomial.Probability(3).ToString(" #0.00000;-#0.00000"));
// Log probability density
Console.WriteLine(@"{0} - Log probability mass at location '3'", binomial.ProbabilityLn(3).ToString(" #0.00000;-#0.00000"));
// Entropy
Console.WriteLine(@"{0} - Entropy", binomial.Entropy.ToString(" #0.00000;-#0.00000"));
// Largest element in the domain
Console.WriteLine(@"{0} - Largest element in the domain", binomial.Maximum.ToString(" #0.00000;-#0.00000"));
// Smallest element in the domain
Console.WriteLine(@"{0} - Smallest element in the domain", binomial.Minimum.ToString(" #0.00000;-#0.00000"));
// Mean
Console.WriteLine(@"{0} - Mean", binomial.Mean.ToString(" #0.00000;-#0.00000"));
// Median
Console.WriteLine(@"{0} - Median", binomial.Median.ToString(" #0.00000;-#0.00000"));
// Variance
Console.WriteLine(@"{0} - Variance", binomial.Variance.ToString(" #0.00000;-#0.00000"));
// Standard deviation
Console.WriteLine(@"{0} - Standard deviation", binomial.StdDev.ToString(" #0.00000;-#0.00000"));
// 3. Generate 10 samples of the Categorical distribution
Console.WriteLine(@"3. Generate 10 samples of the Categorical distribution");
for (var i = 0; i < 10; i++)
{
Console.Write(binomial.Sample().ToString("N05") + @" ");
}
Console.WriteLine();
Console.WriteLine();
// 4. Generate 100000 samples of the Categorical(new []{ 0.1, 0.2, 0.25, 0.45 }) distribution and display histogram
Console.WriteLine(@"4. Generate 100000 samples of the Categorical(0.2, 20) distribution and display histogram");
var data = new double[100000];
for (var i = 0; i < data.Length; i++)
{
data[i] = binomial.Sample();
}
ConsoleHelper.DisplayHistogram(data);
Console.WriteLine();
// 5. Generate 100000 samples of the Categorical(new []{ 0.6, 0.2, 0.1, 0.1 }) distribution and display histogram
Console.WriteLine(@"5. Generate 100000 samples of the Categorical(0.7, 20) distribution and display histogram");
binomial.P = new[] { 0.6, 0.2, 0.1, 0.1 };
for (var i = 0; i < data.Length; i++)
{
data[i] = binomial.Sample();
}
ConsoleHelper.DisplayHistogram(data);
}
public void CategoricalCreateFailsWithAllZeroRatios()
{
var m = new Categorical(badP2);
}
public void CanSetProbability()
{
var b = new Categorical(largeP);
b.P = smallP;
}
public void CanSample()
{
var n = new Categorical(largeP);
var d = n.Sample();
}
public void CanCreateCategorical()
{
var m = new Categorical(largeP);
}
public void SetProbabilityFails()
{
var b = new Categorical(largeP);
b.P = badP;
}
public void CategoricalCreateFailsWithNullHistogram()
{
Histogram h = null;
var m = new Categorical(h);
}
public void CategoricalCreateFailsWithNegativeRatios()
{
var m = new Categorical(badP);
}
