public void FitTest7()
{
UnivariateContinuousDistribution target = CreateUnivariateContinuousDistribution();
double[] observations = { 0.12, 2, 0.52 };
double[] weights = { 0.25, 0.25, 0.50 };
target.Fit(observations, weights);
double expected = 0.79;
double actual = target.Mean;
Assert.AreEqual(expected, actual);
}
public void GetRangeTest2()
{
UnivariateContinuousDistribution target = CreateUnivariateContinuousDistribution();
var range2 = target.GetRange(0.0 + Double.Epsilon);
Assert.AreEqual(-55.834722290615161, range2.Min);
Assert.AreEqual(Double.PositiveInfinity, range2.Max);
var range3 = target.GetRange(1.0 - Constants.DoubleEpsilon);
Assert.AreEqual(-55.834722290615161, range2.Min);
Assert.AreEqual(Double.PositiveInfinity, range2.Max);
}
public void FitTest4()
{
UnivariateContinuousDistribution target = CreateUnivariateContinuousDistribution();
double[] observations = { 0.12, 2, 0.52 };
IFittingOptions options = null;
target.Fit(observations, options);
double expected = 0.88;
double actual = target.Mean;
Assert.AreEqual(expected, actual);
}
public void GetRangeTest_RangeException()
{
UnivariateContinuousDistribution target = CreateUnivariateContinuousDistribution();
bool thrown = false;
try { target.GetRange(0 - Double.Epsilon); }
catch (ArgumentOutOfRangeException) { thrown = true; }
Assert.IsTrue(thrown);
thrown = false;
try { target.GetRange(1.0 + 2 * Constants.DoubleEpsilon); }
catch (ArgumentOutOfRangeException) { thrown = true; }
Assert.IsTrue(thrown);
}
public void GetRangeTest()
{
UnivariateContinuousDistribution target = CreateUnivariateContinuousDistribution();
var range1 = target.GetRange(0.95);
var range2 = target.GetRange(0.99);
var range3 = target.GetRange(0.01);
Assert.AreEqual(19.629053173483637, range1.Min);
Assert.AreEqual(26.370946826516363, range1.Max);
Assert.AreEqual(18.232405574041742, range2.Min);
Assert.AreEqual(27.767594425958258, range2.Max);
Assert.AreEqual(18.232405574041742, range3.Min);
Assert.AreEqual(27.767594425958258, range3.Max);
}
public void EmpiricalDistributionTest()
{
double[] sample = { 1, 5, 3, 1, 5, 2, 1 };
UnivariateContinuousDistribution distribution = NormalDistribution.Standard;
var target = new KolmogorovSmirnovTest(sample, distribution);
EmpiricalDistribution actual = target.EmpiricalDistribution;
Assert.AreNotSame(sample, actual.Samples);
Array.Sort(sample);
for (int i = 0; i < sample.Length; i++)
{
Assert.AreEqual(sample[i], actual.Samples[i]);
}
}
private static double[] GenerateRandom(DistributionsEvaluator evaluator, Dictionary <string, DistributionSettings> randomSource, int samples)
{
double[] random = new double[samples];
var order = evaluator.Parameters;
int argsCount = order.Length;
UnivariateContinuousDistribution[] orderedDistributions = new UnivariateContinuousDistribution[argsCount];
ThreadLocal <double[]> argsLocal = new ThreadLocal <double[]>(() => new double[argsCount]);
for (int i = 0; i < argsCount; i++)
{
string arg = order[i];
if (randomSource.TryGetValue(arg, out DistributionSettings value))
{
orderedDistributions[i] = value.GetUnivariateContinuoisDistribution();
}
else
{
// TODO: check this out
// throw new DistributionsArgumentException($"Parameter value \"{arg}\" is missing", $"Отсутствует значение параметра \"{arg}\"");
throw new DistributionsArgumentException(DistributionsArgumentExceptionType.ParameterValueIsMissing, arg);
}
}
Parallel.For(0, samples, i =>
{
double[] args = argsLocal.Value;
Random rnd = Rnd.Value;
for (int j = 0; j < argsCount; j++)
{
args[j] = orderedDistributions[j].Generate(rnd);
}
random[i] = evaluator.EvaluateCompiled(args);
});
return(random);
}
public void EmpiricalDistributionTest_with_reestimation()
{
Accord.Math.Random.Generator.Seed = 1;
double[] sample = { 1, 5, 3, 1, 5, 2, 1 };
UnivariateContinuousDistribution distribution = NormalDistribution.Standard;
var target = new LillieforsTest(sample, distribution);
EmpiricalDistribution actual = target.EmpiricalDistribution;
Assert.AreNotSame(sample, actual.Samples);
Array.Sort(sample);
for (int i = 0; i < sample.Length; i++)
{
Assert.AreEqual(sample[i], actual.Samples[i]);
}
}
/// <summary>
/// Creates a new One-Sample Kolmogorov test.
/// </summary>
///
/// <param name="sample">The sample we would like to test as belonging to the <paramref name="hypothesizedDistribution"/>.</param>
/// <param name="hypothesizedDistribution">A fully specified distribution (which must NOT have been estimated from the data).</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public KolmogorovSmirnovTest(double[] sample, UnivariateContinuousDistribution hypothesizedDistribution,
KolmogorovSmirnovTestHypothesis alternate = KolmogorovSmirnovTestHypothesis.SampleIsDifferent)
{
this.Hypothesis = alternate;
double N = sample.Length;
// Create the test statistic distribution with given degrees of freedom
StatisticDistribution = new KolmogorovSmirnovDistribution(sample.Length);
// Create a copy of the samples to prevent altering the
// constructor's original arguments in the sorting step
double[] Y = (double[])sample.Clone();
double[] D = new double[sample.Length];
// Sort sample
Array.Sort(Y);
// Create the theoretical and empirical distributions
this.TheoreticalDistribution = hypothesizedDistribution;
this.EmpiricalDistribution = new EmpiricalDistribution(Y, smoothing: 0);
Func <double, double> F = TheoreticalDistribution.DistributionFunction;
// Finally, compute the test statistic and perform actual testing.
if (alternate == KolmogorovSmirnovTestHypothesis.SampleIsDifferent)
{
// Test if the sample's distribution is just significantly
// "different" than the given theoretical distribution.
// This is a correction on the common formulation found in many places
// such as in Wikipedia. Please see the Engineering Statistics Handbook,
// section "1.3.5.16. Kolmogorov-Smirnov Goodness-of-Fit Test" for more
// details: http://www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm
for (int i = 0; i < sample.Length; i++)
{
D[i] = Math.Max(Math.Abs(F(Y[i]) - i / N), Math.Abs((i + 1) / N - F(Y[i])));
}
base.Statistic = D.Max(); // This is the two-sided "Dn" statistic.
base.PValue = StatisticDistribution.ComplementaryDistributionFunction(Statistic);
base.Tail = Testing.DistributionTail.TwoTail;
}
else if (alternate == KolmogorovSmirnovTestHypothesis.SampleIsGreater)
{
// Test if the sample's distribution is "larger" than the
// given theoretical distribution, in a statistical sense.
for (int i = 0; i < sample.Length; i++)
{
D[i] = Math.Max(i / N - F(Y[i]), (i + 1) / N - F(Y[i]));
}
base.Statistic = D.Max(); // This is the one-sided "Dn+" statistic.
base.PValue = StatisticDistribution.OneSideDistributionFunction(Statistic);
base.Tail = Testing.DistributionTail.OneUpper;
}
else
{
// Test if the sample's distribution is "smaller" than the
// given theoretical distribution, in a statistical sense.
for (int i = 0; i < sample.Length; i++)
{
D[i] = Math.Max(F(Y[i]) - i / N, F(Y[i]) - (i + 1) / N);
}
base.Statistic = D.Max(); // This is the one-sided "Dn-" statistic.
base.PValue = StatisticDistribution.OneSideDistributionFunction(Statistic);
base.Tail = Testing.DistributionTail.OneLower;
}
}
/// <summary>
/// Creates a new One-Sample Kolmogorov test.
/// </summary>
///
/// <param name="sample">The sample we would like to test as belonging to the <paramref name="hypothesizedDistribution"/>.</param>
/// <param name="hypothesizedDistribution">A fully specified distribution (which must NOT have been estimated from the data).</param>
///
public KolmogorovSmirnovTest(double[] sample, UnivariateContinuousDistribution hypothesizedDistribution)
: this(sample, hypothesizedDistribution, KolmogorovSmirnovTestHypothesis.SampleIsDifferent)
{
}
internal ContinuousDistribution(UnivariateContinuousDistribution baseDistribution, int samples, double coeff, double offset)
: this(baseDistribution, samples)
{
Coefficient = coeff;
Offset = offset;
}
internal ContinuousDistribution(UnivariateContinuousDistribution baseDistribution)
{
BaseDistribution = baseDistribution ?? throw new ArgumentNullException(nameof(baseDistribution));
}
/// <summary>
/// Initializes a new instance of the <see cref="CustomDistribution"/> class
/// based on <paramref name="distribution"/>, reference for <see cref="Accord.Statistics"/> required.
/// </summary>
/// <param name="distribution">Base distribution, reference for <see cref="Accord.Statistics"/> required.</param>
public CustomDistribution(UnivariateContinuousDistribution distribution)
{
Distribution = distribution;
}
