public void Bukin()
{
// Burkin has a narrow valley, not aligned with any axis, punctuated with many tiny "wells" along its bottom.
// The deepest well is at (-10,1)-> 0.
Func <IList <double>, double> function = (IList <double> x) => 100.0 * Math.Sqrt(Math.Abs(x[1] - 0.01 * x[0] * x[0])) + 0.01 * Math.Abs(x[0] + 10.0);
IList <Interval> box = new Interval[] { Interval.FromEndpoints(-15.0, 0.0), Interval.FromEndpoints(-3.0, 3.0) };
EvaluationSettings settings = new EvaluationSettings()
{
RelativePrecision = 0.0, AbsolutePrecision = 1.0E-4, EvaluationBudget = 1000000
};
/*
* settings.Update += (object result) => {
* MultiExtremum e = (MultiExtremum) result;
* Console.WriteLine("After {0} evaluations, best value {1}", e.EvaluationCount, e.Value);
* };
*/
MultiExtremum minimum = MultiFunctionMath.FindGlobalMinimum(function, box, settings);
Console.WriteLine(minimum.EvaluationCount);
Console.WriteLine("{0} ({1})", minimum.Value, minimum.Precision);
Console.WriteLine("{0} {1}", minimum.Location[0], minimum.Location[1]);
// We do not end up finding the global minimum.
}
public void EllipticKIntegrals()
{
Interval i = Interval.FromEndpoints(0.0, 1.0);
// http://mathworld.wolfram.com/CatalansConstant.html equation 37
Assert.IsTrue(TestUtilities.IsNearlyEqual(
FunctionMath.Integrate(
k => AdvancedMath.EllipticK(k),
Interval.FromEndpoints(0.0, 1.0)),
2.0 * AdvancedMath.Catalan
));
Assert.IsTrue(TestUtilities.IsNearlyEqual(
FunctionMath.Integrate(
k => AdvancedMath.EllipticK(k) * k,
Interval.FromEndpoints(0.0, 1.0)),
1.0
));
Assert.IsTrue(TestUtilities.IsNearlyEqual(
FunctionMath.Integrate(
k => AdvancedMath.EllipticK(k) / (1.0 + k),
Interval.FromEndpoints(0.0, 1.0)),
Math.PI * Math.PI / 8.0
));
}
public void FitToFunctionPolynomialCompatibilityTest()
{
// specify a cubic function
Interval r = Interval.FromEndpoints(-10.0, 10.0);
Func <double, double> fv = delegate(double x) {
return(0.0 - 1.0 * x + 2.0 * x * x - 3.0 * x * x * x);
};
Func <double, double> fu = delegate(double x) {
return(1.0 + 0.5 * Math.Cos(x));
};
// create a data set from it
UncertainMeasurementSample set = CreateDataSet(r, fv, fu, 60);
// fit it to a cubic polynomial
UncertainMeasurementFitResult pFit = set.FitToPolynomial(3);
// fit it to a cubic polynomial
Func <double[], double, double> ff = delegate(double[] p, double x) {
return(p[0] + p[1] * x + p[2] * x * x + p[3] * x * x * x);
};
UncertainMeasurementFitResult fFit = set.FitToFunction(ff, new double[] { 0, 0, 0, 0 });
// dimension
Assert.IsTrue(pFit.Parameters.Count == fFit.Parameters.Count);
// chi squared
Assert.IsTrue(TestUtilities.IsNearlyEqual(pFit.GoodnessOfFit.Statistic, fFit.GoodnessOfFit.Statistic, Math.Sqrt(TestUtilities.TargetPrecision)));
// don't demand super-high precision agreement of parameters and covariance matrix
// parameters
Assert.IsTrue(TestUtilities.IsNearlyEqual(pFit.Parameters.ValuesVector, fFit.Parameters.ValuesVector, Math.Pow(TestUtilities.TargetPrecision, 0.3)));
// covariance
Assert.IsTrue(TestUtilities.IsNearlyEqual(pFit.Parameters.CovarianceMatrix, fFit.Parameters.CovarianceMatrix, Math.Pow(TestUtilities.TargetPrecision, 0.3)));
}
public void FitDataToLinearFunctionTest()
{
// create a data set from a linear combination of sine and cosine
Interval r = Interval.FromEndpoints(-4.0, 6.0);
double[] c = new double[] { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };
Func <double, double> fv = delegate(double x) {
return(2.0 * Math.Cos(x) + 1.0 * Math.Sin(x));
};
Func <double, double> fu = delegate(double x) {
return(0.1 + 0.1 * Math.Abs(x));
};
UncertainMeasurementSample set = CreateDataSet(r, fv, fu, 20, 2);
// fit the data set to a linear combination of sine and cosine
Func <double, double>[] fs = new Func <double, double>[]
{ delegate(double x) { return(Math.Cos(x)); }, delegate(double x) { return(Math.Sin(x)); } };
FitResult result = set.FitToLinearFunction(fs);
// the fit should be right right dimension
Assert.IsTrue(result.Dimension == 2);
// the coefficients should match
Console.WriteLine(result.Parameter(0));
Console.WriteLine(result.Parameter(1));
Assert.IsTrue(result.Parameter(0).ConfidenceInterval(0.95).ClosedContains(2.0));
Assert.IsTrue(result.Parameter(1).ConfidenceInterval(0.95).ClosedContains(1.0));
// diagonal covarainces should match errors
Assert.IsTrue(TestUtilities.IsNearlyEqual(Math.Sqrt(result.Covariance(0, 0)), result.Parameter(0).Uncertainty));
Assert.IsTrue(TestUtilities.IsNearlyEqual(Math.Sqrt(result.Covariance(1, 1)), result.Parameter(1).Uncertainty));
}
public void TestBivariateRegression()
{
// Do a bunch of linear regressions. r^2 should be distributed as expected.
double a0 = 1.0;
double b0 = 0.0;
Random rng = new Random(1001110000);
ContinuousDistribution xDistribution = new UniformDistribution(Interval.FromEndpoints(-2.0, 4.0));
ContinuousDistribution eDistribution = new NormalDistribution();
List <double> r2Sample = new List <double>();
for (int i = 0; i < 500; i++)
{
BivariateSample xySample = new BivariateSample();
for (int k = 0; k < 10; k++)
{
double x = xDistribution.GetRandomValue(rng);
double y = a0 + b0 * x + eDistribution.GetRandomValue(rng);
xySample.Add(x, y);
}
LinearRegressionResult fit = xySample.LinearRegression();
double a = fit.Intercept.Value;
double b = fit.Slope.Value;
r2Sample.Add(fit.RSquared);
}
ContinuousDistribution r2Distribution = new BetaDistribution((2 - 1) / 2.0, (10 - 2) / 2.0);
TestResult ks = r2Sample.KolmogorovSmirnovTest(r2Distribution);
Assert.IsTrue(ks.Probability > 0.05);
}
public void EllipticKCatalanIntegral()
{
System.Diagnostics.Stopwatch timer = System.Diagnostics.Stopwatch.StartNew();
Interval i = Interval.FromEndpoints(0.0, 1.0);
// http://mathworld.wolfram.com/CatalansConstant.html equation 37
Func <double, double> f1 = delegate(double k) {
return(AdvancedMath.EllipticK(k));
};
Assert.IsTrue(TestUtilities.IsNearlyEqual(
FunctionMath.Integrate(f1, i), 2.0 * AdvancedMath.Catalan
));
Func <double, double> f2 = delegate(double k) {
return(AdvancedMath.EllipticK(k) * k);
};
Assert.IsTrue(TestUtilities.IsNearlyEqual(
FunctionMath.Integrate(f2, i), 1.0
));
timer.Stop();
Console.WriteLine(timer.ElapsedTicks);
}
public void RootOfEi()
{
double x = FunctionMath.FindZero(AdvancedMath.IntegralEi, Interval.FromEndpoints(0.1, 1.0));
Assert.IsTrue(TestUtilities.IsNearlyEqual(x, 0.37250741078136663446));
Assert.IsTrue(Math.Abs(AdvancedMath.IntegralEi(x)) < TestUtilities.TargetPrecision);
}
public void ThompsonGlobal()
{
for (int n = 2; n < 8; n++)
{
Console.WriteLine("n={0}", n);
Func <IList <double>, double> f = GetThompsonFunction(n);
Interval[] box = new Interval[2 * (n - 1)];
for (int i = 0; i < (n - 1); i++)
{
box[2 * i] = Interval.FromEndpoints(-Math.PI, Math.PI);
box[2 * i + 1] = Interval.FromEndpoints(-Math.PI / 2.0, Math.PI / 2.0);
}
MultiExtremum minimum = MultiFunctionMath.FindGlobalMinimum(f, box);
Console.WriteLine(minimum.EvaluationCount);
Console.WriteLine("{0} ({1})", minimum.Value, minimum.Precision);
for (int i = 0; i < (n - 1); i++)
{
Console.WriteLine("{0} {1}", minimum.Location[2 * i], minimum.Location[2 * i + 1]);
}
Console.WriteLine("0.0 0.0");
Assert.IsTrue(minimum.Dimension == 2 * (n - 1));
Assert.IsTrue(TestUtilities.IsNearlyEqual(minimum.Value, thompsonSolutions[n], new EvaluationSettings()
{
AbsolutePrecision = 2.0 * minimum.Precision
}));
}
}
public void FindExtremaNegativeGamma()
{
// https://en.wikipedia.org/wiki/Particular_values_of_the_Gamma_function#Other_constants
Tuple <double, double>[] extrema = new Tuple <double, double>[] {
Tuple.Create(-0.5040830082644554092582693045, -3.5446436111550050891219639933),
Tuple.Create(-1.5734984731623904587782860437, 2.3024072583396801358235820396),
Tuple.Create(-2.6107208684441446500015377157, -0.8881363584012419200955280294),
Tuple.Create(-3.6352933664369010978391815669, 0.2451275398343662504382300889)
};
foreach (Tuple <double, double> extremum in extrema)
{
// We use brackets since we know the extremum must lie between the singularities.
// We should be able to use the actual singularities at endpoints, but this doesn't work; look into it.
Interval bracket = Interval.FromEndpoints(Math.Floor(extremum.Item1) + 0.01, Math.Ceiling(extremum.Item1) - 0.01);
Extremum result;
if (extremum.Item2 < 0.0)
{
result = FunctionMath.FindMaximum(AdvancedMath.Gamma, bracket);
}
else
{
result = FunctionMath.FindMinimum(AdvancedMath.Gamma, bracket);
}
Assert.IsTrue(result.Bracket.OpenContains(extremum.Item1));
Assert.IsTrue(result.Bracket.OpenContains(result.Location));
Assert.IsTrue(TestUtilities.IsNearlyEqual(result.Value, extremum.Item2));
}
}
public void WatsonIntegrals()
{
// Watson defined and analytically integrated three complicated triple integrals related to random walks in three dimension
// See http://mathworld.wolfram.com/WatsonsTripleIntegrals.html
Interval watsonWidth = Interval.FromEndpoints(0.0, Math.PI);
Interval[] watsonBox = new Interval[] { watsonWidth, watsonWidth, watsonWidth };
Assert.IsTrue(
MultiFunctionMath.Integrate(
(IReadOnlyList <double> x) => 1.0 / (1.0 - Math.Cos(x[0]) * Math.Cos(x[1]) * Math.Cos(x[2])), watsonBox
).Estimate.ConfidenceInterval(0.99).ClosedContains(
MoreMath.Pow(AdvancedMath.Gamma(1.0 / 4.0), 4) / 4.0
)
);
Assert.IsTrue(
MultiFunctionMath.Integrate(
(IReadOnlyList <double> x) => 1.0 / (3.0 - Math.Cos(x[0]) * Math.Cos(x[1]) - Math.Cos(x[1]) * Math.Cos(x[2]) - Math.Cos(x[0]) * Math.Cos(x[2])), watsonBox
).Estimate.ConfidenceInterval(0.99).ClosedContains(
3.0 * MoreMath.Pow(AdvancedMath.Gamma(1.0 / 3.0), 6) / Math.Pow(2.0, 14.0 / 3.0) / Math.PI
)
);
Assert.IsTrue(
MultiFunctionMath.Integrate(
(IReadOnlyList <double> x) => 1.0 / (3.0 - Math.Cos(x[0]) - Math.Cos(x[1]) - Math.Cos(x[2])), watsonBox
).Estimate.ConfidenceInterval(0.99).ClosedContains(
Math.Sqrt(6.0) / 96.0 * AdvancedMath.Gamma(1.0 / 24.0) * AdvancedMath.Gamma(5.0 / 24.0) * AdvancedMath.Gamma(7.0 / 24.0) * AdvancedMath.Gamma(11.0 / 24.0)
)
);
}
public void JacobiIntegrals()
{
foreach (double k in TestUtilities.GenerateRealValues(1.0E-1, 1.0, 4))
{
// DLMF 22.14.18 says
// \int_{0}^{K(k)} \ln(\dn(t, k)) \, dt = \frac{1}{2} K(k) \ln k'
double K = AdvancedMath.EllipticK(k);
double k1 = Math.Sqrt(1.0 - k * k);
double I1 = FunctionMath.Integrate(
x => Math.Log(AdvancedMath.JacobiDn(x, k)),
Interval.FromEndpoints(0.0, K)
);
Assert.IsTrue(TestUtilities.IsNearlyEqual(I1, K / 2.0 * Math.Log(k1)));
// If k is small, log(k1) is subject to cancellation error, so don't pick k too small.
// It also gives values for the analogous integrals with \sn and \cn,
// but their values involve K'(k), for which we don't have a method.
// Mathworld (http://mathworld.wolfram.com/CompleteEllipticIntegraloftheSecondKind.html) says
// \int_{0}^{K(k)} \dn^2(t, k) = E(k)
double I2 = FunctionMath.Integrate(
u => MoreMath.Sqr(AdvancedMath.JacobiDn(u, k)),
Interval.FromEndpoints(0.0, K)
);
Assert.IsTrue(TestUtilities.IsNearlyEqual(I2, AdvancedMath.EllipticE(k)));
}
}
public void HypergeometricIntegral()
{
// A&S 15.3.1
// F(a, b, c, x) = \frac{\Gamma(c)}{\Gamma(b) \Gamma(c - b)}
// \int_{0}^{1} \! dt \, t^{b-1} (1 - t)^{c - b - 1} (1 - x t)^{-a}
// Choose limits on a, b, c so that singularities of integrand are numerically integrable.
foreach (double a in TestUtilities.GenerateRealValues(1.0, 10.0, 2))
{
foreach (double b in TestUtilities.GenerateRealValues(0.6, 10.0, 2))
{
foreach (double c in TestUtilities.GenerateRealValues(b + 0.6, 10.0, 2))
{
foreach (double x in xs)
{
double I = FunctionMath.Integrate(
t => Math.Pow(t, b - 1.0) * Math.Pow(1.0 - t, c - b - 1.0) * Math.Pow(1.0 - x * t, -a),
Interval.FromEndpoints(0.0, 1.0)
);
double B = AdvancedMath.Beta(b, c - b);
Assert.IsTrue(TestUtilities.IsNearlyEqual(AdvancedMath.Hypergeometric2F1(a, b, c, x), I / B));
}
}
}
}
}
public void IntervalMidpoint()
{
Interval ab = Interval.FromEndpoints(a, b);
Assert.IsTrue(ab.Midpoint == (a + b) / 2.0);
Assert.IsTrue(ab.Contains(ab.Midpoint));
}
public void FitDataToProportionalityTest()
{
Interval r = Interval.FromEndpoints(0.0, 0.1);
Func <double, double> fv = delegate(double x) {
return(0.5 * x);
};
Func <double, double> fu = delegate(double x) {
return(0.02);
};
UncertainMeasurementSample set = CreateDataSet(r, fv, fu, 20);
// fit to proportionality
FitResult prop = set.FitToProportionality();
Assert.IsTrue(prop.Dimension == 1);
Assert.IsTrue(prop.Parameter(0).ConfidenceInterval(0.95).ClosedContains(0.5));
Assert.IsTrue(prop.GoodnessOfFit.LeftProbability < 0.95);
// fit to line
FitResult line = set.FitToLine();
Assert.IsTrue(line.Dimension == 2);
// line's intercept should be compatible with zero and slope with proportionality constant
Assert.IsTrue(line.Parameter(0).ConfidenceInterval(0.95).ClosedContains(0.0));
Assert.IsTrue(line.Parameter(1).ConfidenceInterval(0.95).ClosedContains(prop.Parameter(0).Value));
// the fit should be better, but not too much better
Assert.IsTrue(line.GoodnessOfFit.Statistic < prop.GoodnessOfFit.Statistic);
}
public void MultivariateLinearRegressionAgreement2()
{
// A multivariate linear regression with just one x-column should be the same as a bivariate linear regression.
double intercept = 1.0;
double slope = -2.0;
ContinuousDistribution yErrDist = new NormalDistribution(0.0, 3.0);
UniformDistribution xDist = new UniformDistribution(Interval.FromEndpoints(-2.0, 3.0));
Random rng = new Random(1111111);
MultivariateSample multi = new MultivariateSample("x", "y");
for (int i = 0; i < 10; i++)
{
double x = xDist.GetRandomValue(rng);
double y = intercept + slope * x + yErrDist.GetRandomValue(rng);
multi.Add(x, y);
}
// Old multi linear regression code.
MultiLinearRegressionResult result1 = multi.LinearRegression(1);
// Simple linear regression code.
LinearRegressionResult result2 = multi.TwoColumns(0, 1).LinearRegression();
Assert.IsTrue(TestUtilities.IsNearlyEqual(result1.Parameters["Intercept"].Estimate, result2.Parameters["Intercept"].Estimate));
// New multi linear regression code.
MultiLinearRegressionResult result3 = multi.Column(1).ToList().MultiLinearRegression(multi.Column(0).ToList());
Assert.IsTrue(TestUtilities.IsNearlyEqual(result1.Parameters["Intercept"].Estimate, result3.Parameters["Intercept"].Estimate));
}
public void AssociatedLegendreOrthonormalityL()
{
// don't let l and m get too big, or numerical integration will fail due to heavily oscilatory behavior
int[] ells = TestUtilities.GenerateIntegerValues(1, 10, 4);
for (int ki = 0; ki < ells.Length; ki++)
{
int k = ells[ki];
for (int li = 0; li <= ki; li++)
{
int l = ells[li];
foreach (int m in TestUtilities.GenerateUniformIntegerValues(0, Math.Min(k, l), 4))
{
Func <double, double> f = delegate(double x) {
return(OrthogonalPolynomials.LegendreP(k, m, x) * OrthogonalPolynomials.LegendreP(l, m, x));
};
double I = FunctionMath.Integrate(f, Interval.FromEndpoints(-1.0, 1.0));
Console.WriteLine("k={0} l={1} m={2} I={3}", k, l, m, I);
if (k == l)
{
Assert.IsTrue(TestUtilities.IsNearlyEqual(
I, 2.0 / (2 * k + 1) * Math.Exp(AdvancedIntegerMath.LogFactorial(l + m) - AdvancedIntegerMath.LogFactorial(l - m))
));
}
else
{
Assert.IsTrue(Math.Abs(I) < TestUtilities.TargetPrecision);
}
}
}
}
}
public void FitDataToPolynomialChiSquaredTest()
{
// we want to make sure that the chi^2 values we are producing from polynomial fits are distributed as expected
// create a sample to hold chi^2 values
Sample chis = new Sample();
// define a model
Interval r = Interval.FromEndpoints(-5.0, 15.0);
Func <double, double> fv = delegate(double x) {
return(1.0 * x - 2.0 * x * x);
};
Func <double, double> fu = delegate(double x) {
return(1.0 + 0.5 * Math.Sin(x));
};
// draw 50 data sets from the model and fit year
// store the resulting chi^2 value in the chi^2 set
for (int i = 0; i < 50; i++)
{
UncertainMeasurementSample xs = CreateDataSet(r, fv, fu, 10, i);
FitResult fit = xs.FitToPolynomial(2);
double chi = fit.GoodnessOfFit.Statistic;
chis.Add(chi);
}
// sanity check the sample
Assert.IsTrue(chis.Count == 50);
// test whether the chi^2 values are distributed as expected
ContinuousDistribution chiDistribution = new ChiSquaredDistribution(7);
TestResult ks = chis.KolmogorovSmirnovTest(chiDistribution);
Assert.IsTrue(ks.LeftProbability < 0.95);
}
public void IntervalEndpoints()
{
Interval ab = Interval.FromEndpoints(a, b);
Assert.IsTrue(ab.LeftEndpoint == a);
Assert.IsTrue(ab.RightEndpoint == b);
}
public void IntervalZeroWidth()
{
Interval aa = Interval.FromEndpoints(a, a);
Assert.IsTrue(aa.Width == 0.0);
Assert.IsTrue(aa.LeftEndpoint == aa.RightEndpoint);
Assert.IsTrue(aa.Midpoint == a);
}
public void RootOfJ0()
{
Func <double, double> f = delegate(double x) {
return(AdvancedMath.BesselJ(0, x));
};
double y = FunctionMath.FindZero(f, Interval.FromEndpoints(2.0, 4.0));
Assert.IsTrue(TestUtilities.IsNearlyEqual(y, 2.40482555769577276862));
}
// Returns a d-dimensional symmetric unit cube [-1,+1]^d
public Interval[] SymmetricUnitCube(int d)
{
Interval[] box = new Interval[d];
for (int j = 0; j < d; j++)
{
box[j] = Interval.FromEndpoints(-1.0, 1.0);
}
return(box);
}
public void BesselWeberIntegral()
{
Func <double, double> f = delegate(double t) {
return(Math.Exp(-t * t) * AdvancedMath.BesselJ(0, t) * t);
};
Interval r = Interval.FromEndpoints(0.0, Double.PositiveInfinity);
Assert.IsTrue(TestUtilities.IsNearlyEqual(FunctionMath.Integrate(f, r).Estimate.Value, Math.Exp(-1.0 / 4.0) / 2.0));
}
public void IntervalClosedContains()
{
Interval ac = Interval.FromEndpoints(a, c);
Assert.IsTrue(ac.ClosedContains(a));
Assert.IsTrue(ac.ClosedContains(c));
Assert.IsTrue(ac.ClosedContains(b));
Assert.IsFalse(ac.ClosedContains(d));
}
public void MultivariateLinearRegressionSimple()
{
// define model y = a + b0 * x0 + b1 * x1 + noise
double a = 1.0;
double b0 = -2.0;
double b1 = 3.0;
ContinuousDistribution x0distribution = new CauchyDistribution(10.0, 5.0);
ContinuousDistribution x1distribution = new UniformDistribution(Interval.FromEndpoints(-10.0, 20.0));
ContinuousDistribution noise = new NormalDistribution(0.0, 10.0);
// draw a sample from the model
Random rng = new Random(1);
MultivariateSample sample = new MultivariateSample("x0", "x1", "y");
FrameTable table = new FrameTable();
table.AddColumns <double>("x0", "x1", "y");
for (int i = 0; i < 100; i++)
{
double x0 = x0distribution.GetRandomValue(rng);
double x1 = x1distribution.GetRandomValue(rng);
double eps = noise.GetRandomValue(rng);
double y = a + b0 * x0 + b1 * x1 + eps;
sample.Add(x0, x1, y);
table.AddRow(x0, x1, y);
}
// do a linear regression fit on the model
ParameterCollection oldResult = sample.LinearRegression(2).Parameters;
MultiLinearRegressionResult newResult = table["y"].As <double>().MultiLinearRegression(
table["x0"].As <double>(), table["x1"].As <double>()
);
// the result should have the appropriate dimension
Assert.IsTrue(oldResult.Count == 3);
Assert.IsTrue(newResult.Parameters.Count == 3);
// The parameters should match the model
Assert.IsTrue(oldResult[0].Estimate.ConfidenceInterval(0.90).ClosedContains(b0));
Assert.IsTrue(oldResult[1].Estimate.ConfidenceInterval(0.90).ClosedContains(b1));
Assert.IsTrue(oldResult[2].Estimate.ConfidenceInterval(0.90).ClosedContains(a));
Assert.IsTrue(newResult.CoefficientOf(0).ConfidenceInterval(0.99).ClosedContains(b0));
Assert.IsTrue(newResult.CoefficientOf("x1").ConfidenceInterval(0.99).ClosedContains(b1));
Assert.IsTrue(newResult.Intercept.ConfidenceInterval(0.99).ClosedContains(a));
// The residuals should be compatible with the model predictions
for (int i = 0; i < table.Rows.Count; i++)
{
FrameRow row = table.Rows[i];
double x0 = (double)row["x0"];
double x1 = (double)row["x1"];
double yp = newResult.Predict(x0, x1).Value;
double y = (double)row["y"];
Assert.IsTrue(TestUtilities.IsNearlyEqual(newResult.Residuals[i], y - yp));
}
}
public void IntervalEquality()
{
Interval ab = Interval.FromEndpoints(a, b);
Interval ac = Interval.FromEndpoints(a, c);
Assert.IsTrue(ab.Equals(ab));
Assert.IsTrue(ab.Equals((object)ab));
Assert.IsFalse(ab.Equals(ac));
Assert.IsFalse(ab.Equals((object)ac));
}
public void IntegralSiDefinition()
{
Func <double, double> f = t => Math.Sin(t) / t;
foreach (double x in TestUtilities.GenerateRealValues(1.0E-2, 1.0E2, 8))
{
Interval r = Interval.FromEndpoints(0.0, x);
Assert.IsTrue(TestUtilities.IsNearlyEqual(AdvancedMath.IntegralSi(x), FunctionMath.Integrate(f, r)));
}
}
public void BesselLipshitzIntegral()
{
// \int_{0}^{\infty} e^{-x} J_0(x) \, dx = \frac{1}{\sqrt{2}}
Func <double, double> f = delegate(double t) {
return(Math.Exp(-t) * AdvancedMath.BesselJ(0, t));
};
Interval r = Interval.FromEndpoints(0.0, Double.PositiveInfinity);
Assert.IsTrue(TestUtilities.IsNearlyEqual(FunctionMath.Integrate(f, r).Estimate.Value, 1.0 / Math.Sqrt(2.0)));
}
public void GammaIntegral()
{
foreach (double x in TestUtilities.GenerateRealValues(1.0, 1.0E2, 4))
{
Func <double, double> f = delegate(double t) {
return(Math.Pow(t, x - 1.0) * Math.Exp(-t));
};
Interval r = Interval.FromEndpoints(0.0, Double.PositiveInfinity);
Assert.IsTrue(TestUtilities.IsNearlyEqual(AdvancedMath.Gamma(x), FunctionMath.Integrate(f, r)));
}
}
public void AiryAiIntegral()
{
// DLMF 9.10.11
Func <double, double> f = delegate(double t) {
return(AdvancedMath.AiryAi(t));
};
Interval r = Interval.FromEndpoints(0.0, Double.PositiveInfinity);
double I = FunctionMath.Integrate(f, r);
Assert.IsTrue(TestUtilities.IsNearlyEqual(I, 1.0 / 3.0));
}
public void IntervalContainsEndpoints()
{
Interval ab = Interval.FromEndpoints(a, b);
Assert.IsTrue(ab.Contains(a, IntervalType.Closed));
Assert.IsFalse(ab.Contains(a, IntervalType.Open));
Assert.IsTrue(ab.Contains(a, IntervalType.Closed, IntervalType.Open));
Assert.IsTrue(ab.Contains(b, IntervalType.Closed));
Assert.IsFalse(ab.Contains(b, IntervalType.Open));
Assert.IsFalse(ab.Contains(b, IntervalType.Closed, IntervalType.Open));
}
