public void DataSetManipulationsTest()
{
UncertainMeasurement <double> d1 = new UncertainMeasurement <double>(3.0, new UncertainValue(2.0, 1.0));
UncertainMeasurement <double> d2 = new UncertainMeasurement <double>(-3.0, new UncertainValue(2.0, 1.0));
UncertainMeasurement <double> d3 = new UncertainMeasurement <double>(3.0, new UncertainValue(-2.0, 1.0));
Assert.IsTrue(d1 != null);
UncertainMeasurement <double>[] data = new UncertainMeasurement <double>[] { d1, d2 };
UncertainMeasurementSample set = new UncertainMeasurementSample();
set.Add(data);
Assert.IsFalse(set.Contains(d3));
Assert.IsTrue(set.Count == data.Length);
set.Add(d3);
Assert.IsTrue(set.Contains(d3));
Assert.IsTrue(set.Count == data.Length + 1);
set.Remove(d3);
Assert.IsFalse(set.Contains(d3));
Assert.IsTrue(set.Count == data.Length);
set.Clear();
Assert.IsTrue(set.Count == 0);
}
public void Bug6162()
{
// When UncertianMeasurementSample.FitToPolynomial used Cholesky inversion of (A^T A), this inversion
// would fail when roundoff errors would made the matrix non-positive-definite. We have now changed
// to QR decomposition, which is more robust.
//real data
double[] X_axis = new double[] { 40270.65625, 40270.6569444444, 40270.6576388888, 40270.6583333332, 40270.6590277776,
40270.659722222, 40270.6604166669, 40270.6611111113, 40270.6618055557, 40270.6625000001 };
double[] Y_axis = new double[] { 246.824996948242, 246.850006103516, 245.875, 246.225006103516, 246.975006103516,
247.024993896484, 246.949996948242, 246.875, 247.5, 247.100006103516 };
UncertainMeasurementSample DataSet = new UncertainMeasurementSample();
for (int i = 0; i < 10; i++)
{
DataSet.Add(X_axis[i], Y_axis[i], 1);
}
UncertainMeasurementFitResult DataFit = DataSet.FitToPolynomial(3);
BivariateSample bs = new BivariateSample();
for (int i = 0; i < 10; i++)
{
bs.Add(X_axis[i], Y_axis[i]);
}
PolynomialRegressionResult bsFit = bs.PolynomialRegression(3);
foreach (Parameter p in bsFit.Parameters)
{
Console.WriteLine(p);
}
}
public void FitDataToFunctionTest()
{
// create a data set from a nonlinear function
/*
* Interval r = Interval.FromEndpoints(-3.0, 5.0);
* double[] c = new double[] { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };
* Function<double, double> fv = delegate(double x) {
* return (3.0 * Math.Cos(2.0 * Math.PI * x / 2.0 - 1.0));
* };
* Function<double, double> fu = delegate(double x) {
* return (0.1 + 0.1 * Math.Abs(x));
* };
* DataSet set = CreateDataSet(r, fv, fu, 20, 2);
*/
UncertainMeasurementSample set = new UncertainMeasurementSample();
set.Add(new UncertainMeasurement <double>(1.0, 1.0, 0.1));
set.Add(new UncertainMeasurement <double>(2.0, 0.7, 0.1));
set.Add(new UncertainMeasurement <double>(3.0, 0.0, 0.1));
set.Add(new UncertainMeasurement <double>(4.0, -0.7, 0.1));
set.Add(new UncertainMeasurement <double>(5.0, -1.0, 0.1));
set.Add(new UncertainMeasurement <double>(6.0, -0.7, 0.1));
set.Add(new UncertainMeasurement <double>(7.0, 0.0, 0.1));
set.Add(new UncertainMeasurement <double>(8.0, 0.7, 0.1));
set.Add(new UncertainMeasurement <double>(9.0, 1.0, 0.1));
// fit it to a parameterized fit function
/*
* Function<double[], double, double> ff = delegate(double[] p, double x) {
* return (p[0] * Math.Cos(2.0 * Math.PI / p[1] + p[2]));
* };
*/
Func <double[], double, double> ff = delegate(double[] p, double x) {
//Console.WriteLine(" p[0]={0}, x={1}", p[0], x);
return(p[1] * Math.Cos(x / p[0] + p[2]));
//return (x / p[0]);
};
FitResult fit = set.FitToFunction(ff, new double[] { 1.3, 1.1, 0.1 });
Console.WriteLine(fit.Parameter(0));
Console.WriteLine(fit.Parameter(1));
Console.WriteLine(fit.Parameter(2));
}
private UncertainMeasurementSample CreateDataSet(double[] xs, Func <double, double> fv, Func <double, double> fu, int seed)
{
UncertainMeasurementSample set = new UncertainMeasurementSample();
Random rng = new Random(seed);
foreach (double x in xs)
{
double ym = fv(x);
double ys = fu(x);
NormalDistribution yd = new NormalDistribution(ym, ys);
double y = yd.InverseLeftProbability(rng.NextDouble());
UncertainMeasurement <double> point = new UncertainMeasurement <double>(x, y, ys);
set.Add(point);
}
return(set);
}
private UncertainMeasurementSample CreateDataSet(Interval r, Func <double, double> fv, Func <double, double> fu, int n, int seed)
{
UncertainMeasurementSample set = new UncertainMeasurementSample();
UniformDistribution xd = new UniformDistribution(r);
Random rng = new Random(seed);
for (int i = 0; i < n; i++)
{
double x = xd.InverseLeftProbability(rng.NextDouble());
double ym = fv(x);
double ys = fu(x);
NormalDistribution yd = new NormalDistribution(ym, ys);
double y = yd.InverseLeftProbability(rng.NextDouble());
//Console.WriteLine("{0}, {1}", x, new UncertainValue(y, ys));
UncertainMeasurement <double> point = new UncertainMeasurement <double>(x, y, ys);
set.Add(point);
}
return(set);
}
public void Bug6162()
{
// When UncertianMeasurementSample.FitToPolynomial used Cholesky inversion of (A^T A), this inversion
// would fail when roundoff errors would made the matrix non-positive-definite. We have now changed
// to QR decomposition, which is more robust.
//real data
double[] X_axis = new double[] { 40270.65625, 40270.6569444444, 40270.6576388888, 40270.6583333332, 40270.6590277776,
40270.659722222, 40270.6604166669, 40270.6611111113, 40270.6618055557, 40270.6625000001 };
double[] Y_axis = new double[] { 246.824996948242, 246.850006103516, 245.875, 246.225006103516, 246.975006103516,
247.024993896484, 246.949996948242, 246.875, 247.5, 247.100006103516 };
UncertainMeasurementSample DataSet = new UncertainMeasurementSample();
for (int i = 0; i < 10; i++) DataSet.Add(X_axis[i], Y_axis[i], 1);
//for (int i = 0; i < 10; i++) DataSet.Add(X_axis[i] - 40270.0, Y_axis[i] - 247.0, 1);
FitResult DataFit = DataSet.FitToPolynomial(3);
for (int i = 0; i < DataFit.Dimension; i++)
Console.WriteLine("a" + i.ToString() + " = " + DataFit.Parameter(i).Value);
BivariateSample bs = new BivariateSample();
for (int i = 0; i < 10; i++) bs.Add(X_axis[i], Y_axis[i]);
FitResult bsFit = bs.PolynomialRegression(3);
for (int i = 0; i < bsFit.Dimension; i++) Console.WriteLine(bsFit.Parameter(i));
}
