public void fit_example()
{
#region doc_fit
// Let's say we have a UnivariateContinuousDistribution that we have
// built somehow, either using a constructor for a common distribution
// or that we have received as the output of a method we have called:
UnivariateContinuousDistribution dist = new NormalDistribution();
// Let's say we have a set of observations, and some optional weights:
double[] observations = { 0.12, 2, 0.52 };
// Note: the weights are optional. You do not need to have different weights
// for the different observations you would like to fit, but we will use them
// as an example to show that it is also possible to specify them if we would
// like to, but we could also set them to null in case we do not need them:
double[] weights = { 0.25, 0.25, 0.50 }; // could also be null
// Now, we can finally fit the distribution to the observations that we have:
dist.Fit(observations, weights); // changes 'dist' to become the dist we need
// Now we can verify that the distribution has been updated:
double mean = dist.Mean; // should be 0.79
double var = dist.Variance; // should be 0.82352
double stdDev = dist.StandardDeviation; // should be 0.90748002732842559
#endregion
Assert.AreEqual(0.79, mean);
Assert.AreEqual(0.82352, var);
Assert.AreEqual(0.90748002732842559, stdDev);
}
public void FitTest2()
{
NormalDistribution target;
target = new NormalDistribution();
double[] observations = { 1, 1, 1, 1 };
bool thrown = false;
try { target.Fit(observations); }
catch (ArgumentException) { thrown = true; }
Assert.IsTrue(thrown);
}
public NormalDistributionInfo GetPeakNormalDistribution(double peak, int index, int numOfPoints)
{
NormalDistributionInfo Info = new NormalDistributionInfo();
List <double> ls = new List <double>();
Dictionary <double, int> dict = CalculateTimesEachX(index);
double[] keyArr = dict.Keys.ToArray();
int i = 0;
for (i = 0; i < keyArr.Length; i++)
{
if (keyArr[i] == peak)
{
break;
}
}
for (int j = i - numOfPoints; j < i; j++)
{
if (j < 0)
{
j = 0;
}
int value = dict[keyArr[j]];
for (int k = 0; k < value; k++)
{
ls.Add(keyArr[j]);
}
}
for (int j = i; j < i + numOfPoints + 1; j++)
{
if (j >= keyArr.Length)
{
break;
}
int value = dict[keyArr[j]];
for (int k = 0; k < value; k++)
{
ls.Add(keyArr[j]);
}
}
NormalDistribution ND = new NormalDistribution();
ND.Fit(ls.ToArray());
Info.mean = ND.Mean;
Info.SD = ND.StandardDeviation;
return(Info);
}
public FuncionNormal(double[] eventos) : base(eventos)
{
try
{
DistribucionContinua = new NormalDistribution();
DistribucionContinua.Fit(eventos);
media = ((NormalDistribution)DistribucionContinua).Mean.ToString("0.0000");
sigma = ((NormalDistribution)DistribucionContinua).StandardDeviation.ToString("0.0000");
Resultado = new ResultadoAjuste(StringFDP, StringInversa, DistribucionContinua.StandardDeviation, DistribucionContinua.Mean, DistribucionContinua.Variance, this);
}
catch (Exception)
{
Resultado = null;
}
}
public void FitExtensionTest()
{
NormalDistribution target = new NormalDistribution();
double[] observations = { 0.10, 0.40, 2.00, 2.00 };
target.Fit(observations);
NormalDistribution same = observations.Fit <NormalDistribution>();
Assert.AreNotSame(same, target);
Assert.AreEqual(same.ToString(), target.ToString());
NormalDistribution copy = target.FitNew(observations);
Assert.AreNotSame(copy, target);
Assert.AreEqual(copy.ToString(), target.ToString());
}
public NormalDistributionInfo GetNormalDistributionFitWithNumOfSamePoints(int index)
{
NormalDistributionInfo Info = new NormalDistributionInfo();
int length = MD.Length;
double[] arr = new double[length];
for (int i = 0; i < length; i++)
{
arr[i] = MD[i].GetParameters()[index];
}
NormalDistribution ND = new NormalDistribution();
ND.Fit(arr);
Info.mean = ND.Mean;
Info.SD = ND.StandardDeviation;
return(Info);
}
public void FitTest()
{
double expectedMean = 1.125;
double expectedSigma = 1.01775897605147;
NormalDistribution target;
target = new NormalDistribution();
double[] observations = { 0.10, 0.40, 2.00, 2.00 };
double[] weights = { 0.25, 0.25, 0.25, 0.25 };
target.Fit(observations, weights);
Assert.AreEqual(expectedMean, target.Mean);
Assert.AreEqual(expectedSigma, target.StandardDeviation, 1e-6);
target = new NormalDistribution();
double[] observations2 = { 0.10, 0.10, 0.40, 2.00 };
double[] weights2 = { 0.125, 0.125, 0.25, 0.50 };
target.Fit(observations2, weights2);
Assert.AreEqual(expectedMean, target.Mean);
}
public void FitExtensionTest_options()
{
NormalDistribution target = new NormalDistribution();
double[] observations = { 0.10, 0.40, 2.00, 2.00 };
double[] weights = { 0.25, 0.25, 0.25, 0.25 };
target.Fit(observations, weights);
NormalDistribution same = observations.Fit <NormalDistribution, NormalOptions>(new NormalOptions()
{
Regularization = 10
}, weights);
Assert.AreNotSame(same, target);
Assert.AreEqual(same.ToString(), target.ToString());
NormalDistribution copy = target.FitNew(observations, new NormalOptions()
{
Regularization = 10
}, weights);
Assert.AreNotSame(copy, target);
Assert.AreEqual(copy.ToString(), target.ToString());
}
public void FitTest()
{
double expectedMean = 1.125;
double expectedSigma = 1.01775897605147;
NormalDistribution target;
target = new NormalDistribution();
double[] observations = { 0.10, 0.40, 2.00, 2.00 };
double[] weights = { 0.25, 0.25, 0.25, 0.25 };
target.Fit(observations, weights);
Assert.AreEqual(expectedMean, target.Mean);
Assert.AreEqual(expectedSigma, target.StandardDeviation, 1e-6);
target = new NormalDistribution();
double[] observations2 = { 0.10, 0.10, 0.40, 2.00 };
double[] weights2 = { 0.125, 0.125, 0.25, 0.50 };
target.Fit(observations2, weights2);
Assert.AreEqual(expectedMean, target.Mean);
}
public void FitTest2()
{
NormalDistribution target;
target = new NormalDistribution();
double[] observations = { 1, 1, 1, 1 };
bool thrown = false;
try { target.Fit(observations); }
catch (ArgumentException) { thrown = true; }
Assert.IsTrue(thrown);
}
