public static DataTable TestData()
{
DataTable results = new DataTable();
results.Columns.Add(new DataColumn("R", typeof(double)));
results.Columns.Add(new DataColumn("Vorg", typeof(double)));
results.Columns.Add(new DataColumn("Vmath", typeof(double)));
results.Columns.Add(new DataColumn("Vmc", typeof(double)));
results.Columns.Add(new DataColumn("Vgum", typeof(double)));
Optimizations.UseAnalyticalConvolution = false;
Optimizations.UseFftConvolution = false;
int experiments = 10;
int samples = 1000;
int randoms = (int)10e2;
int testType = 5;
double m1 = 0;
double m2 = 0; //для тестов 6, 7 - положение левой границы
for (int i = 0; i < experiments; i++)
{
double s1 = 1;
double s2 = s1 * InterpolateLiner(0.1, 10, i, experiments);
var pair = GetData(testType, m1, m2, s1, s2, out double vOriginal, out double gum, out string formula);
DistributionsEvaluator evaluator = new DistributionsEvaluator(formula);
Dictionary <string, BaseDistribution> keyValuePairs = new Dictionary <string, BaseDistribution>();
keyValuePairs.Add("A", pair[0].GetDistribution(samples));
keyValuePairs.Add("B", pair[1].GetDistribution(samples));
BaseDistribution resultMath = evaluator.EvaluateDistributions(keyValuePairs, new List <CorrelatedPair>());
List <double> monteCarloQ = new List <double>();
for (int j = 0; j < 10; j++)
{
var resultMonteCarlo = new MonteCarloDistribution(formula, new Dictionary <string, DistributionSettings> {
{ "A", pair[0] }, { "B", pair[1] }
}, randoms, 100);
monteCarloQ.Add(resultMonteCarlo.Variance);
}
double monteCarloQresult = Math.Sqrt(monteCarloQ.Sum(x => Math.Pow(x - vOriginal, 2)) / (monteCarloQ.Count - 1)) * 1.96;
results.Rows.Add(s2 / s1, vOriginal, resultMath.Variance, vOriginal + monteCarloQresult, gum);
}
return(results);
}
private static BasicDistributionData GenerateBasicData(DistributionsEvaluator evaluator, Dictionary <string, DistributionSettings> univariateDistributions, Dictionary <string[], MultivariateDistributionSettings> multivariateDistributions, int samples, int pockets)
{
if (evaluator == null)
{
throw new ArgumentNullException(nameof(evaluator));
}
if (univariateDistributions == null)
{
throw new ArgumentNullException(nameof(univariateDistributions));
}
if (samples < 3)
{
throw new DistributionsArgumentException(DistributionsArgumentExceptionType.NumberOfExperimentsMustBeGreaterThenTwo);
}
if (pockets < 3)
{
throw new DistributionsArgumentException(DistributionsArgumentExceptionType.NumberOfPocketsMustBeGreaterThenTwo);
}
BasicDistributionData data = new BasicDistributionData();
double[] random;
if (multivariateDistributions == null)
{
random = GenerateRandom(evaluator, univariateDistributions, samples);
}
else
{
random = GenerateRandom(evaluator, univariateDistributions, multivariateDistributions, samples);
}
Array.Sort(random);
double[] xAxis = CommonRandomMath.GenerateXAxis(random[0], random[samples - 1], pockets, out _);
double[] cdf = GenerateCDF(xAxis, random);
data.RandomSorted = random;
data.XAxis = xAxis;
data.CDF = cdf;
data.PDF = Derivate(cdf, xAxis[pockets - 1] - xAxis[0]);
return(data);
}
private static double[] GenerateRandom(DistributionsEvaluator evaluator, Dictionary <string, DistributionSettings> univariateDistributions, Dictionary <string[], MultivariateDistributionSettings> multivariateDistributions, int samples)
{
double[] random = new double[samples];
var order = evaluator.Parameters;
MultivariateGenerator generator = new MultivariateGenerator(order, univariateDistributions, multivariateDistributions);
Parallel.For(0, samples, i =>
{
Random rnd = Rnd.Value;
double[] args = generator.Generate(rnd);
random[i] = evaluator.EvaluateCompiled(args);
});
return(random);
}
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);
}
