public void SampleTest(double mean, double sdv, int seed)
{
var dis = new Normal(mean, sdv);
var hybrid = new UnivariateHybridMC(0, dis.DensityLn, 10, 0.1, 1000, 1, new System.Random(seed))
{
BurnInterval = 0
};
double[] sample = hybrid.Sample(10000);
double effective = MCMCDiagnostics.EffectiveSize(sample, x => x);
var stats = new DescriptiveStatistics(sample);
//Testing the mean using the normal distribution.
double meanConvergence = 3 * sdv / Math.Sqrt(effective);
Assert.AreEqual(stats.Mean, mean, meanConvergence, "Mean");
double deviationRation = Math.Pow(stats.StandardDeviation / sdv, 2);
//Approximating chi-square with normal distribution. (Degree of freedom is large)
double deviationConvergence = 3 * Math.Sqrt(2) / Math.Sqrt(effective);
Assert.AreEqual(deviationRation, 1, deviationConvergence, "Standard Deivation");
}
public PointPairList getDensityCurve(IList <double> data, double bandwidth)
{
int intervals = 1000;
var result = new PointPairList {
Capacity = intervals
};
//generate a base line
var statistics = new DescriptiveStatistics(data);
double minValue = data.Min() - (2 * statistics.StandardDeviation);
double maxValue = data.Max() + (2 * statistics.StandardDeviation);
double interval = (maxValue - minValue) / intervals * 1.0;
for (int i = 0; i < intervals; i++)
{
result.Add(minValue + i * interval, 0);
}
if (bandwidth == 0)
{
bandwidth = 1.06 * statistics.StandardDeviation * Math.Pow(data.Count, -1.0 / 5);
}
var orderedData = data.OrderBy(o => o);
foreach (var value in orderedData)
{
Normal nD = new Normal(0, 1);
for (int q = 0; q < intervals; q++)
{
result[q].Y += (1 / (data.Count * bandwidth)) * nD.Density((value - result[q].X) / bandwidth);
}
}
return(result);
}
public SyncRunResult RunTest()
{
var waitTime = _startAt - DateTime.Now;
_startEvent.Signal();
Thread.Sleep((int)(waitTime.TotalMilliseconds));
_runEvent.Signal();
var wr = _worker.Run(_workerId);
double totalElapsed = 0;
wr.InsertPerformance.ForEach(i => { totalElapsed += i; });
var sample = new DescriptiveStatistics(wr.InsertPerformance.ToArray());
Console.WriteLine($"{_workerId:000}: MIN/AVG/MAX: {sample.Minimum:#.00}, {sample.Mean:#.00}, {sample.Maximum:#.00} - STDEV/VAR: {sample.StandardDeviation:#.00}, {sample.Variance:#.00} - SUM: {totalElapsed}");
_finishEvent.Signal();
return(new SyncRunResult()
{
Documents = wr.InsertedDocuments, ElapsedMilliseconds = totalElapsed
});
}
public void SetMonthlyWeatherFileStats(List <HourlyWeatherData> avails, string monthName, int monthNumber, int numDays, string[] interests)
{
Monthly m = new Monthly();
m.SimpleName = monthName;
m.Id = monthNumber;
m.ExpectedValCount = 24 * numDays;
m.ActualValCount = avails.Count();
//loop through desired properties of interest and gather monthly statistics
for (int s = 0; s < interests.Count(); s++)
{
string interest = interests[s];
List <double> values = new List <double>();
foreach (HourlyWeatherData wd in avails)
{
values.Add(Convert.ToDouble(wd.GetType().GetProperty(interest).GetValue(wd, null))); //reflection
}
PropertyInfo p = m.GetType().GetProperty(interest);
var stats = new DescriptiveStatistics(values);
if (p != null)
{
p.SetValue(m, stats, null);
}
else
{
//exception
}
if (s == interests.Count() - 1)
{
this.Monthly.Add(m);
}
}
}
public void SampleTest()
{
double Mean = 5 * rnd.NextDouble();
double Sdv = 5 * rnd.NextDouble();
Normal dis = new Normal(Mean, Sdv);
UnivariateHybridMC Hybrid = new UnivariateHybridMC(0, dis.DensityLn, 10, 0.1, 1000);
Hybrid.BurnInterval = 0;
double[] Sample = Hybrid.Sample(10000);
double Effective = MCMCDiagnostics.EffectiveSize(Sample, x => x);
DescriptiveStatistics Stats = new DescriptiveStatistics(Sample);
//Testing the mean using the normal distribution.
double MeanConvergence = 3 * Sdv / Math.Sqrt(Effective);
Assert.AreEqual(Stats.Mean, Mean, MeanConvergence, "Mean");
double DeviationRation = Math.Pow(Stats.StandardDeviation / Sdv, 2);
//Approximating chi-square with normal distribution. (Degree of freedom is large)
double DeviationConvergence = 3 * Math.Sqrt(2) / Math.Sqrt(Effective);
Assert.AreEqual(DeviationRation, 1, DeviationConvergence, "Standard Deivation");
}
public void ShortSequences()
{
var stats0 = new DescriptiveStatistics(new double[0]);
Assert.That(stats0.Skewness, Is.NaN);
Assert.That(stats0.Kurtosis, Is.NaN);
var stats1 = new DescriptiveStatistics(new[] { 1.0 });
Assert.That(stats1.Skewness, Is.NaN);
Assert.That(stats1.Kurtosis, Is.NaN);
var stats2 = new DescriptiveStatistics(new[] { 1.0, 2.0 });
Assert.That(stats2.Skewness, Is.NaN);
Assert.That(stats2.Kurtosis, Is.NaN);
var stats3 = new DescriptiveStatistics(new[] { 1.0, 2.0, -3.0 });
Assert.That(stats3.Skewness, Is.Not.NaN);
Assert.That(stats3.Kurtosis, Is.NaN);
var stats4 = new DescriptiveStatistics(new[] { 1.0, 2.0, -3.0, -4.0 });
Assert.That(stats4.Skewness, Is.Not.NaN);
Assert.That(stats4.Kurtosis, Is.Not.NaN);
}
public void ZeroVarianceSequence()
{
var stats = new DescriptiveStatistics(new[] { 2.0, 2.0, 2.0, 2.0 });
Assert.That(stats.Skewness, Is.NaN);
Assert.That(stats.Kurtosis, Is.NaN);
}
public void Feed(IEnumerable <Row> rows, ColumnViewModel numerical)
{
DescriptiveStatisticsViewModel result = DescriptiveStatistics.Analyze(
DataContext as MainPageViewModel,
rows.Select(r => (Double)r.Cells[numerical.Index].Content)
);
HistogramElement.VerticalAxisTitle = Const.Loader.GetString("Frequency");
HistogramElement.Width = this.Width - 20; // (Double)App.Current.Resources["ParagraphWidth"] - 20;
HistogramElement.Height = this.Height - 70;
BoxPlotElement.Width = this.Width - 90; // (Double)App.Current.Resources["ParagraphWidth"] - 90;
BoxPlotElement.Min = result.Min;
BoxPlotElement.Max = result.Max;
BoxPlotElement.FirstQuartile = result.FirstQuartile;
BoxPlotElement.Median = result.Median;
BoxPlotElement.ThirdQuartile = result.ThirdQuartile;
BoxPlotElement.Mean = result.Mean;
BoxPlotElement.Update();
d3.Scale.Linear linear = new d3.Scale.Linear()
{
DomainStart = result.Min,
DomainEnd = result.Max,
RangeStart = 0,
RangeEnd = 1
};
linear.Nice();
List <Bin> bins = HistogramCalculator.Bin(
linear.DomainStart,
linear.DomainEnd,
linear.Step,
rows,
numerical
).ToList();
if (numerical.SortOption == SortOption.Descending)
{
bins = bins.OrderByDescending(b => b.Min).ToList();
}
else
{
bins = bins.OrderBy(b => b.Min).ToList();
}
HistogramElement.Data =
bins
.Select(d => new BarChartDatum()
{
Key = $"~{Formatter.Auto(d.Max)}",
ColumnViewModel = numerical,
Value = d.Rows.Count(),
EnvelopeValue = d.Rows.Count(),
Rows = null,
EnvelopeRows = d.Rows
}).ToList();
}
public static stats GenStats(List <List <round> > input)
{
List <double> faction_damage = new List <double> ();
List <double> asset_damage = new List <double> ();
foreach (var i in input)
{
faction_damage.Add(i.Select(e => Convert.ToDouble(e.direct_faction_damage)).Sum());
asset_damage.Add(i.Select(e => Convert.ToDouble(e.damage)).Sum());
}
var statistics = new DescriptiveStatistics(faction_damage);
var stats = new stats();
stats.faction_stats.Maximum = statistics.Maximum;
stats.faction_stats.Minimum = statistics.Minimum;
// stats.values.Median = statistics.Median;
stats.faction_stats.Mean = statistics.Mean;
stats.faction_stats.Variance = statistics.Variance;
stats.faction_stats.StandardDeviation = statistics.StandardDeviation;
stats.faction_stats.Kurtosis = statistics.Kurtosis;
stats.faction_stats.Skewness = statistics.Skewness;
statistics = new DescriptiveStatistics(asset_damage);
stats.asset_stats.Maximum = statistics.Maximum;
stats.asset_stats.Minimum = statistics.Minimum;
// stats.values.Median = statistics.Median;
stats.asset_stats.Mean = statistics.Mean;
stats.asset_stats.Variance = statistics.Variance;
stats.asset_stats.StandardDeviation = statistics.StandardDeviation;
stats.asset_stats.Kurtosis = statistics.Kurtosis;
stats.asset_stats.Skewness = statistics.Skewness;
Normal fac_normal = new Normal(stats.faction_stats.Mean, stats.faction_stats.StandardDeviation, Program.rand);
Normal asset_normal = new Normal(stats.asset_stats.Mean, stats.asset_stats.StandardDeviation, Program.rand);
for (int i = Convert.ToInt32(Math.Floor(stats.faction_stats.Minimum)); i < stats.faction_stats.Maximum; i++)
{
double item1 = fac_normal.CumulativeDistribution(i);
stats.faction_stats.normalized_dist.Add((i, item1));
}
for (int i = Convert.ToInt32(Math.Floor(stats.asset_stats.Minimum)); i < stats.asset_stats.Maximum; i++)
{
double item1 = fac_normal.CumulativeDistribution(i);
stats.asset_stats.normalized_dist.Add((i, item1));
}
return(stats);
}
public void TestDescriptiveStatisticsMeanVariance()
{
// Test around 10^9, potential stability issues
NormalDistribution gaussian = new NormalDistribution(1e+9, 2);
Assert.That(DescriptiveStatistics.Mean(gaussian.EnumerateDoubles(10000)), NumericIs.AlmostEqualTo(1e+9, 0.2), "Mean of (1e+9,2)");
Assert.That(DescriptiveStatistics.Variance(gaussian.EnumerateDoubles(10000)), NumericIs.AlmostEqualTo((double)4, 0.5), "Variance of (1e+9,2)");
Assert.That(DescriptiveStatistics.StandardDeviation(gaussian.EnumerateDoubles(10000)), NumericIs.AlmostEqualTo((double)2, 0.5), "StdDev of (1e+9,2)");
}
public void D2D10DivideByD2MultiplyBy2DistributionShouldBeGood()
{
var statistics = new DescriptiveStatistics(rolls);
Assert.AreEqual(d2d10div2mul2.Minimum, statistics.Minimum, "Expected min differs from actual min");
Assert.AreEqual(d2d10div2mul2.Maximum, statistics.Maximum, "Expected max differs from actual max");
Assert.AreEqual(12.25, statistics.Mean, 1, "Expected mean differs to much from actual mean");
Assert.Greater(statistics.Skewness, 0.8, "Distribution is not skewed enough");
Assert.Less(statistics.Kurtosis, 0, "Distribution is not platykurtic at all");
}
public void TestDescriptiveStatisticsOrderMedian()
{
// -3 -1 -0.5 0 1 4 5 6 10
double[] samples = new double[] { -1, 5, 0, -3, 10, -0.5, 4, 1, 6 };
Assert.That(DescriptiveStatistics.Median(samples), Is.EqualTo(1), "Median");
Assert.That(DescriptiveStatistics.OrderStatistic(samples, 1), Is.EqualTo(-3), "Order-1");
Assert.That(DescriptiveStatistics.OrderStatistic(samples, 3), Is.EqualTo(-0.5), "Order-3");
Assert.That(DescriptiveStatistics.OrderStatistic(samples, 7), Is.EqualTo(5), "Order-7");
Assert.That(DescriptiveStatistics.OrderStatistic(samples, 9), Is.EqualTo(10), "Order-9");
}
public void PopulateStatsFromArrayAndRound(double[] array, int roundDigits)
{
var ArrayStats = new DescriptiveStatistics(array);
Max = Math.Round(ArrayStats.Maximum, roundDigits);
Min = Math.Round(ArrayStats.Minimum, roundDigits);
Mean = Math.Round(ArrayStats.Mean, roundDigits);
//StdDev = Math.Round(ArrayStats.StandardDeviation, roundDigits); uses Bessel's correction (N-1 pop size)
StdDev = Math.Round(Statistics.PopulationStandardDeviation(array), roundDigits);
}
public HistogramDataAndProperties(DenseVector data, int countOfBins = 10)
{
dvData = (DenseVector)data.Clone();
binsCount = countOfBins;
color = Color.Black;
description = "histogram";
stats = new DescriptiveStatistics(dvData);
calculateProbDens();
CalculateQuantiles();
}
//пока что возводит в степень
//надо посчитать Фурье, отрезать высокие гармоники, вернуть обратно
//пока отрежем значения, лежащие за пределами интервала 3*sigma от среднего
private ImageOperationalValue ImageOperationalValueCut(ImageOperationalValue a, double sigmaCount)
{
ImageOperationalValue res = new ImageOperationalValue();
if (a.operandType() == OperandTypes.DenseMatrix)
{
//DenseMatrix dmRes = (DenseMatrix)a.DmImageComponent.Clone();
//determining the new mean value
DenseMatrix dmTmp = a.DmImageComponent.Copy();
DescriptiveStatistics stats = new DescriptiveStatistics(dmTmp.Values);
double dataMeanTmp = stats.Mean;
double standDevTmp = stats.StandardDeviation;
double deviationMarginTmp = sigmaCount * standDevTmp;
dmTmp.MapInplace(new Func <double, double>((dVal) =>
{
if (Math.Abs(dVal - dataMeanTmp) > deviationMarginTmp)
{
//double theSign = (dVal - dataMeanTmp) / Math.Abs(dVal - dataMeanTmp);
// return double.NaN;
return(dataMeanTmp);
}
else
{
return(dVal);
}
}));
//stats = new DescriptiveStatistics(dmTmp.Values);
//double dataMean = stats.Mean;
//double standDev = stats.StandardDeviation;
//double deviationMargin = sigmaCount * standDev;
////dmRes.MapInplace(new Func<double, double>((x) => { return Math.Pow(x, sigmaCount); }));
////stats = new DescriptiveStatistics(dmRes.Values);
//dmRes.MapInplace(new Func<double,double>((dVal) =>
//{
// if (Math.Abs(dVal - dataMean) > deviationMargin)
// {
// //double theSign = (dVal - dataMean)/Math.Abs(dVal - dataMean);
// return dataMean;
// }
// else return dVal;
//}));
res.DmImageComponent = dmTmp.Copy();
}
else
{
res.dNumber = a.dNumber;
}
return(res);
}
public ITextRender Report(ITextRender output = null)
{
if (output == null)
{
output = new builderForMarkdown();
}
var scores = items.Select(x => x.score);
output.AppendHeading("Granularity");
var distinct = items.GetDistinctScores();
Int32 dC = distinct.Count();
output.AppendPair("Distinct", dC);
output.AppendPair("Entries", scores.Count());
Double r = (Double)dC.GetRatio(scores.Count());
output.AppendPair("Distinct / Entries", r);
output.AppendHeading("Cumulative histogram");
for (int i = 1; i < 11; i++)
{
Double l_min = (i - 1).GetRatio(10);
Double l_max = i.GetRatio(10);
var bin = scores.Where(x => (x > l_min) && (x < l_max));
Double per = bin.Count().GetRatio(scores.Count());
output.AppendPair("Bin [" + i + "][" + l_max.ToString("F2") + "]", per.ToString("P2"));
}
output.AppendHeading("Descriptive statistics");
DescriptiveStatistics desc = scores.GetStatistics(true);
desc.Describe(output);
output.AppendHeading("Document selection result");
foreach (DocumentSelectResultEntry result in items)
{
output.AppendLine(result.score.ToString("F5") + "\t\t" + result.AssignedID);
}
output.AppendHorizontalLine();
query.Describe(output);
output.AppendHorizontalLine();
return(output);
}
/// <summary>
/// Publishes a new time-bound observation value of type 'Statistics' to the server.
/// </summary>
/// <param name="observationId">Observation identifier.</param>
/// <param name="timestamp">Date and time in UTC.</param>
/// <param name="value">Observation value.</param>
/// <returns>True if successful, False otherwise.</returns>
public bool PublishObservation(int observationId, DateTime timestamp, DescriptiveStatistics value)
{
StatisticsObservation o = new StatisticsObservation()
{
Timestamp = timestamp,
Value = value
};
string json = JsonConvert.SerializeObject(o);
return(PublishObservationMessage(observationId, Encoding.UTF8.GetBytes(json)));
}
public HistogramDataAndProperties()
{
dvData = DenseVector.Create(1, i => 0.0d);
binsCount = 1;
color = Color.Black;
description = "histogram";
dvbinsCenters = (DenseVector)dvData.Clone();
stats = new DescriptiveStatistics(dvData);
calculateProbDens();
CalculateQuantiles();
}
/// <summary> Determines whether a specified values are distributed in a 'uniformish' way:
/// mean value is as expected, distribution is platykurtic and not skewed. </summary>
/// <param name="randomValues">The random values.</param>
/// <param name="minValue">The minimum possible value.</param>
/// <param name="maxValue">The maximum possible value.</param>
public static void IsUniformishDistribution(double[] randomValues, double minValue, double maxValue)
{
var statistics = new DescriptiveStatistics(randomValues);
double meanMargin = (maxValue < 1) ? maxValue / 5 : 1;
Assert.AreEqual((minValue + maxValue) / 2, statistics.Mean, meanMargin,
"Expected mean differs to much from actual mean");
Assert.AreEqual(minValue, statistics.Minimum, "Expected min differs from actual min");
Assert.AreEqual(maxValue, statistics.Maximum, "Expected max differs from actual max");
Assert.AreEqual(0, statistics.Skewness, 0.1, "Distribution is skewed too much");
Assert.Less(statistics.Kurtosis, 0, "Distribution is not platykurtic at all");
}
public void D4D6DistributionShouldBeGood()
{
// We're combining several random dice, so distribution is not uniform at all,
// since sums in lower range have higher chance of appearing.
var statistics = new DescriptiveStatistics(rolls);
Assert.AreEqual(cd4d6mr2t5.Minimum, statistics.Minimum, "Expected min differs from actual min");
Assert.AreEqual(cd4d6mr2t5.Maximum, statistics.Maximum, "Expected max differs from actual max");
Assert.AreEqual(5.25, statistics.Mean, 1, "Expected mean differs to much from actual mean");
Assert.Greater(statistics.Skewness, 0.2, "Distribution is not skewed enough");
Assert.Less(statistics.Kurtosis, 0, "Distribution is not platykurtic at all");
}
public void SampleTest()
{
//Variances and Means
double[] Sdv = new double[2];
double[] Mean = new double[2];
for (int i = 0; i < 2; i++)
{
Sdv[i] = RandomVar();
Mean[i] = 5 * rnd.NextDouble();
}
//Cross Variance
double rho = 1.0 - RandomVar();
DensityLn <double[]> pdfLn = new DensityLn <double[]>(x => LogDen(x, Sdv, Mean, rho));
HybridMC Hybrid = new HybridMC(new double[2] {
0, 0
}, pdfLn, 10, 0.1, 1000);
Hybrid.BurnInterval = 0;
int SampleSize = 10000;
double[][] Sample = Hybrid.Sample(SampleSize);
double[][] NewSamples = ArrangeSamples(SampleSize, Sample);
double[] Convergence = new double[2];
double[] SampleMean = new double[2];
double[] SampleSdv = new double[2];
for (int i = 0; i < 2; i++)
{
Convergence[i] = 1 / Math.Sqrt(MCMCDiagnostics.EffectiveSize(Sample, x => x[i]));
DescriptiveStatistics Stats = new DescriptiveStatistics(NewSamples[i]);
SampleMean[i] = Stats.Mean;
SampleSdv[i] = Stats.StandardDeviation;
}
double SampleRho = Correlation.Pearson(NewSamples[0], NewSamples[1]);
for (int i = 0; i < 2; i++)
{
string index = i.ToString();
Assert.AreEqual(Mean[i], SampleMean[i], 10 * Convergence[i], index + "Mean");
Assert.AreEqual(SampleSdv[i] * SampleSdv[i], Sdv[i] * Sdv[i], 10 * Convergence[i], index + "Standard Deviation");
}
double ConvergenceRho = 1 / Math.Sqrt(MCMCDiagnostics.EffectiveSize(Sample, x => (x[0] - SampleMean[0]) * (x[1] - SampleMean[1])));
Assert.AreEqual(SampleRho * SampleSdv[0] * SampleSdv[1], rho * Sdv[0] * Sdv[1], 10 * ConvergenceRho, "Rho");
}
public void IEnumerableDoubleHighAccuracy(string dataSet, double skewness, double kurtosis, double median, double min, double max, int count)
{
var data = _data[dataSet];
var stats = new DescriptiveStatistics(data.Data, true);
AssertHelpers.AlmostEqual(data.Mean, stats.Mean, 15);
AssertHelpers.AlmostEqual(data.StandardDeviation, stats.StandardDeviation, 15);
AssertHelpers.AlmostEqual(skewness, stats.Skewness, 9);
AssertHelpers.AlmostEqual(kurtosis, stats.Kurtosis, 9);
Assert.AreEqual(stats.Minimum, min);
Assert.AreEqual(stats.Maximum, max);
Assert.AreEqual(stats.Count, count);
}
public DemoStats(double[][] dataMatrix)
{
var transpMatrix = MatrixFunction.TransposeMatrix(dataMatrix);
Correlations = new CorrelationsAnalysis(transpMatrix, dataMatrix[0].Length - 1);
Regressions = new RegressionAnalysis(dataMatrix, dataMatrix[0].Length - 1);
Descriptive = new DescriptiveStatistics[transpMatrix.Length];
for (int i = 0; i < Descriptive.Length; ++i)
{
Descriptive[i] = new DescriptiveStatistics(transpMatrix[i]);
}
Forecast = new Forecast(dataMatrix, transpMatrix.Length - 1);
}
public void IEnumerableNullableDoubleLowAccuracy(string dataSet, int digits, double skewness, double kurtosis, double median, double min, double max, int count)
{
var data = _data[dataSet];
var stats = new DescriptiveStatistics(data.DataWithNulls, false);
AssertHelpers.AlmostEqualRelative(data.Mean, stats.Mean, 14);
AssertHelpers.AlmostEqualRelative(data.StandardDeviation, stats.StandardDeviation, digits);
AssertHelpers.AlmostEqualRelative(skewness, stats.Skewness, 7);
AssertHelpers.AlmostEqualRelative(kurtosis, stats.Kurtosis, 7);
Assert.AreEqual(stats.Minimum, min);
Assert.AreEqual(stats.Maximum, max);
Assert.AreEqual(stats.Count, count);
}
public TimeSeries <T> ExtractDataDeviationValues()
{
if (typeof(T) != typeof(double))
{
return(null);
}
DescriptiveStatistics stats = new DescriptiveStatistics(dataSeria as List <double>);
List <double> lDevValues = (dataSeria as List <double>).ConvertAll <double>(dVal => dVal - stats.Mean);
return(new TimeSeries <T>(lDevValues as List <T>, lDateTimeStamps));
}
void LoadFile(string path)
{
Table = new Table(path);
Table.Normalize();
DescriptiveStatistics[] ds = new DescriptiveStatistics[Table.ColumnsCount];
for (int i = 0; i < Table.ColumnsCount; i++)
{
ds[i] = new DescriptiveStatistics(Table.Headers[i], Table.ColumnsValues[i]);
}
DS = ds;
ChiSquaredIntervalsCount = 5;
Correlations = new Correlations(Table, DS);
}
public void ProcessATaxon(int taxon, double[] values)
{
var row = _dataTable.NewRow();
if (values.Length < _quantiles * LabTests.MinCountInQuantile)
{
return;
}
var stats = new DescriptiveStatistics(values);
row["taxon"] = taxon;
row["Mean"] = VerifyRange(stats.Mean);
row["Minimum"] = VerifyRange(stats.Minimum);
row["Maximum"] = VerifyRange(stats.Maximum);
row["Skewness"] = VerifyRange(stats.Skewness);
row["Kurtosis"] = VerifyRange(stats.Kurtosis);
row["StandardDeviation"] = VerifyRange(stats.StandardDeviation);
row["Variance"] = VerifyRange(stats.Variance);
row["Count"] = VerifyRange(stats.Count);
var index = 0.5 * (double)stats.Count;
var indexLow = (int)Math.Floor(index);
var indexHigh = (int)Math.Ceiling(index);
if (indexHigh > values.Length - 1)
{
indexHigh = indexLow;
}
var qValue = (values[indexLow] + values[indexHigh]) / 2;
row["Median"] = qValue;
row["Mode"] = values.GroupBy(v => v)
.OrderByDescending(g => g.Count())
.First()
.Key;
Array.Sort(values);
for (double q = 1; q < _quantiles; q++)
{
index = q / _quantiles * (double)stats.Count;
indexLow = (int)Math.Floor(index);
indexHigh = (int)Math.Ceiling(index);
if (indexHigh > values.Length - 1)
{
indexHigh = indexLow;
}
qValue = (values[indexLow] + values[indexHigh]) / 2;
var colName = $"q{_quantiles}_{(int)q}";
row[colName] = qValue;
}
_dataTable.Rows.Add(row);
}
public static MedidasEstadisticas DeterminarMedidasEstadisticas(IEnumerable <double> valores)
{
var medidas = new MedidasEstadisticas();
medidas.ValorMaximo = Statistics.Maximum(valores);
medidas.ValorMinimo = Statistics.Minimum(valores);
medidas.Media = Statistics.Mean(valores);
medidas.TamanioDeLaMuestra = valores.Count();
medidas.Desviacion = Statistics.StandardDeviation(valores);
var desc = new DescriptiveStatistics(samples);
return(medidas);
}
public void IEnumerableDouble(string dataSet, int digits, double skewness, double kurtosis, double median, double min, double max, int count)
{
StatTestData data = mData[dataSet];
DescriptiveStatistics stats = new DescriptiveStatistics(data.Data);
AssertHelpers.AlmostEqual(data.Mean, stats.Mean, 15);
AssertHelpers.AlmostEqual(data.StandardDeviation, stats.StandardDeviation, digits);
AssertHelpers.AlmostEqual(skewness, stats.Skewness, 7);
AssertHelpers.AlmostEqual(kurtosis, stats.Kurtosis, 7);
AssertHelpers.AlmostEqual(median, stats.Median, 15);
Assert.AreEqual(stats.Minimum, min);
Assert.AreEqual(stats.Maximum, max);
Assert.AreEqual(stats.Count, count);
}
public void DescriptiveStatisticsDataContractSerializationTest()
{
var expected = new DescriptiveStatistics(new[] { 1.0, 2.0, 3.0 });
var serializer = new DataContractSerializer(typeof(DescriptiveStatistics));
var stream = new MemoryStream();
serializer.WriteObject(stream, expected);
stream.Position = 0;
var actual = (DescriptiveStatistics)serializer.ReadObject(stream);
Assert.That(actual.Count, Is.EqualTo(expected.Count));
Assert.That(actual.Maximum, Is.EqualTo(expected.Maximum));
Assert.That(actual.Mean, Is.EqualTo(expected.Mean));
}
public void RunningStatisticsConsistentWithDescriptiveStatistics(string dataSet)
{
var data = _data[dataSet];
var running = new RunningStatistics(data.Data);
var descriptive = new DescriptiveStatistics(data.Data);
Assert.That(running.Minimum, Is.EqualTo(descriptive.Minimum), "Minimum");
Assert.That(running.Maximum, Is.EqualTo(descriptive.Maximum), "Maximum");
Assert.That(running.Mean, Is.EqualTo(descriptive.Mean).Within(1e-15), "Mean");
Assert.That(running.Variance, Is.EqualTo(descriptive.Variance).Within(1e-15), "Variance");
Assert.That(running.StandardDeviation, Is.EqualTo(descriptive.StandardDeviation).Within(1e-15), "StandardDeviation");
Assert.That(running.Skewness, Is.EqualTo(descriptive.Skewness).Within(1e-15), "Skewness");
Assert.That(running.Kurtosis, Is.EqualTo(descriptive.Kurtosis).Within(1e-14), "Kurtosis");
}
public void IEnumerableNullableDoubleLowAccuracy(string dataSet, int digits, double skewness, double kurtosis, double median, double min, double max, int count)
{
var data = _data[dataSet];
var stats = new DescriptiveStatistics(data.DataWithNulls, false);
AssertHelpers.AlmostEqual(data.Mean, stats.Mean, 15);
AssertHelpers.AlmostEqual(data.StandardDeviation, stats.StandardDeviation, digits);
AssertHelpers.AlmostEqual(skewness, stats.Skewness, 7);
AssertHelpers.AlmostEqual(kurtosis, stats.Kurtosis, 7);
AssertHelpers.AlmostEqual(median, stats.Median, 15);
Assert.AreEqual(stats.Minimum, min);
Assert.AreEqual(stats.Maximum, max);
Assert.AreEqual(stats.Count, count);
}
/// <summary>
/// Adds anova statistics to the gadget
/// </summary>
/// <param name="strataValue">The strata value associated with the results</param>
/// <param name="stats">The descriptive statistics to process</param>
private void AddAnova(string strataValue, DescriptiveStatistics stats)
{
Expander anovaExpander = new Expander();
anovaExpander.Margin = (Thickness)this.Resources["expanderMargin"];
anovaExpander.IsExpanded = true;
StackPanel pnl = new StackPanel();
anovaExpander.Content = pnl;
if (this.StrataCount > 1)
{
Expander expander = GetStrataExpander(strataValue);
if (expander.Content is StackPanel)
{
StackPanel sPanel = expander.Content as StackPanel;
sPanel.Children.Add(anovaExpander);
}
}
else
{
panelMain.Children.Add(anovaExpander);
}
KeyValuePair<string, DescriptiveStatistics> ANOVAStatistics = new KeyValuePair<string, DescriptiveStatistics>(strataValue, stats);
pnl.Tag = ANOVAStatistics;
pnl.HorizontalAlignment = HorizontalAlignment.Center;
pnl.VerticalAlignment = VerticalAlignment.Center;
pnl.Margin = new Thickness(5);
anovaBlocks.Add(pnl);
TextBlock txt1 = new TextBlock();
txt1.Text = SharedStrings.ANOVA_DESCRIPTION_FOR_MEANS;
txt1.HorizontalAlignment = HorizontalAlignment.Center;
txt1.Margin = new Thickness(5);
txt1.FontWeight = FontWeights.Bold;
//pnl.Children.Add(txt1);
anovaExpander.Header = txt1;
#region Grid1
TextBlock txt2 = new TextBlock();
txt2.Text = SharedStrings.ANOVA_DESCRIPTION_FOR_MEANS_SUBTITLE;
txt2.HorizontalAlignment = HorizontalAlignment.Center;
pnl.Children.Add(txt2);
Grid grid1 = new Grid();
grid1.SnapsToDevicePixels = true;
grid1.HorizontalAlignment = HorizontalAlignment.Center;
grid1.RowDefinitions.Add(new RowDefinition() { Height = new GridLength(30) });
grid1.RowDefinitions.Add(new RowDefinition() { Height = new GridLength(30) });
grid1.RowDefinitions.Add(new RowDefinition() { Height = new GridLength(30) });
grid1.RowDefinitions.Add(new RowDefinition() { Height = new GridLength(30) });
grid1.ColumnDefinitions.Add(new ColumnDefinition() { Width = GridLength.Auto });
grid1.ColumnDefinitions.Add(new ColumnDefinition() { Width = GridLength.Auto });
grid1.ColumnDefinitions.Add(new ColumnDefinition() { Width = GridLength.Auto });
grid1.ColumnDefinitions.Add(new ColumnDefinition() { Width = GridLength.Auto });
grid1.ColumnDefinitions.Add(new ColumnDefinition() { Width = GridLength.Auto });
pnl.Children.Add(grid1);
for (int y = 0; y < grid1.ColumnDefinitions.Count; y++)
{
Rectangle rctHeader = new Rectangle();
rctHeader.Style = this.Resources["gridHeaderCellRectangle"] as Style;
Grid.SetRow(rctHeader, 0);
Grid.SetColumn(rctHeader, y);
grid1.Children.Add(rctHeader);
}
for (int y = 1; y < grid1.RowDefinitions.Count; y++)
{
Rectangle rctHeader = new Rectangle();
rctHeader.Style = this.Resources["gridHeaderCellRectangle"] as Style;
Grid.SetRow(rctHeader, y);
Grid.SetColumn(rctHeader, 0);
grid1.Children.Add(rctHeader);
}
TextBlock lblVariation = new TextBlock();
lblVariation.Text = SharedStrings.ANOVA_VARIATION;
lblVariation.Margin = new Thickness(4, 0, 4, 0);
lblVariation.FontWeight = FontWeights.Bold;
lblVariation.Foreground = Brushes.White;
lblVariation.HorizontalAlignment = System.Windows.HorizontalAlignment.Center;
lblVariation.VerticalAlignment = System.Windows.VerticalAlignment.Center;
grid1.Children.Add(lblVariation);
Grid.SetRow(lblVariation, 0);
Grid.SetColumn(lblVariation, 0);
TextBlock lblBetween = new TextBlock();
lblBetween.Text = SharedStrings.ANOVA_BETWEEN;
lblBetween.Margin = new Thickness(4, 0, 4, 0);
lblBetween.FontWeight = FontWeights.Bold;
lblBetween.Foreground = Brushes.White;
lblBetween.HorizontalAlignment = System.Windows.HorizontalAlignment.Center;
lblBetween.VerticalAlignment = System.Windows.VerticalAlignment.Center;
grid1.Children.Add(lblBetween);
Grid.SetRow(lblBetween, 1);
Grid.SetColumn(lblBetween, 0);
TextBlock lblWithin = new TextBlock();
lblWithin.Text = SharedStrings.ANOVA_WITHIN;
lblWithin.Margin = new Thickness(4, 0, 4, 0);
lblWithin.FontWeight = FontWeights.Bold;
lblWithin.Foreground = Brushes.White;
lblWithin.HorizontalAlignment = System.Windows.HorizontalAlignment.Center;
lblWithin.VerticalAlignment = System.Windows.VerticalAlignment.Center;
grid1.Children.Add(lblWithin);
Grid.SetRow(lblWithin, 2);
Grid.SetColumn(lblWithin, 0);
TextBlock lblTotal = new TextBlock();
lblTotal.Text = SharedStrings.ANOVA_TOTAL;
lblTotal.Margin = new Thickness(4, 0, 4, 0);
lblTotal.HorizontalAlignment = System.Windows.HorizontalAlignment.Center;
lblTotal.VerticalAlignment = System.Windows.VerticalAlignment.Center;
lblTotal.FontWeight = FontWeights.Bold;
lblTotal.Foreground = Brushes.White;
grid1.Children.Add(lblTotal);
Grid.SetRow(lblTotal, 3);
Grid.SetColumn(lblTotal, 0);
TextBlock lblSS = new TextBlock();
lblSS.Text = SharedStrings.ANOVA_SS;
lblSS.Margin = new Thickness(4, 0, 4, 0);
lblSS.HorizontalAlignment = System.Windows.HorizontalAlignment.Center;
lblSS.VerticalAlignment = System.Windows.VerticalAlignment.Center;
lblSS.FontWeight = FontWeights.Bold;
lblSS.Foreground = Brushes.White;
grid1.Children.Add(lblSS);
Grid.SetRow(lblSS, 0);
Grid.SetColumn(lblSS, 1);
TextBlock lblDf = new TextBlock();
lblDf.Text = SharedStrings.ANOVA_DF_SHORT;
lblDf.Margin = new Thickness(4, 0, 4, 0);
lblDf.HorizontalAlignment = System.Windows.HorizontalAlignment.Center;
lblDf.VerticalAlignment = System.Windows.VerticalAlignment.Center;
lblDf.FontWeight = FontWeights.Bold;
lblDf.Foreground = Brushes.White;
grid1.Children.Add(lblDf);
Grid.SetRow(lblDf, 0);
Grid.SetColumn(lblDf, 2);
TextBlock lblMS = new TextBlock();
lblMS.Text = SharedStrings.ANOVA_MS;
lblMS.Margin = new Thickness(4, 0, 4, 0);
lblMS.HorizontalAlignment = System.Windows.HorizontalAlignment.Center;
lblMS.VerticalAlignment = System.Windows.VerticalAlignment.Center;
lblMS.FontWeight = FontWeights.Bold;
lblMS.Foreground = Brushes.White;
grid1.Children.Add(lblMS);
Grid.SetRow(lblMS, 0);
Grid.SetColumn(lblMS, 3);
TextBlock lblF = new TextBlock();
lblF.Text = SharedStrings.ANOVA_F;
lblF.Margin = new Thickness(4, 0, 4, 0);
lblF.HorizontalAlignment = System.Windows.HorizontalAlignment.Center;
lblF.VerticalAlignment = System.Windows.VerticalAlignment.Center;
lblF.FontWeight = FontWeights.Bold;
lblF.Foreground = Brushes.White;
grid1.Children.Add(lblF);
Grid.SetRow(lblF, 0);
Grid.SetColumn(lblF, 4);
TextBlock txtSSBetween = new TextBlock();
txtSSBetween.Text = stats.ssBetween.Value.ToString("N4");
txtSSBetween.Margin = new Thickness(4, 0, 4, 0);
txtSSBetween.HorizontalAlignment = System.Windows.HorizontalAlignment.Right;
txtSSBetween.VerticalAlignment = System.Windows.VerticalAlignment.Center;
grid1.Children.Add(txtSSBetween);
Grid.SetRow(txtSSBetween, 1);
Grid.SetColumn(txtSSBetween, 1);
TextBlock txtSSWithin = new TextBlock();
txtSSWithin.Text = stats.ssWithin.Value.ToString("N4");
txtSSWithin.Margin = new Thickness(4, 0, 4, 0);
txtSSWithin.HorizontalAlignment = System.Windows.HorizontalAlignment.Right;
txtSSWithin.VerticalAlignment = System.Windows.VerticalAlignment.Center;
grid1.Children.Add(txtSSWithin);
Grid.SetRow(txtSSWithin, 2);
Grid.SetColumn(txtSSWithin, 1);
TextBlock txtSSTotal = new TextBlock();
txtSSTotal.Text = (stats.ssWithin.Value + stats.ssBetween.Value).ToString("N4");
txtSSTotal.Margin = new Thickness(4, 0, 4, 0);
txtSSTotal.HorizontalAlignment = System.Windows.HorizontalAlignment.Right;
txtSSTotal.VerticalAlignment = System.Windows.VerticalAlignment.Center;
grid1.Children.Add(txtSSTotal);
Grid.SetRow(txtSSTotal, 3);
Grid.SetColumn(txtSSTotal, 1);
TextBlock txtDFBetween = new TextBlock();
txtDFBetween.Text = stats.dfBetween.Value.ToString();
txtDFBetween.Margin = new Thickness(4, 0, 4, 0);
txtDFBetween.HorizontalAlignment = System.Windows.HorizontalAlignment.Right;
txtDFBetween.VerticalAlignment = System.Windows.VerticalAlignment.Center;
grid1.Children.Add(txtDFBetween);
Grid.SetRow(txtDFBetween, 1);
Grid.SetColumn(txtDFBetween, 2);
TextBlock txtDFWithin = new TextBlock();
txtDFWithin.Text = stats.dfWithin.Value.ToString();
txtDFWithin.Margin = new Thickness(4, 0, 4, 0);
txtDFWithin.HorizontalAlignment = System.Windows.HorizontalAlignment.Right;
txtDFWithin.VerticalAlignment = System.Windows.VerticalAlignment.Center;
grid1.Children.Add(txtDFWithin);
Grid.SetRow(txtDFWithin, 2);
Grid.SetColumn(txtDFWithin, 2);
TextBlock txtDFTotal = new TextBlock();
txtDFTotal.Text = (stats.dfWithin.Value + stats.dfBetween.Value).ToString();
txtDFTotal.Margin = new Thickness(4, 0, 4, 0);
txtDFTotal.HorizontalAlignment = System.Windows.HorizontalAlignment.Right;
txtDFTotal.VerticalAlignment = System.Windows.VerticalAlignment.Center;
grid1.Children.Add(txtDFTotal);
Grid.SetRow(txtDFTotal, 3);
Grid.SetColumn(txtDFTotal, 2);
TextBlock txtMSBetween = new TextBlock();
txtMSBetween.Text = stats.msBetween.Value.ToString("N4");
txtMSBetween.Margin = new Thickness(4, 0, 4, 0);
txtMSBetween.HorizontalAlignment = System.Windows.HorizontalAlignment.Right;
txtMSBetween.VerticalAlignment = System.Windows.VerticalAlignment.Center;
grid1.Children.Add(txtMSBetween);
Grid.SetRow(txtMSBetween, 1);
Grid.SetColumn(txtMSBetween, 3);
TextBlock txtMSWithin = new TextBlock();
txtMSWithin.Text = stats.msWithin.Value.ToString("N4");
txtMSWithin.Margin = new Thickness(4, 0, 4, 0);
txtMSWithin.HorizontalAlignment = System.Windows.HorizontalAlignment.Right;
txtMSWithin.VerticalAlignment = System.Windows.VerticalAlignment.Center;
grid1.Children.Add(txtMSWithin);
Grid.SetRow(txtMSWithin, 2);
Grid.SetColumn(txtMSWithin, 3);
TextBlock txtFStat = new TextBlock();
txtFStat.Text = stats.fStatistic.Value.ToString("N4");
txtFStat.Margin = new Thickness(4, 0, 4, 0);
txtFStat.HorizontalAlignment = System.Windows.HorizontalAlignment.Center;
txtFStat.VerticalAlignment = System.Windows.VerticalAlignment.Center;
grid1.Children.Add(txtFStat);
Grid.SetRow(txtFStat, 1);
Grid.SetColumn(txtFStat, 4);
int rdcount = 0;
foreach (RowDefinition rd in grid1.RowDefinitions)
{
int cdcount = 0;
foreach (ColumnDefinition cd in grid1.ColumnDefinitions)
{
Border b = new Border();
b.Style = this.Resources["gridCellBorder"] as Style;
if (rdcount == 0)
{
b.BorderThickness = new Thickness(b.BorderThickness.Left, 1, b.BorderThickness.Right, b.BorderThickness.Bottom);
}
if (cdcount == 0)
{
b.BorderThickness = new Thickness(1, b.BorderThickness.Top, b.BorderThickness.Right, b.BorderThickness.Bottom);
}
Grid.SetRow(b, rdcount);
Grid.SetColumn(b, cdcount);
grid1.Children.Add(b);
cdcount++;
}
rdcount++;
}
#endregion // Grid1
#region Grid2
Grid grid2 = new Grid();
grid2.SnapsToDevicePixels = true;
grid2.Margin = new Thickness(5);
grid2.HorizontalAlignment = HorizontalAlignment.Center;
grid2.RowDefinitions.Add(new RowDefinition() { Height = new GridLength(30) });
grid2.ColumnDefinitions.Add(new ColumnDefinition() { Width = new GridLength(110) });
grid2.ColumnDefinitions.Add(new ColumnDefinition() { Width = new GridLength(95) });
pnl.Children.Add(grid2);
for (int y = 0; y < grid2.RowDefinitions.Count; y++)
{
Rectangle rctHeader = new Rectangle();
rctHeader.Style = this.Resources["gridHeaderCellRectangle"] as Style;
Grid.SetRow(rctHeader, y);
Grid.SetColumn(rctHeader, 0);
grid2.Children.Add(rctHeader);
}
TextBlock lblPValue = new TextBlock();
lblPValue.Text = SharedStrings.ANOVA_P_VAL;
lblPValue.HorizontalAlignment = HorizontalAlignment.Center;
lblPValue.VerticalAlignment = VerticalAlignment.Center;
lblPValue.FontWeight = FontWeights.Bold;
lblPValue.Foreground = Brushes.White;
Grid.SetRow(lblPValue, 0);
Grid.SetColumn(lblPValue, 0);
grid2.Children.Add(lblPValue);
TextBlock txtPValue = new TextBlock();
txtPValue.Text = stats.anovaPValue.Value.ToString("N4");
txtPValue.HorizontalAlignment = HorizontalAlignment.Center;
txtPValue.VerticalAlignment = VerticalAlignment.Center;
Grid.SetRow(txtPValue, 0);
Grid.SetColumn(txtPValue, 1);
grid2.Children.Add(txtPValue);
rdcount = 0;
foreach (RowDefinition rd in grid2.RowDefinitions)
{
int cdcount = 0;
foreach (ColumnDefinition cd in grid2.ColumnDefinitions)
{
Border b = new Border();
b.Style = this.Resources["gridCellBorder"] as Style;
if (rdcount == 0)
{
b.BorderThickness = new Thickness(b.BorderThickness.Left, 1, b.BorderThickness.Right, b.BorderThickness.Bottom);
}
if (cdcount == 0)
{
b.BorderThickness = new Thickness(1, b.BorderThickness.Top, b.BorderThickness.Right, b.BorderThickness.Bottom);
}
Grid.SetRow(b, rdcount);
Grid.SetColumn(b, cdcount);
grid2.Children.Add(b);
cdcount++;
}
rdcount++;
}
#endregion // Grid2
#region Grid3
TextBlock txt3 = new TextBlock();
txt3.Text = SharedStrings.ANOVA_BARTLETT;
txt3.HorizontalAlignment = HorizontalAlignment.Center;
txt3.Margin = new Thickness(5);
txt3.FontWeight = FontWeights.Bold;
pnl.Children.Add(txt3);
Grid grid3 = new Grid();
grid3.SnapsToDevicePixels = true;
grid3.Margin = new Thickness(5);
grid3.HorizontalAlignment = HorizontalAlignment.Center;
grid3.RowDefinitions.Add(new RowDefinition() { Height = new GridLength(30) });
grid3.RowDefinitions.Add(new RowDefinition() { Height = new GridLength(30) });
grid3.RowDefinitions.Add(new RowDefinition() { Height = new GridLength(30) });
grid3.ColumnDefinitions.Add(new ColumnDefinition() { Width = new GridLength(110) });
grid3.ColumnDefinitions.Add(new ColumnDefinition() { Width = new GridLength(95) });
pnl.Children.Add(grid3);
for (int y = 0; y < grid3.RowDefinitions.Count; y++)
{
Rectangle rctHeader = new Rectangle();
rctHeader.Style = this.Resources["gridHeaderCellRectangle"] as Style;
Grid.SetRow(rctHeader, y);
Grid.SetColumn(rctHeader, 0);
grid3.Children.Add(rctHeader);
}
TextBlock lblBartlettChi = new TextBlock();
lblBartlettChi.Text = SharedStrings.ANOVA_CHI;
lblBartlettChi.HorizontalAlignment = HorizontalAlignment.Center;
lblBartlettChi.VerticalAlignment = VerticalAlignment.Center;
lblBartlettChi.FontWeight = FontWeights.Bold;
lblBartlettChi.Foreground = Brushes.White;
Grid.SetRow(lblBartlettChi, 0);
Grid.SetColumn(lblBartlettChi, 0);
grid3.Children.Add(lblBartlettChi);
TextBlock lblBartlettDf = new TextBlock();
lblBartlettDf.Text = SharedStrings.ANOVA_DF_LONG;
lblBartlettDf.HorizontalAlignment = HorizontalAlignment.Center;
lblBartlettDf.VerticalAlignment = VerticalAlignment.Center;
lblBartlettDf.FontWeight = FontWeights.Bold;
lblBartlettDf.Foreground = Brushes.White;
Grid.SetRow(lblBartlettDf, 1);
Grid.SetColumn(lblBartlettDf, 0);
grid3.Children.Add(lblBartlettDf);
TextBlock lblBartlettP = new TextBlock();
lblBartlettP.Text = SharedStrings.ANOVA_P_VAL;
lblBartlettP.HorizontalAlignment = HorizontalAlignment.Center;
lblBartlettP.VerticalAlignment = VerticalAlignment.Center;
lblBartlettP.FontWeight = FontWeights.Bold;
lblBartlettP.Foreground = Brushes.White;
Grid.SetRow(lblBartlettP, 2);
Grid.SetColumn(lblBartlettP, 0);
grid3.Children.Add(lblBartlettP);
TextBlock txtBartlettChi = new TextBlock();
txtBartlettChi.Text = stats.chiSquare.Value.ToString("N4");
txtBartlettChi.HorizontalAlignment = HorizontalAlignment.Center;
txtBartlettChi.VerticalAlignment = VerticalAlignment.Center;
Grid.SetRow(txtBartlettChi, 0);
Grid.SetColumn(txtBartlettChi, 1);
grid3.Children.Add(txtBartlettChi);
TextBlock txtBartlettDf = new TextBlock();
txtBartlettDf.Text = stats.dfBetween.Value.ToString();
txtBartlettDf.HorizontalAlignment = HorizontalAlignment.Center;
txtBartlettDf.VerticalAlignment = VerticalAlignment.Center;
Grid.SetRow(txtBartlettDf, 1);
Grid.SetColumn(txtBartlettDf, 1);
grid3.Children.Add(txtBartlettDf);
TextBlock txtBartlettP = new TextBlock();
txtBartlettP.Text = stats.bartlettPValue.Value.ToString("N4");
txtBartlettP.HorizontalAlignment = HorizontalAlignment.Center;
txtBartlettP.VerticalAlignment = VerticalAlignment.Center;
Grid.SetRow(txtBartlettP, 2);
Grid.SetColumn(txtBartlettP, 1);
grid3.Children.Add(txtBartlettP);
rdcount = 0;
foreach (RowDefinition rd in grid3.RowDefinitions)
{
int cdcount = 0;
foreach (ColumnDefinition cd in grid3.ColumnDefinitions)
{
Border b = new Border();
b.Style = this.Resources["gridCellBorder"] as Style;
if (rdcount == 0)
{
b.BorderThickness = new Thickness(b.BorderThickness.Left, 1, b.BorderThickness.Right, b.BorderThickness.Bottom);
}
if (cdcount == 0)
{
b.BorderThickness = new Thickness(1, b.BorderThickness.Top, b.BorderThickness.Right, b.BorderThickness.Bottom);
}
Grid.SetRow(b, rdcount);
Grid.SetColumn(b, cdcount);
grid3.Children.Add(b);
cdcount++;
}
rdcount++;
}
TextBlock txt4 = new TextBlock();
txt4.Text = SharedStrings.ANOVA_SMALL_P;
txt4.HorizontalAlignment = HorizontalAlignment.Center;
txt4.Margin = new Thickness(5);
pnl.Children.Add(txt4);
TextBlock txt5 = new TextBlock();
txt5.Text = SharedStrings.ANOVA_MWWTST;
txt5.HorizontalAlignment = HorizontalAlignment.Center;
txt5.Margin = new Thickness(5);
txt5.FontWeight = FontWeights.Bold;
pnl.Children.Add(txt5);
#endregion // Grid3
#region Grid4
Grid grid4 = new Grid();
grid4.SnapsToDevicePixels = true;
grid4.Margin = new Thickness(5);
grid4.HorizontalAlignment = HorizontalAlignment.Center;
grid4.RowDefinitions.Add(new RowDefinition() { Height = new GridLength(30) });
grid4.RowDefinitions.Add(new RowDefinition() { Height = new GridLength(30) });
grid4.RowDefinitions.Add(new RowDefinition() { Height = new GridLength(30) });
grid4.ColumnDefinitions.Add(new ColumnDefinition() { Width = new GridLength(110) });
grid4.ColumnDefinitions.Add(new ColumnDefinition() { Width = new GridLength(95) });
pnl.Children.Add(grid4);
for (int y = 0; y < grid4.RowDefinitions.Count; y++)
{
Rectangle rctHeader = new Rectangle();
rctHeader.Style = this.Resources["gridHeaderCellRectangle"] as Style;
Grid.SetRow(rctHeader, y);
Grid.SetColumn(rctHeader, 0);
grid4.Children.Add(rctHeader);
}
TextBlock lblKWChi = new TextBlock();
lblKWChi.Text = SharedStrings.ANOVA_KWH;
lblKWChi.HorizontalAlignment = HorizontalAlignment.Center;
lblKWChi.VerticalAlignment = VerticalAlignment.Center;
lblKWChi.FontWeight = FontWeights.Bold;
lblKWChi.Foreground = Brushes.White;
Grid.SetRow(lblKWChi, 0);
Grid.SetColumn(lblKWChi, 0);
grid4.Children.Add(lblKWChi);
TextBlock lblKWDf = new TextBlock();
lblKWDf.Text = SharedStrings.ANOVA_DF_LONG;
lblKWDf.HorizontalAlignment = HorizontalAlignment.Center;
lblKWDf.VerticalAlignment = VerticalAlignment.Center;
lblKWDf.FontWeight = FontWeights.Bold;
lblKWDf.Foreground = Brushes.White;
Grid.SetRow(lblKWDf, 1);
Grid.SetColumn(lblKWDf, 0);
grid4.Children.Add(lblKWDf);
TextBlock lblKWP = new TextBlock();
lblKWP.Text = SharedStrings.ANOVA_P_VAL;
lblKWP.HorizontalAlignment = HorizontalAlignment.Center;
lblKWP.VerticalAlignment = VerticalAlignment.Center;
lblKWP.FontWeight = FontWeights.Bold;
lblKWP.Foreground = Brushes.White;
Grid.SetRow(lblKWP, 2);
Grid.SetColumn(lblKWP, 0);
grid4.Children.Add(lblKWP);
TextBlock txtKWChi = new TextBlock();
txtKWChi.Text = stats.kruskalWallisH.Value.ToString("N4");
txtKWChi.HorizontalAlignment = HorizontalAlignment.Center;
txtKWChi.VerticalAlignment = VerticalAlignment.Center;
Grid.SetRow(txtKWChi, 0);
Grid.SetColumn(txtKWChi, 1);
grid4.Children.Add(txtKWChi);
TextBlock txtWKDf = new TextBlock();
txtWKDf.Text = stats.dfBetween.Value.ToString();
txtWKDf.HorizontalAlignment = HorizontalAlignment.Center;
txtWKDf.VerticalAlignment = VerticalAlignment.Center;
Grid.SetRow(txtWKDf, 1);
Grid.SetColumn(txtWKDf, 1);
grid4.Children.Add(txtWKDf);
TextBlock txtKWP = new TextBlock();
txtKWP.Text = stats.kruskalPValue.Value.ToString("N4");
txtKWP.HorizontalAlignment = HorizontalAlignment.Center;
txtKWP.VerticalAlignment = VerticalAlignment.Center;
Grid.SetRow(txtKWP, 2);
Grid.SetColumn(txtKWP, 1);
grid4.Children.Add(txtKWP);
rdcount = 0;
foreach (RowDefinition rd in grid4.RowDefinitions)
{
int cdcount = 0;
foreach (ColumnDefinition cd in grid4.ColumnDefinitions)
{
Border b = new Border();
b.Style = this.Resources["gridCellBorder"] as Style;
if (rdcount == 0)
{
b.BorderThickness = new Thickness(b.BorderThickness.Left, 1, b.BorderThickness.Right, b.BorderThickness.Bottom);
}
if (cdcount == 0)
{
b.BorderThickness = new Thickness(1, b.BorderThickness.Top, b.BorderThickness.Right, b.BorderThickness.Bottom);
}
Grid.SetRow(b, rdcount);
Grid.SetColumn(b, cdcount);
grid4.Children.Add(b);
cdcount++;
}
rdcount++;
}
#endregion // Grid4
//panelMain.Children.Add(pnl);
}
public void IEnumerableDouble(string dataSet, int digits, double skewness, double kurtosis, double median, double min, double max, int count)
{
StatTestData data = mData[dataSet];
DescriptiveStatistics stats = new DescriptiveStatistics(data.Data);
AssertHelpers.AlmostEqual(data.Mean, stats.Mean, 15);
AssertHelpers.AlmostEqual(data.StandardDeviation, stats.StandardDeviation, digits);
AssertHelpers.AlmostEqual(skewness, stats.Skewness, 7);
AssertHelpers.AlmostEqual(kurtosis, stats.Kurtosis, 7);
AssertHelpers.AlmostEqual(median, stats.Median, 15);
Assert.AreEqual(stats.Minimum, min);
Assert.AreEqual(stats.Maximum, max);
Assert.AreEqual(stats.Count, count);
}
/// <summary>
/// Converts the gadget's output to Html
/// </summary>
/// <returns></returns>
public override string ToHTML(string htmlFileName = "", int count = 0)
{
StringBuilder htmlBuilder = new StringBuilder();
CustomOutputHeading = headerPanel.Text;
CustomOutputDescription = descriptionPanel.Text;
if (CustomOutputHeading == null || (string.IsNullOrEmpty(CustomOutputHeading) && !CustomOutputHeading.Equals("(none)")))
{
htmlBuilder.AppendLine("<h2 class=\"gadgetHeading\">Means</h2>");
}
else if (CustomOutputHeading != "(none)")
{
htmlBuilder.AppendLine("<h2 class=\"gadgetHeading\">" + CustomOutputHeading + "</h2>");
}
htmlBuilder.AppendLine("<p class=\"gadgetOptions\"><small>");
htmlBuilder.AppendLine("<em>Main variable:</em> <strong>" + cbxField.Text + "</strong>");
htmlBuilder.AppendLine("<br />");
if (cbxFieldCrosstab.SelectedIndex >= 0)
{
htmlBuilder.AppendLine("<em>Crosstab variable:</em> <strong>" + cbxFieldCrosstab.Text + "</strong>");
htmlBuilder.AppendLine("<br />");
}
if (cbxFieldWeight.SelectedIndex >= 0)
{
htmlBuilder.AppendLine("<em>Weight variable:</em> <strong>" + cbxFieldWeight.Text + "</strong>");
htmlBuilder.AppendLine("<br />");
}
if (cbxFieldStrata.SelectedIndex >= 0)
{
htmlBuilder.AppendLine("<em>Strata variable:</em> <strong>" + cbxFieldStrata.Text + "</strong>");
htmlBuilder.AppendLine("<br />");
}
htmlBuilder.AppendLine("</small></p>");
if (!string.IsNullOrEmpty(CustomOutputDescription))
{
htmlBuilder.AppendLine("<p class=\"gadgetsummary\">" + CustomOutputDescription + "</p>");
}
if (!string.IsNullOrEmpty(messagePanel.Text) && messagePanel.Visibility == Visibility.Visible)
{
htmlBuilder.AppendLine("<p><small><strong>" + messagePanel.Text + "</strong></small></p>");
}
if (!string.IsNullOrEmpty(infoPanel.Text) && infoPanel.Visibility == Visibility.Visible)
{
htmlBuilder.AppendLine("<p><small><strong>" + infoPanel.Text + "</strong></small></p>");
}
// Each grid in the 'strataGridList' has a tag associated with it which tells us which strata
// that grid is for. For example, if we stratify by 'Sex' in the Oswego table (in Sample.prj)
// we will have two grids in strataGridList, one for males and one for females. The tags will
// be 'Sex = Male' and 'Sex = Female', respectively.
foreach (Grid grid in this.strataGridList)
{
string gridName = grid.Tag.ToString();
string summaryText = "This tables contains several descriptive statistics for the field " + cbxField.Text + ". ";
if (!string.IsNullOrEmpty(cbxFieldWeight.Text)) { summaryText += "The field " + cbxFieldWeight.Text + " has been specified as a weight. "; }
if (!string.IsNullOrEmpty(cbxFieldStrata.Text)) { summaryText += "The data has been stratified. The data in this table is for the strata value " + grid.Tag.ToString() + ". "; }
if (!string.IsNullOrEmpty(cbxFieldCrosstab.Text)) { summaryText += "The data has been cross-tabulated; there will be one data row for each value of " + cbxFieldCrosstab.Text + ". "; }
summaryText += "The column headings are: The description of the data, the total number of observations, the sum of the observations, the mean, the variance, the standard deviation, the minimum observed value, the 25% value, the median value, the 75% value, the maximum, and the mode.";
htmlBuilder.AppendLine("<div style=\"height: 7px;\"></div>");
htmlBuilder.AppendLine("<table border=\"0\" cellpadding=\"0\" cellspacing=\"0\" summary=\"" + summaryText + "\">");
htmlBuilder.AppendLine("<caption>" + gridName + "</caption>");
foreach (UIElement control in grid.Children)
{
if (control is TextBlock)
{
int rowNumber = Grid.GetRow(control);
int columnNumber = Grid.GetColumn(control);
string tableDataTagOpen = "<td>";
string tableDataTagClose = "</td>";
if (rowNumber == 0)
{
tableDataTagOpen = "<th>";
tableDataTagClose = "</th>";
}
if (columnNumber == 0)
{
htmlBuilder.AppendLine("<tr>");
}
if (columnNumber == 0 && rowNumber > 0)
{
tableDataTagOpen = "<td class=\"value\">";
}
string value = ((TextBlock)control).Text;
string formattedValue = value;
htmlBuilder.AppendLine(tableDataTagOpen + formattedValue + tableDataTagClose);
if (columnNumber >= grid.ColumnDefinitions.Count - 1)
{
htmlBuilder.AppendLine("</tr>");
}
}
}
htmlBuilder.AppendLine("</table>");
// We must find the specific stack panel that corresponds to this strata value from the list of
// ANOVA panels in the gadget. The 'tag' property of the panel is a key value pair that contains
// the strata value we need, plus the set of descriptive statistics that we must display in the
// output.
StackPanel anovaPanel = null;
DescriptiveStatistics statistics = new DescriptiveStatistics();
foreach (StackPanel panel in anovaBlocks)
{
if(panel.Tag is KeyValuePair<string, DescriptiveStatistics>)
{
KeyValuePair<string, DescriptiveStatistics> kvp = ((KeyValuePair<string, DescriptiveStatistics>)panel.Tag);
if (kvp.Key.Equals(gridName))
{
anovaPanel = panel;
statistics = kvp.Value;
break; // no sense in continuning
}
}
}
// check to make sure we actually found one
if (!(anovaPanel == null))
{
string strssBetweenValue = SharedStrings.UNDEFINED;
string strdfBetweenValue = SharedStrings.UNDEFINED;
string strmsBetweenValue = SharedStrings.UNDEFINED;
string strssWithinValue = SharedStrings.UNDEFINED;
string strdfWithinValue = SharedStrings.UNDEFINED;
string strmsWithinValue = SharedStrings.UNDEFINED;
string strfStatisticValue = SharedStrings.UNDEFINED;
string stranovaPValueValue = SharedStrings.UNDEFINED;
string stranovaTValueValue = SharedStrings.UNDEFINED;
string strchiSquareValue = SharedStrings.UNDEFINED;
string strbartlettPValue = SharedStrings.UNDEFINED;
string strTotalSSValue = SharedStrings.UNDEFINED;
string strTotalDFValue = SharedStrings.UNDEFINED;
string strKruskalWallisH = SharedStrings.UNDEFINED;
string strKruskalPValue = SharedStrings.UNDEFINED;
if (statistics.ssBetween.HasValue) { strssBetweenValue = statistics.ssBetween.Value.ToString("F4"); }
if (statistics.dfBetween.HasValue) { strdfBetweenValue = statistics.dfBetween.Value.ToString("F0"); }
if (statistics.msBetween.HasValue) { strmsBetweenValue = statistics.msBetween.Value.ToString("F4"); }
if (statistics.ssWithin.HasValue) { strssWithinValue = statistics.ssWithin.Value.ToString("F4"); }
if (statistics.dfWithin.HasValue) { strdfWithinValue = statistics.dfWithin.Value.ToString("F0"); }
if (statistics.msWithin.HasValue) { strmsWithinValue = statistics.msWithin.Value.ToString("F4"); }
if (statistics.fStatistic.HasValue) { strfStatisticValue = statistics.fStatistic.Value.ToString("F4"); }
if (statistics.anovaPValue.HasValue) { stranovaPValueValue = statistics.anovaPValue.Value.ToString("F4"); }
if (statistics.chiSquare.HasValue) { strchiSquareValue = statistics.chiSquare.Value.ToString("F4"); }
if (statistics.bartlettPValue.HasValue) { strbartlettPValue = statistics.bartlettPValue.Value.ToString("F4"); }
if (statistics.ssBetween.HasValue && statistics.ssWithin.HasValue) { strTotalSSValue = (statistics.ssBetween.Value + statistics.ssWithin.Value).ToString("F4"); }
if (statistics.dfBetween.HasValue && statistics.dfWithin.HasValue) { strTotalDFValue = (statistics.dfBetween.Value + statistics.dfWithin.Value).ToString("F0"); }
if (statistics.kruskalWallisH.HasValue) { strKruskalWallisH = statistics.kruskalWallisH.Value.ToString("F4"); }
if (statistics.kruskalPValue.HasValue) { strKruskalPValue = statistics.kruskalPValue.Value.ToString("F4"); }
summaryText = "This table contains analysis of variance (ANOVA) statistics for the field " + cbxField.Text + ", cross-tabulated by " + cbxFieldCrosstab.Text + ". ";
summaryText += "The column headings for this table are: The variation, the SS value, the degrees of freedom, the MS value, and the F-statistic. There are three rows: The between, the within, and the total.";
// ANOVA
htmlBuilder.AppendLine("<div style=\"height: 7px;\"></div>");
htmlBuilder.AppendLine("<p><strong>" + SharedStrings.ANOVA_DESCRIPTION_FOR_MEANS + "</strong><br />");
htmlBuilder.AppendLine("<small>" + SharedStrings.ANOVA_DESCRIPTION_FOR_MEANS_SUBTITLE + "</small></p>");
htmlBuilder.AppendLine("<table border=\"0\" cellpadding=\"0\" cellspacing=\"0\" summary=\"" + summaryText + "\">");
htmlBuilder.AppendLine(" <tr>");
htmlBuilder.AppendLine(" <th>Variation</th>");
htmlBuilder.AppendLine(" <th>SS</th>");
htmlBuilder.AppendLine(" <th>df</th>");
htmlBuilder.AppendLine(" <th>MS</th>");
htmlBuilder.AppendLine(" <th>F statistic</th>");
htmlBuilder.AppendLine(" </tr>");
htmlBuilder.AppendLine(" <tr>");
htmlBuilder.AppendLine(" <th>Between</th>");
htmlBuilder.AppendLine(" <td>" + strssBetweenValue + "</td>");
htmlBuilder.AppendLine(" <td>" + strdfBetweenValue + "</td>");
htmlBuilder.AppendLine(" <td>" + strmsBetweenValue + "</td>");
htmlBuilder.AppendLine(" <td>" + strfStatisticValue + "</td>");
htmlBuilder.AppendLine(" </tr>");
htmlBuilder.AppendLine(" <tr>");
htmlBuilder.AppendLine(" <th>Within</th>");
htmlBuilder.AppendLine(" <td>" + strssWithinValue + "</td>");
htmlBuilder.AppendLine(" <td>" + strdfWithinValue + "</td>");
htmlBuilder.AppendLine(" <td>" + strmsWithinValue + "</td>");
htmlBuilder.AppendLine(" <td> </td>");
htmlBuilder.AppendLine(" </tr>");
htmlBuilder.AppendLine(" <tr>");
htmlBuilder.AppendLine(" <th>Total</th>");
htmlBuilder.AppendLine(" <td>" + strTotalSSValue + "</td>");
htmlBuilder.AppendLine(" <td>" + strTotalDFValue + "</td>");
htmlBuilder.AppendLine(" <td> </td>");
htmlBuilder.AppendLine(" <td> </td>");
htmlBuilder.AppendLine(" </tr>");
htmlBuilder.AppendLine("</table>");
htmlBuilder.AppendLine("<div style=\"height: 7px;\"></div>");
htmlBuilder.AppendLine("<table border=\"0\" cellpadding=\"0\" cellspacing=\"0\" summary=\"This table contains the ANOVA p-value.\">");
htmlBuilder.AppendLine(" <tr>");
htmlBuilder.AppendLine(" <th>P Value</th>");
htmlBuilder.AppendLine(" <td>" + stranovaPValueValue + "</td>");
htmlBuilder.AppendLine(" </tr>");
htmlBuilder.AppendLine("</table>");
// Bartlett
htmlBuilder.AppendLine("<div style=\"height: 7px;\"></div>");
htmlBuilder.AppendLine("<p><strong>Bartlett's Test for Inequality of Population Variances</strong></p>");
htmlBuilder.AppendLine("<table border=\"0\" cellpadding=\"0\" cellspacing=\"0\">");
htmlBuilder.AppendLine(" <tr>");
htmlBuilder.AppendLine(" <th>Chi Square</th>");
htmlBuilder.AppendLine(" <td>" + strchiSquareValue + "</td>");
htmlBuilder.AppendLine(" </tr>");
htmlBuilder.AppendLine(" <tr>");
htmlBuilder.AppendLine(" <th>Degrees of freedom</th>");
htmlBuilder.AppendLine(" <td>" + strdfBetweenValue + "</td>");
htmlBuilder.AppendLine(" </tr>");
htmlBuilder.AppendLine(" <tr>");
htmlBuilder.AppendLine(" <th>P Value</th>");
htmlBuilder.AppendLine(" <td>" + strbartlettPValue + "</td>");
htmlBuilder.AppendLine("</table>");
htmlBuilder.AppendLine("<p><small>A small p-value (e.g., less than 0.05) suggests that the variances are not homogeneous and that the ANOVA may not be appropriate.</small></p>");
// Kruskal-Wallis
htmlBuilder.AppendLine("<div style=\"height: 7px;\"></div>");
htmlBuilder.AppendLine("<p><strong>Mann-Whitney/Wilcoxon Two-Sample Test (Kruskal-Wallis test for two groups)</strong></p>");
htmlBuilder.AppendLine("<table border=\"0\" cellpadding=\"0\" cellspacing=\"0\" >");
htmlBuilder.AppendLine(" <tr>");
htmlBuilder.AppendLine(" <th>Kruskal-Wallis H</th>");
htmlBuilder.AppendLine(" <td>" + strKruskalWallisH + "</td>");
htmlBuilder.AppendLine(" </tr>");
htmlBuilder.AppendLine(" <tr>");
htmlBuilder.AppendLine(" <th>Degrees of freedom</th>");
htmlBuilder.AppendLine(" <td>" + strdfBetweenValue + "</td>");
htmlBuilder.AppendLine(" </tr>");
htmlBuilder.AppendLine(" <tr>");
htmlBuilder.AppendLine(" <th>P Value</th>");
htmlBuilder.AppendLine(" <td>" + strKruskalPValue + "</td>");
htmlBuilder.AppendLine("</table>");
}
}
return htmlBuilder.ToString();
}
public void IEnumerableDoubleHighAccuracy(string dataSet, double skewness, double kurtosis, double median, double min, double max, int count)
{
var data = _data[dataSet];
var stats = new DescriptiveStatistics(data.Data, true);
AssertHelpers.AlmostEqualRelative(data.Mean, stats.Mean, 14);
AssertHelpers.AlmostEqualRelative(data.StandardDeviation, stats.StandardDeviation, 14);
AssertHelpers.AlmostEqualRelative(skewness, stats.Skewness, 9);
AssertHelpers.AlmostEqualRelative(kurtosis, stats.Kurtosis, 9);
Assert.AreEqual(stats.Minimum, min);
Assert.AreEqual(stats.Maximum, max);
Assert.AreEqual(stats.Count, count);
}
public void ShortSequences()
{
var stats0 = new DescriptiveStatistics(new double[0]);
Assert.That(stats0.Skewness, Is.NaN);
Assert.That(stats0.Kurtosis, Is.NaN);
var stats1 = new DescriptiveStatistics(new[] { 1.0 });
Assert.That(stats1.Skewness, Is.NaN);
Assert.That(stats1.Kurtosis, Is.NaN);
var stats2 = new DescriptiveStatistics(new[] { 1.0, 2.0 });
Assert.That(stats2.Skewness, Is.NaN);
Assert.That(stats2.Kurtosis, Is.NaN);
var stats3 = new DescriptiveStatistics(new[] { 1.0, 2.0, -3.0 });
Assert.That(stats3.Skewness, Is.Not.NaN);
Assert.That(stats3.Kurtosis, Is.NaN);
var stats4 = new DescriptiveStatistics(new[] { 1.0, 2.0, -3.0, -4.0 });
Assert.That(stats4.Skewness, Is.Not.NaN);
Assert.That(stats4.Kurtosis, Is.Not.NaN);
}
public void ZeroVarianceSequence()
{
var stats = new DescriptiveStatistics(new[] { 2.0, 2.0, 2.0, 2.0 });
Assert.That(stats.Skewness, Is.NaN);
Assert.That(stats.Kurtosis, Is.NaN);
}
public void IEnumerableDouble(
[Values("lottery", "lew", "mavro", "michelso", "numacc1", "numacc2", "numacc3", "numacc4")] string dataSet,
[Values(15, 15, 12, 12, 15, 13, 9, 8)] int digits,
[Values(-0.09333165310779, -0.050606638756334, 0.64492948110824, -0.0185388637725746, 0, 0, 0, 0)] double skewness,
[Values(-1.19256091074856, -1.49604979214447, -0.82052379677456, 0.33968459842539, 0, -2.003003003003, -2.003003003003, -2.00300300299913)] double kurtosis,
[Values(522.5, -162, 2.0018, 299.85, 10000002, 1.2, 1000000.2, 10000000.2)] double median,
[Values(4, -579, 2.0013, 299.62, 10000001, 1.1, 1000000.1, 10000000.1)] double min,
[Values(999, 300, 2.0027, 300.07, 10000003, 1.3, 1000000.3, 10000000.3)] double max,
[Values(218, 200, 50, 100, 3, 1001, 1001, 1001)] int count)
{
var data = _data[dataSet];
var stats = new DescriptiveStatistics(data.Data);
AssertHelpers.AlmostEqual(data.Mean, stats.Mean, 15);
AssertHelpers.AlmostEqual(data.StandardDeviation, stats.StandardDeviation, digits);
AssertHelpers.AlmostEqual(skewness, stats.Skewness, 7);
AssertHelpers.AlmostEqual(kurtosis, stats.Kurtosis, 7);
AssertHelpers.AlmostEqual(median, stats.Median, 15);
Assert.AreEqual(stats.Minimum, min);
Assert.AreEqual(stats.Maximum, max);
Assert.AreEqual(stats.Count, count);
}
