//
// Summary:
// Computes the Range of a sequence of nullable System.Double values.
//
// Parameters:
// source:
// A sequence of nullable System.Double values to calculate the Range of.
//
// Returns:
// The Range of the sequence of values, or null if the source sequence is
// empty or contains only values that are null.
//
// Exceptions:
// System.ArgumentNullException:
// source is null.
public static double?Range(this IEnumerable <double?> source)
{
IEnumerable <double> values = source.Coalesce();
if (values.Any())
{
return(values.Range());
}
return(null);
}
private static KendoData GettableByKendofilter(IEnumerable <Struct_Joing_AllTable> enumerable, string kendoQuery)
{
//applichiamo where
QueryDescriptor Qd = JsonConvert.DeserializeObject <QueryDescriptor>(kendoQuery);
enumerable = enumerable.Where(Qd)
.OrderBy(Qd);
return(new KendoData(
enumerable.Count(),
enumerable
.Range(Qd)
.ToList()
));
}
public static void CalculateThresholds(IClassificationProblemData problemData, IEnumerable <double> estimatedValues, IEnumerable <double> targetClassValues, out double[] classValues, out double[] thresholds)
{
var estimatedTargetValues = Enumerable.Zip(estimatedValues, targetClassValues, (e, t) => new { EstimatedValue = e, TargetValue = t }).ToList();
double estimatedValuesRange = estimatedValues.Range();
Dictionary <double, double> classMean = new Dictionary <double, double>();
Dictionary <double, double> classStdDev = new Dictionary <double, double>();
// calculate moments per class
foreach (var group in estimatedTargetValues.GroupBy(p => p.TargetValue))
{
IEnumerable <double> estimatedClassValues = group.Select(x => x.EstimatedValue);
double classValue = group.Key;
double mean, variance;
OnlineCalculatorError meanErrorState, varianceErrorState;
OnlineMeanAndVarianceCalculator.Calculate(estimatedClassValues, out mean, out variance, out meanErrorState, out varianceErrorState);
if (meanErrorState == OnlineCalculatorError.None && varianceErrorState == OnlineCalculatorError.None)
{
classMean[classValue] = mean;
classStdDev[classValue] = Math.Sqrt(variance);
}
}
double[] originalClasses = classMean.Keys.OrderBy(x => x).ToArray();
int nClasses = originalClasses.Length;
List <double> thresholdList = new List <double>();
for (int i = 0; i < nClasses - 1; i++)
{
for (int j = i + 1; j < nClasses; j++)
{
double x1, x2;
double class0 = originalClasses[i];
double class1 = originalClasses[j];
// calculate all thresholds
CalculateCutPoints(classMean[class0], classStdDev[class0], classMean[class1], classStdDev[class1], out x1, out x2);
// if the two cut points are too close (for instance because the stdDev=0)
// then move them by 0.1% of the range of estimated values
if (x1.IsAlmost(x2))
{
x1 -= 0.001 * estimatedValuesRange;
x2 += 0.001 * estimatedValuesRange;
}
if (!double.IsInfinity(x1) && !thresholdList.Any(x => x.IsAlmost(x1)))
{
thresholdList.Add(x1);
}
if (!double.IsInfinity(x2) && !thresholdList.Any(x => x.IsAlmost(x2)))
{
thresholdList.Add(x2);
}
}
}
thresholdList.Sort();
// add small value and large value for the calculation of most influential class in each thresholded section
thresholdList.Insert(0, double.NegativeInfinity);
thresholdList.Add(double.PositiveInfinity);
// find the most likely class for the points between thresholds m
List <double> filteredThresholds = new List <double>();
List <double> filteredClassValues = new List <double>();
for (int i = 0; i < thresholdList.Count - 1; i++)
{
// determine class with maximal density mass between the thresholds
double maxDensity = DensityMass(thresholdList[i], thresholdList[i + 1], classMean[originalClasses[0]], classStdDev[originalClasses[0]]);
double maxDensityClassValue = originalClasses[0];
foreach (var classValue in originalClasses.Skip(1))
{
double density = DensityMass(thresholdList[i], thresholdList[i + 1], classMean[classValue], classStdDev[classValue]);
if (density > maxDensity)
{
maxDensity = density;
maxDensityClassValue = classValue;
}
}
if (maxDensity > double.NegativeInfinity &&
(filteredClassValues.Count == 0 || !maxDensityClassValue.IsAlmost(filteredClassValues.Last())))
{
filteredThresholds.Add(thresholdList[i]);
filteredClassValues.Add(maxDensityClassValue);
}
}
if (filteredThresholds.Count == 0 || !double.IsNegativeInfinity(filteredThresholds.First()))
{
// this happens if there are no thresholds (distributions for all classes are exactly the same)
// or when the CDF up to the first threshold is zero
// -> all samples should be classified as the class with the most observations
// group observations by target class and select the class with largest count
double mostFrequentClass = targetClassValues.GroupBy(c => c)
.OrderBy(g => g.Count())
.Last().Key;
filteredThresholds.Insert(0, double.NegativeInfinity);
filteredClassValues.Insert(0, mostFrequentClass);
}
thresholds = filteredThresholds.ToArray();
classValues = filteredClassValues.ToArray();
}
/// <summary>
/// Returns a specific page of a list
/// </summary>
/// <typeparam name="T"></typeparam>
/// <param name="list"></param>
/// <param name="pageIndex"></param>
/// <param name="pageSize"></param>
/// <returns></returns>
public static List <T> GetPageOfList <T>(this IEnumerable <T> list, int pageIndex, int pageSize)
{
int startIndex = (pageIndex - 1) * pageSize;
return(list.Range(startIndex, pageSize * pageIndex));
}
public static void CalculateThresholds(IClassificationProblemData problemData, IEnumerable<double> estimatedValues, IEnumerable<double> targetClassValues, out double[] classValues, out double[] thresholds) {
var estimatedTargetValues = Enumerable.Zip(estimatedValues, targetClassValues, (e, t) => new { EstimatedValue = e, TargetValue = t }).ToList();
double estimatedValuesRange = estimatedValues.Range();
Dictionary<double, double> classMean = new Dictionary<double, double>();
Dictionary<double, double> classStdDev = new Dictionary<double, double>();
// calculate moments per class
foreach (var group in estimatedTargetValues.GroupBy(p => p.TargetValue)) {
IEnumerable<double> estimatedClassValues = group.Select(x => x.EstimatedValue);
double classValue = group.Key;
double mean, variance;
OnlineCalculatorError meanErrorState, varianceErrorState;
OnlineMeanAndVarianceCalculator.Calculate(estimatedClassValues, out mean, out variance, out meanErrorState, out varianceErrorState);
if (meanErrorState == OnlineCalculatorError.None && varianceErrorState == OnlineCalculatorError.None) {
classMean[classValue] = mean;
classStdDev[classValue] = Math.Sqrt(variance);
}
}
double[] originalClasses = classMean.Keys.OrderBy(x => x).ToArray();
int nClasses = originalClasses.Length;
List<double> thresholdList = new List<double>();
for (int i = 0; i < nClasses - 1; i++) {
for (int j = i + 1; j < nClasses; j++) {
double x1, x2;
double class0 = originalClasses[i];
double class1 = originalClasses[j];
// calculate all thresholds
CalculateCutPoints(classMean[class0], classStdDev[class0], classMean[class1], classStdDev[class1], out x1, out x2);
// if the two cut points are too close (for instance because the stdDev=0)
// then move them by 0.1% of the range of estimated values
if (x1.IsAlmost(x2)) {
x1 -= 0.001 * estimatedValuesRange;
x2 += 0.001 * estimatedValuesRange;
}
if (!double.IsInfinity(x1) && !thresholdList.Any(x => x.IsAlmost(x1))) thresholdList.Add(x1);
if (!double.IsInfinity(x2) && !thresholdList.Any(x => x.IsAlmost(x2))) thresholdList.Add(x2);
}
}
thresholdList.Sort();
// add small value and large value for the calculation of most influential class in each thresholded section
thresholdList.Insert(0, double.NegativeInfinity);
thresholdList.Add(double.PositiveInfinity);
// find the most likely class for the points between thresholds m
List<double> filteredThresholds = new List<double>();
List<double> filteredClassValues = new List<double>();
for (int i = 0; i < thresholdList.Count - 1; i++) {
// determine class with maximal density mass between the thresholds
double maxDensity = DensityMass(thresholdList[i], thresholdList[i + 1], classMean[originalClasses[0]], classStdDev[originalClasses[0]]);
double maxDensityClassValue = originalClasses[0];
foreach (var classValue in originalClasses.Skip(1)) {
double density = DensityMass(thresholdList[i], thresholdList[i + 1], classMean[classValue], classStdDev[classValue]);
if (density > maxDensity) {
maxDensity = density;
maxDensityClassValue = classValue;
}
}
if (maxDensity > double.NegativeInfinity &&
(filteredClassValues.Count == 0 || !maxDensityClassValue.IsAlmost(filteredClassValues.Last()))) {
filteredThresholds.Add(thresholdList[i]);
filteredClassValues.Add(maxDensityClassValue);
}
}
if (filteredThresholds.Count == 0 || !double.IsNegativeInfinity(filteredThresholds.First())) {
// this happens if there are no thresholds (distributions for all classes are exactly the same)
// or when the CDF up to the first threshold is zero
// -> all samples should be classified as the class with the most observations
// group observations by target class and select the class with largest count
double mostFrequentClass = targetClassValues.GroupBy(c => c)
.OrderBy(g => g.Count())
.Last().Key;
filteredThresholds.Insert(0, double.NegativeInfinity);
filteredClassValues.Insert(0, mostFrequentClass);
}
thresholds = filteredThresholds.ToArray();
classValues = filteredClassValues.ToArray();
}
void Start()
{
// Initialize the list with levels
scenes = new List <int>(IEnumerable <int> .Range(1, MAX)); // This creates a list with values from 1 to 50
}
public void RangeTest()
{
var subSet = testEnumerable.Range(3, 5).ToList();
CollectionAssert.AreEqual(subSet, Enumerable.Range(3, 5).ToList());
}
public static IEnumerable <T> Limit <T>(this IEnumerable <T> list, int end)
{
return(list.Range(0, end));
}
protected override object ProcessSubgroup(IEnumerable<double> data)
{
return data != null && data.Any() ? data.Range() : 0;
}
private List <int> FindIndexes(Predicate <Bar> test)
{
return(IEnumerable.Range(0, bar.Count).Where(i => test(bar[i])).ToList());
}
