Namespaces

Types in MathNet.Numerics.Statistics

Type WeightedDescriptiveStatistics

Namespace MathNet.Numerics.Statistics

Computes the basic statistics of data set. The class meets the NIST standard of accuracy for mean, variance, and standard deviation (the only statistics they provide exact values for) and exceeds them in increased accuracy mode. Recommendation: consider to use RunningWeightedStatistics instead.
This type declares a DataContract for out of the box ephemeral serialization with engines like DataContractSerializer, Protocol Buffers and FsPickler, but does not guarantee any compatibility between versions. It is not recommended to rely on this mechanism for durable persistence.

Constructors

Methods

Properties

Public Constructors

WeightedDescriptiveStatistics(IEnumerable<Tuple<double, double>> data, bool increasedAccuracy)

Initializes a new instance of the WeightedDescriptiveStatistics class.
Don't use increased accuracy for data sets containing large values (in absolute value). This may cause the calculations to overflow.
Parameters
IEnumerable<Tuple<double, double>> data

The sample data.

bool increasedAccuracy

If set to true , increased accuracy mode used. Increased accuracy mode uses Decimal types for internal calculations.

Public Methods

bool Equals(object obj)

int GetHashCode()

Type GetType()

string ToString()

Public Properties

long Count get; set;

Gets the size of the sample.
Value:

double EffectiveSampleSize get; set;

The Kish's effective sample size https://en.wikipedia.org/wiki/Effective_sample_size
Value:

double Kurtosis get; set;

Gets the unbiased estimator of the population excess kurtosis using the G_2 estimator.
Returns zero if Count is less than four.
Value:

double Maximum get; set;

Gets the maximum sample value.
Value:

double Mean get; set;

Gets the sample mean.
Value:

double Minimum get; set;

Gets the minimum sample value.
Value:

double Skewness get; set;

Gets the unbiased estimator of the population skewness.
Returns zero if Count is less than three.
Value:

double StandardDeviation get; set;

Gets the unbiased population standard deviation (on a dataset of size N will use an N-1 normalizer).
Value:

double TotalWeight get; set;

Gets the total weight. When used with unweighted data, returns the number of samples.
Value:

double Variance get; set;

Gets the unbiased population variance estimator (on a dataset of size N will use an N-1 normalizer).
Value: