AnyVector Class

SystemObject
  Meta.Numerics.MatricesAnyMatrixDouble
    Meta.Numerics.MatricesAnyRectangularMatrix
      Meta.Numerics.MatricesAnyVector
        Meta.Numerics.MatricesColumnVector
        Meta.Numerics.MatricesRowVector

public abstract class AnyVector : AnyRectangularMatrix, 
	IEnumerable, IEnumerable<double>, ICollection<double>, 
	IList<double>, IReadOnlyCollection<double>, IReadOnlyList<double>

Public MustInherit Class AnyVector
	Inherits AnyRectangularMatrix
	Implements IEnumerable, IEnumerable(Of Double), 
	ICollection(Of Double), IList(Of Double), IReadOnlyCollection(Of Double), 
	IReadOnlyList(Of Double)

public ref class AnyVector abstract : public AnyRectangularMatrix, 
	IEnumerable, IEnumerable<double>, ICollection<double>, 
	IList<double>, IReadOnlyCollection<double>, IReadOnlyList<double>

[<AbstractClassAttribute>]
type AnyVector =  
    class
        inherit AnyRectangularMatrix
        interface IEnumerable
        interface IEnumerable<float>
        interface ICollection<float>
        interface IList<float>
        interface IReadOnlyCollection<float>
        interface IReadOnlyList<float>
    end

	Name	Description
	ColumnCount	Gets the number of matrix columns. (Inherited from AnyMatrixT.)
	Dimension	Gets the dimension of the vector.
	IsReadOnly	Gets a flag indicating whether the matrix is read-only. (Inherited from AnyMatrixT.)
	ItemInt32	Gets or sets the specified vector component.
	ItemInt32, Int32	Gets or sets the value of a matrix entry. (Inherited from AnyMatrixT.)
	RowCount	Gets the number of matrix rows. (Inherited from AnyMatrixT.)

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	Name	Description
	Column	Gets a copy of the specified column. (Inherited from AnyRectangularMatrix.)
	Equals(Object)	Determines whether the given object is an equal matrix. (Inherited from AnyMatrixT.)
	Equals(AnyMatrixT)	Determines whether the given matrix equals the current matrix. (Inherited from AnyMatrixT.)
	Fill	Sets all matrix entries according to a supplied fill function. (Inherited from AnyMatrixT.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	FrobeniusNorm	Computes the Frobenius-norm of the matrix. (Inherited from AnyRectangularMatrix.)
	GetEnumerator	Gets an enumerator of the vector components.
	GetHashCode	Not a valid operation. (Inherited from AnyMatrixT.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	InfinityNorm	Computes the ∞-norm of the matrix. (Inherited from AnyRectangularMatrix.)
	MaxNorm	Computes the max-norm of the matrix. (Inherited from AnyRectangularMatrix.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	MultiplySelfByTranspose	Computes the product of the matrix and its transpose. (Inherited from AnyRectangularMatrix.)
	MultiplyTransposeBySelf	Computes the product of the matrix's transpose and itself. (Inherited from AnyRectangularMatrix.)
	Norm	Computes the magnitude of the vector.
	OneNorm	Computes the 1-norm of the matrix. (Inherited from AnyRectangularMatrix.)
	Row	Gets a copy of the specified row. (Inherited from AnyRectangularMatrix.)
	ToArray	Returns the vector elements in an independent array.
	ToString	Returns a string that represents the current object. (Inherited from Object.)

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	Name	Description
	Autocovariance	Overloaded. Computes the auto-covariance for all lags. (Defined by Series.)
	Autocovariance(Int32)	Overloaded. Computes the auto-covariance of the series at the given lag. (Defined by Series.)
	CentralMoment	Computes the given sample central moment. (Defined by Univariate.)
	CorrectedStandardDeviation	Computes the Bessel-corrected standard deviation. (Defined by Univariate.)
	CorrelationCoefficient	Computes the correlation coefficient between the two variables. (Defined by Bivariate.)
	Covariance	Computes the covariance of the two variables. (Defined by Bivariate.)
	FitToAR1	Fits an AR(1) model to the time series. (Defined by Series.)
	FitToBeta	Finds the Beta distribution that best fits the given sample. (Defined by Univariate.)
	FitToExponential	Finds the exponential distribution that best fits the given sample. (Defined by Univariate.)
	FitToGamma	Finds the Gamma distribution that best fits the given sample. (Defined by Univariate.)
	FitToGumbel	Find the Gumbel distribution that best fit the given sample. (Defined by Univariate.)
	FitToLognormal	Finds the log-normal distribution that best fits the given sample. (Defined by Univariate.)
	FitToMA1	Fits an MA(1) model to the time series. (Defined by Series.)
	FitToNormal	Finds the normal distribution that best fits the given sample. (Defined by Univariate.)
	FitToRayleigh	Finds the Rayleigh distribution that best fits the given sample. (Defined by Univariate.)
	FitToWald	Finds the Wald distribution that best fits a sample. (Defined by Univariate.)
	FitToWeibull	Finds the Weibull distribution that best fits the given sample. (Defined by Univariate.)
	InterquartileRange	Finds the interquartile range. (Defined by Univariate.)
	InverseLeftProbability	Finds the sample value corresponding to a given percentile. (Defined by Univariate.)
	KolmogorovSmirnovTest	Tests whether the sample is compatible with the given distribution. (Defined by Univariate.)
	KuiperTest	Tests whether the sample is compatible with the given distribution. (Defined by Univariate.)
	LeftProbability	Gets the fraction of values equal to or less than the given value. (Defined by Univariate.)
	LinearRegression	Computes the best-fit linear regression from the data. (Defined by Bivariate.)
	LjungBoxTest	Overloaded. Performs a Ljung-Box test for non-correlation. (Defined by Series.)
	LjungBoxTest(Int32)	Overloaded. Performs a Ljung-Box test for non-correlation with the given number of lags. (Defined by Series.)
	Maximum	Finds the maximum value. (Defined by Univariate.)
	MaximumLikelihoodFit	Finds the parameters that make an arbitrary, parameterized distribution best fit the sample. (Defined by Univariate.)
	Mean	Computes the sample mean. (Defined by Univariate.)
	Median	Finds the median. (Defined by Univariate.)
	Minimum	Finds the minimum value. (Defined by Univariate.)
	MultiLinearRegression(IReadOnlyDictionaryString, IReadOnlyListDouble)	Overloaded. Performs a multivariate linear regression on the named columns. (Defined by Multivariate.)
	MultiLinearRegression(IReadOnlyListDouble)	Overloaded. Performs a multivariate linear regression. (Defined by Multivariate.)
	NonlinearRegression(IReadOnlyListDouble, FuncIReadOnlyDictionaryString, Double, Double, Double, IReadOnlyDictionaryString, Double)	Overloaded. Finds the parameterized function that best fits the data. (Defined by Bivariate.)
	NonlinearRegression(IReadOnlyListDouble, FuncIReadOnlyListDouble, Double, Double, IReadOnlyListDouble)	Overloaded. Finds the parameterized function that best fits the data. (Defined by Bivariate.)
	PolynomialRegression	Computes the polynomial of given degree which best fits the data. (Defined by Bivariate.)
	PopulationCentralMoment	Estimates the given central moment of the underlying population. (Defined by Univariate.)
	PopulationCovariance	Estimates the covariance of the two variables in the population. (Defined by Bivariate.)
	PopulationMean	Estimates the mean of the underlying population. (Defined by Univariate.)
	PopulationRawMoment	Estimates the given raw moment of the underlying population. (Defined by Univariate.)
	PopulationStandardDeviation	Estimates of the standard deviation of the underlying population. (Defined by Univariate.)
	PopulationVariance	Estimates of the variance of the underlying population. (Defined by Univariate.)
	PowerSpectrum	Computes the power spectrum of the time series. (Defined by Series.)
	RawMoment	Computes the given sample raw moment. (Defined by Univariate.)
	SeriesPopulationStatistics	Computes estimates for the moments of the population from which the time series is drawn. (Defined by Series.)
	ShapiroFranciaTest	Performs a Shapiro-Francia test of normality on the sample. (Defined by Univariate.)
	SignTest	Tests whether the sample median is compatible with the given reference value. (Defined by Univariate.)
	Skewness	Computes the sample skewness. (Defined by Univariate.)
	StandardDeviation	Computes the sample standard deviation. (Defined by Univariate.)
	StudentTTest	Tests whether the sample mean is compatible with the reference mean. (Defined by Univariate.)
	Trimean	Finds the tri-mean. (Defined by Univariate.)
	Variance	Computes the sample variance. (Defined by Univariate.)
	ZTest	Performs a z-test to test whether the given sample is compatible with the given normal reference population. (Defined by Univariate.)

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Reference

Meta.Numerics.Matrices Namespace

Meta.Numerics.MatricesRowVector

Meta.Numerics.MatricesColumnVector