AnyVector Class

Implements functionality shared between row and column vectors.

Definition

Namespace: Meta.Numerics.Matrices
Assembly: Meta.Numerics (in Meta.Numerics.dll) Version: 4.2.0+6d77d64445f7d5d91b12e331399c4362ecb25333
C#
public abstract class AnyVector : AnyRectangularMatrix, 
	IEnumerable, IEnumerable<double>, ICollection<double>, 
	IList<double>, IReadOnlyCollection<double>, IReadOnlyList<double>
Inheritance
Object    AnyMatrixDouble    AnyRectangularMatrix    AnyVector
Derived
Implements
ICollectionDouble, IEnumerableDouble, IListDouble, IReadOnlyCollectionDouble, IReadOnlyListDouble, IEnumerable

Properties

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)

Methods

Column Gets the specified column.
(Inherited from AnyRectangularMatrix)
Equals(AnyMatrixT) Determines whether the given matrix equals the current matrix.
(Inherited from AnyMatrixT)
Equals(Object) Determines whether the given object is an equal matrix.
(Inherited from AnyMatrixT)
Fill Sets all matrix entries according to a supplied fill function.
(Inherited from AnyMatrixT)
FinalizeAllows 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)
GetTypeGets 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)
MemberwiseCloneCreates 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.
ToStringReturns a string that represents the current object.
(Inherited from Object)

Extension Methods

Autocovariance Computes the auto-covariance for all lags.
(Defined by Series)
Autocovariance 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 Performs a Ljung-Box test for non-correlation.
(Defined by Series)
LjungBoxTest 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 Performs a multivariate linear regression on the named columns.
(Defined by Multivariate)
MultiLinearRegression Performs a multivariate linear regression.
(Defined by Multivariate)
NonlinearRegression Finds the parameterized function that best fits the data.
(Defined by Bivariate)
NonlinearRegression 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)

See Also