Meta.Numerics Library

## AnyVector Class |

Implements functionality shared between row and column vectors.

Inheritance Hierarchy

SystemObject

Meta.Numerics.MatricesAnyMatrixDouble

Meta.Numerics.MatricesAnyRectangularMatrix

Meta.Numerics.MatricesAnyVector

Meta.Numerics.MatricesColumnVector

Meta.Numerics.MatricesRowVector

Meta.Numerics.MatricesAnyMatrixDouble

Meta.Numerics.MatricesAnyRectangularMatrix

Meta.Numerics.MatricesAnyVector

Meta.Numerics.MatricesColumnVector

Meta.Numerics.MatricesRowVector

Syntax

The AnyVector type exposes the following members.

Properties

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.) |

Methods

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.) |

Extension Methods

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.) |

See Also