Meta.Numerics Library

## AnyVector Methods |

The AnyVector type exposes the following members.

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