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