Class MultivariateNormalDistribution
java.lang.Object
org.apache.commons.math3.distribution.AbstractMultivariateRealDistribution
org.apache.commons.math3.distribution.MultivariateNormalDistribution
- All Implemented Interfaces:
MultivariateRealDistribution
Implementation of the multivariate normal (Gaussian) distribution.
- Since:
- 3.1
- See Also:
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Field Summary
Fields inherited from class org.apache.commons.math3.distribution.AbstractMultivariateRealDistribution
random -
Constructor Summary
ConstructorsConstructorDescriptionMultivariateNormalDistribution(double[] means, double[][] covariances) Creates a multivariate normal distribution with the given mean vector and covariance matrix.MultivariateNormalDistribution(RandomGenerator rng, double[] means, double[][] covariances) Creates a multivariate normal distribution with the given mean vector and covariance matrix. -
Method Summary
Modifier and TypeMethodDescriptiondoubledensity(double[] vals) Returns the probability density function (PDF) of this distribution evaluated at the specified pointx.Gets the covariance matrix.double[]getMeans()Gets the mean vector.double[]Gets the square root of each element on the diagonal of the covariance matrix.double[]sample()Generates a random value vector sampled from this distribution.Methods inherited from class org.apache.commons.math3.distribution.AbstractMultivariateRealDistribution
getDimension, reseedRandomGenerator, sample
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Constructor Details
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MultivariateNormalDistribution
public MultivariateNormalDistribution(double[] means, double[][] covariances) throws SingularMatrixException, DimensionMismatchException, NonPositiveDefiniteMatrixException Creates a multivariate normal distribution with the given mean vector and covariance matrix.
The number of dimensions is equal to the length of the mean vector and to the number of rows and columns of the covariance matrix. It is frequently written as "p" in formulae.Note: this constructor will implicitly create an instance of
Well19937cas random generator to be used for sampling only (seesample()andAbstractMultivariateRealDistribution.sample(int)). In case no sampling is needed for the created distribution, it is advised to passnullas random generator via the appropriate constructors to avoid the additional initialisation overhead.- Parameters:
means- Vector of means.covariances- Covariance matrix.- Throws:
DimensionMismatchException- if the arrays length are inconsistent.SingularMatrixException- if the eigenvalue decomposition cannot be performed on the provided covariance matrix.NonPositiveDefiniteMatrixException- if any of the eigenvalues is negative.
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MultivariateNormalDistribution
public MultivariateNormalDistribution(RandomGenerator rng, double[] means, double[][] covariances) throws SingularMatrixException, DimensionMismatchException, NonPositiveDefiniteMatrixException Creates a multivariate normal distribution with the given mean vector and covariance matrix.
The number of dimensions is equal to the length of the mean vector and to the number of rows and columns of the covariance matrix. It is frequently written as "p" in formulae.- Parameters:
rng- Random Number Generator.means- Vector of means.covariances- Covariance matrix.- Throws:
DimensionMismatchException- if the arrays length are inconsistent.SingularMatrixException- if the eigenvalue decomposition cannot be performed on the provided covariance matrix.NonPositiveDefiniteMatrixException- if any of the eigenvalues is negative.
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Method Details
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getMeans
public double[] getMeans()Gets the mean vector.- Returns:
- the mean vector.
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getCovariances
Gets the covariance matrix.- Returns:
- the covariance matrix.
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density
Returns the probability density function (PDF) of this distribution evaluated at the specified pointx. In general, the PDF is the derivative of the cumulative distribution function. If the derivative does not exist atx, then an appropriate replacement should be returned, e.g.Double.POSITIVE_INFINITY,Double.NaN, or the limit inferior or limit superior of the difference quotient.- Parameters:
vals- Point at which the PDF is evaluated.- Returns:
- the value of the probability density function at point
x. - Throws:
DimensionMismatchException
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getStandardDeviations
public double[] getStandardDeviations()Gets the square root of each element on the diagonal of the covariance matrix.- Returns:
- the standard deviations.
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sample
public double[] sample()Generates a random value vector sampled from this distribution.- Specified by:
samplein interfaceMultivariateRealDistribution- Specified by:
samplein classAbstractMultivariateRealDistribution- Returns:
- a random value vector.
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