public class CorrelatedRandomVectorGenerator extends Object implements RandomVectorGenerator
RandomVectorGenerator
that generates vectors with with
correlated components.
Random vectors with correlated components are built by combining the uncorrelated components of another random vector in such a way that the resulting correlations are the ones specified by a positive definite covariance matrix.
The main use for correlated random vector generation is for Monte-Carlo simulation of physical problems with several
variables, for example to generate error vectors to be added to a nominal vector. A particularly interesting case is
when the generated vector should be drawn from a Multivariate Normal Distribution. The
approach using a Cholesky decomposition is quite usual in this case. However, it can be extended to other cases as
long as the underlying random generator provides normalized values
like
GaussianRandomGenerator
or UniformRandomGenerator
.
Sometimes, the covariance matrix for a given simulation is not strictly positive definite. This means that the
correlations are not all independent from each other. In this case, however, the non strictly positive elements found
during the Cholesky decomposition of the covariance matrix should not be negative either, they should be null.
Another non-conventional extension handling this case is used here. Rather than computing
C = UT.U
where C
is the covariance matrix and U
is an
upper-triangular matrix, we compute C = B.BT
where B
is a rectangular matrix
having more rows than columns. The number of columns of B
is the rank of the covariance matrix, and it
is the dimension of the uncorrelated random vector that is needed to compute the component of the correlated vector.
This class handles this situation automatically.
Constructor and Description |
---|
CorrelatedRandomVectorGenerator(double[] meanIn,
RealMatrix covariance,
double small,
NormalizedRandomGenerator generatorIn)
Builds a correlated random vector generator from its mean
vector and covariance matrix.
|
CorrelatedRandomVectorGenerator(RealMatrix covariance,
double small,
NormalizedRandomGenerator generatorIn)
Builds a null mean random correlated vector generator from its
covariance matrix.
|
Modifier and Type | Method and Description |
---|---|
NormalizedRandomGenerator |
getGenerator()
Get the underlying normalized components generator.
|
int |
getRank()
Get the rank of the covariance matrix.
|
RealMatrix |
getRootMatrix()
Get the root of the covariance matrix.
|
double[] |
nextVector()
Generate a correlated random vector.
|
public CorrelatedRandomVectorGenerator(double[] meanIn, RealMatrix covariance, double small, NormalizedRandomGenerator generatorIn)
meanIn
- Expected mean values for all components.covariance
- Covariance matrix.small
- Diagonal elements threshold under which column are
considered to be dependent on previous ones and are discardedgeneratorIn
- underlying generator for uncorrelated normalized
components.NonPositiveDefiniteMatrixException
- if the covariance matrix is not strictly positive definite.DimensionMismatchException
- if the mean and covariance
arrays dimensions do not match.public CorrelatedRandomVectorGenerator(RealMatrix covariance, double small, NormalizedRandomGenerator generatorIn)
covariance
- Covariance matrix.small
- Diagonal elements threshold under which column are
considered to be dependent on previous ones and are discarded.generatorIn
- Underlying generator for uncorrelated normalized
components.NonPositiveDefiniteMatrixException
- if the covariance matrix is not strictly positive definite.public NormalizedRandomGenerator getGenerator()
public int getRank()
getRootMatrix()
public RealMatrix getRootMatrix()
B
such that
the covariance matrix is equal to B.BT
getRank()
public double[] nextVector()
nextVector
in interface RandomVectorGenerator
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