case class RandomMatD(dim1: Int = 5, dim2: Int = 10, max: Double = 20.0, min: Double = 0.0, density: Double = 1.0, stream: Int = 0) extends VariateMat with Product with Serializable
The RandomMatD class generates a random matrix of doubles.
- dim1
the number of rows in the matrix
- dim2
the number of columns in the matrix
- max
generate integers in the range 0 (inclusive) to max (inclusive)
- min
generate integers in the range 0 (inclusive) to max (inclusive)
- density
sparsity basis = 1 - density
- stream
the random number stream
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Instance Constructors
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new
RandomMatD(dim1: Int = 5, dim2: Int = 10, max: Double = 20.0, min: Double = 0.0, density: Double = 1.0, stream: Int = 0)
- dim1
the number of rows in the matrix
- dim2
the number of columns in the matrix
- max
generate integers in the range 0 (inclusive) to max (inclusive)
- min
generate integers in the range 0 (inclusive) to max (inclusive)
- density
sparsity basis = 1 - density
- stream
the random number stream
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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var
_discrete: Boolean
Indicates whether the distribution is discrete or continuous (default)
Indicates whether the distribution is discrete or continuous (default)
- Attributes
- protected
- Definition Classes
- VariateMat
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final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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- val density: Double
- val dim1: Int
- val dim2: Int
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def
discrete: Boolean
Determine whether the distribution is discrete or continuous.
Determine whether the distribution is discrete or continuous.
- Definition Classes
- VariateMat
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final
def
eq(arg0: AnyRef): Boolean
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def
finalize(): Unit
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final
def
flaw(method: String, message: String): Unit
Show the flaw by printing the error message.
Show the flaw by printing the error message.
- method
the method where the error occurred
- message
the error message
- Definition Classes
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def
gen: MatrixD
Determine the next random double matrix for the particular distribution.
Determine the next random double matrix for the particular distribution.
- Definition Classes
- RandomMatD → VariateMat
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final
def
getClass(): Class[_]
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def
igen: MatrixI
Determine the next random integer matrix for the particular distribution.
Determine the next random integer matrix for the particular distribution. It is only valid for discrete random variates.
- Definition Classes
- RandomMatD → VariateMat
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final
def
isInstanceOf[T0]: Boolean
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- val max: Double
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def
mean: MatrixD
Compute the matrix mean for the particular distribution.
Compute the matrix mean for the particular distribution.
- Definition Classes
- RandomMatD → VariateMat
- val min: Double
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
pf(z: MatrixD): Double
Compute the probability function (pf): The probability density function (pdf) for continuous RVM's or the probability mass function (pmf) for discrete RVM's.
Compute the probability function (pf): The probability density function (pdf) for continuous RVM's or the probability mass function (pmf) for discrete RVM's.
- z
the mass point/matrix whose probability is sought
- Definition Classes
- RandomMatD → VariateMat
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val
r: Random
Random number stream selected by the stream number
Random number stream selected by the stream number
- Attributes
- protected
- Definition Classes
- VariateMat
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def
rlegenc: RleMatrixD
Determine the next random Rle matrix for the particular distribution.
Determine the next random Rle matrix for the particular distribution. Repetition based upon runLength is used to create column vectors.
- Definition Classes
- RandomMatD → VariateMat
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def
rlegenr: RleMatrixD
Determine the next random Rle matrix for the particular distribution.
Determine the next random Rle matrix for the particular distribution. Repetition based upon runLength is used to create row vectors.
- Definition Classes
- RandomMatD → VariateMat
- val stream: Int
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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final
def
wait(): Unit
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def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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