case class RandomVecTrend(dim: Int = 10, trend: FunctionS2S = (x: Double) => 2.0 * x, noise: Variate = Uniform (), stream: Int = 0) extends VariateVec with Product with Serializable
The RandomVecTrend class generates random variate vectors useful for
for creating synthetic time-series data. Applies an additive noise model.
- dim
the dimension/size of the vector (number of elements)
- trend
the function representing deterministic component of the time-series
- noise
the ditribution/variate used to produce the random noise
- stream
the random number stream
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Instance Constructors
- new RandomVecTrend(dim: Int = 10, trend: FunctionS2S = (x: Double) => 2.0 * x, noise: Variate = Uniform (), stream: Int = 0)
- dim
the dimension/size of the vector (number of elements)
- trend
the function representing deterministic component of the time-series
- noise
the ditribution/variate used to produce the random noise
- stream
the random number stream
Value Members
- 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)
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- protected
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- VariateVec
- final def asInstanceOf[T0]: T0
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
- val dim: Int
- def discrete: Boolean
Determine whether the distribution is discrete or continuous.
Determine whether the distribution is discrete or continuous.
- Definition Classes
- VariateVec
- final def eq(arg0: AnyRef): Boolean
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- final def flaw(method: String, message: String): Unit
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- def gen: VectoD
Determine the next random double vector for the particular distribution.
Determine the next random double vector for the particular distribution.
- Definition Classes
- RandomVecTrend → VariateVec
- final def getClass(): Class[_ <: AnyRef]
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- AnyRef → Any
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- @native() @HotSpotIntrinsicCandidate()
- def igen: VectoI
Determine the next random integer vector for the particular distribution.
Determine the next random integer vector for the particular distribution. It is only valid for discrete random variates.
- Definition Classes
- RandomVecTrend → VariateVec
- final def isInstanceOf[T0]: Boolean
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- val mean: VectoD
Compute the vector mean for the particular distribution.
Compute the vector mean for the particular distribution.
- Definition Classes
- RandomVecTrend → VariateVec
- final def ne(arg0: AnyRef): Boolean
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- val noise: Variate
- final def notify(): Unit
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- @native() @HotSpotIntrinsicCandidate()
- final def notifyAll(): Unit
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- @native() @HotSpotIntrinsicCandidate()
- def pf(z: VectoD): Double
Compute the probability function (pf): The probability density function (pdf) for continuous RVV's or the probability mass function (pmf) for discrete RVV's.
Compute the probability function (pf): The probability density function (pdf) for continuous RVV's or the probability mass function (pmf) for discrete RVV's.
- z
the mass point/vector whose probability is sought
- Definition Classes
- RandomVecTrend → VariateVec
- def productElementNames: Iterator[String]
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- Product
- val r: Random
Random number stream selected by the stream number
Random number stream selected by the stream number
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- protected
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- VariateVec
- val stream: Int
- final def synchronized[T0](arg0: => T0): T0
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- val trend: FunctionS2S
- final def wait(arg0: Long, arg1: Int): Unit
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
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- final def wait(): Unit
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- def finalize(): Unit
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