scalation
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scalation/scalation/scalation.random

scalation.random

package scalation.random

Members list

Type members

Classlikes

case class Bernoulli(p: Double, stream: Int) extends Variate

This class generates Bernoulli random variates. This discrete RV models the one trial (success is 1, failure is 0). Note, the gen2 method gives a range of 2 (-1 to 1).

This class generates Bernoulli random variates. This discrete RV models the one trial (success is 1, failure is 0). Note, the gen2 method gives a range of 2 (-1 to 1).

Value parameters

p

the probability of success

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class Beta(alpha: Double, beta: Double, stream: Int) extends Variate

This class generates Beta random variates. This continuous RV models random proportions. Beta = Gamma1 / (Gamma1 + Gamma2).

This class generates Beta random variates. This continuous RV models random proportions. Beta = Gamma1 / (Gamma1 + Gamma2).

Value parameters

alpha

the shape parameter for Gamma1

beta

the shape parameter for Gamma2

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class Binomial(p: Double, n: Int, stream: Int) extends Variate

This class generates Binomial random variates. This discrete RV models the number of successes in n trials.

This class generates Binomial random variates. This discrete RV models the number of successes in n trials.

Value parameters

n

the number of independent trials

p

the probability of success

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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object CDF

The CDF object contains methods for computing 'F(x)', the Cumulative Distribution Functions 'CDF's for popular distributions: Uniform Exponential Weibel Empirical StandardNormal StudentT ChiSquare Fisher For a given CDF 'F' with argument 'x', compute 'p = F(x)'.

The CDF object contains methods for computing 'F(x)', the Cumulative Distribution Functions 'CDF's for popular distributions: Uniform Exponential Weibel Empirical StandardNormal StudentT ChiSquare Fisher For a given CDF 'F' with argument 'x', compute 'p = F(x)'.

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
CDF.type
case class Cauchy(alpha: Double, beta: Double, stream: Int) extends Variate

This class generates Cauchy (or Lorentz) random variates. This continuous RV models data with heavier tails than normally distributed.

This class generates Cauchy (or Lorentz) random variates. This continuous RV models data with heavier tails than normally distributed.

Value parameters

alpha

the location parameter (median)

beta

the scale parameter

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class ChiSquare(df: Int, stream: Int) extends Variate

This class generates ChiSquare random variates. This continuous RV models the variance of a distribution.

This class generates ChiSquare random variates. This continuous RV models the variance of a distribution.

Value parameters

df

the degrees of freedom

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class Dice(cdf: Array[Double], stream: Int) extends Variate

This class generates Dice random variates for a given distribution specified using a cumulative distribution function (cdf). This discrete RV models the roll of dice numbered 0, 1, ..., n-1. Add 1 for 1 to n.

This class generates Dice random variates for a given distribution specified using a cumulative distribution function (cdf). This discrete RV models the roll of dice numbered 0, 1, ..., n-1. Add 1 for 1 to n.

Value parameters

cdf

the distribution function (cdf)

stream

the random number stream

Attributes

See also

Randi for an easy way to use fair dice 'Randi (1, 6)'.

Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class Dir(alpha: VectorD, stream: Int)

The Dir class generates Dirichlet random variables. The Dirichlet distribution is the distribution over the space of multinomial distributions.

The Dir class generates Dirichlet random variables. The Dirichlet distribution is the distribution over the space of multinomial distributions.

Value parameters

alpha

the concentration parameters

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Object
trait Matchable
class Any
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case class Discrete(dist: VectorD, x: VectorD, cumulative: Boolean, stream: Int) extends Variate

This class generates generalized Discrete random variates for a given distribution specified using either a probability mass function (pmf) or a cumulative distribution function (cdf). This discrete RV models arbitrary experiments with discrete outcomes.

This class generates generalized Discrete random variates for a given distribution specified using either a probability mass function (pmf) or a cumulative distribution function (cdf). This discrete RV models arbitrary experiments with discrete outcomes.

Value parameters

cumulative

whether 'dist' is cumulative (cdf) or not (pmf)

dist

the distribution function (pdf or cdf)

stream

the random number stream

x

the x-coordinate values (mass points)

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class Erlang(mu: Double, k: Int, stream: Int) extends Variate

This class generates Erlang random variates. This continuous RV models the time until k stages complete.

This class generates Erlang random variates. This continuous RV models the time until k stages complete.

Value parameters

k

the number of stages (or Exponential samples)

mu

the mean of exponential samples (Erlang mean = mu * k)

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class Exponential(mu: Double, stream: Int) extends Variate

This class generates Exponential random variates. This continuous RV models the time until an event occurs.

This class generates Exponential random variates. This continuous RV models the time until an event occurs.

Value parameters

mu

the mean

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class Fisher(df1: Int, df2: Int, stream: Int) extends Variate

This class generates Fisher (F-Distribution) random variates. This continuous RV models the ratio of variances.

This class generates Fisher (F-Distribution) random variates. This continuous RV models the ratio of variances.

Value parameters

df1

the degrees of freedom for numerator Chi-Square

df2

the degrees of freedom for denominator Chi-Square

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class Gamma(alpha: Double, beta: Double, stream: Int) extends Variate

This class generates Gamma random variates. This continuous RV models the time until an event occurs. Note: variance = alpha * beta ^ 2.

This class generates Gamma random variates. This continuous RV models the time until an event occurs. Note: variance = alpha * beta ^ 2.

Value parameters

alpha

the shape parameter

beta

the scale parameter

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class Geometric(p: Double, stream: Int) extends Variate

This class generates Geometric random variates. This discrete RV models the number of failures before the first success.

This class generates Geometric random variates. This discrete RV models the number of failures before the first success.

Value parameters

p

the probability of success

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class HyperExponential(p: Double, mu1: Double, mu2: Double, stream: Int) extends Variate

This class generates HyperExponential random variates (two rates). This continuous RV models the time until an event occurs (higher coefficient of variation than exponential distribution).

This class generates HyperExponential random variates (two rates). This continuous RV models the time until an event occurs (higher coefficient of variation than exponential distribution).

Value parameters

mu1

the first mean (1 / lambda1)

mu2

the second mean (1 / lambda2)

p

the probability of first vs. second rates

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class HyperExponential_(mu: Double, sigma: Double, stream: Int) extends Variate

This class generates HyperExponential_ random variates. This continuous RV models the time until an event occurs (higher coefficient of variation than exponential distribution). FIX

This class generates HyperExponential_ random variates. This continuous RV models the time until an event occurs (higher coefficient of variation than exponential distribution). FIX

Value parameters

mu

the mean

sigma

the standard deviation

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class HyperGeometric(p: Double, n: Int, pop: Int, stream: Int) extends Variate

This class generates HyperGeometric random variates. This discrete RV models the number of successes in n draws from a finite population.

This class generates HyperGeometric random variates. This discrete RV models the number of successes in n draws from a finite population.

Value parameters

n

the number of draws (balls drawn)

p

the probability of success (red balls)

pop

the size of the finite population (total number of balls)

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
Show all
case class Known(values: Array[Double], stream: Int) extends Variate

This class generates Known random variates. The values come from a know array of values (useful for testing).

This class generates Known random variates. The values come from a know array of values (useful for testing).

Value parameters

stream

the random number stream

values

the known array of values

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
Show all
case class LogNormal(mu: Double, sigma2: Double, stream: Int) extends Variate

This class generates LogNormal random variates. This continuous RV models data that is normally distributed after a log transformation.

This class generates LogNormal random variates. This continuous RV models data that is normally distributed after a log transformation.

Value parameters

mu

the mean for underlying Normal

sigma2

the variance (sigma squared) for underlying Normal

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
Show all
case class Logistic(a: Double, b: Double, stream: Int) extends Variate

This class generates Logistic random variates. This continuous RV models logistically distributed data (stretched Normal).

This class generates Logistic random variates. This continuous RV models logistically distributed data (stretched Normal).

Value parameters

a

the location parameter

b

the scale parameter

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class Multinomial(p: Array[Double], n: Int, stream: Int) extends VariateVec

The Multinomial class generates random variate vectors following the multinomial distribution. This discrete RV models the multinomial trials, which generalize Bernoulli trials ({0, 1} to the case where the outcome is in {0, 1, ..., k-1}.

The Multinomial class generates random variate vectors following the multinomial distribution. This discrete RV models the multinomial trials, which generalize Bernoulli trials ({0, 1} to the case where the outcome is in {0, 1, ..., k-1}.

Value parameters

n

the number of independent trials

p

array of cumulative probabilities as CDF values

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class VariateVec
class Object
trait Matchable
class Any
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case class NHPoissonProcess(lambda: VectorD, dt: Double, stream: Int) extends TimeVariate

This class generates arrival times according to a NHPoissonProcess, an Non-Homogeneous Process Process (NHPP), where the arrival rate function lambda(t) is piece-wise constant. Rates are constant over basic time intervals of length 'dt'.

This class generates arrival times according to a NHPoissonProcess, an Non-Homogeneous Process Process (NHPP), where the arrival rate function lambda(t) is piece-wise constant. Rates are constant over basic time intervals of length 'dt'.

Value parameters

dt

the length the basic time intervals

lambda

the vector of arrival rates

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class TimeVariate
class Variate
class Object
trait Matchable
class Any
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case class NegativeBinomial(p: Double, s: Int, stream: Int) extends Variate

This class generates NegativeBinomial random variates. This discrete RV models the number of failures before s-th success.

This class generates NegativeBinomial random variates. This discrete RV models the number of failures before s-th success.

Value parameters

p

the probability of success

s

the number of successes

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class Normal(mu: Double, sigma2: Double, stream: Int) extends Variate

This class generates Normal (Gaussian) random variates. This continuous RV models normally distributed data (bell curve). When summed, most distributions tend to Normal (Central Limit Theorem).

This class generates Normal (Gaussian) random variates. This continuous RV models normally distributed data (bell curve). When summed, most distributions tend to Normal (Central Limit Theorem).

Value parameters

mu

the mean

sigma2

the variance (sigma squared)

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class NormalMat(dim: Int, dim2: Int, mu: Double, sig2: Double, stream: Int) extends VariateMat

The NormalMat class generates Normal (Gaussian) random variate matrices according to the Normal distribution with scalar mean 'mu' and variance 'sig2'. This continuous RVM models multiple instances of normally distributed multidimensional data and treats the variables as identical and independent.

The NormalMat class generates Normal (Gaussian) random variate matrices according to the Normal distribution with scalar mean 'mu' and variance 'sig2'. This continuous RVM models multiple instances of normally distributed multidimensional data and treats the variables as identical and independent.

Value parameters

dim

the number of rows in the matrix

dim2

the number of columns in the matrix

mu

the mean

sig2

the variance (stdev^2)

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class VariateMat
class Object
trait Matchable
class Any
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case class NormalTen(dim: Int, dim2: Int, dim3: Int, mu: Double, sig2: Double, stream: Int) extends VariateTen

The NormalTen class generates Normal (Gaussian) random variate matrices according to the Normal distribution with scalar mean 'mu' and variance 'sig2'. This continuous RVT models multiple instances of normally distributed multidimensional data and treats the variables as identical and independent.

The NormalTen class generates Normal (Gaussian) random variate matrices according to the Normal distribution with scalar mean 'mu' and variance 'sig2'. This continuous RVT models multiple instances of normally distributed multidimensional data and treats the variables as identical and independent.

Value parameters

dim

the number of rows in the tensor

dim2

the number of columns in the tensor

dim3

the number of sheets in the tensor

mu

the mean

sig2

the variance (stdev^2)

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class VariateTen
class Object
trait Matchable
class Any
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case class NormalVec(mu: VectorD, cov: MatrixD, stream: Int) extends VariateVec

The NormalVec class generates Normal (Gaussian) random variate vectors according to the Multivariate Normal distribution with mean 'mu' and covariance 'cov'. This continuous RVV models normally distributed multidimensional data.

The NormalVec class generates Normal (Gaussian) random variate vectors according to the Multivariate Normal distribution with mean 'mu' and covariance 'cov'. This continuous RVV models normally distributed multidimensional data.

Value parameters

cov

the covariance matrix

mu

the mean vector

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class VariateVec
class Object
trait Matchable
class Any
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case class NormalVec_(mu: VectorD, sig: VectorD, stream: Int) extends VariateVec

The NormalVec_ class generates Normal (Gaussian) random variate vectors according to the Multivariate Normal distribution with vector mean 'mu' and standard deviation 'sig'. This continuous RVV models normally distributed multidimensional data and treats the variables as independent.

The NormalVec_ class generates Normal (Gaussian) random variate vectors according to the Multivariate Normal distribution with vector mean 'mu' and standard deviation 'sig'. This continuous RVV models normally distributed multidimensional data and treats the variables as independent.

Value parameters

mu

the mean vector

sig

the standard deviation vector

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class VariateVec
class Object
trait Matchable
class Any
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case class NormalVec_c(n: Int, mu: Double, sig2: Double, stream: Int) extends VariateVec

The NormalVec_c class generates Normal (Gaussian) random variate vectors according to the Multivariate Normal distribution with constant mean 'mu' and variance 'sig2'. This continuous RVV models normally distributed multidimensional data and treats the variables as independent.

The NormalVec_c class generates Normal (Gaussian) random variate vectors according to the Multivariate Normal distribution with constant mean 'mu' and variance 'sig2'. This continuous RVV models normally distributed multidimensional data and treats the variables as independent.

Value parameters

mu

the common mean

n

the number of elements in the vector

sig2

the common variance (standard deviation squared)

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class VariateVec
class Object
trait Matchable
class Any
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case class PermutedVecD(x: VectorD, stream: Int) extends VariateVec

The PermutedVecD class generates random permutations of a vector of doubles.

The PermutedVecD class generates random permutations of a vector of doubles.

Value parameters

stream

the random number stream

x

the vector of doubles to permute

Attributes

See also

maths-people.anu.edu.au/~brent/pd/Arndt-thesis.pdf

Supertypes
trait Serializable
trait Product
trait Equals
class VariateVec
class Object
trait Matchable
class Any
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case class PermutedVecI(x: VectorI, stream: Int) extends VariateVec

The PermutedVecI class generates random permutations of a vector of integers.

The PermutedVecI class generates random permutations of a vector of integers.

Value parameters

stream

the random number stream

x

the vector of integers to permute

Attributes

See also

maths-people.anu.edu.au/~brent/pd/Arndt-thesis.pdf

Supertypes
trait Serializable
trait Product
trait Equals
class VariateVec
class Object
trait Matchable
class Any
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case class Poisson(mu: Double, stream: Int) extends Variate

This class generates Poisson random variates (discrete). This discrete RV models the number of events in a time interval of unit length.

This class generates Poisson random variates (discrete). This discrete RV models the number of events in a time interval of unit length.

Value parameters

mu

the mean

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
Show all
case class PoissonProcess(lambda: Double, stream: Int) extends TimeVariate

The PoissonProcess class generates arrival times according to a Poisson Process. Given the current arrival time 't', generate the next arrival time.

The PoissonProcess class generates arrival times according to a Poisson Process. Given the current arrival time 't', generate the next arrival time.

Value parameters

lambda

the arrival rate (arrivals per unit time)

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class TimeVariate
class Variate
class Object
trait Matchable
class Any
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case class PowerLaw(a: Double, b: Double, y: Double, stream: Int) extends Variate

This class generates PowerLaw random variates: 'cx^-y' for 'x in [a, b]'. This continuous RV models power-law distributions.

This class generates PowerLaw random variates: 'cx^-y' for 'x in [a, b]'. This continuous RV models power-law distributions.

Value parameters

a

the minimum value parameter

b

the maximum value parameter

stream

the random number stream

y

the power parameter to be used

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class ProbabilityVec(n: Int, d: Double, stream: Int) extends VariateVec

The ProbabilityVec class generates a probability vector where the 'i'th probability is '1/n' with a +/- randomizing displacement of at most 'd'. Note, the probability vector must add to one.

The ProbabilityVec class generates a probability vector where the 'i'th probability is '1/n' with a +/- randomizing displacement of at most 'd'. Note, the probability vector must add to one.

Value parameters

d

the randomizing displacement, must be in [0, 1]

n

the dimension/size of the probability vector

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class VariateVec
class Object
trait Matchable
class Any
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object Quantile

The Quantile object contains methods for computing 'Finv', the "inverse" Cumulative Distribution Functions (iCDF's) for popular sampling distributions: StandardNormal, StudentT, ChiSquare and Fisher. For a given CDF F and probability/quantile p, compute x such that F(x) = p. The iCDF may be thought of as giving value of x for which the area under the curve from -infinity to x of the probability density function (pdf) is equal to p.

The Quantile object contains methods for computing 'Finv', the "inverse" Cumulative Distribution Functions (iCDF's) for popular sampling distributions: StandardNormal, StudentT, ChiSquare and Fisher. For a given CDF F and probability/quantile p, compute x such that F(x) = p. The iCDF may be thought of as giving value of x for which the area under the curve from -infinity to x of the probability density function (pdf) is equal to p.

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
Quantile.type
trait RNG(stream: Int)

The RNG trait is the base class for all ScalaTion Random Number Generators. The extending classes must implement a 'gen' method that generates random real numbers in the range (0, 1). They must also implement an 'igen' methods to return stream values.

The RNG trait is the base class for all ScalaTion Random Number Generators. The extending classes must implement a 'gen' method that generates random real numbers in the range (0, 1). They must also implement an 'igen' methods to return stream values.

Value parameters

stream

the random number stream index (0 to N_STREAMS - 1 = 999)

Attributes

Supertypes
class Object
trait Matchable
class Any
Known subtypes
class Random
class Random0
class Random2
class Random3
object RNGStream

The RNGStream object allows for random selection of streams for applications where reproducibility of random numbers is not desired.

The RNGStream object allows for random selection of streams for applications where reproducibility of random numbers is not desired.

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
RNGStream.type
object RNGTester

The RNGTester object conducts tests of Random Number Generators: (1) meansTest: Means Test (including a speed test). (2) distributionTest: Chi-square Goodness of Fit Test. (3) distributionTest_KS: K-S Goodness of Fit Test. (4) correlationTest: Correlation Test.

The RNGTester object conducts tests of Random Number Generators: (1) meansTest: Means Test (including a speed test). (2) distributionTest: Chi-square Goodness of Fit Test. (3) distributionTest_KS: K-S Goodness of Fit Test. (4) correlationTest: Correlation Test.

runMain scalation.random.RNGTest

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
RNGTester.type
case class Randi(a: Int, b: Int, stream: Int) extends Variate

This class generates Randi random variates (random integers: a, ..., b). This discrete RV models equi-probable integral outcomes.

This class generates Randi random variates (random integers: a, ..., b). This discrete RV models equi-probable integral outcomes.

Value parameters

a

the lower bound (inclusive)

b

the upper bound (inclusive)

stream

the random number stream

Attributes

See also
Companion
object
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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object Randi

Attributes

Companion
class
Supertypes
trait Product
trait Mirror
class Object
trait Matchable
class Any
Self type
Randi.type
case class Randi0(b: Int, stream: Int) extends Variate

This class generates Randi0 random variates (random integers: 0, ..., b). This discrete RV models equi-probable integral outcomes starting with 0.

This class generates Randi0 random variates (random integers: 0, ..., b). This discrete RV models equi-probable integral outcomes starting with 0.

Value parameters

b

the upper bound (>= 0) (inclusive)

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class RandiU0(b: Int, stream: Int) extends Variate

This class generates RandiU0 random variates (unique random integers: 0, ..., b). This discrete RV models equi-probable integral outcomes starting with 0.

This class generates RandiU0 random variates (unique random integers: 0, ..., b). This discrete RV models equi-probable integral outcomes starting with 0.

Value parameters

b

the upper bound (>= 0) (inclusive)

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class Random(stream: Int) extends RNG

The Random class generates random real numbers in the range (0, 1). It implements, using 64-bit integers (Long's), the 'MRG31k3p' generator developed by L'Ecuyer and Touzin, described in "FAST COMBINED MULTIPLE RECURSIVE GENERATORS WITH MULTIPLIERS OF THE FORM a = 2^q +/- 2^r". MRG31k3p is a Combined Multiple Recursive Generator (CMRG) shown to have good performance and statistical properties for simulations. It has a period of about 2^185 and is considered to be a faster alternative to the popular 'MRG32k3' generator. MRG31k3p combines MRG1 and MRG2.

The Random class generates random real numbers in the range (0, 1). It implements, using 64-bit integers (Long's), the 'MRG31k3p' generator developed by L'Ecuyer and Touzin, described in "FAST COMBINED MULTIPLE RECURSIVE GENERATORS WITH MULTIPLIERS OF THE FORM a = 2^q +/- 2^r". MRG31k3p is a Combined Multiple Recursive Generator (CMRG) shown to have good performance and statistical properties for simulations. It has a period of about 2^185 and is considered to be a faster alternative to the popular 'MRG32k3' generator. MRG31k3p combines MRG1 and MRG2.

MRG1: x_i = (0          + a_12 x_i-2 + a_13 x_i-3) % M1
MRG2: x_i = (a_21 x_i-1 + 0          + a_23 x_i-3) % M2

where a_12 = 2^22, a_13 = 2^7+1, a_21 = 2^15 and a_23 = 2^15+1.

Value parameters

stream

the random number stream index

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
trait RNG
class Object
trait Matchable
class Any
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case class Random0(stream: Int) extends RNG

The Random0 class generates random real numbers in the range (0, 1). It implements, using 32-bit integers (Int's).

The Random0 class generates random real numbers in the range (0, 1). It implements, using 32-bit integers (Int's).

x_i = (x_i-1 + 1) % m

Value parameters

stream

the random number stream index

Attributes

See also

Random for a better random number generator

Supertypes
trait Serializable
trait Product
trait Equals
trait RNG
class Object
trait Matchable
class Any
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case class Random2(stream: Int) extends RNG

The Random2 class generates random real numbers in the range (0, 1). It implements, using 32-bit integers (Int's), the 'MRG31k3p' generator developed by L'Ecuyer and Touzin, described in "FAST COMBINED MULTIPLE RECURSIVE GENERATORS WITH MULTIPLIERS OF THE FORM a = 2^q +/- 2^r". MRG31k3p is a Combined Multiple Recursive Generator (CMRG) shown to have good performance and statistical properties for simulations. It has a period of about 2^185 and is considered to be a faster alternative to the popular 'MRG32k3' generator. MRG31k3p combines MRG1 and MRG2.

The Random2 class generates random real numbers in the range (0, 1). It implements, using 32-bit integers (Int's), the 'MRG31k3p' generator developed by L'Ecuyer and Touzin, described in "FAST COMBINED MULTIPLE RECURSIVE GENERATORS WITH MULTIPLIERS OF THE FORM a = 2^q +/- 2^r". MRG31k3p is a Combined Multiple Recursive Generator (CMRG) shown to have good performance and statistical properties for simulations. It has a period of about 2^185 and is considered to be a faster alternative to the popular 'MRG32k3' generator. MRG31k3p combines MRG1 and MRG2.

MRG1: x_i = (0          + a_12 x_i-2 + a_13 x_i-3) % M1
MRG2: x_i = (a_21 x_i-1 + 0          + a_23 x_i-3) % M2

where a_12 = 2^22, a_13 = 2^7+1, a_21 = 2^15 and a_23 = 2^15+1.

Value parameters

stream

the random number stream index

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
trait RNG
class Object
trait Matchable
class Any
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case class Random3(stream: Int) extends RNG

The Random3 class generates random real numbers in the range (0, 1). It implements, using 64-bit integers (Int's), the 'MINSTD' generator, which is a multiplicative Linear Congruential Generator (LCG). These generators were commonly used in the last century.

The Random3 class generates random real numbers in the range (0, 1). It implements, using 64-bit integers (Int's), the 'MINSTD' generator, which is a multiplicative Linear Congruential Generator (LCG). These generators were commonly used in the last century.

x_i = a x_i-1 % m

Value parameters

stream

the random number stream index

Attributes

See also

http://random.mat.sbg.ac.at/results/karl/server/node4.html#SECTION00042000000000000000 In case a better generator is needed, a Multiple Recursive Generator (MRG) or Composite Multiple Recursive Generator (CMRG) should be used.

Random

Supertypes
trait Serializable
trait Product
trait Equals
trait RNG
class Object
trait Matchable
class Any
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case class RandomMatD(dim: Int, dim2: Int, max: Double, min: Double, density: Double, stream: Int) extends VariateMat

The RandomMatD class generates a random matrix of doubles.

The RandomMatD class generates a random matrix of doubles.

Value parameters

density

sparsity basis = 1 - density

dim

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)

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class VariateMat
class Object
trait Matchable
class Any
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object RandomSeeds

The first 1000 seeds for the 'MRG31k3p' random number generator.

The first 1000 seeds for the 'MRG31k3p' random number generator.

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
object RandomSeeds3

The first 1000 seeds for the LCG random number generator.

The first 1000 seeds for the LCG random number generator.

Attributes

Supertypes
class Object
trait Matchable
class Any
Self type
case class RandomSet(count: Int, max: Int, skip: Int, stream: Int) extends VariateSet

The RandomSet class generates a random set/subset of integers.

The RandomSet class generates a random set/subset of integers.

Value parameters

count

the size of the set (number of integer elements)

max

generate integers in the range 0 (inclusive) to max (inclusive)

skip

skip this number, i.e, do not use it

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class VariateSet
class Object
trait Matchable
class Any
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case class RandomSetS(count: Int, stream: Int) extends VariateSet

The RandomSetS class generates a random set/subset of strings.

The RandomSetS class generates a random set/subset of strings.

Value parameters

count

the size of the set (number of strings)

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class VariateSet
class Object
trait Matchable
class Any
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case class RandomSetW(count: Int, nWords: Int, lRange: Range, cRange: Range, stream: Int) extends VariateSet

The RandomSetW class generates a random set/subset of words.

The RandomSetW class generates a random set/subset of words.

Value parameters

cRange

the range of characters to generate

count

the size of the set (number of words)

lRange

the range of string lengths to generate

nWords

the numbers of words to predetermine.

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class VariateSet
class Object
trait Matchable
class Any
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case class RandomStr(lRange: Range, cRange: Range, stream: Int) extends Variate

The RandomStr class generates random strings.

The RandomStr class generates random strings.

Value parameters

cRange

the range of characters to generate

lRange

the range of string lengths to generate

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class RandomTenD(dim: Int, dim2: Int, dim3: Int, max: Double, min: Double, density: Double, stream: Int) extends VariateTen

The RandomTenD class generates a random tensor of doubles.

The RandomTenD class generates a random tensor of doubles.

Value parameters

density

sparsity basis = 1 - density

dim

the number of rows in the tensor

dim2

the number of columns in the tensor

dim3

the number of sheets in the tensor

max

generate integers in the range 0 (inclusive) to max (inclusive)

min

generate integers in the range 0 (inclusive) to max (inclusive)

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class VariateTen
class Object
trait Matchable
class Any
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case class RandomVecD(dim: Int, max: Double, min: Double, density: Double, runLength: Int, stream: Int) extends VariateVec

The RandomVecD class generates a random vector of doubles. Ex: (3.0, 2.0, 0.0, 4.0, 1.0) has dim = 5 and max = 4.

The RandomVecD class generates a random vector of doubles. Ex: (3.0, 2.0, 0.0, 4.0, 1.0) has dim = 5 and max = 4.

Value parameters

density

sparsity basis = 1 - density

dim

the dimension/size of the vector (number of elements)

max

generate doubles in the range min to max

min

generate doubles in the range min to max

runLength

the maximum run length

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class VariateVec
class Object
trait Matchable
class Any
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case class RandomVecD_(dim: Int, max: VectorD, min: VectorD, stream: Int) extends VariateVec

The RandomVecD_ class generates a random vector of doubles. Ex: (3.0, 2.0, 0.0, 4.0, 1.0) has dim = 5. This version does not consider density or runLength.

The RandomVecD_ class generates a random vector of doubles. Ex: (3.0, 2.0, 0.0, 4.0, 1.0) has dim = 5. This version does not consider density or runLength.

Value parameters

dim

the dimension/size of the vector (number of elements)

max

generate doubles in the range min to max

min

generate doubles in the range min to max

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class VariateVec
class Object
trait Matchable
class Any
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case class RandomVecI(dim: Int, max: Int, min: Int, skip: Int, unique: Boolean, stream: Int) extends VariateVec

The RandomVecI class generates a random vector of integers. Ex: (3, 2, 0, 4, 1) has dim = 5 and max = 4.

The RandomVecI class generates a random vector of integers. Ex: (3, 2, 0, 4, 1) has dim = 5 and max = 4.

Value parameters

dim

the dimension/size of the vector (number of elements)

max

generate integers in the range min (inclusive) to max (inclusive)

min

generate integers in the range min (inclusive) to max (inclusive)

skip

skip this number, i.e, do not use it

stream

the random number stream

unique

whether the integers must be unique

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class VariateVec
class Object
trait Matchable
class Any
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case class RandomVecS(dim: Int, unique: Boolean, stream: Int) extends VariateVec

The RandomVecS class generates a random vector of strings. Ex: ("3", "2", "0", "4", "1") has dim = 5 and max = 4.

The RandomVecS class generates a random vector of strings. Ex: ("3", "2", "0", "4", "1") has dim = 5 and max = 4.

Value parameters

dim

the dimension/size of the vector (number of elements)

stream

the random number stream

unique

whether the strings must be unique

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class VariateVec
class Object
trait Matchable
class Any
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case class RandomVecSample(pop: Int, samp: Int, stream: Int) extends VariateVec

The RandomVecSample class generates random sample from a population.

The RandomVecSample class generates random sample from a population.

Value parameters

pop

the size of the population (0, 1, ... pop-1)

samp

the size of the random samples

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class VariateVec
class Object
trait Matchable
class Any
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case class RandomVecTrend(dim: Int, trend: Double => Double, noise: Variate, stream: Int) extends VariateVec

The RandomVecTrend class generates random variate vectors useful for for creating synthetic time-series data. Applies an additive noise model.

The RandomVecTrend class generates random variate vectors useful for for creating synthetic time-series data. Applies an additive noise model.

Value parameters

dim

the dimension/size of the vector (number of elements)

noise

the ditribution/variate used to produce the random noise

stream

the random number stream

trend

the function representing deterministic component of the time-series

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class VariateVec
class Object
trait Matchable
class Any
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case class RandomWord(nWords: Int, lRange: Range, cRange: Range, stream: Int) extends Variate

The RandomWord class generates random words from a predetermined set.

The RandomWord class generates random words from a predetermined set.

Value parameters

cRange

the range of characters to generate

lRange

the range of string lengths to generate

nWords

the numbers of words to predetermine.

stream

the random number stream

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class Sharp(x: Double, stream: Int) extends Variate

This class generates Sharp (Deterministic) random variates. This discrete RV models the case when the variance is 0.

This class generates Sharp (Deterministic) random variates. This discrete RV models the case when the variance is 0.

Value parameters

stream

the random number stream

x

the value for this constant distribution

Attributes

Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class StdNormal(stream: Int) extends Variate

This class generates StdNormal random variates using the acceptance-rejection method.

This class generates StdNormal random variates using the acceptance-rejection method.

Value parameters

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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object StreamMaker

The StreamMaker object computes seeds for Random and Random2, both of which implement the 'MRG31k3p' random number generator. This generator has a period length around 2^185. Each seed is a 6-dimensional vector of 32-bit integers.

The StreamMaker object computes seeds for Random and Random2, both of which implement the 'MRG31k3p' random number generator. This generator has a period length around 2^185. Each seed is a 6-dimensional vector of 32-bit integers.

Attributes

See also
Supertypes
class Object
trait Matchable
class Any
Self type
case class StudentT(df: Int, stream: Int) extends Variate

This class generates StudentT (Student's t) random variates. This continuous RV models cases where data are normally distributed, but variability increases since the variance is unknown.

This class generates StudentT (Student's t) random variates. This continuous RV models cases where data are normally distributed, but variability increases since the variance is unknown.

Value parameters

df

the degrees of freedom

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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abstract class TimeVariate(stream: Int) extends Variate

The TimeVariate abstract class serves as a superclass for time-based random variates such Poisson Processes.

The TimeVariate abstract class serves as a superclass for time-based random variates such Poisson Processes.

Value parameters

stream

the random number stream

Attributes

Supertypes
class Variate
class Object
trait Matchable
class Any
Known subtypes
case class Trapezoidal(a: Double, c: Double, d: Double, b: Double, stream: Int) extends Variate

This class generates Trapezoidal random variates. This continuous RV models cases where outcomes cluster between two modes. Both Uniform andTriangularare special cases ofTrapezoidal`.

This class generates Trapezoidal random variates. This continuous RV models cases where outcomes cluster between two modes. Both Uniform andTriangularare special cases ofTrapezoidal`.

Value parameters

a

the minimum

b

the maximum

c

the first mode

d

the second mode

stream

the random number stream

Attributes

See also

iopscience.iop.org/0026-1394/44/2/003/pdf/0026-1394_44_2_003.pdf

Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class Triangular(a: Double, b: Double, c: Double, stream: Int) extends Variate

This class generates simple Triangular random variates with the mode in the middle. This continuous RV models cases where outcomes cluster around the mode.

This class generates simple Triangular random variates with the mode in the middle. This continuous RV models cases where outcomes cluster around the mode.

Value parameters

a

the lower bound

b

the upper bound

c

the mode

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
Show all
case class Trinomial(p: Double, q: Double, n: Int, stream: Int) extends Variate

This class generates Trinomial random variates. While Binomial is based on trials with two outcomes, success (1) or failure (0). Trinomial is based on trials with three outcomes, high (2), medium (1) or low (0). This discrete RV models the result of 'n' trials.

This class generates Trinomial random variates. While Binomial is based on trials with two outcomes, success (1) or failure (0). Trinomial is based on trials with three outcomes, high (2), medium (1) or low (0). This discrete RV models the result of 'n' trials.

Value parameters

n

the number of independent trials

p

the probability of high (2)

q

the probability of medium (1)

stream

the random number stream

Attributes

See also

onlinecourses.science.psu.edu/stat414/node/106 FIX - fails distribution test - maybe due to 'pf' method

Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
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case class Uniform(a: Double, b: Double, stream: Int) extends Variate

This class generates Uniform random variates in the range (a, b). This continuous RV models equi-probable outcomes.

This class generates Uniform random variates in the range (a, b). This continuous RV models equi-probable outcomes.

Value parameters

a

the lower bound

b

the upper bound

stream

the random number stream

Attributes

See also
Companion
object
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
Show all
object Uniform

Attributes

Companion
class
Supertypes
trait Product
trait Mirror
class Object
trait Matchable
class Any
Self type
Uniform.type
abstract class Variate(stream: Int)

The Variate abstract class serves as a base class for all the Random Variate (RV) generators. They use one of the Random Number Generators (RNG's) from Random to generate numbers following their particular distribution. Random Variate Generators (RVG's) for thirty popular probability distributions are implemented as extensions of Variate. Still need to add one.

The Variate abstract class serves as a base class for all the Random Variate (RV) generators. They use one of the Random Number Generators (RNG's) from Random to generate numbers following their particular distribution. Random Variate Generators (RVG's) for thirty popular probability distributions are implemented as extensions of Variate. Still need to add one.

Value parameters

stream

the random number stream (0 until N_STREAMS)

Attributes

See also
Supertypes
class Object
trait Matchable
class Any
Known subtypes
class Bernoulli
class Beta
class Binomial
class Cauchy
class ChiSquare
class Dice
class Discrete
class Erlang
class Exponential
class Fisher
class Gamma
class Geometric
class Known
class LogNormal
class Logistic
class Normal
class Poisson
class PowerLaw
class Randi
class Randi0
class RandiU0
class RandomStr
class RandomWord
class Sharp
class StdNormal
class StudentT
class TimeVariate
class Trapezoidal
class Triangular
class Trinomial
class Uniform
class Weibull
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abstract class VariateMat(stream: Int)
object VariateMat

The VariateMat companion object provides a method to add correlation to a matrix.

The VariateMat companion object provides a method to add correlation to a matrix.

Attributes

Companion
class
Supertypes
class Object
trait Matchable
class Any
Self type
VariateMat.type
abstract class VariateSet(stream: Int)

The VariateSet abstract class serves as a base class for all the Random Variate Set (RVS) generators. They use one of the Random Number Generators (RNG's) from Random.scala to generate numbers following their particular multivariate distribution.

The VariateSet abstract class serves as a base class for all the Random Variate Set (RVS) generators. They use one of the Random Number Generators (RNG's) from Random.scala to generate numbers following their particular multivariate distribution.

Value parameters

stream

the random number stream (0 until N_STREAMS)

Attributes

Supertypes
class Object
trait Matchable
class Any
Known subtypes
class RandomSet
class RandomSetS
class RandomSetW
abstract class VariateTen(stream: Int)
abstract class VariateVec(stream: Int)
case class Weibull(alpha: Double, beta: Double, stream: Int) extends Variate

This class generates Weibull random variates. This continuous RV models the time for an event to occur.

This class generates Weibull random variates. This continuous RV models the time for an event to occur.

Value parameters

alpha

the shape parameter

beta

the scale parameter

stream

the random number stream

Attributes

See also
Supertypes
trait Serializable
trait Product
trait Equals
class Variate
class Object
trait Matchable
class Any
Show all
final class cDFTest_ChiSquare

Attributes

Supertypes
class Object
trait Matchable
class Any
final class cDFTest_Empirical

Attributes

Supertypes
class Object
trait Matchable
class Any
final class cDFTest_Exponential

Attributes

Supertypes
class Object
trait Matchable
class Any
final class cDFTest_Fisher

Attributes

Supertypes
class Object
trait Matchable
class Any
final class cDFTest_Fisher2

Attributes

Supertypes
class Object
trait Matchable
class Any
final class cDFTest_Normal

Attributes

Supertypes
class Object
trait Matchable
class Any
final class cDFTest_Normal_Diff

Attributes

Supertypes
class Object
trait Matchable
class Any
final class cDFTest_StudentT

Attributes

Supertypes
class Object
trait Matchable
class Any
final class cDFTest_Uniform

Attributes

Supertypes
class Object
trait Matchable
class Any
final class cDFTest_Weibull

Attributes

Supertypes
class Object
trait Matchable
class Any
final class cLTTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class diceTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class poissonProcessTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class quantileTest_ChiSquare

Attributes

Supertypes
class Object
trait Matchable
class Any
final class quantileTest_Empirical

Attributes

Supertypes
class Object
trait Matchable
class Any
final class quantileTest_Exponential

Attributes

Supertypes
class Object
trait Matchable
class Any
final class quantileTest_Fisher

Attributes

Supertypes
class Object
trait Matchable
class Any
final class quantileTest_Normal

Attributes

Supertypes
class Object
trait Matchable
class Any
final class quantileTest_StudentT

Attributes

Supertypes
class Object
trait Matchable
class Any
final class quantileTest_Uniform

Attributes

Supertypes
class Object
trait Matchable
class Any
final class rNGTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class randomStrTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class randomWordTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class streamMaker3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class streamMakerGen

Attributes

Supertypes
class Object
trait Matchable
class Any
final class variateMatTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class variateSetTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class variateSetTest2

Attributes

Supertypes
class Object
trait Matchable
class Any
final class variateSetTest3

Attributes

Supertypes
class Object
trait Matchable
class Any
final class variateTenTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class variateTest

Attributes

Supertypes
class Object
trait Matchable
class Any
final class variateVecTest

Attributes

Supertypes
class Object
trait Matchable
class Any

Types

type Distribution = (Double, Parameters) => Double

The function type for distribution functions, including (1) Cumulative Distribution Function (CDF) (2) inverse Cumulative Distribution Function (iCDF) The arguments are Double for coordinate 'x' or probability 'p' and a Vector of parameters, e.g., degrees of freedom.

The function type for distribution functions, including (1) Cumulative Distribution Function (CDF) (2) inverse Cumulative Distribution Function (iCDF) The arguments are Double for coordinate 'x' or probability 'p' and a Vector of parameters, e.g., degrees of freedom.

Attributes

type Parameters = Vector[Double]

Type definition for parameters to a distribution. Vector is used instead of Array since they are covariant, while Scala arrays are not.

Type definition for parameters to a distribution. Vector is used instead of Array since they are covariant, while Scala arrays are not.

Attributes

Value members

Concrete methods

def cDFTest_ChiSquare(): Unit

The cDFTest_ChiSquare main function tests the 'CDF.chiSquareCDF' method.

The cDFTest_ChiSquare main function tests the 'CDF.chiSquareCDF' method.

runMain scalation.random.cDFTest_ChiSquare

Attributes

def cDFTest_Empirical(): Unit

The cDFTest_Empirical main function tests the 'CDF.buildEmpiricalCDF' method.

The cDFTest_Empirical main function tests the 'CDF.buildEmpiricalCDF' method.

runMain scalation.random.cDFTest_Empirical

Attributes

def cDFTest_Exponential(): Unit

The cDFTest_Exponential main function tests the 'CDF.exponentialCDF' method.

The cDFTest_Exponential main function tests the 'CDF.exponentialCDF' method.

runMain scalation.random.cDFTest_Exponential

Attributes

def cDFTest_Fisher(): Unit

The cDFTest_Fisher main function tests the 'CDF.fisherCDF' method.

The cDFTest_Fisher main function tests the 'CDF.fisherCDF' method.

runMain scalation.random.cDFTest_Fisher

Attributes

def cDFTest_Fisher2(): Unit

The cDFTest_Fisher2 main function tests the 'CDF.fisherCDF' method.

The cDFTest_Fisher2 main function tests the 'CDF.fisherCDF' method.

runMain scalation.random.cDFTest_Fisher2

Attributes

def cDFTest_Normal(): Unit

The cDFTest_Normal main function tests the 'CDF.normalCDF' method.

The cDFTest_Normal main function tests the 'CDF.normalCDF' method.

runMain scalation.random.cDFTest_Normal

Attributes

def cDFTest_Normal_Diff(): Unit

The cDFTest_Normal main function tests difference between different implementations of the 'normalCDF' method in the CDF object.

The cDFTest_Normal main function tests difference between different implementations of the 'normalCDF' method in the CDF object.

runMain scalation.random.cDFTest_Normal_Diff

Attributes

def cDFTest_StudentT(): Unit

The cDFTest_StudentT main function tests the 'CDF.studentTCDF' method.

The cDFTest_StudentT main function tests the 'CDF.studentTCDF' method.

runMain scalation.random.cDFTest_StudentT

Attributes

def cDFTest_Uniform(): Unit

The cDFTest_Uniform main function tests the 'CDF.uniformCDF' method.

The cDFTest_Uniform main function tests the 'CDF.uniformCDF' method.

runMain scalation.random.cDFTest_Uniform

Attributes

def cDFTest_Weibull(): Unit

The cDFTest_Weibull main function tests the 'CDF.weibullCDF' method.

The cDFTest_Weibull main function tests the 'CDF.weibullCDF' method.

runMain scalation.random.cDFTest_Weibull

Attributes

def cLTTest(): Unit

The cLTTest main function is used to test the Uniform random variate generator for illustrating the Central Lmiit Theorem. Try adding difference numbers of random values and other distributions.

The cLTTest main function is used to test the Uniform random variate generator for illustrating the Central Lmiit Theorem. Try adding difference numbers of random values and other distributions.

runMain scalation.random.cLTTest

Attributes

def diceTest(): Unit

The diceTest main function is used to test the Randi random variate generator for the case of rolling two dice. The probability mass function (pmf) is plotted.

The diceTest main function is used to test the Randi random variate generator for the case of rolling two dice. The probability mass function (pmf) is plotted.

runMain scalation.random.diceTest

Attributes

def poissonProcessTest(): Unit

The poissonProcessTest main function is used to test both the PoissonProcess and NHPoissonProcess classes.

The poissonProcessTest main function is used to test both the PoissonProcess and NHPoissonProcess classes.

Attributes

def quantileTest_ChiSquare(): Unit

The quantileTest_ChiSquare main function tests the 'Quantile.chiSquareInv' method.

The quantileTest_ChiSquare main function tests the 'Quantile.chiSquareInv' method.

runMain scalation.random.quantileTest_ChiSquare

Attributes

def quantileTest_Empirical(): Unit

The quantileTest_Empirical main function tests the 'Quantile.empiricalInv' method.

The quantileTest_Empirical main function tests the 'Quantile.empiricalInv' method.

runMain scalation.random.quantileTest_Empirical

Attributes

def quantileTest_Exponential(): Unit

The quantileTest_Exponential main function tests the 'Quantile.exponentialInv' method.

The quantileTest_Exponential main function tests the 'Quantile.exponentialInv' method.

runMain scalation.random.quantileTest_Exponential

Attributes

def quantileTest_Fisher(): Unit

The quantileTest_Fisher main function tests the 'Quantile.fisherInv' method.

The quantileTest_Fisher main function tests the 'Quantile.fisherInv' method.

runMain scalation.random.quantileTest_Fisher

Attributes

def quantileTest_Normal(): Unit

The quantileTest_Normal main function tests the 'Quantile.normalInv' method.

The quantileTest_Normal main function tests the 'Quantile.normalInv' method.

runMain scalation.random.quantileTest_Normal

Attributes

def quantileTest_StudentT(): Unit

The quantileTest_StudentT main function tests the 'Quantile.studentTInv' method.

The quantileTest_StudentT main function tests the 'Quantile.studentTInv' method.

runMain scalation.random.quantileTest_StudentT

Attributes

def quantileTest_Uniform(): Unit

The quantileTest_Uniform main function tests the 'Quantile.uniformInv' method.

The quantileTest_Uniform main function tests the 'Quantile.uniformInv' method.

runMain scalation.random.quantileTest_Uniform

Attributes

def rNGTest(): Unit

The rNGTest main function conducts three simple tests of the Random Number Generators.

The rNGTest main function conducts three simple tests of the Random Number Generators.

runMain scalation.random.rNGTest

Attributes

def randomStrTest(): Unit

The randomStrTest main function is used to test the Random Variate String (RVS) generator from the RandomStr class.

The randomStrTest main function is used to test the Random Variate String (RVS) generator from the RandomStr class.

runMain scalation.random.randomStrTest

Attributes

def randomWordTest(): Unit

The randomWordTest main function is used to test the Random Variate Word (RVW) generator from the RandomWord class.

The randomWordTest main function is used to test the Random Variate Word (RVW) generator from the RandomWord class.

runMain scalation.random.randomWordTest

Attributes

def streamMaker3(): Unit

The streamMaker3 main function finds seeds for the Random3 random number generator. This generator has a period length around 2^31. Each seed is a 32-bit integer.

The streamMaker3 main function finds seeds for the Random3 random number generator. This generator has a period length around 2^31. Each seed is a 32-bit integer.

runMain scalation.random.streamMaker3

Attributes

def streamMakerGen(): Unit

The streamMakerGen main function generates and prints the first k seeds for the the 'MRG31k3p' random number generator's streams.

The streamMakerGen main function generates and prints the first k seeds for the the 'MRG31k3p' random number generator's streams.

runMain scalation.random.streamMakerGen

Attributes

def variateMatTest(): Unit

The variateMatTest main function is used to test the Random Variate Matrix (RVM) generators from the classes derived from VariateMat.

The variateMatTest main function is used to test the Random Variate Matrix (RVM) generators from the classes derived from VariateMat.

runMain scalation.random.variateMatTest

Attributes

def variateSetTest(): Unit

The variateSetTest object is used to test the Random Variate Set (RVS) generators from the classes derived from VariateSet.

The variateSetTest object is used to test the Random Variate Set (RVS) generators from the classes derived from VariateSet.

runMain scalation.random.variateSetTest

Attributes

def variateSetTest2(): Unit

The variateSetTest2 object is used to test the Random Variate Set (RVS) generators from the classes derived from VariateSet.

The variateSetTest2 object is used to test the Random Variate Set (RVS) generators from the classes derived from VariateSet.

runMain scalation.random.variateSetTest2

Attributes

def variateSetTest3(): Unit

The variateSetTest3 object is used to test the Random Variate Set (RVS) generators from the classes derived from VariateSet.

The variateSetTest3 object is used to test the Random Variate Set (RVS) generators from the classes derived from VariateSet.

runMain scalation.random.variateSetTest3

Attributes

def variateTenTest(): Unit

The variateTenTest main function is used to test the Random Variate Matrix (RVT) generators from the classes derived from VariateTen.

The variateTenTest main function is used to test the Random Variate Matrix (RVT) generators from the classes derived from VariateTen.

runMain scalation.random.variateTenTest

Attributes

def variateTest(): Unit

The variateTest main function conducts two simple tests of the Random Variate Generators: (1) Means Test and (2) Chi-square Goodness of Fit Test. FIX: need to add (3) Variance Test and (4) K-S Goodness of Fit Test.

The variateTest main function conducts two simple tests of the Random Variate Generators: (1) Means Test and (2) Chi-square Goodness of Fit Test. FIX: need to add (3) Variance Test and (4) K-S Goodness of Fit Test.

runMain scalation.random.variateTest

Attributes

def variateVecTest(): Unit

The variateVecTest main function is used to test the Random Variate Vector (RVV) generators from the classes derived from VariateVec.

The variateVecTest main function is used to test the Random Variate Vector (RVV) generators from the classes derived from VariateVec.

runMain scalation.random.variateVecTest

Attributes

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