Random¶
Usage
use Random;
or
import Random;
Submodules
Support for pseudorandom number generation.
This module defines a randomStream
type (based upon the PCG
algorithm) that can be used to generate pseudorandom numbers in parallel.
The randomStream
type can be used to generate individual random numbers
via randomStream.getNext
or to fill an array with random numbers via
randomStream.fill
. There are also several other methods available
for manipulating arrays, parallel iteration, or adjusting the stream’s
position.
This module contains a few top-level procedures that can be used for manipulating arrays:
fillRandom
fills an array with random numbers in parallelshuffle
randomly re-arranges the elements of an arraypermutation
creates a random permutation of an array’s domain (unstable).
Note
Note about deprecations:
Before Chapel 1.33, this module was unstable and defined an abstract random stream interface (
RandomStreamInterface
) and two implementations of that interface (based on the PCG and NPB algorithms).As of Chapel 1.33, this module is partially stable and defines a single
randomStream
type that is based on the PCG algorithm. The NPB algorithm is still available via theNPBRandom
package module. TheRandomStreamInterface
is now deprecated.Various symbols used to switch between the two algorithms have also been deprecated. These include:
overloads of the top-level methods that accept an
algorithm
argumentIn a future release, we intend to use Chapel’s interface features to define one or more interfaces for random streams. At that point, the
randomStream
type will be an implementation of the interface(s) for generating a seedable stream of random numbers.
- type RNG = _RNG¶
Warning
‘RNG’ is deprecated; please use the PCG algorithm via the
randomStream
type or the NPB algorithm via theNPBRandom
package moduleSelect between different supported RNG algorithms. See
PCGRandom
andNPBRandom
for details on these algorithms.
- param defaultRNG = _defaultRNG¶
Warning
‘defaultRNG’ is deprecated; please use the PCG algorithm via the
randomStream
type or the NPB algorithm via theNPBRandom
package moduleThe default RNG. The current default is PCG - see
PCGRandom
.
- type RandomStream = if _defaultRNG == _RNG.PCG then PCGRandomStreamInternal else NPBRandom.NPBRandomStream¶
Warning
the RandomStream class is deprecated; please use the
randomStream
record instead
- proc fillRandom(ref arr: [], seed: int(64) = _SeedGenerator.oddCurrentTime, param algorithm = _defaultRNG) where isNumericOrBoolType(arr.eltType)¶
Warning
The overload of fillRandom that accepts an ‘algorithm’ argument is deprecated; please remove the ‘algorithm’ argument
- proc fillRandom(ref arr: [] ?t, seed: int) where isNumericOrBoolType(t) && arr.isRectangular()
Fill a rectangular array of numeric values with pseudorandom values in parallel using a new
randomStream
. The first arr.size values from the stream will be assigned to the array’s elements in row-major order. The parallelization strategy is determined by the array.- Arguments
arr – An array of numeric values
seed – The seed to use to create the
randomStream
- proc fillRandom(ref arr: [] ?t) where isNumericOrBoolType(t) && arr.isRectangular()
Warning
the overload of fillRandom that generates its own seed is unstable
Fill a rectangular array of numeric values with pseudorandom values in parallel using a new
randomStream
. The first arr.size values from the stream will be assigned to the array’s elements in row-major order. The parallelization strategy is determined by the array.Note
a seed will be generated in an implementation specific manner that depends on the current time.
- Arguments
arr – An array of numeric values
- proc fillRandom(ref arr: [] ?t, min: t, max: t, seed: int) where isNumericOrBoolType(t) && arr.isRectangular()
Fill a rectangular array of numeric values with pseudorandom values in the range [
min
,max
] (inclusive) in parallel using a newrandomStream
. The first arr.size values from the stream will be assigned to the array’s elements in row-major order. The parallelization strategy is determined by the array.- Arguments
arr – An array of numeric values
min – The (inclusive) lower bound for the random values
max – The (inclusive) upper bound for the random values
seed – The seed to use to create the
randomStream
- proc fillRandom(ref arr: [] ?t, min: t, max: t) where isNumericOrBoolType(t) && arr.isRectangular()
Warning
the overload of fillRandom that generates its own seed is unstable
Fill a rectangular array of numeric values with pseudorandom values in the range [
min
,max
] (inclusive) in parallel using a newrandomStream
. The first arr.size values from the stream will be assigned to the array’s elements in row-major order. The parallelization strategy is determined by the array.Note
a seed will be generated in an implementation specific manner that depends on the current time.
- Arguments
arr – An array of numeric values
min – The (inclusive) lower bound for the random values
max – The (inclusive) upper bound for the random values
- proc shuffle(ref arr: [], seed: int(64) = _SeedGenerator.oddCurrentTime, param algorithm = _RNG.PCG)¶
Warning
The overload of ‘shuffle’ that accepts an ‘algorithm’ argument is deprecated; please remove the ‘algorithm’ argument
Shuffle the elements of a rectangular array into a random order.
- Arguments
arr – a rectangular 1-D non-strided array
seed – the seed to use when shuffling. Defaults to oddCurrentTime from
RandomSupport.SeedGenerator
.algorithm :
RNG
– A param indicating which algorithm to use. Defaults to PCG.
- proc shuffle(ref arr: [?d], seed: int) where is1DRectangularDomain(d)
Use a new
randomStream
to shuffle an array in place.- Arguments
arr – A non-strided default rectangular 1D array
seed – The seed to initialize a
randomStream
with
- proc shuffle(ref arr: [?d]) where is1DRectangularDomain(d)
Warning
the overload of shuffle that generates its own seed is unstable
Use a new
randomStream
to shuffle an array in place.Note
a seed will be generated in an implementation specific manner that depends on the current time.
- Arguments
arr – A non-strided default rectangular 1D array
- proc permutation(ref arr: [], seed: int(64) = _SeedGenerator.oddCurrentTime, param algorithm = _RNG.PCG)¶
Warning
The overload of ‘permutation’ that accepts an ‘algorithm’ argument is deprecated; please remove the ‘algorithm’ argument
Produce a random permutation, storing it in a 1-D array. The resulting array will include each value from low..high exactly once, where low and high refer to the array’s domain.
- Arguments
arr – a rectangular 1-D non-strided array
seed – the seed to use when creating the permutation. Defaults to oddCurrentTime from
RandomSupport.SeedGenerator
.algorithm :
RNG
– A param indicating which algorithm to use. Defaults to PCG.
- proc permutation(ref arr: [?d] ?t, seed: int) where isCoercible(d.idxType, t) && is1DRectangularDomain(d)
Warning
‘permutation’ is unstable and subject to change
Use a new
randomStream
to produce a random permutation of an array’s domain. The valuesd.dim(0).low..d.dim(0).high
will appear exactly once in the array in a pseudo-random order.- Arguments
arr – The array to store the permutation in
seed – The seed to use when creating the
randomStream
- proc permutation(ref arr: [?d] ?t) where isCoercible(d.idxType, t) && is1DRectangularDomain(d)
Warning
‘permutation’ is unstable and subject to change
Use a new
randomStream
to produce a random permutation of an array’s domain. The valuesd.dim(0).low..d.dim(0).high
will appear exactly once in the array in a pseudo-random order.Note
a seed will be generated in an implementation specific manner that depends on the current time.
- Arguments
arr – The array to store the permutation in
- record randomStream: writeSerializable¶
A
randomStream
represents a stream of pseudorandom numbers of a particular type. Numeric and bool types are supported.Conceptually it can be thought of as an indexed sequence of numbers ranging from 0 to infinity. Each index in the sequence corresponds to a random number of the specified type. This allows for the generation of random numbers in parallel, where each task involved in the parallel iteration can request random numbers within a particular range and traverse that range of the sequence independently of other tasks (see
randomStream.iterate
(unstable)).Although parallel iteration is supported via the
iterate
method, the type itself is not thread-safe. In particular, it is not safe to call methods such asgetNext
orfill
on arandomStream
from multiple tasks concurrently.Several high-level methods are provided to generate random numbers or to manipulate arrays using random numbers:
randomStream.fill
to fill an array with random numbersrandomStream.shuffle
to randomly re-arrange the elements of an arrayrandomStream.permutation
to create a random permutation of an arrays domain, and store it in the array (unstable)randomStream.choice
to randomly sample from an array or range (unstable)
Note that these methods have top-level counterparts that will internally create a
randomStream
and then call the corresponding method on it. These can be convenient for one-off uses, but if you are generating many random numbers, it is generally more efficient to create arandomStream
and use it repeatedly.An individual random number can be requested using
randomStream.getNext
which will advance the stream to the next position and return the value at that position. The position of the stream can also be manipulated using:randomStream.skipToNth
to skip to a particular position in the streamrandomStream.getNth
to skip to a particular position in the stream and return the value at that position (unstable)
A
randomStream
can be initialized with a seed value. When not provided explicitly, a seed will be generated in an implementation specific manner that depends upon the current time (this behavior is currently unstable).When copied, the
randomStream
’s seed, state, and position will also be copied. This means that the copy will produce the same sequence of random numbers as the original without affecting the original.Note
Implementation Details:
This stream is implemented using the PCG random number generator algorithm. See http://www.pcg-random.org/ and the paper, PCG: A Family of Simple Fast Space-Efficient Statistically Good Algorithms for Random Number Generation by M.E. O’Neill.
This record builds upon the
pcg_setseq_64_xsh_rr_32_rng
PCG RNG which has 64 bits of state and 32 bits of output.While the PCG RNG used here is believed to have good statistical properties, it is not suitable for generating key material for encryption since the output of this RNG may be predictable. Additionally, if statistical properties of the random numbers are very important, another strategy may be required.
We have good confidence that the random numbers generated by this record match the C PCG reference implementation and have specifically verified equal output given the same seed. However, this implementation differs from the C PCG reference implementation in how it produces random integers within particular bounds (with
randomStream.getNext
usingmin
andmax
arguments). In addition, this implementation directly supports the generation of randomreal
values, unlike the C PCG implementation.Smaller numbers, such as
uint(8)
oruint(16)
, are generated from the high-order bits of the 32-bit output.To generate larger numbers, several 32-bit-output RNGs are composed together. Each of these 32-bit RNGs has a different sequence constant and so will be independent and uncorrelated. For example, to generate 128-bit complex numbers, this RNG will use four 32-bit PCG RNGs with different sequence constants. One impact of this approach is that this implementation will only generate 2**64 different complex numbers with a given seed (for example).
This record also supports generating integers within particular bounds. When that is required, it uses a strategy different from the PCG reference implementation to support efficient parallel iteration. In particular, when more than 1 random value is required as part of generating a value in a range, conceptually it uses more composed RNGs (as with the 32x2 strategy). Each new value beyond the first that is computed will be computed with a different RNG. This strategy is meant to avoid statistical bias. While we have tested this strategy to our satisfaction, it has not been subject to rigorous analysis and may have undesirable statistical properties.
When generating a real, imaginary, or complex number, this implementation uses the strategy of generating a 64-bit unsigned integer and then multiplying it by 2.0**-64 in order to convert it to a floating point number. While this does construct a uniform distribution on rounded floating point values, it leaves out many possible real values (for example, 2**-128). We believe that this strategy has reasonable statistical properties. One side effect of this strategy is that the real number 1.0 can be generated because of rounding. The real number 0.0 can be generated because PCG can produce the value 0 as a random integer.
We have tested this implementation with TestU01 (available at http://simul.iro.umontreal.ca/testu01/tu01.html ). We measured our implementation with TestU01 1.2.3 and the Crush suite, which consists of 144 statistical tests. The results were:
no failures for generating uniform reals
1 failure for generating 32-bit values (which is also true for the reference version of PCG with the same configuration)
0 failures for generating 64-bit values (which we provided to TestU01 as 2 different 32-bit values since it only accepts 32 bits at a time)
0 failures for generating bounded integers (which we provided to TestU01 by requesting values in [0..,2**31+2**30+1) until we had two values < 2**31, removing the top 0 bit, and then combining the top 16 bits into the value provided to TestU01).
- type eltType¶
Specifies the type of value generated by the random stream. Currently, numeric and bool types are supported.
- const seed: int¶
The seed value for the PCG random number generator.
When not provided explicitly, a seed will be generated in an implementation specific manner that depends upon the current time.
- proc init(type eltType, seed: int) where isNumericOrBoolType(eltType)¶
Create a new
randomStream
using the specified seed.
- proc init(type eltType) where isNumericOrBoolType(eltType)
Warning
The
randomStream
initializer that generates a seed is unstable and subject to changeCreate a new
randomStream
.A seed value will be generated in an implementation specific manner that depends on the current time.
- proc ref fill(ref arr: []) where arr.isRectangular()¶
Fill the array with pseudorandom values sampled from this stream.
- Arguments
arr – The rectangular array to be filled
- proc ref fill(ref arr: [] ?t, min: t, max: t) where arr.isRectangular()
Fill the array with pseudorandom values within a particular range. Each array element is set to a number in [
min
,max
] (inclusive) sampled from this stream.- Arguments
arr – The rectangular array to be filled
min – The minimum value to sample
max – The maximum value to sample
- proc ref fillRandom(ref arr: [])¶
Warning
‘randomStream.fillRandom’ is deprecated; please use ‘fill’ instead
- proc ref fillRandom(ref arr: [], min, max)
Warning
‘randomStream.fillRandom’ is deprecated; please use ‘fill’ instead
- proc ref shuffle(ref arr: [?d]) where is1DRectangularDomain(d) && isCoercible(this.eltType, d.idxType)¶
Randomly rearrange a 1D array using values from this random stream.
- Arguments
arr – The array to shuffle. Its domain’s
idxType
should be coercible to this stream’seltType
.
- proc ref permutation(ref arr: [?d] ?t) where isCoercible(this.eltType, d.idxType) && isCoercible(d.idxType, t) && is1DRectangularDomain(d)¶
Warning
‘permutation’ is unstable and subject to change
Produce a random permutation of an array’s domain. The values
d.dim(0).low..d.dim(0).high
will appear exactly once in the array in a pseudo-random order.- Arguments
arr – The array to store the permutation in
- proc ref choice(const x: [?d], size: ?sizeType = none, replace = true, prob: ?probType = none) throws where is1DRectangularDomain(d) && isCoercible(this.eltType, d.idxType)¶
Warning
‘choice’ is unstable and subject to change
Return a random sample from a given 1D array,
x
.- Arguments
x – The array to take a sample from.
size – An optional integral value specifying the number of elements to choose, or a domain specifying the dimensions of the sampled array to be filled, otherwise a single element will be chosen.
replace – an optional
bool
specifying whether or not to sample with replacement, i.e. elements will only be chosen up to one time whenreplace=false
.prob – an optional 1D array that contains probabilities of choosing each element of
x
, otherwise elements will be chosen over a uniform distribution.prob
must have integral or real element type, with no negative values and at least one non-zero value. The size must be equal to that ofx.domain
.
- Returns
An element chosen from
x
ifsize == 1
, or an array of elements chosen fromx
ifsize > 1
orsize
is a domain.- Throws
IllegalArgumentError – if
x.size == 0
, ifx.size != prob.size
, ifprob
contains a negative value, ifprob
has no non-zero values, ifsize < 1 || size.size < 1
, ifreplace=false
andsize > x.size || size.size > x.size
- proc ref choice(x: range(?), size: ?sizeType = none, replace = true, prob: ?probType = none) throws where isCoercible(this.eltType, x.idxType)
Warning
‘choice’ is unstable and subject to change
Return a random sample from a given bounded range,
x
.- Arguments
x – a bounded range with values that will be sampled from.
size – An optional integral value specifying the number of elements to choose, or a domain specifying the dimensions of the sampled array to be filled, otherwise a single element will be chosen.
replace – an optional
bool
specifying whether or not to sample with replacement, i.e. elements will only be chosen up to one time whenreplace=false
.prob – an optional 1D array that contains probabilities of choosing each element of
x
, otherwise elements will be chosen over a uniform distribution.prob
must have integral or real element type, with no negative values and at least one non-zero value. The size must be equal to that ofx
.
- Returns
An element chosen from
x
ifsize == 1
, or an array of element chosen fromx
ifsize > 1
orsize
is a domain.- Throws
IllegalArgumentError – if
x.size == 0
, ifx.size != prob.size
, ifprob
contains a negative value, ifprob
has no non-zero values, ifsize < 1 || size.size < 1
, ifreplace=false
andsize > x.size || size.size > x.size
- proc ref choice(x: domain, size: ?sizeType = none, replace = true, prob: ?probType = none) throws where is1DRectangularDomain(x) && isCoercible(this.eltType, x.idxType)
Warning
‘choice’ is unstable and subject to change
Return a random sample from a given 1D domain,
x
.- Arguments
x – a 1D domain with values that will be sampled from.
size – An optional integral value specifying the number of elements to choose, or a domain specifying the dimensions of the sampled array to be filled, otherwise a single element will be chosen.
replace – an optional
bool
specifying whether or not to sample with replacement, i.e. elements will only be chosen up to one time whenreplace=false
.prob – an optional 1D array that contains probabilities of choosing each element of
x
, otherwise elements will be chosen over a uniform distribution.prob
must have integral or real element type, with no negative values and at least one non-zero value. The size must be equal to that ofx
.
- Returns
An element chosen from
x
ifsize == 1
, or an array of element chosen fromx
ifsize > 1
orsize
is a domain.- Throws
IllegalArgumentError – if
x.size == 0
, ifx.size != prob.size
, ifprob
contains a negative value, ifprob
has no non-zero values, ifsize < 1 || size.size < 1
, ifreplace=false
andsize > x.size || size.size > x.size
.
- proc ref getNext(): eltType¶
Get the next value in the random stream and advance its position by one.
- proc ref getNext(min: eltType, max: eltType): eltType
Get the next random value from the stream within a given range. Returns a number in [
min
,max
] (inclusive).This method will halt if checks are enabled and
min > max
.Note
For integers, this type uses a strategy for generating a value in a particular range that has not been subject to rigorous study and may have statistical problems.
For real numbers, this type generates a random value in [max, min] by computing a random value in [0,1] and scaling and shifting that value. Note that not all possible floating point values in the interval [min, max] can be constructed in this way.
- Arguments
min – The minimum value to sample
max – The maximum value to sample
- proc ref skipToNth(n: integral) throws¶
Warning
‘skipToNth’ is unstable and subject to change
Advance or rewind the random stream to the
n
-th position in the pseudorandom sequence (wheren=0
is the starting position)- Arguments
n – The position to skip to
- Throws
IllegalArgumentError – Thrown if
n
is negative
- proc ref getNth(n: integral): eltType throws¶
Warning
‘getNth’ is unstable and subject to change
Advance or rewind the random stream to the
n
-th position in the pseudorandom sequence and return the value. This is equivalent toskipToNth
followed bygetNext
.- Arguments
n – The position to skip to and retrieve (must be greater than zero)
- Throws
IllegalArgumentError – Thrown if
n
is negative
- proc ref iterate(D: domain)¶
Warning
‘iterate’ is unstable and subject to change
- proc ref iterate(D: domain, min: eltType, max: eltType)
Warning
‘iterate’ is unstable and subject to change
- proc serialize(writer, ref serializer) throws¶
serialize the
randomStream
as a record with two fields:eltType
andseed
.
- proc createRandomStream(type eltType, seed: int(64) = _SeedGenerator.oddCurrentTime, param parSafe: bool = true, param algorithm = _defaultRNG)¶
Warning
‘createRandomStream’ is deprecated; please use ‘new randomStream’ instead
Constructs a new stream of random numbers using the specified seed and parallel safety. Ensures that the seed value meets the PRNG’s constraints.
Note
The
NPBRandom
RNG will halt if provided an even seed.PCGRandom
has no restrictions on the provided seed value.- Arguments
eltType : type – The element type to be generated.
seed : int(64) – The seed to use for the PRNG. Defaults to oddCurrentTime from
RandomSupport.SeedGenerator
.parSafe : bool – The parallel safety setting. Defaults to true.
algorithm :
RNG
– A param indicating which algorithm to use. Defaults todefaultRNG
.
- Returns
an owned RandomStream
- class RandomStreamInterface: writeSerializable¶
Warning
‘RandomStreamInterface’ is deprecated
Models a stream of pseudorandom numbers. This class is defined for documentation purposes and should not be instantiated. See
PCGRandom
andNPBRandom
for RNGs that can be instantiated. To create a random stream, usecreateRandomStream
.Note
At present, different implementations of this interface can vary in whether or not they can generate 0.0 and/or 1.0. (e.g. They can be generated by
PCGRandom
but not byNPBRandom
).Note
We plan to support general serial and parallel iterator methods on
RandomStreamInterface
; however, providing the full suite of iterators is not possible with our current parallel iterator framework. Specifically, ifRandomStreamInterface
is a follower in a zippered iteration context, there is no way for it to update the total number of random numbers generated in a safe/sane/coordinated way. We are exploring a revised leader-follower iterator framework that would support this idiom (and other cursor-based ones). With Chapel’s recent support for standalone parallel iterators, one could define a standalone parallel iterator forRandomStreamInterface
, but this effort has not yet been taken on.Note
The
RandomStreamInterface
is included here only for documentation and does not help with compilation in any way. In the future, we hope to turn it into an interface.- type eltType = real(64)¶
Specifies the type of value generated by the RandomStream. Not all RandomStream implementations support all types.
- param parSafe: bool = true¶
Indicates whether or not the RandomStream needs to be parallel-safe by default. If multiple tasks interact with it in an uncoordinated fashion, this must be set to true. If it will only be called from a single task, or if only one task will call into it at a time, setting to false will reduce overhead related to ensuring mutual exclusion.
- const seed: int(64)¶
The seed value for the PRNG. There may be constraints upon legal values depending on the specific RNG.
- proc getNext(): eltType¶
Returns the next value in the random stream.
- Returns
The next value in the random stream as type
eltType
.
- proc skipToNth(n: integral) throws¶
Advances/rewinds the stream to the n-th value in the sequence. The first value corresponds to n=0. n must be >= 0, otherwise an IllegalArgumentError is thrown.
- Arguments
n : integral – The position in the stream to skip to. Must be >= 0.
- Throws
IllegalArgumentError – When called with negative n value.
- proc getNth(n: integral): eltType throws¶
Advance/rewind the stream to the n-th value and return it (advancing the stream by one). n must be >= 0, otherwise an IllegalArgumentError is thrown. This is equivalent to
skipToNth()
followed bygetNext()
.- Arguments
n : integral – The position in the stream to skip to. Must be >= 0.
- Returns
The n-th value in the random stream as type
eltType
.- Throws
IllegalArgumentError – When called with negative n value.
- proc fillRandom(ref arr: [] eltType)¶
Fill the argument array with pseudorandom values. This method is identical to the standalone
fillRandom
procedure, except that it consumes random values from theRandomStreamInterface
object on which it’s invoked rather than creating a new stream for the purpose of the call.- Arguments
arr : []
eltType
– The array to be filled
- proc choice(const x: [], size: ?sizeType = none, replace = true, prob: ?probType = none) throws¶
Returns a random sample from a given 1-D array,
x
.- Arguments
x – a 1-D array with values that will be sampled from.
size – An optional integral value specifying the number of elements to choose, or a domain specifying the dimensions of the sampled array to be filled, otherwise a single element will be chosen.
replace – an optional
bool
specifying whether or not to sample with replacement, i.e. elements will only be chosen up to one time whenreplace=false
.prob – an optional 1-D array that contains probabilities of choosing each element of
x
, otherwise elements will be chosen over a uniform distribution.prob
must have integral or real element type, with no negative values and at least one non-zero value. The size must be equal to that ofx.domain
.
- Returns
An element chosen from
x
ifsize == 1
, or an array of element chosen fromx
ifsize > 1
orsize
is a domain.- Throws
IllegalArgumentError – if
x.size == 0
, ifx.size != prob.size
, ifprob
contains a negative value, ifprob
has no non-zero values, ifsize < 1 || size.size < 1
, ifreplace=false
andsize > x.size || size.size > x.size
- proc choice(x: range(strides = ?), size: ?sizeType = none, replace = true, prob: ?probType = none) throws
Returns a random sample from a given bounded range,
x
.- Arguments
x – a bounded range with values that will be sampled from.
size – An optional integral value specifying the number of elements to choose, or a domain specifying the dimensions of the sampled array to be filled, otherwise a single element will be chosen.
replace – an optional
bool
specifying whether or not to sample with replacement, i.e. elements will only be chosen up to one time whenreplace=false
.prob – an optional 1-D array that contains probabilities of choosing each element of
x
, otherwise elements will be chosen over a uniform distribution.prob
must have integral or real element type, with no negative values and at least one non-zero value. The size must be equal to that ofx
.
- Returns
An element chosen from
x
ifsize == 1
, or an array of element chosen fromx
ifsize > 1
orsize
is a domain.- Throws
IllegalArgumentError – if
x.size == 0
, ifx.size != prob.size
, ifprob
contains a negative value, ifprob
has no non-zero values, ifsize < 1 || size.size < 1
, ifreplace=false
andsize > x.size || size.size > x.size
.
- proc choice(x: domain, size: ?sizeType = none, replace = true, prob: ?probType = none) throws
Returns a random sample from a given 1-D domain,
x
.- Arguments
x – a 1-D dom with values that will be sampled from.
size – An optional integral value specifying the number of elements to choose, or a domain specifying the dimensions of the sampled array to be filled, otherwise a single element will be chosen.
replace – an optional
bool
specifying whether or not to sample with replacement, i.e. elements will only be chosen up to one time whenreplace=false
.prob – an optional 1-D array that contains probabilities of choosing each element of
x
, otherwise elements will be chosen over a uniform distribution.prob
must have integral or real element type, with no negative values and at least one non-zero value. The size must be equal to that ofx
.
- Returns
An element chosen from
x
ifsize == 1
, or an array of element chosen fromx
ifsize > 1
orsize
is a domain.- Throws
IllegalArgumentError – if
x.size == 0
, ifx.size != prob.size
, ifprob
contains a negative value, ifprob
has no non-zero values, ifsize < 1 || size.size < 1
, ifreplace=false
andsize > x.size || size.size > x.size
.
- proc iterate(D: domain, type resultType = eltType)¶
Returns an iterable expression for generating D.size random numbers. The RNG state will be immediately advanced by D.size before the iterable expression yields any values.
The returned iterable expression is useful in parallel contexts, including standalone and zippered iteration. The domain will determine the parallelization strategy.
- Arguments
D – a domain
resultType – the type of number to yield
- Returns
an iterable expression yielding random resultType values