LowDiscrepancySequence¶

class
LowDiscrepancySequence
(*args)¶ Base class to generate low discrepancy sequences.
 Available constructors:
 LowDiscrepancySequence(dimension=1)
Parameters: dimension : int
Dimension of the points of the low discrepancy sequence.
Notes
The low discrepancy sequences, also called ‘quasirandom’ sequences, are a deterministic alternative to random sequences for use in Monte Carlo methods. These sequences are sets of equidistributed points which the error in uniformity is measured by its discrepancy.
The discrepancy of a set is defined, using Niederreiter’s notation, as:
where is the sdimensional Lebesgue measure, is the number of points in that fall into , and is the set of sdimensional intervals or boxes of the form:
where .
The stardiscrepancy is defined similarly, except that the supremum is taken over the set of intervals of the form:
where is in the halfopen interval .
A lowdiscrepancy sequence can be generated only through the derived classes of LowDiscrepancySequence. OpenTURNS proposes the
Faure
,Halton
,Reverse Halton
,Haselgrove
andSobol
sequences.Examples
>>> import openturns as ot >>> # Create a sequence of 3 points of 2 dimensions >>> sequence = ot.LowDiscrepancySequence(ot.SobolSequence(2)) >>> print(sequence.generate(3)) 0 : [ 0.5 0.5 ] 1 : [ 0.75 0.25 ] 2 : [ 0.25 0.75 ]
Methods
computeStarDiscrepancy
(sample)Compute the star discrepancy of a sample uniformly distributed over [0, 1). generate
(*args)Generate a sample of pseudorandom vectors of numbers uniformly distributed over [0, 1). getClassName
()Accessor to the object’s name. getDimension
()Accessor to the dimension of the points of the low discrepancy sequence. getId
()Accessor to the object’s id. getImplementation
(*args)Accessor to the underlying implementation. getName
()Accessor to the object’s name. initialize
(dimension)Initialize the sequence. setName
(name)Accessor to the object’s name. 
__init__
(*args)¶ x.__init__(…) initializes x; see help(type(x)) for signature

computeStarDiscrepancy
(sample)¶ Compute the star discrepancy of a sample uniformly distributed over [0, 1).
Parameters: sample : 2d sequence of float
Returns: starDiscrepancy : float
Star discrepancy of a sample uniformly distributed over [0, 1).
Examples
>>> import openturns as ot >>> # Create a sequence of 3 points of 2 dimensions >>> sequence = ot.LowDiscrepancySequence(ot.SobolSequence(2)) >>> sample = sequence.generate(16) >>> print(sequence.computeStarDiscrepancy(sample)) 0.12890625 >>> sample = sequence.generate(64) >>> print(sequence.computeStarDiscrepancy(sample)) 0.0537109375

generate
(*args)¶ Generate a sample of pseudorandom vectors of numbers uniformly distributed over [0, 1).
Parameters: size : int
Number of points to be generated. Default is 1.
Returns: sample :
Sample
Sample of pseudorandom vectors of numbers uniformly distributed over [0, 1).
Examples
>>> import openturns as ot >>> # Create a sequence of 3 points of 2 dimensions >>> sequence = ot.LowDiscrepancySequence(ot.SobolSequence(2)) >>> print(sequence.generate(3)) 0 : [ 0.5 0.5 ] 1 : [ 0.75 0.25 ] 2 : [ 0.25 0.75 ]

getClassName
()¶ Accessor to the object’s name.
Returns: class_name : str
The object class name (object.__class__.__name__).

getDimension
()¶ Accessor to the dimension of the points of the low discrepancy sequence.
Returns: dimension : int
Dimension of the points of the low discrepancy sequence.

getId
()¶ Accessor to the object’s id.
Returns: id : int
Internal unique identifier.

getImplementation
(*args)¶ Accessor to the underlying implementation.
Returns: impl : Implementation
The implementation class.

getName
()¶ Accessor to the object’s name.
Returns: name : str
The name of the object.

initialize
(dimension)¶ Initialize the sequence.
Parameters: dimension : int
Dimension of the points of the low discrepancy sequence.
Examples
>>> import openturns as ot >>> # Create a sequence of 3 points of 2 dimensions >>> sequence = ot.LowDiscrepancySequence(ot.SobolSequence(2)) >>> print(sequence.generate(3)) 0 : [ 0.5 0.5 ] 1 : [ 0.75 0.25 ] 2 : [ 0.25 0.75 ] >>> print(sequence.generate(3)) 0 : [ 0.375 0.375 ] 1 : [ 0.875 0.875 ] 2 : [ 0.625 0.125 ] >>> sequence.initialize(2) >>> print(sequence.generate(3)) 0 : [ 0.5 0.5 ] 1 : [ 0.75 0.25 ] 2 : [ 0.25 0.75 ]

setName
(name)¶ Accessor to the object’s name.
Parameters: name : str
The name of the object.