SobolSequence¶

class SobolSequence(*args)

Sobol sequence.

Parameters:
dimensionpositive int,

Dimension of the points.

Examples

>>> import openturns as ot
>>> sequence = ot.SobolSequence(2)
>>> print(sequence.generate(5))
0 : [ 0.5   0.5   ]
1 : [ 0.75  0.25  ]
2 : [ 0.25  0.75  ]
3 : [ 0.375 0.375 ]
4 : [ 0.875 0.875 ]


Methods

 ComputeStarDiscrepancy(sample) Compute the star discrepancy of a sample uniformly distributed over [0, 1). generate(*args) Generate a sample of pseudo-random vectors of numbers uniformly distributed over [0, 1). Accessor to the object's name. Accessor to the dimension of the points of the low discrepancy sequence. Accessor to the object's name. Accessor to the linear congruential generator (LCG) used to scramble the sequences. Test if the object is named. initialize(dimension) Initialize the sequence. setName(name) Accessor to the object's name. setScramblingState(state) Accessor to the linear congruential generator (LCG) used to scramble the sequences.
__init__(*args)
static ComputeStarDiscrepancy(sample)

Compute the star discrepancy of a sample uniformly distributed over [0, 1).

Parameters:
sample2-d sequence of float
Returns:
starDiscrepancyfloat

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 pseudo-random vectors of numbers uniformly distributed over [0, 1).

Parameters:
sizeint

Number of points to be generated. Default is 1.

Returns:
sampleSample

Sample of pseudo-random 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_namestr

The object class name (object.__class__.__name__).

getDimension()

Accessor to the dimension of the points of the low discrepancy sequence.

Returns:
dimensionint

Dimension of the points of the low discrepancy sequence.

getName()

Accessor to the object’s name.

Returns:
namestr

The name of the object.

getScramblingState()

Accessor to the linear congruential generator (LCG) used to scramble the sequences.

Returns:
stateint

The state of the LCG, defined by the recursion .

hasName()

Test if the object is named.

Returns:
hasNamebool

True if the name is not empty.

initialize(dimension)

Initialize the sequence.

Parameters:
dimensionint

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:
namestr

The name of the object.

setScramblingState(state)

Accessor to the linear congruential generator (LCG) used to scramble the sequences.

Parameters:
stateint

The state of the LCG, defined by the recursion .

Examples using the class¶

Create a polynomial chaos metamodel by integration on the cantilever beam

Create a polynomial chaos metamodel by integration on the cantilever beam

Use a randomized QMC algorithm

Use a randomized QMC algorithm

Time variant system reliability problem

Time variant system reliability problem

Various design of experiments

Various design of experiments

Generate low discrepancy sequences

Generate low discrepancy sequences

Optimization of the Rastrigin test function

Optimization of the Rastrigin test function