modea.Sampling module

This module contains several sampling options that can be used when drawing random values for mutations.

Some of the sampling options in this module can be considered base-samplers. This means that they produce a set of values without requiring any input. The remaining options will have a base_sampler optional argument, as they need input from some other sampler to produce values, as they perform operations on them such as mirroring.

Indirect samplers

class modea.Sampling.GaussianSampling(n, shape='col')

Bases: object

A sampler to create random vectors using a Gaussian distribution

Parameters:
  • n – Dimensionality of the vectors to be sampled
  • shape – String to select between whether column ('col') or row ('row') vectors should be returned. Defaults to column vectors.
next()

Draw the next sample from the Sampler

Returns:A new vector sampled from a Gaussian distribution with mean 0 and standard deviation 1
class modea.Sampling.MirroredOrthogonalSampling(n, lambda_, shape='col', base_sampler=None)

Bases: object

Factory method returning a pre-defined mirrored orthogonal sampler in the right order: orthogonalize first, mirror second.

Parameters:
  • n – Dimensionality of the vectors to be sampled
  • shape – String to select between whether column ('col') or row ('row') vectors should be returned. Defaults to column vectors
  • base_sampler – A different Sampling object from which samples to be mirrored are drawn. If no base_sampler is given, a GaussianSampling object will be created and used.
Returns:

A MirroredSampling object with as base sampler an OrthogonalSampling object initialized with the given parameters.
next()

Draw the next sample from the Sampler

Returns:A new vector, alternating between a new orthogonalized sample from the base_sampler and a mirror of the last.
reset()

Reset the internal state of this sampler, so the next sample is forced to be taken new.

class modea.Sampling.MirroredSampling(n, shape='col', base_sampler=None)

Bases: object

A sampler to create mirrored samples using some base sampler (Gaussian by default) Returns a single vector each time, while remembering the internal state of whether the next() should return a new sample, or the mirror of the previous one.

Parameters:
  • n – Dimensionality of the vectors to be sampled
  • shape – String to select between whether column ('col') or row ('row') vectors should be returned. Defaults to column vectors
  • base_sampler – A different Sampling object from which samples to be mirrored are drawn. If no base_sampler is given, a GaussianSampling object will be created and used.
next()

Draw the next sample from the Sampler

Returns:A new vector, alternating between a new sample from the base_sampler and a mirror of the last.
reset()

Reset the internal state of this sampler, so the next sample is forced to be taken new.

class modea.Sampling.OrthogonalSampling(n, lambda_, shape='col', base_sampler=None)

Bases: object

A sampler to create orthogonal samples using some base sampler (Gaussian as default)

Parameters:
  • n – Dimensionality of the vectors to be sampled
  • lambda – Number of samples to be drawn and orthonormalized per generation
  • shape – String to select between whether column ('col') or row ('row') vectors should be returned. Defaults to column vectors
  • base_sampler – A different Sampling object from which samples to be mirrored are drawn. If no base_sampler is given, a GaussianSampling object will be created and used.
next()

Draw the next sample from the Sampler

Returns:A new vector sampled from a set of orthonormalized vectors, originally drawn from base_sampler
reset()

Reset the internal state of this sampler, so the next sample is forced to be taken new.

class modea.Sampling.QuasiGaussianHaltonSampling(n, shape='col')

Bases: object

A quasi-Gaussian sampler based on a Halton sequence

Parameters:
  • n – Dimensionality of the vectors to be sampled
  • shape – String to select between whether column ('col') or row ('row') vectors should be returned. Defaults to column vectors
next()

Draw the next sample from the Sampler

Returns:A new vector sampled from a Halton sequence with mean 0 and standard deviation 1
class modea.Sampling.QuasiGaussianSobolSampling(n, shape='col', seed=None)

Bases: object

A quasi-Gaussian sampler based on a Sobol sequence

Parameters:
  • n – Dimensionality of the vectors to be sampled
  • shape – String to select between whether column ('col') or row ('row') vectors should be returned. Defaults to column vectors
next()

Draw the next sample from the Sampler

Returns:A new vector sampled from a Sobol sequence with mean 0 and standard deviation 1