Sampler

Contains Sampler-classes.

arithmetize(cls)

Add arithmetic operations to Sampler-class.

class Sampler(*args, **kwargs)

Base class Sampler that implements algebra of Samplers.

weight

weight of Sampler self in mixtures.

Type

float

sample(size)

Sampling method of a sampler.

Parameters

size (int) – lentgh of sample to be generated.

Returns

Array of size (len, Sampler’s dimension).

Return type

np.ndarray

apply(transform)

Apply a transformation to the sampler. Build new sampler, which sampling function is given by transform(self.sample(size))`.

Parameters

transform (callable) – function, that takes ndarray of shape (size, dim_sampler) and produces ndarray of shape (size, new_dim_sampler).

Returns

instance of class Sampler with redefined method sample.

Return type

Sampler

truncate(high=None, low=None, expr=None, prob=0.5, max_iters=None, sample_anyways=False)

Truncate a sampler. Resulting sampler produces points satisfying low <= pts <= high. If expr is suplied, the condition is low <= expr(pts) <= high.

Uses while-loop to obtain a sample from the region of interest of needed size. The behaviour of the while loop is controlled by parameters max_iters and sample_anyways-parameters.

Parameters

• high (ndarray, list, float) – upper truncation-bound.

• low (ndarray, list, float) – lower truncation-bound.

• expr (callable, optional.) – Some vectorized function. Accepts points of sampler, returns either bool or float. In case of float, either high or low should also be supplied.

• prob (float, optional) – estimate of P(truncation-condtion is satisfied). When supplied, can improve the performance of sampling-method of truncated sampler.

• max_iters (float, optional) – if the number of iterations needed for obtaining the sample exceeds this number, either a warning or error is raised. By default is set to 1e7 (constant of TruncateSampler-class).

• sample_anyways (bool, optional) – If set to True, when exceeding self.max_iters number of iterations the procedure throws a warning but continues. If set to False, the error is raised.

Returns

new Sampler-instance, truncated version of self.

Return type

Sampler

class OrSampler(left, right, *args, **kwargs)

Class for implementing | (mixture) operation on Sampler-instances.

sample(size)

Sampling procedure of a mixture of two samplers. Samples points with probabilities defined by weights (self.weight-attr) from two samplers invoked (self.bases-attr) and mixes them in one sample of needed size.

class AndSampler(left, right, *args, **kwargs)

Class for implementing & (coordinates stacking) operation on Sampler-instances.

sample(size)

Sampling procedure of a product of two samplers. Check out the docstring of Sampler.__and__ for more info.

class ApplySampler(sampler, transform, *args, **kwargs)

Class for implementing apply (adding transform) operation on Sampler-instances.

sample(size)

Sampling procedure of a sampler subjugated to a transform. Check out the docstring of Sampler.apply for more info.

class TruncateSampler(sampler, high=None, low=None, expr=None, prob=0.5, max_iters=None, sample_anyways=False, *args, **kwargs)

Class for implementing truncate (truncation by a condition) operation on Sampler-instances.

max_iters = 10000000.0

sample(size)

Sampling method of a sampler subjugated to truncation. Check out the docstring of Sampler.truncation for more info.

class BaseOperationSampler(left, right, *args, **kwargs)

Base class for implementing all arithmetic operations on Sampler-instances.

operation = None

sample(size)

Sampling method of a sampler.

Parameters

size (int) – lentgh of sample to be generated.

Returns

Array of size (len, Sampler’s dimension).

Return type

np.ndarray

class AddSampler(left, right, *args, **kwargs)

operation = '__add__'

class MulSampler(left, right, *args, **kwargs)

operation = '__mul__'

class TruedivSampler(left, right, *args, **kwargs)

operation = '__truediv__'

class SubSampler(left, right, *args, **kwargs)

operation = '__sub__'

class PowSampler(left, right, *args, **kwargs)

operation = '__pow__'

class FloordivSampler(left, right, *args, **kwargs)

operation = '__floordiv__'

class ModSampler(left, right, *args, **kwargs)

operation = '__mod__'

class RAddSampler(left, right, *args, **kwargs)

operation = '__radd__'

class RMulSampler(left, right, *args, **kwargs)

operation = '__rmul__'

class RTruedivSampler(left, right, *args, **kwargs)

operation = '__rtruediv__'

class RSubSampler(left, right, *args, **kwargs)

operation = '__rsub__'

class RPowSampler(left, right, *args, **kwargs)

operation = '__rpow__'

class RFloordivSampler(left, right, *args, **kwargs)

operation = '__rfloordiv__'

class RModSampler(left, right, *args, **kwargs)

operation = '__rmod__'

class ConstantSampler(constant, **kwargs)

Sampler of a constant.

Parameters

constant (int, str, float, list) – constant, associated with the Sampler. Can be multidimensional, e.g. list or np.ndarray.

constant

vectorized constant, associated with the Sampler.

Type

np.array

sample(size)

Sampling method of ConstantSampler. Repeats sampler’s constant size times.

Parameters

size (int) – the size of sample to be generated.

Returns

array of shape (size, 1) containing Sampler’s constant.

Return type

np.ndarray

class NumpySampler(name, seed=None, **kwargs)

Sampler based on a distribution from numpy random.

Parameters

• name (str) – a distribution name (a method from numpy random) or its alias.

• seed (int) – random seed for setting up sampler’s state (see make_rng()).

• **kwargs – additional keyword-arguments defining properties of specific distribution (e.g. loc for ‘normal’).

name

a distribution name (a method from numpy random).

Type

str

state

a random number generator

Type

numpy.random.Generator

_params

dict of args for Sampler’s distribution.

Type

dict

sample(size)

Generates random samples from distribution self.name.

Parameters

size (int) – the size of sample to be generated.

Returns

array of shape (size, Sampler’s dimension).

Return type

np.ndarray

class ScipySampler(name, seed=None, **kwargs)

Sampler based on a distribution from scipy.stats.

Parameters

• name (str) – a distribution name, a class from scipy.stats, or its alias.

• seed (int) – random seed for setting up sampler’s state (see make_rng()).

• **kwargs – additional parameters for specification of the distribution. For instance, scale for name=’gamma’.

name

a distribution name (a class from scipy.stats).

Type

str

state

a random number generator

Type

numpy.random.Generator

distr

a distribution class

sample(size)

Sampling method of ScipySampler. Generates random samples from distribution self.name.

Parameters

size (int) – the size of sample to be generated.

Returns

array of shape (size, Sampler’s dimension).

Return type

np.ndarray

class HistoSampler(histo=None, edges=None, seed=None, **kwargs)

Sampler based on a histogram, output of np.histogramdd.

Parameters

• histo (tuple) – histogram, on which the sampler is based. Make sure that it is unnormalized (normed=False in np.histogramdd).

• edges (list) – list of len=histo_dimension, contains edges of bins along axes.

• seed (int) – random seed for setting up sampler’s state (see make_rng()).

bins

bins of base-histogram (see np.histogramdd).

Type

np.ndarray

edges

edges of base-histogram.

Type

list

Notes

The sampler should be based on unnormalized histogram. if histo-arg is supplied, it is used for histo-initilization. Otherwise, edges should be supplied. In this case all bins are empty.

sample(size)

Sampling method of HistoSampler. Generates random samples from distribution, represented by histogram (self.bins, self.edges).

Parameters

size (int) – the size of sample to be generated.

Returns

array of shape (size, histo dimension).

Return type

np.ndarray

update(points)

Update bins of sampler’s histogram by throwing in additional points.

Parameters

points (np.ndarray) – Array of points of shape (n_points, histo_dimension).

cart_prod(*arrs)

Get array of cartesian tuples from arbitrary number of arrays. Faster version of itertools.product. The order of tuples is lexicographic.

Parameters

arrs (tuple, list or ndarray.) – Any sequence of ndarrays.

Returns

2d-array with rows (arr[0][i], arr[2][j],…,arr[n][k]).

Return type

ndarray