prophesy.sampling package

Sampling is the package to get a rough image of the parameter space by considering single points in the parameter space.

Sampler: How to sample?

The basic interface for sampling is the sampler interface. Besides sampling on given points, it supports uniform sampling as some samplers might perform better.

prophesy.sampling.sampler module

class Sampler

Bases: object

Base class for performing sampling of given set of points

abstract perform_sampling(samplepoints, ensure_welldefinedness=False)

Given some parameter instantiations, perform sampling on these instantiations.


samplepoints – An iterable yielding parameter instantiations


A collection with the results of these samples

Return type


perform_uniform_sampling(parameters, region, samples_per_dimension)

Samples a uniform grid of points.

Given a list of intervals (i.e., the first and last point; for each dimension, in order) and the number of samples per dimension, a uniformly-spaced grid of points (the cartesian product) is sampled.

  • parameters – Parameters together with their region.

  • samples_per_dimension – In how many points should the region be divided.

prophesy.sampling.sampler_ratfunc module

class RatFuncSampling(ratfunc, parameters)

Bases: prophesy.sampling.sampler.Sampler

Simple sampler based on pycarl rational function


samplepoints – iterable of SamplePoint


Return type


Sampling via probabilistic model checking

One can also sample via probabilistic model checkers, see prophesy.modelcheckers.pmc module.

Sampling: Where to sample?

To determine sample points based on the already sampled information, we provide sample generators.

prophesy.sampling.sample_generator module

class SampleGenerator(sampler, parameters, region, samples=None)

Bases: object

Class to generate samples given a sampler. SampleGenerator uses the iteration interface to do so.

prophesy.sampling.sampling_linear module

class LinearRefinement(sampler, parameters, region, samples, threshold)

Bases: prophesy.sampling.sample_generator.SampleGenerator

Based on an initial set of samples, refines the samples by means of linear interpolation to approximate the threshold

prophesy.sampling.sampling_uniform module

class UniformSampleGenerator(sampler, parameters, region, samples, samples_per_dimension)

Bases: prophesy.sampling.sample_generator.SampleGenerator

Generates a uniform grid of samples

High-level convenience functions

prophesy.sampling.sampling module

Helper module for simplified sampling access.

refine_samples(sampler, parameters, region, samples, iterations, threshold)

Refine samples over several iterations.

  • sampler – The sampler to use

  • parameters – The parameters of the problem

  • samples – The already known samples

  • iterations – The number of iterations the refinement generator should be called.

  • threshold – The threshold value we are most interested in


uniform_samples(sampler, parameters, region, samples_per_dim)

Generate a uniform grid of samples.

  • sampler – The sampler to use

  • parameters – The parameters in our problem

  • samples_per_dim – The number of samples per dimension