Created on 16 aug. 2011
This module contains various classes that can be used for specifying different types of samplers. These different samplers implement basic sampling techniques including Full Factorial sampling, Latin Hypercube sampling, and Monte Carlo sampling.
Code author: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
generates a Latin Hypercube sample for each of the uncertainties in case of categorical uncertainties, it handles the transform as well
generate a Latin Hypercupe Sample.
Parameters: 


generates a Monte Carlo sample for each of the uncertainties. In case of a Categorical Uncertainty it also handles the transform
generate a Monte Carlo Sample.
Parameters: 


generates a full factorial sample. If the uncertainty is non categorical, resolution is used to set the samples. If the uncertainty is an integer, their wont be duplicates in the sample. So, samples is equal to or smaller then the specified resolution
Helper function for determining the number of experiments that will be generated given the sampled uncertainties.
Parameters:  sampled_uncertainties – a list of sampled uncertainties, as the values return by generate_samples 

This method provides an alternative implementation to the default implementation provided by Sampler. This version returns a full factorial design across the uncertainties.
Parameters:  sampled_uncertainties – a list of sampled uncertainties, as the values return by generate_samples 

Return type:  a generator object that yields the designs resulting from combining the uncertainties 
The main method of :class: ~sampler.Sampler and its children. This will call the sample method for each of the uncertainties and return the resulting samples
Parameters: 


Return type:  dict with the uncertainty.name as key, and the sample as value 
base class from which different samplers can be derived
Helper function for determining the number of experiments that will be generated given the sampled uncertainties.
Parameters:  sampled_uncertainties – a list of sampled uncertainties, as the values return by generate_samples 

types of distributions known by the sampler. by default it knows the uniform continuous distribution for sampling floats, and the uniform discrete distribution for sampling integers.
This method provides an alternative implementation to the default implementation provided by Sampler. This version returns a full factorial design across the uncertainties.
Parameters:  sampled_uncertainties – a list of sampled uncertainties, as the values return by generate_samples 

Return type:  a generator object that yields the designs resulting from combining the uncertainties 
The main method of :class: ~sampler.Sampler and its children. This will call the sample method for each of the uncertainties and return the resulting designs.
Parameters: 


Return type:  dict with the uncertainty.name as key, and the sample as value 
method for sampling a number of samples from a particular distribution. The various samplers differ with respect to their implementation of this method.
Parameters: 

