Python code to sample pairs of a given set of particles in n dims, where the probability for each pair is Gaussian

# smrfeld/sample-pairs-gaussian

Switch branches/tags
Nothing to show

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

## Files

Failed to load latest commit information.
Type
Name
Commit time

# Sample pairs of particles according to a discrete Gaussian

Python code to sample pairs of a given set of particles in n dims, where the probability for each pair is Gaussian

## Requirements

Python 3 & Numpy.

## Installation and usage

Use `pip`:

``````pip install samplePairsGaussian
``````

or manually:

``````python setup.py install
``````

``````from samplePairsGaussian import *
``````

## Idea

Given a set of `n` particles with positions in `d`-dimensional space denoted by `x_i` for `i=0,1,...,n`.

We want to sample a pair of particles `i,j` where `i =/= j`, where the probability for sampling this pair is given by:

``````p(i,j) ~ exp( - |x_i - x_j|^2 / 2 sigma^2 )
``````

where we use `|x|` to denote the `L_2` norm, and `sigma` is some chosen standard deviation.

This problem is easy to write down, but difficult to implement for large numbers of particles since it requires computing `N^2` distances.

A further problem is that we may want to:

2. Remove a particle.
3. Move a particle.

In this case, not all distances are affected - these operations should be of order `N`. However, if we sample the discrete distribution by forming the CDF, we will need to recalculate it, which is expensive. Alternatively, if we use rejection sampling, we must have a good candidate (envelope) distribution such that the acceptance ratio is high.

This library attempts to come up with the most efficient way to perform these operations in Python.

A key way this library reduces computational cost is by introducing a cutoff for particle distances, where pairs of particles separated by a distance greater than the cutoff are not considered for sampling. It is natural to let this be some chosen multiple of the std. dev., i.e. `m*sigma` for some `m`. If we use rejection sampling where candidates are drawn from a uniform distribution, the acceptance ratio should be approximately `( sqrt(2 * pi) * sigma ) / ( 2 * m * sigma ) = 1.253 / m`. (in the first equation: the area of the Gaussian is `1`, divided by the area of the uniform distribution of width `2 * m * sigma` and height `1 / (sqrt(2 * pi) * sigma )`).

In general, we avoid all use of for loops, and rely extensively on array operations using numpy.

### Multiple species

Multiple species are also supported, where we have multiple species but want to draw two particles of the same species (two particles of any species can be done by simply ignoring the species labels).

Specifically, the classes `ProbCalculatorMultiSpecies` and `SamplerMultiSpecies` implement this.

## Examples

See the examples folder.

Python code to sample pairs of a given set of particles in n dims, where the probability for each pair is Gaussian

## Releases 2

samplePairsGaussian v1.1 Latest
Jul 3, 2019

## Packages 0

No packages published