cosmoHammer.pso package

Submodules

cosmoHammer.pso.BestFitPositionGenerator module

cosmoHammer.pso.CurvatureFitter module

cosmoHammer.pso.MpiParticleSwarmOptimizer module

Created on Oct 28, 2013

@author: J.Akeret

class cosmoHammer.pso.MpiParticleSwarmOptimizer.MpiParticleSwarmOptimizer(func, low, high, particleCount=25, threads=1)[source]

Bases: cosmoHammer.pso.ParticleSwarmOptimizer.ParticleSwarmOptimizer

PSO with support for MPI to distribute the workload over multiple nodes

isMaster()[source]

cosmoHammer.pso.ParticleSwarmOptimizer module

Created on Sep 30, 2013

@author: J. Akeret

class cosmoHammer.pso.ParticleSwarmOptimizer.Particle(position, velocity, fitness=0)[source]

Bases: object

Implementation of a single particle

Parameters:
  • position – the position of the particle in the parameter space
  • velocity – the velocity of the particle
  • fitness – the current fitness of the particle
copy()[source]

Creates a copy of itself

classmethod create(paramCount)[source]

Creates a new particle without position, velocity and -inf as fitness

updatePBest()[source]

Sets the current particle representation as personal best

class cosmoHammer.pso.ParticleSwarmOptimizer.ParticleSwarmOptimizer(func, low, high, particleCount=25, threads=1, pool=None)[source]

Bases: object

Optimizer using a swarm of particles

Parameters:
  • func – A function that takes a vector in the parameter space as input and returns the natural logarithm of the posterior probability for that position.
  • low – array of the lower bound of the parameter space
  • high – array of the upper bound of the parameter space
  • particleCount – the number of particles to use.
  • threads – (optional) The number of threads to use for parallelization. If threads == 1, then the multiprocessing module is not used but if threads > 1, then a Pool object is created and calls to lnpostfn are run in parallel.
  • pool – (optional) An alternative method of using the parallelized algorithm. If provided, the value of threads is ignored and the object provided by pool is used for all parallelization. It can be any object with a map method that follows the same calling sequence as the built-in map function.
isMaster()[source]
optimize(maxIter=1000, c1=1.193, c2=1.193, p=0.7, m=0.001, n=0.01)[source]

Runs the complete optimiziation.

Parameters:
  • maxIter – maximum iterations
  • c1 – cognitive weight
  • c2 – social weight
  • p – stop criterion, percentage of particles to use
  • m – stop criterion, difference between mean fitness and global best
  • n – stop criterion, difference between norm of the particle vector and norm of the global best
Return swarms, gBests:
 

the swarms and the global bests of all iterations
sample(maxIter=1000, c1=1.193, c2=1.193, p=0.7, m=0.001, n=0.01)[source]

Launches the PSO. Yields the complete swarm per iteration

Parameters:
  • maxIter – maximum iterations
  • c1 – cognitive weight
  • c2 – social weight
  • p – stop criterion, percentage of particles to use
  • m – stop criterion, difference between mean fitness and global best
  • n – stop criterion, difference between norm of the particle vector and norm of the global best

Module contents