Source code for pyswarms.backend.swarms

# -*- coding: utf-8 -*-

Swarm Class Backend

This module implements a Swarm class that holds various attributes in
the swarm such as position, velocity, options, etc. You can use this
as input to most backend cases.

# Import modules
import numpy as np
from attr import attrib, attrs
from attr.validators import instance_of

[docs]@attrs class Swarm(object): """A Swarm Class This class offers a generic swarm that can be used in most use-cases such as single-objective optimization, etc. It contains various attributes that are commonly-used in most swarm implementations. To initialize this class, **simply supply values for the position and velocity matrix**. The other attributes are automatically filled. If you want to initialize random values, take a look at: * :func:`pyswarms.backend.generators.generate_swarm`: for generating positions randomly. * :func:`pyswarms.backend.generators.generate_velocity`: for generating velocities randomly. If your swarm requires additional parameters (say c1, c2, and w in gbest PSO), simply pass them to the :code:`options` dictionary. As an example, say we want to create a swarm by generating particles randomly. We can use the helper methods above to do our job: .. code-block:: python import pyswarms.backend as P from pyswarms.backend.swarms import Swarm # Let's generate a 10-particle swarm with 10 dimensions init_positions = P.generate_swarm(n_particles=10, dimensions=10) init_velocities = P.generate_velocity(n_particles=10, dimensions=10) # Say, particle behavior is governed by parameters `foo` and `bar` my_options = {'foo': 0.4, 'bar': 0.6} # Initialize the swarm my_swarm = Swarm(position=init_positions, velocity=init_velocities, options=my_options) From there, you can now use all the methods in :mod:`pyswarms.backend`. Of course, the process above has been abstracted by the method :func:`pyswarms.backend.generators.create_swarm` so you don't have to write the whole thing down. Attributes ---------- position : numpy.ndarray position-matrix at a given timestep of shape :code:`(n_particles, dimensions)` velocity : numpy.ndarray velocity-matrix at a given timestep of shape :code:`(n_particles, dimensions)` n_particles : int number of particles in a swarm. dimensions : int number of dimensions in a swarm. options : dict various options that govern a swarm's behavior. pbest_pos : numpy.ndarray personal best positions of each particle of shape :code:`(n_particles, dimensions)` Default is `None` best_pos : numpy.ndarray best position found by the swarm of shape :code:`(dimensions, )` for the :obj:`pyswarms.backend.topology.Star` topology and :code:`(dimensions, particles)` for the other topologies pbest_cost : numpy.ndarray personal best costs of each particle of shape :code:`(n_particles, )` best_cost : float best cost found by the swarm, default is :obj:`numpy.inf` current_cost : numpy.ndarray the current cost found by the swarm of shape :code:`(n_particles, dimensions)` """ # Required attributes position = attrib(type=np.ndarray, validator=instance_of(np.ndarray)) velocity = attrib(type=np.ndarray, validator=instance_of(np.ndarray)) # With defaults n_particles = attrib(type=int, validator=instance_of(int)) dimensions = attrib(type=int, validator=instance_of(int)) options = attrib(type=dict, default={}, validator=instance_of(dict)) pbest_pos = attrib(type=np.ndarray, validator=instance_of(np.ndarray)) best_pos = attrib( type=np.ndarray, default=np.array([]), validator=instance_of(np.ndarray), ) pbest_cost = attrib( type=np.ndarray, default=np.array([]), validator=instance_of(np.ndarray), ) best_cost = attrib( type=float, default=np.inf, validator=instance_of((int, float)) ) current_cost = attrib( type=np.ndarray, default=np.array([]), validator=instance_of(np.ndarray), ) @n_particles.default def n_particles_default(self): return self.position.shape[0] @dimensions.default def dimensions_default(self): return self.position.shape[1] @pbest_pos.default def pbest_pos_default(self): return self.position