# -*- coding: utf-8 -*-
"""
A Pyramid Network Topology
This class implements a pyramid topology. In this topology, the particles are connected by N-dimensional simplices.
"""
# Import standard library
import logging
# Import modules
import numpy as np
from scipy.spatial import Delaunay
from .. import operators as ops
from ..handlers import BoundaryHandler, VelocityHandler
from ...utils.reporter import Reporter
from .base import Topology
[docs]class Pyramid(Topology):
[docs] def __init__(self, static=False):
"""Initialize the class
Parameters
----------
static : bool (Default is :code:`False`)
a boolean that decides whether the topology
is static or dynamic
"""
super(Pyramid, self).__init__(static)
self.rep = Reporter(logger=logging.getLogger(__name__))
[docs] def compute_gbest(self, swarm, **kwargs):
"""Update the global best using a pyramid neighborhood approach
This topology uses the :code:`Delaunay` class from :code:`scipy`. To
prevent precision errors in the Delaunay class, custom
:code:`qhull_options` were added. Namely, :code:`QJ0.001 Qbb Qc Qx`.
The meaning of those options is explained in [qhull]. This method is
used to triangulate N-dimensional space into simplices. The vertices of
the simplicies consist of swarm particles. This method is adapted from
the work of Lane et al.[SIS2008]
[SIS2008] J. Lane, A. Engelbrecht and J. Gain, "Particle swarm optimization with spatially
meaningful neighbours," 2008 IEEE Swarm Intelligence Symposium, St. Louis, MO, 2008,
pp. 1-8. doi: 10.1109/SIS.2008.4668281
[qhull] http://www.qhull.org/html/qh-optq.htm
Parameters
----------
swarm : pyswarms.backend.swarms.Swarm
a Swarm instance
Returns
-------
numpy.ndarray
Best position of shape :code:`(n_dimensions, )`
float
Best cost
"""
try:
# If there are less than (swarm.dimensions + 1) particles they are all connected
if swarm.n_particles < swarm.dimensions + 1:
self.neighbor_idx = np.tile(
np.arange(swarm.n_particles), (swarm.n_particles, 1)
)
best_pos = swarm.pbest_pos[np.argmin(swarm.pbest_cost)]
best_cost = np.min(swarm.pbest_cost)
else:
# Check if the topology is static or dynamic and assign neighbors
if (
self.static and self.neighbor_idx is None
) or not self.static:
pyramid = Delaunay(
swarm.position, qhull_options="QJ0.001 Qbb Qc Qx"
)
indices, index_pointer = pyramid.vertex_neighbor_vertices
# Insert all the neighbors for each particle in the idx array
self.neighbor_idx = np.array(
[
index_pointer[indices[i] : indices[i + 1]]
for i in range(swarm.n_particles)
]
)
idx_min = np.array(
[
swarm.pbest_cost[self.neighbor_idx[i]].argmin()
for i in range(len(self.neighbor_idx))
]
)
best_neighbor = np.array(
[
self.neighbor_idx[i][idx_min[i]]
for i in range(len(self.neighbor_idx))
]
).astype(int)
# Obtain best cost and position
best_cost = np.min(swarm.pbest_cost[best_neighbor])
best_pos = swarm.pbest_pos[best_neighbor]
except AttributeError:
self.rep.logger.exception(
"Please pass a Swarm class. You passed {}".format(type(swarm))
)
raise
else:
return (best_pos, best_cost)
[docs] def compute_velocity(
self,
swarm,
clamp=None,
vh=VelocityHandler(strategy="unmodified"),
bounds=None,
):
"""Compute the velocity matrix
This method updates the velocity matrix using the best and current
positions of the swarm. The velocity matrix is computed using the
cognitive and social terms of the swarm.
A sample usage can be seen with the following:
.. code-block :: python
import pyswarms.backend as P
from pyswarms.backend.swarm import Swarm
from pyswarms.backend.handlers import VelocityHandler
from pyswarms.backend.topology import Pyramid
my_swarm = P.create_swarm(n_particles, dimensions)
my_topology = Pyramid(static=False)
my_vh = VelocityHandler(strategy="zero")
for i in range(iters):
# Inside the for-loop
my_swarm.velocity = my_topology.update_velocity(my_swarm, clamp, my_vh,
bounds=bounds)
Parameters
----------
swarm : pyswarms.backend.swarms.Swarm
a Swarm instance
clamp : tuple of floats (default is :code:`None`)
a tuple of size 2 where the first entry is the minimum velocity
and the second entry is the maximum velocity. It
sets the limits for velocity clamping.
vh : pyswarms.backend.handlers.VelocityHandler
a VelocityHandler instance
bounds : tuple of :code:`np.ndarray` or list (default is :code:`None`)
a tuple of size 2 where the first entry is the minimum bound while
the second entry is the maximum bound. Each array must be of shape
:code:`(dimensions,)`.
Returns
-------
numpy.ndarray
Updated velocity matrix
"""
return ops.compute_velocity(swarm, clamp, vh, bounds=bounds)
[docs] def compute_position(
self, swarm, bounds=None, bh=BoundaryHandler(strategy="periodic")
):
"""Update the position matrix
This method updates the position matrix given the current position and
the velocity. If bounded, it waives updating the position.
Parameters
----------
swarm : pyswarms.backend.swarms.Swarm
a Swarm instance
bounds : tuple of :code:`np.ndarray` or list (default is :code:`None`)
a tuple of size 2 where the first entry is the minimum bound while
the second entry is the maximum bound. Each array must be of shape
:code:`(dimensions,)`.
bh : a BoundaryHandler instance
Returns
-------
numpy.ndarray
New position-matrix
"""
return ops.compute_position(swarm, bounds, bh)