from numpy import rint as np_rint
from numpy.typing import ArrayLike
from star_pso.engines.generic_pso import GenericPSO
from star_pso.utils.auxiliary import (nb_clip_inplace,
nb_median_taxicab_distance)
# Public interface.
__all__ = ["IntegerPSO"]
[docs]
class IntegerPSO(GenericPSO):
"""
Description:
This implements an Integer variant of the original PSO algorithm that operates
similarly to the StandardPSO, but rounds the positions to the nearest integer.
"""
def __init__(self, x_min: ArrayLike, x_max: ArrayLike, **kwargs) -> None:
"""
Default initializer of the IntegerPSO class.
:param x_min: lower search space bound.
:param x_max: upper search space bound.
"""
# Call the super initializer with the input parameters.
super().__init__(lower_bound=x_min, upper_bound=x_max, **kwargs)
# Generate initial particle velocities.
self._velocities = GenericPSO.rng.uniform(-1.0, +1.0, size=(self.n_rows,
self.n_cols))
# _end_def_
[docs]
def update_positions(self) -> None:
"""
Updates the positions of the particles in the swarm.
:return: None.
"""
# Round the new positions and convert them to type int.
new_positions = np_rint(self.swarm.positions_as_array() +
self._velocities).astype(int)
# Ensure the particle stays within bounds.
nb_clip_inplace(new_positions,
self.lower_bound,
self.upper_bound)
# Update all particle positions.
self.swarm.set_positions(new_positions)
# _end_def_
[docs]
def generate_random_positions(self) -> None:
"""
Generate the population of particles positions by sampling
uniformly random integer numbers within the [x_min, x_max]
bounds.
:return: None.
"""
# Generate uniform INTEGER positions Int(x_min, x_max).
integer_positions = GenericPSO.rng.integers(self.lower_bound,
self.upper_bound,
endpoint=True,
size=(self.n_rows,
self.n_cols))
# Assign the new positions in the swarm.
self.swarm.set_positions(integer_positions)
# _end_def_
[docs]
def reset_all(self) -> None:
"""
Resets the particle positions, velocities
and clear all the statistics dictionary.
:return: None.
"""
# Reset particle velocities.
self._velocities = GenericPSO.rng.uniform(-1.0, +1.0,
size=(self.n_rows,
self.n_cols))
# Generate random integer positions.
self.generate_random_positions()
# Clear all the internal bookkeeping.
self.clear_all()
# _end_def_
[docs]
def calculate_spread(self) -> float:
"""
Calculates a spread measure for the particle positions
using the normalized median Taxi-Cab distance.
A value close to '0' indicates the swarm is converging
to a single value. On the contrary a value close to '1'
indicates the swarm is still spread around the search space.
:return: an estimated measure (float) for the spread of
the particles.
"""
# Extract the positions in a 2D numpy array.
positions = self.swarm.positions_as_array()
# Normalized median Taxi-Cab/Manhattan distance.
return nb_median_taxicab_distance(positions, normal=True)
# _end_def_
# _end_class_