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Genetic Algorithms for Swarm Parameter Tuning.py
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Genetic Algorithms for Swarm Parameter Tuning.py
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"""Genetic Algorithms for Swarm Parameter Tuning.py
Swarming behaviour is based on aggregation of simple drones exhibiting basic instinctive reactions to stimuli. However,
to achieve overall balanced/interesting behaviour the relative importance of these instincts, as well their internal
parameters, must be tuned. In this project, you will learn how to apply Genetic Programming as means of such tuning,
and attempt to achieve a series of non-trivial swarm-level behaviours.
"""
from __future__ import barry_as_FLUFL
__all__ = None
__author__ = "#CHEE JUN YUAN GLENN#"
__copyright__ = "Copyright © 2019, Cheejyg"
__email__ = "[email protected]"
__license__ = "MIT"
__maintainer__ = "#CHEE JUN YUAN GLENN#"
__status__ = "Development"
__version__ = "1.0"
import argparse
import json
# import matplotlib
import matplotlib.animation
import matplotlib.pyplot
from mpl_toolkits.mplot3d import Axes3D
import numpy
# import os
import random
# import scipy
# import sys
# import tensorflow
# import time
random.seed(24)
numpy.random.seed(24)
dT = 0.1
boundary_type = 1 # [1 = Bound (Velocity), 2 = Wrap (Position)]
animation = True
animation_type = 1 if animation else None # [1 = Normal (Standard), 2 = History (Path)]
measure = True
inputFilename = None
sceneFilename = None
outputFilename = None
verbosity = None
outputFile = None
output = None
canvas = None
ax = None
scatter = None
scatterPredators = None
scatterPreys = None
boidSize = None
radiusSeparationSquared = None
radiusAlignmentSquared = None
radiusCohesionSquared = None
radiusPredatorSquared = None
radiusPreySquared = None
weightSeparation = None
weightAlignment = None
weightCohesion = None
weightPredator = None
weightPredatorBoost = None
weightPrey = None
weightPreyBoost = None
maximumSpeed = None
maximumSpeedSquared = None
dimension = None
width = None
height = None
depth = None
ticks = None
n = None
nPredators = None
nPreys = None
positions = None
positionsPredator = None
positionsPrey = None
rotations = None
rotationsPredator = None
rotationsPrey = None
velocities = None
velocitiesPredator = None
velocitiesPrey = None
typePredator = None
typePrey = None
waypointsPredator = None
matrixSeparations = None
matrixAlignments = None
matrixCohesions = None
matrixPredators = None
matrixPreys = None
separations = None
alignments = None
cohesions = None
predator = None
prey = None
differences = None
differencesPredator = None
differencesPrey = None
distances = None
distancesPredator = None
distancesPrey = None
predatorsWaypoint = None
measurement = None
def __main__() -> None:
global verbosity
global canvas
global ax
global scatter
global scatterPredators
global scatterPreys
global dimension
global ticks
global n
__argparse__()
__global__()
if animation:
if 0 < dimension < 4:
canvas = matplotlib.pyplot.figure(1)
if dimension == 1:
matplotlib.pyplot.xlabel("x")
matplotlib.pyplot.xlim(-width, width)
matplotlib.pyplot.ylabel("y")
matplotlib.pyplot.ylim(-0, 0)
scatter = matplotlib.pyplot.scatter(
positions[:, 0], numpy.zeros((n, dimension), dtype=float, order=None), s=8, c="Blue", marker="o"
)
scatterPredators = matplotlib.pyplot.scatter(
positionsPredator[:, 0], numpy.zeros((nPredators, dimension), dtype=float, order=None),
s=8, c="Red", marker="s"
)
scatterPreys = matplotlib.pyplot.scatter(
positionsPrey[:, 0], numpy.zeros((nPreys, dimension), dtype=float, order=None),
s=8, c="Green", marker="^"
)
elif dimension == 2:
matplotlib.pyplot.xlabel("x")
matplotlib.pyplot.xlim(-width, width)
matplotlib.pyplot.ylabel("y")
matplotlib.pyplot.ylim(-height, height)
scatter = matplotlib.pyplot.scatter(
positions[:, 0], positions[:, 1], s=8, c="Blue", marker="o"
)
scatterPredators = matplotlib.pyplot.scatter(
positionsPredator[:, 0], positionsPredator[:, 1], s=8, c="Red", marker="s"
)
scatterPreys = matplotlib.pyplot.scatter(
positionsPrey[:, 0], positionsPrey[:, 1], s=8, c="Green", marker="^"
)
elif dimension == 3:
ax = canvas.add_subplot(111, projection="3d")
ax.set_xlabel("x")
ax.set_xlim(-width, width)
ax.set_ylabel("y")
ax.set_ylim(-height, height)
ax.set_zlabel("z")
ax.set_zlim(-depth, depth)
scatter = ax.scatter(
positions[:, 0], positions[:, 1], positions[:, 2], s=8, c="Blue", marker="o"
)
scatterPredators = ax.scatter(
positionsPredator[:, 0], positionsPredator[:, 1], positionsPredator[:, 2], s=8, c="Red", marker="s"
)
scatterPreys = ax.scatter(
positionsPrey[:, 0], positionsPrey[:, 1], positionsPrey[:, 2], s=8, c="Green", marker="^"
)
func_animation = matplotlib.animation.FuncAnimation(
fig=canvas, func=__update__, frames=ticks, init_func=__global__, fargs=(), save_count=(1024 % ticks),
interval=1, repeat=False
)
matplotlib.pyplot.show()
else:
for tick in range(ticks):
__update__(tick)
try:
print(str((
(
"velocities",
"predators",
"preys",
"distances",
"distancesPredator",
"distancesPrey"
),
output["fitness"]
)).replace("), [", "), \n["))
except (UnboundLocalError, KeyError, IndexError) as e:
print(str((
(
"velocities",
"predators",
"preys",
"distances",
"distancesPredator",
"distancesPrey"
),
e
)).replace("), [", "), \n["))
if verbosity > 0:
if verbosity > 1:
json.dump(output, outputFile)
outputFile.close()
return
def __argparse__() -> None:
global inputFilename
global sceneFilename
global outputFilename
global verbosity
parser = argparse.ArgumentParser(
description="Swarming behaviour is based on aggregation of simple drones exhibiting basic instinctive reactions"
"to stimuli. However, to achieve overall balanced/interesting behaviour the relative importance of these"
"instincts, as well their internalparameters, must be tuned. In this project, you will learn how to apply"
"Genetic Programming as means of such tuning, and attempt to achieve a series of non-trivial swarm-level"
"behaviours.",
add_help=True
)
parser.add_argument(
"-i", "--input", type=str, default="parameters.json",
help="json parameters file"
)
parser.add_argument(
"-s", "--scene", type=str, default="scene/scene.json",
help="json scene file"
)
parser.add_argument(
"-o", "--output", type=str, default="Genetic Algorithms for Swarm Parameter Tuning.log",
help="output debug file"
)
parser.add_argument("-v", "--verbosity", type=int, choices=[0, 1, 2], default=0, help="verbosity")
args = parser.parse_args()
inputFilename = str(args.input)
sceneFilename = str(args.scene)
outputFilename = str(args.output)
verbosity = args.verbosity if args.verbosity else 0
return
def __global__() -> None:
global inputFilename
global sceneFilename
global outputFilename
global verbosity
global outputFile
global output
global boidSize
global radiusSeparationSquared
global radiusAlignmentSquared
global radiusCohesionSquared
global radiusPredatorSquared
global radiusPreySquared
global weightSeparation
global weightAlignment
global weightCohesion
global weightPredator
global weightPredatorBoost
global weightPrey
global weightPreyBoost
global maximumSpeed
global maximumSpeedSquared
global dimension
global width
global height
global depth
global ticks
global n
global nPredators
global nPreys
global positions
global positionsPredator
global positionsPrey
global rotations
global rotationsPredator
global rotationsPrey
global velocities
global velocitiesPredator
global velocitiesPrey
global typePredator
global typePrey
global waypointsPredator
global separations
global alignments
global cohesions
global predator
global prey
global predatorsWaypoint
global measurement
output = {}
try:
with open(inputFilename, "rt") as input_file:
inputs = json.load(input_file)
boidSize = inputs["boidSize"]
radius_separation = inputs["radii"]["separation"]
radius_alignment = inputs["radii"]["alignment"]
radius_cohesion = inputs["radii"]["cohesion"]
radius_predator = inputs["radii"]["predator"]
radius_prey = inputs["radii"]["prey"]
weightSeparation = inputs["weights"]["separation"]
weightAlignment = inputs["weights"]["alignment"]
weightCohesion = inputs["weights"]["cohesion"]
weightPredator = inputs["weights"]["predator"]
weightPredatorBoost = inputs["weights"]["predatorBoost"]
weightPrey = inputs["weights"]["prey"]
weightPreyBoost = inputs["weights"]["preyBoost"]
maximumSpeed = inputs["maximumSpeed"]
except (FileNotFoundError, json.decoder.JSONDecodeError, UnboundLocalError, KeyError, IndexError, TypeError) as e:
if verbosity > 0:
print("EXCEPTION: %s" % (str(e)))
boidSize = random.random() * 10
radius_separation = random.random() + 1
radius_alignment = random.random() * 100
radius_cohesion = random.random() * 100
radius_predator = random.random() * 100
radius_prey = random.random() * 100
weightSeparation = random.random()
weightAlignment = random.random()
weightCohesion = random.random()
weightPredator = random.random()
weightPredatorBoost = random.random() + 1
weightPrey = random.random()
weightPreyBoost = random.random() + 1
maximumSpeed = random.random() * 100
radiusSeparationSquared = (radius_separation + (radius_separation * boidSize) + boidSize) ** 2
radiusAlignmentSquared = (radius_alignment + (radius_alignment * boidSize) + boidSize) ** 2
radiusCohesionSquared = (radius_cohesion + (radius_cohesion * boidSize) + boidSize) ** 2
radiusPredatorSquared = (radius_predator + (radius_predator * boidSize) + boidSize) ** 2
radiusPreySquared = (radius_prey + (radius_prey * boidSize) + boidSize) ** 2
maximumSpeedSquared = maximumSpeed ** 2
try:
with open(sceneFilename, "rt") as scene_file:
scene = json.load(scene_file)
ticks = scene["ticks"]
positions = numpy.array(
scene["boids"]["positions"], dtype=float, copy=False, order=None, subok=False, ndmin=0
)
positionsPredator = numpy.array(
scene["predators"]["positions"], dtype=float, copy=False, order=None, subok=False, ndmin=0
)
positionsPrey = numpy.array(
scene["preys"]["positions"], dtype=float, copy=False, order=None, subok=False, ndmin=0
)
rotations = numpy.array(
scene["boids"]["rotations"], dtype=float, copy=False, order=None, subok=False, ndmin=0
)
rotationsPredator = numpy.array(
scene["predators"]["rotations"], dtype=float, copy=False, order=None, subok=False, ndmin=0
)
rotationsPrey = numpy.array(
scene["preys"]["rotations"], dtype=float, copy=False, order=None, subok=False, ndmin=0
)
velocities = numpy.array(
scene["boids"]["velocities"], dtype=float, copy=False, order=None, subok=False, ndmin=0
)
velocitiesPredator = numpy.array(
scene["predators"]["velocities"], dtype=float, copy=False, order=None, subok=False, ndmin=0
)
velocitiesPrey = numpy.array(
scene["preys"]["velocities"], dtype=float, copy=False, order=None, subok=False, ndmin=0
)
n = positions.shape[0]
nPredators = positionsPredator.shape[0]
nPreys = positionsPrey.shape[0]
dimension = positions.shape[1]
width = scene["window"]["width"]
height = scene["window"]["height"]
if dimension > 2:
depth = scene["window"]["depth"]
typePredator = scene["predators"]["type"]
typePrey = scene["preys"]["type"]
if typePredator == 2:
waypointsPredator = numpy.array(
scene["predators"]["waypoints"], dtype=float, copy=False, order=None, subok=False, ndmin=0
)
if nPredators < 1:
positionsPredator.shape = (0, dimension)
rotationsPredator.shape = (0, dimension)
velocitiesPredator.shape = (0, dimension)
if nPreys < 1:
positionsPrey.shape = (0, dimension)
rotationsPrey.shape = (0, dimension)
velocitiesPrey.shape = (0, dimension)
if positions.shape[0] != n or positions.shape[1] != dimension \
or positionsPredator.shape[0] != nPredators or positionsPredator.shape[1] != dimension \
or positionsPrey.shape[0] != nPreys or positionsPrey.shape[1] != dimension:
raise IndexError
except (FileNotFoundError, json.decoder.JSONDecodeError, UnboundLocalError, KeyError, IndexError, TypeError) as e:
if verbosity > 0:
print("EXCEPTION: %s" % (str(e)))
dimension = random.randint(1, 3) \
if dimension is None else dimension
width = random.randint(10, 300) \
if width is None else width
height = random.randint(10, 300) \
if height is None else height
if dimension > 2 and depth is None:
depth = random.randint(10, 300) # if depth is None else depth
ticks = random.randint(1, 1024) \
if ticks is None else ticks
n = random.randint(2, 2048) \
if n is None else n
nPredators = random.randint(0, round(n * 0.1)) \
if nPredators is None else nPredators
nPreys = random.randint(0, round(n * 0.1)) \
if nPreys is None else nPreys
positions = (min(width, height) / (40 / 9)) * numpy.random.randn(n, dimension) \
if positions is None else positions
positionsPredator = (min(width, height) / (40 / 9)) * numpy.random.randn(nPredators, dimension) \
if positionsPredator is None else positionsPredator
positionsPrey = (min(width, height) / (40 / 9)) * numpy.random.randn(nPreys, dimension) \
if positionsPrey is None else positionsPrey
rotations = numpy.random.rand(n, dimension) \
if rotations is None else rotations
rotationsPredator = numpy.random.rand(nPredators, dimension) \
if rotationsPredator is None else rotationsPredator
rotationsPrey = numpy.random.rand(nPreys, dimension) \
if rotationsPrey is None else rotationsPrey
velocities = numpy.random.randn(n, dimension) \
if velocities is None else velocities
velocitiesPredator = numpy.random.randn(nPredators, dimension) \
if velocitiesPredator is None else velocitiesPredator
velocitiesPrey = numpy.random.randn(nPreys, dimension) \
if velocitiesPrey is None else velocitiesPrey
typePredator = random.randint(1, 2) \
if typePredator is None else typePredator
typePrey = random.randint(1, 2) \
if typePrey is None else typePrey
waypointsPredator = (
numpy.random.rand(nPredators, random.randint(2, 24), dimension) * (2 * min(width, height))
) - min(width, height) \
if typePredator == 2 and waypointsPredator is None else waypointsPredator
positions[0] = numpy.array(
numpy.ones(dimension, dtype=float, order=None), dtype=float, copy=False, order=None, subok=False, ndmin=0
)
rotations[0] = numpy.array(
numpy.ones(dimension, dtype=float, order=None), dtype=float, copy=False, order=None, subok=False, ndmin=0
)
velocities[0] = numpy.array(
numpy.ones(dimension, dtype=float, order=None), dtype=float, copy=False, order=None, subok=False, ndmin=0
)
# OVERRIDE
if __name__ == "__main__":
# verbosity = 2
dimension = 2
# n = 400
# nPredators = 4
# nPreys = 4
# ticks = 1024
if positions.shape[0] != n or positions.shape[1] != dimension \
or positionsPredator.shape[0] != nPredators or positionsPredator.shape[1] != dimension \
or positionsPrey.shape[0] != nPreys or positionsPrey.shape[1] != dimension:
if verbosity > 0:
print("OVERRIDE")
dimension = random.randint(1, 3) \
if dimension is None else dimension
width = random.randint(10, 300) \
if width is None else width
height = random.randint(10, 300) \
if height is None else height
if dimension > 2 and depth is None:
depth = random.randint(10, 300) # if depth is None else depth
ticks = random.randint(1, 1024) \
if ticks is None else ticks
n = random.randint(2, 2048) \
if n is None else n
nPredators = random.randint(0, round(n * 0.1)) \
if nPredators is None else nPredators
nPreys = random.randint(0, round(n * 0.1)) \
if nPreys is None else nPreys
positions = (min(width, height) / (40 / 9)) * numpy.random.randn(n, dimension) \
if positions is None or positions.shape[0] != n or positions.shape[1] != dimension else positions
positionsPredator = (min(width, height) / (40 / 9)) * numpy.random.randn(nPredators, dimension) \
if positionsPredator is None or positionsPredator.shape[0] != nPredators \
or positionsPredator.shape[1] != dimension else positionsPredator
positionsPrey = (min(width, height) / (40 / 9)) * numpy.random.randn(nPreys, dimension) \
if positionsPrey is None or positionsPrey.shape[0] != nPreys \
or positionsPrey.shape[1] != dimension else positionsPrey
rotations = numpy.random.rand(n, dimension) \
if rotations is None or rotations.shape[0] != n or rotations.shape[1] != dimension else rotations
rotationsPredator = numpy.random.rand(nPredators, dimension) \
if rotationsPredator is None or rotationsPredator.shape[0] != nPredators \
or rotationsPredator.shape[1] != dimension else rotationsPredator
rotationsPrey = numpy.random.rand(nPreys, dimension) \
if rotationsPrey is None or rotationsPrey.shape[0] != nPreys \
or rotationsPrey.shape[1] != dimension else rotationsPrey
velocities = numpy.random.randn(n, dimension) \
if velocities is None or velocities.shape[0] != n or velocities.shape[1] != dimension else velocities
velocitiesPredator = numpy.random.randn(nPredators, dimension) \
if velocitiesPredator is None or velocitiesPredator.shape[0] != nPredators \
or velocitiesPredator.shape[1] != dimension else velocitiesPredator
velocitiesPrey = numpy.random.randn(nPreys, dimension) \
if velocitiesPrey is None or velocitiesPrey.shape[0] != nPreys \
or velocitiesPrey.shape[1] != dimension else velocitiesPrey
typePredator = random.randint(1, 2) \
if typePredator is None else typePredator
typePrey = random.randint(1, 2) \
if typePrey is None else typePrey
if typePredator == 2:
waypointsPredator = (
numpy.random.rand(nPredators, random.randint(2, 24), dimension) * (2 * min(width, height))
) - min(width, height) \
if waypointsPredator is None or waypointsPredator.shape[0] != nPredators \
or waypointsPredator.shape[2] != dimension else waypointsPredator
positions[0] = numpy.array(
numpy.ones(dimension, dtype=float, order=None), dtype=float, copy=False, order=None, subok=False,
ndmin=0
)
rotations[0] = numpy.array(
numpy.ones(dimension, dtype=float, order=None), dtype=float, copy=False, order=None, subok=False,
ndmin=0
)
velocities[0] = numpy.array(
numpy.ones(dimension, dtype=float, order=None), dtype=float, copy=False, order=None, subok=False,
ndmin=0
)
if verbosity > 0:
print(
"------------------------------------------------------------"
"------------------------------------------------------------"
)
print("{")
print(
"\t\"n\": %d, "
"\"nPredators\": %d, "
"\"nPreys\": %d, "
"\"ticks\": %d, " % (n, nPredators, nPreys, ticks)
)
print(
"\t\"boids\": {\n"
"\t\t\"positions\": %s, \n"
"\t\t\"velocities\": %s \n"
"\t}, "
% (str(positions.tolist()), str(velocities.tolist()))
)
if nPredators > 0:
print(
"\t\"predators\": {\n"
"\t\t\"type\": %s, \n"
"\t\t\"positions\": %s, \n"
"\t\t\"velocities\": %s \n"
"\t}, "
% (typePredator, str(positionsPredator.tolist()), str(velocitiesPredator.tolist()))
)
if nPreys > 0:
print(
"\t\"preys\": {\n"
"\t\t\"type\": %s, \n"
"\t\t\"positions\": %s, \n"
"\t\t\"velocities\": %s \n"
"\t}, "
% (typePrey, str(positionsPrey.tolist()), str(velocitiesPrey.tolist()))
)
print(
"\t\"parameters\": {\n"
"\t\t\"boidSize\": %f,\n"
"\t\t\"radiuses\": {\n"
"\t\t\t\"separation\": %f, \n"
"\t\t\t\"alignment\": %f, \n"
"\t\t\t\"cohesion\": %f, \n"
"\t\t\t\"predator\": %f, \n"
"\t\t\t\"prey\": %f\n"
"\t\t}, \n"
"\t\t\"weights\": {\n"
"\t\t\t\"separation\": %f, \n"
"\t\t\t\"alignment\": %f, \n"
"\t\t\t\"cohesion\": %f, \n"
"\t\t\t\"predator\": %f, \n"
"\t\t\t\"predatorBoost\": %f, \n"
"\t\t\t\"prey\": %f\n"
"\t\t\t\"preyBoost\": %f\n"
"\t\t}, \n"
"\t\t\"maximumSpeed\": %f\n"
"\t}" % (
boidSize,
radius_separation, radius_alignment, radius_cohesion, radius_predator, radius_prey,
weightSeparation, weightAlignment, weightCohesion, weightPredator, weightPredatorBoost, weightPrey,
weightPreyBoost,
maximumSpeed
)
)
print("}")
print(
"------------------------------------------------------------"
"------------------------------------------------------------"
)
if verbosity > 1:
outputFile = open(outputFilename, "at")
output["n"] = n
output["nPredators"] = nPredators
output["nPreys"] = nPreys
output["ticks"] = ticks
output["boids"] = {
"positions": str(positions).replace('\n', ""),
"rotations": str(rotations).replace('\n', ""),
"velocities": str(velocities).replace('\n', "")
}
output["predators"] = {
"type": typePredator,
"positions": str(positionsPredator).replace('\n', ""),
"rotations": str(rotationsPredator).replace('\n', ""),
"velocities": str(velocitiesPredator).replace('\n', "")
}
output["preys"] = {
"type": typePrey,
"positions": str(positionsPrey).replace('\n', ""),
"rotations": str(rotationsPrey).replace('\n', ""),
"velocities": str(velocitiesPrey).replace('\n', "")
}
output["measurementFitness"] = []
output["measurement"] = []
output["fitness"] = []
'''# Normalise
total_weights = \
weightSeparation + weightAlignment + weightCohesion \
+ weightPredator + weightPrey
weightSeparation /= total_weights
weightAlignment /= total_weights
weightCohesion /= total_weights
weightPredator /= total_weights
weightPrey /= total_weights'''
separations = numpy.zeros((n, dimension), dtype=float, order=None)
alignments = numpy.zeros((n, dimension), dtype=float, order=None)
cohesions = numpy.zeros((n, dimension), dtype=float, order=None)
predator = numpy.zeros((n, dimension), dtype=float, order=None)
prey = numpy.zeros((n, dimension), dtype=float, order=None)
predatorsWaypoint = [0]
measurement = [] if measure else None
return
def __update__(tick: int) -> None:
global scatter
global scatterPredators
global scatterPreys
global dimension
global width
global height
global depth
global n
global nPredators
global nPreys
global positions
global positionsPredator
global positionsPrey
global velocities
global velocitiesPredator
global velocitiesPrey
global matrixSeparations
global matrixAlignments
global matrixCohesions
global matrixPredators
global matrixPreys
global separations
global alignments
global cohesions
global predator
global prey
global differences
global differencesPredator
global differencesPrey
global distances
global distancesPredator
global distancesPrey
global predatorsWaypoint
differences = positions - positions.reshape(n, 1, dimension)
differencesPredator = positionsPredator - positions.reshape(n, 1, dimension)
differencesPrey = positionsPrey - positions.reshape(n, 1, dimension)
distances = numpy.einsum("...i,...i", differences, differences)
distancesPredator = numpy.einsum("...i,...i", differencesPredator, differencesPredator)
distancesPrey = numpy.einsum("...i,...i", differencesPrey, differencesPrey)
# Separation
matrixSeparations = (distances < radiusSeparationSquared) * (distances != 0)
separations = numpy.nan_to_num(
numpy.sum(differences * matrixSeparations.reshape(n, n, 1), axis=1) * -1,
copy=False
)
# Alignment
matrixAlignments = (distances < radiusAlignmentSquared) * (distances != 0)
alignments = numpy.nan_to_num(
numpy.sum(
matrixAlignments.reshape(n, n, 1) * numpy.repeat(velocities.reshape(1, n, dimension), n, axis=0), axis=1
) / numpy.sum(matrixAlignments, axis=0).reshape(n, 1),
copy=False
)
# Cohesion
matrixCohesions = (distances < radiusCohesionSquared) * (distances != 0)
cohesions = numpy.nan_to_num(
(positions + numpy.sum(
matrixCohesions.reshape(n, n, 1) * numpy.repeat(positions.reshape(1, n, dimension), n, axis=0),
axis=1
)) / (numpy.ones(n) + numpy.sum(matrixCohesions, axis=0)).reshape(n, 1),
copy=False
) - positions
# Predator
matrixPredators = (distancesPredator < radiusPredatorSquared)
predator = numpy.nan_to_num(
numpy.sum(
differencesPredator * numpy.repeat(matrixPredators.reshape(n, nPredators, 1), dimension, axis=2),
axis=1
) * -1,
copy=False
)
# Prey
matrixPreys = (distancesPrey < radiusPreySquared)
if nPreys > 0:
prey = numpy.nan_to_num(
differencesPrey[numpy.arange(n), numpy.argmin(distancesPrey, axis=1)]
* (numpy.sum(matrixPreys, axis=1) > 0).reshape(n, 1),
copy=False
)
# Others
# Predators
if typePredator == 1:
'''differencesPredator = positions - positionsPredator.reshape(nPredators, 1, dimension)
distancesPredator = numpy.einsum("...i,...i", differencesPredator, differencesPredator)
predators = numpy.nan_to_num(positions[numpy.argmin(distancesPredator, axis=1)] - positionsPredator, copy=False)'''
predators = numpy.nan_to_num(positions[numpy.argmin(distancesPredator, axis=0)] - positionsPredator, copy=False)
elif typePredator == 2:
predators = waypointsPredator[0][predatorsWaypoint[0]] - positionsPredator
if round(waypointsPredator[0][predatorsWaypoint[0]][0]) == round(positionsPredator[0][0]) \
and round(waypointsPredator[0][predatorsWaypoint[0]][1]) == round(positionsPredator[0][1]):
predatorsWaypoint[0] = (predatorsWaypoint[0] + 1) % len(waypointsPredator[0])
# Preys
if tick % 200 == 0:
if typePrey == 1:
if dimension == 1:
positionsPrey = numpy.random.uniform(-width, width, (nPreys, 1))
elif dimension == 2:
positionsPrey = numpy.concatenate(
(
numpy.random.uniform(-width, width, (nPreys, 1)),
numpy.random.uniform(-height, height, (nPreys, 1))
),
axis=1
)
elif dimension == 3:
positionsPrey = numpy.concatenate(
(
numpy.random.uniform(-width, width, (nPreys, 1)),
numpy.random.uniform(-width, width, (nPreys, 1)),
numpy.random.uniform(-depth, depth, (nPreys, 1))
),
axis=1
)
elif typePrey == 2:
preyY = None
preyM = None
preyX = None
preyC = None
preyR = None
if dimension == 1:
positionsPrey = numpy.random.uniform(-width, width, (nPreys, 1))
elif dimension == 2:
preyY = None
preyM = (
waypointsPredator[0][(predatorsWaypoint[0] + 1) % len(waypointsPredator[0])][1]
- waypointsPredator[0][predatorsWaypoint[0]][1]
) / (
waypointsPredator[0][(predatorsWaypoint[0] + 1) % len(waypointsPredator[0])][0]
- waypointsPredator[0][predatorsWaypoint[0]][0]
)
preyX = random.uniform(-width, width)
preyC = 0
preyY = preyM * preyX + preyC
preyR = random.randint(0, int(min(width, height) / 4))
positionsPrey = numpy.concatenate(
(
numpy.random.uniform(preyX - preyR, preyX + preyR, (nPreys, 1)),
numpy.random.uniform(preyY - preyR, preyY + preyR, (nPreys, 1))
),
axis=1
)
elif dimension == 3:
positionsPrey = numpy.concatenate(
(
numpy.random.uniform(-width, width, (nPreys, 1)),
numpy.random.uniform(-width, width, (nPreys, 1)),
numpy.random.uniform(-depth, depth, (nPreys, 1))
),
axis=1
)
# ------------------------------------------------------------------------------------------------------------------------
'''position = None
position_other = None
neighbours = None
c = None'''
# ------------------------------------------------------------------------------------------------------------------------
'''
# Separation
for boid in range(n):
position = positions[boid, :]
c = numpy.zeros(dimension, dtype=float, order=None)
for other in range(n):
if other != boid:
position_other = positions[other]
difference = position_other - position
if numpy.einsum("...i,...i", difference, difference) < radiusSeparationSquared:
c -= difference
separations[boid] = c
# Alignment
for boid in range(n):
position = positions[boid, :]
c = numpy.zeros(dimension, dtype=float, order=None)
neighbours = 0.0
for other in range(n):
if other != boid:
position_other = positions[other]
velocity_other = velocities[other]
if numpy.einsum(
"...i,...i", position_other - position, position_other - position
) < radiusAlignmentSquared:
c += velocity_other
neighbours += 1.0
alignments[boid] = c if neighbours < 1 else c / neighbours
# Cohesion
for boid in range(n):
position = positions[boid, :]
c = numpy.copy(position)
neighbours = 1.0
for other in range(n):
if other != boid:
position_other = positions[other]
if numpy.einsum(
"...i,...i", position_other - position, position_other - position
) < radiusCohesionSquared:
c += position_other
neighbours += 1.0
cohesions[boid] = c if neighbours < 1 else (c / neighbours) - position
'''
# ------------------------------------------------------------------------------------------------------------------------
# Normalise
separations = numpy.nan_to_num(
separations / numpy.sqrt(numpy.einsum("...i,...i", separations, separations).reshape(n, 1)), copy=False
)
alignments = numpy.nan_to_num(
alignments / numpy.sqrt(numpy.einsum("...i,...i", alignments, alignments).reshape(n, 1)), copy=False
)
cohesions = numpy.nan_to_num(
cohesions / numpy.sqrt(numpy.einsum("...i,...i", cohesions, cohesions).reshape(n, 1)), copy=False
)
predator = numpy.nan_to_num(
predator / numpy.sqrt(numpy.einsum("...i,...i", predator, predator).reshape(n, 1)), copy=False
)
prey = numpy.nan_to_num(
prey / numpy.sqrt(numpy.einsum("...i,...i", prey, prey).reshape(n, 1)), copy=False
)
# Normalise Others
predators = numpy.nan_to_num(
predators / numpy.sqrt(numpy.einsum("...i,...i", predators, predators).reshape(nPredators, 1)), copy=False
)
'''preys = numpy.nan_to_num(
preys / numpy.sqrt(numpy.einsum("...i,...i", preys, preys).reshape(nPreys, 1)), copy=False
)'''
target = \
(
(weightSeparation * separations) + (weightAlignment * alignments) + (weightCohesion * cohesions)
+ (weightPredator * weightPredatorBoost * predator) + (weightPrey * weightPreyBoost * prey)
)
velocities += target + numpy.random.randn(n, dimension)
velocitiesPredator += predators # + numpy.random.randn(n, dimension)
# velocitiesPrey += preys + numpy.random.randn(n, dimension)
boundaries = positions + (dT * velocities)
out_of_bounds = numpy.array(
(boundaries[:, 0] < -width) + (boundaries[:, 0] > width),
dtype=float, copy=False, order=None, subok=False, ndmin=0
).reshape(n, 1)
if dimension > 1:
out_of_bounds += numpy.array(
(boundaries[:, 1] > height) + (boundaries[:, 1] < -height),
dtype=float, copy=False, order=None, subok=False, ndmin=0
).reshape(n, 1)
if dimension > 2:
out_of_bounds += numpy.array(
(boundaries[:, 2] > depth) + (boundaries[:, 2] > depth),
dtype=float, copy=False, order=None, subok=False, ndmin=0
).reshape(n, 1)
if boundary_type == 1:
velocities += out_of_bounds * (velocities * -2)
elif boundary_type == 2:
positions += out_of_bounds * (positions * -2)
'''velocities = numpy.clip(velocities, -maximumSpeed, maximumSpeed)
velocitiesPredator = numpy.clip(velocitiesPredator, -maximumSpeed, maximumSpeed)
velocitiesPrey = numpy.clip(velocitiesPrey, -maximumSpeed, maximumSpeed)'''
velocities_squared = numpy.einsum("...i,...i", velocities, velocities).reshape(n, 1)
velocities_predator_squared = numpy.einsum(
"...i,...i", velocitiesPredator, velocitiesPredator
).reshape(nPredators, 1)
'''velocities_prey_squared = numpy.einsum(
"...i,...i", velocitiesPrey, velocitiesPrey
).reshape(nPreys, 1)'''
velocities = (
velocities * (velocities_squared < maximumSpeedSquared)
+ numpy.nan_to_num(
velocities / numpy.sqrt(velocities_squared)
) * (velocities_squared > maximumSpeedSquared)
* maximumSpeed
)
velocitiesPredator = (
velocitiesPredator * (velocities_predator_squared < maximumSpeedSquared)
+ numpy.nan_to_num(
velocitiesPredator / numpy.sqrt(velocities_predator_squared)
) * (velocities_predator_squared > maximumSpeedSquared)
* maximumSpeed
)
positions += dT * velocities
positionsPredator += dT * velocitiesPredator
# positionsPrey += dT * velocitiesPrey
velocities *= 0.9
velocitiesPredator *= 0.9
# velocitiesPrey *= 0.9
if animation:
matplotlib.pyplot.title("Tick %d" % tick)
if animation_type == 1:
if dimension == 1:
scatter.set_offsets(numpy.insert(positions, [1], [0], axis=1))
scatterPredators.set_offsets(numpy.insert(positionsPredator, [1], [0], axis=1))
scatterPreys.set_offsets(numpy.insert(positionsPrey, [1], [0], axis=1))
elif dimension == 2:
scatter.set_offsets(positions)
scatterPredators.set_offsets(positionsPredator)
scatterPreys.set_offsets(positionsPrey)
elif dimension == 3:
scatter._offsets3d = (positions[:, 0], positions[:, 1], positions[:, 2])
scatterPredators._offsets3d = (
positionsPredator[:, 0], positionsPredator[:, 1], positionsPredator[:, 2]
)
scatterPreys._offsets3d = (positionsPrey[:, 0], positionsPrey[:, 1], positionsPrey[:, 2])
elif animation_type == 2:
if dimension == 1:
scatter = matplotlib.pyplot.scatter(
positions[:, 0], numpy.zeros((n, dimension), dtype=float, order=None), s=8, marker="o"
)
scatterPredators = matplotlib.pyplot.scatter(
positionsPredator[:, 0], numpy.zeros((nPredators, dimension), dtype=float, order=None),
s=8, c="Red", marker="s"
)
scatterPreys = matplotlib.pyplot.scatter(
positionsPrey[:, 0], numpy.zeros((nPreys, dimension), dtype=float, order=None),
s=8, c="Green", marker="^"
)
elif dimension == 2: