def test_distance(self):
"""Test distance."""
for i in range(20):
point = matrix(random.random(3))
assert 0 == distance(point, point)
pa = matrix([5, 3, 4])
pb = matrix([10, 3, 4])
assert 5 == distance(pa, pb)
pa = matrix([5, 3, 4])
pb = matrix([5, 7, 4])
assert 4 == distance(pa, pb)
pa = matrix([5, 3, 4])
pb = matrix([5, 3, 11])
assert 7 == distance(pa, pb)
def test_distance(self):
"""Test distance."""
for i in range(20):
point = matrix(random.random(3))
assert 0 == distance(point, point)
pa = matrix([5, 3, 4])
pb = matrix([10, 3, 4])
assert 5 == distance(pa, pb)
pa = matrix([5, 3, 4])
pb = matrix([5, 7, 4])
assert 4 == distance(pa, pb)
pa = matrix([5, 3, 4])
pb = matrix([5, 3, 11])
assert 7 == distance(pa, pb)
def test_generate_sequential_points(self):
"""Test generate sequential points."""
gen = generate_sequential_points(matrix([0, 0, 0]), random)
point = next(gen)
for i in range(30):
next_point = next(gen)
assert distance(point, next_point) < 0.87
point = next_point
def test_generate_sequential_points(self):
"""Test generate sequential points."""
gen = generate_sequential_points(matrix([0, 0, 0]), random)
point = next(gen)
for i in range(30):
next_point = next(gen)
assert distance(point, next_point) < 0.87
point = next_point
def builder(base, point, params, random_generator, position_initializator):
"""Sistematically generate environments from parameters combination."""
index = 1
for param in parameters_matrix(params):
# read parameters from the matrix
error = param[0]
inertial_weight = param[1]
cognition = param[2]
social = param[3]
num_particles = param[4]
velocity_max = param[5]
iterations_per_particle = param[6]
# point disturbed by a gaussian noise
disturbed_point_gen = apply_noise_linear(point=point, error=error)
# compute radius
radius = matrix([distance(b, next(disturbed_point_gen)) for b in base])
# build environment configuration
env_config = {
'inertial_weight': inertial_weight,
'cognition': cognition,
'social': social,
'random': random_generator,
'base': base,
'radius': radius
}
env = Environment(config=env_config)
# particle position generator
position_generator = position_initializator(env)
# particles inside env
for i in range(num_particles):
# particles
PSOParticle(
environment=env,
id="P{}env{}".format(i, index),
current=Information(position=next(position_generator)),
velocity=velocity_max * random_generator.random(),
vmax=velocity_max
)
# engine for env
yield (param, ParticleEngine(
config={
'max_iterations': iterations_per_particle
},
environment=env
))
side_length=side_length,
num_of_points=1)
base = next(base_generator)
point = next(point_generator)
random_generator = Random()
# Load logging configuration
log_config = "examples/benchmark_1.log.json"
with open(log_config) as data_file:
data = json.load(data_file)
logging.config.dictConfig(data)
logger = logging.getLogger("benchmark")
for index, (param, engine) in enumerate(
builder(base, point, params, random_generator, around_beacons)):
engine.start()
engine.join()
bestParticle = engine.environment.neighborBest
best_position = bestParticle.best.position
error = distance(point, best_position)
fitness = bestParticle.best.fitness
logger.debug(
"{} {:.3f} {:.3f} {:.3f} {:.3f} {:.3f} {:.3f} {:.3f} {:.3f}".format(
index, param[0], param[1], param[2], param[3], param[4], param[5],
param[6], error))
center=center, side_length=side_length, num_of_points=1)
base = next(base_generator)
point = next(point_generator)
random_generator = Random()
# Load logging configuration
log_config = "examples/benchmark_1.log.json"
with open(log_config) as data_file:
data = json.load(data_file)
logging.config.dictConfig(data)
logger = logging.getLogger("benchmark")
for index, (param, engine) in enumerate(builder(base, point, params,
random_generator,
around_beacons)):
engine.start()
engine.join()
bestParticle = engine.environment.neighborBest
best_position = bestParticle.best.position
error = distance(point, best_position)
fitness = bestParticle.best.fitness
logger.debug(
"{} {:.3f} {:.3f} {:.3f} {:.3f} {:.3f} {:.3f} {:.3f} {:.3f}".format(
index, param[0], param[1], param[2], param[3], param[4], param[5],
param[6], error))
random_generator = Random()
center = [1000, 1000, 1000]
side_length = 100
base_generator = generate_matrix_of_points_in_cube(
center=center, side_length=side_length, num_of_points=4)
point_generator = generate_matrix_of_points_in_cube(
center=center, side_length=side_length, num_of_points=1)
base = next(base_generator)
point = next(point_generator)
error = 10
disturbed_point_gen = apply_noise_linear(point=point, error=error)
radius = matrix([distance(b, next(disturbed_point_gen)) for b in base])
env_config = {
'inertial_weight': 2,
'cognition': 2,
'social': 1,
'random': random_generator,
'base': base,
'radius': radius
}
env1 = Environment(config=env_config)
position_generator = around_beacons(env1)
vmax = 5
center = [1000, 1000, 1000]
side_length = 100
base_generator = generate_matrix_of_points_in_cube(center=center,
side_length=side_length,
num_of_points=4)
point_generator = generate_matrix_of_points_in_cube(center=center,
side_length=side_length,
num_of_points=1)
base = next(base_generator)
point = next(point_generator)
error = 10
disturbed_point_gen = apply_noise_linear(point=point, error=error)
radius = matrix([distance(b, next(disturbed_point_gen)) for b in base])
env_config = {
'inertial_weight': 2,
'cognition': 2,
'social': 1,
'random': random_generator,
'base': base,
'radius': radius
}
env1 = Environment(config=env_config)
position_generator = around_beacons(env1)
vmax = 5