Source code for surrogate.mutation.mutGaussian
# Copyright 2016 Quan Pan
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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# Author: Quan Pan <quanpan302@hotmail.com>
# License: Apache License, Version 2.0
# Create: 2016-12-02
import random
from collections import Sequence
from itertools import repeat
[docs]def mutGaussian(variable, mu=1, sigma=0.5, prob=0.5):
"""This function applies a gaussian mutation of mean *mu* and standard
deviation *sigma* on the input individual. This mutation expects a
:term:`sequence` individual composed of real valued attributes.
The *prob* argument is the probability of each attribute to be mutated.
:param variable: Decision Variable to be mutated.
:param mu: Mean or :term:`python:sequence` of means for the
gaussian addition mutation.
:param sigma: Standard deviation or :term:`python:sequence` of
standard deviations for the gaussian addition mutation.
:param prob: Independent probability for each attribute to be mutated.
:returns: A tuple of one variable.
This function uses the :func:`~random.random` and :func:`~random.gauss`
functions from the python base :mod:`random` module.
"""
size = len(variable)
# size = variable.size
if not isinstance(mu, Sequence):
mu = repeat(mu, size)
elif len(mu) < size:
raise IndexError("mu must be at least the size of variable: %d < %d" % (len(mu), size))
if not isinstance(sigma, Sequence):
sigma = repeat(sigma, size)
elif len(sigma) < size:
raise IndexError("sigma must be at least the size of variable: %d < %d" % (len(sigma), size))
for i, m, s in zip(xrange(size), mu, sigma):
if random.random() < prob:
variable[i] += random.gauss(m, s)
return variable