# Source code for surrogate.selection.selStochasticUniversalSampling

# 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
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# 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 operator import attrgetter
[docs]def selStochasticUniversalSampling(individuals, k=1):
"""Select the *k* individuals among the input *individuals*.
The selection is made by using a single random value to sample all of the
individuals by choosing them at evenly spaced intervals. The list returned
contains references to the input *individuals*.
:param individuals: A list of individuals to select from.
:param k: The number of individuals to select.
:return: A list of selected individuals.
This function uses the :func:`~random.uniform` function from the python base
:mod:`random` module.
"""
s_inds = sorted(individuals, key=attrgetter("fitness"), reverse=True)
# TODO 20161205 individual property fitness.values[]
# sum_fits = sum(ind.fitness.values[0] for ind in individuals)
sum_fits = sum(ind.fitness for ind in individuals)
distance = sum_fits / float(k)
start = random.uniform(0, distance)
points = [start + i * distance for i in xrange(k)]
chosen = []
for p in points:
i = 0
# TODO 20161205 individual property fitness.values[]
# sum_ = s_inds[i].fitness.values[0]
sum_ = s_inds[i].fitness
while sum_ < p:
i += 1
# TODO 20161205 individual property fitness.values[]
# sum_ += s_inds[i].fitness.values[0]
sum_ += s_inds[i].fitness
chosen.append(s_inds[i])
return chosen