fmder

from collections.abc import Sequence
from copy import deepcopy
from functools import wraps
from typing import Callable

import numpy


MAXIMIZE = 1.0
MINIMIZE = -1.0

fmder

from math import factorial

from pymoo.optimize import minimize
from pymoo.util import plotting
from pymoo.util.reference_direction import UniformReferenceDirectionFactory
from pymop.factory import get_problem
from pymoo.algorithms.nsga3 import nsga3
import matplotlib.pyplot as plt
import numpy

fmder

import itertools

import numpy

from deap import cma

class CovarianceConditionError(Exception):
    pass

class StrategyMixedInteger(cma.Strategy):

fmder

#    This file is part of DEAP.
#
#    DEAP is free software: you can redistribute it and/or modify
#    it under the terms of the GNU Lesser General Public License as
#    published by the Free Software Foundation, either version 3 of
#    the License, or (at your option) any later version.
#
#    DEAP is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the

fmder

def inflate(d, sep="_"):
    items = dict()
    for k, v in d.items():
        keys = k.split(sep)
        sub_items = items
        for ki in keys[:-1]:
            try:
                sub_items = sub_items[ki]
            except KeyError:

fmder

End of evolution after 80 generations
 MU = 10, LAMBDA = 100
Population of the generation number 79 :
(array('d', [0.3384816385414553, 0.2913862946297661, 0.6391631079035605, 99.78019922362375, 0.9999999999999991]), (100.78019922362375, 102.04923026469854))
(array('d', [0.12725983751030595, 0.06852642494740502, 0.2944790143036873, -80.78193202686674, 0.9999999999999988]), (-79.78193202686674, -79.29166675010535))
(array('d', [0.32497537960481293, 0.30232682417375684, 0.6611237261340428, 34.76077835597124, 0.999999999999999]), (35.76077835597124, 37.04920428588385))
(array('d', [0.12518823446474206, 0.05982068209343572, 0.2846256754372451, -24.98563318764257, 0.9999999999999986]), (-23.98563318764257, -23.515998595647147))
(array('d', [0.16447172340744912, -0.37862460917923624, 0.2425460722676009, -0.8978315694776402, 0.9999999999999983]), (0.10216843052235813, 0.13056161701817193))
(array('d', [0.17269510499090013, -0.3647635822434079, 0.27057986499062364, 8.828981876870646, 0.9999999999999986]), (9.828981876

fmder

def shuffled_iterator(iterator, look_ahead=5000, skip=0, seed=None):
    """Return an iterator on the shuffled (part) of the provided *iterator*.
    This iterator consumes the provided iterator to turn it into a shuffled
    version. The cunsumption can be limited by the *lookahead* argument that
    limits the view of the operator. The *skip* argument flushes the first n
    items of the provided *iterator* and the *seed* allow for repetability.
    """
    rng = numpy.random.RandomState(seed)
    
    batch = list(itertools.islice(iterator, skip, skip+look_ahead))

fmder

from lasagne.layers import *
from lasagne.nonlinearities import sigmoid
import lasagne
import numpy
import theano
import theano.tensor as T

def build_network(input_shape, input_var, num_units):
    nnet = dict()
    nnet["input"] = InputLayer(input_shape, input_var, name="input")

fmder

import lasagne
import numpy
import theano
import theano.tensor as T

def get_data():
    return numpy.random.randn(200, 32, 32), numpy.random.randint(2, size=(200, 1))

def build_ae(input_shape, input_var):

fmder

import numpy
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.


    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in