fmder

class countCalls(object):
    """Decorator that keeps track of the number of times a function is called.
    ::
    
        >>> @countCalls
        ... def foo():
        ...     return "spam"
        ... 
        >>> for _ in range(10)
        ...     foo()

fmder

import random
from deap import algorithms, base, creator, tools
import sortingnetwork as sn

INIT_SIZE, INPUTS, MAXGEN = 40, 12, 250
H_CXPB, H_MUTPB = 0.5, 0.3
P_CXPB, PMUT_PB = 0.5, 0.3

def evalNetwork(host, parasite, dimension):
    network = sn.SortingNetwork(dimension, host)

fmder

from scoop.futures import map

def root():
    for _ in range(100):
        r = list(map(node, [range(1000)] * 100))


def node(data):
    for _ in range(5):
        r1 = sum(map(leaf, [range(1000)] * 10))

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

try:
    import numpy
except ImportError:
    import platform
    if platform.python_implementation() == "PyPy":
        import numpypy as numpy
    else:
        raise ImportError("DEAP requires Numpy.")

from collections import OrderedDict

fmder

import ghalton
import random
import json

from deap import base
from deap import creator
from deap import tools

creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

fmder

from operator import attrgetter

logbook = tools.Logbook()
logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])

for gen in xrange(ngen):
    # Generate a new population
    population = toolbox.generate()
    # Evaluate the individuals
    fitnesses = toolbox.map(toolbox.evaluate, population)

fmder

if(!function.isFeasible(m_pop[mu()+i].searchPoint())){
	RealVector t(m_pop[mu()+i].searchPoint());
	function.closestFeasible( t );
	double dist = norm_sqr( t - m_pop[mu()+i].searchPoint() );
	std::cout << dist << std::endl;
	m_invalidCount++;
}

fmder

import subprocess
try:
    tree = subprocess.check_output(["git", "rev-parse", "HEAD"]).strip()
except OSError:
    import warnings
    warnings.warn("Cannot link examples because we cannot retrieve the git version", Warning)
else:
    extlinks = {'example': ('https://github.com/DEAP/deap/blob/{tree}/examples/%s.py'.format(tree=tree), "examples/")}

fmder

class ClosestValidMultiPenality(object):
    def __init__(self, feasibility, feasible, alpha, distance=None):
        self.fbty_fct = feasibility
        self.fbl_fct = feasible
        self.alpha = alpha
        self.dist_fct = distance

    def __call__(self, func):
        @wraps(func)
        def wrapper(individual, *args, **kwargs):