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python-deap-python3.patch
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File python-deap-python3.patch of Package python-deap
Index: deap-1.3.1/deap/algorithms.py =================================================================== --- deap-1.3.1.orig/deap/algorithms.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/algorithms.py 2020-09-10 17:06:16.380968200 +0200 @@ -27,7 +27,7 @@ you really want them to do. import random -import tools +from deap import tools def varAnd(population, toolbox, cxpb, mutpb): @@ -157,7 +157,7 @@ def eaSimple(population, toolbox, cxpb, record = stats.compile(population) if stats else {} logbook.record(gen=0, nevals=len(invalid_ind), **record) if verbose: - print logbook.stream + print(logbook.stream) # Begin the generational process for gen in range(1, ngen + 1): @@ -184,7 +184,7 @@ def eaSimple(population, toolbox, cxpb, record = stats.compile(population) if stats else {} logbook.record(gen=gen, nevals=len(invalid_ind), **record) if verbose: - print logbook.stream + print(logbook.stream) return population, logbook @@ -227,10 +227,10 @@ def varOr(population, toolbox, lambda_, "or equal to 1.0.") offspring = [] - for _ in xrange(lambda_): + for _ in range(lambda_): op_choice = random.random() if op_choice < cxpb: # Apply crossover - ind1, ind2 = map(toolbox.clone, random.sample(population, 2)) + ind1, ind2 = list(map(toolbox.clone, random.sample(population, 2))) ind1, ind2 = toolbox.mate(ind1, ind2) del ind1.fitness.values offspring.append(ind1) @@ -308,7 +308,7 @@ def eaMuPlusLambda(population, toolbox, record = stats.compile(population) if stats is not None else {} logbook.record(gen=0, nevals=len(invalid_ind), **record) if verbose: - print logbook.stream + print(logbook.stream) # Begin the generational process for gen in range(1, ngen + 1): @@ -332,7 +332,7 @@ def eaMuPlusLambda(population, toolbox, record = stats.compile(population) if stats is not None else {} logbook.record(gen=gen, nevals=len(invalid_ind), **record) if verbose: - print logbook.stream + print(logbook.stream) return population, logbook @@ -409,7 +409,7 @@ def eaMuCommaLambda(population, toolbox, record = stats.compile(population) if stats is not None else {} logbook.record(gen=0, nevals=len(invalid_ind), **record) if verbose: - print logbook.stream + print(logbook.stream) # Begin the generational process for gen in range(1, ngen + 1): @@ -433,7 +433,7 @@ def eaMuCommaLambda(population, toolbox, record = stats.compile(population) if stats is not None else {} logbook.record(gen=gen, nevals=len(invalid_ind), **record) if verbose: - print logbook.stream + print(logbook.stream) return population, logbook @@ -477,7 +477,7 @@ def eaGenerateUpdate(toolbox, ngen, hall logbook = tools.Logbook() logbook.header = ['gen', 'nevals'] + (stats.fields if stats else []) - for gen in xrange(ngen): + for gen in range(ngen): # Generate a new population population = toolbox.generate() # Evaluate the individuals @@ -494,6 +494,6 @@ def eaGenerateUpdate(toolbox, ngen, hall record = stats.compile(population) if stats is not None else {} logbook.record(gen=gen, nevals=len(population), **record) if verbose: - print logbook.stream + print(logbook.stream) return population, logbook Index: deap-1.3.1/deap/base.py =================================================================== --- deap-1.3.1.orig/deap/base.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/base.py 2020-09-10 16:36:03.282483352 +0200 @@ -66,7 +66,7 @@ class Toolbox(object): The following code block is an example of how the toolbox is used. :: >>> def func(a, b, c=3): - ... print a, b, c + ... print("%d %d %d" % (a, b, c)) ... >>> tools = Toolbox() >>> tools.register("myFunc", func, 2, c=4) @@ -189,12 +189,12 @@ class Fitness(object): self.wvalues = tuple(map(mul, values, self.weights)) except TypeError: _, _, traceback = sys.exc_info() - raise TypeError, ("Both weights and assigned values must be a " - "sequence of numbers when assigning to values of " - "%r. Currently assigning value(s) %r of %r to a " - "fitness with weights %s." - % (self.__class__, values, type(values), - self.weights)), traceback + raise TypeError(("Both weights and assigned values must be a " + "sequence of numbers when assigning to values of " + "%r. Currently assigning value(s) %r of %r to a " + "fitness with weights %s." + % (self.__class__, values, type(values), + self.weights)), traceback) def delValues(self): self.wvalues = () @@ -267,3 +267,4 @@ class Fitness(object): """Return the Python code to build a copy of the object.""" return "%s.%s(%r)" % (self.__module__, self.__class__.__name__, self.values if self.valid else tuple()) + Index: deap-1.3.1/deap/benchmarks/movingpeaks.py =================================================================== --- deap-1.3.1.orig/deap/benchmarks/movingpeaks.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/benchmarks/movingpeaks.py 2020-09-10 17:06:16.380968200 +0200 @@ -393,6 +393,6 @@ def diversity(population): if __name__ == "__main__": mpb = MovingPeaks(dim=2, npeaks=[1,1,10], number_severity=0.1) - print mpb.maximums() + print(mpb.maximums()) mpb.changePeaks() - print mpb.maximums() + print(mpb.maximums()) Index: deap-1.3.1/deap/creator.py =================================================================== --- deap-1.3.1.orig/deap/creator.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/creator.py 2020-09-10 16:20:31.428896520 +0200 @@ -142,7 +142,7 @@ def create(name, base, **kargs): dict_inst = {} dict_cls = {} - for obj_name, obj in kargs.iteritems(): + for obj_name, obj in list(kargs.items()): if isinstance(obj, type): dict_inst[obj_name] = obj else: @@ -161,7 +161,7 @@ def create(name, base, **kargs): """Replace the __init__ function of the new type, in order to add attributes that were defined with **kargs to the instance. """ - for obj_name, obj in dict_inst.iteritems(): + for obj_name, obj in list(dict_inst.items()): setattr(self, obj_name, obj()) if base.__init__ is not object.__init__: base.__init__(self, *args, **kargs) Index: deap-1.3.1/deap/gp.py =================================================================== --- deap-1.3.1.orig/deap/gp.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/gp.py 2020-09-10 17:06:16.380968200 +0200 @@ -31,7 +31,7 @@ from functools import partial, wraps from inspect import isclass from operator import eq, lt -import tools # Needed by HARM-GP +from deap import tools # Needed by HARM-GP ###################################### # GP Data structure # @@ -197,7 +197,7 @@ class Primitive(object): self.arity = len(args) self.args = args self.ret = ret - args = ", ".join(map("{{{0}}}".format, range(self.arity))) + args = ", ".join(map("{{{0}}}".format, list(range(self.arity)))) self.seq = "{name}({args})".format(name=self.name, args=args) def format(self, *args): @@ -298,7 +298,7 @@ class PrimitiveSetTyped(object): def addType(dict_, ret_type): if ret_type not in dict_: new_list = [] - for type_, list_ in dict_.items(): + for type_, list_ in list(dict_.items()): if issubclass(type_, ret_type): for item in list_: if item not in new_list: @@ -478,11 +478,11 @@ def compile(expr, pset): return eval(code, pset.context, {}) except MemoryError: _, _, traceback = sys.exc_info() - raise MemoryError, ("DEAP : Error in tree evaluation :" - " Python cannot evaluate a tree higher than 90. " - "To avoid this problem, you should use bloat control on your " - "operators. See the DEAP documentation for more information. " - "DEAP will now abort."), traceback + raise MemoryError(("DEAP : Error in tree evaluation :" + " Python cannot evaluate a tree higher than 90. " + "To avoid this problem, you should use bloat control on your " + "operators. See the DEAP documentation for more information. " + "DEAP will now abort."), traceback) def compileADF(expr, psets): @@ -504,7 +504,7 @@ def compileADF(expr, psets): """ adfdict = {} func = None - for pset, subexpr in reversed(zip(psets, expr)): + for pset, subexpr in reversed(list(zip(psets, expr))): pset.context.update(adfdict) func = compile(subexpr, pset) adfdict.update({pset.name: func}) @@ -613,9 +613,9 @@ def generate(pset, min_, max_, condition term = random.choice(pset.terminals[type_]) except IndexError: _, _, traceback = sys.exc_info() - raise IndexError, "The gp.generate function tried to add " \ - "a terminal of type '%s', but there is " \ - "none available." % (type_,), traceback + raise IndexError("The gp.generate function tried to add " \ + "a terminal of type '%s', but there is " \ + "none available." % (type_,), traceback) if isclass(term): term = term() expr.append(term) @@ -624,9 +624,9 @@ def generate(pset, min_, max_, condition prim = random.choice(pset.primitives[type_]) except IndexError: _, _, traceback = sys.exc_info() - raise IndexError, "The gp.generate function tried to add " \ - "a primitive of type '%s', but there is " \ - "none available." % (type_,), traceback + raise IndexError("The gp.generate function tried to add " \ + "a primitive of type '%s', but there is " \ + "none available." % (type_,), traceback) expr.append(prim) for arg in reversed(prim.args): stack.append((depth + 1, arg)) @@ -654,8 +654,8 @@ def cxOnePoint(ind1, ind2): types2 = defaultdict(list) if ind1.root.ret == __type__: # Not STGP optimization - types1[__type__] = xrange(1, len(ind1)) - types2[__type__] = xrange(1, len(ind2)) + types1[__type__] = list(range(1, len(ind1))) + types2[__type__] = list(range(1, len(ind2))) common_types = [__type__] else: for idx, node in enumerate(ind1[1:], 1): @@ -1009,8 +1009,8 @@ def harm(population, toolbox, cxpb, mutp opRandom = random.random() if opRandom < cxpb: # Crossover - aspirant1, aspirant2 = toolbox.mate(*map(toolbox.clone, - toolbox.select(population, 2))) + aspirant1, aspirant2 = toolbox.mate(*list(map(toolbox.clone, + toolbox.select(population, 2)))) del aspirant1.fitness.values, aspirant2.fitness.values if acceptfunc(len(aspirant1)): producedpop.append(aspirant1) @@ -1058,7 +1058,7 @@ def harm(population, toolbox, cxpb, mutp record = stats.compile(population) if stats else {} logbook.record(gen=0, nevals=len(invalid_ind), **record) if verbose: - print logbook.stream + print(logbook.stream) # Begin the generational process for gen in range(1, ngen + 1): @@ -1125,7 +1125,7 @@ def harm(population, toolbox, cxpb, mutp record = stats.compile(population) if stats else {} logbook.record(gen=gen, nevals=len(invalid_ind), **record) if verbose: - print logbook.stream + print(logbook.stream) return population, logbook @@ -1186,7 +1186,7 @@ def graph(expr): <http://networkx.lanl.gov/pygraphviz/>`_ as the nodes might be plotted out of order when using `NetworX <http://networkx.github.com/>`_. """ - nodes = range(len(expr)) + nodes = list(range(len(expr))) edges = list() labels = dict() Index: deap-1.3.1/deap/tools/support.py =================================================================== --- deap-1.3.1.orig/deap/tools/support.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/tools/support.py 2020-09-10 17:06:16.380968200 +0200 @@ -1,7 +1,7 @@ -from __future__ import division + try: - import cPickle as pickle + import pickle as pickle except ImportError: import pickle @@ -205,7 +205,7 @@ class Statistics(object): values = tuple(self.key(elem) for elem in data) entry = dict() - for key, func in self.functions.iteritems(): + for key, func in list(self.functions.items()): entry[key] = func(values) return entry @@ -236,7 +236,7 @@ class MultiStatistics(dict): :param data: Sequence of objects on which the statistics are computed. """ record = {} - for name, stats in self.items(): + for name, stats in list(self.items()): record[name] = stats.compile(data) return record @@ -255,7 +255,7 @@ class MultiStatistics(dict): automatically to the registered function when called, optional. """ - for stats in self.values(): + for stats in list(self.values()): stats.register(name, function, *args, **kargs) class Logbook(list): @@ -302,7 +302,7 @@ class Logbook(list): >>> # [...] >>> record = mstats.compile(population) >>> logbook.record(**record) - >>> print logbook + >>> print(logbook) fitness length ------------ ------------ max mean max mean @@ -339,8 +339,8 @@ class Logbook(list): in the dictionary are recorded in a chapter entitled as the name of the key part of the pair. Chapters are also Logbook. """ - apply_to_all = {k: v for k, v in infos.items() if not isinstance(v, dict)} - for key, value in infos.items(): + apply_to_all = {k: v for k, v in list(infos.items()) if not isinstance(v, dict)} + for key, value in list(infos.items()): if isinstance(value, dict): chapter_infos = value.copy() chapter_infos.update(apply_to_all) @@ -388,11 +388,11 @@ class Logbook(list): >>> log = Logbook() >>> log.append({'gen' : 0}) - >>> print log.stream # doctest: +NORMALIZE_WHITESPACE + >>> print(log.stream) # doctest: +NORMALIZE_WHITESPACE gen 0 >>> log.append({'gen' : 1}) - >>> print log.stream # doctest: +NORMALIZE_WHITESPACE + >>> print(log.stream) # doctest: +NORMALIZE_WHITESPACE 1 """ startindex, self.buffindex = self.buffindex, len(self) @@ -402,11 +402,11 @@ class Logbook(list): if isinstance(key, slice): for i, in range(*key.indices(len(self))): self.pop(i) - for chapter in self.chapters.values(): + for chapter in list(self.chapters.values()): chapter.pop(i) else: self.pop(key) - for chapter in self.chapters.values(): + for chapter in list(self.chapters.values()): chapter.pop(key) def pop(self, index=0): @@ -431,11 +431,11 @@ class Logbook(list): if not columns: columns = sorted(self[0].keys()) + sorted(self.chapters.keys()) if not self.columns_len or len(self.columns_len) != len(columns): - self.columns_len = map(len, columns) + self.columns_len = list(map(len, columns)) chapters_txt = {} offsets = defaultdict(int) - for name, chapter in self.chapters.items(): + for name, chapter in list(self.chapters.items()): chapters_txt[name] = chapter.__txt__(startindex) if startindex == 0: offsets[name] = len(chapters_txt[name]) - len(self) @@ -458,17 +458,17 @@ class Logbook(list): header = [] nlines = 1 if len(self.chapters) > 0: - nlines += max(map(len, chapters_txt.values())) - len(self) + 1 - header = [[] for i in xrange(nlines)] + nlines += max(list(map(len, list(chapters_txt.values())))) - len(self) + 1 + header = [[] for i in range(nlines)] for j, name in enumerate(columns): if name in chapters_txt: length = max(len(line.expandtabs()) for line in chapters_txt[name]) blanks = nlines - 2 - offsets[name] - for i in xrange(blanks): + for i in range(blanks): header[i].append(" " * length) header[blanks].append(name.center(length)) header[blanks+1].append("-" * length) - for i in xrange(offsets[name]): + for i in range(offsets[name]): header[blanks+2+i].append(chapters_txt[name][i]) else: length = max(len(line[j].expandtabs()) for line in str_matrix) @@ -653,3 +653,4 @@ if __name__ == "__main__": doctest.run_docstring_examples(MultiStatistics, globals()) doctest.run_docstring_examples(MultiStatistics.register, globals()) doctest.run_docstring_examples(MultiStatistics.compile, globals()) + Index: deap-1.3.1/deap/benchmarks/__init__.py =================================================================== --- deap-1.3.1.orig/deap/benchmarks/__init__.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/benchmarks/__init__.py 2020-09-10 16:20:31.432896544 +0200 @@ -489,7 +489,7 @@ def dtlz1(individual, obj): """ g = 100 * (len(individual[obj-1:]) + sum((xi-0.5)**2 - cos(20*pi*(xi-0.5)) for xi in individual[obj-1:])) f = [0.5 * reduce(mul, individual[:obj-1], 1) * (1 + g)] - f.extend(0.5 * reduce(mul, individual[:m], 1) * (1 - individual[m]) * (1 + g) for m in reversed(xrange(obj-1))) + f.extend(0.5 * reduce(mul, individual[:m], 1) * (1 - individual[m]) * (1 + g) for m in reversed(range(obj-1))) return f def dtlz2(individual, obj): @@ -588,7 +588,7 @@ def dtlz5(ind, n_objs): theta = lambda x: pi / (4.0 * (1 + gval)) * (1 + 2 * gval * x) fit = [(1 + gval) * cos(pi / 2.0 * ind[0]) * reduce(lambda x,y: x*y, [cos(theta(a)) for a in ind[1:]])] - for m in reversed(range(1, n_objs)): + for m in reversed(list(range(1, n_objs))): if m == 1: fit.append((1 + gval) * sin(pi / 2.0 * ind[0])) else: @@ -608,7 +608,7 @@ def dtlz6(ind, n_objs): fit = [(1 + gval) * cos(pi / 2.0 * ind[0]) * reduce(lambda x,y: x*y, [cos(theta(a)) for a in ind[1:]])] - for m in reversed(range(1, n_objs)): + for m in reversed(list(range(1, n_objs))): if m == 1: fit.append((1 + gval) * sin(pi / 2.0 * ind[0])) else: Index: deap-1.3.1/deap/benchmarks/binary.py =================================================================== --- deap-1.3.1.orig/deap/benchmarks/binary.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/benchmarks/binary.py 2020-09-10 16:20:31.432896544 +0200 @@ -13,7 +13,7 @@ # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see <http://www.gnu.org/licenses/>. -from __future__ import division + from functools import wraps import math @@ -29,7 +29,7 @@ def bin2float(min_, max_, nbits): # User must take care to make nelem an integer. nelem = len(individual)//nbits decoded = [0] * nelem - for i in xrange(nelem): + for i in range(nelem): gene = int("".join(map(str, individual[i*nbits:i*nbits+nbits])), 2) @@ -68,10 +68,10 @@ def chuang_f1(individual): """ total = 0 if individual[-1] == 0: - for i in xrange(0, len(individual)-1, 4): + for i in range(0, len(individual)-1, 4): total += inv_trap(individual[i:i+4]) else: - for i in xrange(0, len(individual)-1, 4): + for i in range(0, len(individual)-1, 4): total += trap(individual[i:i+4]) return total, @@ -85,16 +85,16 @@ def chuang_f2(individual): """ total = 0 if individual[-2] == 0 and individual[-1] == 0: - for i in xrange(0, len(individual)-2, 8): + for i in range(0, len(individual)-2, 8): total += inv_trap(individual[i:i+4]) + inv_trap(individual[i+4:i+8]) elif individual[-2] == 0 and individual[-1] == 1: - for i in xrange(0, len(individual)-2, 8): + for i in range(0, len(individual)-2, 8): total += inv_trap(individual[i:i+4]) + trap(individual[i+4:i+8]) elif individual[-2] == 1 and individual[-1] == 0: - for i in xrange(0, len(individual)-2, 8): + for i in range(0, len(individual)-2, 8): total += trap(individual[i:i+4]) + inv_trap(individual[i+4:i+8]) else: - for i in xrange(0, len(individual)-2, 8): + for i in range(0, len(individual)-2, 8): total += trap(individual[i:i+4]) + trap(individual[i+4:i+8]) return total, @@ -108,10 +108,10 @@ def chuang_f3(individual): """ total = 0 if individual[-1] == 0: - for i in xrange(0, len(individual)-1, 4): + for i in range(0, len(individual)-1, 4): total += inv_trap(individual[i:i+4]) else: - for i in xrange(2, len(individual)-3, 4): + for i in range(2, len(individual)-3, 4): total += inv_trap(individual[i:i+4]) total += trap(individual[-2:]+individual[:2]) return total, @@ -125,7 +125,7 @@ def royal_road1(individual, order): nelem = len(individual) // order max_value = int(2**order - 1) total = 0 - for i in xrange(nelem): + for i in range(nelem): value = int("".join(map(str, individual[i*order:i*order+order])), 2) total += int(order) * int(value/max_value) return total, Index: deap-1.3.1/deap/benchmarks/tools.py =================================================================== --- deap-1.3.1.orig/deap/benchmarks/tools.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/benchmarks/tools.py 2020-09-10 16:20:31.432896544 +0200 @@ -285,7 +285,7 @@ def convergence(first_front, optimal_fro distances.append(float("inf")) for opt_ind in optimal_front: dist = 0. - for i in xrange(len(opt_ind)): + for i in range(len(opt_ind)): dist += (ind.fitness.values[i] - opt_ind[i])**2 if dist < distances[-1]: distances[-1] = dist Index: deap-1.3.1/deap/cma.py =================================================================== --- deap-1.3.1.orig/deap/cma.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/cma.py 2020-09-10 16:20:31.432896544 +0200 @@ -24,7 +24,7 @@ import copy from math import sqrt, log, exp import numpy -import tools +from . import tools class Strategy(object): @@ -118,7 +118,7 @@ class Strategy(object): """ arz = numpy.random.standard_normal((self.lambda_, self.dim)) arz = self.centroid + self.sigma * numpy.dot(arz, self.BD.T) - return map(ind_init, arz) + return list(map(ind_init, arz)) def update(self, population): """Update the current covariance matrix strategy from the @@ -286,7 +286,7 @@ class StrategyOnePlusLambda(object): # self.y = numpy.dot(self.A, numpy.random.standard_normal(self.dim)) arz = numpy.random.standard_normal((self.lambda_, self.dim)) arz = self.parent + self.sigma * numpy.dot(arz, self.A.T) - return map(ind_init, arz) + return list(map(ind_init, arz)) def update(self, population): """Update the current covariance matrix strategy from the Index: deap-1.3.1/deap/tests/test_benchmarks.py =================================================================== --- deap-1.3.1.orig/deap/tests/test_benchmarks.py 2020-09-10 16:20:31.420896473 +0200 +++ deap-1.3.1/deap/tests/test_benchmarks.py 2020-09-10 16:20:31.432896544 +0200 @@ -33,7 +33,7 @@ class BenchmarkTest(unittest.TestCase): full_individual = creator.Individual([1] * 10) two_individiual = creator.Individual(8*[0] + [1, 0]) population = [zero_individual, full_individual, two_individiual] - fitnesses = map(self.toolbox.evaluate, population) + fitnesses = list(map(self.toolbox.evaluate, population)) for ind, fit in zip(population, fitnesses): ind.fitness.values = fit assert population[0].fitness.values == (0.0, ) @@ -45,7 +45,7 @@ class BenchmarkTest(unittest.TestCase): wrong_population = [wrong_size_individual] # It is up the user to make sure that bin2float gets an individual with # an adequate length; no exceptions are raised. - fitnesses = map(self.toolbox.evaluate, wrong_population) + fitnesses = list(map(self.toolbox.evaluate, wrong_population)) for ind, fit in zip(wrong_population, fitnesses): # In python 2.7 operator.mul works in a different way than in # python3. Thus an error occurs in python2.7 but an assignment is Index: deap-1.3.1/deap/tests/test_init.py =================================================================== --- deap-1.3.1.orig/deap/tests/test_init.py 2020-09-10 16:20:31.420896473 +0200 +++ deap-1.3.1/deap/tests/test_init.py 2020-09-10 16:20:31.432896544 +0200 @@ -8,6 +8,6 @@ from deap import tools class InitTest(unittest.TestCase): def test_statistics_compile(self): l = 10 - gen_idx = partial(random.sample, range(l), l) + gen_idx = partial(random.sample, list(range(l)), l) i = tools.initIterate(list, gen_idx) self.assertSetEqual(set(i), set(range(l))) Index: deap-1.3.1/deap/tests/test_logbook.py =================================================================== --- deap-1.3.1.orig/deap/tests/test_logbook.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/tests/test_logbook.py 2020-09-10 17:06:16.380968200 +0200 @@ -7,7 +7,7 @@ class LogbookTest(unittest.TestCase): def setUp(self): self.logbook = tools.Logbook() - print + print() def test_multi_chapters(self): self.logbook.record(gen=0, evals=100, fitness={'obj 1' : {'avg' : 1.0, 'max' : 10}, Index: deap-1.3.1/deap/tools/_hypervolume/pyhv.py =================================================================== --- deap-1.3.1.orig/deap/tools/_hypervolume/pyhv.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/tools/_hypervolume/pyhv.py 2020-09-10 17:06:16.380968200 +0200 @@ -64,7 +64,7 @@ class _HyperVolume: """ def weaklyDominates(point, other): - for i in xrange(len(point)): + for i in range(len(point)): if point[i] > other[i]: return False return True @@ -152,7 +152,7 @@ class _HyperVolume: hvol = qPrevDimIndex.volume[dimIndex] + qPrevDimIndex.area[dimIndex] * (qCargo[dimIndex] - qPrevDimIndex.cargo[dimIndex]) else: qArea[0] = 1 - qArea[1:dimIndex+1] = [qArea[i] * -qCargo[i] for i in xrange(dimIndex)] + qArea[1:dimIndex+1] = [qArea[i] * -qCargo[i] for i in range(dimIndex)] q.volume[dimIndex] = hvol if q.ignore >= dimIndex: qArea[dimIndex] = qPrevDimIndex.area[dimIndex] @@ -184,7 +184,7 @@ class _HyperVolume: dimensions = len(self.referencePoint) nodeList = _MultiList(dimensions) nodes = [_MultiList.Node(dimensions, point) for point in front] - for i in xrange(dimensions): + for i in range(dimensions): self.sortByDimension(nodes, i) nodeList.extend(nodes, i) self.list = nodeList @@ -239,7 +239,7 @@ class _MultiList: def __str__(self): strings = [] - for i in xrange(self.numberLists): + for i in range(self.numberLists): currentList = [] node = self.sentinel.next[i] while node != self.sentinel: @@ -292,7 +292,7 @@ class _MultiList: def remove(self, node, index, bounds): """Removes and returns 'node' from all lists in [0, 'index'[.""" - for i in xrange(index): + for i in range(index): predecessor = node.prev[i] successor = node.next[i] predecessor.next[i] = successor @@ -309,7 +309,7 @@ class _MultiList: nodes of the node that is reinserted are in the list. """ - for i in xrange(index): + for i in range(index): node.prev[i].next[i] = node node.next[i].prev[i] = node if bounds[i] > node.cargo[i]: Index: deap-1.3.1/deap/tools/crossover.py =================================================================== --- deap-1.3.1.orig/deap/tools/crossover.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/tools/crossover.py 2020-09-10 16:20:31.432896544 +0200 @@ -1,4 +1,4 @@ -from __future__ import division + import random import warnings @@ -84,7 +84,7 @@ def cxUniform(ind1, ind2, indpb): :mod:`random` module. """ size = min(len(ind1), len(ind2)) - for i in xrange(size): + for i in range(size): if random.random() < indpb: ind1[i], ind2[i] = ind2[i], ind1[i] @@ -115,7 +115,7 @@ def cxPartialyMatched(ind1, ind2): p1, p2 = [0] * size, [0] * size # Initialize the position of each indices in the individuals - for i in xrange(size): + for i in range(size): p1[ind1[i]] = i p2[ind2[i]] = i # Choose crossover points @@ -127,7 +127,7 @@ def cxPartialyMatched(ind1, ind2): cxpoint1, cxpoint2 = cxpoint2, cxpoint1 # Apply crossover between cx points - for i in xrange(cxpoint1, cxpoint2): + for i in range(cxpoint1, cxpoint2): # Keep track of the selected values temp1 = ind1[i] temp2 = ind2[i] @@ -166,11 +166,11 @@ def cxUniformPartialyMatched(ind1, ind2, p1, p2 = [0] * size, [0] * size # Initialize the position of each indices in the individuals - for i in xrange(size): + for i in range(size): p1[ind1[i]] = i p2[ind2[i]] = i - for i in xrange(size): + for i in range(size): if random.random() < indpb: # Keep track of the selected values temp1 = ind1[i] @@ -209,7 +209,7 @@ def cxOrdered(ind1, ind2): optimization and machine learning. Addison Wesley, 1989 """ size = min(len(ind1), len(ind2)) - a, b = random.sample(xrange(size), 2) + a, b = random.sample(range(size), 2) if a > b: a, b = b, a @@ -321,7 +321,7 @@ def cxSimulatedBinaryBounded(ind1, ind2, elif len(up) < size: raise IndexError("up must be at least the size of the shorter individual: %d < %d" % (len(up), size)) - for i, xl, xu in zip(xrange(size), low, up): + for i, xl, xu in zip(range(size), low, up): if random.random() <= 0.5: # This epsilon should probably be changed for 0 since # floating point arithmetic in Python is safer Index: deap-1.3.1/deap/tools/emo.py =================================================================== --- deap-1.3.1.orig/deap/tools/emo.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/tools/emo.py 2020-09-10 16:20:31.432896544 +0200 @@ -1,4 +1,4 @@ -from __future__ import division + import bisect from collections import defaultdict, namedtuple from itertools import chain @@ -74,7 +74,7 @@ def sortNondominated(individuals, k, fir map_fit_ind = defaultdict(list) for ind in individuals: map_fit_ind[ind.fitness].append(ind) - fits = map_fit_ind.keys() + fits = list(map_fit_ind.keys()) current_front = [] next_front = [] @@ -129,7 +129,7 @@ def assignCrowdingDist(individuals): nobj = len(individuals[0].fitness.values) - for i in xrange(nobj): + for i in range(nobj): crowd.sort(key=lambda element: element[0][i]) distances[crowd[0][1]] = float("inf") distances[crowd[-1][1]] = float("inf") @@ -184,7 +184,7 @@ def selTournamentDCD(individuals, k): individuals_2 = random.sample(individuals, len(individuals)) chosen = [] - for i in xrange(0, k, 4): + for i in range(0, k, 4): chosen.append(tourn(individuals_1[i], individuals_1[i+1])) chosen.append(tourn(individuals_1[i+2], individuals_1[i+3])) chosen.append(tourn(individuals_2[i], individuals_2[i+1])) @@ -248,7 +248,7 @@ def sortLogNondominated(individuals, k, #Launch the sorting algorithm obj = len(individuals[0].fitness.wvalues)-1 - fitnesses = unique_fits.keys() + fitnesses = list(unique_fits.keys()) front = dict.fromkeys(fitnesses, 0) # Sort the fitnesses lexicographically. @@ -284,7 +284,7 @@ def sortNDHelperA(fitnesses, obj, front) front[s2] = max(front[s2], front[s1] + 1) elif obj == 1: sweepA(fitnesses, front) - elif len(frozenset(map(itemgetter(obj), fitnesses))) == 1: + elif len(frozenset(list(map(itemgetter(obj), fitnesses)))) == 1: #All individuals for objective M are equal: go to objective M-1 sortNDHelperA(fitnesses, obj-1, front) else: @@ -615,7 +615,7 @@ def associate_to_niche(fitnesses, refere # Retrieve min distance niche index niches = numpy.argmin(distances, axis=1) - distances = distances[range(niches.shape[0]), niches] + distances = distances[list(range(niches.shape[0])), niches] return niches, distances @@ -705,7 +705,7 @@ def selSPEA2(individuals, k): K = math.sqrt(N) strength_fits = [0] * N fits = [0] * N - dominating_inds = [list() for i in xrange(N)] + dominating_inds = [list() for i in range(N)] for i, ind_i in enumerate(individuals): for j, ind_j in enumerate(individuals[i+1:], i+1): @@ -716,19 +716,19 @@ def selSPEA2(individuals, k): strength_fits[j] += 1 dominating_inds[i].append(j) - for i in xrange(N): + for i in range(N): for j in dominating_inds[i]: fits[i] += strength_fits[j] # Choose all non-dominated individuals - chosen_indices = [i for i in xrange(N) if fits[i] < 1] + chosen_indices = [i for i in range(N) if fits[i] < 1] if len(chosen_indices) < k: # The archive is too small - for i in xrange(N): + for i in range(N): distances = [0.0] * N - for j in xrange(i + 1, N): + for j in range(i + 1, N): dist = 0.0 - for l in xrange(L): + for l in range(L): val = individuals[i].fitness.values[l] - \ individuals[j].fitness.values[l] dist += val * val @@ -737,7 +737,7 @@ def selSPEA2(individuals, k): density = 1.0 / (kth_dist + 2.0) fits[i] += density - next_indices = [(fits[i], i) for i in xrange(N) + next_indices = [(fits[i], i) for i in range(N) if not i in chosen_indices] next_indices.sort() #print next_indices @@ -745,12 +745,12 @@ def selSPEA2(individuals, k): elif len(chosen_indices) > k: # The archive is too large N = len(chosen_indices) - distances = [[0.0] * N for i in xrange(N)] - sorted_indices = [[0] * N for i in xrange(N)] - for i in xrange(N): - for j in xrange(i + 1, N): + distances = [[0.0] * N for i in range(N)] + sorted_indices = [[0] * N for i in range(N)] + for i in range(N): + for j in range(i + 1, N): dist = 0.0 - for l in xrange(L): + for l in range(L): val = individuals[chosen_indices[i]].fitness.values[l] - \ individuals[chosen_indices[j]].fitness.values[l] dist += val * val @@ -759,8 +759,8 @@ def selSPEA2(individuals, k): distances[i][i] = -1 # Insert sort is faster than quick sort for short arrays - for i in xrange(N): - for j in xrange(1, N): + for i in range(N): + for j in range(1, N): l = j while l > 0 and distances[i][j] < distances[i][sorted_indices[i][l - 1]]: sorted_indices[i][l] = sorted_indices[i][l - 1] @@ -772,8 +772,8 @@ def selSPEA2(individuals, k): while size > k: # Search for minimal distance min_pos = 0 - for i in xrange(1, N): - for j in xrange(1, size): + for i in range(1, N): + for j in range(1, size): dist_i_sorted_j = distances[i][sorted_indices[i][j]] dist_min_sorted_j = distances[min_pos][sorted_indices[min_pos][j]] @@ -784,11 +784,11 @@ def selSPEA2(individuals, k): break # Remove minimal distance from sorted_indices - for i in xrange(N): + for i in range(N): distances[i][min_pos] = float("inf") distances[min_pos][i] = float("inf") - for j in xrange(1, size - 1): + for j in range(1, size - 1): if sorted_indices[i][j] == min_pos: sorted_indices[i][j] = sorted_indices[i][j + 1] sorted_indices[i][j + 1] = min_pos Index: deap-1.3.1/deap/tools/indicator.py =================================================================== --- deap-1.3.1.orig/deap/tools/indicator.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/tools/indicator.py 2020-09-10 16:20:31.432896544 +0200 @@ -41,7 +41,7 @@ def hypervolume(front, **kargs): return hv.hypervolume(numpy.concatenate((wobj[:i], wobj[i+1:])), ref) # Parallelization note: Cannot pickle local function - contrib_values = map(contribution, range(len(front))) + contrib_values = list(map(contribution, list(range(len(front))))) # Select the maximum hypervolume value (correspond to the minimum difference) return numpy.argmax(contrib_values) @@ -62,7 +62,7 @@ def additive_epsilon(front, **kargs): mwobj[i] = numpy.ma.masked return numpy.min(numpy.max(wobj[i] - mwobj, axis=1)) - contrib_values = map(contribution, range(len(front))) + contrib_values = list(map(contribution, list(range(len(front))))) # Select the minimum contribution value return numpy.argmin(contrib_values) @@ -84,7 +84,7 @@ def multiplicative_epsilon(front, **karg mwobj[i] = numpy.ma.masked return numpy.min(numpy.max(wobj[i] / mwobj, axis=1)) - contrib_values = map(contribution, range(len(front))) + contrib_values = list(map(contribution, list(range(len(front))))) # Select the minimum contribution value return numpy.argmin(contrib_values) Index: deap-1.3.1/deap/tools/init.py =================================================================== --- deap-1.3.1.orig/deap/tools/init.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/tools/init.py 2020-09-10 16:20:31.432896544 +0200 @@ -1,4 +1,4 @@ -from __future__ import division + def initRepeat(container, func, n): """Call the function *container* with a generator function corresponding @@ -22,7 +22,7 @@ def initRepeat(container, func, n): See the :ref:`list-of-floats` and :ref:`population` tutorials for more examples. """ - return container(func() for _ in xrange(n)) + return container(func() for _ in range(n)) def initIterate(container, generator): """Call the function *container* with an iterable as @@ -42,7 +42,7 @@ def initIterate(container, generator): >>> import random >>> from functools import partial >>> random.seed(42) - >>> gen_idx = partial(random.sample, range(10), 10) + >>> gen_idx = partial(random.sample, list(range(10)), 10) >>> initIterate(list, gen_idx) # doctest: +SKIP [1, 0, 4, 9, 6, 5, 8, 2, 3, 7] @@ -72,7 +72,7 @@ def initCycle(container, seq_func, n=1): See the :ref:`funky` tutorial for an example. """ - return container(func() for _ in xrange(n) for func in seq_func) + return container(func() for _ in range(n) for func in seq_func) __all__ = ['initRepeat', 'initIterate', 'initCycle'] @@ -87,3 +87,4 @@ if __name__ == "__main__": doctest.run_docstring_examples(initIterate, globals()) doctest.run_docstring_examples(initCycle, globals()) + Index: deap-1.3.1/deap/tools/migration.py =================================================================== --- deap-1.3.1.orig/deap/tools/migration.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/tools/migration.py 2020-09-10 16:20:31.432896544 +0200 @@ -1,4 +1,4 @@ -from __future__ import division + def migRing(populations, k, selection, replacement=None, migarray=None): @@ -31,12 +31,12 @@ def migRing(populations, k, selection, r """ nbr_demes = len(populations) if migarray is None: - migarray = range(1, nbr_demes) + [0] + migarray = list(range(1, nbr_demes)) + [0] - immigrants = [[] for i in xrange(nbr_demes)] - emigrants = [[] for i in xrange(nbr_demes)] + immigrants = [[] for i in range(nbr_demes)] + emigrants = [[] for i in range(nbr_demes)] - for from_deme in xrange(nbr_demes): + for from_deme in range(nbr_demes): emigrants[from_deme].extend(selection(populations[from_deme], k)) if replacement is None: # If no replacement strategy is selected, replace those who migrate Index: deap-1.3.1/deap/tools/mutation.py =================================================================== --- deap-1.3.1.orig/deap/tools/mutation.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/tools/mutation.py 2020-09-10 16:20:31.432896544 +0200 @@ -1,4 +1,4 @@ -from __future__ import division + import math import random @@ -41,7 +41,7 @@ def mutGaussian(individual, mu, sigma, i elif len(sigma) < size: raise IndexError("sigma must be at least the size of individual: %d < %d" % (len(sigma), size)) - for i, m, s in zip(xrange(size), mu, sigma): + for i, m, s in zip(range(size), mu, sigma): if random.random() < indpb: individual[i] += random.gauss(m, s) @@ -72,7 +72,7 @@ def mutPolynomialBounded(individual, eta elif len(up) < size: raise IndexError("up must be at least the size of individual: %d < %d" % (len(up), size)) - for i, xl, xu in zip(xrange(size), low, up): + for i, xl, xu in zip(range(size), low, up): if random.random() <= indpb: x = individual[i] delta_1 = (x - xl) / (xu - xl) @@ -110,7 +110,7 @@ def mutShuffleIndexes(individual, indpb) functions from the python base :mod:`random` module. """ size = len(individual) - for i in xrange(size): + for i in range(size): if random.random() < indpb: swap_indx = random.randint(0, size - 2) if swap_indx >= i: @@ -135,7 +135,7 @@ def mutFlipBit(individual, indpb): This function uses the :func:`~random.random` function from the python base :mod:`random` module. """ - for i in xrange(len(individual)): + for i in range(len(individual)): if random.random() < indpb: individual[i] = type(individual[i])(not individual[i]) @@ -166,7 +166,7 @@ def mutUniformInt(individual, low, up, i elif len(up) < size: raise IndexError("up must be at least the size of individual: %d < %d" % (len(up), size)) - for i, xl, xu in zip(xrange(size), low, up): + for i, xl, xu in zip(range(size), low, up): if random.random() < indpb: individual[i] = random.randint(xl, xu) @@ -207,7 +207,7 @@ def mutESLogNormal(individual, c, indpb) n = random.gauss(0, 1) t0_n = t0 * n - for indx in xrange(size): + for indx in range(size): if random.random() < indpb: individual.strategy[indx] *= math.exp(t0_n + t * random.gauss(0, 1)) individual[indx] += individual.strategy[indx] * random.gauss(0, 1) Index: deap-1.3.1/deap/tools/selection.py =================================================================== --- deap-1.3.1.orig/deap/tools/selection.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/deap/tools/selection.py 2020-09-10 16:20:31.432896544 +0200 @@ -1,4 +1,4 @@ -from __future__ import division + import random import numpy as np @@ -21,7 +21,7 @@ def selRandom(individuals, k): This function uses the :func:`~random.choice` function from the python base :mod:`random` module. """ - return [random.choice(individuals) for i in xrange(k)] + return [random.choice(individuals) for i in range(k)] def selBest(individuals, k, fit_attr="fitness"): @@ -63,7 +63,7 @@ def selTournament(individuals, k, tourns :mod:`random` module. """ chosen = [] - for i in xrange(k): + for i in range(k): aspirants = selRandom(individuals, tournsize) chosen.append(max(aspirants, key=attrgetter(fit_attr))) return chosen @@ -90,7 +90,7 @@ def selRoulette(individuals, k, fit_attr s_inds = sorted(individuals, key=attrgetter(fit_attr), reverse=True) sum_fits = sum(getattr(ind, fit_attr).values[0] for ind in individuals) chosen = [] - for i in xrange(k): + for i in range(k): u = random.random() * sum_fits sum_ = 0 for ind in s_inds: @@ -147,7 +147,7 @@ def selDoubleTournament(individuals, k, def _sizeTournament(individuals, k, select): chosen = [] - for i in xrange(k): + for i in range(k): # Select two individuals from the population # The first individual has to be the shortest prob = parsimony_size / 2. @@ -167,7 +167,7 @@ def selDoubleTournament(individuals, k, def _fitTournament(individuals, k, select): chosen = [] - for i in xrange(k): + for i in range(k): aspirants = select(individuals, k=fitness_size) chosen.append(max(aspirants, key=attrgetter(fit_attr))) return chosen @@ -198,7 +198,7 @@ def selStochasticUniversalSampling(indiv distance = sum_fits / float(k) start = random.uniform(0, distance) - points = [start + i*distance for i in xrange(k)] + points = [start + i*distance for i in range(k)] chosen = [] for p in points: @@ -234,9 +234,9 @@ def selLexicase(individuals, k): if fit_weights[cases[0]] > 0: f = max - best_val_for_case = f(map(lambda x: x.fitness.values[cases[0]], candidates)) + best_val_for_case = f([x.fitness.values[cases[0]] for x in candidates]) - candidates = list(filter(lambda x: x.fitness.values[cases[0]] == best_val_for_case, candidates)) + candidates = list([x for x in candidates if x.fitness.values[cases[0]] == best_val_for_case]) cases.pop(0) selected_individuals.append(random.choice(candidates)) @@ -266,13 +266,13 @@ def selEpsilonLexicase(individuals, k, e while len(cases) > 0 and len(candidates) > 1: if fit_weights[cases[0]] > 0: - best_val_for_case = max(map(lambda x: x.fitness.values[cases[0]], candidates)) + best_val_for_case = max([x.fitness.values[cases[0]] for x in candidates]) min_val_to_survive_case = best_val_for_case - epsilon - candidates = list(filter(lambda x: x.fitness.values[cases[0]] >= min_val_to_survive_case, candidates)) + candidates = list([x for x in candidates if x.fitness.values[cases[0]] >= min_val_to_survive_case]) else : - best_val_for_case = min(map(lambda x: x.fitness.values[cases[0]], candidates)) + best_val_for_case = min([x.fitness.values[cases[0]] for x in candidates]) max_val_to_survive_case = best_val_for_case + epsilon - candidates = list(filter(lambda x: x.fitness.values[cases[0]] <= max_val_to_survive_case, candidates)) + candidates = list([x for x in candidates if x.fitness.values[cases[0]] <= max_val_to_survive_case]) cases.pop(0) @@ -307,11 +307,11 @@ def selAutomaticEpsilonLexicase(individu if fit_weights[cases[0]] > 0: best_val_for_case = max(errors_for_this_case) min_val_to_survive = best_val_for_case - median_absolute_deviation - candidates = list(filter(lambda x: x.fitness.values[cases[0]] >= min_val_to_survive, candidates)) + candidates = list([x for x in candidates if x.fitness.values[cases[0]] >= min_val_to_survive]) else : best_val_for_case = min(errors_for_this_case) max_val_to_survive = best_val_for_case + median_absolute_deviation - candidates = list(filter(lambda x: x.fitness.values[cases[0]] <= max_val_to_survive, candidates)) + candidates = list([x for x in candidates if x.fitness.values[cases[0]] <= max_val_to_survive]) cases.pop(0) Index: deap-1.3.1/doc/code/benchmarks/ackley.py =================================================================== --- deap-1.3.1.orig/doc/code/benchmarks/ackley.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/benchmarks/ackley.py 2020-09-10 16:20:31.432896544 +0200 @@ -19,7 +19,7 @@ ax = Axes3D(fig) X = np.arange(-30, 30, 0.5) Y = np.arange(-30, 30, 0.5) X, Y = np.meshgrid(X, Y) -Z = np.fromiter(map(ackley_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) +Z = np.fromiter(list(map(ackley_arg0, list(zip(X.flat,Y.flat)))), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, norm=LogNorm(), cmap=cm.jet, linewidth=0.2) Index: deap-1.3.1/doc/code/benchmarks/bohachevsky.py =================================================================== --- deap-1.3.1.orig/doc/code/benchmarks/bohachevsky.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/benchmarks/bohachevsky.py 2020-09-10 16:20:31.432896544 +0200 @@ -19,7 +19,7 @@ ax = Axes3D(fig, azim = -29, elev = 50) X = np.arange(-15, 15, 0.5) Y = np.arange(-15, 15, 0.5) X, Y = np.meshgrid(X, Y) -Z = np.fromiter(map(bohachevsky_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) +Z = np.fromiter(list(map(bohachevsky_arg0, list(zip(X.flat,Y.flat)))), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, norm=LogNorm(), cmap=cm.jet, linewidth=0.2) Index: deap-1.3.1/doc/code/benchmarks/griewank.py =================================================================== --- deap-1.3.1.orig/doc/code/benchmarks/griewank.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/benchmarks/griewank.py 2020-09-10 16:20:31.432896544 +0200 @@ -18,7 +18,7 @@ ax = Axes3D(fig, azim = -29, elev = 40) X = np.arange(-50, 50, 0.5) Y = np.arange(-50, 50, 0.5) X, Y = np.meshgrid(X, Y) -Z = np.fromiter(map(griewank_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) +Z = np.fromiter(list(map(griewank_arg0, list(zip(X.flat,Y.flat)))), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2) Index: deap-1.3.1/doc/code/benchmarks/h1.py =================================================================== --- deap-1.3.1.orig/doc/code/benchmarks/h1.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/benchmarks/h1.py 2020-09-10 16:20:31.432896544 +0200 @@ -19,7 +19,7 @@ ax = Axes3D(fig) X = np.arange(-25, 25, 0.5) Y = np.arange(-25, 25, 0.5) X, Y = np.meshgrid(X, Y) -Z = np.fromiter(map(h1_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) +Z = np.fromiter(list(map(h1_arg0, list(zip(X.flat,Y.flat)))), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, norm=LogNorm(), cmap=cm.jet, linewidth=0.2) Index: deap-1.3.1/doc/code/benchmarks/himmelblau.py =================================================================== --- deap-1.3.1.orig/doc/code/benchmarks/himmelblau.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/benchmarks/himmelblau.py 2020-09-10 16:20:31.432896544 +0200 @@ -18,7 +18,7 @@ ax = Axes3D(fig, azim = -29, elev = 49) X = np.arange(-6, 6, 0.1) Y = np.arange(-6, 6, 0.1) X, Y = np.meshgrid(X, Y) -Z = np.fromiter(map(himmelblau_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) +Z = np.fromiter(list(map(himmelblau_arg0, list(zip(X.flat,Y.flat)))), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, norm=LogNorm(), cmap=cm.jet, linewidth=0.2) Index: deap-1.3.1/doc/code/benchmarks/movingsc1.py =================================================================== --- deap-1.3.1.orig/doc/code/benchmarks/movingsc1.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/benchmarks/movingsc1.py 2020-09-10 16:20:31.432896544 +0200 @@ -24,7 +24,7 @@ ax = Axes3D(fig) X = np.arange(0, 100, 1.0) Y = np.arange(0, 100, 1.0) X, Y = np.meshgrid(X, Y) -Z = np.fromiter(map(lambda x: mp(x)[0], zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) +Z = np.fromiter([mp(x)[0] for x in zip(X.flat,Y.flat)], dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2) Index: deap-1.3.1/doc/code/benchmarks/rastrigin.py =================================================================== --- deap-1.3.1.orig/doc/code/benchmarks/rastrigin.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/benchmarks/rastrigin.py 2020-09-10 16:20:31.432896544 +0200 @@ -17,7 +17,7 @@ ax = Axes3D(fig, azim = -29, elev = 50) X = np.arange(-5, 5, 0.1) Y = np.arange(-5, 5, 0.1) X, Y = np.meshgrid(X, Y) -Z = np.fromiter(map(rastrigin_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) +Z = np.fromiter(list(map(rastrigin_arg0, list(zip(X.flat,Y.flat)))), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2) Index: deap-1.3.1/doc/code/benchmarks/rosenbrock.py =================================================================== --- deap-1.3.1.orig/doc/code/benchmarks/rosenbrock.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/benchmarks/rosenbrock.py 2020-09-10 16:20:31.432896544 +0200 @@ -19,7 +19,7 @@ ax = Axes3D(fig) X = np.arange(-2, 2, 0.1) Y = np.arange(-1, 3, 0.1) X, Y = np.meshgrid(X, Y) -Z = np.fromiter(map(rosenbrock_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) +Z = np.fromiter(list(map(rosenbrock_arg0, list(zip(X.flat,Y.flat)))), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, norm=LogNorm(), cmap=cm.jet, linewidth=0.2) Index: deap-1.3.1/doc/code/benchmarks/schaffer.py =================================================================== --- deap-1.3.1.orig/doc/code/benchmarks/schaffer.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/benchmarks/schaffer.py 2020-09-10 16:20:31.432896544 +0200 @@ -18,7 +18,7 @@ ax = Axes3D(fig, azim = -29, elev = 60) X = np.arange(-25, 25, 0.25) Y = np.arange(-25, 25, 0.25) X, Y = np.meshgrid(X, Y) -Z = np.fromiter(map(schaffer_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) +Z = np.fromiter(list(map(schaffer_arg0, list(zip(X.flat,Y.flat)))), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2) Index: deap-1.3.1/doc/code/benchmarks/schwefel.py =================================================================== --- deap-1.3.1.orig/doc/code/benchmarks/schwefel.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/benchmarks/schwefel.py 2020-09-10 16:20:31.432896544 +0200 @@ -18,7 +18,7 @@ ax = Axes3D(fig) X = np.arange(-500, 500, 10) Y = np.arange(-500, 500, 10) X, Y = np.meshgrid(X, Y) -Z = np.fromiter(map(schwefel_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) +Z = np.fromiter(list(map(schwefel_arg0, list(zip(X.flat,Y.flat)))), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.2) Index: deap-1.3.1/doc/code/benchmarks/shekel.py =================================================================== --- deap-1.3.1.orig/doc/code/benchmarks/shekel.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/benchmarks/shekel.py 2020-09-10 16:20:31.432896544 +0200 @@ -26,7 +26,7 @@ ax = Axes3D(fig) X = np.arange(0, 1, 0.01) Y = np.arange(0, 1, 0.01) X, Y = np.meshgrid(X, Y) -Z = np.fromiter(map(shekel_arg0, zip(X.flat,Y.flat)), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) +Z = np.fromiter(list(map(shekel_arg0, list(zip(X.flat,Y.flat)))), dtype=np.float, count=X.shape[0]*X.shape[1]).reshape(X.shape) ax.plot_surface(X, Y, Z, rstride=1, cstride=1, norm=LogNorm(), cmap=cm.jet, linewidth=0.2) Index: deap-1.3.1/doc/code/tutorials/part_1/1_where_to_start.py =================================================================== --- deap-1.3.1.orig/doc/code/tutorials/part_1/1_where_to_start.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/tutorials/part_1/1_where_to_start.py 2020-09-10 16:20:31.432896544 +0200 @@ -30,7 +30,7 @@ def main(): CXPB, MUTPB, NGEN = 0.5, 0.2, 40 # Evaluate the entire population - fitnesses = map(toolbox.evaluate, pop) + fitnesses = list(map(toolbox.evaluate, pop)) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit @@ -38,7 +38,7 @@ def main(): # Select the next generation individuals offspring = toolbox.select(pop, len(pop)) # Clone the selected individuals - offspring = map(toolbox.clone, offspring) + offspring = list(map(toolbox.clone, offspring)) # Apply crossover and mutation on the offspring for child1, child2 in zip(offspring[::2], offspring[1::2]): @@ -54,7 +54,7 @@ def main(): # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] - fitnesses = map(toolbox.evaluate, invalid_ind) + fitnesses = list(map(toolbox.evaluate, invalid_ind)) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit Index: deap-1.3.1/doc/code/tutorials/part_2/2_2_2_permutation.py =================================================================== --- deap-1.3.1.orig/doc/code/tutorials/part_2/2_2_2_permutation.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/tutorials/part_2/2_2_2_permutation.py 2020-09-10 16:20:31.436896568 +0200 @@ -11,6 +11,6 @@ creator.create("Individual", list, fitne IND_SIZE=10 toolbox = base.Toolbox() -toolbox.register("indices", random.sample, range(IND_SIZE), IND_SIZE) +toolbox.register("indices", random.sample, list(range(IND_SIZE)), IND_SIZE) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.indices) Index: deap-1.3.1/doc/code/tutorials/part_2/2_2_5_particle.py =================================================================== --- deap-1.3.1.orig/doc/code/tutorials/part_2/2_2_5_particle.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/tutorials/part_2/2_2_5_particle.py 2020-09-10 16:20:31.436896568 +0200 @@ -10,8 +10,8 @@ creator.create("Particle", list, fitness smin=None, smax=None, best=None) def initParticle(pcls, size, pmin, pmax, smin, smax): - part = pcls(random.uniform(pmin, pmax) for _ in xrange(size)) - part.speed = [random.uniform(smin, smax) for _ in xrange(size)] + part = pcls(random.uniform(pmin, pmax) for _ in range(size)) + part.speed = [random.uniform(smin, smax) for _ in range(size)] part.smin = smin part.smax = smax return part Index: deap-1.3.1/doc/code/tutorials/part_2/2_3_3_swarm.py =================================================================== --- deap-1.3.1.orig/doc/code/tutorials/part_2/2_3_3_swarm.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/tutorials/part_2/2_3_3_swarm.py 2020-09-10 16:20:31.436896568 +0200 @@ -11,8 +11,8 @@ creator.create("Particle", list, fitness creator.create("Swarm", list, gbest=None, gbestfit=creator.FitnessMax) def initParticle(pcls, size, pmin, pmax, smin, smax): - part = pcls(random.uniform(pmin, pmax) for _ in xrange(size)) - part.speed = [random.uniform(smin, smax) for _ in xrange(size)] + part = pcls(random.uniform(pmin, pmax) for _ in range(size)) + part.speed = [random.uniform(smin, smax) for _ in range(size)] part.smin = smin part.smax = smax return part Index: deap-1.3.1/doc/code/tutorials/part_2/2_3_4_demes.py =================================================================== --- deap-1.3.1.orig/doc/code/tutorials/part_2/2_3_4_demes.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/tutorials/part_2/2_3_4_demes.py 2020-09-10 16:20:31.436896568 +0200 @@ -11,7 +11,7 @@ creator.create("Individual", list, fitne IND_SIZE=10 toolbox = base.Toolbox() -toolbox.register("indices", random.sample, range(IND_SIZE), IND_SIZE) +toolbox.register("indices", random.sample, list(range(IND_SIZE)), IND_SIZE) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.indices) toolbox.register("deme", tools.initRepeat, list, toolbox.individual) Index: deap-1.3.1/doc/code/tutorials/part_3/3_6_2_tool_decoration.py =================================================================== --- deap-1.3.1.orig/doc/code/tutorials/part_3/3_6_2_tool_decoration.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/tutorials/part_3/3_6_2_tool_decoration.py 2020-09-10 16:20:31.436896568 +0200 @@ -10,7 +10,7 @@ def checkBounds(min, max): def wrapper(*args, **kargs): offspring = func(*args, **kargs) for child in offspring: - for i in xrange(len(child)): + for i in range(len(child)): if child[i] > max: child[i] = max elif child[i] < min: Index: deap-1.3.1/doc/code/tutorials/part_3/3_6_using_the_toolbox.py =================================================================== --- deap-1.3.1.orig/doc/code/tutorials/part_3/3_6_using_the_toolbox.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/tutorials/part_3/3_6_using_the_toolbox.py 2020-09-10 16:20:31.436896568 +0200 @@ -31,7 +31,7 @@ for g in range(NGEN): # Select the next generation individuals offspring = toolbox.select(pop, len(pop)) # Clone the selected individuals - offspring = map(toolbox.clone, offspring) + offspring = list(map(toolbox.clone, offspring)) # Apply crossover on the offspring for child1, child2 in zip(offspring[::2], offspring[1::2]): Index: deap-1.3.1/doc/code/tutorials/part_3/3_7_variations.py =================================================================== --- deap-1.3.1.orig/doc/code/tutorials/part_3/3_7_variations.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/tutorials/part_3/3_7_variations.py 2020-09-10 16:20:31.436896568 +0200 @@ -34,7 +34,7 @@ from deap import algorithms for g in range(NGEN): # Select and clone the next generation individuals - offspring = map(toolbox.clone, toolbox.select(pop, len(pop))) + offspring = list(map(toolbox.clone, toolbox.select(pop, len(pop)))) # Apply crossover and mutation on the offspring offspring = algorithms.varAnd(offspring, toolbox, CXPB, MUTPB) Index: deap-1.3.1/doc/code/tutorials/part_3/3_next_step.py =================================================================== --- deap-1.3.1.orig/doc/code/tutorials/part_3/3_next_step.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/tutorials/part_3/3_next_step.py 2020-09-10 16:20:31.436896568 +0200 @@ -17,8 +17,8 @@ toolbox.register("individual", tools.ini ind1 = toolbox.individual() -print ind1 # [0.86..., 0.27..., 0.70..., 0.03..., 0.87...] -print ind1.fitness.valid # False +print(ind1) # [0.86..., 0.27..., 0.70..., 0.03..., 0.87...] +print(ind1.fitness.valid) # False ## 3.2 Evaluation def evaluate(individual): @@ -28,16 +28,16 @@ def evaluate(individual): return a, 1. / b ind1.fitness.values = evaluate(ind1) -print ind1.fitness.valid # True -print ind1.fitness # (2.73, 0.2) +print(ind1.fitness.valid) # True +print(ind1.fitness) # (2.73, 0.2) ## 3.3 Mutation mutant = toolbox.clone(ind1) ind2, = tools.mutGaussian(mutant, mu=0.0, sigma=0.2, indpb=0.2) del mutant.fitness.values -print ind2 is mutant # True -print mutant is ind1 # False +print(ind2 is mutant) # True +print(mutant is ind1) # False ## 3.4 Crossover child1, child2 = [toolbox.clone(ind) for ind in (ind1, ind2)] @@ -47,7 +47,7 @@ del child2.fitness.values ## 3.5 Selection selected = tools.selBest([child1, child2], 2) -print child1 in selected # True +print(child1 in selected) # True ## 3.5 Note LAMBDA = 10 Index: deap-1.3.1/doc/code/tutorials/part_3/multistats.py =================================================================== --- deap-1.3.1.orig/doc/code/tutorials/part_3/multistats.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/tutorials/part_3/multistats.py 2020-09-10 16:20:31.436896568 +0200 @@ -47,7 +47,7 @@ toolbox.register("compile", gp.compile, pop = toolbox.population(n=100) # Evaluate the individuals -fitnesses = map(toolbox.evaluate, pop) +fitnesses = list(map(toolbox.evaluate, pop)) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit Index: deap-1.3.1/doc/code/tutorials/part_3/stats.py =================================================================== --- deap-1.3.1.orig/doc/code/tutorials/part_3/stats.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/tutorials/part_3/stats.py 2020-09-10 16:20:31.436896568 +0200 @@ -31,7 +31,7 @@ toolbox.register("evaluate", evalOneMax) pop = toolbox.population(n=100) # Evaluate the individuals -fitnesses = map(toolbox.evaluate, pop) +fitnesses = list(map(toolbox.evaluate, pop)) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit Index: deap-1.3.1/doc/code/tutorials/part_4/4_4_Using_Cpp_NSGA.py =================================================================== --- deap-1.3.1.orig/doc/code/tutorials/part_4/4_4_Using_Cpp_NSGA.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/tutorials/part_4/4_4_Using_Cpp_NSGA.py 2020-09-10 16:20:31.436896568 +0200 @@ -34,7 +34,7 @@ def genWire(dimension): def genNetwork(dimension, min_size, max_size): size = random.randint(min_size, max_size) - return [genWire(dimension) for i in xrange(size)] + return [genWire(dimension) for i in range(size)] def mutWire(individual, dimension, indpb): for index, elem in enumerate(individual): @@ -91,8 +91,8 @@ def main(): stats.update(population) # Begin the evolution - for g in xrange(NGEN): - print "-- Generation %i --" % g + for g in range(NGEN): + print("-- Generation %i --" % g) offspring = [toolbox.clone(ind) for ind in population] # Apply crossover and mutation @@ -121,21 +121,21 @@ def main(): for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit - print " Evaluated %i individuals" % len(invalid_ind) + print(" Evaluated %i individuals" % len(invalid_ind)) population = toolbox.select(population+offspring, len(offspring)) hof.update(population) stats.update(population) - print " Min %s" % stats.Min[0][-1][0] - print " Max %s" % stats.Max[0][-1][0] - print " Avg %s" % stats.Avg[0][-1][0] - print " Std %s" % stats.Std[0][-1][0] + print(" Min %s" % stats.Min[0][-1][0]) + print(" Max %s" % stats.Max[0][-1][0]) + print(" Avg %s" % stats.Avg[0][-1][0]) + print(" Std %s" % stats.Std[0][-1][0]) best_network = sn.SortingNetwork(INPUTS, hof[0]) - print best_network - print best_network.draw() - print "%i errors, length %i, depth %i" % hof[0].fitness.values + print(best_network) + print(best_network.draw()) + print("%i errors, length %i, depth %i" % hof[0].fitness.values) return population, stats, hof Index: deap-1.3.1/doc/code/tutorials/part_4/4_5_home_made_eval_func.py =================================================================== --- deap-1.3.1.orig/doc/code/tutorials/part_4/4_5_home_made_eval_func.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/tutorials/part_4/4_5_home_made_eval_func.py 2020-09-10 17:06:16.384968222 +0200 @@ -40,7 +40,7 @@ def genWire(dimension): def genNetwork(dimension, min_size, max_size): size = random.randint(min_size, max_size) - return [genWire(dimension) for i in xrange(size)] + return [genWire(dimension) for i in range(size)] def mutWire(individual, dimension, indpb): for index, elem in enumerate(individual): @@ -97,9 +97,9 @@ def main(): stats.update(population) # Begin the evolution - for g in xrange(NGEN): + for g in range(NGEN): t1 = time.time() - print "-- Generation %i --" % g + print("-- Generation %i --" % g) offspring = [toolbox.clone(ind) for ind in population] t2 = time.time() # Apply crossover and mutation @@ -128,14 +128,14 @@ def main(): for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit - print " Evaluated %i individuals" % len(invalid_ind) + print(" Evaluated %i individuals" % len(invalid_ind)) t5 = time.time() population = toolbox.select(population+offspring, len(offspring)) t6 = time.time() #hof.update(population) stats.update(population) t7 = time.time() - print stats + print(stats) print("Times :") print("Clone : " + str(t2-t1) + " (" + str((t2-t1)/(t7-t1)) +"%)") @@ -148,9 +148,9 @@ def main(): best_network = sn.SortingNetwork(INPUTS, hof[0]) - print best_network - print best_network.draw() - print "%i errors, length %i, depth %i" % hof[0].fitness.values + print(best_network) + print(best_network.draw()) + print("%i errors, length %i, depth %i" % hof[0].fitness.values) return population, stats, hof Index: deap-1.3.1/doc/code/tutorials/part_4/sortingnetwork.py =================================================================== --- deap-1.3.1.orig/doc/code/tutorials/part_4/sortingnetwork.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/code/tutorials/part_4/sortingnetwork.py 2020-09-10 16:20:31.436896568 +0200 @@ -19,7 +19,7 @@ except ImportError: def product(*args, **kwds): # product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy # product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111 - pools = map(tuple, args) * kwds.get('repeat', 1) + pools = list(map(tuple, args)) * kwds.get('repeat', 1) result = [[]] for pool in pools: result = [x+[y] for x in result for y in pool] @@ -55,7 +55,7 @@ class SortingNetwork(list): self.append({wire1: wire2}) return - for wires in last_level.iteritems(): + for wires in last_level.items(): if wires[1] >= wire1 and wires[0] <= wire2: self.append({wire1: wire2}) return @@ -65,7 +65,7 @@ class SortingNetwork(list): def sort(self, values): """Sort the values in-place based on the connectors in the network.""" for level in self: - for wire1, wire2 in level.iteritems(): + for wire1, wire2 in level.items(): if values[wire1] > values[wire2]: values[wire1], values[wire2] = values[wire2], values[wire1] @@ -75,7 +75,7 @@ class SortingNetwork(list): the network dimensionality. """ if cases is None: - cases = product(range(2), repeat=self.dimension) + cases = product(list(range(2)), repeat=self.dimension) misses = 0 ordered = [[0]*(self.dimension-i) + [1]*i for i in range(self.dimension+1)] @@ -92,19 +92,19 @@ class SortingNetwork(list): str_wires[0][1] = " o" str_spaces = [] - for i in xrange(1, self.dimension): + for i in range(1, self.dimension): str_wires.append(["-"]*7 * self.depth) str_spaces.append([" "]*7 * self.depth) str_wires[i][0] = str(i) str_wires[i][1] = " o" for index, level in enumerate(self): - for wire1, wire2 in level.iteritems(): + for wire1, wire2 in level.items(): str_wires[wire1][(index+1)*6] = "x" str_wires[wire2][(index+1)*6] = "x" - for i in xrange(wire1, wire2): + for i in range(wire1, wire2): str_spaces[i][(index+1)*6+1] = "|" - for i in xrange(wire1+1, wire2): + for i in range(wire1+1, wire2): str_wires[i][(index+1)*6] = "|" network_draw = "".join(str_wires[0]) Index: deap-1.3.1/doc/conf.py =================================================================== --- deap-1.3.1.orig/doc/conf.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/doc/conf.py 2020-09-10 16:20:31.436896568 +0200 @@ -51,8 +51,8 @@ source_encoding = 'utf-8' master_doc = 'index' # General information about the project. -project = u'DEAP' -copyright = u'2009-%s, DEAP Project' % time.strftime('%Y') +project = 'DEAP' +copyright = '2009-%s, DEAP Project' % time.strftime('%Y') # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the @@ -224,8 +224,8 @@ htmlhelp_basename = 'DEAP-doc' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ - ('contents', 'DEAP.tex', u'DEAP Documentation', - u'DEAP Project', 'manual'), + ('contents', 'DEAP.tex', 'DEAP Documentation', + 'DEAP Project', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of @@ -237,7 +237,7 @@ latex_documents = [ #latex_use_parts = False # Additional stuff for the LaTeX preamble. -latex_preamble = '\usepackage{amsmath,amssymb}' +latex_preamble = '\\usepackage{amsmath,amssymb}' # Documents to append as an appendix to all manuals. #latex_appendices = [] Index: deap-1.3.1/examples/bbob.py =================================================================== --- deap-1.3.1.orig/examples/bbob.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/examples/bbob.py 2020-09-10 17:06:46.709139025 +0200 @@ -108,7 +108,7 @@ if __name__ == "__main__": # Iterate over all the instance of a single problem # Rotation, translation, etc. - for instance in chain(range(1, 6), range(21, 31)): + for instance in chain(list(range(1, 6)), list(range(21, 31))): # Set the function to be used (problem) in the logger e.setfun(*bn.instantiate(f_name, iinstance=instance)) Index: deap-1.3.1/examples/coev/coop_evol.py =================================================================== --- deap-1.3.1.orig/examples/coev/coop_evol.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/examples/coev/coop_evol.py 2020-09-10 17:06:16.384968222 +0200 @@ -133,7 +133,7 @@ def main(extended=True, verbose=True): for i in range(len(species)): contributions.append(toolbox.evaluateContribution(representatives, target_set, i)[0]) - for i in reversed(range(len(species))): + for i in reversed(list(range(len(species)))): if contributions[i] < EXTINCTION_TRESHOLD: species.pop(i) species_index.pop(i) @@ -158,7 +158,7 @@ def main(extended=True, verbose=True): for con in contribs: try: - con, g = zip(*con) + con, g = list(zip(*con)) line2, = plt.plot(g, con, "-", color="k") except ValueError: pass Index: deap-1.3.1/examples/de/dynamic.py =================================================================== --- deap-1.3.1.orig/examples/de/dynamic.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/examples/de/dynamic.py 2020-09-10 17:06:16.384968222 +0200 @@ -93,7 +93,7 @@ def main(verbose=True): # Apply exclusion rexcl = (BOUNDS[1] - BOUNDS[0]) / (2 * NPOP**(1.0/NDIM)) - for i, j in itertools.combinations(range(NPOP), 2): + for i, j in itertools.combinations(list(range(NPOP)), 2): if bests[i].fitness.valid and bests[j].fitness.valid: d = sum((bests[i][k] - bests[j][k])**2 for k in range(NDIM)) d = math.sqrt(d) Index: deap-1.3.1/examples/de/sphere.py =================================================================== --- deap-1.3.1.orig/examples/de/sphere.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/examples/de/sphere.py 2020-09-10 17:06:16.384968222 +0200 @@ -50,7 +50,7 @@ def cxExponential(x, y, cr): size = len(x) index = random.randrange(size) # Loop on the indices index -> end, then on 0 -> index - for i in chain(range(index, size), range(0, index)): + for i in chain(list(range(index, size)), list(range(0, index))): x[i] = y[i] if random.random() < cr: break Index: deap-1.3.1/examples/es/cma_bipop.py =================================================================== --- deap-1.3.1.orig/examples/es/cma_bipop.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/examples/es/cma_bipop.py 2020-09-10 17:06:16.384968222 +0200 @@ -189,7 +189,7 @@ def main(verbose=True): # The main axis std has no effect conditions["NoEffectCoor"] = True - stop_causes = [k for k, v in conditions.items() if v] + stop_causes = [k for k, v in list(conditions.items()) if v] print("Stopped because of condition%s %s" % ((":" if len(stop_causes) == 1 else "s:"), ",".join(stop_causes))) i += 1 Index: deap-1.3.1/examples/ga/knn.py =================================================================== --- deap-1.3.1.orig/examples/ga/knn.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/examples/ga/knn.py 2020-09-10 17:06:16.384968222 +0200 @@ -56,14 +56,14 @@ class KNN(object): classes = dict((cls, 0) for cls in self.classes) for n in nns[:self.k]: classes[self.labels[n]] += 1 - labels = sorted(classes.items(), key=operator.itemgetter(1))[-1][0] + labels = sorted(list(classes.items()), key=operator.itemgetter(1))[-1][0] elif data.ndim == 2: labels = list() for i, d in enumerate(data): classes = dict((cls, 0) for cls in self.classes) for n in nns[i, :self.k]: classes[self.labels[n]] += 1 - labels.append(sorted(classes.items(), key=operator.itemgetter(1))[-1][0]) + labels.append(sorted(list(classes.items()), key=operator.itemgetter(1))[-1][0]) return labels Index: deap-1.3.1/examples/ga/mo_rhv.py =================================================================== --- deap-1.3.1.orig/examples/ga/mo_rhv.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/examples/ga/mo_rhv.py 2020-09-10 17:06:16.384968222 +0200 @@ -77,7 +77,7 @@ def hypervolume_contrib(front, **kargs): return total_hv - hv.hypervolume(numpy.concatenate((wobj[:i], wobj[i+1:])), ref) # Parallelization note: Cannot pickle local function - return map(contribution, range(len(front))) + return list(map(contribution, list(range(len(front))))) toolbox.register("attr_float", uniform, BOUND_LOW, BOUND_UP, NDIM) Index: deap-1.3.1/examples/ga/nqueens.py =================================================================== --- deap-1.3.1.orig/examples/ga/nqueens.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/examples/ga/nqueens.py 2020-09-10 16:20:31.468896761 +0200 @@ -62,7 +62,7 @@ creator.create("Individual", list, fitne #Since there is only one queen per line, #individual are represented by a permutation toolbox = base.Toolbox() -toolbox.register("permutation", random.sample, range(NB_QUEENS), NB_QUEENS) +toolbox.register("permutation", random.sample, list(range(NB_QUEENS)), NB_QUEENS) #Structure initializers #An individual is a list that represents the position of each queen. Index: deap-1.3.1/examples/ga/onemax.py =================================================================== --- deap-1.3.1.orig/examples/ga/onemax.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/examples/ga/onemax.py 2020-09-10 17:06:16.384968222 +0200 @@ -129,7 +129,7 @@ def main(): # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] - fitnesses = map(toolbox.evaluate, invalid_ind) + fitnesses = list(map(toolbox.evaluate, invalid_ind)) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit Index: deap-1.3.1/examples/ga/tsp.py =================================================================== --- deap-1.3.1.orig/examples/ga/tsp.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/examples/ga/tsp.py 2020-09-10 16:20:31.472896784 +0200 @@ -38,7 +38,7 @@ creator.create("Individual", array.array toolbox = base.Toolbox() # Attribute generator -toolbox.register("indices", random.sample, range(IND_SIZE), IND_SIZE) +toolbox.register("indices", random.sample, list(range(IND_SIZE)), IND_SIZE) # Structure initializers toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.indices) Index: deap-1.3.1/examples/ga/xkcd.py =================================================================== --- deap-1.3.1.orig/examples/ga/xkcd.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/examples/ga/xkcd.py 2020-09-10 16:20:31.472896784 +0200 @@ -53,14 +53,14 @@ def evalXKCD(individual, target_price): taken by the order if the chef can cook everything in parallel.""" price = 0.0 times = list() - for item, number in individual.items(): + for item, number in list(individual.items()): price += ITEMS[item][0] * number times.append(ITEMS[item][1]) return abs(price - target_price), max(times) def cxCounter(ind1, ind2, indpb): """Swaps the number of perticular items between two individuals""" - for key in ITEMS.keys(): + for key in list(ITEMS.keys()): if random.random() < indpb: ind1[key], ind2[key] = ind2[key], ind1[key] return ind1, ind2 Index: deap-1.3.1/examples/pso/basic.py =================================================================== --- deap-1.3.1.orig/examples/pso/basic.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/examples/pso/basic.py 2020-09-10 17:06:16.384968222 +0200 @@ -38,9 +38,9 @@ def generate(size, pmin, pmax, smin, sma def updateParticle(part, best, phi1, phi2): u1 = (random.uniform(0, phi1) for _ in range(len(part))) u2 = (random.uniform(0, phi2) for _ in range(len(part))) - v_u1 = map(operator.mul, u1, map(operator.sub, part.best, part)) - v_u2 = map(operator.mul, u2, map(operator.sub, best, part)) - part.speed = list(map(operator.add, part.speed, map(operator.add, v_u1, v_u2))) + v_u1 = list(map(operator.mul, u1, list(map(operator.sub, part.best, part)))) + v_u2 = list(map(operator.mul, u2, list(map(operator.sub, best, part)))) + part.speed = list(map(operator.add, part.speed, list(map(operator.add, v_u1, v_u2)))) for i, speed in enumerate(part.speed): if abs(speed) < part.smin: part.speed[i] = math.copysign(part.smin, speed) Index: deap-1.3.1/examples/pso/multiswarm.py =================================================================== --- deap-1.3.1.orig/examples/pso/multiswarm.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/examples/pso/multiswarm.py 2020-09-10 17:06:16.384968222 +0200 @@ -26,7 +26,7 @@ import random import numpy try: - from itertools import imap + except: # Python 3 nothing to do pass @@ -82,15 +82,15 @@ def convertQuantum(swarm, rcloud, centre def updateParticle(part, best, chi, c): ce1 = (c * random.uniform(0, 1) for _ in range(len(part))) ce2 = (c * random.uniform(0, 1) for _ in range(len(part))) - ce1_p = map(operator.mul, ce1, map(operator.sub, best, part)) - ce2_g = map(operator.mul, ce2, map(operator.sub, part.best, part)) - a = map(operator.sub, - map(operator.mul, + ce1_p = list(map(operator.mul, ce1, list(map(operator.sub, best, part)))) + ce2_g = list(map(operator.mul, ce2, list(map(operator.sub, part.best, part)))) + a = list(map(operator.sub, + list(map(operator.mul, itertools.repeat(chi), - map(operator.add, ce1_p, ce2_g)), - map(operator.mul, + list(map(operator.add, ce1_p, ce2_g)))), + list(map(operator.mul, itertools.repeat(1 - chi), - part.speed)) + part.speed)))) part.speed = list(map(operator.add, part.speed, a)) part[:] = list(map(operator.add, part, part.speed)) @@ -201,7 +201,7 @@ def main(verbose=True): # Apply exclusion reinit_swarms = set() - for s1, s2 in itertools.combinations(range(len(population)), 2): + for s1, s2 in itertools.combinations(list(range(len(population))), 2): # Swarms must have a best and not already be set to reinitialize if population[s1].best and population[s2].best and not (s1 in reinit_swarms or s2 in reinit_swarms): dist = 0 Index: deap-1.3.1/examples/pso/speciation.py =================================================================== --- deap-1.3.1.orig/examples/pso/speciation.py 2020-01-21 02:17:45.000000000 +0100 +++ deap-1.3.1/examples/pso/speciation.py 2020-09-10 17:06:16.384968222 +0200 @@ -26,7 +26,7 @@ import random import numpy try: - from itertools import imap + except: # Python 3 nothing to do pass @@ -81,15 +81,15 @@ def convert_quantum(swarm, rcloud, centr def updateParticle(part, best, chi, c): ce1 = (c*random.uniform(0, 1) for _ in range(len(part))) ce2 = (c*random.uniform(0, 1) for _ in range(len(part))) - ce1_p = map(operator.mul, ce1, map(operator.sub, best, part)) - ce2_g = map(operator.mul, ce2, map(operator.sub, part.best, part)) - a = map(operator.sub, - map(operator.mul, + ce1_p = list(map(operator.mul, ce1, list(map(operator.sub, best, part)))) + ce2_g = list(map(operator.mul, ce2, list(map(operator.sub, part.best, part)))) + a = list(map(operator.sub, + list(map(operator.mul, itertools.repeat(chi), - map(operator.add, ce1_p, ce2_g)), - map(operator.mul, + list(map(operator.add, ce1_p, ce2_g)))), + list(map(operator.mul, itertools.repeat(1-chi), - part.speed)) + part.speed)))) part.speed = list(map(operator.add, part.speed, a)) part[:] = list(map(operator.add, part, part.speed))
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