Skip to content

Commit

Permalink
Whitespace fixes (PEP8 messages E203, E231, W291, W293). Verified with
Browse files Browse the repository at this point in the history 
diff -w (no changes).
  • Loading branch information
@bje-
bje- committed May 4, 2015
1 parent 883c837 commit cd1d1f0
Show file tree
Hide file tree
Showing 5 changed files with 213 additions and 213 deletions.
88 changes: 44 additions & 44 deletions deap/algorithms.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -15,8 +15,8 @@

"""The :mod:`algorithms` module is intended to contain some specific algorithms
in order to execute very common evolutionary algorithms. The method used here
are more for convenience than reference as the implementation of every
evolutionary algorithm may vary infinitely. Most of the algorithms in this
are more for convenience than reference as the implementation of every
evolutionary algorithm may vary infinitely. Most of the algorithms in this
module use operators registered in the toolbox. Generally, the keyword used are
:meth:`mate` for crossover, :meth:`mutate` for mutation, :meth:`~deap.select`
for selection and :meth:`evaluate` for evaluation.
Expand All @@ -34,15 +34,15 @@ def varAnd(population, toolbox, cxpb, mutpb):
(crossover **and** mutation). The modified individuals have their
fitness invalidated. The individuals are cloned so returned population is
independent of the input population.
:param population: A list of individuals to vary.
:param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
operators.
:param cxpb: The probability of mating two individuals.
:param mutpb: The probability of mutating an individual.
:returns: A list of varied individuals that are independent of their
parents.
The variation goes as follow. First, the parental population
:math:`P_\mathrm{p}` is duplicated using the :meth:`toolbox.clone` method
and the result is put into the offspring population :math:`P_\mathrm{o}`.
Expand All @@ -56,7 +56,7 @@ def varAnd(population, toolbox, cxpb, mutpb):
probability *mutpb*. When an individual is mutated it replaces its not
mutated version in :math:`P_\mathrm{o}`. The resulting
:math:`P_\mathrm{o}` is returned.
This variation is named *And* beceause of its propention to apply both
crossover and mutation on the individuals. Note that both operators are
not applied systematicaly, the resulting individuals can be generated from
Expand All @@ -65,25 +65,25 @@ def varAnd(population, toolbox, cxpb, mutpb):
:math:`[0, 1]`.
"""
offspring = [toolbox.clone(ind) for ind in population]

# Apply crossover and mutation on the offspring
for i in range(1, len(offspring), 2):
if random.random() < cxpb:
offspring[i-1], offspring[i] = toolbox.mate(offspring[i-1], offspring[i])
del offspring[i-1].fitness.values, offspring[i].fitness.values
offspring[i - 1], offspring[i] = toolbox.mate(offspring[i - 1], offspring[i])
del offspring[i - 1].fitness.values, offspring[i].fitness.values

for i in range(len(offspring)):
if random.random() < mutpb:
offspring[i], = toolbox.mutate(offspring[i])
del offspring[i].fitness.values

return offspring

def eaSimple(population, toolbox, cxpb, mutpb, ngen, stats=None,
halloffame=None, verbose=__debug__):
"""This algorithm reproduce the simplest evolutionary algorithm as
presented in chapter 7 of [Back2000]_.
:param population: A list of individuals.
:param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
operators.
Expand All @@ -98,7 +98,7 @@ def eaSimple(population, toolbox, cxpb, mutpb, ngen, stats=None,
:returns: The final population
:returns: A class:`~deap.tools.Logbook` with the statistics of the
evolution
The algorithm takes in a population and evolves it in place using the
:meth:`varAnd` method. It returns the optimized population and a
:class:`~deap.tools.Logbook` with the statistics of the evolution. The
Expand Down Expand Up @@ -131,11 +131,11 @@ def eaSimple(population, toolbox, cxpb, mutpb, ngen, stats=None,
Using a non-stochastic selection method will result in no selection as
the operator selects *n* individuals from a pool of *n*.
This function expects the :meth:`toolbox.mate`, :meth:`toolbox.mutate`,
:meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be
registered in the toolbox.
.. [Back2000] Back, Fogel and Michalewicz, "Evolutionary Computation 1 :
Basic Algorithms and Operators", 2000.
"""
Expand All @@ -157,31 +157,31 @@ def eaSimple(population, toolbox, cxpb, mutpb, ngen, stats=None,
print logbook.stream

# Begin the generational process
for gen in range(1, ngen+1):
for gen in range(1, ngen + 1):
# Select the next generation individuals
offspring = toolbox.select(population, len(population))

# Vary the pool of individuals
offspring = varAnd(offspring, toolbox, cxpb, mutpb)

# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit

# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)

# Replace the current population by the offspring
population[:] = offspring

# Append the current generation statistics to the logbook
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

Expand All @@ -190,7 +190,7 @@ def varOr(population, toolbox, lambda_, cxpb, mutpb):
(crossover, mutation **or** reproduction). The modified individuals have
their fitness invalidated. The individuals are cloned so returned
population is independent of the input population.
:param population: A list of individuals to vary.
:param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
operators.
Expand All @@ -200,7 +200,7 @@ def varOr(population, toolbox, lambda_, cxpb, mutpb):
:returns: The final population
:returns: A class:`~deap.tools.Logbook` with the statistics of the
evolution
The variation goes as follow. On each of the *lambda_* iteration, it
selects one of the three operations; crossover, mutation or reproduction.
In the case of a crossover, two individuals are selected at random from
Expand All @@ -214,15 +214,15 @@ def varOr(population, toolbox, lambda_, cxpb, mutpb):
:math:`P_\mathrm{o}`. In the case of a reproduction, one individual is
selected at random from :math:`P_\mathrm{p}`, cloned and appended to
:math:`P_\mathrm{o}`.
This variation is named *Or* beceause an offspring will never result from
both operations crossover and mutation. The sum of both probabilities
shall be in :math:`[0, 1]`, the reproduction probability is
1 - *cxpb* - *mutpb*.
"""
assert (cxpb + mutpb) <= 1.0, ("The sum of the crossover and mutation "
"probabilities must be smaller or equal to 1.0.")

offspring = []
for _ in xrange(lambda_):
op_choice = random.random()
Expand All @@ -238,13 +238,13 @@ def varOr(population, toolbox, lambda_, cxpb, mutpb):
offspring.append(ind)
else: # Apply reproduction
offspring.append(random.choice(population))

return offspring

def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
stats=None, halloffame=None, verbose=__debug__):
"""This is the :math:`(\mu + \lambda)` evolutionary algorithm.
:param population: A list of individuals.
:param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
operators.
Expand All @@ -261,7 +261,7 @@ def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
:returns: The final population
:returns: A class:`~deap.tools.Logbook` with the statistics of the
evolution.
The algorithm takes in a population and evolves it in place using the
:func:`varOr` function. It returns the optimized population and a
:class:`~deap.tools.Logbook` with the statistics of the evolution. The
Expand Down Expand Up @@ -307,16 +307,16 @@ def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
print logbook.stream

# Begin the generational process
for gen in range(1, ngen+1):
for gen in range(1, ngen + 1):
# Vary the population
offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)

# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit

# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
Expand All @@ -331,12 +331,12 @@ def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
print logbook.stream

return population, logbook

def eaMuCommaLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
stats=None, halloffame=None, verbose=__debug__):
"""This is the :math:`(\mu~,~\lambda)` evolutionary algorithm.
:param population: A list of individuals.
:param population: A list of individuals.
:param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
operators.
:param mu: The number of individuals to select for the next generation.
Expand All @@ -352,7 +352,7 @@ def eaMuCommaLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
:returns: The final population
:returns: A class:`~deap.tools.Logbook` with the statistics of the
evolution
The algorithm takes in a population and evolves it in place using the
:func:`varOr` function. It returns the optimized population and a
:class:`~deap.tools.Logbook` with the statistics of the evolution. The
Expand Down Expand Up @@ -381,7 +381,7 @@ def eaMuCommaLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
selection will result in no selection at all as
the operator selects *lambda* individuals from a pool of *mu*.
This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`,
:meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be
registered in the toolbox. This algorithm uses the :func:`varOr`
Expand All @@ -407,7 +407,7 @@ def eaMuCommaLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
print logbook.stream

# Begin the generational process
for gen in range(1, ngen+1):
for gen in range(1, ngen + 1):
# Vary the population
offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)

Expand All @@ -433,11 +433,11 @@ def eaMuCommaLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,

return population, logbook

def eaGenerateUpdate(toolbox, ngen, halloffame=None, stats=None,
def eaGenerateUpdate(toolbox, ngen, halloffame=None, stats=None,
verbose=__debug__):
"""This is algorithm implements the ask-tell model proposed in
"""This is algorithm implements the ask-tell model proposed in
[Colette2010]_, where ask is called `generate` and tell is called `update`.
:param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
operators.
:param ngen: The number of generation.
Expand Down Expand Up @@ -480,16 +480,16 @@ def eaGenerateUpdate(toolbox, ngen, halloffame=None, stats=None,
fitnesses = toolbox.map(toolbox.evaluate, population)
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit

if halloffame is not None:
halloffame.update(population)

# Update the strategy with the evaluated individuals
toolbox.update(population)

record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(population), **record)
if verbose:
print logbook.stream

return population, logbook
return population, logbook
Loading

0 comments on commit cd1d1f0

Please sign in to comment.