def __call__(self, func):
if self.on_individual:
if self.names:
for name in self.names:
GLOBAL_ESDL_FUNCTIONS[name] = OnIndividual(name, func)
else:
GLOBAL_ESDL_FUNCTIONS[func.__name__] = OnIndividual(
func.__name__, func)
else:
if self.names:
for name in self.names:
GLOBAL_ESDL_FUNCTIONS[name] = func
else:
GLOBAL_ESDL_FUNCTIONS[func.__name__] = func
return func
def test_recombiners_Discrete_Selection_All():
population = make_pop()
_gen = OnIndividual('crossover_discrete')(_source=iter(population),
two_children=True)
offspring = list(_gen)
print "len(offspring) = %d, len(population) = %d" % (len(offspring),
len(population))
assert len(offspring) == len(population), "Did not select all individuals"
print "intersection = %s" % set(offspring).intersection(set(population))
assert len(set(offspring).intersection(
set(population))) == 0, "Did not modify some individuals"
def test_recombiners_Segmented_None():
population = make_pop()
_gen = OnIndividual('crossover_segmented')(_source=iter(population),
per_pair_rate=0.0,
two_children=True)
offspring = list(_gen)
print "len(offspring) = %d, len(population) = %d" % (len(offspring),
len(population))
assert len(offspring) == len(population), "Did not select all individuals"
print "difference = %s" % set(offspring).difference(set(population))
assert len(set(offspring).difference(
set(population))) == 0, "Modified some individuals"
def test_recombiners_PerGeneTuple_None():
population = make_pop()
joined = list(joiners.DistinctRandomTuples(_source=[population] * 2))
_gen = OnIndividual('crossover_tuple')(_source=iter(joined),
per_indiv_rate=1.0,
greediness=1.0)
offspring = list(_gen)
print "len(offspring) = %d, len(population) = %d" % (len(offspring),
len(population))
assert len(offspring) == len(population), "Did not select all individuals"
assert all(g1 is g2 for g1, g2 in izip(
population, offspring)), "Did not return first individual"
def test_recombiners_SingleDifferent_Selection_Half():
population = make_pop_variable(shortest=4)
modify_count = 0
for _ in xrange(100):
_gen = OnIndividual('crossover_one_different')(
_source=iter(population), per_pair_rate=0.5, two_children=True)
offspring = list(_gen)
assert len(offspring) == len(
population), "Did not select all individuals"
modify_count += len(set(offspring).difference(set(population)))
modify_rate = float(modify_count) / (100.0 * len(population))
assert 0.4 <= modify_rate <= 0.6, "Did not modify approximately 50% of individuals"
def test_recombiners_SingleDifferent_Selection_All():
population = make_pop_variable(shortest=4)
_gen = OnIndividual('crossover_different')(_source=iter(population),
points=1,
two_children=True)
offspring = list(_gen)
print "len(offspring) = %d, len(population) = %d" % (len(offspring),
len(population))
assert len(offspring) == len(population), "Did not select all individuals"
print "intersection = %s" % set(offspring).intersection(set(population))
assert len(set(offspring).intersection(
set(population))) == 0, "Did not modify some individuals"
_gen = OnIndividual('crossover_different')(_source=iter(population),
points=2,
two_children=True)
offspring = list(_gen)
print "len(offspring) = %d, len(population) = %d" % (len(offspring),
len(population))
assert len(offspring) == len(population), "Did not select all individuals"
print "intersection = %s" % set(offspring).intersection(set(population))
assert len(set(offspring).intersection(
set(population))) == 0, "Did not modify some individuals"
def test_recombiners_Discrete_Values():
population = make_pop()
_gen = OnIndividual('crossover_discrete')(_source=iter(population),
two_children=True)
children = list(_gen)
child1, child2 = children[0:2]
print "len(child1) = %d, len(child2) = %d, len(population[0]) = %d" % (
len(child1), len(child2), len(population[0]))
assert len(child1) == len(child2) == len(
population[0]), "Individual's length was changed"
print '\n'.join(str(set(child)) for child in children)
assert not all(len(set(child)) != 2
for child in children), "Number of distinct values is not 2"
child1_set, child2_set = set(child1.genome), set(child2.genome)
print "set(child1) = %s" % child1_set
print "set(child2) = %s" % child2_set
assert child1_set == child2_set, "Distinct values are not matched"
def test_recombiners_PerGeneTuple_NotFromFirst():
population = make_pop()
joined = list(joiners.DistinctRandomTuples(_source=[population] * 2))
_gen = OnIndividual('crossover_tuple')(_source=iter(joined),
per_indiv_rate=1.0,
greediness=0.0)
offspring = list(_gen)
print "len(offspring) = %d, len(population) = %d" % (len(offspring),
len(population))
assert len(offspring) == len(population), "Did not select all individuals"
assert all(
g1 is not g2
for g1, g2 in izip(population, offspring)), "DistinctRandom failed"
assert all(g not in population
for g in offspring), "Some individuals were not cloned"
assert sum(g.statistic.get('recombined', 0) for g in offspring) == len(
offspring), "Some individuals were not recombined"
def test_recombiners_Segmented_Normal():
population = make_pop()
_gen = OnIndividual('crossover_segmented')(_source=iter(population),
per_pair_rate=1.0,
switch_rate=0.9,
two_children=True)
offspring = list(_gen)
print "len(offspring) = %d, len(population) = %d" % (len(offspring),
len(population))
assert len(offspring) == len(population), "Did not select all individuals"
print "intersection = %s" % set(offspring).intersection(set(population))
assert len(set(offspring).intersection(
set(population))) == 0, "Did not modify some individuals"
for indiv in offspring:
distinct_genes = set(indiv.genome)
if len(distinct_genes) != 2: print indiv.genome
assert len(distinct_genes) == 2, "Did not cross individuals"
def test_recombiners_SingleSame_Values():
population = make_pop()
_gen = OnIndividual('crossover_one')(_source=iter(population),
two_children=True)
child1, child2 = next(_gen), next(_gen)
print "len(child1) = %d, len(child2) = %d, len(population[0]) = %d" % (
len(child1), len(child2), len(population[0]))
assert len(child1) == len(child2) == len(
population[0]), "Individual's length was changed"
child1_set, child2_set = set(child1.genome), set(child2.genome)
print "len(set(child1)) = %d, len(set(child2)) = %d" % (len(child1_set),
len(child2_set))
assert len(child1_set) == len(
child2_set) == 2, "Number of distinct values is not 2"
print "set(child1) = %s" % child1_set
print "set(child2) = %s" % child2_set
assert child1_set == child2_set, "Distinct values are not matched"
def test_recombiners_SingleDifferent_Values():
population = make_pop_variable(shortest=4)
_gen = OnIndividual('crossover_one_different')(_source=iter(population),
two_children=True)
child1, child2 = next(_gen), next(_gen)
print "len(child1) = %d, len(child2) = %d, len(pop[0]) = %d, len(pop[1]) = %d" % (
len(child1), len(child2), len(population[0]), len(population[1]))
assert len(child1) + len(child2) == len(population[0]) + len(
population[1]), "Genes were gained/lost"
child1_set, child2_set = set(child1.genome), set(child2.genome)
source_set = set(population[0].genome).union(set(population[1].genome))
print "len(set(child1)) = %d, len(set(child2)) = %d" % (len(child1_set),
len(child2_set))
assert len(child1_set) <= 2 and len(
child2_set) <= 2, "Number of distinct values is not 2 or less"
print "set(child1) = %s" % child1_set
print "set(child2) = %s" % child2_set
print "set(pop[0] U pop[1]) = %s" % source_set
assert child1_set.issubset(source_set) and child2_set.issubset(
source_set), "Genes were invented"
def __init__(self, cfg, lscape=None, monitor=None):
# Merge syntax and default details
self.syntax = merge_cls_dicts(self, 'syntax')
self.cfg = ConfigDict(merge_cls_dicts(self, 'default'))
# Merge in all globally defined ESDL functions
for key, value in GLOBAL_ESDL_FUNCTIONS.iteritems():
self.cfg.system[key.lower()] = value
# Now apply user cfg details and test against syntax
self.cfg.overlay(cfg)
# If no default evaluator has been provided, use `lscape`
cfg_validate(self.cfg, self.syntax, type(self), warnings=False)
# Initialise empty members
self._code = None
self._code_string = None
self.monitor = None
self.selector = None
self.selector_current = None
self._in_step = False
self._next_block = []
self._block_cache = {}
# Compile code
self.definition = self.cfg.system.definition
self._context = context = {
'config': self.cfg,
'rand': random.Random(cfg.random_seed),
'notify': self._do_notify
}
# Add species settings to context
for cls in SPECIES:
inst = context[cls.name] = cls(self.cfg, lscape)
try:
for key, value in inst.public_context.iteritems():
context[key.lower()] = value
except AttributeError: pass
# Add external values to context
for key, value in self.cfg.system.iteritems():
if isinstance(key, str):
key_lower = key.lower()
if key_lower in context:
warn("Overriding variable/function '%s'" % key_lower)
context[key_lower] = value
else:
warn('System dictionary contains non-string key %r' % key)
model, self.validation_result = compileESDL(self.definition, context)
if not self.validation_result:
raise ESDLCompilerError(self.validation_result, "Errors occurred while compiling system.")
self._code_string, internal_context = emit(model, out=None, optimise_level=0, profile='_profiler' in context)
internal_context['_yield'] = lambda name, group: self.monitor.on_yield(self, name, group)
internal_context['_alias'] = GroupAlias
for key, value in internal_context.iteritems():
if key in context:
warn("Variable/function '%s' is overridden by internal value" % key)
context[key] = value
esec.context._context.context = context
esec.context._context.config = context['config']
esec.context._context.rand = context['rand']
esec.context._context.notify = context['notify']
self.monitor = monitor or MonitorBase()
self.selector = self.cfg['selector'] or [name for name in model.block_names if name != model.INIT_BLOCK_NAME]
self.selector_current = iter(self.selector)
for func in model.externals.iterkeys():
if func not in context:
context[func] = OnIndividual(func)
self._code = compile(self._code_string, 'ESDL Definition', 'exec')