def _extrapolate_xs(self, current):
'''generate a new individual based on individuals in the frontier'''
sample = self._sample_frontier_exclude(current, n=3)
rv_list = [x for x in current.xs]
# randomly pick at least one x-position to change
change_indices = [i for i in xrange(len(rv_list))
if random.random() < self.spec.p_crossover]
if not change_indices:
change_indices = [base.random_index(rv_list)]
# extrapolate a new value for each of the chosen indices
for i in change_indices:
rv_list[i] = self._extrapolate_x(x, (s.xs[i] for s in sample), self.model.xs[i])
return tuple(rv_list)
def _extrapolate_xs(self, current):
'''generate a new individual based on individuals in the frontier'''
a, b, c = self._sample_frontier_exclude(current, n=3)
rv_list = [x for x in current.xs]
# randomly pick at least one x-position to change
change_indices = [
i for i in xrange(len(rv_list))
if random.random() < self.spec.p_crossover
]
if not change_indices:
change_indices = [base.random_index(rv_list)]
# extrapolate a new value for each of the chosen indices
for i in change_indices:
extrapolated = a.xs[i] + self.spec.f * (b.xs[i] - c.xs[i])
rv_list[i] = self.model.xs[i].clip(extrapolated)
return tuple(rv_list)
def run(self, text_report=True):
'''run MaxWalkSat on self.model'''
# current ModelIO to evaluate and mutate
self._current = self.model.random_model_io()
self._best = self._current
# initialize and update log variables to track values by era
self._current_era = NumberLog()
self._current_era += self._current.energy
self._best_era = None
# bookkeeping variables
self._evals = 0
self._lives = 4
self._report = StringBuilder() if text_report else NullObject()
self._terminate = False
while self._evals < self.spec.iterations and not self._terminate:
# get the generator for a random independent variable
if self.spec.p_mutation > random.random():
# if not searching a dimension, mutate randomly
self._update('+')
else:
# if doing a local search, choose a dimension
dimension = base.random_index(self._current.xs)
search_iv = self.model.xs[dimension]
# then try points all along the dimension
lo, hi = search_iv.lo, search_iv.hi
for j in self._local_search_xs(lo, hi, 10):
self._update('|', dimension=dimension, value=j)
return SearchReport(best=self._best.energy,
best_era=self._best_era,
evaluations=self._evals,
searcher=self.__class__,
spec=self.spec,
report=self._report)
def _update(self, improvement_char, dimension=None, value=None):
'''calculate the next value from the model and update state as
necessary'''
# check for invalid input
if value is not None and dimension is None:
err = 'cannot call _update with specified value but no dimension'
raise ValueError(err)
if dimension is None:
dimension = base.random_index(self._current.xs)
if value is None:
# get random value if no value input
value = self.model.xs[dimension]()
updated = False
while not updated:
new_xs = tuple_replace(self._current.xs, dimension, value)
try:
self._current = self.model(new_xs, io=True)
updated = True
except ModelInputException:
value = self.model.xs[dimension]()
self._evals += 1
self._current_era += self._current.energy
# compare to previous best and update as necessary
if self._current.energy < self._best.energy:
self._best = self._current
self._report += improvement_char
else:
self._report += '.'
# end-of-era bookkeeping
if self._evals % self.spec.era_length == 0:
self._end_era()
def run(self, text_report=True):
'''run MaxWalkSat on self.model'''
# current ModelIO to evaluate and mutate
self._current = self.model.random_model_io()
self._best = self._current
# initialize and update log variables to track values by era
self._current_era = NumberLog()
self._current_era += self._current.energy
self._best_era = None
# bookkeeping variables
self._evals = 0
self._lives = 4
self._report = StringBuilder() if text_report else NullObject()
self._terminate = False
while self._evals < self.spec.iterations and not self._terminate:
# get the generator for a random independent variable
if self.spec.p_mutation > random.random():
# if not searching a dimension, mutate randomly
self._update('+')
else:
# if doing a local search, choose a dimension
dimension = base.random_index(self._current.xs)
search_iv = self.model.xs[dimension]
# then try points all along the dimension
lo, hi = search_iv.lo, search_iv.hi
for j in self._local_search_xs(lo, hi, 10):
self._update('|', dimension=dimension, value=j)
return SearchReport(best=self._best.energy,
best_era=self._best_era,
evaluations=self._evals,
searcher=self.__class__,
spec=self.spec,
report=self._report)
def _update(self, improvement_char, dimension=None, value=None):
'''calculate the next value from the model and update state as
necessary'''
# check for invalid input
if value is not None and dimension is None:
err = 'cannot call _update with specified value but no dimension'
raise ValueError(err)
if dimension is None:
dimension = base.random_index(self._current.xs)
if value is None:
# get random value if no value input
value = self.model.xs[dimension]()
updated = False
while not updated:
new_xs = tuple_replace(self._current.xs, dimension, value)
try:
self._current = self.model(new_xs, io=True)
updated = True
except ModelInputException:
value = self.model.xs[dimension]()
self._evals += 1
self._current_era += self._current.energy
# compare to previous best and update as necessary
if self._current.energy < self._best.energy:
self._best = self._current
self._report += improvement_char
else:
self._report += '.'
# end-of-era bookkeeping
if self._evals % self.spec.era_length == 0:
self._end_era()
def _mutate(self, child):
i = base.random_index(child)
return base.tuple_replace(child, i, self.model.xs[i]())
def random_index(self):
return base.random_index(self._cache)
