class SubmodularityCoverage(object):
def __init__(self, item_pool_dict, initial_covered_set=None, kernel=math.sqrt):
if not initial_covered_set:
initial_covered_set = Counter()
self.covered_elems = initial_covered_set
self.item_pool_dict = item_pool_dict
self.kernel = kernel
self._build_queue()
def _build_queue(self):
from asractivelearning.datastructures.priorityqueue import FastBucketQueue
self.queue = FastBucketQueue()
for (item_key, elem_set) in self.item_pool.iteritems():
gain = self._compute_gain(elem_set, self.covered_elems)
self.queue.push(item_key, gain)
def _compute_gain(self, elem_set, covered_elem):
accum_gain = 0.0
# # Save repeated function-pointer reevaluation in loop
sqrtFct = self.kernel
for key, value in elem_set.viewitems():
accum_gain += sqrtFct(value+covered_elem[key]) - sqrtFct(covered_elem[key])
return accum_gain
def draw_sample(self):
def __iter__(self):
return self
def next(self):
self.draw_sample()
def compute_scores(self, item_pool, scoring_fct, duration_itemgetter, batch_size_seconds):
self._loop(item_pool, scoring_fct, duration_itemgetter, batch_size_seconds)
def _build_queue(self):
from asractivelearning.datastructures.priorityqueue import FastBucketQueue
self.queue = FastBucketQueue()
for (item_key, elem_set) in self.item_pool.iteritems():
gain = self._compute_gain(elem_set, self.covered_elems)
self.queue.push(item_key, gain)
