def _create_filter(self):
"Creates a new filter"
# Get the initial parameters
capacity, prob = self.init_capacity, self.prob
# Bound the ultimate probability by adjusting for the initial
# From "Scalable Bloom Filters", Almeida 2007
# We use : P <= P0 * (1 / (1 - r))
prob = (1 - self.prob_reduction) * prob
# Update our parameters if we have filters
if len(self.filters) > 0:
# Grow by the scale size
capacity = self.filters[-1].info["capacity"] * self.scale_size
# Reduce the probability by the probability reduction
prob = self.filters[-1].info["prob"] * self.prob_reduction
# Compute the new size and K
length, k = BloomFilter.params_for_capacity(capacity, prob)
# Invoke our callback to get a bitmap
bitmap = self.callback(length)
# Create a new bloom filter
filter = BloomFilter(bitmap, k)
# Add the new properties
filter.info["prob"] = prob
filter.info["capacity"] = capacity
return filter
def _initialize(self):
"Initializes the probability and capacity of existing filters"
# Bound the ultimate probability by adjusting for the initial
# From "Scalable Bloom Filters", Almeida 2007
# We use : P <= P0 * (1 / (1 - r))
prob = (1 - self.prob_reduction) * self.prob
for filt in self.filters:
size = len(filt.bitmap) - 8 * filt.extra_buffer()
filt.info["prob"] = prob
filt.info["capacity"] = int(BloomFilter.expected_capacity(size, prob))
prob *= self.prob_reduction
