def call_main(author_name):
bf = Bloom()
count = bf.search("/Users/vivekris/GS/code/gsbloomboost/input/filters/",author_name)
lookup = get_id_lookup()
return_id = get_id(count, lookup)
print return_id
def getMeme(cid, sub):
path = str(os.getcwd() + "/stats/" + str(cid) + "/")
os.makedirs(path, exist_ok=True)
f = Path(path + "bloom.txt")
print(f)
filter = Bloom()
if f.is_file():
file = open(f, "r")
filter.getTable(file)
file.close()
else:
file = open(f, "w+")
global reddit
for s in reddit.subreddit(sub).hot():
if not filter.find(str(s)) and s.is_reddit_media_domain:
filter.insert(str(s))
with open(f, "w") as file:
filter.writeTable(file)
break
request.urlretrieve(s.url, "temp.jpg")
return open("temp.jpg", "rb")
#getMeme(123,"dankmemes")
def create_collection(self, genome):
section_size = int(len(genome) / (self.k))
for i in range(self.k):
section = genome[i * (section_size):(i + 1) * (section_size)]
bloom = Bloom(1000000000, 0.3)
for k in range(0, len(section) - segment_size + 1):
s = section[k:k + segment_size]
bloom.bloom_add(s)
self.collection.append(bloom)
print("Collection created successfully")
self.collection = tuple(self.collection)
def post(self):
bloom = memcache.get("filter")
if bloom is None:
bloom = Bloom(2**21, 5)
value = self.request.POST["value"]
value_already_included = False
if value in bloom:
value_already_included = True
bloom.add(value)
memcache.set("filter", bloom)
self.redirect("/?success=" + str(not value_already_included)
+ "&value=" + value)
def call_main(lists_folder, bloom_folder):
list_files = get_files(lists_folder)
#bloom_files = get_files(bloom_folder)
for filee in list_files:
# Create a new bloom filter file if it does not exist..
#Get abc in /Users/xya/asdf/abc.2bloom
file_name = filee.rsplit('/',1)[1].split('.')[0]
bf = Bloom(bloom_folder+"/"+file_name+".bloom")
# Get the list of elements from the file.
list_of_authors = [ element.strip() for element in file(filee) ]
print list_of_authors
# Now start adding elements
bf.add_elements(list_of_authors)
def train(self, xs, ys, epochs):
"""
Train the model and setup the two amqs.
"""
# Filter pos/neg examples
# TODO: make more efficient (don't necessarily need to compute pos/negs here)
positives = [x for x, y in zip(xs, ys) if y]
negatives = [x for x, y in zip(xs, ys) if not y]
# Setup first filter
self.amq1.add_set(positives)
# Train the neural net on reported positives of first filter
amq1_pos_indices = [
i for i, x in enumerate(xs) if self.amq1.contains(x)
]
amq1_pos_xs = [xs[i] for i in amq1_pos_indices]
amq1_pos_ys = [ys[i] for i in amq1_pos_indices]
self.model.train(amq1_pos_xs, amq1_pos_ys, epochs)
# Tune tau
self.tau, fpr, fnr = self._choose_tau(amq1_pos_xs, amq1_pos_ys)
# Get false negatives from model
model_false_negs = [
x for x in amq1_pos_xs if not (self.model(x) > self.tau)
]
num_model_false_negs = len(model_false_negs)
# Setup second filter if we have false negs
if num_model_false_negs > 0 and fnr > 0:
# Compute optimal bitarray size ratio for second filter
inside = fpr / ((1 - fpr) * (1 / fnr - 1))
m2 = int(0 if inside == 0 else -log2(inside) / log(2))
if m2 == 0:
self.amq2 = WordBloom(
Bloom.init_ne(num_model_false_negs, self.err))
else:
self.amq2 = WordBloom(Bloom.init_nm(num_model_false_negs, m2))
self.amq2.add_set(model_false_negs)
def simulation():
input = np.random.randint(1, 1000000, size=10000)
test = np.random.randint(1, 1000000, size=1000)
b = Bloom(input, 0.01)
b.train_bloom()
b.test_bloom(test)
def __init__(self, n, c, err, set_size, err1k):
"""
n: number of letters in string
c: size of alphabet
err: total error rate of sandwich
"""
self.n = n
self.c = c
self.model = WordNet(n, c)
self.tau = 0.5 # default value, adjust by tuning later
self.alpha = 0.618503137801576 # 2 ** -log(2)
# AMQs can only be set up after training model
self.err = err
self.err1 = self.err * err1k
self.amq1 = WordBloom(Bloom.init_ne(set_size, self.err1))
self.amq2 = None # Determine size after training
def train(self, xs, ys, epochs):
"""
Train on examples for a certain number of epochs
"""
# Train neural net
# Note: torch dataloader takes care of shuffling
self.model.train(xs, ys, epochs)
# Tune tau
self.tau = self._choose_tau(xs, ys)
# Get false negatives
positives = [x for x, y in zip(xs, ys) if y]
false_negs = [x for x in positives if not (self.model(x) > self.tau)]
# Build filter for negatives
if len(false_negs) > 0:
self.amq = WordBloom(Bloom.init_ne(len(false_negs), self.err / 2))
self.amq.add_set(false_negs)
def bloom_test(xs, ys, num_pos, num_neg, n, c, e):
"""
Perform a test on the Bloom filter
"""
bloom = WordBloom(Bloom.init_ne(num_pos, e))
positives = [x for x, y in zip(xs, ys) if y]
bloom.add_set(positives)
false_pos = false_neg = 0
for x, y in zip(xs, ys):
filter_contains = bloom.contains(x)
false_pos += not y and filter_contains
false_neg += y and not filter_contains
print(bloom)
print("fpr: {}, fnr: {}, correct%: {}".format(
false_pos / num_neg, false_neg / num_pos,
1 - (false_pos + false_neg) / (num_pos + num_neg)))
def main():
"""Main function."""
bloom = Bloom(12)
# Add some values to the set
bloom.add("Curie")
bloom.add("Laplace")
# Now test some values
person = "Pasteur"
if (bloom.check(person)):
print("%s is probably in the set." % person)
else:
print("%s is definitely not in the set." % person)
# Test some more values
person = "Curie"
if (bloom.check(person)):
print("%s is probably in the set." % person)
else:
print("%s is definitely not in the set." % person)
class TestBloomMethods(unittest.TestCase):
"""Test the Bloom Filter."""
def setUp(self):
"""Set up the Bloom Filter table."""
self.size = 10
self.b = Bloom(self.size)
def test_add(self):
"""Make sure that add properly activates bits in the table."""
self.b.add('Archimedes')
self.assertEqual(self.b.table, [1, 0, 0, 1, 0, 0, 0, 0, 0, 0])
def test_check_element_in_table(self):
"""Ensure check method returns true if element was added."""
self.b.add('Copernicus')
self.assertEqual(self.b.check('Copernicus'), True)
def test_check_element_not_in_table(self):
"""Ensure check method returns false if elmt definitely not in set."""
self.assertEqual(self.b.check('Galileo'), False)
# rings, steps = gen_circle(rings=None, pixels_per=pixels_per, offset=0, invert=False)
# layout = layout_from_rings(rings, origin=(0, 0, 0), z_diff=8)
# driver = DriverSimPixel(sum(pixels_per), layout=layout)
# led = LEDCircle(driver, rings=rings, maxAngleDiff=0)
def shutdown():
log.debug('Force close server')
driver.server.close()
time.sleep(2)
sys.exit()
try:
if isinstance(led, LEDMatrix):
anim = Bloom(led, dir=True)
anim.run(amt=2, fps=60)
elif isinstance(led, LEDCube):
# anim = cuby(led, color_list=c_list)
# anim.run(amt=1, fps=8)
anim = Simplex(led, freq=16, octaves=1)
anim.run(amt=1, fps=30)
# anim = Spectrum(led, vis_list=['Spread'], steps_per_vis=None,
# bins=12, max_freq=4000, log_scale=True, auto_gain=False, gain=3)
anim.run(amt=1, fps=8)
elif isinstance(led, LEDCircle):
anim = Diag(led, turns=1, angle=6, direction=False)
anim.run(amt=6, fps=20)
except Exception as e:
shutdown()
raise
def setUp(self):
"""Set up the Bloom Filter table."""
self.size = 10
self.b = Bloom(self.size)
return [word]
letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm',
'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
words = []
for char in word:
if char == '*':
for letter in letters:
words.append(word.replace('*', letter, 1))
for item in list(words):
for char in list(item):
if char == '*':
for letter in letters:
words.append(item.replace('*', letter, 1))
return words
bloom = Bloom(15000000, 8)
parser = argparse.ArgumentParser(description="Bloom filter. Builds a\
bloom filter. Searches for a word.\
Wildcards(*) allowed.")
parser.add_argument('file', type=str, help="File location")
parser.add_argument('word', type=str, help="Word to search")
args = parser.parse_args()
with open(args.file, 'r') as reader:
for line in reader:
bloom.insert(line.rstrip())
print("Bloom filter built.")
print("Searching for \"{}\"...".format(args.word))
if '*' in args.word:
print("The more wildcards, the longer this takes...")
matches = []
for word in replace(args.word):
import datetime
from bloom import Bloom
f = open("data/emerson_essays.txt", "r")
sample = f.read()
lines = sample.splitlines()
mb = Bloom()
false_positive = 0
new_key = 0
start_time = datetime.datetime.now()
for line in lines:
if not mb.Add(line):
false_positive += 1
else:
new_key += 1
end_time = datetime.datetime.now()
print(" false_positive : %s - new_key: %s on %s " % (false_positive,new_key, len(lines)))
diff = (start_time-end_time).microseconds
ratio = diff / len(lines)
print("{} ms for ratio: {}".format(diff,ratio))
print(" {} LEN ".format(mb.Len()))
