def __init__(self,
dim,
lshashes=None,
distance=None,
fetch_vector_filters=None,
vector_filters=None,
storage=None):
""" Keeps the configuration. """
if lshashes is None:
lshashes = [RandomBinaryProjections('default', 10)]
self.lshashes = lshashes
if distance is None: distance = EuclideanDistance()
self.distance = distance
if vector_filters is None: vector_filters = [NearestFilter(10)]
self.vector_filters = vector_filters
if fetch_vector_filters is None:
fetch_vector_filters = [UniqueFilter()]
self.fetch_vector_filters = fetch_vector_filters
if storage is None: storage = MemoryStorage()
self.storage = storage
# Initialize all hashes for the data space dimension.
for lshash in self.lshashes:
lshash.reset(dim)
print('*** engine init done ***')
def __init__(self,
dim,
lshashes=[RandomBinaryProjections('default', 10)],
distance=EuclideanDistance(),
vector_filters=[NearestFilter(10)],
storage=MemoryStorage()):
""" Keeps the configuration. """
self.lshashes = lshashes
self.distance = distance
self.vector_filters = vector_filters
self.storage = storage
# Initialize all hashes for the data space dimension.
for lshash in self.lshashes:
lshash.reset(dim)
def __init__(self,
measure="EuclideanDistance",
data_path='data/classed_data/'):
self.res = ResnetSimilarity()
self.pbar = ProgressBar()
# Dimension of our vector space
self.dimension = 2048
self.data_path = data_path
# Create a random binary hash with 10 bits
self.rbp = RandomBinaryProjections('rbp', 10)
self.measure = measure
self.msote = MemoryStorage()
if measure == "EuclideanDistance":
self.engine = Engine(self.dimension,
lshashes=[self.rbp],
storage=self.msote,
distance=EuclideanDistance())
else:
self.engine = Engine(self.dimension,
lshashes=[self.rbp],
storage=self.msote,
distance=CosineDistance())
dimension = 1000
# Create permutations meta-hash
permutations2 = HashPermutationMapper('permut2')
# Create binary hash as child hash
rbp_perm2 = RandomBinaryProjections('rbp_perm2', 14)
# Add rbp as child hash of permutations hash
permutations2.add_child_hash(rbp_perm2)
engine = Engine(dimension,
lshashes=[permutations2],
distance=CosineDistance(),
vector_filters=[NearestFilter(5)],
storage=MemoryStorage())
i = 0
query = numpy.zeros(dimension)
f = open('features2.txt', 'r')
# Opening, reading from the file::
for next_read_line in f:
next_read_line = next_read_line.rstrip()
split_arr = next_read_line.split(" ")
split_arr = split_arr[1:]
split_arr = list(map(float, split_arr))
vector = numpy.asarray(split_arr)
def setUp(self):
self.memory = MemoryStorage()
self.redis_object = Redis(host='localhost', port=6379, db=0)
self.redis_storage = RedisStorage(self.redis_object)
def setUp(self):
self.storage = MemoryStorage()
super(MemoryStorageTest, self).setUp()
def setUp(self):
self.memory = MemoryStorage()
self.redis_object = Redis()
self.redis_storage = RedisStorage(self.redis_object)
numpy.random.seed(16)
def setUp(self):
self.memory = MemoryStorage()
self.redis_object = Redis()
self.redis_storage = RedisStorage(self.redis_object)
def memoryStorage():
return MemoryStorage()
