class TestRandomBinaryProjections(unittest.TestCase):
def setUp(self):
self.rbp = RandomBinaryProjections('testHash', 10)
self.rbp.reset(100)
def test_hash_format(self):
h = self.rbp.hash_vector(numpy.random.randn(100))
self.assertEqual(len(h), 1)
self.assertEqual(type(h[0]), type(''))
self.assertEqual(len(h[0]), 10)
for c in h[0]:
self.assertTrue(c == '1' or c == '0')
def test_hash_deterministic(self):
x = numpy.random.randn(100)
first_hash = self.rbp.hash_vector(x)[0]
for k in range(100):
self.assertEqual(first_hash, self.rbp.hash_vector(x)[0])
class RBP_hasher(object):
def __init__(self, dimension, n_bit, alpha):
self.n_bit = n_bit
self.dim = dimension
self.alpha = alpha
self.sample_space = 2**n_bit
self.rbp = RandomBinaryProjections('rbp', self.n_bit)
self.engine = Engine(dimension, lshashes=[self.rbp])
@property
def params(self):
return self.rbp.get_config()
def load(self, config):
self.rbp.apply_config(config)
def _string2int(self, s):
return int(s, 2)
def __call__(self, v):
'''
Convert the returned string into a integer.
Return a dict based off the weights.
'''
s = self.rbp.hash_vector(v)[0]
weights = {
self._string2int(s): 1.0,
}
if not self.alpha:
return weights
# If alpha is non-zero, deposit weight into nearby bins
slist = map(bool, map(int, list(s)))
for n in range(len(s)):
s2list = slist[:]
s2list[n] = not slist[n]
s2list = map(str, map(int, s2list))
s2 = ''.join(s2list)
idx = self._string2int(s2)
weights[idx] = self.alpha
return weights
# Dimension of our vector space
dimension = 288
# Create a random binary hash with 10 bits
rbp = RandomBinaryProjections('rbp', 12)
# Create engine with pipeline configuration
engine = Engine(dimension, lshashes=[rbp])
for each_image in open("C:/Users/Administrator/Desktop/pk.txt").readlines():
each_image = each_image.strip('\n')
each_vector = []
each_vector = select_by_pk(each_image)
engine.store_vector(each_vector, '%s' % each_image)
each_bucket = ''.join(rbp.hash_vector(each_vector))
update_bucket(each_image, each_bucket)
for each_image in open("C:/Users/Administrator/Desktop/pk.txt").readlines():
each_image = each_image.strip('\n')
each_vector = []
each_vector = select_by_pk(each_image)
# Get nearest neighbours
N = engine.neighbours(
each_vector
) # print(cosine_similarity(query, p_matrix[p_index.index(int(ele[1].strip('data_')))]))
similarlist = []
for ele in N:
class DoubleEngine:
def _build_rdp_engine(self,matrix,rdp,normals):
# Dimension of our vector space
dimension = np.shape(matrix)[1]
n = np.shape(matrix)[0]
# Create a random binary hash with 10 bits
# Create engine with pipeline configuration
engine = Engine(dimension, lshashes=[rdp],storage = MemoryStorage())
rdp.vectors = normals
for index in range(n):
v = matrix[index]
engine.store_vector(v, '%d' % index)
return engine
def process2(self,vectors1,vectors2,num_bit,bin_width):
# build engine
self.dimension = np.shape(vectors1)[1]
self.rdp = RandomDiscretizedProjections('rdp',num_bit,bin_width)
self.rbp = RandomBinaryProjections('rbp',num_bit)
self.rdp.reset(self.dimension)
self.rbp.reset(self.dimension)
self.normals = self.rdp.vectors
self.rbp.normals = self.normals
self.engine1 = self._build_rdp_engine(vectors1,self.rdp,self.normals)
self.engine2 = self._build_rdp_engine(vectors2,self.rdp,self.normals)
# create new key
buckets1 = self.engine1.storage.buckets['rdp']
buckets2 = self.engine2.storage.buckets['rdp']
self.rbdp = {}
print 'len of buckets1', len(buckets1)
print 'len of buckets2', len(buckets2)
keys_int1 = []
keys_int2 = []
for key in buckets1:
ks = [int(x) for x in key.split('_')]
keys_int1.append(ks)
for key in buckets2:
ks = [int(x) for x in key.split('_')]
keys_int2.append(ks)
for idx1,key1 in enumerate(buckets1):
if idx1 % 100 == 0:
logging.info('{} {}/{}'.format(key1,idx1,len(buckets1)))
for idx2,key2 in enumerate(buckets2):
ks1 = keys_int1[idx1]
ks2 = keys_int2[idx2]
new_key = [ks1[i] + ks2[i] for i in xrange(len(ks1))]
new_key = ''.join(['1' if x>=0 else '0' for x in new_key])
if not new_key in self.rbdp:
self.rbdp[new_key] = []
self.rbdp[new_key].append((key1,key2))
def build_permute_index(self,num_permutation,beam_size,hamming_beam_size):
self.num_permutation = num_permutation
self.hamming_beam_size = hamming_beam_size
self.beam_size = beam_size
self.projection_count = self.rbp.projection_count
# add permutations
self.permutations = []
for i in xrange(self.num_permutation):
p = Permutation(self.projection_count)
self.permutations.append(p)
# convert current buckets to an array of bitarray
buckets = self.rbdp
original_keys = []
for key in buckets:
ba = bitarray(key)
original_keys.append(ba)
# build permutation lists
self.permuted_lists = []
i = 0
for p in self.permutations:
logging.info('Creating Permutation Index: #{}/{}'.format(i,len(self.permutations)))
i+=1
permuted_list = []
for ba in original_keys:
c = ba.copy()
p.permute(c)
permuted_list.append((c,ba))
# sort the list
permuted_list = sorted(permuted_list)
self.permuted_lists.append(permuted_list)
def get_neighbour_keys(self,bucket_key,k):
# O( np*beam*log(np*beam) )
# np = number of permutations
# beam = self.beam_size
# np * beam == 200 * 100 Still really fast
query_key = bitarray(bucket_key)
topk = set()
for i in xrange(len(self.permutations)):
p = self.permutations[i]
plist = self.permuted_lists[i]
candidates = p.search_revert(plist,query_key,self.beam_size)
topk = topk.union(set(candidates))
topk = list(topk)
topk = sorted(topk, key = lambda x : hamming_distance(x,query_key))
topk_bin = [x.to01() for x in topk[:k]]
return topk_bin
def n2(self,key1,key2,v):
#return [(cos_dist,(idx1,idx2))]
def matrix_list(engine,key):
# return a matrix and a list of keys
items = engine.storage.buckets['rdp'][key]
m = []
l = []
for v,key in items:
m.append(v)
l.append(int(key))
m = np.array(m)
return m,l
m1,l1 = matrix_list(self.engine1,key1)
m2,l2 = matrix_list(self.engine2,key2)
len1 = len(l1)
len2 = len(l2)
# a . v
av = np.dot(m1,v)
av = np.repeat(av,len2).reshape(len1,len2)
# b . v
bv = np.dot(m2,v)
bv = np.repeat(bv,len1).reshape(len2,len1).T
# nominator = a.v + b.v
nomi = av + bv
# |v|
nv = np.linalg.norm(v,2)
# a.a
aa = np.sum(m1*m1,axis = 1)
aa = np.repeat(aa,len2).reshape(len1,len2)
# b.b
bb = np.sum(m2*m2,axis = 1)
bb = np.repeat(bb,len1).reshape(len2,len1).T
# a.b
ab = np.dot(m1,m2.T)
# denominator
deno = np.sqrt(aa + bb + 2 * ab) * nv
# distance matrix
dism = nomi / deno
dist = []
for i in xrange(len1):
for j in xrange(len2):
dis = dism[i,j]
dist.append((dis,(l1[i],l2[j])))
return dist
def neighbours2(self,v,n):
# one important assumption: just have one hash method
# Collect candidates from all buckets from all hashes
candidates = []
direct_bucket_keys = self.rbp.hash_vector(v)
# Get the neighbours of candidate_bucket_keys
candidate_bucket_keys = []
for bucket_key in direct_bucket_keys:
neighbour_keys = self.get_neighbour_keys(bucket_key,self.hamming_beam_size)
candidate_bucket_keys.extend(neighbour_keys)
dists = []
for bucket_key in candidate_bucket_keys:
comb = self.rbdp[bucket_key]
print bucket_key, len(comb)
for key1,key2 in comb:
dist = self.n2(key1,key2,v)
dists.extend(dist)
dists = sorted(dists,key = lambda x: -x[0])
return dists[:n]
# If there is no vector filter, just return list of candidates
return dists
rbp = RandomBinaryProjections('rbp', 5)
rbp.reset(1)
#engine = Engine(10, lshashes=[rbp])
file = open(sys.argv[1]).readlines()
df = pd.DataFrame()
bigdict = dict()
i = 0
for line in file:
data = dict()
triples = line.split(" ")[1:]
for t in triples:
update(data, rbp.hash_vector([float(t.split(",")[1])])[0])
# for p in range(0, 200-len(data.values())):
# data[p]=0
# for bucket_key in rbp.hash_vector([float(x) for x in data.values()]):
# print(bucket_key)
# bigdict[i] = {'key' : bucket_key}
#df=df.append(data, ignore_index=True)
bigdict[i] = data
i = i + 1
df = pd.DataFrame(bigdict)
print(len(bigdict.keys()))
df = df.transpose().fillna(0)
df.to_csv('test.csv')
print(df.head(2))
