def __init__(self, b, t, **kw):
super(KLSH, self).__init__(b=b, t=t, **kw)
#self._check_parameters()
self.hash = Hashing(b=b, t=t)
self.bucket = KlshBucket()
self.storage = PickleStorage()
class KLSH(KLSHBase):
'''
>>>from klsh import KLSH
>>>klsh = KLSH(
... b=b, #number of hash bits
... t=t, #number of
...)
'''
def __init__(self, b, t, **kw):
super(KLSH, self).__init__(b=b, t=t, **kw)
#self._check_parameters()
self.hash = Hashing(b=b, t=t)
self.bucket = KlshBucket()
self.storage = PickleStorage()
def loadDataSet(self, filename, delim=','):
self.dataMat = np.loadtxt(filename, delimiter=delim)
def preprocessing(self):
dataMat = self.dataMat
numOfData = dataMat[:, 0].size
self.insert_matrix(dataMat, numOfData)
self.store_Wmat()
self.bucket.store_buckets()
def insert_matrix(self, matrix, num):
KerMat = self.hash.kernelMatrix(matrix)
CenterMat = self.hash.center(KerMat)
(HashTable, self.W) = self.hash.creatHashTable(CenterMat, self.b,
self.t)
for i in xrange(num):
self.bucket.insert_buckets(matrix[i, :], HashTable[i, :])
def knn(self, vector, knum, stored=False):
if stored == False:
hashed_array = self.hash.do_hashing(
vector, self.dataMat,
self.W) #vector must be the first argument
knn_vectors = self.bucket.select_knn(knum, hashed_array)
else:
self.W = self.load_Wmat()
self.bucket.load_buckets()
hashed_array = self.hash.do_hashing(vector, self.dataMat, self.W)
#hashed_array = self.hash.do_hashing(self.dataMat, vector, self.W)
knn_vectors = self.bucket.select_knn(knum, hashed_array)
return knn_vectors
def store_Wmat(self):
fw = open('Wmat.data', 'wb')
self.storage.save(self.W, fw)
fw.close()
def load_Wmat(self):
fr = open('Wmat.data')
W = self.storage.load(fr)
fr.close()
return W
def __init__(self, b, t, **kw):
super(KLSH, self).__init__(b=b, t=t, **kw)
# self._check_parameters()
self.hash = Hashing(b=b, t=t)
self.bucket = KlshBucket()
self.storage = PickleStorage()
class KLSH(KLSHBase):
"""
>>>from klsh import KLSH
>>>klsh = KLSH(
... b=b, #number of hash bits
... t=t, #number of
...)
"""
def __init__(self, b, t, **kw):
super(KLSH, self).__init__(b=b, t=t, **kw)
# self._check_parameters()
self.hash = Hashing(b=b, t=t)
self.bucket = KlshBucket()
self.storage = PickleStorage()
def loadDataSet(self, filename, delim=","):
self.dataMat = np.loadtxt(filename, delimiter=delim)
def preprocessing(self):
dataMat = self.dataMat
numOfData = dataMat[:, 0].size
self.insert_matrix(dataMat, numOfData)
self.store_Wmat()
self.bucket.store_buckets()
def insert_matrix(self, matrix, num):
KerMat = self.hash.kernelMatrix(matrix)
CenterMat = self.hash.center(KerMat)
(HashTable, self.W) = self.hash.creatHashTable(CenterMat, self.b, self.t)
for i in xrange(num):
self.bucket.insert_buckets(matrix[i, :], HashTable[i, :])
def knn(self, vector, knum, stored=False):
if stored == False:
hashed_array = self.hash.do_hashing(vector, self.dataMat, self.W) # vector must be the first argument
knn_vectors = self.bucket.select_knn(knum, hashed_array)
else:
self.W = self.load_Wmat()
self.bucket.load_buckets()
hashed_array = self.hash.do_hashing(vector, self.dataMat, self.W)
# hashed_array = self.hash.do_hashing(self.dataMat, vector, self.W)
knn_vectors = self.bucket.select_knn(knum, hashed_array)
return knn_vectors
def store_Wmat(self):
fw = open("Wmat.data", "wb")
self.storage.save(self.W, fw)
fw.close()
def load_Wmat(self):
fr = open("Wmat.data")
W = self.storage.load(fr)
fr.close()
return W