def __init__(self, N, D, sketch_size=0.20):
self.N = N
self.D = D
self.blk_size = math.ceil(D // 32) * 32
self.range = int(N * sketch_size)
self.kernel = cupyKernel(kernel, "cms_hash_update_retrieve")
self.cms = torch.cuda.FloatTensor(self.range, D).fill_(0)
print(N, "CMS Flat", self.cms.size())
def __init__(self, N, D, sketch_size=0.20):
self.N = N
self.D = D
self.blk_size = 32
self.range = max(int(D * sketch_size), 1)
self.kernel = cupyKernel(kernel, "dense_cms_update")
self.cms = torch.cuda.FloatTensor(N, self.range).fill_(0)
print(N, "Dense CMS", self.cms.size())
def __init__(self, N, D, sketch_size=0.20):
self.N = N
self.D = D
self.blk_size = math.ceil(D // 32) * 32
self.range = int(N * sketch_size / 3.)
self.width = self.range * D
self.kernel = cupyKernel(kernel, "hash_update_retrieve")
self.cms = torch.zeros(3, self.range, D).float().cuda()
print(N, "CMS", self.cms.size())
def __init__(self, N, D, sketch_size=0.20):
self.N = N
self.D = D
self.blk_size = math.ceil(D // 32) * 32
self.range = int(N * sketch_size / 3.)
self.width = self.range * D
self.kernel = cupyKernel(kernel, "hash_update_retrieve")
self.sketch = torch.cuda.FloatTensor(3, self.range, D).fill_(0)
print(N, "Count Sketch", self.sketch.size())
def __init__(self, d_, k_, L_, weights=None, seed_=8191):
self.d = d_
self.k = k_
self.L = L_
self.fp = cupyKernel(kernel, "fingerprint")
if weights is None:
self.rp = SimHash.generate(d_, k_, L_, seed_)
else:
self.rp = SimHash.generate_from_weight(weights)
def __init__(self, d_, k_, L_, seed_=8191, srp_list=None):
self.d = d_
self.k = k_
self.L = L_
self.fp = cupyKernel(kernel, "fingerprint")
if srp_list is None:
self.rp = SimHash.generate(d_, k_, L_, seed_)
else:
self.rp = SimHash.generate_from_list(srp_list)
def initialize(self):
if self.kernel is None:
self.kernel = cupyKernel(kernel, "cs_hash_update_retrieve")
def initialize(self):
if self.kernel is None:
if self.D == self.range:
self.kernel = cupyKernel(kernel, "dense_update")
else:
self.kernel = cupyKernel(kernel, "dense_cms_update")
def __init__(self, d_, k_, L_, seed_=8192, srp_list=None):
self.d = d_
self.k = k_
self.L = L_
self.fp = cupyKernel(kernel, "fingerprint")
self.rp = SimHash.generate(d_, k_, L_, seed_)
