def get_bucket(self, hash_name, bucket_key):
"""
Returns bucket content as list of tuples (vector, data).
"""
redis_key = "nearpy_%s_%s" % (hash_name, bucket_key)
items = self.redis_object.lrange(redis_key, 0, -1)
results = []
for item_str in items:
val_dict = json.loads(want_string(item_str))
vector = numpy.fromiter(val_dict["vector"], dtype=numpy.float64)
if "data" in val_dict:
results.append((vector, val_dict["data"]))
else:
results.append((vector, None))
return results
def get_bucket(self, hash_name, bucket_key):
"""
Returns bucket content as list of tuples (vector, data).
"""
redis_key = 'nearpy_%s_%s' % (hash_name, bucket_key)
items = self.redis_object.lrange(redis_key, 0, -1)
results = []
for item_str in items:
val_dict = json.loads(want_string(item_str))
# Depending on type (sparse or not) reconstruct vector
if 'sparse' in val_dict:
# Fill these for COO creation
row = []
col = []
data = []
# For each non-zero element, append values
for e in val_dict['nonzeros']:
row.append(e[0]) # Row index
data.append(e[1]) # Value
col.append(0) # Column index (always 0)
# Create numpy arrays for COO creation
coo_row = numpy.array(row, dtype=numpy.int32)
coo_col = numpy.array(col, dtype=numpy.int32)
coo_data = numpy.array(data)
# Create COO sparse vector
vector = scipy.sparse.coo_matrix(
(coo_data, (coo_row, coo_col)), shape=(val_dict['dim'], 1))
else:
vector = numpy.fromiter(val_dict['vector'],
dtype=numpy.float64)
# Add data to result tuple, if present
if 'data' in val_dict:
results.append((vector, val_dict['data']))
else:
results.append((vector, None))
return results
def get_bucket(self, hash_name, bucket_key):
"""
Returns bucket content as list of tuples (vector, data).
"""
redis_key = 'nearpy_%s_%s' % (hash_name, bucket_key)
items = self.redis_object.lrange(redis_key, 0, -1)
results = []
for item_str in items:
val_dict = json.loads(want_string(item_str))
# Depending on type (sparse or not) reconstruct vector
if 'sparse' in val_dict:
# Fill these for COO creation
row = []
col = []
data = []
# For each non-zero element, append values
for e in val_dict['nonzeros']:
row.append(e[0]) # Row index
data.append(e[1]) # Value
col.append(0) # Column index (always 0)
# Create numpy arrays for COO creation
coo_row = numpy.array(row, dtype=numpy.int32)
coo_col = numpy.array(col, dtype=numpy.int32)
coo_data = numpy.array(data)
# Create COO sparse vector
vector = scipy.sparse.coo_matrix( (coo_data,(coo_row,coo_col)), shape=(val_dict['dim'],1) )
else:
vector = numpy.fromiter(val_dict['vector'], dtype=numpy.float64)
# Add data to result tuple, if present
if 'data' in val_dict:
results.append((vector, val_dict['data']))
else:
results.append((vector, None))
return results
