def new_method(begin_query, end_query, begin_target, end_target):
N = 16 # divide data into 16 chunks
store = pd.HDFStore(new_data, )
query = store.select('data', start=begin_query, stop=end_query)
target = store.select('data', start=begin_target, stop=end_target)
if (len(query) < (end_query - begin_query) or len(target) <
(end_target - begin_target)):
print("Warning: did not read all requested entries.")
query_fp = np.empty((end_query - begin_query, N), dtype=np.uint64)
query_fp = query['RDK5FPhex'].apply(lambda x: fphex2int64(x, N))
target_fp = np.empty((end_target - begin_target, N), dtype=np.uint64)
target_fp = target['RDK5FPhex'].apply(lambda x: fphex2int64(x, N))
query_list = list(itertools.chain.from_iterable(query_fp.values))
target_list = list(itertools.chain.from_iterable(target_fp.values))
merged_query = np.reshape(query_list, (end_query - begin_query, N))
merged_target = np.reshape(target_list, (end_target - begin_target, N))
similarity = GPUtanimoto(merged_query, merged_target)
store.close()
return similarity
def test_empty(self):
query = np.empty(shape=(1, 1))
target = np.empty(shape=(1, 2))
output = GPUtanimoto(query, target)
expect = np.empty(shape=(1, 1))
self.assertTrue((output - expect).any())
def test_double(self):
query = np.array([[0, 1]], np.uint64)
target = np.array([[0, 1], [1, 0]], np.uint64)
output = GPUtanimoto(query, target)
expect = [(0, 1.0), (1, 0.0)]
self.assertEqual(output, expect)
def test_ones(self):
query = np.array([[1]], np.uint64)
target = np.array([[1]], np.uint64)
output = GPUtanimoto(query, target)
expect = [(0, 1.0)]
self.assertEqual(output, expect)