Merge pull request #56 from Dobatymo/new-release

v0.1.1

pynear/__init__.py

@@ -1,6 +1,8 @@
 from typing import List
 from typing import Tuple
 
+import numpy as np
+
 from _pynear import BKTreeBinaryIndex64
 from _pynear import BKTreeBinaryIndex128
 from _pynear import BKTreeBinaryIndex256
@@ -21,16 +23,13 @@
 from _pynear import dist_hamming_512
 from _pynear import dist_l1
 from _pynear import dist_l2
-import numpy as np
 
 from ._version import __version__
 
 
 def dist_hamming(a: List, b: List):
     if len(a) != len(b):
-        raise ValueError(
-            f"invalid data dimension: a and b dimensions must agree."
-        )
+        raise ValueError("invalid data dimension: a and b dimensions must agree.")
     dim = len(a)
     if dim == 64:
         return dist_hamming_512(a, b)
@@ -41,10 +40,7 @@ def dist_hamming(a: List, b: List):
     elif dim == 8:
         return dist_hamming_64(a, b)
     else:
-        raise ValueError(
-            f"invalid data dimension: hamming distance only supports 64, 32, 16 or 8 bytes of data"
-        )
-
+        raise ValueError("invalid data dimension: hamming distance only supports 64, 32, 16 or 8 bytes of data")
 
 
 class VPTreeBinaryIndex:

pynear/_version.py

@@ -3,6 +3,6 @@
 _version = {
     "major": 0,
     "minor": 1,
-    "revis": 0,
+    "revis": 1,
 }
 __version__ = ".".join([str(a) for a in _version.values()])

pynear/benchmark/index_adapters.py

@@ -71,6 +71,7 @@ def build_index(self, data: np.ndarray):
     def _search_implementation(self, query, k: int):
         self._index.searchKNN(query, k)
 
+
 class PyNearBKTreeAdapter(IndexAdapter):
     def __init__(self):
         self._index = pynear.BKTreeBinaryIndex()
@@ -83,6 +84,7 @@ def build_index(self, data: np.ndarray):
     def _search_implementation(self, query, k: int):
         self._index.find_threshold(query, self._dimensions)
 
+
 class FaissIndexFlatL2Adapter(IndexAdapter):
     def __init__(self):
         self._index = None

pynear/tests/test_vptree.py

@@ -26,6 +26,7 @@
 
 np.set_printoptions(threshold=sys.maxsize)
 
+
 def hamming_distance_pairwise(a: np.ndarray, b: np.ndarray) -> np.ndarray:
     result = []
     for ai in a:
@@ -37,6 +38,7 @@ def hamming_distance_pairwise(a: np.ndarray, b: np.ndarray) -> np.ndarray:
 
     return np.array(result).astype(np.uint8)
 
+
 def euclidean_distance_pairwise(a: np.ndarray, b: np.ndarray) -> np.ndarray:
     result = []
     for ai in a:
@@ -60,6 +62,7 @@ def manhattan_distance_pairwise(a: np.ndarray, b: np.ndarray) -> np.ndarray:
 
     return np.array(result)
 
+
 def chebyshev_distance_pairwise(a: np.ndarray, b: np.ndarray) -> np.ndarray:
     result = []
     for ai in a:
@@ -211,9 +214,9 @@ def test_k_equals_dataset(vptree_cls, exaustive_metric):
 
     vptree_indices = np.array(vptree_indices, dtype=np.uint64)[:, ::-1]
     vptree_distances = np.array(vptree_distances, dtype=np.float64)[:, ::-1]
-    dist_diff = vptree_distances - exaustive_distances 
+    dist_diff = vptree_distances - exaustive_distances
     ind_diff = vptree_indices - exaustive_indices
-    print(">>>>>>>>>>>>", dist_diff[dist_diff > 1e-7] )
+    print(">>>>>>>>>>>>", dist_diff[dist_diff > 1e-7])
     print(">>>>>>>>>>>>", np.argwhere(ind_diff != 0))
 
     np.testing.assert_allclose(exaustive_distances, vptree_distances, rtol=1e-06)