add point store and candidate set #9
+436
−245
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pengweisong
reviewed
Aug 1, 2022
| create tag point(vector string); | ||
| create edge e(); | ||
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| insert vertex point(vector) VALUES 0:("")) |
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an extra bracket in the end?
| points = self.executor.map(self.worker,id_list) | ||
| for point in points: | ||
| if point.id in self.visited: | ||
| return |
| @@ -43,7 +43,8 @@ def get_neighbors(self, vid) -> Tuple[str, Dict]: | |||
| ''' | |||
| # todo: replace t1 as tag, col1 as property | |||
| # todo: use int id? | |||
| query = "FETCH PROP ON t1 \'{}\' YIELD properties(vertex).col1".format(vid) | |||
| query = "FETCH PROP ON t1 \'{}\' YIELD properties(vertex).col1".format( | |||
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no need slice to escape
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| if curr.distance(q) > furthest_res.distance(q): | ||
| break | ||
| visited = set([p.id for p in candidates]) |
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It's equal to {p.id for p in candidates}
| return result.values | ||
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| def knn_search(self, q: Point, k: int, ef: int) -> List[Point]: | ||
| points.sort(key=lambda x: x.distance(q)) |
| def __select_neighbors_simple(self, q: Point, candidates: List[Point], m: int) -> List[Point]: | ||
| candidate_set = CandidateSet( | ||
| q, candidates, candidate_count, self.point_store) | ||
| while True: |
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simple BFS may be slower than the method in the paper
| return max(self._points_pair)[1] | ||
| if self.id ==0 or other.id == 0: | ||
| return float('inf') | ||
| return np.linalg.norm(self.vector - other.vector) |
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could cache some distance here in id granularity.
| self.max_degree = 10 | ||
| self.layer_factor = 4 | ||
| self.max_layer = 5 | ||
| self.candidate_set_size = 100 |
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all this config should calculate according with the data size?
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