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72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
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from data_structures.kd_tree.kd_node import KDNode
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def nearest_neighbour_search(
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root: KDNode | None, query_point: list[float]
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) -> tuple[list[float] | None, float, int]:
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"""
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Performs a nearest neighbor search in a KD-Tree for a given query point.
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Args:
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root (KDNode | None): The root node of the KD-Tree.
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query_point (list[float]): The point for which the nearest neighbor
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is being searched.
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Returns:
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tuple[list[float] | None, float, int]:
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- The nearest point found in the KD-Tree to the query point,
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or None if no point is found.
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- The squared distance to the nearest point.
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- The number of nodes visited during the search.
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"""
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nearest_point: list[float] | None = None
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nearest_dist: float = float("inf")
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nodes_visited: int = 0
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def search(node: KDNode | None, depth: int = 0) -> None:
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"""
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Recursively searches for the nearest neighbor in the KD-Tree.
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Args:
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node: The current node in the KD-Tree.
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depth: The current depth in the KD-Tree.
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"""
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nonlocal nearest_point, nearest_dist, nodes_visited
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if node is None:
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return
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nodes_visited += 1
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# Calculate the current distance (squared distance)
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current_point = node.point
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current_dist = sum(
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(query_coord - point_coord) ** 2
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for query_coord, point_coord in zip(query_point, current_point)
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)
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# Update nearest point if the current node is closer
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if nearest_point is None or current_dist < nearest_dist:
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nearest_point = current_point
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nearest_dist = current_dist
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# Determine which subtree to search first (based on axis and query point)
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k = len(query_point) # Dimensionality of points
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axis = depth % k
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if query_point[axis] <= current_point[axis]:
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nearer_subtree = node.left
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further_subtree = node.right
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else:
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nearer_subtree = node.right
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further_subtree = node.left
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# Search the nearer subtree first
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search(nearer_subtree, depth + 1)
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# If the further subtree has a closer point
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if (query_point[axis] - current_point[axis]) ** 2 < nearest_dist:
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search(further_subtree, depth + 1)
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search(root, 0)
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return nearest_point, nearest_dist, nodes_visited
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