349.intersection-of-two-arrays

本文介绍了一个计算两个数组交集的方法,通过将第一个数组存入字典,并遍历第二个数组检查是否存在相同元素,若存在则加入结果列表并移除字典中的对应项。

给定两个数组,写一个函数来计算它们的交集。

例子:

 给定 num1[1, 2, 2, 1]nums2 = [2, 2], 返回 [2].

提示:

  • 每个在结果中的元素必定是唯一的。
  • 我们可以不考虑输出结果的顺序。

解题思路:

    遍历第一个数组,并存到一个集合中去,然后再遍历第二个数组,如果第二个数组中存在集合中的元素,则将其放入ans中,并删除集合中的这个元素。

解题代码:

    

        d={}
        ans=[]
        for num in nums1:
            d[num]=d.get(num,1)
        for num in nums2:
            if num in d:
                ans.append(num)
                del d[num]
        return ans

### Wise Inner Shape IoU Implementation and Usage In the context of computer vision or 3D shape analysis, Wise Inner Shape Intersection over Union (IoU) serves as an evaluation metric that measures how well two shapes align with each other internally. This method focuses on comparing inner structures rather than outer boundaries to assess similarity more accurately. The formula for calculating this specific type of IoU can be defined as follows: \[ \text{WiseInnerShapeIoU} = \frac{\left| A_{inner} \cap B_{inner}\right|}{\left|A_{inner} \cup B_{inner}\right|} \] where \(A_{inner}\) represents the set of points inside object A but not part of its boundary, while \(B_{inner}\) denotes those within object B under similar conditions[^1]. For practical implementation purposes, one approach involves converting both objects into voxel grids where occupied cells represent solid parts whereas empty ones signify void spaces between surfaces. Afterward, operations such as intersection (\(\cap\)) and union (\(\cup\)) become straightforward bitwise logic applied across corresponding voxels from either grid. Below is a Python code snippet demonstrating how to compute Wise Inner Shape IoU using NumPy arrays representing binary volumes: ```python import numpy as np def calculate_wise_inner_shape_iou(volume_a: np.ndarray, volume_b: np.ndarray) -> float: """ Calculate the wise inner shape IoU given two binary volumetric representations. Args: volume_a (np.ndarray): Binary array indicating presence/absence of material in Volume A. volume_b (np.ndarray): Similar representation for Volume B. Returns: float: Computed value of Wise Inner Shape IoU ranging [0., 1.]. """ # Ensure inputs are boolean-valued ndarrays mask_a = volume_a.astype(bool) mask_b = volume_b.astype(bool) # Compute intersections & unions considering only internal regions intersec = ((mask_a ^ (~volume_a)) * (mask_b ^ (~volume_b))).sum() union = (((mask_a | ~volume_a) | (mask_b | ~volume_b)) != False).sum() return max(0., min(intersec / union, 1.)) ``` This function takes two three-dimensional Numpy arrays (`volume_a`, `volume_b`) containing zeros and ones which indicate whether certain positions belong to respective solids' interiors. It returns a floating-point number signifying their degree of overlap according to the described criterion. --related questions-- 1. How does changing resolution affect accuracy when computing Wise Inner Shape IoU? 2. What preprocessing steps should precede applying this technique effectively? 3. Can you provide examples illustrating scenarios wherein traditional metrics fail compared against Wise Inner Shape IoU? 4. Are there alternative methods offering comparable performance yet simpler computation requirements?
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