有效计数numpy的唯一子阵列出现的次数？ [英] Efficiently count the number of occurrences of unique subarrays in NumPy?

问题描述

我有形状阵列（128，36，8），我想找到的最后一个长度为8的唯一子阵列出现的次数尺寸。

I have an array of shape (128, 36, 8) and I'd like to find the number of occurrences of the unique subarrays of length 8 in the last dimension.

我所知道的 np.unique 和 np.bincount ，但这些似乎是元素，而比子阵。我见过这个问题，但它是寻找特定的第一次出现子阵，而不是将所有子阵独特的计数。

I'm aware of np.unique and np.bincount, but those seem to be for elements rather than subarrays. I've seen this question but it's about finding the first occurrence of a particular subarray, rather than the counts of all unique subarrays.

推荐答案

的问题指出，输入数组是形状（128，36，8）和我们有意在最后一维发现长度 8 独特的子阵列。
所以，我假设的独特之处是沿着前两个维度被合并在一起。让我们假设 A 作为输入三维阵列。

The question states that the input array is of shape (128, 36, 8) and we are interested in finding unique subarrays of length 8 in the last dimension. So, I am assuming that the uniqueness is along the first two dimensions being merged together. Let us assume A as the input 3D array.

获取独特的子阵数

# Reshape the 3D array to a 2D array merging the first two dimensions
Ar = A.reshape(-1,A.shape[2])

# Perform lex sort and get the sorted indices and xy pairs
sorted_idx = np.lexsort(Ar.T)
sorted_Ar =  Ar[sorted_idx,:]

# Get the count of rows that have at least one TRUE value 
# indicating presence of unique subarray there
unq_out = np.any(np.diff(sorted_Ar,axis=0),1).sum()+1

样运行 -

In [159]: A # A is (2,2,3)
Out[159]: 
array([[[0, 0, 0],
        [0, 0, 2]],

       [[0, 0, 2],
        [2, 0, 1]]])

In [160]: unq_out
Out[160]: 3

获取独特的子阵中出现的次数

# Reshape the 3D array to a 2D array merging the first two dimensions
Ar = A.reshape(-1,A.shape[2])

# Perform lex sort and get the sorted indices and xy pairs
sorted_idx = np.lexsort(Ar.T)
sorted_Ar =  Ar[sorted_idx,:]

# Get IDs for each element based on their uniqueness
id = np.append([0],np.any(np.diff(sorted_Ar,axis=0),1).cumsum())

# Get counts for each ID as the final output
unq_count = np.bincount(id) 

样运行 -

In [64]: A
Out[64]: 
array([[[0, 0, 2],
        [1, 1, 1]],

       [[1, 1, 1],
        [1, 2, 0]]])

In [65]: unq_count
Out[65]: array([1, 2, 1], dtype=int64)

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