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问题:
I'm trying to rewrite a function using numpy which is originally in MATLAB. There's a logical indexing part which is as follows in MATLAB:
X = reshape(1:16, 4, 4).'; idx = [true, false, false, true]; X(idx, idx) ans = 1 4 13 16
When I try to make it in numpy, I can't get the correct indexing:
X = np.arange(1, 17).reshape(4, 4) idx = [True, False, False, True] X[idx, idx] # Output: array([6, 1, 1, 6])
What's the proper way of getting a grid from the matrix via logical indexing?
回答1:
You could also write:
>>> X[np.ix_(idx,idx)] array([[ 1, 4], [13, 16]])
回答2:
In [1]: X = np.arange(1, 17).reshape(4, 4) In [2]: idx = np.array([True, False, False, True]) # note that here idx has to # be an array (not a list) # or boolean values will be # interpreted as integers In [3]: X[idx][:,idx] Out[3]: array([[ 1, 4], [13, 16]])
回答3:
In numpy this is called fancy indexing. To get the items you want you should use a 2D array of indices.
You can use an outer to make from your 1D idx a proper 2D array of indices. The outers, when applied to two 1D sequences, compare each element of one sequence to each element of the other. Recalling that True*True=True and False*True=False, the np.multiply.outer(), which is the same as np.outer(), can give you the 2D indices:
idx_2D = np.outer(idx,idx) #array([[ True, False, False, True], # [False, False, False, False], # [False, False, False, False], # [ True, False, False, True]], dtype=bool)
Which you can use:
x[ idx_2D ] array([ 1, 4, 13, 16])
In your real code you can use x=[np.outer(idx,idx)] but it does not save memory, working the same as if you included a del idx_2D after doing the slice.
