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Is it possible to convert an array of indices to an array of ones and zeros, given the range? i.e. [2,3] -> [0, 0, 1, 1, 0], in range of 5

I'm trying to automate something like this:

>>> index_array = np.arange(200,300)
array([200, 201, ... , 299])

>>> mask_array = ???           # some function of index_array and 500
array([0, 0, 0, ..., 1, 1, 1, ... , 0, 0, 0])

>>> train(data[mask_array])    # trains with 200~299
>>> predict(data[~mask_array]) # predicts with 0~199, 300~499
share|improve this question
    
scipy has a masked array module. It is related to the question. docs.scipy.org/doc/numpy/reference/maskedarray.html –  K.Chen Sep 3 at 22:57
    
[x in index_array for x in range(500)] sort of does it, but with True and False instead of 1 and 0. –  genisage Sep 3 at 23:03
    
@genisage Can you please make your comment as an answer? I want to choose yours. It's the exact thing I was looking for. Thank you for the answer! –  Efreet Sep 4 at 4:47

2 Answers 2

up vote 1 down vote accepted

As requested, here it is in an answer. The code:

[x in index_array for x in range(500)]

will give you a mask like you asked for, but it will use Bools instead of 0's and 1's.

share|improve this answer
up vote 2 down vote

Here's one way:

In [1]: index_array = np.array([3, 4, 7, 9])

In [2]: n = 15

In [3]: mask_array = np.zeros(n, dtype=int)

In [4]: mask_array[index_array] = 1

In [5]: mask_array
Out[5]: array([0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0])

If the mask is always a range, you can eliminate index_array, and assign 1 to a slice:

In [6]: mask_array = np.zeros(n, dtype=int)

In [7]: mask_array[5:10] = 1

In [8]: mask_array
Out[8]: array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0])

If you want an array of boolean values instead of integers, change the dtype of mask_array when it is created:

In [11]: mask_array = np.zeros(n, dtype=bool)

In [12]: mask_array
Out[12]: 
array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False], dtype=bool)

In [13]: mask_array[5:10] = True

In [14]: mask_array
Out[14]: 
array([False, False, False, False, False,  True,  True,  True,  True,
        True, False, False, False, False, False], dtype=bool)
share|improve this answer
    
+1 This is a very nice answer too, especially if someone wants their mask_array to be an np.array. –  Efreet Sep 5 at 5:08

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