Set Values in Numpy Array Based Upon Another Array

Question

1 `term_map` tracks which term is in which position.

In [256]: term_map = np.array([2, 2, 3, 4, 4, 4, 2, 0, 0, 0])

In [257]: term_map
Out[257]: array([2, 2, 3, 4, 4, 4, 2, 0, 0, 0])

2 `term_scores` tracks the weight of each term at each position.

In [258]: term_scores = np.array([5, 6, 9, 8, 9, 4, 5, 1, 2, 1])

In [259]: term_scores
Out[259]: array([5, 6, 9, 8, 9, 4, 5, 1, 2, 1])

3 Get the unique values and the inverse indices.

In [260]: unqID, idx = np.unique(term_map, return_inverse=True)

In [261]: unqID
Out[261]: array([0, 2, 3, 4])

4 Compute the scores for the unique values.

In [262]: value_sums = np.bincount(idx, term_scores)

In [263]: value_sums
Out[263]: array([  4.,  16.,   9.,  21.])

5 Initialize Array To Update. The indices correspond to the values in the `term_map` variable.

In [254]: vocab = np.zeros(13)

In [255]: vocab
Out[255]: array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])

6 DESIRED: Insert the values 4 corresponding to the positions listed in 3 into the `vocab` variable.

In [255]: updated_vocab
Out[255]: array([ 4.,  0.,  16.,  9.,  21.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])

How do I create 6?

yeah @TonyTannous that's correct. Sorry for not making it more clear. — mattyd2
– mattyd2, Commented Feb 16, 2017 at 19:18
I would think that's trivial given the 5 more difficult hurdles covered before that. — Divakar
– Divakar, Commented Feb 16, 2017 at 19:18
I'd like to not use a for loop if possible. This is pretty close - stackoverflow.com/questions/8373079/… — mattyd2
– mattyd2, Commented Feb 16, 2017 at 19:19

Divakar · Accepted Answer · 2017-02-16 19:36:15Z

3

As it turns out, we can avoid the np.unique step to directly get to the desired output by feeding in term_map and term_scores to np.bincount and also mention the length of the output array with its optional argument minlength.

Thus, we could simply do -

final_output = np.bincount(term_map, term_scores, minlength=13)

Sample run -

In [142]: term_map = np.array([2, 2, 3, 4, 4, 4, 2, 0, 0, 0])
     ...: term_scores = np.array([5, 6, 9, 8, 9, 4, 5, 1, 2, 1])
     ...: 

In [143]: np.bincount(term_map, term_scores, minlength=13)
Out[143]: 
array([  4.,   0.,  16.,   9.,  21.,   0.,   0.,   0.,   0.,   0.,   0.,
         0.,   0.])

answered Feb 16, 2017 at 19:36

Divakar

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Tony Tannous · Accepted Answer · 2017-02-16 19:20:59Z

2

import numpy as np

term_map = np.array([2, 2, 3, 4, 4, 4, 2, 0, 0, 0])
term_scores = np.array([5, 6, 9, 8, 9, 4, 5, 1, 2, 1])
unqID, idx = np.unique(term_map, return_inverse=True)
value_sums = np.bincount(idx, term_scores)

vocab = np.zeros(13)
vocab[unqID] = value_sums
print(vocab)

OUT: [ 4. 0. 16. 9. 21. 0. 0. 0. 0. 0. 0. 0. 0.]

answered Feb 16, 2017 at 19:20

Tony Tannous

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Set Values in Numpy Array Based Upon Another Array

1 `term_map` tracks which term is in which position.

2 `term_scores` tracks the weight of each term at each position.

3 Get the unique values and the inverse indices.

4 Compute the scores for the unique values.

5 Initialize Array To Update. The indices correspond to the values in the `term_map` variable.

6 DESIRED: Insert the values 4 corresponding to the positions listed in 3 into the `vocab` variable.

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Collectives™ on Stack Overflow

1 term_map tracks which term is in which position.

2 term_scores tracks the weight of each term at each position.

3 Get the unique values and the inverse indices.

4 Compute the scores for the unique values.

5 Initialize Array To Update. The indices correspond to the values in the term_map variable.

6 DESIRED: Insert the values 4 corresponding to the positions listed in 3 into the vocab variable.

2 Answers 2

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1 `term_map` tracks which term is in which position.

2 `term_scores` tracks the weight of each term at each position.

5 Initialize Array To Update. The indices correspond to the values in the `term_map` variable.

6 DESIRED: Insert the values 4 corresponding to the positions listed in 3 into the `vocab` variable.