CODEWITHSUNDEEP
pythonarraysnumpyindexing
KRL
KRL

Reputation: 195

How to add elements to a 2D array using python fancy indexing?

I am writing a program in python and I want to vectorize it as much as possible. I have the following variables

  1. 2D array of zeros E with shape (L,T).
  2. array w with shape (N,) with arbitrary values.
  3. array index with shape (A,) whose values are integers between 0 and N-1. The values are unique.
  4. array labels with a shape the same as w ((A,)), whose values are integers between 0 and L-1. The values are not necessarily unique.
  5. Integer t between 0 and T-1.

We want to add the values of w at indices index to the array E at rows labels and column t. I used the following code:

E[labels,t] += w[index]

But this approach does not give desired results. For example,

import numpy as np

E = np.zeros([10,1])
w = np.arange(0,100)
index = np.array([1,3,4,12,80])
labels = np.array([0,0,5,5,2])
t = 0
E[labels,t] += w[index]

Gives 

array([[ 3.],
   [ 0.],
   [80.],
   [ 0.],
   [ 0.],
   [12.],
   [ 0.],
   [ 0.],
   [ 0.],
   [ 0.]])

But the correct answer would be 

array([[ 4.],
       [ 0.],
       [80.],
       [ 0.],
       [ 0.],
       [16.],
       [ 0.],
       [ 0.],
       [ 0.],
       [ 0.]])

Is there a way to achieve this behavior without using a for loop?

I realized I can use this: np.add.at(E,[labels,t],w[index]) but it gives me this warning: 

FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.

Upvotes: 1

Views: 112

Answers (1)

Luke DeLuccia
Luke DeLuccia

Reputation: 541

Pulled from a similar question, you can use np.bincount() to achieve your goal:

import numpy as np
import time

E = np.zeros([10,1])
w = np.arange(0,100)
index = np.array([1,3,4,12,80])
labels = np.array([0,0,5,5,2])
t = 0

# --------- Using np.bincount()
start = time.perf_counter()
for _ in range(10000):
    E = np.zeros([10,1])
    values = w[index]
    result = np.bincount(labels, values, E.shape[0])
    E[:, t] += result
print("Bin count time: {}".format(time.perf_counter() - start))
print(E)


# --------- Using for loop
for _ in range(10000):
    E = np.zeros([10,1])
    for i, in_ in enumerate(index):
        E[labels[i], t] += w[in_]
print("For loop time: {}".format(time.perf_counter() - start))
print(E)

Gives:

Bin count time: 0.045003452
[[ 4.]
 [ 0.]
 [80.]
 [ 0.]
 [ 0.]
 [16.]
 [ 0.]
 [ 0.]
 [ 0.]
 [ 0.]]
For loop time: 0.09853353699999998
[[ 4.]
 [ 0.]
 [80.]
 [ 0.]
 [ 0.]
 [16.]
 [ 0.]
 [ 0.]
 [ 0.]
 [ 0.]]

Upvotes: 1

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