CODEWITHSUNDEEP
pythonarraysnumpyarray-broadcasting
nullgeppetto
nullgeppetto

Reputation: 569

Zero (row-wise) a `numpy` array's elements than are less than the values of a given vector

I have a numpy array, e.g., 

import numpy as np    
A = np.exp(np.random.randn(3,10))

i.e., the array

array([[ 1.17164655,  1.39153953,  0.68628548,  0.1051013 ],
       [ 0.45604269,  2.21059251,  1.79624195,  0.37553947],
       [ 1.03063907,  0.28035114,  1.70371105,  3.66090236]])

and I compute the maximum of row as follows

np.max(A, axis=1)
array([ 1.39153953,  2.21059251,  3.66090236])

I want to zero the elements of A, whose values are less than a fraction of the maximum value of the corresponding row. For instance, for the above example, if we set this fraction to 0.9, I would like to zero the following elements:

1st row: Zero the elements that are less than 0.9 * maximum = 1.25238557

2nd row: Zero the elements that are less than 0.9 * maximum = 1.98953326

3rd row: Zero the elements that are less than 0.9 * maximum = 3.29481212

I took a look at numpy's documentation, but I had no luck. I also tried 

A < np.max(A, axis=1)

which I'd expect to work, but it doesn't.

Upvotes: 1

Views: 135

Answers (1)

user2357112
user2357112

Reputation: 281958

Use the keepdims argument to keep a length-1 axis instead of removing the collapsed axis, so the axes line up with the original shape for broadcasting:

A[A < 0.9*np.amax(A, axis=1, keepdims=True)] = 0

Upvotes: 3

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