CODEWITHSUNDEEP
pythonnumpymatrixmultidimensional-array
fact
fact

Reputation: 557

Evaluating Python lambda function with numpys np.fromfunction

Let A1 and A2 be numpy arrays of the same shape, say ((d1,d2)). I want to build ((d1,d1)) array from it such that its [i,j]th entry is defined by applying a function to the tuple A1[i],A2[j]. I use np.fromfunction in the form

f=lambda i,j: np.inner(A1[i],A2[j])
A=np.fromfunction(f, shape=(d1, d1))

(as suggested in Fastest way to initialize numpy array with values given by function) .

However I get the error ''IndexError: arrays used as indices must be of integer (or boolean) type''. This is strange because changing the lambda function to for example

 f=lambda i,j: i*j

works fine! It seems calling another function in the lambda function leads to trouble with 

np.fromfunction

(np.inner is just an example and I'd like to be able to replace it by other such functions).

Upvotes: 3

Views: 1631

Answers (1)

unutbu
unutbu

Reputation: 879691

To debug the situation, make f a proper function and add a print statement to see the value of i and j:

import numpy as np
np.random.seed(2015)
d1, d2 = 5, 3
A1 = np.random.random((d1,d2))
A2 = np.random.random((d1,d2))
def f(i, j):
    print(i, j)
    return np.inner(A1[i],A2[j])
A = np.fromfunction(f, shape=(d1, d1))

You'll see (i, j) equals:

(array([[ 0.,  0.,  0.,  0.,  0.],
       [ 1.,  1.,  1.,  1.,  1.],
       [ 2.,  2.,  2.,  2.,  2.],
       [ 3.,  3.,  3.,  3.,  3.],
       [ 4.,  4.,  4.,  4.,  4.]]), array([[ 0.,  1.,  2.,  3.,  4.],
       [ 0.,  1.,  2.,  3.,  4.],
       [ 0.,  1.,  2.,  3.,  4.],
       [ 0.,  1.,  2.,  3.,  4.],
       [ 0.,  1.,  2.,  3.,  4.]]))

Aha. The problem is that these arrays are float-valued. As the error message says, indices have to be of integer or boolean type.

Perusing the docstring for np.fromfunction reveals it has a third parameter, dtype, which controls the data type of coordinate arrays:

Parameters
dtype : data-type, optional
    Data-type of the coordinate arrays passed to `function`.
    By default, `dtype` is float.

Therefore the solution is to add dtype=int to the call to np.fromfunction:

A = np.fromfunction(f, shape=(d1, d1), dtype=int)

Upvotes: 7

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