形状不匹配：形状X的值数组无法广播到形状的索引结果。 。 。 X？

Question

Consider: I'm trying to generate s samples the von mises distribution for every element of k , which raises an error for k=0 (which is my null hypothesis, so I want it as accurate as possible). I'm trying to "fudge" it by giving a low k and randomizing the bias direction.

Assume

import numpy as np
s = 1000
k = np.arange(10)
theta = np.zeros_like(k)
shp = (10,)

Then the following code

import scipy.stats as stat    
rpt = (s,) + tuple(np.ones_like(shp))
theta = np.tile(theta, rpt)
k_zero = np.logical_not(k)
theta[:, k_zero] = np.random.rand(np.sum(k_zero), s) * 2 * np.pi - np.pi
k[k_zero] = .001
ks = np.tile(k, rpt)    

gives the error

Traceback (most recent call last):

  File "<ipython-input-blah>", line 1, in <module>
    theta[:, k_zero] = np.random.rand(np.sum(k_zero), s) * np.pi - np.pi / 2

ValueError: shape mismatch: value array of shape (1,1000) could not be broadcast to indexing result of shape (1,1000)

But . . . those shapes are the same. Why can't I do that?

EDIT: as pointed out below -

theta[:, k_zero] = np.random.rand(s, np.sum(k_zero)) * np.pi - np.pi / 2

works. Is this just a bug in the error message?

Answer 1

The error message

It appears that there is a bug in the print out that you received

• Broadcast error on assignment prints dimensions in wrong order
• setitem shape mismatch error is incorrect

The code fix

The problem is because your shape in the random number assignment is wrong. Look at the following...

theta[:, k_zero] = np.random.rand(np.sum(k_zero), s).reshape(s,1)

The way you can debug such a problem is by investigating the shapes of the assigned variable and the variable you want to assign it to.

For example what I did was

theta.shape
np.random.rand(np.sum(k_zero), s).shape

and I saw that the random number came out with transposed dimensions