Add a vector to array

Question

A really stupid question, but I could not figure the right way.. - A is a 2 by 2 matrix, and B is a 2 by 1 matrix. - In a 10 iterations loop, B_new=A*B. B_new is 2 by 1. - Save B_new to an output matrix B_final after each iteration. So in the end, B_final is 2 by 10. However, I have problem of adding B to B_new in a loop. Below is my code, can anyone give me some suggestions? ``` import numpy as np a=np.ones(shape=(2,2)) b=np.ones(shape=(2,1)) c_final=np.zeros(shape=(2,10)) for i in range(0,10): c=np.dot(a,b) b=c c_final[:,i]=c ``` Here is the error message: ``` c_final[:,i]=c ValueError: output operand requires a reduction, but reduction is not enabled ```

Accepted Answer

The error you're seeing is because when numpy broadcasts `c_final[:,i]` and `np.dot(a,b)` together it produces an array with shape `(2,2)`, which then can't be assigned to `c_final[:,i]` since it has a shape of `(2,1)`. I think it's much clearer if you just play around with it in the interpreter:

```
>>> import numpy as np
>>> a = np.ones((2,2))
>>> b = np.ones((2,1))
>>> c_final = np.zeros((2,10))
>>> np.dot(a,b)
array([[ 2.],
       [ 2.]])
>>> np.dot(a,b).shape
(2, 1)
>>> c_final[:,0]
array([ 0.,  0.])
>>> c_final[:,0].shape
(2,)
>>> np.broadcast(c_final[:,0],np.dot(a,b)).shape
(2, 2)
```

The way around this is to flatten `np.dot(a,b)` by using `np.squeeze` or something similar so that when they are broadcast together they produce a 2 element array. For example:

```
>>> c_final[:,0] = np.dot(a,b).squeeze()
```

You're not alone in finding the error message unhelpful. Someone filed a ticket about this about a year ago.

Add a vector to array

Solution

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