python numpy ValueError: operands could not be broadcast together with shapes

numpy, python

Solution

`dot` is matrix multiplication, but `*` does something else.

We have two arrays:

- `X`, shape (97,2)

- `y`, shape (2,1)

With Numpy arrays, the operation

X * y

is done element-wise, but one or both of the values can be expanded in one or more dimensions to make them compatible. This operation is called broadcasting. Dimensions, where size is 1 or which are missing, can be used in broadcasting.

In the example above the dimensions are incompatible, because:

97   2
 2   1

Here there are conflicting numbers in the first dimension (97 and 2). That is what the ValueError above is complaining about. The second dimension would be ok, as number 1 does not conflict with anything.

For more information on broadcasting rules: http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html

(Please note that if `X` and `y` are of type `numpy.matrix`, then asterisk can be used as matrix multiplication. My recommendation is to keep away from `numpy.matrix`, it tends to complicate more than simplifying things.)

Your arrays should be fine with `numpy.dot`; if you get an error on `numpy.dot`, you must have some other bug. If the shapes are wrong for `numpy.dot`, you get a different exception:

ValueError: matrices are not aligned

If you still get this error, please post a minimal example of the problem. An example multiplication with arrays shaped like yours succeeds:

In [1]: import numpy

In [2]: numpy.dot(numpy.ones([97, 2]), numpy.ones([2, 1])).shape
Out[2]: (97, 1)

Problem

In numpy, I have two "arrays", `X` is `(m,n)` and `y` is a vector `(n,1)` using ``` X*y ``` I am getting the error ``` ValueError: operands could not be broadcast together with shapes (97,2) (2,1) ``` When `(97,2)x(2,1)` is clearly a legal matrix operation and should give me a `(97,1)` vector EDIT: I have corrected this using `X.dot(y)` but the original question still remains.

Original source