Numpy transpose multiplication problem

Question

I tried to find the eigenvalues of a matrix multiplied by its transpose but I couldn't do it using numpy.

testmatrix = numpy.array([[1,2],[3,4],[5,6],[7,8]])
prod = testmatrix * testmatrix.T
print eig(prod)

I expected to get the following result for the product:

5    11    17    23
11    25    39    53
17    39    61    83
23    53    83   113

and eigenvalues:

0.0000
0.0000
0.3929
203.6071

Instead I got ValueError: shape mismatch: objects cannot be broadcast to a single shape when multiplying testmatrix with its transpose.

This works (the multiplication, not the code) in MatLab but I need to use it in a python application.

Can someone tell me what I'm doing wrong?

Solution 1

You might find this tutorial useful since you know MATLAB.

Also, try multiplying testmatrix with the dot() function, i.e. numpy.dot(testmatrix,testmatrix.T)

Apparently numpy.dot is used between arrays for matrix multiplication! The * operator is for element-wise multiplication (.* in MATLAB).

Solution 2

You're using element-wise multiplication - the * operator on two Numpy matrices is equivalent to the .* operator in Matlab. Use

prod = numpy.dot(testmatrix, testmatrix.T)

Comments

[BallpointBen]

PEP 465 allows the use of the infix @ operator: mat1 @ mat2