# Numpy transpose multiplication problem

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?

## Answers

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).

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)
```

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