The overhead of the np.matrix class is quite high for small matrices. See for example the following code:

import time

import math

import numpy as np

def rot2D(phi):

c=math.cos(phi);

return np.matrix(c)

_b=np.matrix(np.zeros( (1,)))

def rot2Dx(phi):

global _b

r=_b.copy()

c=math.cos(phi);

r.itemset(0, c)

return r

phi=.023

%timeit rot2D(phi)

%timeit rot2Dx(phi)

The second implementation performs much better by using a copy instead of a constructor. Is there a way to efficiency create a new np.matrix object? For other functions in my code I do not have the option to copy an existing matrix, but I need to construct a new object or perform a cast from np.array to np.matrix.

I am already aware of two alternatives:

- Using the new multiplication operator (https://www.python.org/dev/peps/pep-0465/). This is a good solution, but only python 3.5

- Using the .dot functions from np.array. This works, but personally I like the notation using np.matrix much better.

I also created an issue on github: https://github.com/numpy/numpy/issues/6186

With kind regards,

Pieter Eendebak