I'd like to know how to calculate the factorial of a matrix elementwise. For example,
import numpy as np
mat = np.array([[1,2,3],[2,3,4]])
np.the_function_i_want(mat)
would give a matrix mat2
such that mat2[i,j] = mat[i,j]!
. I've tried something like
np.fromfunction(lambda i,j: np.math.factorial(mat[i,j]))
but it passes the entire matrix as argument for np.math.factorial
. I've also tried to use scipy.vectorize
but for matrices larger than 10x10 I get an error. This is the code I wrote:
import scipy as sp
javi = sp.fromfunction(lambda i,j: i+j, (15,15))
fact = sp.vectorize(sp.math.factorial)
fact(javi)
OverflowError: Python int too large to convert to C long
Such an integer number would be greater than 2e9, so I don't understand what this means.
There's a
factorial
function inscipy.special
which allows element-wise computations on arrays:The function returns an array of float values and so can compute "larger" factorials up to the accuracy floating point numbers allow:
You may need to adjust the print precision of NumPy arrays if you want to avoid the number being displayed in scientific notation.
Regarding
scipy.vectorize
: theOverflowError
implies that the result of some of the calculations are too big to be stored as integers (normallyint32
orint64
).If you want to vectorize
sp.math.factorial
and want arbitrarily large integers, you'll need to specify that the function return an output array with the'object'
datatype. For instance:Specifying the
'object'
type allows Python integers to be returned byfact
. These are not limited in size and so you can calculate factorials as large as your computer's memory will permit. Be aware that arrays of this type lose some of the speed and efficiency benefits which regular NumPy arrays have.