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I want to raise a 2-dimensional numpy array, let's call it A, to the power of some number n, but I have thus far failed to find the function or operator to do that.
I'm aware that I could cast it to the matrix type and use the fact that then (similar to what would be the behaviour in Matlab), A**n does just what I want, (for array the same expression means elementwise exponentiation). Casting to matrix and back seems like a rather ugly workaround though.
Surely there must be a good way to perform that calculation while keeping the format to array?
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The numpy. linalg. matrix_power() method is used to raise a square matrix to the power n. It will take two parameters, The 1st parameter is an input matrix that is created using a NumPy array and the 2nd parameter is the exponent n, which refers to the power that can be zero or non-zero integers.
To raise a square matrix to the power n in Linear Algebra, use the numpy. linalg. matrix_power() in Python For positive integers n, the power is computed by repeated matrix squarings and matrix multiplications. If n == 0, the identity matrix of the same shape as M is returned.
To multiply two matrices use the dot() function of NumPy. It takes only 2 arguments and returns the product of two matrices.
I believe you want numpy.linalg.matrix_power
As a quick example:
import numpy as np x = np.arange(9).reshape(3,3) y = np.matrix(x) a = y**3 b = np.linalg.matrix_power(x, 3) print a print b assert np.all(a==b) This yields:
In [19]: a Out[19]: matrix([[ 180, 234, 288], [ 558, 720, 882], [ 936, 1206, 1476]]) In [20]: b Out[20]: array([[ 180, 234, 288], [ 558, 720, 882], [ 936, 1206, 1476]]) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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