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# MATRIX_POWER

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The MATRIX_POWER node is based on a numpy or scipy function. The description of that function is as follows: Raise a square matrix to the (integer) power 'n'. 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. If "n < 0", the inverse is computed and then raised to "abs(n)". Note: Stacks of object matrices are not currently supported. Params: a : (..., M, M) array_like Matrix to be "powered". n : int The exponent can be any integer or long integer, positive, negative, or zero. Returns: out : DataContainer type 'ordered pair', 'scalar', or 'matrix'
Python Code
from flojoy import flojoy, Matrix, Scalar
import numpy as np

import numpy.linalg

@flojoy
def MATRIX_POWER(
default: Matrix,
n: int = 2,
) -> Matrix | Scalar:
"""The MATRIX_POWER node is based on a numpy or scipy function.

The description of that function is as follows:

Raise a square matrix to the (integer) power 'n'.

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. If "n < 0", the inverse is computed and then raised to "abs(n)".

Note: Stacks of object matrices are not currently supported.

Parameters
----------
a : (..., M, M) array_like
Matrix to be "powered".
n : int
The exponent can be any integer or long integer, positive, negative, or zero.

Returns
-------
DataContainer
type 'ordered pair', 'scalar', or 'matrix'
"""

result = numpy.linalg.matrix_power(
a=default.m,
n=n,
)

if isinstance(result, np.ndarray):
result = Matrix(m=result)
else:
assert isinstance(
result, np.number | float | int
), f"Expected np.number, float or int for result, got {type(result)}"
result = Scalar(c=float(result))

return result


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