Structure and Evaluation of Multivariate Polynomials [ex201.1]ΒΆ

Out:

-- 1 --
(array([-0.3  ,  0.08 , -0.004]),), (array([0, 2, 4]),)
-- 2 --
0.09599999999999997
-- 3 --
[-0.3   -0.224 -0.044  0.096 -0.044]
-- 4 --
0.007680000000000015
-- 5 --
[ 0.00768 -0.00352 -0.0224  -0.042  ]
shape:  4
-- 6 --
shape:  (3, 2, 5)

import numpy as np
import lmlib as lm

expos = ([0, 2, 4],)
coefs = ([-0.3, 0.08, -0.004],)
m_poly = lm.MPoly(coefs, expos)

print("-- 1 --")
print(m_poly)

print("-- 2 --")
print(m_poly.eval((3,)))

print("-- 3 --")
print(m_poly.eval((np.arange(5),)))

expos = ([0, 1, 2], [0, 2, 4])
coefs = ([0.1, -0.03, 0.01], [-0.3, 0.08, -0.004])
m_poly = lm.MPoly(coefs, expos)

print("-- 4 --")
# Scalar variable inputs yields a scalar output
print(m_poly.eval((1, 3)))

print("-- 5 --")
# Array_like variable inputs yields into a array_like output of the same shape
variables = ([1, 2, 3, 4], [3, 2, 1, 0])
out = m_poly.eval(variables)
print(out)
print('shape: ', len(out))

print("-- 6 --")
x = np.arange(3*2*5).reshape([3, 2, 5])
y = np.arange(3*2*5).reshape([3, 2, 5])-10
print('shape: ', m_poly.eval((x, y)).shape)

Total running time of the script: ( 0 minutes 0.038 seconds)

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