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import numpy as np
from generalized_unscented_transform import generalized_ut
from evaluate_from_sigma_points import Evaluate_sample_statistics
def quadratic_transform(sigma_points):
sigma_points = np.array(sigma_points)[0]
y = []
for x in sigma_points:
y.append(3*x + 2*(x**2))
y = np.matrix(y)
return y
# Gaussian case
def test_Gaussian():
m1 = 1
m2 = 4
m3 = 0
m4 = 48
sigma_points, weights = generalized_ut(np.matrix([m1]), np.matrix([m2]), np.matrix([m3]), np.matrix([m4]))
sigma_mean, sigma_cov, sigma_skew, sigma_kurt = Evaluate_sample_statistics(quadratic_transform(sigma_points), weights)
print(sigma_mean)
print(sigma_cov)
print(sigma_skew)
print(sigma_kurt)
def test_Exp():
m1 = 0.5
m2 = 0.25
m3 = 0.25
m4 = 0.5625
sigma_points, weights = generalized_ut(np.matrix([m1]), np.matrix([m2]), np.matrix([m3]), np.matrix([m4]))
sigma_mean, sigma_cov, sigma_skew, sigma_kurt = Evaluate_sample_statistics(quadratic_transform(sigma_points), weights)
print(sigma_mean)
print(sigma_cov)
print(sigma_skew)
print(sigma_kurt)
if __name__ == "__main__":
test_Exp()