def residual(xi, n, input_lags):
d1i = xi.reshape(n, n).transpose() / mu
m = MAP(D0, d1i, check=False)
estimated_lags = [m.lag(i + 1) for i in range(len(input_lags))]
system_diff = np.asarray(A.dot(xi) - b).flatten() * 1000
lags_diff = np.asarray(input_lags - estimated_lags)
diff = np.hstack((system_diff, lags_diff))
return diff
def residual(x, n, input_moments, input_lags):
m = MAP(*decompose(x, n), check=False)
estimated_moments = [
m.moment(i + 1) for i in range(len(input_moments))
]
estimated_lags = [m.lag(i + 1) for i in range(len(input_lags))]
estimated_moments_lags = np.r_[estimated_moments, estimated_lags]
input_moments_lags = np.r_[input_moments, input_lags]
return estimated_moments_lags - input_moments_lags
