Source code for smpl.envs.helpers.helper_funcs

import numpy as np


# fit calculation
[docs]def fitting_score( y, yhat ): ey = y - yhat em = y - np.mean(y) return 100.0 * (1 - np.linalg.norm(ey) / np.linalg.norm(em))
# mean squared error
[docs]def get_mse( y, yhat ): ey = y - yhat N = len(y) return (1 / N) * np.matmul(ey, ey)
# Kalman filter
[docs]def state_estimator( x, u, ym, A, B, C, K ): xe = np.matmul(A, x) \ + np.matmul(B, u) \ + np.matmul(K, ym - np.matmul(C, x)) return np.array(xe).ravel()