(self, Ts, Ps)
return np.zeros(dX+dU), Phi, 0, self._hyperparams['strength']
Evaluate the policy prior.
Evaluate the policy prior.
Evaluate the policy prior .
def eval(self, Ts, Ps):
        """ Evaluate the policy prior. """
        dX, dU = Ts.shape[-1], Ps.shape[-1]
        prior_fd = np.zeros((dU, dX))
        prior_cond = 1e-5 * np.eye(dU)
        sig = np.eye(dX)
        Phi = self._hyperparams['strength'] * np.vstack([
            np.hstack([sig, sig.dot(prior_fd.T)]),
            np.hstack([prior_fd.dot(sig),
                       prior_fd.dot(sig).dot(prior_fd.T) + prior_cond])
        ])
        return np.zeros(dX+dU), Phi, 0, self._hyperparams['strength']
def eval ( self , Ts , Ps ) : dX , dU = Ts . shape [ - 1 ] , Ps . shape [ - 1 ] prior_fd = np . zeros ( ( dU , dX ) ) prior_cond = 1e-5 * np . eye ( dU ) sig = np . eye ( dX ) Phi = self . _hyperparams [ 'strength' ] * np . vstack ( [ np . hstack ( [ sig , sig . dot ( prior_fd . T ) ] ) , np . hstack ( [ prior_fd . dot ( sig ) , prior_fd . dot ( sig ) . dot ( prior_fd . T ) + prior_cond ] ) ] ) return np . zeros ( dX + dU ) , Phi , 0 , self . _hyperparams [ 'strength' ]