(self, Dx, Du, pts)
return mu0, Phi, m, n0
Evaluate prior.
        Args:
            pts: A N x Dx+Du+Dx matrix.
Evaluate prior.
        Args:
            pts: A N x Dx+Du+Dx matrix.
Evaluate prior . Args : pts : A N x Dx + Du + Dx matrix .
def eval(self, Dx, Du, pts):
        """
        Evaluate prior.
        Args:
            pts: A N x Dx+Du+Dx matrix.
        """
        # Construct query data point by rearranging entries and adding
        # in reference.
        assert pts.shape[1] == Dx + Du + Dx

        # Perform query and fix mean.
        mu0, Phi, m, n0 = self.gmm.inference(pts)

        # Factor in multiplier.
        n0 = n0 * self._strength
        m = m * self._strength

        # Multiply Phi by m (since it was normalized before).
        Phi *= m
        return mu0, Phi, m, n0
def eval ( self , Dx , Du , pts ) : # Construct query data point by rearranging entries and adding # in reference. assert pts . shape [ 1 ] == Dx + Du + Dx # Perform query and fix mean. mu0 , Phi , m , n0 = self . gmm . inference ( pts ) # Factor in multiplier. n0 = n0 * self . _strength m = m * self . _strength # Multiply Phi by m (since it was normalized before). Phi *= m return mu0 , Phi , m , n0