def P(self, value): """ covariance matrix""" self._P = setter_scalar(value, self.dim_x) self._P1_2 = cholesky(self._P, lower=True)
def P(self, value): self._P = setter_scalar(value, self.dim_x) self._P1_2 = cholesky(self._P, lower=True)
def Q(self, value): self._Q = setter_scalar(value, self.dim_x)
def Q(self, value): """ Process uncertainty matrix""" self._Q = setter_scalar(value, self.dim_x)
def R(self, value): """ measurement uncertainty""" self._R = setter_scalar(value, self.dim_z)
def P(self, value): self._P = setter_scalar(value, self.dim_x)
def W(self, value): self._W = setter_scalar(value, self.dim_x)
def R_inv(self, value): self._R_inv = setter_scalar(value, self.dim_z)
def P_inv(self, value): self._P_inv = setter_scalar(value, self.dim_x)
def R(self, value): """ measurement uncertainty""" self._R = setter_scalar(value, self.dim_z) self._R1_2 = cholesky(self._R, lower=True)
def Q(self, value): """ Process uncertainty""" self._Q = setter_scalar(value, self.dim_x) self._Q1_2 = cholesky(self._Q, lower=True)
def P(self, value): """ state covariance matrix""" self._P = setter_scalar(value, self.dim_x)
def R(self, value): self._R = setter_scalar(value, self.dim_z)
def V(self, value): self._V = setter_scalar(value, self.dim_z) self._V_inv = linalg.inv(self.V)
def Q(self, value): self._Q = setter_scalar(value, self.dim_x) self._Q1_2 = cholesky(self._Q, lower=True)
def R(self, value): self._R = setter_scalar(value, self.dim_z) self._R1_2 = cholesky(self._R, lower=True)
def Q(self, value): self._Q = setter_scalar(value, self.dim_x) self._Q1_2 = cholesky (self._Q, lower=True)
def R(self, value): self._R = setter_scalar(value, self.dim_z) self._R1_2 = cholesky (self._R, lower=True)