def test_vec_and_unvec(self): a = np.array([[ 5., 1., 14., 2., 42.], [132., 2., 429., 1., 1.], [ 1., 2., 1430., 2., 2.]]) b = npu.col(5., 132., 1., 1., 2., 2., 14., 429., 1430., 2., 1., 2., 42., 1., 2.) npt.assert_almost_equal(npu.vec(a), b) npt.assert_almost_equal(npu.unvec(b, 3), a)
def noise_covariance(self, time_delta): mrf_squared = self.mean_reversion_factor_squared(time_delta) eye_minus_mrf_squared = np.eye( self.process_dim * self.process_dim) - mrf_squared return npu.unvec( np.dot(np.dot(self._transition_x_2_inverse, eye_minus_mrf_squared), self._cov_vec), self.process_dim)
def noisecovariance(self, timedelta): mrfsquared = self.meanreversionfactorsquared(timedelta) eyeminusmrfsquared = np.eye(self.processdim) - mrfsquared return npu.unvec( np.dot(np.dot(self.__transitionx2inverse, eyeminusmrfsquared), self.__covvec), self.processdim)