93 lines
2.6 KiB
Python
93 lines
2.6 KiB
Python
"""
|
|
GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
|
|
Atlanta, Georgia 30332-0415
|
|
All Rights Reserved
|
|
|
|
See LICENSE for the license information
|
|
|
|
JacobianFactor unit tests.
|
|
Author: Frank Dellaert & Duy Nguyen Ta (Python)
|
|
"""
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
import gtsam
|
|
from gtsam.utils.test_case import GtsamTestCase
|
|
|
|
|
|
class TestJacobianFactor(GtsamTestCase):
|
|
|
|
def test_eliminate(self):
|
|
# Recommended way to specify a matrix (see cython/README)
|
|
Ax2 = np.array(
|
|
[[-5., 0.],
|
|
[+0., -5.],
|
|
[10., 0.],
|
|
[+0., 10.]], order='F')
|
|
|
|
# This is good too
|
|
Al1 = np.array(
|
|
[[5, 0],
|
|
[0, 5],
|
|
[0, 0],
|
|
[0, 0]], dtype=float, order = 'F')
|
|
|
|
# Not recommended for performance reasons, but should still work
|
|
# as the wrapper should convert it to the correct type and storage order
|
|
Ax1 = np.array(
|
|
[[0, 0], # f4
|
|
[0, 0], # f4
|
|
[-10, 0], # f2
|
|
[0, -10]]) # f2
|
|
|
|
x2 = 1
|
|
l1 = 2
|
|
x1 = 3
|
|
|
|
# the RHS
|
|
b2 = np.array([-1., 1.5, 2., -1.])
|
|
sigmas = np.array([1., 1., 1., 1.])
|
|
model4 = gtsam.noiseModel.Diagonal.Sigmas(sigmas)
|
|
combined = gtsam.JacobianFactor(x2, Ax2, l1, Al1, x1, Ax1, b2, model4)
|
|
|
|
# eliminate the first variable (x2) in the combined factor, destructive
|
|
# !
|
|
ord = gtsam.Ordering()
|
|
ord.push_back(x2)
|
|
actualCG, lf = combined.eliminate(ord)
|
|
|
|
# create expected Conditional Gaussian
|
|
R11 = np.array([[11.1803, 0.00],
|
|
[0.00, 11.1803]])
|
|
S12 = np.array([[-2.23607, 0.00],
|
|
[+0.00, -2.23607]])
|
|
S13 = np.array([[-8.94427, 0.00],
|
|
[+0.00, -8.94427]])
|
|
d = np.array([2.23607, -1.56525])
|
|
expectedCG = gtsam.GaussianConditional(
|
|
x2, d, R11, l1, S12, x1, S13, gtsam.noiseModel.Unit.Create(2))
|
|
# check if the result matches
|
|
self.gtsamAssertEquals(actualCG, expectedCG, 1e-4)
|
|
|
|
# the expected linear factor
|
|
Bl1 = np.array([[4.47214, 0.00],
|
|
[0.00, 4.47214]])
|
|
|
|
Bx1 = np.array(
|
|
# x1
|
|
[[-4.47214, 0.00],
|
|
[+0.00, -4.47214]])
|
|
|
|
# the RHS
|
|
b1 = np.array([0.0, 0.894427])
|
|
|
|
model2 = gtsam.noiseModel.Diagonal.Sigmas(np.array([1., 1.]))
|
|
expectedLF = gtsam.JacobianFactor(l1, Bl1, x1, Bx1, b1, model2)
|
|
|
|
# check if the result matches the combined (reduced) factor
|
|
self.gtsamAssertEquals(lf, expectedLF, 1e-4)
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|