def testMakeInverseUpdateOpsNoEigenDecomp(self):
with tf_ops.Graph().as_default(), self.test_session() as sess:
random_seed.set_random_seed(200)
cov = np.array([[5., 2.], [2.,
4.]]) # NOTE(mattjj): must be symmetric
factor = InverseProvidingFactorTestingDummy(cov.shape)
factor._cov = array_ops.constant(cov, dtype=dtypes.float32)
damping_func = make_damping_func(0)
factor.register_inverse(damping_func)
factor.instantiate_inv_variables()
ops = factor.make_inverse_update_ops()
self.assertEqual(1, len(ops))
sess.run(tf_variables.global_variables_initializer())
# The inverse op will assign the damped inverse of cov to the inv var.
old_inv = sess.run(factor.get_inverse(damping_func))
self.assertAllClose(
sess.run(ff.inverse_initializer(cov.shape, dtypes.float32)),
old_inv)
sess.run(ops)
new_inv = sess.run(factor.get_inverse(damping_func))
self.assertAllClose(new_inv, np.linalg.inv(cov))
def testMakeInverseUpdateOpsNoEigenDecomp(self):
with tf_ops.Graph().as_default(), self.test_session() as sess:
random_seed.set_random_seed(200)
cov = np.array([[5., 2.], [2., 4.]]) # NOTE(mattjj): must be symmetric
factor = InverseProvidingFactorTestingDummy(cov.shape)
factor._cov = array_ops.constant(cov, dtype=dtypes.float32)
factor.register_damped_inverse(0)
ops = factor.make_inverse_update_ops()
self.assertEqual(1, len(ops))
sess.run(tf_variables.global_variables_initializer())
# The inverse op will assign the damped inverse of cov to the inv var.
old_inv = sess.run(factor._inverses_by_damping[0])
self.assertAllClose(
sess.run(ff.inverse_initializer(cov.shape, dtypes.float32)), old_inv)
sess.run(ops)
new_inv = sess.run(factor._inverses_by_damping[0])
self.assertAllClose(new_inv, np.linalg.inv(cov))