def setUp(self):
init_pdf = pb.UniPdf(np.array([-5.]), np.array([5.]))
p_xt_xtp = pb.MLinGaussCPdf(np.array([[2.]]), np.array([[1.]]),
np.array([0.]))
p_yt_xt = pb.MLinGaussCPdf(np.array([[1.]]), np.array([[1.]]),
np.array([0.]))
self.pf = pb.ParticleFilter(20, init_pdf, p_xt_xtp, p_yt_xt)
def test_rvs(self):
self.assertEqual(self.uni.rv.dimension, 1)
self.assertEqual(self.uni.cond_rv.dimension, 0)
self.assertEqual(self.multiuni.rv.dimension, 3)
self.assertEqual(self.multiuni.cond_rv.dimension, 0)
a = pb.RVComp(2, "a")
b = pb.RVComp(1, "b")
test_uni = pb.UniPdf(np.array([0., -1., 2.]), np.array([1., 1., 4.]),
pb.RV(a, b))
self.assertTrue(test_uni.rv.contains(a))
self.assertTrue(test_uni.rv.contains(b))
test_uni = pb.UniPdf(np.array([0., -1., 2.]), np.array([1., 1., 4.]),
[a, b])
self.assertTrue(test_uni.rv.contains(a))
self.assertTrue(test_uni.rv.contains(b))
def test_sample(self):
# we can only somehow test unifrom pdf, so we create a product of them
uni_list = []
for i in range(10):
uni_list.append(pb.UniPdf(np.array([i + 0.]), np.array([i + 1.])))
uni_prod = pb.ProdPdf(uni_list) # a product of 10 UniPdfs
for i in range(100):
sample = uni_prod.sample()
for j in range(10): # test each component..
self.assertTrue(j <= sample[j] <= j + 1) # ..is within bounds
def run_pf(timer, pf_opts, nr_particles, pf_class):
nr_steps = pf_opts.nr_steps # number of time steps
# prepare initial particle density:
init_pdf = pb.UniPdf(pf_opts.init_range[0], pf_opts.init_range[1])
# construct particle filter
if pf_class is pb.ParticleFilter:
pf = pf_class(nr_particles, init_pdf, pf_opts.p_xt_xtp,
pf_opts.p_yt_xt)
elif pf_class is pb.MarginalizedParticleFilter:
pf = pf_class(nr_particles, init_pdf, pf_opts.p_bt_btp,
pf_opts.kalman_args)
else:
raise NotImplementedError("This switch case not handled")
x_t = pf_opts.x_t
y_t = pf_opts.y_t
mean = np.empty((nr_steps, 2))
timer.start()
for i in range(nr_steps):
pf.bayes(y_t[i])
mean[i] = pf.posterior().mean()
timer.stop()
cumerror = np.sum((mean - x_t)**2, 0)
print((" {0}-{3} cummulative error for {1} steps: {2}".format(
nr_particles, nr_steps, np.sqrt(cumerror), pf_class.__name__)))
plt = None # disable plotting for now
if plt:
x = np.arange(nr_steps)
plt.plot(x,
mean[:, 0],
'x',
label="{0}: {1}".format(nr_particles, pf_class.__name__))
plt.plot(x,
mean[:, 1],
'+',
label="{0}: {1}".format(nr_particles, pf_class.__name__))
if plt and nr_particles == 90 and pf_class == pb.MarginalizedParticleFilter:
plt.plot(x, x_t[:, 0], '-')
plt.plot(x, x_t[:, 1], '--')
plt.legend()
plt.show()
def test_rvs(self):
self.assertEqual(self.prod.rv.dimension, 3)
# test that child rv components are copied into parent ProdPdf
a, b, c = pb.RVComp(1, "a"), pb.RVComp(1, "b"), pb.RVComp(1, "c")
uni = pb.UniPdf(np.array([0., 0.]), np.array([1., 2.]), pb.RV(a, b))
gauss = pb.GaussPdf(np.array([0.]), np.array([[1.]]), pb.RV(c))
prod = pb.ProdPdf((uni, gauss))
self.assertEquals(prod.rv.name, "[a, b, c]")
for rv_comp in a, b, c:
self.assertTrue(prod.rv.contains(rv_comp))
# that that custom rv passed to constructor is accepted
d = pb.RVComp(3, "d")
prod_custom = pb.ProdPdf((uni, gauss), pb.RV(d))
self.assertEquals(prod_custom.rv.name, "[d]")
self.assertTrue(prod_custom.rv.contains(d))
self.assertFalse(prod_custom.rv.contains(a))
self.assertFalse(prod_custom.rv.contains(b))
self.assertFalse(prod_custom.rv.contains(c))
def setUp(self):
self.uni = pb.UniPdf(np.array([0., 0.]), np.array([1., 2.]))
self.gauss = pb.GaussPdf(np.array([0.]), np.array([[1.]]))
self.prod = pb.ProdPdf((self.uni, self.gauss))
def setUp(self):
self.uni = pb.UniPdf(np.array([-10.]), np.array([20.]))
(self.a, self.b) = (np.array([0., -1., 2.]), np.array([1., 1., 4.]))
self.multiuni = pb.UniPdf(self.a, self.b)
def setUp(self):
ide = np.array([[1.]]) # 1x1 identity matrix
self.gauss = pb.MLinGaussCPdf(ide, ide, np.array([0.]))
self.uni = pb.UniPdf(np.array([0.]), np.array([2.]))
self.prod = pb.ProdCPdf((self.gauss, self.uni))