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fix(mobile): sample particles from weighted CDF intervals #926

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fix(mobile): sample particles from weighted CDF intervals #926

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13 changes: 3 additions & 10 deletions src/roboticstoolbox/mobile/ParticleFilter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,6 @@

import matplotlib.pyplot as plt
import numpy as np
import scipy as sp
import spatialmath.base as smb
from matplotlib import animation
from matplotlib.axes import Axes
Expand Down Expand Up @@ -696,21 +695,15 @@ def _select(self) -> None:
#
# particles with large weights will occupy a greater percentage of the
# y axis in a cummulative plot
cdf = np.cumsum(self.weight) / self.weight.sum()
cdf = np.cumsum(self.weight)
cdf = cdf / cdf[-1]

# so randomly (uniform) choosing y values is more likely to correspond to
# better particles...
iselect = self.random.uniform(0, 1, size=(self.nparticles,))

# find the particle that corresponds to each y value (just a look up)
interpfun = sp.interpolate.interp1d(
cdf,
np.arange(self.nparticles),
assume_sorted=True,
kind="nearest",
fill_value="extrapolate",
)
inextgen = interpfun(iselect).astype(int)
inextgen = np.searchsorted(cdf, iselect, side="right")

# copy selected particles for next generation..
self.x = self.x[inextgen, :]
Expand Down
18 changes: 18 additions & 0 deletions tests/test_particle_filter_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,24 @@ def test_map_is_the_map_of_the_sensor() -> None:
assert pf.map is pf.sensor.map


@pytest.mark.parametrize("first_weight", [0.2, 0.8])
def test_resampling_uses_weighted_cdf_intervals(first_weight: float) -> None:
pf = _filter()
particles = np.column_stack(
(np.arange(pf.nparticles), np.zeros((pf.nparticles, 2)))
)
pf.x = particles.copy()
pf.weight = np.zeros(pf.nparticles)
pf.weight[[0, -1]] = [first_weight, 1 - first_weight]
pf._random = np.random.default_rng(47)
draws = np.random.default_rng(47).uniform(0, 1, size=pf.nparticles)
expected_indices = np.where(draws < first_weight, 0, pf.nparticles - 1)

pf._select()

np.testing.assert_array_equal(pf.x, particles[expected_indices])


@pytest.mark.parametrize("verbose", [False, True])
def test_verbose_is_what_was_asked(verbose: bool) -> None:
assert _filter(verbose=verbose).verbose is verbose
Expand Down
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