def plot_residual_resample(a):
N = len(a)
a_norm = np.asarray(a) / np.sum(a)
cumsum = np.cumsum(a_norm)
cumsum = np.insert(cumsum, 0, 0)
cmap = mpl.colors.ListedColormap([[0., .4, 1.],
[0., .8, 1.],
[1., .8, 0.],
[1., .4, 0.]]*(int(N/4) + 1))
fig = plt.figure(figsize=(6,3))
ax = fig.add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
drawedges=False,
spacing='proportional',
orientation='horizontal')
indexes = residual_resample(a_norm)
bins = np.bincount(indexes)
for i in range(1, N):
n = bins[i-1] # number particles in this sample
if n > 0:
b = np.linspace(cumsum[i-1], cumsum[i], n+2)[1:-1]
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('residual resampling')
plt.show()
def plot_residual_resample(a):
N = len(a)
a_norm = np.asarray(a) / np.sum(a)
cumsum = np.cumsum(a_norm)
cumsum = np.insert(cumsum, 0, 0)
cmap = mpl.colors.ListedColormap([[0., .4, 1.],
[0., .8, 1.],
[1., .8, 0.],
[1., .4, 0.]]*(int(N/4) + 1))
fig = plt.figure()
ax = plt.gcf().add_axes([0.05, 0.475, 0.9, 0.15])
norm = mpl.colors.BoundaryNorm(cumsum, cmap.N)
bar = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
norm=norm,
drawedges=False,
spacing='proportional',
orientation='horizontal')
indexes = residual_resample(a_norm)
bins = np.bincount(indexes)
for i in range(1, N):
n = bins[i-1] # number particles in this sample
if n > 0:
b = np.linspace(cumsum[i-1], cumsum[i], n+2)[1:-1]
plt.scatter(b, [.5]*len(b), s=60, facecolor='k', edgecolor='k')
bar.set_ticks([])
plt.title('residual resampling')
def resample(self):
self.weights = normalize(self.weights[:, np.newaxis],
axis=0,
norm='l1').ravel()
self.states = self.states[residual_resample(self.weights)]
self.weights = np.ones(
(self.num_particles), dtype=int) * (1 / float(self.num_particles))
def resample(self):
self.weights = normalize(self.weights[:, np.newaxis],
axis=0,
norm='l1').ravel()
#print np.square(self.weights)
ESS = 1 / float(np.sum(np.square(self.weights)))
#print ESS,THRESHOLD
if ESS > THRESHOLD:
newStates = np.empty((len(self.weights), self.DIM), dtype=int)
self.states = self.states[residual_resample(self.weights)]
self.weights = np.ones((self.num_particles),
dtype=int) * (1 / float(self.num_particles))
def resample(self):
indexes = residual_resample(self.weights)
self.particles = self.particles[indexes,:]
self.weights = self.weights[indexes]
