def compare_to_matlab(measurement='Near'):
prior = GaussianMixture(
weights=[1, 1, 1, 1, 1],
means=[
[-2, -4], # GM1 mean
[-1, -2], # GM2 mean
[0, 0], # GM3 mean
[1, -2], # GM4 mean
[2, -4], # GM5 mean
],
covariances=[
[
[0.1, 0], # GM1 mean
[0, 0.1]
],
[
[0.2, 0], # GM2 mean
[0, 0.2]
],
[
[0.3, 0], # GM3 mean
[0, 0.3]
],
[
[0.2, 0], # GM4 mean
[0, 0.2]
],
[
[0.1, 0], # GM5 mean
[0, 0.1]
],
])
# prior = GaussianMixture(weights=[1],
# means=[[-2, -4], # GM1 mean
# ],
# covariances=[[[0.1, 0], # GM1 mean
# [0, 0.1]
# ],
# ])
# Define sensor likelihood
brm = range_model()
file_ = open('/Users/nick/Downloads/VBIS GM Fusion/nick_output.csv', 'w')
for i in range(30):
# Do a VBIS update
logging.info('Starting VB update...')
vb = VariationalBayes()
mu_hat, var_hat, beta_hat = vb.update(measurement, brm, prior)
# Flatten values
flat = np.hstack((beta_hat, mu_hat.flatten(), var_hat.flatten()))
# Save Flattened values
np.savetxt(file_, np.atleast_2d(flat), delimiter=',')
file_.close()
def gmm_sm_test(measurement='Outside'):
# Define prior
# prior = GaussianMixture(weights=[1, 4, 5],
# means=[[0.5, 1.3], # GM1 mean
# [-0.7, -0.6], # GM2 mean
# [0.2, -3], # GM3 mean
# ],
# covariances=[[[0.4, 0.3], # GM1 mean
# [0.3, 0.4]
# ],
# [[0.3, 0.1], # GM2 mean
# [0.1, 0.3]
# ],
# [[0.5, 0.4], # GM3 mean
# [0.4, 0.5]],
# ])
prior = GaussianMixture(
weights=[1, 1, 1, 1, 1],
means=[
[-2, -4], # GM1 mean
[-1, -2], # GM2 mean
[0, 0], # GM3 mean
[1, -2], # GM4 mean
[2, -4], # GM5 mean
],
covariances=[
[
[0.1, 0], # GM1 mean
[0, 0.1]
],
[
[0.2, 0], # GM2 mean
[0, 0.2]
],
[
[0.3, 0], # GM3 mean
[0, 0.3]
],
[
[0.2, 0], # GM4 mean
[0, 0.2]
],
[
[0.1, 0], # GM5 mean
[0, 0.1]
],
])
# prior = GaussianMixture(weights=[1],
# means=[[-2, -4], # GM1 mean
# ],
# covariances=[[[0.1, 0], # GM1 mean
# [0, 0.1]
# ],
# ])
# Define sensor likelihood
brm = range_model()
# Do a VBIS update
logging.info('Starting VB update...')
vb = VariationalBayes()
mu_hat, var_hat, beta_hat = vb.update(measurement,
brm,
prior,
use_LWIS=True)
vbis_posterior = GaussianMixture(weights=beta_hat,
means=mu_hat,
covariances=var_hat)
# Define gridded space for graphing
min_x, max_x = -5, 5
min_y, max_y = -5, 5
res = 100
x_space, y_space = np.mgrid[min_x:max_x:1 / res, min_y:max_y:1 / res]
pos = np.empty(x_space.shape + (2, ))
pos[:, :, 0] = x_space
pos[:, :, 1] = y_space
levels_res = 50
max_prior = np.max(prior.pdf(pos))
prior_levels = np.linspace(0, max_prior, levels_res)
brm.probability()
max_lh = np.max(brm.probs)
lh_levels = np.linspace(0, max_lh, levels_res)
max_post = np.max(vbis_posterior.pdf(pos))
post_levels = np.linspace(0, max_post, levels_res)
# Plot results
fig = plt.figure()
likelihood_label = 'Likelihood of \'{}\''.format(measurement)
prior_ax = plt.subplot2grid((2, 32), (0, 0), colspan=14)
prior_cax = plt.subplot2grid((2, 32), (0, 14), colspan=1)
prior_c = prior_ax.contourf(x_space,
y_space,
prior.pdf(pos),
levels=prior_levels)
cbar = plt.colorbar(prior_c, cax=prior_cax)
prior_ax.set_xlabel('x1')
prior_ax.set_ylabel('x2')
prior_ax.set_title('Prior Distribution')
lh_ax = plt.subplot2grid((2, 32), (0, 17), colspan=14)
lh_cax = plt.subplot2grid((2, 32), (0, 31), colspan=1)
brm.classes[measurement].plot(ax=lh_ax,
label=likelihood_label,
ls='--',
levels=lh_levels,
show_plot=False,
plot_3D=False)
# plt.colorbar(sm.probs, cax=lh_cax)
lh_ax.set_title(likelihood_label)
posterior_ax = plt.subplot2grid((2, 32), (1, 0), colspan=31)
posterior_cax = plt.subplot2grid((2, 32), (1, 31), colspan=1)
posterior_c = posterior_ax.contourf(x_space,
y_space,
vbis_posterior.pdf(pos),
levels=post_levels)
plt.colorbar(posterior_c, cax=posterior_cax)
posterior_ax.set_xlabel('x1')
posterior_ax.set_ylabel('x2')
posterior_ax.set_title('VBIS Posterior Distribution')
logging.info(
'Prior Weights: \n {} \n Means: \n {} \n Variances: \n {} \n'.format(
prior.weights, prior.means, prior.covariances))
logging.info(
'Posterior Weights: \n {} \n Means: \n {} \n Variances: \n {} \n'.
format(vbis_posterior.weights, vbis_posterior.means,
vbis_posterior.covariances))
plt.show()