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Fixed covariance computation, included dimensions in plotting functions.

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Rozo Leonel (CR/PJ-AI-R31)
2019-10-25 11:50:04 +02:00
parent d70108e320
commit 7551e27743
1 changed files with 6 additions and 5 deletions
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pyrobolearn/models/gaussian.py
+6 -5
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@@ -605,7 +605,7 @@ class Gaussian(object):
# compute conditional
c = self.cov[np.ix_(o, i)].dot(np.linalg.inv(self.cov[np.ix_(i, i)]))
mu = self.mean[o] + c.dot(value - self.mean[i])
cov = self.cov[np.ix_(o, o)] - c.dot(self.cov[i, o])
cov = self.cov[np.ix_(o, o)] - c.dot(self.cov[np.ix_(i, o)])
return Gaussian(mu, cov)
def marginalize(self, idx):
@@ -1280,9 +1280,10 @@ def plot_3d_and_2d_countour(gaussians, step=500, bound=10, fig=None, title='', b
plt.show(block=block)
def plot_2d_ellipse(ax, gaussian, color='g', fill=False, plot_2devs=False, plot_arrows=True):
def plot_2d_ellipse(ax, gaussian, dims, color='g', fill=False, plot_2devs=False, plot_arrows=True):
# alias
g = gaussian
g = Gaussian(mean=gaussian.mean[np.ix_(dims)], covariance=gaussian.cov[np.ix_(dims, dims)])
# g = gaussian
# compute std deviation and eigenvectors from the gaussian
std_dev, evecs = g.ellipsoid_axes()
@@ -1311,8 +1312,8 @@ def plot_2d_ellipse(ax, gaussian, color='g', fill=False, plot_2devs=False, plot_
# compute scaled eigenvectors
p = std_dev * evecs
# draw arrows
ax.arrow(g.mean[0], g.mean[1], p[0, 0], p[1, 0], length_includes_head=True, head_width=0.15, color='r')
ax.arrow(g.mean[0], g.mean[1], p[0, 1], p[1, 1], length_includes_head=True, head_width=0.15, color='r')
ax.arrow(g.mean[dims[0]], g.mean[dims[1]], p[0, 0], p[1, 0], length_includes_head=True, head_width=0.15, color='r')
ax.arrow(g.mean[dims[0]], g.mean[dims[1]], p[0, 1], p[1, 1], length_includes_head=True, head_width=0.15, color='r')
return ellipse_2std