daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Fixed display of marginals on 3d plots of Gaussian

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The contours were displaying multiple curves, which doesn't have
a lot of meaning. Replaced with the marginal distribution.
This commit is contained in:
Roger Labbe 2015-08-08 10:06:29 -07:00
parent d481f3aaf4
commit be713acc68
1 changed files with 31 additions and 8 deletions
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39
code/mkf_internal.py
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@ -300,17 +300,30 @@ def plot_3d_covariance(mean, cov):
xv, yv = np.meshgrid (xs, ys)
zs = np.array([100.* stats.multivariate_gaussian(np.array([x,y]),mean,cov) \
for x,y in zip(np.ravel(xv), np.ravel(yv))])
for x, y in zip(np.ravel(xv), np.ravel(yv))])
zv = zs.reshape(xv.shape)
ax = plt.figure().add_subplot(111, projection='3d')
maxz = np.max(zs)
#ax = plt.figure().add_subplot(111, projection='3d')
ax = plt.gca(projection='3d')
ax.plot_surface(xv, yv, zv, rstride=1, cstride=1, cmap=cm.autumn)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.contour(xv, yv, zv, zdir='x', offset=minx-1, cmap=cm.autumn)
ax.contour(xv, yv, zv, zdir='y', offset=maxy, cmap=cm.BuGn)
x = mean[0]
zs = np.array([100.* stats.multivariate_gaussian(np.array([x, y]),mean,cov)
for _, y in zip(np.ravel(xv), np.ravel(yv))])
zv = zs.reshape(xv.shape)
ax.contour(xv, yv, zv, zdir='x', offset=minx-1, cmap=cm.binary)
y = mean[1]
zs = np.array([100.* stats.multivariate_gaussian(np.array([x, y]),mean,cov)
for x, _ in zip(np.ravel(xv), np.ravel(yv))])
zv = zs.reshape(xv.shape)
ax.contour(xv, yv, zv, zdir='y', offset=maxy, cmap=cm.binary)
def plot_3d_sampled_covariance(mean, cov):
@ -364,8 +377,17 @@ def plot_3d_sampled_covariance(mean, cov):
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.contour(xv, yv, zv, zdir='x', offset=minx-1, cmap=cm.autumn)
ax.contour(xv, yv, zv, zdir='y', offset=maxy, cmap=cm.BuGn)
x = mean[0]
zs = np.array([100.* stats.multivariate_gaussian(np.array([x, y]),mean,cov)
for _, y in zip(np.ravel(xv), np.ravel(yv))])
zv = zs.reshape(xv.shape)
ax.contour(xv, yv, zv, zdir='x', offset=minx-1, cmap=cm.binary)
y = mean[1]
zs = np.array([100.* stats.multivariate_gaussian(np.array([x, y]),mean,cov)
for x, _ in zip(np.ravel(xv), np.ravel(yv))])
zv = zs.reshape(xv.shape)
ax.contour(xv, yv, zv, zdir='y', offset=maxy, cmap=cm.binary)
def plot_3_covariances():
@ -464,9 +486,10 @@ def plot_covariance(P, index=(0, 0)):
if __name__ == "__main__":
pass
#show_position_chart()
#plot_3d_covariance((2,7), np.array([[8.,0],[0,4.]]))
plot_3d_covariance((2,7), np.array([[8.,0],[0,1.]]))
#plot_3d_sampled_covariance([2,7], [[8.,0],[0,4.]])
#show_residual_chart()