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following naming convention better, lots of inverses which should be able to get rid of one or two, unsure if it works

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This commit is contained in:
Alan Saul 2013-03-18 15:59:12 +00:00
parent 34ae852eea
commit 2bf1cf0eb6
3 changed files with 39 additions and 30 deletions
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17
python/examples/laplace_approximations.py
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@ -41,18 +41,21 @@ def student_t_approx():
cov = kernel.K(X)
lap.fit_full(cov)
#Get one sample (just look at a single Y
mode = float(lap.f_hat[0])
variance = float((deg_free/(deg_free-2))) #BUG: Not convinced this is giving reasonable variables
#mode = float(lap.f_hat[0])
#variance = float((deg_free/(deg_free-2))) #BUG: Not convinced this is giving reasonable variables
#variance = float((deg_free/(deg_free-2)) + np.diagonal(lap.hess_hat)[0]) #BUG: Not convinced this is giving reasonable variables
normalised_approx = norm(loc=mode, scale=variance)
print "Normal with mode %f, and variance %f" % (mode, variance)
print lap.height_unnormalised
test_range = np.arange(0, 10, 0.1)
print np.diagonal(lap.hess_hat)
plt.plot(test_range, t_rv.pdf(test_range))
plt.plot(test_range, normalised_approx.pdf(test_range))
for i in xrange(X.shape[0]):
mode = lap.f_hat[i]
covariance = lap.hess_hat_i[i,i]
scaling = np.exp(lap.ln_z_hat)
normalised_approx = norm(loc=mode, scale=covariance)
print "Normal with mode %f, and variance %f" % (mode, covariance)
plt.plot(test_range, normalised_approx.pdf(test_range))
plt.show()
import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
def noisy_laplace_approx():