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Added timing and realised mdot can be faster as its almost always a diagonal matrix its multiplying with

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Alan Saul 2013-04-05 17:56:02 +01:00
parent 4a14a82dfb
commit 31d8faecf8
2 changed files with 21 additions and 13 deletions
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9
python/examples/laplace_approximations.py
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@ -8,11 +8,12 @@ from coxGP.python.likelihoods.likelihood_function import student_t
def timing():
real_var = 0.1
times = 1000
times = 1
deg_free = 10
real_sd = np.sqrt(real_var)
the_is = np.zeros(times)
X = np.linspace(0.0, 10.0, 30)[:, None]
X = np.linspace(0.0, 10.0, 500)[:, None]
for a in xrange(times):
Y = np.sin(X) + np.random.randn(*X.shape)*real_var
Yc = Y.copy()
@ -21,6 +22,8 @@ def timing():
Yc[25] += 10
Yc[23] += 10
Yc[24] += 10
Yc[300] += 10
Yc[400] += 10000
edited_real_sd = real_sd
kernel1 = GPy.kern.rbf(X.shape[1])
@ -33,9 +36,9 @@ def timing():
m.optimize()
the_is[a] = m.likelihood.i
import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
print the_is
print np.mean(the_is)
import ipdb; ipdb.set_trace() ### XXX BREAKPOINT
def student_t_approx():