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Minor reorganising

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Alan Saul 2014-02-11 14:06:42 +00:00
parent 9eef4ebded
commit c76f1a4d6d
3 changed files with 8 additions and 9 deletions
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5
GPy/inference/latent_function_inference/laplace.py
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@ -92,12 +92,11 @@ class LaplaceInference(object):
iteration = 0 iteration = 0
while difference > self._mode_finding_tolerance and iteration < self._mode_finding_max_iter: while difference > self._mode_finding_tolerance and iteration < self._mode_finding_max_iter:
W = -likelihood.d2logpdf_df2(f, Y, extra_data=Y_metadata) W = -likelihood.d2logpdf_df2(f, Y, extra_data=Y_metadata)
W_f = W*f
grad = likelihood.dlogpdf_df(f, Y, extra_data=Y_metadata) grad = likelihood.dlogpdf_df(f, Y, extra_data=Y_metadata)
W_f = W*f
b = W_f + grad # R+W p46 line 6. b = W_f + grad # R+W p46 line 6.
#W12BiW12Kb, B_logdet = self._compute_B_statistics(K, W.copy(), np.dot(K, b), likelihood.log_concave)
W12BiW12, _, _ = self._compute_B_statistics(K, W, likelihood.log_concave) W12BiW12, _, _ = self._compute_B_statistics(K, W, likelihood.log_concave)
W12BiW12Kb = np.dot(W12BiW12, np.dot(K, b)) W12BiW12Kb = np.dot(W12BiW12, np.dot(K, b))

4
GPy/testing/likelihood_tests.py
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@ -1,10 +1,10 @@
import numpy as np import numpy as np
import unittest import unittest
import GPy import GPy
from GPy.models import GradientChecker from ..models import GradientChecker
import functools import functools
import inspect import inspect
from GPy.likelihoods import link_functions from ..likelihoods import link_functions
from ..core.parameterization import Param from ..core.parameterization import Param
from functools import partial from functools import partial
#np.random.seed(300) #np.random.seed(300)