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moved linalg function to GPy.linalg

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This commit is contained in:
Nicolo Fusi 2013-05-13 17:04:59 +01:00
parent cbfbcfb411
commit 80c25c9aaf
3 changed files with 14 additions and 8 deletions
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10
GPy/inference/SGD.py
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@ -206,8 +206,9 @@ class opt_SGD(Optimizer):
f, fp = f_fp(self.x_opt[j])
step[j] = self.momentum * step[j] + self.learning_rate[j] * fp
self.x_opt[j] -= step[j]
self.restore_constraints(ci)
self.model.grads[j] = fp
# restore likelihood _bias and _scale, otherwise when we call set_data(y) on
# the next feature, it will get normalized with the mean and std of this one.
self.model.likelihood._bias = 0
@ -217,6 +218,8 @@ class opt_SGD(Optimizer):
def opt(self, f_fp=None, f=None, fp=None):
self.x_opt = self.model._get_params_transformed()
self.model.grads = np.zeros_like(self.x_opt)
X, Y = self.model.X.copy(), self.model.likelihood.Y.copy()
self.model.likelihood.YYT = None
@ -287,7 +290,10 @@ class opt_SGD(Optimizer):
self.model.likelihood.N = N
self.model.likelihood.D = D
self.model.likelihood.Y = Y
sigma = self.model.likelihood._variance
self.model.likelihood._variance = None # invalidate cache
self.model.likelihood._set_params(sigma)
self.trace.append(self.f_opt)
if self.iteration_file is not None:
f = open(self.iteration_file + "iteration%d.pickle" % it, 'w')

7
GPy/models/sparse_GP.py
View file

@ -3,17 +3,12 @@
import numpy as np
import pylab as pb
from ..util.linalg import mdot, jitchol, tdot, symmetrify
from ..util.linalg import mdot, jitchol, tdot, symmetrify, backsub_both_sides
from ..util.plot import gpplot
from .. import kern
from GP import GP
from scipy import linalg
def backsub_both_sides(L, X):
""" Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
tmp, _ = linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(X), lower=1, trans=1)
return linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(tmp.T), lower=1, trans=1)[0].T
class sparse_GP(GP):
"""
Variational sparse GP model

5
GPy/util/linalg.py
View file

@ -303,3 +303,8 @@ def cholupdate(L,x):
x = x.copy()
N = x.size
weave.inline(code, support_code=support_code, arg_names=['N','L','x'], type_converters=weave.converters.blitz)
def backsub_both_sides(L, X):
""" Return L^-T * X * L^-1, assumuing X is symmetrical and L is lower cholesky"""
tmp, _ = linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(X), lower=1, trans=1)
return linalg.lapack.flapack.dtrtrs(L, np.asfortranarray(tmp.T), lower=1, trans=1)[0].T