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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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This commit is contained in:
Ricardo 2013-05-13 15:09:43 +01:00
commit 3cc9547cab
2 changed files with 21 additions and 8 deletions
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8
GPy/likelihoods/EP.py
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@ -113,11 +113,11 @@ class EP(likelihood):
#Site parameters update #Site parameters update
Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma[i,i]) Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma[i,i])
Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma[i,i]) Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma[i,i])
self.tau_tilde[i] = self.tau_tilde[i] + Delta_tau self.tau_tilde[i] += Delta_tau
self.v_tilde[i] = self.v_tilde[i] + Delta_v self.v_tilde[i] += Delta_v
#Posterior distribution parameters update #Posterior distribution parameters update
si=Sigma[:,i].reshape(self.N,1) si=Sigma[:,i:i+1]
Sigma = Sigma - Delta_tau/(1.+ Delta_tau*Sigma[i,i])*np.dot(si,si.T) Sigma -= Delta_tau/(1.+ Delta_tau*Sigma[i,i])*np.dot(si,si.T)#DSYR
mu = np.dot(Sigma,self.v_tilde) mu = np.dot(Sigma,self.v_tilde)
self.iterations += 1 self.iterations += 1
#Sigma recomptutation with Cholesky decompositon #Sigma recomptutation with Cholesky decompositon

21
GPy/util/linalg.py
View file

@ -236,6 +236,18 @@ def tdot(*args, **kwargs):
else: else:
return tdot_numpy(*args,**kwargs) return tdot_numpy(*args,**kwargs)
def DSYR(A,x,alpha=1.):
N = c_int(A.shape[0])
LDA = c_int(A.shape[0])
UPLO = c_char('l')
ALPHA = c_double(alpha)
A_ = A.ctypes.data_as(ctypes.c_void_p)
x_ = x.ctypes.data_as(ctypes.c_void_p)
INCX = c_int(1)
_blaslib.dsyr_(byref(UPLO), byref(N), byref(ALPHA),
x_, byref(INCX), A_, byref(LDA))
symmetrify(A,upper=True)
def symmetrify(A,upper=False): def symmetrify(A,upper=False):
""" """
Take the square matrix A and make it symmetrical by copting elements from the lower half to the upper Take the square matrix A and make it symmetrical by copting elements from the lower half to the upper
@ -259,19 +271,20 @@ def symmetrify(A,upper=False):
} }
""" """
if A.flags['C_CONTIGUOUS'] and upper: if A.flags['C_CONTIGUOUS'] and upper:
weave.inline(f_contig_code,['A','N']) weave.inline(f_contig_code,['A','N'], extra_compile_args=['-O3'])
elif A.flags['C_CONTIGUOUS'] and not upper: elif A.flags['C_CONTIGUOUS'] and not upper:
weave.inline(c_contig_code,['A','N']) weave.inline(c_contig_code,['A','N'], extra_compile_args=['-O3'])
elif A.flags['F_CONTIGUOUS'] and upper: elif A.flags['F_CONTIGUOUS'] and upper:
weave.inline(c_contig_code,['A','N']) weave.inline(c_contig_code,['A','N'], extra_compile_args=['-O3'])
elif A.flags['F_CONTIGUOUS'] and not upper: elif A.flags['F_CONTIGUOUS'] and not upper:
weave.inline(f_contig_code,['A','N']) weave.inline(f_contig_code,['A','N'], extra_compile_args=['-O3'])
else: else:
tmp = np.tril(A) tmp = np.tril(A)
A[:] = 0.0 A[:] = 0.0
A += tmp A += tmp
A += np.tril(tmp,-1).T A += np.tril(tmp,-1).T
def symmetrify_murray(A): def symmetrify_murray(A):
A += A.T A += A.T
nn = A.shape[0] nn = A.shape[0]