def proceed(self, meanfunc, covfunc, likfunc, x, y, nargout=1):
if not isinstance(covfunc, cov.FITCOfKernel):
raise Exception('Only covFITC supported.')
tol = 1e-6 # tolerance for when to stop the Newton iterations
smax = 2; Nline = 100; thr = 1e-4 # line search parameters
maxit = 20 # max number of Newton steps in f
inffunc = infLaplace()
diagK,Kuu,Ku = covfunc.proceed(x) # evaluate the covariance matrix
m = meanfunc.proceed(x) # evaluate the mean vector
if likfunc.hyp: # hard coded inducing inputs noise
sn2 = np.exp(2.*likfunc.hyp[-1])
snu2 = 1.e-6*sn2 # similar to infFITC
else:
snu2 = 1.e-6
n, D = x.shape
nu = Kuu.shape[0]
rot180 = lambda A: np.rot90(np.rot90(A)) # little helper functions
chol_inv = lambda A: np.linalg.solve( rot180( np.linalg.cholesky(rot180(A)) ),np.eye(nu)) # chol(inv(A))
R0 = chol_inv(Kuu+snu2*np.eye(nu)) # initial R, used for refresh O(nu^3)
V = np.dot(R0,Ku); d0 = diagK - np.array([(V*V).sum(axis=0)]).T # initial d, needed
Psi_old = np.inf # make sure while loop starts by the largest old objective val
if self.last_alpha == None: # find a good starting point for alpha and f
alpha = np.zeros((n,1))
f = self.mvmK(alpha,V,d0) + m # start at mean if sizes not match
vargout = likfunc.proceed(y, f, None, inffunc, None, 3)
lp = vargout[0]; dlp = vargout[1]; d2lp = vargout[2]
W=-d2lp; Psi_new = -lp.sum()
else:
alpha = self.last_alpha
f = self.mvmK(alpha,V,d0) + m # try last one
vargout = likfunc.proceed(y, f, None, inffunc, None, 3)
lp = vargout[0]; dlp = vargout[1]; d2lp = vargout[2]
W=-d2lp
Psi_new = np.dot(alpha.T,(f-m))/2. - lp.sum() # objective for last alpha
vargout = - likfunc.proceed(y, m, None, inffunc, None, 1)
Psi_def = vargout[0] # objective for default init f==m
if Psi_def < Psi_new: # if default is better, we use it
alpha = np.zeros((n,1))
f = self.mvmK(alpha,V,d0) + m
vargout = likfunc.proceed(y, f, None, inffunc, None, 3)
lp = vargout[0]; dlp = vargout[1]; d2lp = vargout[2]
W=-d2lp; Psi_new = -lp.sum()
isWneg = np.any(W<0) # flag indicating whether we found negative values of W
it = 0 # this happens for the Student's t likelihood
while (Psi_old - Psi_new > tol) and it<maxit: # begin Newton
Psi_old = Psi_new
it += 1
if isWneg: # stabilise the Newton direction in case W has negative values
W = np.maximum(W,0) # stabilise the Hessian to guarantee postive definiteness
tol = 1e-8 # increase accuracy to also get the derivatives right
b = W*(f-m) + dlp; dd = 1/(1+W*d0)
RV = np.dot( chol_inv( np.eye(nu) + np.dot(V*np.tile((W*dd).T,(nu,1)),V.T)),V )
dalpha = dd*b - (W*dd)*np.dot(RV.T,np.dot(RV,(dd*b))) - alpha # Newt dir + line search
vargout = brentmin(0,smax,Nline,thr,self.Psi_lineFITC,4,dalpha,alpha,V,d0,m,likfunc,y,inffunc)
s = vargout[0]; Psi_new = vargout[1]; Nfun = vargout[2]; alpha = vargout[3]
f = vargout[4]; dlp = vargout[5]; W = vargout[6]
isWneg = np.any(W<0)
self.last_alpha = alpha # remember for next call
vargout = likfunc.proceed(y,f,None,inffunc,None,4)
lp = vargout[0]; dlp = vargout[1]; d2lp = vargout[2]; d3lp = vargout[3]
W=-d2lp; isWneg = np.any(W<0)
post = postStruct()
post.alpha = np.dot(R0.T,np.dot(V,alpha)) # return the posterior parameters
post.sW = np.sqrt(np.abs(W))*np.sign(W) # preserve sign in case of negative
dd = 1/(1+d0*W) # temporary variable O(n)
A = np.eye(nu) + np.dot(V*np.tile((W*dd).T,(nu,1)),V.T) # temporary variable O(n*nu^2)
R0tV = np.dot(R0.T,V); B = R0tV*np.tile((W*dd).T,(nu,1)) # temporary variables O(n*nu^2)
post.L = -np.dot(B,R0tV.T) # L = -R0'*V*inv(Kt+diag(1./ttau))*V'*R0, first part
if np.any(1+d0*W<0):
raise Exception('W is too negative; nlZ and dnlZ cannot be computed.')
nlZ = np.dot(alpha.T,(f-m))/2. - lp.sum() - np.log(dd).sum()/2. + \
np.log(np.diag(np.linalg.cholesky(A).T)).sum()
RV = np.dot(chol_inv(A),V)
RVdd = RV * np.tile((W*dd).T,(nu,1)) # RVdd needed for dnlZ
B = np.dot(B,RV.T)
post.L += np.dot(B,B.T)
if nargout>2: # do we want derivatives?
dnlZ = dnlZStruct(meanfunc, covfunc, likfunc) # allocate space for derivatives
[d,P,R] = self.fitcRefresh(d0,Ku,R0,V,W) # g = diag(inv(inv(K)+W))/2
g = d/2 + 0.5*np.atleast_2d((np.dot(np.dot(R,R0),P)**2).sum(axis=0)).T
t = W/(1+W*d0)
dfhat = g*d3lp # deriv. of nlZ wrt. fhat: dfhat=diag(inv(inv(K)+W)).*d3lp/2
for ii in range(len(covfunc.hyp)): # covariance hypers
ddiagK,dKuu,dKu = covfunc.proceed(x, None, ii) # eval cov derivatives
dA = 2.*dKu.T-np.dot(R0tV.T,dKuu) # dQ = dA*R0tV
w = np.atleast_2d((dA*R0tV.T).sum(axis=1)).T
v = ddiagK-w # w = diag(dQ); v = diag(dK)-diag(dQ);
dnlZ.cov[ii] = np.dot(ddiagK.T,t) - np.dot((RVdd*RVdd).sum(axis=0),v) # explicit part
dnlZ.cov[ii] -= (np.dot(RVdd,dA)*np.dot(RVdd,R0tV.T)).sum() # explicit part
dnlZ.cov[ii] = 0.5*dnlZ.cov[ii] - np.dot(alpha.T,np.dot(dA,np.dot(R0tV,alpha))+v*alpha)/2. # explicit
b = np.dot(dA,np.dot(R0tV,dlp)) + v*dlp # b-K*(Z*b) = inv(eye(n)+K*diag(W))*b
KZb = self.mvmK(self.mvmZ(b,RVdd,t),V,d0)
dnlZ.cov[ii] -= np.dot(dfhat.T,(b-KZb)) # implicit part
dnlZ.cov[ii] = dnlZ.cov[ii][0,0]
for ii in range(len(likfunc.hyp)): # likelihood hypers
vargout = likfunc.proceed(y,f,None,inffunc,ii,3)
lp_dhyp = vargout[0]; dlp_dhyp = vargout[1]; d2lp_dhyp = vargout[2]
dnlZ.lik[ii] = -np.dot(g.T,d2lp_dhyp) - lp_dhyp.sum() # explicit part
b = self.mvmK(dlp_dhyp,V,d0) # implicit part
dnlZ.lik[ii] -= np.dot(dfhat.T,b-self.mvmK(self.mvmZ(b,RVdd,t),V,d0))
if ii == len(likfunc.hyp)-1:
# since snu2 is a fixed fraction of sn2, there is a covariance-like term
# in the derivative as well
snu = np.sqrt(snu2);
T = chol_inv(Kuu + snu2*np.eye(nu));
T = np.dot(T.T,np.dot(T,snu*Ku));
t = np.array([(T*T).sum(axis=0)]).T
z = np.dot(alpha.T,np.dot(T.T,np.dot(T,alpha))-t*alpha) - np.dot(np.array([(RVdd*RVdd).sum(axis=0)]),t)
z += (np.dot(RVdd,T.T)**2).sum()
b = (t*dlp-np.dot(T.T,np.dot(T,dlp)))/2.
KZb = self.mvmK(self.mvmZ(b,RVdd,t),V,d0)
z -= np.dot(dfhat.T,b-KZb)
dnlZ.lik[ii] += z
dnlZ.lik[ii] = dnlZ.lik[ii][0,0]
for ii in range(len(meanfunc.hyp)): # mean hypers
dm = meanfunc.proceed(x, ii)
dnlZ.mean[ii] = -np.dot(alpha.T,dm) # explicit part
Zdm = self.mvmZ(dm,RVdd,t)
dnlZ.mean[ii] -= np.dot(dfhat.T,(dm-self.mvmK(Zdm,V,d0))) # implicit part
dnlZ.mean[ii] = dnlZ.mean[ii][0,0]
vargout = [post,nlZ[0,0],dnlZ]
else:
vargout = [post, nlZ[0,0]]
return vargout
def proceed(self, meanfunc, covfunc, likfunc, x, y, nargout=1):
if not isinstance(covfunc, cov.FITCOfKernel):
raise Exception('Only covFITC supported.')
tol = 1e-6 # tolerance for when to stop the Newton iterations
smax = 2
Nline = 100
thr = 1e-4 # line search parameters
maxit = 20 # max number of Newton steps in f
inffunc = Laplace()
diagK, Kuu, Ku = covfunc.proceed(x) # evaluate the covariance matrix
m = meanfunc.proceed(x) # evaluate the mean vector
if likfunc.hyp: # hard coded inducing inputs noise
sn2 = np.exp(2. * likfunc.hyp[-1])
snu2 = 1.e-6 * sn2 # similar to infFITC
else:
snu2 = 1.e-6
n, D = x.shape
nu = Kuu.shape[0]
rot180 = lambda A: np.rot90(np.rot90(A)) # little helper functions
chol_inv = lambda A: np.linalg.solve(
rot180(np.linalg.cholesky(rot180(A))), np.eye(nu)) # chol(inv(A))
R0 = chol_inv(Kuu +
snu2 * np.eye(nu)) # initial R, used for refresh O(nu^3)
V = np.dot(R0, Ku)
d0 = diagK - np.array([(V * V).sum(axis=0)]).T # initial d, needed
Psi_old = np.inf # make sure while loop starts by the largest old objective val
if self.last_alpha == None: # find a good starting point for alpha and f
alpha = np.zeros((n, 1))
f = self.mvmK(alpha, V, d0) + m # start at mean if sizes not match
vargout = likfunc.proceed(y, f, None, inffunc, None, 3)
lp = vargout[0]
dlp = vargout[1]
d2lp = vargout[2]
W = -d2lp
Psi_new = -lp.sum()
else:
alpha = self.last_alpha
f = self.mvmK(alpha, V, d0) + m # try last one
vargout = likfunc.proceed(y, f, None, inffunc, None, 3)
lp = vargout[0]
dlp = vargout[1]
d2lp = vargout[2]
W = -d2lp
Psi_new = np.dot(
alpha.T, (f - m)) / 2. - lp.sum() # objective for last alpha
vargout = -likfunc.proceed(y, m, None, inffunc, None, 1)
Psi_def = vargout[0] # objective for default init f==m
if Psi_def < Psi_new: # if default is better, we use it
alpha = np.zeros((n, 1))
f = self.mvmK(alpha, V, d0) + m
vargout = likfunc.proceed(y, f, None, inffunc, None, 3)
lp = vargout[0]
dlp = vargout[1]
d2lp = vargout[2]
W = -d2lp
Psi_new = -lp.sum()
isWneg = np.any(
W < 0) # flag indicating whether we found negative values of W
it = 0 # this happens for the Student's t likelihood
while (Psi_old - Psi_new > tol) and it < maxit: # begin Newton
Psi_old = Psi_new
it += 1
if isWneg: # stabilise the Newton direction in case W has negative values
W = np.maximum(
W, 0
) # stabilise the Hessian to guarantee postive definiteness
tol = 1e-8 # increase accuracy to also get the derivatives right
b = W * (f - m) + dlp
dd = 1 / (1 + W * d0)
RV = np.dot(
chol_inv(
np.eye(nu) +
np.dot(V * np.tile((W * dd).T, (nu, 1)), V.T)), V)
dalpha = dd * b - (W * dd) * np.dot(RV.T, np.dot(
RV, (dd * b))) - alpha # Newt dir + line search
vargout = brentmin(0, smax, Nline, thr, self.Psi_lineFITC, 4,
dalpha, alpha, V, d0, m, likfunc, y, inffunc)
s = vargout[0]
Psi_new = vargout[1]
Nfun = vargout[2]
alpha = vargout[3]
f = vargout[4]
dlp = vargout[5]
W = vargout[6]
isWneg = np.any(W < 0)
self.last_alpha = alpha # remember for next call
vargout = likfunc.proceed(y, f, None, inffunc, None, 4)
lp = vargout[0]
dlp = vargout[1]
d2lp = vargout[2]
d3lp = vargout[3]
W = -d2lp
isWneg = np.any(W < 0)
post = postStruct()
post.alpha = np.dot(R0.T,
np.dot(V,
alpha)) # return the posterior parameters
post.sW = np.sqrt(np.abs(W)) * np.sign(
W) # preserve sign in case of negative
dd = 1 / (1 + d0 * W) # temporary variable O(n)
A = np.eye(nu) + np.dot(V * np.tile(
(W * dd).T, (nu, 1)), V.T) # temporary variable O(n*nu^2)
R0tV = np.dot(R0.T, V)
B = R0tV * np.tile((W * dd).T,
(nu, 1)) # temporary variables O(n*nu^2)
post.L = -np.dot(
B, R0tV.T) # L = -R0'*V*inv(Kt+diag(1./ttau))*V'*R0, first part
if np.any(1 + d0 * W < 0):
raise Exception(
'W is too negative; nlZ and dnlZ cannot be computed.')
nlZ = np.dot(alpha.T,(f-m))/2. - lp.sum() - np.log(dd).sum()/2. + \
np.log(np.diag(np.linalg.cholesky(A).T)).sum()
RV = np.dot(chol_inv(A), V)
RVdd = RV * np.tile((W * dd).T, (nu, 1)) # RVdd needed for dnlZ
B = np.dot(B, RV.T)
post.L += np.dot(B, B.T)
if nargout > 2: # do we want derivatives?
dnlZ = dnlZStruct(meanfunc, covfunc,
likfunc) # allocate space for derivatives
[d, P, R] = self.fitcRefresh(d0, Ku, R0, V,
W) # g = diag(inv(inv(K)+W))/2
g = d / 2 + 0.5 * np.atleast_2d(
(np.dot(np.dot(R, R0), P)**2).sum(axis=0)).T
t = W / (1 + W * d0)
dfhat = g * d3lp # deriv. of nlZ wrt. fhat: dfhat=diag(inv(inv(K)+W)).*d3lp/2
for ii in range(len(covfunc.hyp)): # covariance hypers
ddiagK, dKuu, dKu = covfunc.proceed(x, None,
ii) # eval cov derivatives
dA = 2. * dKu.T - np.dot(R0tV.T, dKuu) # dQ = dA*R0tV
w = np.atleast_2d((dA * R0tV.T).sum(axis=1)).T # w = diag(dQ)
v = ddiagK - w # v = diag(dK)-diag(dQ);
dnlZ.cov[ii] = np.dot(ddiagK.T, t) - np.dot(
(RVdd * RVdd).sum(axis=0), v) # explicit part
dnlZ.cov[ii] -= (np.dot(RVdd, dA) *
np.dot(RVdd, R0tV.T)).sum() # explicit part
dnlZ.cov[ii] = 0.5 * dnlZ.cov[ii] - np.dot(
alpha.T,
np.dot(dA, np.dot(R0tV, alpha)) +
v * alpha) / 2. # explicit
b = np.dot(dA, np.dot(
R0tV,
dlp)) + v * dlp # b-K*(Z*b) = inv(eye(n)+K*diag(W))*b
KZb = self.mvmK(self.mvmZ(b, RVdd, t), V, d0)
dnlZ.cov[ii] -= np.dot(dfhat.T, (b - KZb)) # implicit part
dnlZ.cov[ii] = dnlZ.cov[ii][0, 0]
for ii in range(len(likfunc.hyp)): # likelihood hypers
vargout = likfunc.proceed(y, f, None, inffunc, ii, 3)
lp_dhyp = vargout[0]
dlp_dhyp = vargout[1]
d2lp_dhyp = vargout[2]
dnlZ.lik[ii] = -np.dot(
g.T, d2lp_dhyp) - lp_dhyp.sum() # explicit part
b = self.mvmK(dlp_dhyp, V, d0) # implicit part
dnlZ.lik[ii] -= np.dot(
dfhat.T, b - self.mvmK(self.mvmZ(b, RVdd, t), V, d0))
if ii == len(likfunc.hyp) - 1:
# since snu2 is a fixed fraction of sn2, there is a covariance-like term
# in the derivative as well
snu = np.sqrt(snu2)
T = chol_inv(Kuu + snu2 * np.eye(nu))
T = np.dot(T.T, np.dot(T, snu * Ku))
t = np.array([(T * T).sum(axis=0)]).T
z = np.dot(
alpha.T,
np.dot(T.T, np.dot(T, alpha)) - t * alpha) - np.dot(
np.array([(RVdd * RVdd).sum(axis=0)]), t)
z += (np.dot(RVdd, T.T)**2).sum()
b = (t * dlp - np.dot(T.T, np.dot(T, dlp))) / 2.
KZb = self.mvmK(self.mvmZ(b, RVdd, t), V, d0)
z -= np.dot(dfhat.T, b - KZb)
dnlZ.lik[ii] += z
dnlZ.lik[ii] = dnlZ.lik[ii][0, 0]
for ii in range(len(meanfunc.hyp)): # mean hypers
dm = meanfunc.proceed(x, ii)
dnlZ.mean[ii] = -np.dot(alpha.T, dm) # explicit part
Zdm = self.mvmZ(dm, RVdd, t)
dnlZ.mean[ii] -= np.dot(
dfhat.T, (dm - self.mvmK(Zdm, V, d0))) # implicit part
dnlZ.mean[ii] = dnlZ.mean[ii][0, 0]
return post, nlZ[0, 0], dnlZ
else:
return post, nlZ[0, 0]
def proceed(self, meanfunc, covfunc, likfunc, x, y, nargout=1):
tol = 1e-6 # tolerance for when to stop the Newton iterations
smax = 2; Nline = 20; thr = 1e-4 # line search parameters
maxit = 20 # max number of Newton steps in f
inffunc = self
K = covfunc.proceed(x) # evaluate the covariance matrix
m = meanfunc.proceed(x) # evaluate the mean vector
n, D = x.shape
Psi_old = np.inf # make sure while loop starts by the largest old objective val
if self.last_alpha == None: # find a good starting point for alpha and f
alpha = np.zeros((n,1))
f = np.dot(K,alpha) + m # start at mean if sizes not match
vargout = likfunc.proceed(y, f, None, inffunc, None, 3)
lp = vargout[0]; dlp = vargout[1]; d2lp = vargout[2]
W= -d2lp
Psi_new = -lp.sum()
else:
alpha = self.last_alpha
f = np.dot(K,alpha) + m # try last one
vargout = likfunc.proceed(y, f, None, inffunc, None, 3)
lp = vargout[0]; dlp = vargout[1]; d2lp = vargout[2]
W= -d2lp
Psi_new = np.dot(alpha.T,(f-m))/2. - lp.sum() # objective for last alpha
vargout = - likfunc.proceed(y, m, None, inffunc, None, 1)
Psi_def = vargout[0] # objective for default init f==m
if Psi_def < Psi_new: # if default is better, we use it
alpha = np.zeros((n,1))
f = np.dot(K,alpha) + m
vargout = likfunc.proceed(y, f, None, inffunc, None, 3)
lp = vargout[0]; dlp = vargout[1]; d2lp = vargout[2]
W=-d2lp; Psi_new = -lp.sum()
isWneg = np.any(W<0) # flag indicating whether we found negative values of W
it = 0 # this happens for the Student's t likelihood
while (Psi_old - Psi_new > tol) and it<maxit: # begin Newton
Psi_old = Psi_new; it += 1
if isWneg: # stabilise the Newton direction in case W has negative values
W = np.maximum(W,0) # stabilise the Hessian to guarantee postive definiteness
tol = 1e-10 # increase accuracy to also get the derivatives right
sW = np.sqrt(W); L = np.linalg.cholesky(np.eye(n) + np.dot(sW,sW.T)*K).T
b = W*(f-m) + dlp;
dalpha = b - sW*solve_chol(L,sW*np.dot(K,b)) - alpha
vargout = brentmin(0,smax,Nline,thr,self.Psi_line,4,dalpha,alpha,K,m,likfunc,y,inffunc)
s = vargout[0]
Psi_new = vargout[1]
Nfun = vargout[2]
alpha = vargout[3]
f = vargout[4]
dlp = vargout[5]
W = vargout[6]
isWneg = np.any(W<0)
self.last_alpha = alpha # remember for next call
vargout = likfunc.proceed(y,f,None,inffunc,None,4)
lp = vargout[0]; dlp = vargout[1]; d2lp = vargout[2]; d3lp = vargout[3]
W = -d2lp; isWneg = np.any(W<0)
post = postStruct()
post.alpha = alpha # return the posterior parameters
post.sW = np.sqrt(np.abs(W))*np.sign(W) # preserve sign in case of negative
if isWneg:
[ldA,iA,post.L] = self.logdetA(K,W,3)
nlZ = np.dot(alpha.T,(f-m))/2. - lp.sum() + ldA/2.
nlZ = nlZ[0]
else:
sW = post.sW
post.L = np.linalg.cholesky(np.eye(n)+np.dot(sW,sW.T)*K).T
nlZ = np.dot(alpha.T,(f-m))/2. + (np.log(np.diag(post.L))-np.reshape(lp,(lp.shape[0],))).sum()
nlZ = nlZ[0]
if nargout>2: # do we want derivatives?
dnlZ = dnlZStruct(meanfunc, covfunc, likfunc) # allocate space for derivatives
if isWneg: # switch between Cholesky and LU decomposition mode
Z = -post.L # inv(K+inv(W))
g = np.atleast_2d((iA*K).sum(axis=1)).T /2 # deriv. of ln|B| wrt W; g = diag(inv(inv(K)+diag(W)))/2
else:
Z = np.tile(sW,(1,n))*solve_chol(post.L,np.diag(np.reshape(sW,(sW.shape[0],)))) #sW*inv(B)*sW=inv(K+inv(W))
C = np.linalg.solve(post.L.T,np.tile(sW,(1,n))*K) # deriv. of ln|B| wrt W
g = np.atleast_2d((np.diag(K)-(C**2).sum(axis=0).T)).T /2. # g = diag(inv(inv(K)+W))/2
dfhat = g* d3lp # deriv. of nlZ wrt. fhat: dfhat=diag(inv(inv(K)+W)).*d3lp/2
for ii in range(len(covfunc.hyp)): # covariance hypers
dK = covfunc.proceed(x, None, ii)
dnlZ.cov[ii] = (Z*dK).sum()/2. - np.dot(alpha.T,np.dot(dK,alpha))/2. # explicit part
b = np.dot(dK,dlp) # b-K*(Z*b) = inv(eye(n)+K*diag(W))*b
dnlZ.cov[ii] -= np.dot(dfhat.T,b-np.dot(K,np.dot(Z,b))) # implicit part
dnlZ.cov[ii] = dnlZ.cov[ii][0][0]
for ii in range(len(likfunc.hyp)): # likelihood hypers
[lp_dhyp,dlp_dhyp,d2lp_dhyp] = likfunc.proceed(y,f,None,inffunc,ii,3)
dnlZ.lik[ii] = -np.dot(g.T,d2lp_dhyp) - lp_dhyp.sum() # explicit part
b = np.dot(K,dlp_dhyp) # b-K*(Z*b) = inv(eye(n)+K*diag(W))*b
dnlZ.lik[ii] -= np.dot(dfhat.T,b-np.dot(K,np.dot(Z,b))) # implicit part
dnlZ.lik[ii] = dnlZ.lik[ii][0][0]
for ii in range(len(meanfunc.hyp)): # mean hypers
dm = meanfunc.proceed(x, ii)
dnlZ.mean[ii] = -np.dot(alpha.T,dm) # explicit part
dnlZ.mean[ii] -= np.dot(dfhat.T,dm-np.dot(K,np.dot(Z,dm))) # implicit part
dnlZ.mean[ii] = dnlZ.mean[ii][0][0]
vargout = [post,nlZ[0],dnlZ]
else:
vargout = [post, nlZ[0]]
return vargout
def proceed(self, meanfunc, covfunc, likfunc, x, y, nargout=1):
tol = 1e-6 # tolerance for when to stop the Newton iterations
smax = 2
Nline = 20
thr = 1e-4 # line search parameters
maxit = 20 # max number of Newton steps in f
inffunc = self
K = covfunc.proceed(x) # evaluate the covariance matrix
m = meanfunc.proceed(x) # evaluate the mean vector
n, D = x.shape
Psi_old = np.inf # make sure while loop starts by the largest old objective val
if self.last_alpha == None: # find a good starting point for alpha and f
alpha = np.zeros((n, 1))
f = np.dot(K, alpha) + m # start at mean if sizes not match
vargout = likfunc.proceed(y, f, None, inffunc, None, 3)
lp = vargout[0]
dlp = vargout[1]
d2lp = vargout[2]
W = -d2lp
Psi_new = -lp.sum()
else:
alpha = self.last_alpha
f = np.dot(K, alpha) + m # try last one
vargout = likfunc.proceed(y, f, None, inffunc, None, 3)
lp = vargout[0]
dlp = vargout[1]
d2lp = vargout[2]
W = -d2lp
Psi_new = np.dot(
alpha.T, (f - m)) / 2. - lp.sum() # objective for last alpha
vargout = -likfunc.proceed(y, m, None, inffunc, None, 1)
Psi_def = vargout[0] # objective for default init f==m
if Psi_def < Psi_new: # if default is better, we use it
alpha = np.zeros((n, 1))
f = np.dot(K, alpha) + m
vargout = likfunc.proceed(y, f, None, inffunc, None, 3)
lp = vargout[0]
dlp = vargout[1]
d2lp = vargout[2]
W = -d2lp
Psi_new = -lp.sum()
isWneg = np.any(
W < 0) # flag indicating whether we found negative values of W
it = 0 # this happens for the Student's t likelihood
while (Psi_old - Psi_new > tol) and it < maxit: # begin Newton
Psi_old = Psi_new
it += 1
if isWneg: # stabilise the Newton direction in case W has negative values
W = np.maximum(
W, 0
) # stabilise the Hessian to guarantee postive definiteness
tol = 1e-10 # increase accuracy to also get the derivatives right
sW = np.sqrt(W)
L = np.linalg.cholesky(np.eye(n) + np.dot(sW, sW.T) * K).T
b = W * (f - m) + dlp
dalpha = b - sW * solve_chol(L, sW * np.dot(K, b)) - alpha
vargout = brentmin(0, smax, Nline, thr, self.Psi_line, 4, dalpha,
alpha, K, m, likfunc, y, inffunc)
s = vargout[0]
Psi_new = vargout[1]
Nfun = vargout[2]
alpha = vargout[3]
f = vargout[4]
dlp = vargout[5]
W = vargout[6]
isWneg = np.any(W < 0)
self.last_alpha = alpha # remember for next call
vargout = likfunc.proceed(y, f, None, inffunc, None, 4)
lp = vargout[0]
dlp = vargout[1]
d2lp = vargout[2]
d3lp = vargout[3]
W = -d2lp
isWneg = np.any(W < 0)
post = postStruct()
post.alpha = alpha # return the posterior parameters
post.sW = np.sqrt(np.abs(W)) * np.sign(
W) # preserve sign in case of negative
if isWneg:
[ldA, iA, post.L] = self.logdetA(K, W, 3)
nlZ = np.dot(alpha.T, (f - m)) / 2. - lp.sum() + ldA / 2.
nlZ = nlZ[0]
else:
sW = post.sW
post.L = np.linalg.cholesky(np.eye(n) + np.dot(sW, sW.T) * K).T
nlZ = np.dot(alpha.T,
(f - m)) / 2. + (np.log(np.diag(post.L)) -
np.reshape(lp,
(lp.shape[0], ))).sum()
nlZ = nlZ[0]
if nargout > 2: # do we want derivatives?
dnlZ = dnlZStruct(meanfunc, covfunc,
likfunc) # allocate space for derivatives
if isWneg: # switch between Cholesky and LU decomposition mode
Z = -post.L # inv(K+inv(W))
g = np.atleast_2d(
(iA * K).sum(axis=1)
).T / 2 # deriv. of ln|B| wrt W; g = diag(inv(inv(K)+diag(W)))/2
else:
Z = np.tile(sW, (1, n)) * solve_chol(
post.L, np.diag(np.reshape(
sW, (sW.shape[0], )))) #sW*inv(B)*sW=inv(K+inv(W))
C = np.linalg.solve(post.L.T,
np.tile(sW, (1, n)) *
K) # deriv. of ln|B| wrt W
g = np.atleast_2d(
(np.diag(K) -
(C**2).sum(axis=0).T)).T / 2. # g = diag(inv(inv(K)+W))/2
dfhat = g * d3lp # deriv. of nlZ wrt. fhat: dfhat=diag(inv(inv(K)+W)).*d3lp/2
for ii in range(len(covfunc.hyp)): # covariance hypers
dK = covfunc.proceed(x, None, ii)
dnlZ.cov[ii] = (Z * dK).sum() / 2. - np.dot(
alpha.T, np.dot(dK, alpha)) / 2. # explicit part
b = np.dot(dK, dlp) # b-K*(Z*b) = inv(eye(n)+K*diag(W))*b
dnlZ.cov[ii] -= np.dot(
dfhat.T, b - np.dot(K, np.dot(Z, b))) # implicit part
dnlZ.cov[ii] = dnlZ.cov[ii][0, 0]
for ii in range(len(likfunc.hyp)): # likelihood hypers
[lp_dhyp, dlp_dhyp,
d2lp_dhyp] = likfunc.proceed(y, f, None, inffunc, ii, 3)
dnlZ.lik[ii] = -np.dot(
g.T, d2lp_dhyp) - lp_dhyp.sum() # explicit part
b = np.dot(K, dlp_dhyp) # b-K*(Z*b) = inv(eye(n)+K*diag(W))*b
dnlZ.lik[ii] -= np.dot(
dfhat.T, b - np.dot(K, np.dot(Z, b))) # implicit part
dnlZ.lik[ii] = dnlZ.lik[ii][0, 0]
for ii in range(len(meanfunc.hyp)): # mean hypers
dm = meanfunc.proceed(x, ii)
dnlZ.mean[ii] = -np.dot(alpha.T, dm) # explicit part
dnlZ.mean[ii] -= np.dot(
dfhat.T, dm - np.dot(K, np.dot(Z, dm))) # implicit part
dnlZ.mean[ii] = dnlZ.mean[ii][0, 0]
return post, nlZ[0], dnlZ
else:
return post, nlZ[0]
