def gp_train(loghyper, covfunc, X, y, R=None, w=None):
''' gp_train() returns the learnt hyperparameters.
Following chapter 5.4.1 in Rasmussen and Williams: GPs for ML (2006).
The original version (MATLAB implementation) of used optimizer minimize.m
is copyright (C) 1999 - 2006, Carl Edward Rasmussen.
The used python adaptation is by Roland Memisevic 2008.
Input R and w is needed for XGP regression! '''
[logtheta, fvals, iter, nml] = minimize.run(loghyper, nlml, dnlml, [covfunc, X, y, R, w], maxnumfuneval=100)
return logtheta, nml
def min_wrapper(hyp, F, Flag, *varargin):
# Utilize scipy.optimize functions, sgc.py, or minimize.py to
# minimize the negative log marginal liklihood.
x = convert_to_array(hyp) # convert the hyperparameter class to an array
if Flag == 'CG':
aa = cg(nlml, x, dnlml, (F,hyp,varargin), maxiter=100, disp=False, full_output=True)
x = aa[0]; fopt = aa[1]; funcCalls = aa[2]; gradcalls = aa[3]
if aa[4] == 1:
print "Maximum number of iterations exceeded."
elif aa[4] == 2:
print "Gradient and/or function calls not changing."
gopt = dnlml(x,F,hyp,varargin)
return convert_to_class(x,hyp), fopt, gopt, funcCalls
elif Flag == 'BFGS':
# Use BFGS
aa = bfgs(nlml, x, dnlml, (F,hyp,varargin), maxiter=100, disp=False, full_output=True)
x = aa[0]; fopt = aa[1]; gopt = aa[2]; Bopt = aa[3]; funcCalls = aa[4]; gradcalls = aa[5]
if aa[6] == 1:
print "Maximum number of iterations exceeded."
elif aa[6] == 2:
print "Gradient and/or function calls not changing."
if isinstance(fopt, ndarray):
fopt = fopt[0]
return convert_to_class(x,hyp), fopt, gopt, funcCalls
elif Flag == 'SCG':
# use sgc.py
aa = scg(x, nlml, dnlml, (F,hyp,varargin), niters = 100)
hyp = convert_to_class(aa[0],hyp)
fopt = aa[1][-1]
gopt = dnlml(aa[0],F,hyp,varargin)
return hyp, fopt, gopt, len(aa[1])
elif Flag == 'Minimize':
# use minimize.py
aa = run(x, nlml, dnlml, (F,hyp,varargin), maxnumfuneval=-100)
hyp = convert_to_class(aa[0],hyp)
fopt = aa[1][-1]
gopt = dnlml(aa[0],F,hyp,varargin)
return hyp, fopt, gopt, len(aa[1])
else:
raise Exception('Incorrect usage of optimization flag in min_wrapper')
def gp_train(loghyper, covfunc, X, y, R=None, w=None):
''' gp_train() returns the learnt hyperparameters.
Following chapter 5.4.1 in Rasmussen and Williams: GPs for ML (2006).
The original version (MATLAB implementation) of used optimizer minimize.m
is copyright (C) 1999 - 2006, Carl Edward Rasmussen.
The used python adaptation is by Roland Memisevic 2008.
Input R and w is needed for XGP regression! '''
if R == None:
print ' gp_train()'
else:
print ' xgp_train()'
[logtheta, fvals, iter] = minimize.run(loghyper,
nlml,
dnlml, [covfunc, X, y, R, w],
maxnumfuneval=100)
return logtheta
def train_by_optimizer(self,
x_val,
y_val,
number_epoch=10,
batch_size=None):
if self.meanfunc == 'zero':
params = {
'sigma_n': self.sigma_n,
'sigma_f': self.sigma_f,
'l_k': self.l_k
}
else:
params = {
'mean': self.mean,
'sigma_n': self.sigma_n,
'sigma_f': self.sigma_f,
'l_k': self.l_k
}
import minimize
if batch_size is None:
self.batch_size = len(x_val)
else:
self.batch_size = batch_size
self.x_val = x_val
self.y_val = y_val
print 'start to optimize'
likelihood = self.get_likelihood(x_val, y_val)
print 'BEGINE Training, Log Likelihood = %.2f' % likelihood
opt_results = minimize.run(self._optimizer_f,
self._get_hypArray(params),
length=number_epoch,
verbose=True)
optimalHyp = deepcopy(opt_results[0])
self._apply_hyp(optimalHyp)
likelihood = self.get_likelihood(x_val, y_val)
print 'END Training, Log Likelihood = %.2f' % likelihood
def train_by_optimizer(self, x_val, y_val,
number_epoch=10, batch_size=None):
if self.meanfunc == 'zero':
params = {'sigma_n':self.sigma_n, 'sigma_f':self.sigma_f, 'l_k':self.l_k}
else:
params = {'mean':self.mean,'sigma_n':self.sigma_n, 'sigma_f':self.sigma_f, 'l_k':self.l_k}
import minimize
if batch_size is None:
self.batch_size = len(x_val)
else:
self.batch_size = batch_size
self.x_val = x_val
self.y_val = y_val
print 'start to optimize'
likelihood = self.get_likelihood(x_val, y_val)
print 'BEGINE Training, Log Likelihood = %.2f'% likelihood
opt_results = minimize.run(self._optimizer_f, self._get_hypArray(params),length=number_epoch,verbose=True)
optimalHyp = deepcopy(opt_results[0])
self._apply_hyp(optimalHyp)
likelihood = self.get_likelihood(x_val, y_val)
print 'END Training, Log Likelihood = %.2f'% likelihood
def min_wrapper(hyp, F, Flag, *varargin):
# Utilize scipy.optimize functions, sgc.py, or minimize.py to
# minimize the negative log marginal liklihood.
x = convert_to_array(hyp) # convert the hyperparameter class to an array
if Flag == 'CG':
aa = cg(nlml,
x,
dnlml, (F, hyp, varargin),
maxiter=100,
disp=False,
full_output=True)
x = aa[0]
fopt = aa[1]
funcCalls = aa[2]
gradcalls = aa[3]
if aa[4] == 1:
print "Maximum number of iterations exceeded."
elif aa[4] == 2:
print "Gradient and/or function calls not changing."
gopt = dnlml(x, F, hyp, varargin)
return convert_to_class(x, hyp), fopt, gopt, funcCalls
elif Flag == 'BFGS':
# Use BFGS
aa = bfgs(nlml,
x,
dnlml, (F, hyp, varargin),
maxiter=100,
disp=False,
full_output=True)
x = aa[0]
fopt = aa[1]
gopt = aa[2]
Bopt = aa[3]
funcCalls = aa[4]
gradcalls = aa[5]
if aa[6] == 1:
print "Maximum number of iterations exceeded."
elif aa[6] == 2:
print "Gradient and/or function calls not changing."
if isinstance(fopt, ndarray):
fopt = fopt[0]
return convert_to_class(x, hyp), fopt, gopt, funcCalls
elif Flag == 'SCG':
# use sgc.py
aa = scg(x, nlml, dnlml, (F, hyp, varargin), niters=100)
hyp = convert_to_class(aa[0], hyp)
fopt = aa[1][-1]
gopt = dnlml(aa[0], F, hyp, varargin)
return hyp, fopt, gopt, len(aa[1])
elif Flag == 'Minimize':
# use minimize.py
aa = run(x, nlml, dnlml, (F, hyp, varargin), maxnumfuneval=-100)
hyp = convert_to_class(aa[0], hyp)
fopt = aa[1][-1]
gopt = dnlml(aa[0], F, hyp, varargin)
return hyp, fopt, gopt, len(aa[1])
else:
raise Exception('Incorrect usage of optimization flag in min_wrapper')
