def test_grad(self):
hyper = opt_hyper(self.gpr, {'covar':self.logtheta})[0]
hyperparams = hyper
# X0 = param_dict_to_list(hyperparams)
# Ifilter_x = {'covar':SP.ones(len(X0),dtype='bool')}
def f(x):
x_ = hyperparams
# x_[Ifilter_x] = x
rv = self.gpr.LML(x_)
#LG.debug("L("+str(x_)+")=="+str(rv))
if SP.isnan(rv):
return 1E6
return rv
def df(x):
x_ = hyperparams
# x_[Ifilter_x] = x
rv = self.gpr.LMLgrad(x_)
#convert to list
rv = param_dict_to_list(rv)
#LG.debug("dL("+str(x_)+")=="+str(rv))
if SP.isnan(rv).any():
In = isnan(rv)
rv[In] = 1E6
return rv#[Ifilter_x]
self.assertAlmostEqual(OPT.check_grad(f,df,hyper['covar']),10**-4,3)
def test_grad(self):
hyper = opt_hyper(self.gpr, {'covar': self.logtheta})[0]
hyperparams = hyper
# X0 = param_dict_to_list(hyperparams)
# Ifilter_x = {'covar':SP.ones(len(X0),dtype='bool')}
def f(x):
x_ = hyperparams
# x_[Ifilter_x] = x
rv = self.gpr.LML(x_)
#LG.debug("L("+str(x_)+")=="+str(rv))
if SP.isnan(rv):
return 1E6
return rv
def df(x):
x_ = hyperparams
# x_[Ifilter_x] = x
rv = self.gpr.LMLgrad(x_)
#convert to list
rv = param_dict_to_list(rv)
#LG.debug("dL("+str(x_)+")=="+str(rv))
if SP.isnan(rv).any():
In = isnan(rv)
rv[In] = 1E6
return rv #[Ifilter_x]
self.assertAlmostEqual(OPT.check_grad(f, df, hyper['covar']), 10**-4,
3)
def optHyper(gpr,logtheta,Ifilter=None,priors=None,maxiter=100,gradcheck=False):
"""optimize hyperparemters of gp gpr starting from gpr
optHyper(gpr,logtheta,filter=None,prior=None)
gpr: GP regression classe
logtheta: starting piont for optimization
Ifilter : filter index vector
prior : non-default prior, otherwise assume first index amplitude, last noise, rest:lengthscales
"""
if priors is None: # use a very crude default prior if we don't get anything else:
priors = defaultPriors(gpr,logtheta)
def fixlogtheta(logtheta,limit=1E3):
"""make a valid logtheta which is non-infinite and non-0"""
rv = logtheta.copy()
I_upper = logtheta>limit
I_lower = logtheta<-limit
rv[I_upper] = +limit
rv[I_lower] = -limit
return rv
def checklogtheta(logtheta,limit=1E3):
"""make a valid logtheta which is non-infinite and non-0"""
I_upper = logtheta>limit
I_lower = logtheta<-limit
return not (I_upper.any() or I_lower.any())
#TODO: mean-function
def f(logtheta):
#logtheta_ = fixlogtheta(logtheta)
logtheta_ = logtheta
if not checklogtheta(logtheta):
print logtheta
#make optimzier/sampler search somewhere else
return 1E6
rv = gpr.lMl(logtheta_,lml=True,dlml=False,priors=priors)
LG.debug("L("+str(logtheta_)+")=="+str(rv))
if isnan(rv):
return 1E6
return rv
def df(logtheta):
#logtheta_ = fixlogtheta(logtheta)
logtheta_ = logtheta
if not checklogtheta(logtheta):
#make optimzier/sampler search somewhere else
print logtheta
return zeros_like(logtheta_)
rv = gpr.lMl(logtheta_,lml=False,dlml=True,priors=priors)
LG.debug("dL("+str(logtheta_)+")=="+str(rv))
#mask out filtered dimensions
if not Ifilter is None:
rv = rv*Ifilter
if isnan(rv).any():
In = isnan(rv)
rv[In] = 1E6
return rv
plotit = True
plotit = False
if(plotit):
X = arange(0.001,0.05,0.001)
Y = zeros(size(X))
dY = zeros(size(X))
k=2
theta = logtheta
for i in range(len(X)):
theta[k] = log(X[i])
Y[i] = f(theta)
dY[i] = df(theta)[k]
plot(X,Y)
hold(True);
plot(X,dY)
show()
#start-parameters
theta0 = logtheta
LG.info("startparameters for opt:"+str(exp(logtheta)))
if gradcheck:
LG.info("check_grad:" + str(OPT.check_grad(f,df,theta0)))
raw_input()
LG.info("start optimization")
#opt_params=OPT.fmin_cg (f, theta0, fprime = df, args = (), gtol = 1.0000000000000001e-005, maxiter =maxiter, full_output = 1, disp = 1, retall = 0)
#opt_params=OPT.fmin_ncg (f, theta0, fprime = df, fhess_p=None, fhess=None, args=(), avextol=1.0000000000000001e-04, epsilon=1.4901161193847656e-08, maxiter=maxiter, full_output=1, disp=1, retall=0)
opt_params=OPT.fmin_bfgs(f, theta0, fprime=df, args=(), gtol=1.0000000000000001e-04, norm=inf, epsilon=1.4901161193847656e-08, maxiter=maxiter, full_output=1, disp=(0), retall=0)
rv = opt_params[0]
LG.info("old parameters:")
LG.info(str(exp(logtheta)))
LG.info("optimized parameters:")
LG.info(str(exp(rv)))
LG.info("grad:"+str(df(rv)))
return rv
def optHyper(gpr,
logtheta,
Ifilter=None,
priors=None,
maxiter=100,
gradcheck=False):
"""optimize hyperparemters of gp gpr starting from gpr
optHyper(gpr,logtheta,filter=None,prior=None)
gpr: GP regression classe
logtheta: starting piont for optimization
Ifilter : filter index vector
prior : non-default prior, otherwise assume first index amplitude, last noise, rest:lengthscales
"""
if priors is None: # use a very crude default prior if we don't get anything else:
priors = defaultPriors(gpr, logtheta)
def fixlogtheta(logtheta, limit=1E3):
"""make a valid logtheta which is non-infinite and non-0"""
rv = logtheta.copy()
I_upper = logtheta > limit
I_lower = logtheta < -limit
rv[I_upper] = +limit
rv[I_lower] = -limit
return rv
def checklogtheta(logtheta, limit=1E3):
"""make a valid logtheta which is non-infinite and non-0"""
I_upper = logtheta > limit
I_lower = logtheta < -limit
return not (I_upper.any() or I_lower.any())
#TODO: mean-function
def f(logtheta):
#logtheta_ = fixlogtheta(logtheta)
logtheta_ = logtheta
if not checklogtheta(logtheta):
print logtheta
#make optimzier/sampler search somewhere else
return 1E6
rv = gpr.lMl(logtheta_, lml=True, dlml=False, priors=priors)
LG.debug("L(" + str(logtheta_) + ")==" + str(rv))
if isnan(rv):
return 1E6
return rv
def df(logtheta):
#logtheta_ = fixlogtheta(logtheta)
logtheta_ = logtheta
if not checklogtheta(logtheta):
#make optimzier/sampler search somewhere else
print logtheta
return zeros_like(logtheta_)
rv = gpr.lMl(logtheta_, lml=False, dlml=True, priors=priors)
LG.debug("dL(" + str(logtheta_) + ")==" + str(rv))
#mask out filtered dimensions
if not Ifilter is None:
rv = rv * Ifilter
if isnan(rv).any():
In = isnan(rv)
rv[In] = 1E6
return rv
plotit = True
plotit = False
if (plotit):
X = arange(0.001, 0.05, 0.001)
Y = zeros(size(X))
dY = zeros(size(X))
k = 2
theta = logtheta
for i in range(len(X)):
theta[k] = log(X[i])
Y[i] = f(theta)
dY[i] = df(theta)[k]
plot(X, Y)
hold(True)
plot(X, dY)
show()
#start-parameters
theta0 = logtheta
LG.info("startparameters for opt:" + str(exp(logtheta)))
if gradcheck:
LG.info("check_grad:" + str(OPT.check_grad(f, df, theta0)))
raw_input()
LG.info("start optimization")
#opt_params=OPT.fmin_cg (f, theta0, fprime = df, args = (), gtol = 1.0000000000000001e-005, maxiter =maxiter, full_output = 1, disp = 1, retall = 0)
#opt_params=OPT.fmin_ncg (f, theta0, fprime = df, fhess_p=None, fhess=None, args=(), avextol=1.0000000000000001e-04, epsilon=1.4901161193847656e-08, maxiter=maxiter, full_output=1, disp=1, retall=0)
opt_params = OPT.fmin_bfgs(f,
theta0,
fprime=df,
args=(),
gtol=1.0000000000000001e-04,
norm=inf,
epsilon=1.4901161193847656e-08,
maxiter=maxiter,
full_output=1,
disp=(0),
retall=0)
rv = opt_params[0]
LG.info("old parameters:")
LG.info(str(exp(logtheta)))
LG.info("optimized parameters:")
LG.info(str(exp(rv)))
LG.info("grad:" + str(df(rv)))
return rv