def findMin(self, x, y):
meanfunc = self.model.meanfunc
covfunc = self.model.covfunc
likfunc = self.model.likfunc
inffunc = self.model.inffunc
hypInArray = self._convert_to_array()
if isinstance(covfunc, pyGPs.cov.SM):
Lm = len(meanfunc.hyp)
Lc = len(covfunc.hyp)
opt = rt_minimize.rt_minimize(hypInArray, self._nlzAnddnlz, length=-40)
optimalHyp = deepcopy(opt[0])
funcValue = opt[1][-1]
if self.searchConfig:
searchRange = self.searchConfig.meanRange + \
self.searchConfig.covRange + self.searchConfig.likRange
if not (self.searchConfig.num_restarts
or self.searchConfig.min_threshold):
raise Exception('Specify at least one of the stop conditions')
while True:
self.trailsCounter += 1 # increase counter
# TODO Replace with better initialization
for i in xrange(hypInArray.shape[0]): # random init of hyp
hypInArray[i] = np.random.uniform(low=searchRange[i][0],
high=searchRange[i][1])
if isinstance(self.model.covfunc, pyGPs.cov.SM):
hyps = cov.initSMhypers(self.model.covfunc.para[0], x, y)
hypInArray[Lm:Lm + Lc] = hyps[:]
# value this time is better than optimal min value
try:
thisopt = rt_minimize.rt_minimize(hypInArray,
self._nlzAnddnlz,
length=-40)
if thisopt[1][-1] < funcValue:
funcValue = thisopt[1][-1]
optimalHyp = thisopt[0]
except:
self.errorCounter += 1
if self.searchConfig.num_restarts and self.errorCounter > self.searchConfig.num_restarts / 2:
print "[RTMinimize] %d out of %d trails failed during optimization" % (
self.errorCounter, self.trailsCounter)
raise Exception(
"Over half of the trails failed for minimize")
# if exceed num_restarts
if self.searchConfig.num_restarts and self.trailsCounter > self.searchConfig.num_restarts - 1:
print "[RTMinimize] %d out of %d trails failed during optimization" % (
self.errorCounter, self.trailsCounter)
return optimalHyp, funcValue
# reach provided mininal
if self.searchConfig.min_threshold and funcValue <= self.searchConfig.min_threshold:
print "[RTMinimize] %d out of %d trails failed during optimization" % (
self.errorCounter, self.trailsCounter)
return optimalHyp, funcValue
return optimalHyp, funcValue
def findMin(self, x, y):
meanfunc = self.model.meanfunc
covfunc = self.model.covfunc
likfunc = self.model.likfunc
inffunc = self.model.inffunc
hypInArray = self._convert_to_array()
if isinstance(covfunc, pyGPs.cov.SM):
Lm = len(meanfunc.hyp)
Lc = len(covfunc.hyp)
opt = rt_minimize.rt_minimize(hypInArray, self._nlzAnddnlz, length=-40)
optimalHyp = deepcopy(opt[0])
funcValue = opt[1][-1]
if self.searchConfig:
searchRange = self.searchConfig.meanRange + \
self.searchConfig.covRange + self.searchConfig.likRange
if not (self.searchConfig.num_restarts or self.searchConfig.min_threshold):
raise Exception('Specify at least one of the stop conditions')
while True:
self.trailsCounter += 1 # increase counter
# TODO Replace with better initialization
for i in xrange(hypInArray.shape[0]): # random init of hyp
hypInArray[i] = np.random.uniform(low=searchRange[i][0], high=searchRange[i][1])
if isinstance(self.model.covfunc, pyGPs.cov.SM):
hyps = cov.initSMhypers(self.model.covfunc.para[0], x, y)
hypInArray[Lm:Lm + Lc] = hyps[:]
# value this time is better than optimal min value
try:
thisopt = rt_minimize.rt_minimize(hypInArray, self._nlzAnddnlz, length=-40)
if thisopt[1][-1] < funcValue:
funcValue = thisopt[1][-1]
optimalHyp = thisopt[0]
except:
self.errorCounter += 1
if self.searchConfig.num_restarts and self.errorCounter > self.searchConfig.num_restarts / 2:
print "[RTMinimize] %d out of %d trails failed during optimization" % (self.errorCounter, self.trailsCounter)
raise Exception(
"Over half of the trails failed for minimize")
# if exceed num_restarts
if self.searchConfig.num_restarts and self.trailsCounter > self.searchConfig.num_restarts - 1:
print "[RTMinimize] %d out of %d trails failed during optimization" % (self.errorCounter, self.trailsCounter)
return optimalHyp, funcValue
# reach provided mininal
if self.searchConfig.min_threshold and funcValue <= self.searchConfig.min_threshold:
print "[RTMinimize] %d out of %d trails failed during optimization" % (self.errorCounter, self.trailsCounter)
return optimalHyp, funcValue
return optimalHyp, funcValue
def bocpdGPTlearn(
X, # Training data
model, # The current GP model
theta_m, # the hyperparameters for the GP model
theta_h, # the hyperparameters for the hazard function
dt=1, # the timestep
):
max_minimize_iter = 30
num_hazard_params = len(theta_h)
if model.ScalePrior:
theta_s = model.ScalePrior[
0] # alpha from the prior on scale (assumed beta is identity)
else:
theta_s = 0
theta = np.append(np.append(theta_h, theta_m), theta_s)
(theta, nlml, i) = rt_minimize(
theta,
dbocpdGP,
-max_minimize_iter,
X,
model,
num_hazard_params,
dt,
)
hazard_params = theta[:num_hazard_params]
model_params = theta[num_hazard_params:-1]
scale_params = theta[-1]
return (hazard_params, model_params, scale_params, nlml[-1])
def bocpdGPTlearn(
X, # Training data
model, # The current GP model
theta_m, # the hyperparameters for the GP model
theta_h, # the hyperparameters for the hazard function
dt=1, # the timestep
):
max_minimize_iter = 30
num_hazard_params = len(theta_h)
if model.ScalePrior:
theta_s = model.ScalePrior[
0] # alpha from the prior on scale (assumed beta is identity)
else:
theta_s = 0
theta = np.append(np.append(theta_h, theta_m), theta_s)
(theta, nlml, i) = rt_minimize(
theta,
dbocpdGP,
-max_minimize_iter,
X,
model,
num_hazard_params,
dt,
)
hazard_params = theta[:num_hazard_params]
model_params = theta[num_hazard_params:-1]
scale_params = theta[-1]
return (hazard_params, model_params, scale_params, nlml[-1])