def findMin(self, x, y, numIters=200):
meanfunc = self.model.meanfunc
covfunc = self.model.covfunc
likfunc = self.model.likfunc
inffunc = self.model.inffunc
hypInArray = self._convert_to_array()
try:
# opt = minimize.run(self._nlzAnddnlz, hypInArray, length=-numIters)
opt = minimize.run(self._nlzAnddnlz, hypInArray, length=numIters)
optimalHyp = deepcopy(opt[0])
funcValue = opt[1][-1]
self.logger.warning("Number of line searches %g", opt[2])
except:
self.errorCounter += 1
if not self.searchConfig:
raise Exception("Can not learn hyperparamters using minimize.")
self.trailsCounter += 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
for i in range(hypInArray.shape[0]): # random init of hyp
hypInArray[i] = np.random.uniform(low=searchRange[i][0],
high=searchRange[i][1])
# value this time is better than optiaml min value
try:
# thisopt = minimize.run(self._nlzAnddnlz, hypInArray, length=-numIters)
thisopt = minimize.run(self._nlzAnddnlz,
hypInArray,
length=numIters)
self.logger.warning("Number of line searches %g",
thisopt[2])
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:
self.logger.warning(
"[Minimize] %d out of %d trails failed during optimization",
self.errorCounter, self.trailsCounter)
raise Exception(
"Over half of the trails failed for minimize")
if self.searchConfig.num_restarts and self.trailsCounter > self.searchConfig.num_restarts - 1: # if exceed num_restarts
self.logger.warning(
"[Minimize] %d out of %d trails failed during optimization",
self.errorCounter, self.trailsCounter)
return optimalHyp, funcValue
if self.searchConfig.min_threshold and funcValue <= self.searchConfig.min_threshold: # reach provided mininal
self.logger.warning(
"[Minimize] %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()
opt = minimize.run(self._nlzAnddnlz, hypInArray, length=-40)
try:
opt = minimize.run(self._nlzAnddnlz, hypInArray, length=-40)
optimalHyp = deepcopy(opt[0])
funcValue = opt[1][-1]
except:
self.errorCounter += 1
if not self.searchConfig:
raise Exception("Can not use minimize. Try other hyparameters")
self.trailsCounter += 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
for i in xrange(hypInArray.shape[0]): # random init of hyp
hypInArray[i] = np.random.uniform(low=searchRange[i][0],
high=searchRange[i][1])
# value this time is better than optiaml min value
try:
thisopt = minimize.run(self._nlzAnddnlz,
hypInArray,
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 "[Minimize] %d out of %d trails failed during optimization" % (
self.errorCounter, self.trailsCounter)
raise Exception(
"Over half of the trails failed for minimize")
if self.searchConfig.num_restarts and self.trailsCounter > self.searchConfig.num_restarts - 1: # if exceed num_restarts
print "[Minimize] %d out of %d trails failed during optimization" % (
self.errorCounter, self.trailsCounter)
return optimalHyp, funcValue
if self.searchConfig.min_threshold and funcValue <= self.searchConfig.min_threshold: # reach provided mininal
print "[Minimize] %d out of %d trails failed during optimization" % (
self.errorCounter, self.trailsCounter)
return optimalHyp, funcValue
return optimalHyp, funcValue
def findMin(self, x, y, numIters = 200):
meanfunc = self.model.meanfunc
covfunc = self.model.covfunc
likfunc = self.model.likfunc
inffunc = self.model.inffunc
hypInArray = self._convert_to_array()
try:
# opt = minimize.run(self._nlzAnddnlz, hypInArray, length=-numIters)
opt = minimize.run(self._nlzAnddnlz, hypInArray, length=numIters)
optimalHyp = deepcopy(opt[0])
funcValue = opt[1][-1]
print("Number of line searches %g" % opt[2])
except:
self.errorCounter += 1
if not self.searchConfig:
raise Exception("Can not learn hyperparamters using minimize.")
self.trailsCounter += 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
for i in xrange(hypInArray.shape[0]): # random init of hyp
hypInArray[i]= np.random.uniform(low=searchRange[i][0], high=searchRange[i][1])
# value this time is better than optiaml min value
try:
# thisopt = minimize.run(self._nlzAnddnlz, hypInArray, length=-numIters)
thisopt = minimize.run(self._nlzAnddnlz, hypInArray, length=numIters)
print("Number of line searches %g" % thisopt[2])
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("[Minimize] %d out of %d trails failed during optimization" % (self.errorCounter, self.trailsCounter))
raise Exception("Over half of the trails failed for minimize")
if self.searchConfig.num_restarts and self.trailsCounter > self.searchConfig.num_restarts-1: # if exceed num_restarts
print("[Minimize] %d out of %d trails failed during optimization" % (self.errorCounter, self.trailsCounter))
return optimalHyp, funcValue
if self.searchConfig.min_threshold and funcValue <= self.searchConfig.min_threshold: # reach provided mininal
print("[Minimize] %d out of %d trails failed during optimization" % (self.errorCounter, self.trailsCounter))
return optimalHyp, funcValue
return optimalHyp, funcValue