def Target_Terms(self, mvals, Order=0, verbose=False, customdir=None):
## This is the objective function; it's a dictionary containing the value, first and second derivatives
Objective = {'X':0.0, 'G':np.zeros(self.FF.np), 'H':np.zeros((self.FF.np,self.FF.np))}
# Loop through the targets, stage the directories and submit the Work Queue processes.
for Tgt in self.Targets:
Tgt.stage(mvals, AGrad = Order >= 1, AHess = Order >= 2, customdir=customdir)
if self.asynchronous:
# Asynchronous evaluation of objective function and Work Queue tasks.
# Create a list of the targets, and remove them from the list as they are finished.
Need2Evaluate = self.Targets[:]
# This ensures that the OrderedDict doesn't get out of order.
for Tgt in self.Targets:
self.ObjDict[Tgt.name] = None
# Loop through the targets and compute the objective function for ones that are finished.
while len(Need2Evaluate) > 0:
for Tgt in Need2Evaluate:
if Tgt.wq_complete():
# List of functions that I can call.
Funcs = [Tgt.get_X, Tgt.get_G, Tgt.get_H]
# Call the appropriate function
Ans = Funcs[Order](mvals, customdir=customdir)
# Print out the qualitative indicators
if verbose:
Tgt.meta_indicate(customdir=customdir)
# Note that no matter which order of function we call, we still increment the objective / gradient / Hessian the same way.
if not in_fd():
self.ObjDict[Tgt.name] = {'w' : Tgt.weight/self.WTot , 'x' : Ans['X']}
for i in range(3):
Objective[Letters[i]] += Ans[Letters[i]]*Tgt.weight/self.WTot
Need2Evaluate.remove(Tgt)
break
else:
pass
else:
wq = getWorkQueue()
if wq is not None:
wq_wait(wq)
for Tgt in self.Targets:
# The first call is always done at the midpoint.
Tgt.bSave = True
# List of functions that I can call.
Funcs = [Tgt.get_X, Tgt.get_G, Tgt.get_H]
# Call the appropriate function
Ans = Funcs[Order](mvals, customdir=customdir)
# Print out the qualitative indicators
if verbose:
Tgt.meta_indicate(customdir=customdir)
# Note that no matter which order of function we call, we still increment the objective / gradient / Hessian the same way.
if not in_fd():
self.ObjDict[Tgt.name] = {'w' : Tgt.weight/self.WTot , 'x' : Ans['X']}
for i in range(3):
Objective[Letters[i]] += Ans[Letters[i]]*Tgt.weight/self.WTot
# The target has evaluated at least once.
for Tgt in self.Targets:
Tgt.evaluated = True
# Safeguard to make sure we don't have exact zeros on Hessian diagonal
for i in range(self.FF.np):
if Objective['H'][i,i] == 0.0:
Objective['H'][i,i] = 1.0
return Objective
def Target_Terms(self, mvals, Order=0, verbose=False, customdir=None):
## This is the objective function; it's a dictionary containing the value, first and second derivatives
Objective = {'X':0.0, 'G':np.zeros(self.FF.np), 'H':np.zeros((self.FF.np,self.FF.np))}
# Loop through the targets, stage the directories and submit the Work Queue processes.
for Tgt in self.Targets:
Tgt.stage(mvals, AGrad = Order >= 1, AHess = Order >= 2, customdir=customdir)
if self.asynchronous:
# Asynchronous evaluation of objective function and Work Queue tasks.
# Create a list of the targets, and remove them from the list as they are finished.
Need2Evaluate = self.Targets[:]
# This ensures that the OrderedDict doesn't get out of order.
for Tgt in self.Targets:
self.ObjDict[Tgt.name] = None
# Loop through the targets and compute the objective function for ones that are finished.
while len(Need2Evaluate) > 0:
for Tgt in Need2Evaluate:
if Tgt.wq_complete():
# List of functions that I can call.
Funcs = [Tgt.get_X, Tgt.get_G, Tgt.get_H]
# Call the appropriate function
Ans = Funcs[Order](mvals, customdir=customdir)
# Print out the qualitative indicators
if verbose:
Tgt.meta_indicate(customdir=customdir)
# Note that no matter which order of function we call, we still increment the objective / gradient / Hessian the same way.
if not in_fd():
self.ObjDict[Tgt.name] = {'w' : Tgt.weight/self.WTot , 'x' : Ans['X']}
for i in range(3):
Objective[Letters[i]] += Ans[Letters[i]]*Tgt.weight/self.WTot
Need2Evaluate.remove(Tgt)
break
else:
pass
else:
wq = getWorkQueue()
if wq != None:
wq_wait(wq)
for Tgt in self.Targets:
# The first call is always done at the midpoint.
Tgt.bSave = True
# List of functions that I can call.
Funcs = [Tgt.get_X, Tgt.get_G, Tgt.get_H]
# Call the appropriate function
Ans = Funcs[Order](mvals, customdir=customdir)
# Print out the qualitative indicators
if verbose:
Tgt.meta_indicate(customdir=customdir)
# Note that no matter which order of function we call, we still increment the objective / gradient / Hessian the same way.
if not in_fd():
self.ObjDict[Tgt.name] = {'w' : Tgt.weight/self.WTot , 'x' : Ans['X']}
for i in range(3):
Objective[Letters[i]] += Ans[Letters[i]]*Tgt.weight/self.WTot
# The target has evaluated at least once.
for Tgt in self.Targets:
Tgt.evaluated = True
# Safeguard to make sure we don't have exact zeros on Hessian diagonal
for i in range(self.FF.np):
if Objective['H'][i,i] == 0.0:
Objective['H'][i,i] = 1.0
return Objective