def restore(self):
print "Restoring Profile."
data = State.get_data(self.version, self.StateID)
q = False
for inputsize in data:
try:
algdata = inputsize["Algorithm"]
except:
inputsize = {
"ResourceVariables": self.variables,
"InputParameters": self.parameters,
"Algorithm": {}
}
State.checkpoint(self.version, self.StateID, data)
break
if algdata != None and algdata != []:
if inputsize["InputParameters"] == self.parameters:
q = True
self.strategy.update_state(algdata)
#if profiling with the input size was finished go to the next one
if not self.strategy.stop_condition():
break
if not q:
return []
return data
def restore(self):
#load state
data = State.get_data(self.version, self.StateID)
for inputsize in data:
try:
algdata = inputsize["Methodology"]
except:
inputsize = {
"Input": self.newinput,
"BenchmarkInput": self.benchmark_input,
"Methodology": {},
}
State.checkpoint(self.version, self.StateID, data)
continue
if not (algdata in [None, {}, []]):
self.benchmark_input = inputsize["BenchmarkInput"]
self.benchmark_solutions = None
for sol in self.profiling_solutions:
if sol["Input"] == self.benchmark_input:
self.benchmark_solutions = sol["Configurations"]
if not self.benchmark_solutions:
#the benchmark input from the save file doesn't have any solution explored
self.benchmark_input = self.profiling_solutions[-1][
"Input"]
self.benchmark_solutions = self.profiling_solutions[-1][
"Configurations"]
self.strategy.update_state(algdata)
#if profiling with the input size was finished go to the next one
if not self.strategy.stop_condition():
break
return data
def restore(self):
if self.Data == {}:
#load state
for v in self.versions:
version = ".".join(map(lambda s: str(s), v))
data = State.get_data(version, self.StateID)
if data in [None, {}, []]:
continue
self.Data[version] = data[0]
def get_explored_solutions(self):
data = State.get_data(self.version, self.StateID)
if data in [None, []]:
return self.variables, None
solutions = []
for info in data:
solutions.append({
"Input": info["InputParameters"],
"Configurations": info["Algorithm"]["solutions"]
})
return self.variables, solutions
def save_state(self):
print "Save state to file."
data = State.get_data(self.version, self.StateID)
algdata = self.strategy.get_state()
info = {
"Input": self.newinput,
"BenchmarkInput": self.benchmark_input,
"Methodology": algdata
}
q = False
for inputsize in data:
if inputsize["Input"] == self.newinput:
inputsize.update(info)
q = True
if not q:
data.append(info)
State.checkpoint(self.version, self.StateID, data)
def save_state(self):
print "Save state to file."
data = State.get_data(self.version, self.StateID)
algdata = self.strategy.get_state()
info = {
"ResourceVariables": self.variables,
"InputParameters": self.parameters,
"Algorithm": algdata
}
q = False
for inputsize in data:
if inputsize["InputParameters"] == self.parameters:
inputsize.update(info)
q = True
if not q:
data.append(info)
State.checkpoint(self.version, self.StateID, data)