def generateRandomCandidate(self):
candidate = []
for ind in range(self.model.nParams):
candidate.append(
random.uniform(
self.model.parameter_values[self.model.params[ind]]["min"],
self.model.parameter_values[
self.model.params[ind]]["max"]))
return creator.Candidate(candidate)
def generateCandidate(self):
candidate = []
for ind in range(self.model.nParams):
#candidate.append(random.uniform(self.model.parameter_values[self.model.params[ind]]["min"], self.model.parameter_values[self.model.params[ind]]["max"]))
while True:
r = random.normal(param_references[ind],np.min([
np.abs(self.model.parameter_values[self.model.params[ind]]["min"] - param_references[ind]),
np.abs(self.model.parameter_values[self.model.params[ind]]["max"] - param_references[ind])
])/3.0)
if r > 0:
break
candidate.append(r)
return creator.Candidate(candidate)