def startAt(self,r):
"""
Define the initial position of the agent and wipe its memory.
Parameters:
----------
position : list, the initial solution to start at
"""
self.r = r
self.car = carObjective.normCarVector_to_car(r)
self.memory = [Solution(r=self.r,score=self.f(),agent_class=type(self))]
def moveTo(self,r):
"""
Change the agent's current solution to r and update it's score.
Also add the new solution to the agent's memory and increment the
agent's move countself.
Parameters:
----------
r : list, shape = [nDims]
the new solution to move to
"""
self.r = r
self.car = carObjective.normCarVector_to_car(r)
self.score = self.f()
self.memory.append(Solution(self.r,self.score,type(self)))
self.nmoves += 1
def constrain(self,r,p):
self.car = carObjective.normCarVector_to_car(r)
carConstrained = carObjective.constrain(self.car)
rConstrained = carObjective.normalizedCarVector(carConstrained)
return rConstrained #[-1 for i in range(len(x))]
def fr(self,new_r):
newCar = carObjective.normCarVector_to_car(new_r)
return carObjective.objective(newCar)
def f(self): #evaluate objective function for this agent's current solution
self.car = carObjective.normCarVector_to_car(self.r)
return carObjective.objective(self.car)
def fr(self,new_r):
newCar = carObjective.normCarVector_to_car(new_r)
return carObjective.objective(newCar,weights=self.obj_weights)