(self,times)
return self._construct_path_from_skeleton([(0,self.startPosition)]+skeleton, times)
currently will only return a random path before time T. Can be connected by brownian bridges
currently will only return a random path before time T. Can be connected by brownian bridges
currently will only return a random path before time T . Can be connected by brownian bridges
def generate_sample_path(self,times):
        "currently will only return a random path before time T. Can be connected by brownian bridges"
        #this algorithm uses the EA1 algorithm by Beskos and 
        # Roberts on exact simulation of diffusions. It's an AR algorithm.
        # For some parameters, the probability of acceptance can be very low.
        time = 0
        endPoint = 0
        skeleton=[]
        T = times[-1]
        while time<T:
            if time+2<T:
                delta=2
            else:
                delta = T-time
            endPoint, tempSkeleton = self.__generate_sample_path(delta,endPoint)
            tk = [(time + x[0],x[1]) for x in tempSkeleton] 
            skeleton+=tk
            
            time+=2
        
     
        
        return self._construct_path_from_skeleton([(0,self.startPosition)]+skeleton, times)
def generate_sample_path ( self , times ) : #this algorithm uses the EA1 algorithm by Beskos and  # Roberts on exact simulation of diffusions. It's an AR algorithm. # For some parameters, the probability of acceptance can be very low. time = 0 endPoint = 0 skeleton = [ ] T = times [ - 1 ] while time < T : if time + 2 < T : delta = 2 else : delta = T - time endPoint , tempSkeleton = self . __generate_sample_path ( delta , endPoint ) tk = [ ( time + x [ 0 ] , x [ 1 ] ) for x in tempSkeleton ] skeleton += tk time += 2 return self . _construct_path_from_skeleton ( [ ( 0 , self . startPosition ) ] + skeleton , times )