the official answer to that question is here - http://docs.python.org/reference/datamodel.html?highlight=containers - at about the 6th paragraph where it talks about "containers".
i think it's a hard question and really in a sense (imho) the real reason is just that this tends to be the best compromise for the kind of language that python is. andrew Cameron Pulsford wrote: > Thanks, that did it! Why is that the case though? Or rather, why do > the assignments to temp.x and temp.y not effect the self.x and self.y? > How come I only run into the problem with the list? > > > On Feb 12, 2009, at 5:15 PM, andrew cooke wrote: > >> >> you're setting the new knight's "sl" to the value self.sl and then >> adding >> values to it. that's the same list - so you are adding values to the >> self.sl list when you add them to the knight's sl. >> >> this is easier to understand just by seeing the fix, which is to use: >> >> temp = Knight(self.x, self.y, self.g, self.h, self.gp, list(self.sl)) >> >> which will make a new copy of the list, so you are only adding to >> the sl >> in the (new) knight. >> >> andrew >> >> >> >> dlocpuwons wrote: >>> Using Python 2.6.1... >>> >>> I am (attempting) to make an A* search for a chess problem, but I am >>> running into a really annoying shared memory issue in my successor >>> function. Here it is stripped down to the important parts that relate >>> to my problem. >>> >>> def successors(self): >>> result = [] >>> moves = [[2, 1], [2, -1], [-2, 1], [-2, -1], [1, 2], [1, -2], >>> [-1, >>> 2], [-1, -2]] #possible moves for a knight >>> >>> for i in moves: >>> temp = Knight(self.x, self.y, self.g, self.h, self.gp, >>> self.sl) >>> temp.x += i[0] >>> temp.y += i[1] >>> temp.sl.append([temp.x, temp.y]) #Adds the new current >>> state to >>> the >>> visited states list >>> result.append(temp) >>> return result >>> >>> The method creates a temporary Knight object, increments it to the >>> new >>> position and then adds this new position to its list of visited >>> states. Then it returns a list of these 8 new objects. The problem >>> seems to be with the "result.sl.append()" line. As the method chugs >>> along and creates the new objects the "temp.sl" lines seems to stay >>> in >>> memory, so when the method is done all the new objects have all the >>> new states that were made over the course of the method instead of >>> just their own created in the loop. For example when I try to get >>> successors for a piece that is initially at (2,2) with no previously >>> visited states, the method prints this out for the sl (state list) >>> value >>> >>> [[2, 2], [4, 3], [4, 1], [0, 3], [0, 1], [3, 4], [3, 0], [1, 4], [1, >>> 0]] >>> [[2, 2], [4, 3], [4, 1], [0, 3], [0, 1], [3, 4], [3, 0], [1, 4], [1, >>> 0]] >>> [[2, 2], [4, 3], [4, 1], [0, 3], [0, 1], [3, 4], [3, 0], [1, 4], [1, >>> 0]] >>> [[2, 2], [4, 3], [4, 1], [0, 3], [0, 1], [3, 4], [3, 0], [1, 4], [1, >>> 0]] >>> [[2, 2], [4, 3], [4, 1], [0, 3], [0, 1], [3, 4], [3, 0], [1, 4], [1, >>> 0]] >>> [[2, 2], [4, 3], [4, 1], [0, 3], [0, 1], [3, 4], [3, 0], [1, 4], [1, >>> 0]] >>> [[2, 2], [4, 3], [4, 1], [0, 3], [0, 1], [3, 4], [3, 0], [1, 4], [1, >>> 0]] >>> [[2, 2], [4, 3], [4, 1], [0, 3], [0, 1], [3, 4], [3, 0], [1, 4], [1, >>> 0]] >>> >>> but what it should print out is >>> >>> [[2, 2], [4, 3]] >>> [[2, 2], [4, 1]] >>> [[2, 2], [0, 3]] >>> [[2, 2], [0, 1]] >>> [[2, 2], [3, 4]] >>> [[2, 2], [3, 0]] >>> [[2, 2], [1, 4]] >>> [[2, 2], [1, 0]] >>> >>> It sort of seems like python is trying to be too smart and is trying >>> to keep things in memory. Is there anyway to work around this? >>> -- >>> http://mail.python.org/mailman/listinfo/python-list >>> >>> >> >> > > -- http://mail.python.org/mailman/listinfo/python-list
