 # Untitled

Mar 16th, 2023
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1. import bisect
2. from copy import deepcopy
3.
4. """
5. Defining a class for the problem structure that we will solve with a search.
6. The Problem class is an abstract class from which we make inheritance to define the basic
7. characteristics of every problem we want to solve
8. """
9.
10. dx = [0, 1, 0, -1]
11. dy = [1, 0, -1, 0]
12. op = [2, 3, 0, 1]
13.
14. class Problem:
15.     def __init__(self, initial, goal=None):
16.         # [red, green, snake]
17.         self.initial = initial
18.         self.goal = goal
19.
20.     def valid(self, x, y):
21.         return (x >= 0 and x < 10 and y >= 0 and y < 10)
22.
23.     def go(self, S, dir):
24.         state = [None, None, None]
25.         state = list(S)
26.         state = list(S)
27.         state = list(S)
28.         current = state[-1]
29.         nx = current + dx[dir]
30.         ny = current + dy[dir]
31.         if not self.valid(nx, ny):
32.             return None
33.
35.         for apple in state:
36.             if apple == nx and apple == ny:
38.                 break
39.
41.             return None
42.
44.         for i in range(len(state)):
45.             if state[i] == nx and state[i] == ny:
47.                 state.pop(i)
48.                 break
49.
50.         # remove the tail
52.             state.pop(0)
53.
55.         state.append((nx, ny))
56.
57.         state = tuple(state)
58.         state = tuple(state)
59.         state = tuple(state)
60.         return tuple(state)
61.
62.     def successor(self, state):
63.         """Given a state, return a dictionary of {action : state} pairs reachable
64.        from this state. If there are many successors, consider an iterator
65.        that yields the successors one at a time, rather than building them
66.        all at once.
67.
68.        :param state: given state
69.        :return:  dictionary of {action : state} pairs reachable
70.                  from this state
71.        :rtype: dict
72.        """
73.
74.         successors = {}
75.
76.         red, green, snake = deepcopy(state)
77.
78.         dir_coming_from = 0
79.         for dir in range(4):
80.             nx = snake[-1] + dx[dir]
81.             ny = snake[-1] + dy[dir]
82.             if nx == snake[-2] and ny == snake[-2]:
83.                 dir_coming_from = dir
84.                 break
85.
86.         # pravo e op[dir_coming_from]
87.         # levo e op[dir_coming_from] - 1
88.         # desno e op[dir_coming_from] + 1
89.
91.         if ahead is not None:
93.
94.         right = self.go(state, (op[dir_coming_from] + 1) % 4)
95.         if right is not None:
96.             successors['SvrtiDesno'] = right
97.
98.         left = self.go(state, (op[dir_coming_from] - 1 + 4) % 4)
99.         if left is not None:
100.             successors['SvrtiLevo'] = left
101.
102.         return successors
103.
104.     def actions(self, state):
105.         """Given a state, return a list of all actions possible
106.        from that state
107.
108.        :param state: given state
109.        :return: list of actions
110.        :rtype: list
111.        """
112.         return self.successor(state).keys()
113.
114.     def result(self, state, action):
115.         """Given a state and action, return the resulting state
116.
117.        :param state: given state
118.        :param action: given action
119.        :return: resulting state
120.        """
121.         return self.successor(state)[action]
122.
123.     def goal_test(self, state):
124.         """Return True if the state is a goal. The default method compares
125.        the state to self.goal, as specified in the constructor. Implement
126.        this method if checking against a single self.goal is not enough.
127.
128.        :param state: given state
129.        :return: is the given state a goal state
130.        :rtype: bool
131.        """
132.         # all green apples are eaten
133.         return len(state) == 0
134.
135.     def path_cost(self, c, state1, action, state2):
136.         """Return the cost of a solution path that arrives at state2 from state1
137.        via action, assuming cost c to get up to state1. If the problem is such
138.        that the path doesn't matter, this function will only look at state2.
139.        If the path does matter, it will consider c and maybe state1 and action.
140.        The default method costs 1 for every step in the path.
141.
142.        :param c: cost of the path to get up to state1
143.        :param state1: given current state
144.        :param action: action that needs to be done
145.        :param state2: state to arrive to
146.        :return: cost of the path after executing the action
147.        :rtype: float
148.        """
149.         return c + 1
150.
151.     def value(self):
152.         """For optimization problems, each state has a value.
153.        Hill-climbing and related algorithms try to maximize this value.
154.
155.        :return: state value
156.        :rtype: float
157.        """
158.         return -len(self.initial)
159.
160.
161. """
162. Definition of the class for node structure of the search.
163. The class Node is not inherited
164. """
165.
166.
167. class Node:
168.     def __init__(self, state, parent=None, action=None, path_cost=0):
169.         """Create node from the search tree,  obtained from the parent by
170.        taking the action
171.
172.        :param state: current state
173.        :param parent: parent state
174.        :param action: action
175.        :param path_cost: path cost
176.        """
177.         self.state = state
178.         self.parent = parent
179.         self.action = action
180.         self.path_cost = path_cost
181.         self.depth = 0  # search depth
182.         if parent:
183.             self.depth = parent.depth + 1
184.
185.     def __repr__(self):
186.         return "<Node %s>" % (self.state,)
187.
188.     def __lt__(self, node):
189.         return self.state < node.state
190.
191.     def expand(self, problem):
192.         """List the nodes reachable in one step from this node.
193.
194.        :param problem: given problem
195.        :return: list of available nodes in one step
196.        :rtype: list(Node)
197.        """
198.         return [self.child_node(problem, action)
199.                 for action in problem.actions(self.state)]
200.
201.     def child_node(self, problem, action):
202.         """Return a child node from this node
203.
204.        :param problem: given problem
205.        :param action: given action
206.        :return: available node  according to the given action
207.        :rtype: Node
208.        """
209.         next_state = problem.result(self.state, action)
210.         return Node(next_state, self, action,
211.                     problem.path_cost(self.path_cost, self.state,
212.                                       action, next_state))
213.
214.     def solution(self):
215.         """Return the sequence of actions to go from the root to this node.
216.
217.        :return: sequence of actions
218.        :rtype: list
219.        """
220.         return [node.action for node in self.path()[1:]]
221.
222.     def solve(self):
223.         """Return the sequence of states to go from the root to this node.
224.
225.        :return: list of states
226.        :rtype: list
227.        """
228.         return [node.state for node in self.path()[0:]]
229.
230.     def path(self):
231.         """Return a list of nodes forming the path from the root to this node.
232.
233.        :return: list of states from the path
234.        :rtype: list(Node)
235.        """
236.         x, result = self, []
237.         while x:
238.             result.append(x)
239.             x = x.parent
240.         result.reverse()
241.         return result
242.
243.     """We want the queue of nodes at breadth_first_search or
244.    astar_search to not contain states-duplicates, so the nodes that
245.    contain the same condition we treat as the same. [Problem: this can
246.    not be desirable in other situations.]"""
247.
248.     def __eq__(self, other):
249.         return isinstance(other, Node) and self.state == other.state
250.
251.     def __hash__(self):
252.         return hash(self.state)
253.
254.
255. """
256. Definitions of helper structures for storing the list of generated, but not checked nodes
257. """
258.
259.
260. class Queue:
261.     """Queue is an abstract class/interface. There are three types:
262.        Stack(): Last In First Out Queue (stack).
263.        FIFOQueue(): First In First Out Queue.
264.        PriorityQueue(order, f): Queue in sorted order (default min-first).
265.    """
266.
267.     def __init__(self):
268.         raise NotImplementedError
269.
270.     def append(self, item):
271.         """Adds the item into the queue
272.
273.        :param item: given element
274.        :return: None
275.        """
276.         raise NotImplementedError
277.
278.     def extend(self, items):
279.         """Adds the items into the queue
280.
281.        :param items: given elements
282.        :return: None
283.        """
284.         raise NotImplementedError
285.
286.     def pop(self):
287.         """Returns the first element of the queue
288.
289.        :return: first element
290.        """
291.         raise NotImplementedError
292.
293.     def __len__(self):
294.         """Returns the number of elements in the queue
295.
296.        :return: number of elements in the queue
297.        :rtype: int
298.        """
299.         raise NotImplementedError
300.
301.     def __contains__(self, item):
302.         """Check if the queue contains the element item
303.
304.        :param item: given element
305.        :return: whether the queue contains the item
306.        :rtype: bool
307.        """
308.         raise NotImplementedError
309.
310.
311. class Stack(Queue):
312.     """Last-In-First-Out Queue."""
313.
314.     def __init__(self):
315.         self.data = []
316.
317.     def append(self, item):
318.         self.data.append(item)
319.
320.     def extend(self, items):
321.         self.data.extend(items)
322.
323.     def pop(self):
324.         return self.data.pop()
325.
326.     def __len__(self):
327.         return len(self.data)
328.
329.     def __contains__(self, item):
330.         return item in self.data
331.
332.
333. class FIFOQueue(Queue):
334.     """First-In-First-Out Queue."""
335.
336.     def __init__(self):
337.         self.data = []
338.
339.     def append(self, item):
340.         self.data.append(item)
341.
342.     def extend(self, items):
343.         self.data.extend(items)
344.
345.     def pop(self):
346.         return self.data.pop(0)
347.
348.     def __len__(self):
349.         return len(self.data)
350.
351.     def __contains__(self, item):
352.         return item in self.data
353.
354.
355. class PriorityQueue(Queue):
356.     """A queue in which the minimum (or maximum) element is returned first
357.     (as determined by f and order). This structure is used in
358.     informed search"""
359.
360.     def __init__(self, order=min, f=lambda x: x):
361.         """
362.        :param order: sorting function, if order is min, returns the element
363.                      with minimal f (x); if the order is max, then returns the
364.                      element with maximum f (x).
365.        :param f: function f(x)
366.        """
367.         assert order in [min, max]
368.         self.data = []
369.         self.order = order
370.         self.f = f
371.
372.     def append(self, item):
373.         bisect.insort_right(self.data, (self.f(item), item))
374.
375.     def extend(self, items):
376.         for item in items:
377.             bisect.insort_right(self.data, (self.f(item), item))
378.
379.     def pop(self):
380.         if self.order == min:
381.             return self.data.pop(0)
382.         return self.data.pop()
383.
384.     def __len__(self):
385.         return len(self.data)
386.
387.     def __contains__(self, item):
388.         return any(item == pair for pair in self.data)
389.
390.     def __getitem__(self, key):
391.         for _, item in self.data:
392.             if item == key:
393.                 return item
394.
395.     def __delitem__(self, key):
396.         for i, (value, item) in enumerate(self.data):
397.             if item == key:
398.                 self.data.pop(i)
399.
400.
401. """
402. Uninformed graph search
403. The main difference is that here we do not allow loops,
404. i.e. repetition of states
405. """
406.
407.
408. def graph_search(problem, fringe):
409.     """Search through the successors of a problem to find a goal.
410.      If two paths reach a state, only use the best one.
411.
412.    :param problem: given problem
413.    :param fringe: empty queue
414.    :return: Node
415.    """
416.     closed = {}
417.     fringe.append(Node(problem.initial))
418.     while fringe:
419.         node = fringe.pop()
420.         if problem.goal_test(node.state):
421.             return node
422.         if node.state not in closed:
423.             closed[node.state] = True
424.             fringe.extend(node.expand(problem))
425.     return None
426.
427.
429.     """Search the shallowest nodes in the search tree first.
430.
431.    :param problem: given problem
432.    :return: Node
433.    """
434.     return graph_search(problem, FIFOQueue())
435.
436.
437. if __name__ == '__main__':
438.     green_apples = int(input())
439.     green = []
440.     for i in range(green_apples):
441.         green.append(tuple(map(int, input().split(','))))
442.
443.     red_apples = int(input())
444.     red = []
445.     for i in range(red_apples):
446.         red.append(tuple(map(int, input().split(','))))
447.
448.     snake = ((0, 9), (0, 8), (0, 7))
449.     initial_state = (tuple(red), tuple(green), snake)
450.     state = Problem(initial_state)
451.