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- #podvizni prepreki
- class Queue:
- """Queue is an abstract class/interface. There are three types:
- Stack(): A Last In First Out Queue.
- FIFOQueue(): A First In First Out Queue.
- PriorityQueue(order, f): Queue in sorted order (default min-first).
- Each type supports the following methods and functions:
- q.append(item) -- add an item to the queue
- q.extend(items) -- equivalent to: for item in items: q.append(item)
- q.pop() -- return the top item from the queue
- len(q) -- number of items in q (also q.__len())
- item in q -- does q contain item?
- Note that isinstance(Stack(), Queue) is false, because we implement stacks
- as lists. If Python ever gets interfaces, Queue will be an interface."""
- def __init__(self):
- raise NotImplementedError
- def extend(self, items):
- for item in items:
- self.append(item)
- def Stack():
- """A Last-In-First-Out Queue."""
- return []
- class FIFOQueue(Queue):
- """A First-In-First-Out Queue."""
- def __init__(self):
- self.A = []
- self.start = 0
- def append(self, item):
- self.A.append(item)
- def __len__(self):
- return len(self.A) - self.start
- def extend(self, items):
- self.A.extend(items)
- def pop(self):
- e = self.A[self.start]
- self.start += 1
- if self.start > 5 and self.start > len(self.A) / 2:
- self.A = self.A[self.start:]
- self.start = 0
- return e
- def __contains__(self, item):
- return item in self.A[self.start:]
- class PriorityQueue(Queue):
- """A queue in which the minimum (or maximum) element (as determined by f and
- order) is returned first. If order is min, the item with minimum f(x) is
- returned first; if order is max, then it is the item with maximum f(x).
- Also supports dict-like lookup. This structure will be most useful in informed searches"""
- def __init__(self, order=min, f=lambda x: x):
- self.A = []
- self.order = order
- self.f = f
- def append(self, item):
- bisect.insort(self.A, (self.f(item), item))
- def __len__(self):
- return len(self.A)
- def pop(self):
- if self.order == min:
- return self.A.pop(0)[1]
- else:
- return self.A.pop()[1]
- def __contains__(self, item):
- return any(item == pair[1] for pair in self.A)
- def __getitem__(self, key):
- for _, item in self.A:
- if item == key:
- return item
- def __delitem__(self, key):
- for i, (value, item) in enumerate(self.A):
- if item == key:
- self.A.pop(i)
- # ______________________________________________________________________________________________
- # Definiranje na klasa za strukturata na problemot koj ke go resavame so prebaruvanje
- # Klasata Problem e apstraktna klasa od koja pravime nasleduvanje za definiranje na osnovnite karakteristiki
- # na sekoj eden problem sto sakame da go resime
- class Problem:
- """The abstract class for a formal problem. You should subclass this and
- implement the method successor, and possibly __init__, goal_test, and
- path_cost. Then you will create instances of your subclass and solve them
- with the various search functions."""
- def __init__(self, initial, goal=None):
- """The constructor specifies the initial state, and possibly a goal
- state, if there is a unique goal. Your subclass's constructor can add
- other arguments."""
- self.initial = initial
- self.goal = goal
- def successor(self, state):
- """Given a state, return a dictionary of {action : state} pairs reachable
- from this state. If there are many successors, consider an iterator
- that yields the successors one at a time, rather than building them
- all at once. Iterators will work fine within the framework. Yielding is not supported in Python 2.7"""
- raise NotImplementedError
- def actions(self, state):
- """Given a state, return a list of all actions possible from that state"""
- raise NotImplementedError
- def result(self, state, action):
- """Given a state and action, return the resulting state"""
- raise NotImplementedError
- def goal_test(self, state):
- """Return True if the state is a goal. The default method compares the
- state to self.goal, as specified in the constructor. Implement this
- method if checking against a single self.goal is not enough."""
- return state == self.goal
- def path_cost(self, c, state1, action, state2):
- """Return the cost of a solution path that arrives at state2 from
- state1 via action, assuming cost c to get up to state1. If the problem
- is such that the path doesn't matter, this function will only look at
- state2. If the path does matter, it will consider c and maybe state1
- and action. The default method costs 1 for every step in the path."""
- return c + 1
- def value(self):
- """For optimization problems, each state has a value. Hill-climbing
- and related algorithms try to maximize this value."""
- raise NotImplementedError
- # ______________________________________________________________________________
- # Definiranje na klasa za strukturata na jazel od prebaruvanje
- # Klasata Node ne se nasleduva
- class Node:
- """A node in a search tree. Contains a pointer to the parent (the node
- that this is a successor of) and to the actual state for this node. Note
- that if a state is arrived at by two paths, then there are two nodes with
- the same state. Also includes the action that got us to this state, and
- the total path_cost (also known as g) to reach the node. Other functions
- may add an f and h value; see best_first_graph_search and astar_search for
- an explanation of how the f and h values are handled. You will not need to
- subclass this class."""
- def __init__(self, state, parent=None, action=None, path_cost=0):
- "Create a search tree Node, derived from a parent by an action."
- self.state = state
- self.parent = parent
- self.action = action
- self.path_cost = path_cost
- self.depth = 0
- if parent:
- self.depth = parent.depth + 1
- def __repr__(self):
- return "<Node %s>" % (self.state,)
- def __lt__(self, node):
- return self.state < node.state
- def expand(self, problem):
- "List the nodes reachable in one step from this node."
- return [self.child_node(problem, action)
- for action in problem.actions(self.state)]
- def child_node(self, problem, action):
- "Return a child node from this node"
- next = problem.result(self.state, action)
- return Node(next, self, action,
- problem.path_cost(self.path_cost, self.state,
- action, next))
- def solution(self):
- "Return the sequence of actions to go from the root to this node."
- return [node.action for node in self.path()[1:]]
- def solve(self):
- "Return the sequence of states to go from the root to this node."
- return [node.state for node in self.path()[0:]]
- def path(self):
- "Return a list of nodes forming the path from the root to this node."
- x, result = self, []
- while x:
- result.append(x)
- x = x.parent
- return list(reversed(result))
- # We want for a queue of nodes in breadth_first_search or
- # astar_search to have no duplicated states, so we treat nodes
- # with the same state as equal. [Problem: this may not be what you
- # want in other contexts.]
- def __eq__(self, other):
- return isinstance(other, Node) and self.state == other.state
- def __hash__(self):
- return hash(self.state)
- # ________________________________________________________________________________________________________
- # Neinformirano prebaruvanje vo ramki na drvo
- # Vo ramki na drvoto ne razresuvame jamki
- def tree_search(problem, fringe):
- """Search through the successors of a problem to find a goal.
- The argument fringe should be an empty queue."""
- fringe.append(Node(problem.initial))
- while fringe:
- node = fringe.pop()
- print(node.state)
- if problem.goal_test(node.state):
- return node
- fringe.extend(node.expand(problem))
- return None
- def breadth_first_tree_search(problem):
- "Search the shallowest nodes in the search tree first."
- return tree_search(problem, FIFOQueue())
- def depth_first_tree_search(problem):
- "Search the deepest nodes in the search tree first."
- return tree_search(problem, Stack())
- # ________________________________________________________________________________________________________
- # Neinformirano prebaruvanje vo ramki na graf
- # Osnovnata razlika e vo toa sto ovde ne dozvoluvame jamki t.e. povtoruvanje na sostojbi
- def graph_search(problem, fringe):
- """Search through the successors of a problem to find a goal.
- The argument fringe should be an empty queue.
- If two paths reach a state, only use the best one."""
- closed = {}
- fringe.append(Node(problem.initial))
- while fringe:
- node = fringe.pop()
- if problem.goal_test(node.state):
- return node
- if node.state not in closed:
- closed[node.state] = True
- fringe.extend(node.expand(problem))
- return None
- def breadth_first_graph_search(problem):
- "Search the shallowest nodes in the search tree first."
- return graph_search(problem, FIFOQueue())
- def depth_first_graph_search(problem):
- "Search the deepest nodes in the search tree first."
- return graph_search(problem, Stack())
- def uniform_cost_search(problem):
- "Search the nodes in the search tree with lowest cost first."
- return graph_search(problem, PriorityQueue(lambda a, b: a.path_cost < b.path_cost))
- def depth_limited_search(problem, limit=50):
- "depth first search with limited depth"
- def recursive_dls(node, problem, limit):
- "helper function for depth limited"
- cutoff_occurred = False
- if problem.goal_test(node.state):
- return node
- elif node.depth == limit:
- return 'cutoff'
- else:
- for successor in node.expand(problem):
- result = recursive_dls(successor, problem, limit)
- if result == 'cutoff':
- cutoff_occurred = True
- elif result != None:
- return result
- if cutoff_occurred:
- return 'cutoff'
- else:
- return None
- # Body of depth_limited_search:
- return recursive_dls(Node(problem.initial), problem, limit)
- def iterative_deepening_search(problem):
- for depth in xrange(sys.maxint):
- result = depth_limited_search(problem, depth)
- if result is not 'cutoff':
- return result
- # _________________________________________________________________________________________________________
- # PRIMER 1 : PROBLEM SO DVA SADA SO VODA
- # OPIS: Dadeni se dva sada J0 i J1 so kapaciteti C0 i C1
- # Na pocetok dvata sada se polni. Inicijalnata sostojba moze da se prosledi na pocetok
- # Problemot e kako da se stigne do sostojba vo koja J0 ke ima G0 litri, a J1 ke ima G1 litri
- # AKCII: 1. Da se isprazni bilo koj od sadovite
- # 2. Da se prefrli tecnosta od eden sad vo drug so toa sto ne moze da se nadmine kapacitetot na sadovite
- # Moze da ima i opcionalen tret vid na akcii 3. Napolni bilo koj od sadovite (studentite da ja implementiraat ovaa varijanta)
- # ________________________________________________________________________________________________________
- class WJ(Problem):
- """STATE: Torka od oblik (3,2) if jug J0 has 3 liters and J1 2 liters
- Opcionalno moze za nekoj od sadovite da se sretne i vrednost '*' sto znaci deka e nebitno kolku ima vo toj sad
- GOAL: Predefinirana sostojba do kade sakame da stigneme. Ako ne interesira samo eden sad za drugiot mozeme da stavime '*'
- PROBLEM: Se specificiraat kapacitetite na sadovite, pocetna sostojba i cel """
- def __init__(self, capacities=(5, 2), initial=(5, 0), goal=(0, 1)):
- self.capacities = capacities
- self.initial = initial
- self.goal = goal
- def goal_test(self, state):
- """ Vraka true ako sostojbata e celna """
- g = self.goal
- return (state[0] == g[0] or g[0] == '*') and \
- (state[1] == g[1] or g[1] == '*')
- def successor(self, J):
- """Vraka recnik od sledbenici na sostojbata"""
- successors = dict()
- J0, J1 = J
- (C0, C1) = self.capacities
- if J0 > 0:
- Jnew = 0, J1
- successors['dump jug 0'] = Jnew
- if J1 > 0:
- Jnew = J0, 0
- successors['dump jug 1'] = Jnew
- if J1 < C1 and J0 > 0:
- delta = min(J0, C1 - J1)
- Jnew = J0 - delta, J1 + delta
- successors['pour jug 0 into jug 1'] = Jnew
- if J0 < C0 and J1 > 0:
- delta = min(J1, C0 - J0)
- Jnew = J0 + delta, J1 - delta
- successors['pour jug 1 into jug 0'] = Jnew
- return successors
- def actions(self, state):
- return self.successor(state).keys()
- def result(self, state, action):
- possible = self.successor(state)
- return possible[action]
- # So vaka definiraniot problem mozeme da gi koristime site neinformirani prebaruvanja
- # Vo prodolzenie se dadeni mozni povici (vnimavajte za da moze da napravite povik mora da definirate problem)
- #
- # WJInstance = WJ((5, 2), (5, 2), ('*', 1))
- # print WJInstance
- #
- # answer1 = breadth_first_tree_search(WJInstance)
- # print answer1.solve()
- #
- # answer2 = depth_first_tree_search(WJInstance) #vnimavajte na ovoj povik, moze da vleze vo beskonecna jamka
- # print answer2.solve()
- #
- # answer3 = breadth_first_graph_search(WJInstance)
- # print answer3.solve()
- #
- # answer4 = depth_first_graph_search(WJInstance)
- # print answer4.solve()
- #
- # answer5 = depth_limited_search(WJInstance)
- # print answer5.solve()
- #
- # answer6 = iterative_deepening_search(WJInstance)
- # print answer6.solve()
- class CrnoBelo(Problem):
- def __init__(self, initial, goal):
- self.initial = str(initial)
- self.goal = str(goal)
- def goal_test(self, state):
- return state == self.goal
- def successor(self, state):
- successors = dict()
- dx = [-1, 0, 1, 0]
- dy = [0, 1, 0, -1]
- for x in range(0, N):
- for y in range(0, N):
- Matrix = eval(state)
- if Matrix[x][y] == 1:
- Matrix[x][y] = 0
- for k in range(4):
- newX = x + dx[k]
- newY = y + dy[k]
- if newX >= 0 and newX < N and newY >= 0 and newY < N:
- Matrix[newX][newY] = (Matrix[newX][newY] + 1) % 2
- stateNew = Matrix
- akcija = "X:" + str(x) + "Y:" + str(y)
- successors[akcija] = str(stateNew)
- return successors
- def actions(self, state):
- return self.successor(state).keys()
- def result(self, state, action):
- possible = self.successor(state)
- return possible[action]
- N = int(input())
- polinja = map(int, input().split(','))
- polinjaList = list(polinja)
- InitialMatrix = [[0 for x in range(N)] for y in range(N)]
- for i in range(0, N):
- for j in range(0, N):
- InitialMatrix[i][j] = polinjaList[i * N + j]
- GoalMatrix = [[0 for x in range(N)] for y in range(N)]
- crnoBeloinstance = CrnoBelo(InitialMatrix, GoalMatrix)
- answer = breadth_first_graph_search(crnoBeloinstance).solution()
- print(answer)
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