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- # Python modul vo koj se implementirani algoritmite za neinformirano i informirano prebaruvanje
- # ____________________________________________________________________________________________
- # Improtiranje na dopolnitelno potrebni paketi za funkcioniranje na kodovite
- import sys
- import bisect
- infinity = float('inf') # sistemski definirana vrednost za beskonecnost
- # ____________________________________________________________________________________________
- # Definiranje na pomosni strukturi za cuvanje na listata na generirani, no neprovereni jazli
- 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 range(sys.maxint):
- result = depth_limited_search(problem, depth)
- if result is not 'cutoff':
- return result
- Prepreki= [(6, 1), (6, 2), (4, 2), (2, 3), (1, 4), (1, 6), (1, 8), (2, 9), (6, 4), (5, 5), (6, 7), (5, 7), (4, 7), (4, 8)]
- ############################## ATOM H1 ########################################
- def goreAtomH1(A):
- while(A[0] > 0 and A[0]<8 and A[1] >0 and A[1] < 10 and ((A[0],A[1]) not in \
- Prepreki) and ((A[0],A[1])not in ((A[2],A[3]), (A[4],A[5])))):
- X = A[0]
- X = X - 1
- A = X, A[1], A[2], A[3], A[4], A[5]
- Anew = A[0] + 1, A[1]
- return Anew
- def doleAtomH1(A):
- while(A[0] > 0 and A[0]<8 and A[1] >0 and A[1] < 10 and ((A[0],A[1]) not in \
- Prepreki) and ((A[0],A[1])not in ((A[2],A[3]), (A[4],A[5])))):
- X = A[0]
- X = X + 1
- A = X, A[1], A[2], A[3], A[4], A[5]
- Anew = A[0] - 1, A[1]
- return Anew
- def levoAtomH1(A):
- while(A[0] > 0 and A[0]<8 and A[1] >0 and A[1] < 10 and ((A[0],A[1]) not in \
- Prepreki) and ((A[0],A[1])not in ((A[2],A[3]), (A[4],A[5])))):
- Y = A[1]
- Y = Y - 1
- A = A[0], Y, A[2], A[3], A[4], A[5]
- Anew = A[0], A[1] + 1
- return Anew
- def desnoAtomH1(A):
- while(A[0] > 0 and A[0]<8 and A[1] >0 and A[1] < 10 and ((A[0],A[1]) not in \
- Prepreki) and ((A[0],A[1])not in ((A[2],A[3]), (A[4],A[5])))):
- Y = A[1]
- Y = Y + 1
- A = A[0], Y, A[2], A[3], A[4], A[5]
- Anew = A[0], A[1] - 1
- return Anew
- # #################### ATOM O ##############################################
- def goreAtomO(A):
- while (A[2] > 0 and A[2] < 8 and A[3] > 0 and A[3] < 10 and ((A[2], A[3]) not in \
- Prepreki) and ((A[2], A[3]) not in ((A[0], A[1]), (A[4], A[5])))):
- X = A[2]
- X = X - 1
- A = A[0], A[1], X, A[3], A[4], A[5]
- Anew = A[2] + 1, A[3]
- return Anew
- def doleAtomO(A):
- while (A[2] > 0 and A[2] < 8 and A[3] > 0 and A[3] < 10 and ((A[2], A[3]) not in \
- Prepreki) and ((A[2], A[3]) not in ((A[0], A[1]), (A[4], A[5])))):
- X = A[2]
- X = X + 1
- A = A[0], A[1], X, A[3], A[4], A[5]
- Anew = A[2] - 1, A[3]
- return Anew
- def levoAtomO(A):
- while (A[2] > 0 and A[2] < 8 and A[3] > 0 and A[3] < 10 and ((A[2], A[3]) not in \
- Prepreki) and ((A[2], A[3]) not in ((A[0], A[1]), (A[4], A[5])))):
- Y = A[3]
- Y = Y - 1
- A = A[0], A[1], A[2], Y, A[4], A[5]
- Anew = A[2], A[3] + 1
- return Anew
- def desnoAtomO(A):
- while (A[2] > 0 and A[2] < 8 and A[3] > 0 and A[3] < 10 and ((A[2], A[3]) not in \
- Prepreki) and ((A[2], A[3]) not in ((A[0], A[1]), (A[4], A[5])))):
- Y = A[3]
- Y = Y + 1
- A = A[0], A[1], A[2], Y, A[4], A[5]
- Anew = A[2], A[3] - 1
- return Anew
- ################################## ATOM H2 ######################################
- def goreAtomH2(A):
- while(A[4] > 0 and A[4]<8 and A[5] >0 and A[5] < 10 and ((A[4],A[5]) not in \
- Prepreki) and ((A[4],A[5])not in ((A[2],A[3]), (A[0],A[1])))):
- X = A[4]
- X = X - 1
- A = A[0], A[1], A[2], A[3], X, A[5]
- Anew = A[4] + 1, A[5]
- return Anew
- def doleAtomH2(A):
- while(A[4] > 0 and A[4]<8 and A[5] >0 and A[5] < 10 and ((A[4],A[5]) not in \
- Prepreki) and ((A[4],A[5])not in ((A[2],A[3]), (A[0],A[1])))):
- X = A[4]
- X = X + 1
- A = A[0], A[1], A[2], A[3], X, A[5]
- Anew = A[4] - 1, A[5]
- return Anew
- def levoAtomH2(A):
- while(A[4] > 0 and A[4]<8 and A[5] >0 and A[5] < 10 and ((A[4],A[5]) not in \
- Prepreki) and ((A[4],A[5])not in ((A[2],A[3]), (A[0],A[1])))):
- Y = A[5]
- Y = Y - 1
- A = A[0], A[1], A[2], A[3], A[4], Y
- Anew = A[4], A[5] + 1
- return Anew
- def desnoAtomH2(A):
- while(A[4] > 0 and A[4]<8 and A[5] >0 and A[5] < 10 and ((A[4],A[5]) not in \
- Prepreki) and ((A[4],A[5])not in ((A[2],A[3]), (A[0],A[1])))):
- Y = A[5]
- Y = Y + 1
- A = A[0], A[1], A[2], A[3], A[4], Y
- Anew = A[4], A[5] - 1
- return Anew
- class Molekula (Problem):
- def _init_ (self, initial):
- self.initial = initial
- def goal_test(self, state):
- H1x = state[0]
- H1y = state[1]
- Ox = state[2]
- Oy = state[3]
- H2x = state[4]
- H2y = state[5]
- return (H1y == Oy and Oy == H2y and Ox == H1x + 1 and H2x == Ox + 1 )
- def successor(self, state):
- """ Vraka recnik od sledbenici na sostojbata {akcija : sostojba}"""
- successors = dict()
- H1 = state[0], state[1]
- O = state[2], state[3]
- H2 = state[4], state[5]
- # Gore H1
- H1new = goreAtomH1(state)
- Statenew = (H1new[0], H1new[1],O[0],O[1],H2[0],H2[1])
- successors['GoreH1'] = Statenew
- # Dole H1
- H1new = doleAtomH1(state)
- Statenew = (H1new[0], H1new[1], O[0], O[1], H2[0], H2[1])
- successors['DoluH1'] = Statenew
- # Desno H1
- H1new = desnoAtomH1(state)
- Statenew = (H1new[0], H1new[1], O[0], O[1], H2[0], H2[1])
- successors['DesnoH1'] = Statenew
- # Levo H1
- H1new = levoAtomH1(state)
- Statenew = (H1new[0], H1new[1], O[0], O[1], H2[0], H2[1])
- successors['LevoH1'] = Statenew
- #######################################################################
- # Gore 0
- Onew = goreAtomO(state)
- Statenew = (H1[0],H1[1],Onew[0],Onew[1],H2[0],H2[1])
- successors['GoreO'] = Statenew
- #Dole O
- Onew = doleAtomO(state)
- Statenew = (H1[0], H1[1], Onew[0], Onew[1], H2[0], H2[1])
- successors['DoluO'] = Statenew
- #Desno O
- Onew = desnoAtomO(state)
- Statenew = (H1[0], H1[1], Onew[0], Onew[1], H2[0], H2[1])
- successors['DesnoO'] = Statenew
- #Levo O
- Onew = levoAtomO(state)
- Statenew = (H1[0], H1[1], Onew[0], Onew[1], H2[0], H2[1])
- successors['LevoO'] = Statenew
- ########################################################################
- #Gore H2
- H2new = goreAtomH2(state)
- Statenew = (H1[0],H1[1],O[0],O[1],H2new[0],H2new[1])
- successors['GoreH2'] = Statenew
- #Dole H2
- H2new = doleAtomH2(state)
- Statenew = (H1[0], H1[1], O[0], O[1], H2new[0], H2new[1])
- successors['DoluH2'] = Statenew
- #Levo H2
- H2new = levoAtomH2(state)
- Statenew = (H1[0], H1[1], O[0], O[1], H2new[0], H2new[1])
- successors['LevoH2'] = Statenew
- # Desno H2
- H2new = desnoAtomH2(state)
- Statenew = (H1[0], H1[1], O[0], O[1], H2new[0], H2new[1])
- successors['DesnoH2'] = Statenew
- return successors
- def actions(self, state):
- return self.successor(state).keys()
- def result(self, state, action):
- possible = self.successor(state)
- return possible[action]
- H1AtomRedica = int(input())
- H1AtomKolona = int(input())
- OAtomRedica = int(input())
- OAtomKolona = int(input())
- H2AtomRedica = int(input())
- H2AtomKolona = int(input())
- MolekulaInstance = Molekula((H1AtomRedica, H1AtomKolona,OAtomRedica, OAtomKolona, H2AtomRedica, H2AtomKolona))
- answer = breadth_first_graph_search(MolekulaInstance)
- print answer.solution()
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