Konj/Lovec (Jovana) Neinformirano

# 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

def mapa(red, kol):
    return (red>0 and red<9 and kol>0 and kol<9)

def ZvezdiK(state):
    K = state[0], state[1]
    Z1 = state[4], state[5]
    Z2 = state[6], state[7]
    Z3 = state[8], state[9]
    if(K==Z1):
        newZ1R = '*'
        newZ1K = '*'
        newState = state[0], state[1], state[2], state[3], newZ1R, newZ1K, state[6], state[7], state[8], state[9]
        state = newState

    if (K == Z2):
        newZ2R = '*'
        newZ2K = '*'
        newState = state[0], state[1], state[2], state[3], state[4], state[5], newZ2R, newZ2K, state[8], state[9]
        state = newState

    if (K == Z3):
        newZ3R = '*'
        newZ3K = '*'
        newState = state[0], state[1], state[2], state[3], state[4], state[5], state[6], state[7], newZ3R, newZ3K
        state = newState
    return state

def ZvezdiL(state):
    L = state[2], state[3]
    Z1 = state[4], state[5]
    Z2 = state[6], state[7]
    Z3 = state[8], state[9]
    if (L == Z1):
        newZ1R = '*'
        newZ1K = '*'
        newState = state[0], state[1], state[2], state[3], newZ1R, newZ1K, state[6], state[7], state[8], state[9]
        state = newState

    if (L == Z2):
        newZ2R = '*'
        newZ2K = '*'
        newState = state[0], state[1], state[2], state[3], state[4], state[5], newZ2R, newZ2K, state[8], state[9]
        state = newState

    if (L == Z3):
        newZ3R = '*'
        newZ3K = '*'
        newState = state[0], state[1], state[2], state[3], state[4], state[5], state[6], state[7], newZ3R, newZ3K
        state = newState
    return state


def kGoreLevo(state):
    newR = state[0]-2
    newK = state[1]-1
    ok = mapa(newR, newK)
    if(ok and ((newR, newK)not in((state[2], state[3])))):

        newState = newR, newK, state[2], state[3], state[4], state[5], state[6], state[7], state[8], state[9]
        state = ZvezdiK(newState)
    return state


def kGoreDesno(state):
    newR = state[0]-2
    newK = state[1]+1
    ok = mapa(newR, newK)
    if (ok and ((newR, newK) not in ((state[2], state[3])))):

        newState = newR, newK, state[2], state[3], state[4], state[5], state[6], state[7], state[8], state[9]
        state = ZvezdiK(newState)
    return state


def kDesnoGore(state):
    newR = state[0]-1
    newK = state[1]+2
    ok = mapa(newR, newK)
    if (ok and ((newR, newK) not in ((state[2], state[3])))):

        newState = newR, newK, state[2], state[3], state[4], state[5], state[6], state[7], state[8], state[9]
        state = ZvezdiK(newState)
    return state

def kDesnoDolu(state):
    newR = state[0]+1
    newK = state[1]+2
    ok = mapa(newR, newK)
    if (ok and ((newR, newK) not in ((state[2], state[3])))):

        newState = newR, newK, state[2], state[3], state[4], state[5], state[6], state[7], state[8], state[9]
        state = ZvezdiK(newState)
    return state

def kDoluDesno(state):
    newR = state[0]+2
    newK = state[1]+1
    ok = mapa(newR, newK)
    if (ok and ((newR, newK) not in ((state[2], state[3])))):

        newState = newR, newK, state[2], state[3], state[4], state[5], state[6], state[7], state[8], state[9]
        state = ZvezdiK(newState)
    return state

def kDoluLevo(state):
    newR = state[0]+2
    newK = state[1]-1
    ok = mapa(newR, newK)
    if (ok and ((newR, newK) not in ((state[2], state[3])))):

        newState = newR, newK, state[2], state[3], state[4], state[5], state[6], state[7], state[8], state[9]
        state = ZvezdiK(newState)
    return state

def kLevoDolu(state):
    newR = state[0]+1
    newK = state[1]-2
    ok = mapa(newR, newK)
    if (ok and ((newR, newK) not in ((state[2], state[3])))):

        newState = newR, newK, state[2], state[3], state[4], state[5], state[6], state[7], state[8], state[9]
        state = ZvezdiK(newState)
    return state

def kLevoGore(state):
    newR = state[0]-1
    newK = state[1]-2
    ok = mapa(newR, newK)
    if (ok and ((newR, newK) not in ((state[2], state[3])))):

        newState = newR, newK, state[2], state[3], state[4], state[5], state[6], state[7], state[8], state[9]
        state = ZvezdiK(newState)
    return state

def lGoreLevo(state):
    newR = state[2]-1
    newK = state[3]-1
    ok = mapa(newR, newK)
    if(ok and ((newR, newK)not in ((state[0], state[1])))):
        newState = state[0], state[1], newR, newK, state[4], state[5], state[6], state[7], state[8], state[9]
        state = ZvezdiL(newState)
    return state

def lGoreDesno(state):
    newR = state[2]-1
    newK = state[3]+1
    ok = mapa(newR, newK)
    if(ok and ((newR, newK)not in ((state[0], state[1])))):
        newState = state[0], state[1], newR, newK, state[4], state[5], state[6], state[7], state[8], state[9]
        state = ZvezdiL(newState)
    return state

def lDoluLevo(state):
    newR = state[2]+1
    newK = state[3]-1
    ok = mapa(newR, newK)
    if(ok and ((newR, newK)not in ((state[0], state[1])))):
        newState = state[0], state[1], newR, newK, state[4], state[5], state[6], state[7], state[8], state[9]
        state = ZvezdiL(newState)
    return state

def lDoluDesno(state):
    newR = state[2]+1
    newK = state[3]+1
    ok = mapa(newR, newK)
    if(ok and ((newR, newK)not in ((state[0], state[1])))):
        newState = state[0], state[1], newR, newK, state[4], state[5], state[6], state[7], state[8], state[9]
        state = ZvezdiL(newState)
    return state

class Sah(Problem):

    def __init__(self, initial):
        self.initial = initial

    def goal_test(self, state):
        """ Vraka true ako sostojbata e celna """
        z1Red = state[4]
        z1Kol = state[5]
        z2Red = state[6]
        z2Kol = state[7]
        z3Red = state[8]
        z3Kol = state[9]
        return (z1Red=='*' and z1Kol=='*' and z2Red=='*' and z2Kol=='*' and z3Red=='*' and z3Kol=='*')

    def successor(self, state):
        """Vraka recnik od sledbenici na sostojbata"""
        successors = dict()

        # 1. Konj GoreLevo
        newState = kGoreLevo(state)
        successors['KonjGoreLevo'] = newState

        # 2. Konj GoreDesno
        newState = kGoreDesno(state)
        successors['KonjGoreDesno'] = newState

        # 3. Konj DesnoGore
        newState = kDesnoGore(state)
        successors['KonjDesnoGore'] = newState

        # 4. Konj DesnoDolu
        newState = kDesnoDolu(state)
        successors['KonjDesnoDolu'] = newState

        # 5. Konj DoluDesno
        newState = kDoluDesno(state)
        successors['KonjDoluDesno'] = newState

        # 6. Konj DoluLevo
        newState = kDoluLevo(state)
        successors['KonjDoluLevo'] = newState

        # 7. Konj LevoDolu
        newState = kLevoDolu(state)
        successors['KonjLevoDolu'] = newState

        # 8. Konj LevoGore
        newState = kLevoGore(state)
        successors['KonjLevoGore'] = newState

        # LOVEC

        # 1. Lovec GoreLevo
        newState = lGoreLevo(state)
        successors['LovecGoreLevo'] = newState

        # 2. Lovec GoreDesno
        newState = lGoreDesno(state)
        successors['LovecGoreDesno'] = newState

        # 3. Lovec DoluLevo
        newState = lDoluLevo(state)
        successors['LovecDoluLevo'] = newState

        # 4. Lovec DoluDesno
        newState = lDoluDesno(state)
        successors['LovecDoluDesno'] = newState

        return successors

    def actions(self, state):
        return self.successor(state).keys()

    def result(self, state, action):
        possible = self.successor(state)
        return possible[action]

kRed = int(input())
kKol = int(input())
lRed = int(input())
lKol = int(input())
z1Red = int(input())
z1Kol = int(input())
z2Red = int(input())
z2Kol = int(input())
z3Red = int(input())
z3Kol = int(input())

state = (kRed, kKol, lRed, lKol, z1Red, z1Kol, z2Red, z2Kol, z3Red, z3Kol)
pr = Sah(state)
resenie = breadth_first_graph_search(pr)
print(resenie.solution())