Graf

# 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()

        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


# ________________________________________________________________________________________________________
#Pomosna funkcija za informirani prebaruvanja
#So pomos na ovaa funkcija gi keshirame rezultatite od funkcijata na evaluacija

def memoize(fn, slot=None):
    """Memoize fn: make it remember the computed value for any argument list.
    If slot is specified, store result in that slot of first argument.
    If slot is false, store results in a dictionary."""
    if slot:
        def memoized_fn(obj, *args):
            if hasattr(obj, slot):
                return getattr(obj, slot)
            else:
                val = fn(obj, *args)
                setattr(obj, slot, val)
                return val
    else:
        def memoized_fn(*args):
            if not memoized_fn.cache.has_key(args):
                memoized_fn.cache[args] = fn(*args)
            return memoized_fn.cache[args]

        memoized_fn.cache = {}
    return memoized_fn


# ________________________________________________________________________________________________________
#Informirano prebaruvanje vo ramki na graf

def best_first_graph_search(problem, f):
    """Search the nodes with the lowest f scores first.
    You specify the function f(node) that you want to minimize; for example,
    if f is a heuristic estimate to the goal, then we have greedy best
    first search; if f is node.depth then we have breadth-first search.
    There is a subtlety: the line "f = memoize(f, 'f')" means that the f
    values will be cached on the nodes as they are computed. So after doing
    a best first search you can examine the f values of the path returned."""

    f = memoize(f, 'f')
    node = Node(problem.initial)
    if problem.goal_test(node.state):
        return node
    frontier = PriorityQueue(min, f)
    frontier.append(node)
    explored = set()
    while frontier:
        node = frontier.pop()
        if problem.goal_test(node.state):
            return node
        explored.add(node.state)
        for child in node.expand(problem):
            if child.state not in explored and child not in frontier:
                frontier.append(child)
            elif child in frontier:
                incumbent = frontier[child]
                if f(child) < f(incumbent):
                    del frontier[incumbent]
                    frontier.append(child)
    return None


def greedy_best_first_graph_search(problem, h=None):
    "Greedy best-first search is accomplished by specifying f(n) = h(n)"
    h = memoize(h or problem.h, 'h')
    return best_first_graph_search(problem, h)


def astar_search(problem, h=None):
    "A* search is best-first graph search with f(n) = g(n)+h(n)."
    h = memoize(h or problem.h, 'h')
    return best_first_graph_search(problem, lambda n: n.path_cost + h(n))


# ________________________________________________________________________________________________________
#Dopolnitelni prebaruvanja
#Recursive_best_first_search e implementiran
#Kako zadaca za studentite da gi implementiraat SMA* i IDA*

def recursive_best_first_search(problem, h=None):
    h = memoize(h or problem.h, 'h')

    def RBFS(problem, node, flimit):
        if problem.goal_test(node.state):
            return node, 0  # (The second value is immaterial)
        successors = node.expand(problem)
        if len(successors) == 0:
            return None, infinity
        for s in successors:
            s.f = max(s.path_cost + h(s), node.f)
        while True:
            # Order by lowest f value
            successors.sort(key=lambda x: x.f)
            best = successors[0]
            if best.f > flimit:
                return None, best.f
            if len(successors) > 1:
                alternative = successors[1].f
            else:
                alternative = infinity
            result, best.f = RBFS(problem, best, min(flimit, alternative))
            if result is not None:
                return result, best.f

    node = Node(problem.initial)
    node.f = h(node)
    result, bestf = RBFS(problem, node, infinity)
    return result


# Graphs and Graph Problems
import math

def distance(a, b):
    """The distance between two (x, y) points."""
    return math.hypot((a[0] - b[0]), (a[1] - b[1]))


class Graph:

    """A graph connects nodes (verticies) by edges (links).  Each edge can also
    have a length associated with it.  The constructor call is something like:
        g = Graph({'A': {'B': 1, 'C': 2})
    this makes a graph with 3 nodes, A, B, and C, with an edge of length 1 from
    A to B,  and an edge of length 2 from A to C.  You can also do:
        g = Graph({'A': {'B': 1, 'C': 2}, directed=False)
    This makes an undirected graph, so inverse links are also added. The graph
    stays undirected; if you add more links with g.connect('B', 'C', 3), then
    inverse link is also added.  You can use g.nodes() to get a list of nodes,
    g.get('A') to get a dict of links out of A, and g.get('A', 'B') to get the
    length of the link from A to B.  'Lengths' can actually be any object at
    all, and nodes can be any hashable object."""

    def __init__(self, dict=None, directed=True):
        self.dict = dict or {}
        self.directed = directed
        if not directed:
            self.make_undirected()

    def make_undirected(self):
        """Make a digraph into an undirected graph by adding symmetric edges."""
        for a in list(self.dict.keys()):
            for (b, dist) in self.dict[a].items():
                self.connect1(b, a, dist)

    def connect(self, A, B, distance=1):
        """Add a link from A and B of given distance, and also add the inverse
        link if the graph is undirected."""
        self.connect1(A, B, distance)
        if not self.directed:
            self.connect1(B, A, distance)

    def connect1(self, A, B, distance):
        """Add a link from A to B of given distance, in one direction only."""
        self.dict.setdefault(A, {})[B] = distance

    def get(self, a, b=None):
        """Return a link distance or a dict of {node: distance} entries.
        .get(a,b) returns the distance or None;
        .get(a) returns a dict of {node: distance} entries, possibly {}."""
        links = self.dict.setdefault(a, {})
        if b is None:
            return links
        else:
            return links.get(b)

    def nodes(self):
        """Return a list of nodes in the graph."""
        return list(self.dict.keys())


def UndirectedGraph(dict=None):
    """Build a Graph where every edge (including future ones) goes both ways."""
    return Graph(dict=dict, directed=False)


def RandomGraph(nodes=list(range(10)), min_links=2, width=400, height=300,
                curvature=lambda: random.uniform(1.1, 1.5)):
    """Construct a random graph, with the specified nodes, and random links.
    The nodes are laid out randomly on a (width x height) rectangle.
    Then each node is connected to the min_links nearest neighbors.
    Because inverse links are added, some nodes will have more connections.
    The distance between nodes is the hypotenuse times curvature(),
    where curvature() defaults to a random number between 1.1 and 1.5."""
    g = UndirectedGraph()
    g.locations = {}
    # Build the cities
    for node in nodes:
        g.locations[node] = (random.randrange(width), random.randrange(height))
    # Build roads from each city to at least min_links nearest neighbors.
    for i in range(min_links):
        for node in nodes:
            if len(g.get(node)) < min_links:
                here = g.locations[node]

                def distance_to_node(n):
                    if n is node or g.get(node, n):
                        return infinity
                    return distance(g.locations[n], here)
                neighbor = argmin(nodes, key=distance_to_node)
                d = distance(g.locations[neighbor], here) * curvature()
                g.connect(node, neighbor, int(d))
    return g



class GraphProblem(Problem):

    """The problem of searching a graph from one node to another."""

    def __init__(self, initial, goal, graph):
        Problem.__init__(self, initial, goal)
        self.graph = graph

    def actions(self, A):
        """The actions at a graph node are just its neighbors."""
        return list(self.graph.get(A).keys())

    def result(self, state, action):
        """The result of going to a neighbor is just that neighbor."""
        return action

    def path_cost(self, cost_so_far, A, action, B):
        return cost_so_far + (self.graph.get(A, B) or infinity)

    def h(self, node):
        """h function is straight-line distance from a node's state to goal."""
        locs = getattr(self.graph, 'locations', None)
        if locs:
            return int(distance(locs[node.state], locs[self.goal]))
        else:
            return infinity

Pocetok = input()
Stanica1 = input()
Stanica2 = input()
Kraj = input()


ABdistance=distance((2,1),(2,4))
BIdistance=distance((2,4),(8,5))
BCdistance=distance((2,4),(2,10))
HIdistance=distance((8,1),(8,5))
IJdistance=distance((8,5),(8,8))
FCdistance=distance((5,9),(2,10))
GCdistance=distance((4,11),(2,10))
CDdistance=distance((2,10),(2,15))
FGdistance=distance((5,9),(4,11))
FJdistance=distance((5,9),(8,8))
KGdistance=distance((8,13),(4,11))
LKdistance=distance((8,15),(8,13))
DEdistance=distance((2,15),(2,19))
DLdistance=distance((2,15),(8,15))
LMdistance=distance((8,15),(8,19))


graph = UndirectedGraph({
    "B": {"A": ABdistance, "I": BIdistance, "C": BCdistance},
    "I": {"H": HIdistance, "J": IJdistance},
    "C": {"F": FCdistance, "G": GCdistance, "D": CDdistance},
    "F": {"G": FGdistance, "J": FJdistance},
    "K": {"G": KGdistance, "L": LKdistance},
    "D": {"E": DEdistance, "L": DLdistance},
    "M": {"L": LMdistance}
})

graph.locations = dict(
A = (2,1) , B = (2,4) , C = (2,10) ,
D = (2,15) , E = (2,19) , F = (5,9) ,
G = (4,11) , H = (8,1) , I = (8,5),
J = (8,8) , K = (8,13) , L = (8,15),
M = (8,19))


gp1 = GraphProblem(Pocetok,Stanica1,graph)
gp2 = GraphProblem(Stanica1,Kraj,graph)

gp11 = GraphProblem(Pocetok,Stanica1,graph)
gp22 = GraphProblem(Stanica1,Kraj,graph)

ans1 = astar_search(gp1).solve()
ans2 = astar_search(gp2).solve()

ans11 = astar_search(gp11).path_cost
ans22 = astar_search(gp22).path_cost

a1 = ans11 + ans22

gp3 = GraphProblem(Pocetok,Stanica2,graph)
gp4 = GraphProblem(Stanica2,Kraj,graph)

gp33 = GraphProblem(Pocetok,Stanica2,graph)
gp44 = GraphProblem(Stanica2,Kraj,graph)

ans3 = astar_search(gp3).solve()
ans4 = astar_search(gp4).solve()

ans33 = astar_search(gp33).path_cost
ans44 = astar_search(gp44).path_cost

a2 = ans33+ans44


if a2 < a1:
	an =ans3[:] + ans4[1:len(ans4)]

else:
	an =ans1[:] + ans2[1:len(ans2)]

print an