# search.py
# ---------
# Licensing Information: Please do not distribute or publish solutions to this
# project. You are free to use and extend these projects for educational
# purposes. The Pacman AI projects were developed at UC Berkeley, primarily by
# John DeNero (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# For more info, see http://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html

"""
In search.py, you will implement generic search algorithms which are called 
by Pacman agents (in searchAgents.py).
"""

import util
from node import Node, test_node_contains, replace_frontier_with_lowest_cost

class SearchProblem:
    """
    This class outlines the structure of a search problem, but doesn't implement
    any of the methods (in object-oriented terminology: an abstract class).
    
    You do not need to change anything in this class, ever.
    """

    def getStartState(self):
        """
        Returns the start state for the search problem 
        """
        util.raiseNotDefined()

    def isGoalState(self, state):
        """
          state: Search state
       
        Returns True if and only if the state is a valid goal state
        """
        util.raiseNotDefined()

    def getSuccessors(self, state):
        """
          state: Search state
        
        For a given state, this should return a list of triples, 
        (successor, action, stepCost), where 'successor' is a 
        successor to the current state, 'action' is the action
        required to get there, and 'stepCost' is the incremental 
        cost of expanding to that successor
        """
        util.raiseNotDefined()

    def getCostOfActions(self, actions):
        """
         actions: A list of actions to take
    
        This method returns the total cost of a particular sequence of actions.  The sequence must
        be composed of legal moves
        """
        util.raiseNotDefined()


def tinyMazeSearch(problem):
    """
    Returns a sequence of moves that solves tinyMaze.  For any other
    maze, the sequence of moves will be incorrect, so only use this for tinyMaze
    """
    from game import Directions
    s = Directions.SOUTH
    w = Directions.WEST
    return [s, s, w, s, w, w, s, w]


def depthFirstSearch(problem):
    """
    Search the deepest nodes in the search tree first
    [2nd Edition: p 75, 3rd Edition: p 87]
    
    Your search algorithm needs to return a list of actions that reaches
    the goal.  Make sure to implement a graph search algorithm 
    [2nd Edition: Fig. 3.18, 3rd Edition: Fig 3.7].
    
    To get started, you might want to try some of these simple commands to
    understand the search problem that is being passed in:
    
    print "Start:", problem.getStartState()
    print "Is the start a goal?", problem.isGoalState(problem.getStartState())
    print "Start's successors:", problem.getSuccessors(problem.getStartState())
    """
    problem.getStartState()
    problem.isGoalState(problem.getStartState())
    problem.getSuccessors(problem.getStartState())
    "*** YOUR CODE HERE ***"
    from util import Stack
    return search(problem, Stack())


def breadthFirstSearch(problem):
    """
    Search the shallowest nodes in the search tree first.
    [2nd Edition: p 73, 3rd Edition: p 82]
    """
    "*** YOUR CODE HERE ***"
    # util.raiseNotDefined()
    from util import Queue
    return search(problem, Queue())


def search(problem, frontier):
    from sets import Set
    import logging
    logging.basicConfig(level=logging.INFO)
    explored = Set()
    root = Node(None, problem.getStartState(), None, 0)
    frontier.push(root)
    level = 0
    while True:
        level += 1
        logging.debug("Level: %d" % level)
        if frontier.isEmpty(): return None
        leafNode = frontier.pop()

        if problem.isGoalState(leafNode.getState()):
            solution = leafNode.solution()
            logging.info("Solution node length: %d" % len(solution))
            return solution

        explored.add(leafNode.getState())
        for (successor, action, stepCost) in problem.getSuccessors(leafNode.getState()):
            expandedNode = Node(leafNode, successor, action, stepCost)
            if (expandedNode.getState() not in explored) and (expandedNode.getState() not in frontier.list):
                frontier.push(expandedNode)


def uniformCostSearch(problem):
    "Search the node of least total cost first. "
    "*** YOUR CODE HERE ***"
    # util.raiseNotDefined()
    return search_with_cost(problem)

def nullHeuristic(state, problem=None):
    """
    A heuristic function estimates the cost from the current state to the nearest
    goal in the provided SearchProblem.  This heuristic is trivial.
    """
    return 0


def aStarSearch(problem, heuristic=nullHeuristic):
    "Search the node that has the lowest combined cost and heuristic first."
    "*** YOUR CODE HERE ***"
    # util.raiseNotDefined()
    return search_with_cost(problem, heuristic)


def search_with_cost(problem, heuristic=None):
    from util import PriorityQueue
    from sets import Set
    import logging
    logging.basicConfig(level=logging.INFO)
    frontier = PriorityQueue()
    explored = Set()
    root = Node(None, problem.getStartState(), None, 0)
    frontier.push(root, root.getTotalPathCost())
    level = 0
    while True:
        level += 1
        logging.debug("Level: %d" % level)
        if frontier.isEmpty(): return None
        leafNode = frontier.pop()

        if problem.isGoalState(leafNode.getState()):
            solution = leafNode.solution()
            logging.info("Solution node length: %d" % len(solution))
            return solution

        explored.add(leafNode.getState())
        for (successor, action, stepCost) in problem.getSuccessors(leafNode.getState()):
            expandedNode = Node(leafNode, successor, action, stepCost)
            existedNodeInFrontier = test_node_contains(frontier.heap, expandedNode.getState())

            if (expandedNode.getState() not in explored) and (existedNodeInFrontier is None):
                if heuristic:
                    frontier.push(expandedNode,
                              expandedNode.getTotalPathCost() + heuristic(expandedNode.getState(), problem))
                else:
                    frontier.push(expandedNode, expandedNode.getTotalPathCost())

            elif (existedNodeInFrontier is not None) and (
                        expandedNode.getTotalPathCost() < existedNodeInFrontier.getTotalPathCost()):
                # replace that frontier node with child (less cost path)
                existedNodeInFrontier = expandedNode


# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch
