# search.py
# ---------
# Licensing Information:  You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
# 
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).


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

import util

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.

    Your search algorithm needs to return a list of actions that reaches the
    goal. Make sure to implement a graph search algorithm.

    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())
    """

    # The only differences between the search functions:
    # 1. Cost: Lambda function to get the total cost of a node
    # 2. Nodes: Data structure to store the nodes
    cost = lambda node: 1
    nodes = util.Stack()
    
    # create a Node structure
    from collections import namedtuple
    Node = namedtuple('Node', ['state', 'stepCost', 'actions'])

    # Initialize start node and push onto nodes, 
    nodes.push(Node(problem.getStartState(), 0, []))
    
    # Lists used to keep track of visited nodes and their costs 
    visitedList = []
    visitedCost = []

    while not nodes.isEmpty():
        node = nodes.pop()

        # If the node has been traversed by a cheaper path, skip it
        if node.state in visitedList and visitedCost[visitedList.index(node.state)] <= cost(node):
            continue

        # Add the node to the visited lists
        visitedList.append(node.state)
        visitedCost.append(cost(node))

        # Check if the current node is the goal state and if it is, return the path
        if problem.isGoalState(node.state):
            return node.actions

        # Get all successors, and set each nodes's path to previous node's 
        # path + nextNode's direction and push it onto stack
        for state, action, stepCost in problem.getSuccessors(node.state):
            nodes.push(Node(state, stepCost, node.actions + [action]))


def breadthFirstSearch(problem):
    """Search the shallowest nodes in the search tree first."""

    # The only differences between the search functions:
    # 1. Cost: Lambda function to get the total cost of a node
    # 2. Nodes: Data structure to store the nodes
    cost = lambda node: 1
    nodes = util.Queue()
    
    # create a Node structure
    from collections import namedtuple
    Node = namedtuple('Node', ['state', 'stepCost', 'actions'])

    # Initialize start node and push onto nodes, 
    nodes.push(Node(problem.getStartState(), 0, []))
    
    # Lists used to keep track of visited nodes and their costs 
    visitedList = []
    visitedCost = []

    while not nodes.isEmpty():
        node = nodes.pop()

        # If the node has been traversed by a cheaper path, skip it
        if node.state in visitedList and visitedCost[visitedList.index(node.state)] <= cost(node):
            continue

        # Add the node to the visited lists
        visitedList.append(node.state)
        visitedCost.append(cost(node))

        # Check if the current node is the goal state and if it is, return the path
        if problem.isGoalState(node.state):
            return node.actions

        # Get all successors, and set each nodes's path to previous node's 
        # path + nextNode's direction and push it onto stack
        for state, action, stepCost in problem.getSuccessors(node.state):
            nodes.push(Node(state, stepCost, node.actions + [action]))


def uniformCostSearch(problem):
    """Search the node of least total cost first."""
    
    # The only differences between the search functions:
    # 1. Cost: Lambda function to get the total cost of a node
    # 2. Nodes: Data structure to store the nodes
    cost = lambda node: problem.getCostOfActions(node.actions)
    nodes = util.PriorityQueueWithFunction(cost)
    
    # create a Node structure
    from collections import namedtuple
    Node = namedtuple('Node', ['state', 'stepCost', 'actions'])

    # Initialize start node and push onto nodes, 
    nodes.push(Node(problem.getStartState(), 0, []))
    
    # Lists used to keep track of visited nodes and their costs 
    visitedList = []
    visitedCost = []

    while not nodes.isEmpty():
        node = nodes.pop()

        # If the node has been traversed by a cheaper path, skip it
        if node.state in visitedList and visitedCost[visitedList.index(node.state)] <= cost(node):
            continue

        # Add the node to the visited lists
        visitedList.append(node.state)
        visitedCost.append(cost(node))

        # Check if the current node is the goal state and if it is, return the path
        if problem.isGoalState(node.state):
            return node.actions

        # Get all successors, and set each nodes's path to previous node's 
        # path + nextNode's direction and push it onto stack
        for state, action, stepCost in problem.getSuccessors(node.state):
            nodes.push(Node(state, stepCost, node.actions + [action]))


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."""
    
    # The only differences between the search functions:
    # 1. Cost: Lambda function to get the total cost of a node + heuristic
    # 2. Nodes: Data structure to store the nodes
    cost = lambda node: problem.getCostOfActions(node.actions) + heuristic(node.state, problem)
    nodes = util.PriorityQueueWithFunction(cost)

    # create a Node structure
    from collections import namedtuple
    Node = namedtuple('Node', ['state', 'stepCost', 'actions'])

    # Initialize start node and push onto nodes, 
    nodes.push(Node(problem.getStartState(), 0, []))
    
    # Lists used to keep track of visited nodes and their costs 
    visitedList = []
    visitedCost = []

    while not nodes.isEmpty():
        node = nodes.pop()

        # If the node has been traversed by a cheaper path, skip it
        if node.state in visitedList and visitedCost[visitedList.index(node.state)] <= cost(node):
            continue

        # Add the node to the visited lists
        visitedList.append(node.state)
        visitedCost.append(cost(node))

        # Check if the current node is the goal state and if it is, return the path
        if problem.isGoalState(node.state):
            return node.actions

        # Get all successors, and set each nodes's path to previous node's 
        # path + nextNode's direction and push it onto stack
        for state, action, stepCost in problem.getSuccessors(node.state):
            nodes.push(Node(state, stepCost, node.actions + [action]))

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