Misc changes, remove comments

graphsearch.py

@@ -9,24 +9,11 @@ from settings import *
 
 
 class Problem:
-    """The abstract class for a formal problem. You should subclass
+    def __init__(self, initial, goal):      
-    this and implement the methods actions and result, 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):
-        """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 actions(self, state):
-        """Return the actions that can be executed in the given
-        state. The result would typically be a list, but if there are
-        many actions, consider yielding them one at a time in an
-        iterator, rather than building them all at once."""
-
         moves = []
         if self.turn_left(state):
             moves.append('Left')
@@ -152,9 +139,6 @@ class Problem:
         return temp_map        
                               
     def result(self, state, action):
-        """Return the state that results from executing the given
-        action in the given state. The action must be one of
-        self.actions(state)."""
         new_state = []
 
         if action == 'Left':
@@ -169,16 +153,6 @@ class Problem:
         return super_new_state
 
     def goal_test(self, state):
-        """Return True if the state is a goal. The default method compares the
-        state to self.goal or checks for state in self.goal if it is a
-        list, as specified in the constructor. Override this method if
-        checking against a single self.goal is not enough."""
-        
-        """if isinstance(self.goal, list):
-            return is_in(state, self.goal)
-        else:
-            return state == self.goal"""
-
         if self.goal == state:
             return True
         return False
@@ -195,15 +169,6 @@ class Problem:
 
 
 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):
         """Create a search tree Node, derived from a parent by an action."""
         self.state = state
@@ -238,12 +203,6 @@ class Node:
 class BFS:
     @staticmethod
     def breadth_first_graph_search(problem):
-        """[Figure 3.11]
-        Note that this function can be implemented in a
-        single line as below:
-        return graph_search(problem, FIFOQueue())
-        """
-
         node = Node(problem.initial)
         if problem.goal_test(node.state):
             return node

problem.py

@@ -1,142 +0,0 @@
-import sys
-from collections import deque
-
-from utils import *
-
-
-class Problem:
-    """The abstract class for a formal problem. You should subclass
-    this and implement the methods actions and result, 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 actions(self, state):
-        """Return the actions that can be executed in the given
-        state. The result would typically be a list, but if there are
-        many actions, consider yielding them one at a time in an
-        iterator, rather than building them all at once."""
-        raise NotImplementedError
-
-    def result(self, state, action):
-        """Return the state that results from executing the given
-        action in the given state. The action must be one of
-        self.actions(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 or checks for state in self.goal if it is a
-        list, as specified in the constructor. Override this method if
-        checking against a single self.goal is not enough."""
-        if isinstance(self.goal, list):
-            return is_in(state, self.goal)
-        else:
-            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, state):
-        """For optimization problems, each state has a value. Hill Climbing
-        and related algorithms try to maximize this value."""
-        raise NotImplementedError
-
-
-
-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 {}>".format(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):
-        """[Figure 3.10]"""
-        next_state = problem.result(self.state, action)
-        next_node = Node(next_state, self, action, problem.path_cost(self.path_cost, self.state, action, next_state))
-        return next_node
-
-    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 path(self):
-        """Return a list of nodes forming the path from the root to this node."""
-        node, path_back = self, []
-        while node:
-            path_back.append(node)
-            node = node.parent
-        return list(reversed(path_back))
-
-    # We want for a queue of nodes in breadth_first_graph_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):
-        # We use the hash value of the state
-        # stored in the node instead of the node
-        # object itself to quickly search a node
-        # with the same state in a Hash Table
-        return hash(self.state)
-
-
-def breadth_first_graph_search(problem):
-    """[Figure 3.11]
-    Note that this function can be implemented in a
-    single line as below:
-    return graph_search(problem, FIFOQueue())
-    """
-    node = Node(problem.initial)
-    if problem.goal_test(node.state):
-        return node
-    frontier = deque([node])
-    explored = set()
-    while frontier:
-        node = frontier.popleft()
-        explored.add(node.state)
-        for child in node.expand(problem):
-            if child.state not in explored and child not in frontier:
-                if problem.goal_test(child.state):
-                    return child
-                frontier.append(child)
-    return None