Misc changes, remove comments
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eugenep
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5c48777709
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100308694c
@ -9,24 +9,11 @@ from settings import *
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class Problem:
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"""The abstract class for a formal problem. You should subclass
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this and implement the methods actions and result, and possibly
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__init__, goal_test, and path_cost. Then you will create instances
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of your subclass and solve them with the various search functions."""
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def __init__(self, initial, goal):
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"""The constructor specifies the initial state, and possibly a goal
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state, if there is a unique goal. Your subclass's constructor can add
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other arguments."""
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self.initial = initial
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self.goal = goal
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def actions(self, state):
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"""Return the actions that can be executed in the given
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state. The result would typically be a list, but if there are
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many actions, consider yielding them one at a time in an
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iterator, rather than building them all at once."""
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moves = []
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if self.turn_left(state):
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moves.append('Left')
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@ -152,9 +139,6 @@ class Problem:
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return temp_map
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def result(self, state, action):
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"""Return the state that results from executing the given
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action in the given state. The action must be one of
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self.actions(state)."""
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new_state = []
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if action == 'Left':
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@ -169,16 +153,6 @@ class Problem:
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return super_new_state
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def goal_test(self, state):
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"""Return True if the state is a goal. The default method compares the
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state to self.goal or checks for state in self.goal if it is a
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list, as specified in the constructor. Override this method if
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checking against a single self.goal is not enough."""
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"""if isinstance(self.goal, list):
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return is_in(state, self.goal)
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else:
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return state == self.goal"""
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if self.goal == state:
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return True
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return False
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@ -195,15 +169,6 @@ class Problem:
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class Node:
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"""A node in a search tree. Contains a pointer to the parent (the node
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that this is a successor of) and to the actual state for this node. Note
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that if a state is arrived at by two paths, then there are two nodes with
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the same state. Also includes the action that got us to this state, and
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the total path_cost (also known as g) to reach the node. Other functions
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may add an f and h value; see best_first_graph_search and astar_search for
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an explanation of how the f and h values are handled. You will not need to
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subclass this class."""
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def __init__(self, state, parent=None, action=None):
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"""Create a search tree Node, derived from a parent by an action."""
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self.state = state
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@ -238,12 +203,6 @@ class Node:
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class BFS:
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@staticmethod
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def breadth_first_graph_search(problem):
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"""[Figure 3.11]
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Note that this function can be implemented in a
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single line as below:
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return graph_search(problem, FIFOQueue())
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"""
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node = Node(problem.initial)
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if problem.goal_test(node.state):
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return node
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142
problem.py
142
problem.py
@ -1,142 +0,0 @@
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import sys
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from collections import deque
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from utils import *
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class Problem:
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"""The abstract class for a formal problem. You should subclass
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this and implement the methods actions and result, and possibly
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__init__, goal_test, and path_cost. Then you will create instances
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of your subclass and solve them with the various search functions."""
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def __init__(self, initial, goal=None):
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"""The constructor specifies the initial state, and possibly a goal
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state, if there is a unique goal. Your subclass's constructor can add
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other arguments."""
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self.initial = initial
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self.goal = goal
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def actions(self, state):
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"""Return the actions that can be executed in the given
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state. The result would typically be a list, but if there are
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many actions, consider yielding them one at a time in an
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iterator, rather than building them all at once."""
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raise NotImplementedError
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def result(self, state, action):
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"""Return the state that results from executing the given
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action in the given state. The action must be one of
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self.actions(state)."""
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raise NotImplementedError
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def goal_test(self, state):
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"""Return True if the state is a goal. The default method compares the
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state to self.goal or checks for state in self.goal if it is a
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list, as specified in the constructor. Override this method if
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checking against a single self.goal is not enough."""
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if isinstance(self.goal, list):
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return is_in(state, self.goal)
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else:
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return state == self.goal
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def path_cost(self, c, state1, action, state2):
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"""Return the cost of a solution path that arrives at state2 from
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state1 via action, assuming cost c to get up to state1. If the problem
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is such that the path doesn't matter, this function will only look at
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state2. If the path does matter, it will consider c and maybe state1
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and action. The default method costs 1 for every step in the path."""
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return c + 1
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def value(self, state):
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"""For optimization problems, each state has a value. Hill Climbing
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and related algorithms try to maximize this value."""
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raise NotImplementedError
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class Node:
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"""A node in a search tree. Contains a pointer to the parent (the node
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that this is a successor of) and to the actual state for this node. Note
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that if a state is arrived at by two paths, then there are two nodes with
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the same state. Also includes the action that got us to this state, and
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the total path_cost (also known as g) to reach the node. Other functions
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may add an f and h value; see best_first_graph_search and astar_search for
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an explanation of how the f and h values are handled. You will not need to
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subclass this class."""
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def __init__(self, state, parent=None, action=None, path_cost=0):
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"""Create a search tree Node, derived from a parent by an action."""
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self.state = state
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self.parent = parent
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self.action = action
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self.path_cost = path_cost
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self.depth = 0
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if parent:
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self.depth = parent.depth + 1
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def __repr__(self):
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return "<Node {}>".format(self.state)
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def __lt__(self, node):
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return self.state < node.state
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def expand(self, problem):
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"""List the nodes reachable in one step from this node."""
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return [self.child_node(problem, action)
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for action in problem.actions(self.state)]
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def child_node(self, problem, action):
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"""[Figure 3.10]"""
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next_state = problem.result(self.state, action)
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next_node = Node(next_state, self, action, problem.path_cost(self.path_cost, self.state, action, next_state))
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return next_node
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def solution(self):
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"""Return the sequence of actions to go from the root to this node."""
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return [node.action for node in self.path()[1:]]
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def path(self):
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"""Return a list of nodes forming the path from the root to this node."""
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node, path_back = self, []
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while node:
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path_back.append(node)
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node = node.parent
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return list(reversed(path_back))
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# We want for a queue of nodes in breadth_first_graph_search or
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# astar_search to have no duplicated states, so we treat nodes
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# with the same state as equal. [Problem: this may not be what you
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# want in other contexts.]
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def __eq__(self, other):
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return isinstance(other, Node) and self.state == other.state
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def __hash__(self):
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# We use the hash value of the state
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# stored in the node instead of the node
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# object itself to quickly search a node
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# with the same state in a Hash Table
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return hash(self.state)
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def breadth_first_graph_search(problem):
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"""[Figure 3.11]
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Note that this function can be implemented in a
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single line as below:
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return graph_search(problem, FIFOQueue())
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"""
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node = Node(problem.initial)
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if problem.goal_test(node.state):
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return node
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frontier = deque([node])
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explored = set()
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while frontier:
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node = frontier.popleft()
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explored.add(node.state)
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for child in node.expand(problem):
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if child.state not in explored and child not in frontier:
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if problem.goal_test(child.state):
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return child
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frontier.append(child)
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return None
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