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eugenep 2021-04-14 14:50:44 +02:00
parent 5c48777709
commit 100308694c
2 changed files with 1 additions and 184 deletions
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43
graphsearch.py
View File

@ -9,24 +9,11 @@ from settings 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):
"""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."""
def __init__(self, initial, goal):
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

142
problem.py
View File

@ -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