def uniform_cost_search(problem):
"""
Uniform cost search reference: page 84 Peter Norvig AI book
"""
color = defaultdict(lambda: WHITE)
distance = defaultdict(lambda: float('inf'))
parent = defaultdict(lambda: None)
color[problem.initial] = GRAY
distance[problem.initial] = 0
#parent[problem.initial] = None
pq = CustomPriorityQueue()
initial_state = (0,problem.initial)
pq.add(initial_state)
while pq.empty() == False:
(pathcost,u) = pq.pop()
children_states = problem.children(u)
if problem.goal_test(u):
return (pathcost,reconstruct_path(parent,problem.goal))
for (child,cost) in children_states:
if color[child] == WHITE:
color[child] = GRAY
distance[child] = pathcost + cost
pq.add((distance[child],child))
parent[child] = u
elif color[child] == GRAY and distance[child] > pathcost + cost:
distance[child] = pathcost + cost
parent[child] = u
pq.replace(child,(distance[child],child))
color[u] = BLACK
def uniform_cost_search(problem):
"""
Uniform cost search reference: page 84 Peter Norvig AI book
"""
color = defaultdict(lambda: WHITE)
distance = defaultdict(lambda: float('inf'))
parent = defaultdict(lambda: None)
color[problem.initial] = GRAY
distance[problem.initial] = 0
#parent[problem.initial] = None
pq = CustomPriorityQueue()
initial_state = (0, problem.initial)
pq.add(initial_state)
while pq.empty() == False:
(pathcost, u) = pq.pop()
children_states = problem.children(u)
if problem.goal_test(u):
return (pathcost, reconstruct_path(parent, problem.goal))
for (child, cost) in children_states:
if color[child] == WHITE:
color[child] = GRAY
distance[child] = pathcost + cost
pq.add((distance[child], child))
parent[child] = u
elif color[child] == GRAY and distance[child] > pathcost + cost:
distance[child] = pathcost + cost
parent[child] = u
pq.replace(child, (distance[child], child))
color[u] = BLACK
def a_star(problem):
"""
A* search reference: http://en.wikipedia.org/wiki/A*_search_algorithm
"""
closedset = Set()
openset = Set()
parent = defaultdict(lambda: None)
g = {}
f = {}
openset.add(problem.initial)
g[problem.initial] = 0
f[problem.initial] = g[problem.initial] + problem.h(
problem.initial, problem.goal)
pq = CustomPriorityQueue()
pq.add((f[problem.initial], problem.initial))
while pq.empty() == False:
(current_f, current) = pq.pop()
if problem.goal_test(current):
return (g[current], reconstruct_path(parent, problem.goal))
openset.remove(current)
closedset.add(current)
children = problem.children(current)
for (child, cost) in children:
tentative_g_score = g[current] + cost
if child in closedset and tentative_g_score >= g[child]:
continue
if child not in closedset or tentative_g_score < g[
child]: # don't need to initialize for each i in g.V: g[i]=inf. The reason is
# if a node x is not initilized that means there was visited.
# That means it the node x is not in openset. Hence the node
# x is also not in closedset
parent[child] = current
g[child] = tentative_g_score
f[child] = g[child] + problem.h(child, problem.goal)
#print "edge:",current,"-->",child,"real:",cost, "approx:",problem.h(current,child)
if child not in openset:
openset.add(child)
pq.add((f[child], child))
elif child in openset:
# Replace the priority queue f[child] value. Wiki algo didn't use a priority queue.
# They choose the current node by selecting the node in openset having the lowest f[] value
# Therefore they are getting the updated f values from f[]. In our case, the lowest f[] value
# is taken from the priority queue. Therefore, we need to keep the most uptodate f values in
# the priority queue. The replace operation is optimized according to python documentation.
pq.replace(child, (f[child], child))
#print "replace"
return None
def a_star(problem):
"""
A* search reference: http://en.wikipedia.org/wiki/A*_search_algorithm
"""
closedset = Set()
openset = Set()
parent = defaultdict(lambda: None)
g = {}
f = {}
openset.add(problem.initial)
g[problem.initial] = 0
f[problem.initial] = g[problem.initial] + problem.h(problem.initial,problem.goal)
pq = CustomPriorityQueue()
pq.add((f[problem.initial],problem.initial))
while pq.empty() == False:
(current_f, current) = pq.pop()
if problem.goal_test(current):
return (g[current],reconstruct_path(parent,problem.goal))
openset.remove(current)
closedset.add(current)
children = problem.children(current)
for (child,cost) in children:
tentative_g_score = g[current] + cost
if child in closedset and tentative_g_score >= g[child]:
continue
if child not in closedset or tentative_g_score < g[child]: # don't need to initialize for each i in g.V: g[i]=inf. The reason is
# if a node x is not initilized that means there was visited.
# That means it the node x is not in openset. Hence the node
# x is also not in closedset
parent[child] = current
g[child] = tentative_g_score
f[child] = g[child] + problem.h(child,problem.goal)
#print "edge:",current,"-->",child,"real:",cost, "approx:",problem.h(current,child)
if child not in openset:
openset.add(child)
pq.add((f[child],child))
elif child in openset:
# Replace the priority queue f[child] value. Wiki algo didn't use a priority queue.
# They choose the current node by selecting the node in openset having the lowest f[] value
# Therefore they are getting the updated f values from f[]. In our case, the lowest f[] value
# is taken from the priority queue. Therefore, we need to keep the most uptodate f values in
# the priority queue. The replace operation is optimized according to python documentation.
pq.replace(child,(f[child],child))
#print "replace"
return None
def a_star_beam_search(problem, beam_size):
"""
A* search reference: http://en.wikipedia.org/wiki/A*_search_algorithm
"""
closedset = Set()
openset = Set()
parent = defaultdict(lambda: None)
g = {}
f = {}
openset.add(problem.initial)
g[problem.initial] = 0
f[problem.initial] = g[problem.initial] + problem.h(
problem.initial, problem.goal)
pq = CustomPriorityQueue()
pq.add((f[problem.initial], problem.initial))
while pq.empty() == False:
(current_f, current) = pq.pop()
if problem.goal_test(current):
#todo path
return (g[current], reconstruct_path(parent, problem.goal))
openset.remove(current)
closedset.add(current)
children = problem.children(current)
children_count = 0
for (child, cost) in children:
tentative_g_score = g[current] + cost
if child in closedset and tentative_g_score >= g[child]:
continue
if (child not in closedset or tentative_g_score < g[child]
) and children_count < beam_size:
parent[child] = current
g[child] = tentative_g_score
f[child] = g[child] + problem.h(child, problem.goal)
children_count += 1
if child not in openset:
openset.add(child)
pq.add((f[child], child))
elif child in openset:
pq.replace(child, (f[child], child))
return None
def a_star_beam_search(problem,beam_size):
"""
A* search reference: http://en.wikipedia.org/wiki/A*_search_algorithm
"""
closedset = Set()
openset = Set()
parent = defaultdict(lambda: None)
g = {}
f = {}
openset.add(problem.initial)
g[problem.initial] = 0
f[problem.initial] = g[problem.initial] + problem.h(problem.initial,problem.goal)
pq = CustomPriorityQueue()
pq.add((f[problem.initial],problem.initial))
while pq.empty() == False:
(current_f, current) = pq.pop()
if problem.goal_test(current):
#todo path
return (g[current],reconstruct_path(parent,problem.goal))
openset.remove(current)
closedset.add(current)
children = problem.children(current)
children_count = 0
for (child,cost) in children:
tentative_g_score = g[current] + cost
if child in closedset and tentative_g_score >= g[child]:
continue
if (child not in closedset or tentative_g_score < g[child]) and children_count < beam_size:
parent[child] = current
g[child] = tentative_g_score
f[child] = g[child] + problem.h(child,problem.goal)
children_count += 1
if child not in openset:
openset.add(child)
pq.add((f[child],child))
elif child in openset:
pq.replace(child,(f[child],child))
return None
