def greedy(matrix, start, goal):
"""
Find the path from start to the goal using Greedy Best-first Search Algorithm
The algorithm is implemented based on the description on Wikipedia:
https://en.wikipedia.org/wiki/Best-first_search#Greedy_BFS
Notice: GBFS is suboptimal algorithm, so the solution MAY NOT BE OPTIMAL!
:param matrix: Search space, as a 2D list
:param start: Start point, as a tuple
:param goal: Goal point, as a tuple
:return: The path (if found) from start to goal, or None
"""
print('Analytics: start node ' + str(start) + ', goal node ' + str(goal))
# The set of nodes already evaluated
visited = set()
partially_expanded = set()
# The set of currently discovered nodes that are not evaluated yet.
# Initially, only the start node is known.
# frontier is implemented as a priority queue
frontier = Frontier()
frontier.add(start, manhattan_dist(start, goal))
# For each node, which node it can most efficiently be reached from.
# If a node can be reached from many nodes, came_from will eventually contain the
# most efficient previous step.
came_from = {}
while frontier:
current, current_distance = frontier.nearest
if current == goal:
print('Analytics: ' + str(len(partially_expanded)) +
' expanded nodes out of ' + str(count_nodes(matrix)) +
' nodes , among which ' + str(len(visited)) +
' are fully expanded (all successors evaluated)')
return reconstruct_path(came_from, current)
partially_expanded.add(current)
is_interrupted = False
for neighbor in expand(current, matrix):
if neighbor not in visited:
neighbor_distance = manhattan_dist(neighbor, goal)
if neighbor not in frontier: # Discover a new node
came_from[neighbor] = current
frontier.add(neighbor, neighbor_distance)
if current_distance > neighbor_distance:
is_interrupted = True
break
if not is_interrupted:
frontier.pop_nearest()
visited.add(current)
return None
def a_star(matrix, start, goal, estimate=manhattan_dist):
"""
Find the path from start to the goal using Greedy Best-first Search Algorithm
The algorithm is implemented based on the description on Wikipedia:
https://en.wikipedia.org/wiki/Best-first_search#Greedy_BFS
Notice: GBFS is suboptimal algorithm, so the solution MAY NOT BE OPTIMAL!
:param estimate: Heuristics used in a_star search
:param matrix: Search space, as a 2D list
:param start: Start point, as a tuple
:param goal: Goal point, as a tuple
:return: The path (if found) from start to goal, or None
"""
print('Analytics: start node ' + str(start) + ', goal node ' + str(goal))
# The set of nodes already evaluated
visited = set()
# For each node, the cost of getting from the start node to that node.
# The cost of going from start to start is zero.
g_score = {start: 0}
# For each node, the total cost of getting from the start node to the goal
# by passing by that node. That value is partly known, partly heuristic.
# For the first node, that value is completely heuristic.
f_score = {start: estimate(start, goal)}
# The set of currently discovered nodes that are not evaluated yet.
# Initially, only the start node is known.
# frontier is implemented as a priority queue
frontier = Frontier()
frontier.add(start, f_score[start])
# For each node, which node it can most efficiently be reached from.
# If a node can be reached from many nodes, came_from will eventually contain the
# most efficient previous step.
came_from = {}
while frontier:
current, current_f_score = frontier.pop_nearest()
if current == goal:
print('Analytics: ' + str(len(visited)) +
' expanded nodes, out of ' + str(count_nodes(matrix)) +
' nodes')
# draw_expanded_nodes(matrix, visited)
return reconstruct_path(came_from, current)
visited.add(current)
for neighbor in expand(current, matrix):
if neighbor not in visited:
g_through_current = g_score[
current] + 1 # every neighbor has distance 1
if (neighbor not in frontier
or g_through_current < g_score[neighbor]):
# Discover a new node or a better path
came_from[neighbor] = current
g_score[neighbor] = g_through_current
f_score[neighbor] = (g_score[neighbor] +
estimate(neighbor, goal))
frontier.add(neighbor, f_score[neighbor])
return None
def a_star_multidots(edges,
start: tuple,
goals: tuple,
estimate=mst_estimator):
"""
Find the path from start to the goal using Greedy Best-first Search Algorithm
The algorithm is implemented based on the description on Wikipedia:
https://en.wikipedia.org/wiki/Best-first_search#Greedy_BFS
Notice: GBFS is suboptimal algorithm, so the solution MAY NOT BE OPTIMAL!
:param estimate: Heuristics used in a_star search
:param edges: Search space, as a 2D list
:param start: Start point, as a tuple
:param goals: Goal points, as a set of all dots
:return: The path (if found) from start to goal, or None
"""
print('Analytics: start node ' + str(start) + ', dots node ' + str(goals))
goals_to_indices = {g: i for i, g in enumerate(goals, 2)}
start = init_state(start, goals)
if start[0:2] in goals:
start = mark_visited(start[0:2], goals_to_indices, start)
# The set of nodes already evaluated
visited = set()
# For each node, the cost of getting from the start node to that node.
# The cost of going from start to start is zero.
g_score = {start: 0}
# For each node, the total cost of getting from the start node to the dots
# by passing by that node. That value is partly known, partly heuristic.
# For the first node, that value is completely heuristic.
# f_score = {start: naive_estimator(start, dots_visited[start], goals)}
f_score = {start: estimate(start, goals, edges)}
# The set of currently discovered nodes that are not evaluated yet.
# Initially, only the start node is known.
# frontier is implemented as a priority queue
frontier = Frontier()
frontier.add(start, f_score[start])
# For each node, which node it can most efficiently be reached from.
# If a node can be reached from many nodes, came_from will eventually contain the
# most efficient previous step.
came_from = {}
while frontier:
current, current_f_score = frontier.pop_nearest()
if current[2:].count(1) == len(current) - 2:
print('Analytics: ' + str(len(visited)) +
' expanded nodes, out of ' +
str(len(edges) * (2**(len(current) - 2))) + ' nodes')
return reconstruct_path(came_from, current)
visited.add(current)
for neighbor in expand_multidots(current, edges):
if neighbor[0:2] in goals:
neighbor = mark_visited(neighbor[0:2], goals_to_indices,
neighbor)
if neighbor not in visited:
# Subtract 1 here because the edge_maps contains both start and end for
# the shortest path between dots
g_through_current = g_score[current] + len(
edges[current[0:2]][neighbor[0:2]]) - 1
if (neighbor not in frontier
or g_through_current < g_score[neighbor]):
# Discover a new node or a better path
came_from[neighbor] = current
g_score[neighbor] = g_through_current
f_score[neighbor] = (g_score[neighbor] +
estimate(neighbor, goals, edges))
frontier.add(neighbor, f_score[neighbor])
return None