class Search:
def __init__(self):
self.x_list = []
self.y_list = []
self.labels = []
self.tree = BinaryTree()
self.maps = Maps(x_list=self.x_list,
y_list=self.y_list,
labels=self.labels,
tree=self.tree)
self.graph = None
self.number_path = None
def dis_in_order(self):
self.x_list, self.y_list, self.labels, self.tree = self.maps.original_map(
)
print(self.tree.displayInOrder(self.tree.root))
def dis_pre_order(self):
self.x_list, self.y_list, self.labels, self.tree = self.maps.original_map(
)
self.tree.displayPreOrder(self.tree.root)
def dis_post_order(self):
self.x_list, self.y_list, self.labels, self.tree = self.maps.original_map(
)
self.tree.displayPostOrder(self.tree.root)
# wrapper around the A* and Greedy Depth First Search method
def isa(self, searchKey=None, algorithm=None):
for i in range(len(self.labels)):
if searchKey == self.labels[i]:
x_coord_holder = self.x_list[i]
y_coord_holder = self.y_list[i]
# print('isa x: ' + str(x_coord_holder))
if algorithm == 'gbfs':
path = self.tree.gbfs(searchKey=searchKey,
x=x_coord_holder,
y=y_coord_holder)
elif algorithm == 'asts':
path = self.tree.asts(searchKey=searchKey,
x=x_coord_holder,
y=y_coord_holder)
return path
# Implement Uninformed Blind Search Algorithms on GUI
def gui_target(self, map=None, algorithm=None, goal=None):
# Arguments:
# algorithm (string): the search algorithm code to be used
# goal (string): the city (Node) being searched for (goal state)
# Returns:
# numpath (int): the number of Nodes visited by the algorithm in use
# x_capa (int list): all the x coordinates of the Nodes in the tree
# y_capa (int list): all the y coordinates of the Nodes in the tree
# x_capitula (int list): all the x coordinates of the Nodes visited by algorithm in use
# y_capitula (int list): all the y coordinates of the Nodes visited by algorithm in use
# labella (string list): all the names of the Nodes visited by algorithm in use
# clear the canvas
self.maps.reset()
# create the binary tree to be used
if map == 'original':
self.x_list, self.y_list, self.labels, self.tree = self.maps.original_map(
)
elif map == 'extended':
self.x_list, self.y_list, self.labels, self.tree = self.maps.extended_map(
)
# instantiate the plot_utils class
pltls = plutils(tree=self.tree,
x_list=self.x_list,
y_list=self.y_list,
labels=self.labels)
if algorithm == 'ucs':
start = datetime.now().microsecond
path = self.tree.ucs(goal)
stop = datetime.now().microsecond
time_taken = stop - start
print('Nodes Traversed in uniform cost: ', end='\t')
for i in range(len(path)):
print(path[i].getKey(), end='\t')
print('\n')
numpath, x_capa, y_capa, x_capitula, y_capitula, labella = pltls.ubs_plotter(
path=path, algorithm='ucs')
elif algorithm == 'dfs':
start = datetime.now().microsecond
path = self.tree.dfs(goal)
stop = datetime.now().microsecond
time_taken = stop - start
print('Nodes Traversed in depth first: ')
for i in range(len(path)):
print(path[i].getKey(), end='\t')
print('\n')
numpath, x_capa, y_capa, x_capitula, y_capitula, labella = pltls.ubs_plotter(
path=path, algorithm='dfs')
elif algorithm == 'bfs':
start = datetime.now().microsecond
path = self.tree.bfs(goal)
stop = datetime.now().microsecond
time_taken = stop - start
print('Nodes Traversed in breadth first: ')
for i in range(len(path)):
print(path[i].getKey(), end='\t')
print('\n')
numpath, x_capa, y_capa, x_capitula, y_capitula, labella = pltls.ubs_plotter(
path=path, algorithm='bfs')
elif algorithm == 'gbfs':
start = datetime.now().microsecond
path = self.isa(searchKey=goal, algorithm=algorithm)
stop = datetime.now().microsecond
time_taken = stop - start
print('Nodes Traversed in greedy best first: ')
for i in range(len(path)):
print(path[i].getKey(), end='\t')
print('\n')
numpath, x_capa, y_capa, x_capitula, y_capitula, labella = pltls.ubs_plotter(
path=path, algorithm='gbfs')
elif algorithm == 'asts':
start = datetime.now().microsecond
path = self.isa(searchKey=goal, algorithm=algorithm)
stop = datetime.now().microsecond
time_taken = stop - start
print('Nodes Traversed in a star best first: ')
for i in range(len(path)):
print(path[i].getKey(), end='\t')
print('\n')
numpath, x_capa, y_capa, x_capitula, y_capitula, labella = pltls.ubs_plotter(
path=path, algorithm='asts')
# return the necessary variables
return time_taken, numpath, x_capa, y_capa, x_capitula, y_capitula, labella
# Implement Uninformed Blind Search Algorithms on console
def console_target(self):
print('Implemented algorithm codes: ucs, dfs, bfs, gbfs, asts')
map = raw_input(
'Map Selection: enter "original" or "extended": ').lower()
# clear the canvas
self.maps.reset()
# create the binary tree to be used
if map == 'original':
self.x_list, self.y_list, self.labels, self.tree = self.maps.original_map(
)
elif map == 'extended':
self.x_list, self.y_list, self.labels, self.tree = self.maps.extended_map(
)
# instantiate the plutils class
pltls = plutils(tree=self.tree,
x_list=self.x_list,
y_list=self.y_list,
labels=self.labels)
while True:
goal = raw_input('Enter the name of the city: ')
algorithm = raw_input('Enter search algorithm code: ').lower()
if algorithm == 'ucs':
path = self.tree.ucs(goal)
num_path, _, _, _, _, _ = pltls.ubs_plotter(
path=path, algorithm=algorithm)
print('number of Nodes visited: {}'.format(num_path))
elif algorithm == 'bfs':
path = self.tree.bfs(goal)
num_path = len(path)
print('number of Nodes visited: {}'.format(num_path))
pltls.ubs_plotter(path=path, algorithm=algorithm)
elif algorithm == 'dfs':
path = self.tree.dfs(goal)
num_path = len(path)
print('number of Nodes visited: {}'.format(num_path))
pltls.ubs_plotter(path=path, algorithm=algorithm)
elif algorithm == 'gbfs':
path = self.isa(searchKey=goal, algorithm=algorithm)
num_path = len(path)
print(path)
print('number of Nodes visited: {}'.format(num_path))
pltls.ubs_plotter(path=path, algorithm=algorithm)
elif algorithm == 'asts':
path = self.isa(searchKey=goal, algorithm=algorithm)
num_path = len(path)
print(path)
print('number of Nodes visited: {}'.format(num_path))
pltls.ubs_plotter(path=path, algorithm=algorithm)
# elif algorithm == 'disno':
# self.dis_in_order()
#
# elif algorithm == 'dpreo':
# self.dis_pre_order()
#
# elif algorithm == 'dposo':
# self.dis_post_order()
else:
raise 'Invalid argument entered'
