def main():
m40 = load_map('map-40.pickle')
m10 = load_map('map-10.pickle')
print("shortest_path(m10, 0, 2) = [0, 5, 3, 2]:", shortest_path(m10, 0, 2))
print("shortest_path(m40, 5, 34) = [5, 16, 37, 12, 34]:",
shortest_path(m40, 5, 34))
print("shortest_path(m40, 8, 24) = [8, 14, 16, 37, 12, 17, 10, 24]",
shortest_path(m40, 8, 24))
print("shortest_path(m40, 5, 5) = [5]:", shortest_path(m40, 5, 5))
print("shortest_path(m10, 6, 8) = None:", shortest_path(m10, 6, 8))
# parameters in the function call.
show_map(map_40, start=8, goal=24, path=[8, 33, 14, 16, 37, 12, 31, 10, 24])
# ### Writing your algorithm
# You should open the file `student_code.py` in another tab and work on your algorithm there. Do that by selecting `File > Open` and then selecting the appropriate file.
#
# The algorithm you write will be responsible for generating a `path` like the one passed into `show_map` above. In fact, when called with the same map, start and goal, as above you algorithm should produce the path `[5, 16, 37, 12, 34]`
#
# ```bash
# > shortest_path(map_40, 5, 34)
# [5, 16, 37, 12, 34]
# ```
# In[37]:
path = shortest_path(map_40, 5, 34)
if path == [5, 16, 37, 12, 34]:
print("great! Your code works for these inputs!")
else:
print("something is off, your code produced the following:")
print(path)
# ### Testing your Code
# If the code below produces no errors, your algorithm is behaving correctly. You are almost ready to submit! Before you submit, go through the following submission checklist:
#
# **Submission Checklist**
#
# 1. Does my code pass all tests?
# 2. Does my code implement `A*` search and not some other search algorithm?
# 3. Do I use an **admissible heuristic** to direct search efforts towards the goal?
# 4. Do I use data structures which avoid unnecessarily slow lookups?
from helpers import load_map_40
from student_code import shortest_path
from test import test
map_40 = load_map_40()
path = shortest_path(map_40, 8, 24)
if path == [8, 14, 16, 37, 12, 17, 10, 24]:
print("great! Your code works for these inputs!")
else:
print("something is off, your code produced the following:")
print(path)
test(shortest_path)
from map_data import map_40 from student_code import shortest_path # Double-check everything runs correctly via the required shortest_path function assert shortest_path(map_40, 5, 34) == [5, 16, 37, 12, 34] assert shortest_path(map_40, 5, 5) == [5] assert shortest_path(map_40, 8, 24) == [8, 14, 16, 37, 12, 17, 10, 24]
show_map(map_40, start=8, goal=24, path=[8, 14, 16, 37, 12, 17, 10, 24])
# ### Writing your algorithm
# You should open the file `student_code.py` in another tab and work on your algorithm there. Do that by selecting `File > Open` and then selecting the appropriate file.
#
# The algorithm you write will be responsible for generating a `path` like the one passed into `show_map` above. In fact, when called with the same map, start and goal, as above you algorithm should produce the path `[5, 16, 37, 12, 34]`
#
# ```bash
# > shortest_path(map_40, 5, 34)
# [5, 16, 37, 12, 34]
# ```
# In[22]:
path = shortest_path(map_40, 5, 34)
if path == [5, 16, 37, 12, 34]:
print("great! Your code works for these inputs!")
else:
print("something is off, your code produced the following:")
print(path)
# In[23]:
path = shortest_path(map_40, 8, 24)
if path == [8, 14, 16, 37, 12, 17, 10, 24]:
print("great! Your code works for these inputs!")
else:
print("something is off, your code produced the following:")
print(path)
#map_10 = load_map('map-10.pickle')
#show_map(map_10)
# map_40 is a bigger map than map_10
map_40 = load_map('map-40.pickle')
#show_map(map_40)
# run this code, note the effect of including the optional
# parameters in the function call.
#show_map(map_40, start=5, goal=34, path=[5,16,37,12,34])
######## Code ###########
start, goal = 5, 3
path = shortest_path(map_40, start, goal)
print("\nShortest path from {} to {} is:\n{}".format(start, goal, path))
if path == [5, 16, 37, 12, 34]:
print("\ngreat! Your code works for these inputs!")
else:
print("\nsomething is off, your code produced the following:")
print(path)
######## Test cases ##########
'''
from test import test
test(shortest_path)
'''
show_map(map_40) # ### Advanced Visualizations # # The map above shows a network of roads which spans 40 different intersections (labeled 0 through 39). # # The `show_map` function which generated this map also takes a few optional parameters which might be useful for visualizaing the output of the search algorithm you will write. # # * `start` - The "start" node for the search algorithm. # * `goal` - The "goal" node. # * `path` - An array of integers which corresponds to a valid sequence of intersection visits on the map. # In[11]: # show Network Map Graph for shortest path from Node 5 to Node 34 using A* full_path = shortest_path(map_40, 5, 34) show_map(map_40, start=5, goal=34, path=full_path) # ### Writing your algorithm # You should open the file `student_code.py` in another tab and work on your algorithm there. Do that by selecting `File > Open` and then selecting the appropriate file. # # The algorithm you write will be responsible for generating a `path` like the one passed into `show_map` above. In fact, when called with the same map, start and goal, as above you algorithm should produce the path `[5, 16, 37, 12, 34]` # # ```bash # > shortest_path(map_40, 5, 34) # [5, 16, 37, 12, 34] # ``` # In[9]: path = shortest_path(map_40, 5, 34)
from time import clock
from bcolors import bcolors
if __name__=='__main__':
parser = OptionParser()
parser.add_option('-f', '--file', dest='map_filename',
help='Map file name in pickle format.')
parser.add_option('-s', '--start',
dest='start', help='Starting node.')
parser.add_option('-g', '--goal',
dest='goal', help='Goal node.')
(options, arg) = parser.parse_args()
# check for options
if not options.start or not options.goal or not options.map_filename:
parser.error(bcolors.FAIL + "you must specify options, please type -h for details." + bcolors.ENDC)
#
start = int(options.start)
goal = int(options.goal)
map_filename = options.map_filename
# load map
M = load_map(map_filename)
start_t = clock()
print('Starting search...', end='', flush=True)
path = shortest_path(M, start, goal)
end_t = clock()
print(bcolors.OKGREEN + '\t[DONE]' + bcolors.ENDC)
print('Best path found {}'.format(path))
print('Lapsed time: {0:2.4f} secs'.format(end_t - start_t))