def find_path(self, latitude, longitude):
# Create home node and connect it with other nodes
self.insert_home(latitude, longitude)
# Retrieve home node
home_node = self.database(self.home_id).is_node(list)[0]
# Initilize the SearchResults object to store search results
result = SearchResults()
# After the reinitilization the old value of the object is kept, so it is reset
result.reset()
# Append home node to the begining of the results
result.factories.append(home_node)
# Find path using nearest neighbour algorithm
result = self.nearest_neighbour(self.home_id, 0, [], [result])
# Retrieve last brewery
last_node = result.factories[-1]
# Append home node to the back
result.factories.append(home_node)
# Remove home node and it's connections
self.remove_home()
return result
def genetic_near_neighbour(self, number_of_runs, latitude, longitude):
"""
Runs genetic algorithm for nearest neigbour algorithm
"""
# Creates SearchResults object to store the final result
final_result = SearchResults()
final_result.reset()
# Sets the initial values
max_number_of_beer = 0
org_weight = 10
new_weight = 10
# Runs the genetic algorithm number_or_runs times
for i in range(number_of_runs):
# sets the weight to the mutated one
self.weight = new_weight
# Runs the neares neighbour algorithm with a mutated weight
temp_result = self.find_path(latitude, longitude)
# Checks if more beers were found than previous maximum
if (len(temp_result.beer) > max_number_of_beer):
# sets the maximum number of beer to the new maximum
max_number_of_beer = len(temp_result.beer)
# sets the orginal best weight to the new weight
org_weight = new_weight
# Updates the final result
final_result = temp_result
else:
# resets the weith
new_weight = org_weight
# Decides if to add or subtract the mutation
is_negative = random.randint(0, 1)
if (is_negative == 1):
# Generates mutation and substrcts it
new_weight -= random.random() * 10
else:
# Generates mutation and adds it
new_weight += random.random() * 10
return final_result
def generate_path(latitude, longitude, number_of_runs):
# Validate user input
try:
# Check if values are the correct type
latitude = float(latitude)
longitude = float(longitude)
number_of_runs = int(number_of_runs)
# Check if latitude is in range
if (latitude < -90 or latitude > 90):
raise ValueError
# Check if longitude is in range
if (longitude < -180 or longitude > 180):
raise ValueError
# Check if number_of_runs is range
if (number_of_runs < 0 or number_of_runs > 20):
raise ValueError
except ValueError:
temp_result = SearchResults()
temp_result.reset()
return temp_result.return_in_json()
# convert to float
latitude = float(latitude)
longitude = float(longitude)
# generate home id
unique_id = uuid.uuid4().hex[:12]
# create graph object
graph = Graph('beer.db', home_id=unique_id, weight=0.504597714410906)
# find path
result = graph.genetic_near_neighbour(number_of_runs, latitude, longitude)
# return result in json
return result.return_in_json()