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How to get python variable within class and method

I am currently creating my genetic algorithm and want to print the number of generations at the very end of the program when it finishes. However I am unsure how to access the counter variable that is the number of generations when it is outside of the class and method. So for example, at the end it would be like
Generation 100, average fit 18966, best fit 18947
Your best chromosone at generation 100
'\x06pzÂ\x8cYÆr¯n0q\x07l¿M8\x93Þ\x19\x87"\x01\x85\x1er\x89[F_VyER\x9b\x0bm=)\x9a\x9a¿¥\x10F\x12A\x84\x0fZ^\x14\x99\x8a4®\x9f¿*\\\xa0yi\x19E\x8aÇ+6(_<¾£cO~\x9c\x99\x932\x06\x0f\x82\x7f¤\x808xǸñA\x13\x0e<%\x06ÿ#í\x91Pô\x98 ®\r\x1b}\x89y¦\x0cqAK\tp\x95\x99ÔNj=Wn\x16\x94\x0cu!¯ñ\x13Qü[e8_ÂóU\x10\x1av_+%Q_¡ù\x87=\x08~ciÎ_Ï[\x8f#AëT\x14©qG\x89#Z«L\x9b¢\x94WL\x1dV¶R03\x84æ^ßr\x1fÃÈ\x1d\x8e Læª&®x\x94?TAÒD\x14£i\x82J\x15=w~\x03\x0c\xa0¾5\x02f5T\x91ol¢bIÞfk¬¡27W16(}6\x92\x87\n®xm0\x1a\n<8(à}ñ\x88̾\x17g\x9bj6\x8fI&\x12\x9aÂ\x9a_F\x1a\r[\x1dK\x15<.±DjcIy`98d>\x197Z\x91£%tIJ\x820\x93|\x07\x8dnÚ QÂ!Pf\x1d\nåòf\x91\x1d#S¾|\x9ff[d>O=T$ݶI\x9e»QÛÂ\x1d"¿U=û´F÷\x83C}wA\xa0É\x8aD\x93x»\x85\x7f\x14^\x0eL'
done:
100 generations
How do I exactly access the 100 from the method in the class?
import random
class GeneticAlgorithm(object):
def __init__(self, genetics):
self.genetics = genetics
pass
def run(self):
population = self.genetics.initial()
while True:
fits_pops = [(self.genetics.fitness(ch), ch) for ch in population]
if self.genetics.check_stop(fits_pops): break
population = self.next(fits_pops)
pass
return population
def next(self, fits):
parents_generator = self.genetics.parents(fits)
size = len(fits)
nexts = []
while len(nexts) < size:
parents = next(parents_generator)
cross = random.random() < self.genetics.probability_crossover()
children = self.genetics.crossover(parents) if cross else parents
for ch in children:
mutate = random.random() < self.genetics.probability_mutation()
nexts.append(self.genetics.mutation(ch) if mutate else ch)
pass
pass
return nexts[0:size]
pass
class GeneticFunctions(object):
def probability_crossover(self):
r"""returns rate of occur crossover(0.0-1.0)"""
return 1.0
def probability_mutation(self):
r"""returns rate of occur mutation(0.0-1.0)"""
return 0.0
def initial(self):
r"""returns list of initial population
"""
return []
def fitness(self, chromosome):
r"""returns domain fitness value of chromosome
"""
return len(chromosome)
def check_stop(self, fits_populations):
r"""stop run if returns True
- fits_populations: list of (fitness_value, chromosome)
"""
return False
def parents(self, fits_populations):
r"""generator of selected parents
"""
gen = iter(sorted(fits_populations))
while True:
f1, ch1 = next(gen)
f2, ch2 = next(gen)
yield (ch1, ch2)
pass
return
def crossover(self, parents):
r"""breed children
"""
return parents
def mutation(self, chromosome):
r"""mutate chromosome
"""
return chromosome
pass
if __name__ == "__main__":
"""
example: Mapped guess prepared Text
"""
class GuessText(GeneticFunctions):
def __init__(self, target_text,
limit=100, size=100,
prob_crossover=0.9, prob_mutation=0.2):
self.target = self.text2chromo(target_text)
self.counter = 0
self.limit = limit
self.size = size
self.prob_crossover = prob_crossover
self.prob_mutation = prob_mutation
pass
# GeneticFunctions interface impls
def probability_crossover(self):
return self.prob_crossover
def probability_mutation(self):
return self.prob_mutation
def initial(self):
return [self.random_chromo() for j in range(self.size)]
def fitness(self, chromo):
# larger is better, matched == 0
return -sum(abs(c - t) for c, t in zip(chromo, self.target))
def check_stop(self, fits_populations):
self.counter += 1
if self.counter % 100 == 0:
best_match = list(sorted(fits_populations))[-1][1]
fits = [f for f, ch in fits_populations]
best = -(max(fits))
ave = -(sum(fits) / len(fits))
print(
"Generation %3d, average fit %4d, best fit %4d" %
(self.counter, ave, best,
))
print("Your best chromosone at generation %3d" % self.counter)
print("%r" % self.chromo2text(best_match))
pass
return self.counter >= self.limit
def parents(self, fits_populations):
while True:
father = self.tournament(fits_populations)
mother = self.tournament(fits_populations)
yield (father, mother)
pass
pass
def crossover(self, parents):
father, mother = parents
index1 = random.randint(1, len(self.target) - 2)
index2 = random.randint(1, len(self.target) - 2)
if index1 > index2: index1, index2 = index2, index1
child1 = father[:index1] + mother[index1:index2] + father[index2:]
child2 = mother[:index1] + father[index1:index2] + mother[index2:]
return (child1, child2)
def mutation(self, chromosome):
index = random.randint(0, len(self.target) - 1)
vary = random.randint(-5, 5)
mutated = list(chromosome)
mutated[index] += vary
return mutated
# internals
def tournament(self, fits_populations):
alicef, alice = self.select_random(fits_populations)
bobf, bob = self.select_random(fits_populations)
return alice if alicef > bobf else bob
def select_random(self, fits_populations):
return fits_populations[random.randint(0, len(fits_populations)-1)]
def text2chromo(self, text):
return [ord(ch) for ch in text]
def chromo2text(self, chromo):
return "".join(chr(max(1, min(ch, 255))) for ch in chromo)
def random_chromo(self):
return [random.randint(1, 255) for i in range(len(self.target))]
pass
GeneticAlgorithm(GuessText("""The smartest and fastest Pixel yet.
Google Tensor: Our first custom-built processor.
The first processor designed by Google and made for Pixel, Tensor makes the new Pixel phones our most powerful yet.
The most advanced Pixel Camera ever.
Capture brilliant color and vivid detail with Pixels best-in-class computational photography and new pro-level lenses.""")).run()
print('done:')
print("%3d " 'generations' % counter)
pass

Define the GuessText first. Then access the counter.
gt = GuessText("""The smartest and fastest Pixel yet.
Google Tensor: Our first custom-built processor.
The first processor designed by Google and made for Pixel, Tensor makes the new Pixel phones our most powerful yet.
The most advanced Pixel Camera ever.
Capture brilliant color and vivid detail with Pixels best-in-class computational photography and new pro-level lenses.""")
GeneticAlgorithm(gt).run()
print('done:')
print("%3d " 'generations' % gt.counter)

How to call a module object? [duplicate]

This question already has answers here:
TypeError: 'module' object is not callable
(16 answers)
Closed 1 year ago.
I tried to run this Ant Colony algorithm code (ant_colony.py) in Python:
from threading import Thread
class ant_colony:
class ant(Thread):
def __init__(self, init_location, possible_locations, pheromone_map, distance_callback, alpha, beta, first_pass=False):
Thread.__init__(self)
self.init_location = init_location
self.possible_locations = possible_locations
self.route = []
self.distance_traveled = 0.0
self.location = init_location
self.pheromone_map = pheromone_map
self.distance_callback = distance_callback
self.alpha = alpha
self.beta = beta
self.first_pass = first_pass
self._update_route(init_location)
self.tour_complete = False
def run(self):
while self.possible_locations:
next = self._pick_path()
self._traverse(self.location, next)
self.tour_complete = True
def _pick_path(self):
if self.first_pass:
import random
return random.choice(self.possible_locations)
attractiveness = dict()
sum_total = 0.0
for possible_next_location in self.possible_locations:
pheromone_amount = float(self.pheromone_map[self.location][possible_next_location])
distance = float(self.distance_callback(self.location, possible_next_location))
attractiveness[possible_next_location] = pow(pheromone_amount, self.alpha)*pow(1/distance, self.beta)
sum_total += attractiveness[possible_next_location]
if sum_total == 0.0:
def next_up(x):
import math
import struct
if math.isnan(x) or (math.isinf(x) and x > 0):
return x
if x == 0.0:
x = 0.0
n = struct.unpack('<q', struct.pack('<d', x))[0]
if n >= 0:
n += 1
else:
n -= 1
return struct.unpack('<d', struct.pack('<q', n))[0]
for key in attractiveness:
attractiveness[key] = next_up(attractiveness[key])
sum_total = next_up(sum_total)
import random
toss = random.random()
cummulative = 0
for possible_next_location in attractiveness:
weight = (attractiveness[possible_next_location] / sum_total)
if toss <= weight + cummulative:
return possible_next_location
cummulative += weight
def _traverse(self, start, end):
self._update_route(end)
self._update_distance_traveled(start, end)
self.location = end
def _update_route(self, new):
self.route.append(new)
self.possible_locations.remove(new)
def _update_distance_traveled(self, start, end):
self.distance_traveled += float(self.distance_callback(start, end))
def get_route(self):
if self.tour_complete:
return self.route
return None
def get_distance_traveled(self):
if self.tour_complete:
return self.distance_traveled
return None
def __init__(self, nodes, distance_callback, start=None, ant_count=50, alpha=.5, beta=1.2, pheromone_evaporation_coefficient=.40, pheromone_constant=1000.0, iterations=80):
if type(nodes) is not dict:
raise TypeError("nodes must be dict")
if len(nodes) < 1:
raise ValueError("there must be at least one node in dict nodes")
self.id_to_key, self.nodes = self._init_nodes(nodes)
self.distance_matrix = self._init_matrix(len(nodes))
self.pheromone_map = self._init_matrix(len(nodes))
self.ant_updated_pheromone_map = self._init_matrix(len(nodes))
if not callable(distance_callback):
raise TypeError("distance_callback is not callable, should be method")
self.distance_callback = distance_callback
if start is None:
self.start = 0
else:
self.start = None
#init start to internal id of node id passed
for key, value in self.id_to_key.items():
if value == start:
self.start = key
#if we didn't find a key in the nodes passed in, then raise
if self.start is None:
raise KeyError("Key: " + str(start) + " not found in the nodes dict passed.")
if type(ant_count) is not int:
raise TypeError("ant_count must be int")
if ant_count < 1:
raise ValueError("ant_count must be >= 1")
self.ant_count = ant_count
if (type(alpha) is not int) and type(alpha) is not float:
raise TypeError("alpha must be int or float")
if alpha < 0:
raise ValueError("alpha must be >= 0")
self.alpha = float(alpha)
if (type(beta) is not int) and type(beta) is not float:
raise TypeError("beta must be int or float")
if beta < 1:
raise ValueError("beta must be >= 1")
self.beta = float(beta)
if (type(pheromone_evaporation_coefficient) is not int) and type(pheromone_evaporation_coefficient) is not float:
raise TypeError("pheromone_evaporation_coefficient must be int or float")
self.pheromone_evaporation_coefficient = float(pheromone_evaporation_coefficient)
#pheromone_constant
if (type(pheromone_constant) is not int) and type(pheromone_constant) is not float:
raise TypeError("pheromone_constant must be int or float")
self.pheromone_constant = float(pheromone_constant)
#iterations
if (type(iterations) is not int):
raise TypeError("iterations must be int")
if iterations < 0:
raise ValueError("iterations must be >= 0")
self.iterations = iterations
#other internal variable init
self.first_pass = True
self.ants = self._init_ants(self.start)
self.shortest_distance = None
self.shortest_path_seen = None
def _get_distance(self, start, end):
if not self.distance_matrix[start][end]:
distance = self.distance_callback(self.nodes[start], self.nodes[end])
if (type(distance) is not int) and (type(distance) is not float):
raise TypeError("distance_callback should return either int or float, saw: "+ str(type(distance)))
self.distance_matrix[start][end] = float(distance)
return distance
return self.distance_matrix[start][end]
def _init_nodes(self, nodes):
id_to_key = dict()
id_to_values = dict()
id = 0
for key in sorted(nodes.keys()):
id_to_key[id] = key
id_to_values[id] = nodes[key]
id += 1
return id_to_key, id_to_values
def _init_matrix(self, size, value=0.0):
ret = []
for row in range(size):
ret.append([float(value) for x in range(size)])
return ret
def _init_ants(self, start):
#allocate new ants on the first pass
if self.first_pass:
return [self.ant(start, self.nodes.keys(), self.pheromone_map, self._get_distance,
self.alpha, self.beta, first_pass=True) for x in range(self.ant_count)]
#else, just reset them to use on another pass
for ant in self.ants:
ant.__init__(start, self.nodes.keys(), self.pheromone_map, self._get_distance, self.alpha, self.beta)
def _update_pheromone_map(self):
#always a square matrix
for start in range(len(self.pheromone_map)):
for end in range(len(self.pheromone_map)):
#decay the pheromone value at this location
#tau_xy <- (1-rho)*tau_xy (ACO)
self.pheromone_map[start][end] = (1-self.pheromone_evaporation_coefficient)*self.pheromone_map[start][end]
#then add all contributions to this location for each ant that travered it
#(ACO)
#tau_xy <- tau_xy + delta tau_xy_k
# delta tau_xy_k = Q / L_k
self.pheromone_map[start][end] += self.ant_updated_pheromone_map[start][end]
def _populate_ant_updated_pheromone_map(self, ant):
route = ant.get_route()
for i in range(len(route)-1):
#find the pheromone over the route the ant traversed
current_pheromone_value = float(self.ant_updated_pheromone_map[route[i]][route[i+1]])
#update the pheromone along that section of the route
#(ACO)
# delta tau_xy_k = Q / L_k
new_pheromone_value = self.pheromone_constant/ant.get_distance_traveled()
self.ant_updated_pheromone_map[route[i]][route[i+1]] = current_pheromone_value + new_pheromone_value
self.ant_updated_pheromone_map[route[i+1]][route[i]] = current_pheromone_value + new_pheromone_value
def mainloop(self):
for _ in range(self.iterations):
#start the multi-threaded ants, calls ant.run() in a new thread
for ant in self.ants:
ant.start()
#source: http://stackoverflow.com/a/11968818/5343977
#wait until the ants are finished, before moving on to modifying shared resources
for ant in self.ants:
ant.join()
for ant in self.ants:
#update ant_updated_pheromone_map with this ant's constribution of pheromones along its route
self._populate_ant_updated_pheromone_map(ant)
#if we haven't seen any paths yet, then populate for comparisons later
if not self.shortest_distance:
self.shortest_distance = ant.get_distance_traveled()
if not self.shortest_path_seen:
self.shortest_path_seen = ant.get_route()
#if we see a shorter path, then save for return
if ant.get_distance_traveled() < self.shortest_distance:
self.shortest_distance = ant.get_distance_traveled()
self.shortest_path_seen = ant.get_route()
#decay current pheromone values and add all pheromone values we saw during traversal (from ant_updated_pheromone_map)
self._update_pheromone_map()
#flag that we finished the first pass of the ants traversal
if self.first_pass:
self.first_pass = False
#reset all ants to default for the next iteration
self._init_ants(self.start)
#reset ant_updated_pheromone_map to record pheromones for ants on next pass
self.ant_updated_pheromone_map = self._init_matrix(len(self.nodes), value=0)
#translate shortest path back into callers node id's
ret = []
for id in self.shortest_path_seen:
ret.append(self.id_to_key[id])
return ret
and my module-test file is:
import ant_colony
import math
test_nodes = {0: (0, 7), 1: (3, 9), 2: (12, 4), 3: (14, 11), 4: (8, 11) ,5: (15, 6), 6: (6, 15), 7: (15, 9), 8: (12, 10), 9: (10, 7)}
def distance(start, end):
x_distance = abs(start[0] - end[0])
y_distance = abs(start[1] - end[1])
return math.sqrt(pow(x_distance, 2) + pow(y_distance, 2))
colony = ant_colony(test_nodes, distance)
answer = colony.mainloop()
print(answer)
but when it runs, this error appears:
TypeError: 'module' object is not callable
I tried a lot of ways but they didn't work at all. I tried to test two coordinates instead of distance, I tried to test using arguments and so on, but they did not work. How can I fix it?

You can see that error says module object is not callable. Because when you wrote
import ant_colony
What you did was import that whole module when what you really wanted was just the class
so you can go ahead and do
from ant_colony import ant_colony
and you are good to go!

You are importing module but not a class. Replace this line of code:
import ant_colony
with this line of code:
from ant_colony import ant_colony
What's the difference? In my example you are importing class called ant_colony from file called ant_colony. The first thing is path to file and the second thing is the name of class, function etc.

You have to call it as ant_colony.ant_colony(...) , not ant_colony(...)
See this thread TypeError: 'module' object is not callable

creating children on Genetic Algorithm

I am writing Python code for the implementation of the Genetic Algorithm.
I am stuck on creating children. I need this for my research. I have implemented the code to the best of my ability.
def initialise_city(num_dim, limit = 100):
X = np.random.randint(0,limit,size=num_dim)
return X
def initialise_cities(num_cities):
cities = []
for i in range(num_cities):
cities.append(initialise_city(2))
return cities
num_cities = 5
cities = initialise_cities(num_cities)
print("City Positions: ", cities)
def distance_function(cities, visit_order):
distance = 0.0
visit_pos = 0
next_pos = 0
for i, txt in enumerate(visit_order):
if (i < len(visit_order)-1):
visit_pos = visit_order[i]
next_pos = visit_order[i+1]
distance = distance + np.sqrt((cities[visit_pos][0]-cities[next_pos][0])**2 +(cities[visit_pos][1]-cities[next_pos][1])**2)
#raise NotImplementedError()
return -1.0*distance
def initialise_chromosome(chromosome_size):
# YOUR CODE HERE
chromosome = np.random.permutation(chromosome_size)
#raise NotImplementedError()
return chromosome
def initialise_population(population_size, chromosome_size):
population = []
# YOUR CODE HERE
for i in range(population_size):
population.append(initialise_chromosome(chromosome_size))
#raise NotImplementedError()
return population
def calculate_fitness(population, cities, fitness_function):
fitness_list = []
# YOUR CODE HERE
d = 0.0
for i,ix in enumerate(population):
d = fitness_function(cities,ix)
fitness_list.append(fitness_function(cities,ix))
#raise NotImplementedError()
return fitness_list
def selection(population, fitness_list):
## Select the top half of the best of the population
population = np.array(population)
sorted_indices = np.argsort(fitness_list)
selection_point = int(1+ len(fitness_list)/2)
# Randomply permute this top half of the poulation
indices = np.random.choice(sorted_indices[:selection_point], len(population))
best_population = population[indices]
return best_population
def pairing(selected_population):
## pair up parents that will be used to reproduce
count = 0
pairs = []
while count < len(selected_population)-1:
index = count
pairs.append([selected_population[index],selected_population[index+1]])
count +=2
return pairs
I am stuck on this part where children are supposed to be created.
def create_child(a,b):
child = []
# YOUR CODE HERE
point = random.randint(1,len(pairs))
#raise NotImplementedError()
return child
def cross_over(pairs):
final_population = []
for a,b in pairs:
child = create_child(a,b)
final_population.append(child)
child = create_child(b,a)
final_population.append(child)
return final_population

How to handle the TypeError: 'int' object is not callable?

I want to implements a-star algorithm and the function (from Route_Algorithm.py) extends the nodes to get the next layor of f_value in the tree . The node can be regard as the station. And the self.settings.station_matrix is the numpy.matrix.
def voluation_station(self, successor, dest_location,bus_stations):
'''initilize all the f(g+h) to the specific nodes'''
length = len(self.settings.station_matrix[successor])
for end_element in range(length):
for station in bus_stations:
if int(station.name) == end_element:
station.get_g(self.settings.station_matrix[successor][end_element])
length_station = len(self.settings.station_matrix[end_element])
for element_station in range(length_station):
if element_station == dest_location:
station.get_h(self.settings.station_matrix[end_element][dest_location])
for element in bus_stations:
element.result_f()
return bus_stations
However, when I run the part of code. It reports the error like this:
Traceback (most recent call last):
File "/home/surface/Final-Year-Project/FYP/Main.py", line 4, in <module>
class main():
File "/home/surface/Final-Year-Project/FYP/Main.py", line 13, in main
new_route.busy_route_matrix()
File "/home/surface/Final-Year-Project/FYP/oop_objects/Route.py", line 87, in busy_route_matrix
self.route_algorithm.A_STAR_Algorithm(start_location,dest_location,self.bus_stations)
File "/home/surface/Final-Year-Project/FYP/Util/Route_Algorithm.py", line 40, in A_STAR_Algorithm
self.voluation_station(successor, dest_location,bus_stations)
File "/home/surface/Final-Year-Project/FYP/Util/Route_Algorithm.py", line 73, in voluation_station
station.get_g(self.settings.station_matrix[successor][end_element])
TypeError: 'int' object is not callable
I search the solution in the Internet, I think the problem may be in the end_element, maybe some inherient problem but I'm not sure. Can some one help me! Please!
Additional codes for other classes:
These classes are Util classes, which helps for handle oop_objects!
The class is for the Route_Algorithm:
from Util.Mergesort_algorithm import mergesort_algorithm
class route_algorithm():
'''generate the route to optimise the profiles'''
def __init__(self,settings):
# a* algorithm
self.open_list = []
self.close_list = []
self.route = []
self.settings = settings
self.flag_find = False
self.lines = []
# merge_sort algorithm
self.mergesort_algorithm = mergesort_algorithm()
def A_STAR_Algorithm(self, start_location, dest_location,bus_stations):
'''search the best route for each passenger to the destination'''
#self.clean_f(bus_stations)
# initial the value of f in start_location
for item in bus_stations:
if int(item.name) == start_location:
item.get_g = 0
for key, value in item.adjacent_station.items():
if int(key.name) == dest_location:
item.get_h = value
self.open_list.append(start_location)
#start_location is the name of station
while self.flag_find == False:
successor = self.open_list[0]
self.open_list.remove(successor)
self.route.append(successor)
self.voluation_station(successor, dest_location,bus_stations)
self.a_brain_judge_1(dest_location,bus_stations)
print(self.flag_find)
if self.flag_find == True:
#end the location
self.route.append(dest_location)
#add the line to the self.line
self.print_lines()
self.flag_find = False
self.open_list = []
else:
#continue to search the minimize
list = self.sort(bus_stations)
for item in list:
print(item.name)
print(item.f)
#疑问如果前两个的预估值一样该如何处理
self.open_list.append(int(list[0].name))
def voluation_station(self, successor, dest_location,bus_stations):
'''initilize all the f(g+h) to the specific nodes'''
length = len(self.settings.station_matrix[successor])
for end_element in range(length):
for station in bus_stations:
if int(station.name) == end_element:
station.get_g(self.settings.station_matrix[successor][end_element])
length_station = len(self.settings.station_matrix[end_element])
for element_station in range(length_station):
if element_station == dest_location:
station.get_h(self.settings.station_matrix[end_element][dest_location])
for element in bus_stations:
element.result_f()
return bus_stations
def a_brain_judge_1(self, dest_location,bus_stations):
'''whether the direct_line is the optimize'''
tmp_dest = bus_stations[0]
self.tmp_nodes = []
self.flag_find = True
for element in bus_stations:
if int(element.name) == dest_location:
tmp_dest = element
for element in bus_stations:
if element == tmp_dest:
pass
else:
if element.f < tmp_dest.f:
self.tmp_nodes.append(element)
self.flag_find = False
if self.flag_find == True:
self.route.append(tmp_dest.name)
return None
else:
return self.tmp_nodes
def sort(self,bus_stations):
'''sort all the f in the next stations'''
return self.mergesort_algorithm.Merge_Sort(bus_stations)
def print_lines(self):
#print(len(self.route))
for item in self.route:
print(item)
print("NEXT PASSENGER!---------")
def clean_f(self,stations):
for item in stations:
item.clean_data()
The class is Random_Algorithm, which helps for generate the random passengers.
import random
from Data.Settings import settings
from oop_objects.Bus_Station import bus_station
from oop_objects.Passenger import passenger
class random_algorithm():
'''generate the random bus-stations and passengers'''
def __init__(self):
self.setting = settings()
def random_passenger(self,number):
'''generate random passengers for bus-station,
and assumes there are 6 stations now. Furthermore, the data will be crawled by the creeper'''
passengers = []
for i in range(number):
new_passenger = passenger()
random.seed(self.setting.seed)
new_passenger.Change_Name(random.randint(1,self.setting.bus_station_number))
# generate the start-location
self.setting.seed +=1
end_location = random.randint(1,self.setting.bus_station_number)
# generate the end-location
while new_passenger.name == end_location:
self.setting.seed += 1
end_location = random.randint(1,self.setting.bus_station_number)
#judge whether the start-location same as the end-location
new_passenger.change_end_location(end_location)
passengers.append(new_passenger)
return passengers
def random_station(self,number):
'''generate the name of random stations '''
bus_stations = []
for i in range(number):
new_bus_station = bus_station()
new_bus_station.Name(str(i))
bus_stations.append(new_bus_station)
return bus_stations
def random_edge(self,bus_stations):
'''generate the edge information for the stations'''
for location1 in bus_stations:
#print("The information add in "+location1.name)
for location2 in bus_stations:
if location1 != location2:
#print("the "+location2.name+" was added in the "+location1.name)
if location2 not in location1.adjacent_station and location1 not in location2.adjacent_station:
random.seed(self.setting.seed)
edge = random.randint(1,self.setting.edge_distance)
location1.add_adjacent_station(location2,edge)
#print("the edge is "+str(edge))
self.setting.seed += 1
return bus_stations
The class is the mergesort_algorithm, which compare the f for different stations
class mergesort_algorithm():
def Merge_Sort(self,stations):
length = len(stations)
middle = int(length/2)
if length<=1:
return stations
else:
list1 = self.Merge_Sort(stations[:middle])
list2 = self.Merge_Sort(stations[middle:])
return self.Merge(list1,list2)
def Merge(self,list1,list2):
list3 = []
length1 = len(list1)
length2 = len(list2)
point1 = 0
point2 = 0
while point1<=length1-1 and point2<=length2-1:
if list1[point1].f<list2[point2].f:
list3.append(list1[point1])
point1 += 1
else:
list3.append(list2[point2])
point2 += 1
if point1>=length1:
for i in range(length2):
if i>=point2:
list3.append(list2[point2])
if point2>=length2:
for i in range(length1):
if i>=point1:
list3.append(list1[point1])
return list3
#def print_sort_result(self):
The following class are oop.classes
The class is for the Route:
from Util.Random_Algorithm import random_algorithm
from Data.Settings import settings
from Util.Route_Algorithm import route_algorithm
import numpy as np
class route():
def __init__(self):
self.bus_stations = []
self.passengers = []
self.settings = settings()
#random algorithm
self.random_algorithm = random_algorithm()
#route_algorithm
self.route_algorithm = route_algorithm(self.settings)
def start_route(self):
'''The raw route Information(TEXT) for bus_stations '''
stations = self.random_algorithm.random_station(self.settings.bus_station_number)
finsih_edge_stations = self.random_algorithm.random_edge(stations)
'''
for item in finsih_edge_stations:
print("\nthe information for " + item.name + " is: \t")
for key, value in item.adjacent_station.items():
print("the station is " + key.name)
print(" the distace is " + str(value))
'''
self.bus_stations = finsih_edge_stations
'''The raw route Information(Text) for passengers'''
self.passengers = self.random_algorithm.random_passenger(self.settings.passengers_number)
def bus_stations_matrix(self):
'''trasfer the raw text to the matrix'''
#create zero_matrix
length = len(self.bus_stations)
tmp_matrix = np.zeros(length*length)
station_matrix = tmp_matrix.reshape(length,length)
for item in self.bus_stations:
for key,value in item.adjacent_station.items():
station_matrix[int(item.name)][int(key.name)] = value
station_matrix[int(key.name)][int(item.name)] = value
print(station_matrix)
self.settings.station_matrix = station_matrix
def passengers_matrix(self):
'''trasfer the raw text to the matrix'''
length = len(self.bus_stations)
tmp_matrix = np.zeros(length*length)
passenger_matrix = tmp_matrix.reshape(length,length)
for item in self.passengers:
#print("the start location of passenger is "+str(item.name))
#print("the end location of passenger is "+str(item.end_location))
#print(" ")
passenger_matrix[item.name-1][item.end_location-1]+=1;
print(passenger_matrix)
self.settings.passenger_matrix = passenger_matrix
def busy_route_matrix(self):
'''generate the bus_busy_route matrix'''
#read the requirements of passengers
length = self.settings.bus_station_number
for start_location in range(length):
for dest_location in range(length):
if self.settings.passenger_matrix[start_location][dest_location] == 0:
pass
else:
magnitude = self.settings.passenger_matrix[start_location][dest_location]
#运行a*算法去寻找最短路径/run the a* algorithm to search the path
self.route_algorithm.A_STAR_Algorithm(start_location,dest_location,self.bus_stations)
print("------------------------------------")
def practice(self):
'''practice some programming'''
for element in self.bus_stations:
print((element.f))
The class is for the Passenger
class passenger():
def __init__(self):
self.name = 0
self.end_location = "null"
def Change_Name(self,name):
'''change the name of passenger'''
self.name = name
def change_end_location(self,location):
'''generate the end_location'''
self.end_location = location
The class for the bus_station
class bus_station():
'''the class represents the bus station'''
def __init__(self):
'''the attribute of name means the name of bus-station
the attribute of passenger means the passenger now in the bus-station'''
self.name = "null"
self.passenger = []
self.adjacent_station = {}
#A* algorithm
self.g = 0
self.h = 0
self.f = 0
def Name(self,name):
'''change the name of the station'''
self.name = name
def add_passengers(self,*passenger):
'''add the passenger in the bus-station'''
self.passenger.append(passenger)
def add_adjacent_station(self,station,edge):
'''add the adjacent station in the this station'''
self.adjacent_station[station] = edge
def get_g(self,value):
'''get the value of g （线路值）'''
self.g =self.g+ value
def get_h(self,value):
'''get the value of f （预估值）'''
self.h = value
def result_f(self):
'''print the value of f （实际值）'''
self.f = self.g+self.h
def add_self_cost(self):
self.add_adjacent_station()
The following class is storing the data.
The class is for the setting:
class settings():
def __init__(self):
self.seed = 5
self.bus_station_number = 10
self.passengers_number = 10
self.edge_distance = 50
self.station_matrix = None
self.passenger_matrix = None
And the main class to run the whole project:
from oop_objects.Route import route
class main():
new_route = route()
new_route.start_route()
print("The distance between different bus station :")
new_route.bus_stations_matrix()
print("The location information for passengers :")
new_route.passengers_matrix()
new_route.busy_route_matrix()
#new_route.practice()practice
#new_route.sort()bus_stations

You should avoid the station of successor because you have assigned the value by using the get_g
The result should be:
def voluation_station(self, successor, dest_location,bus_stations):
'''initilize all the f(g+h) to the specific nodes'''
length = len(self.settings.station_matrix[successor])
for end_element in range(length):
if end_element == successor:
pass
else:
for station in bus_stations:
if int(station.name) == end_element:
station.get_g(self.settings.station_matrix[successor][end_element])
length_station = len(self.settings.station_matrix[end_element])
for element_station in range(length_station):
if element_station == dest_location:
station.get_h(self.settings.station_matrix[end_element][dest_location])
for element in bus_stations:
element.result_f()
return bus_stations

Python - high disk usage in SumTree

I've encountered some weird behaviour of my python program. Basically when I tried to create adn fill a SumTree of length larger than 1000, my disk usage increases a lot to ~300MB/s then the programme died.
I'm pretty sure there's no file r/w involved in this process, and the problem is with the add function. The code is shown below.
import numpy as np
class SumTree():
trans_idx = 0
def __init__(self, capacity):
self.num_samples = 0
self.capacity = capacity
self.tree = np.zeros(2 * capacity - 1)
self.transitions = np.empty(self.capacity, dtype=object)
def add(self, p, experience):
tree_idx = self.trans_idx + self.capacity - 1
self.transitions[self.trans_idx] = experience
self.transitions.append(experience)
self.update(tree_idx, p)
self.trans_idx += 1
if self.trans_idx >= self.capacity:
self.trans_idx = 0
self.num_samples = min(self.num_samples + 1, self.capacity)
def update(self, tree_idx, p):
diff = p - self.tree[tree_idx]
self.tree[tree_idx] = p
while tree_idx != 0:
tree_idx = (tree_idx - 1) // 2
self.tree[tree_idx] += diff
def get_leaf(self, value):
parent_idx = 0
while True:
childleft_idx = 2 * parent_idx + 1
childright_idx = childleft_idx + 1
if childleft_idx >= len(self.tree):
leaf_idx = parent_idx
break
else:
if value <= self.tree[childleft_idx]:
parent_idx = childleft_idx
else:
value -= self.tree[childleft_idx]
parent_idx = childright_idx
data_idx = leaf_idx - self.capacity + 1
return leaf_idx, self.tree[leaf_idx], self.transitions[data_idx]
#property
def total_p(self):
return self.tree[0] # the root
#property
def volume(self):
return self.num_samples # number of transistions stored
Here's an example where this SumTree object will be used:
def add(self, experience)
max_p = np.max(self.tree.tree[-self.tree.capacity:])
if max_p == 0:
max_p = 1.0
exp = self.Experience(*experience)
self.tree.add(max_p, exp)
where Experience is a named tuple and self.tree is a Sumtree instance, when I removed the last line the high disk usage disappears.
Can anyone help me with this?

I finally sort this out because each experience is a tuple of namedtuple and I'm creating another namedtuple Experience from it. Fixed by changing experience to a tuple of numpy arrays.