def main():
# num_cores = multiprocessing.cpu_count()
# results = Parallel(n_jobs=num_cores)(delayed(run_testcase)(n[i], d[i], x[i], y[i])
# for i in trange(t, desc='Total', ascii=True))
#
# with open("output.txt", 'w') as outfile:
# for case, val, chromosome in enumerate(results):
# outfile.write('Case: %d \n' % (i+1))
# for f in chromosome:
# outfile.write(str(f) + ' ')
# outfile.write(" value: %d \n" % val)
infile = open('input_examples.txt', 'r')
outfile = open('output.txt', 'w')
# outfile2 = open('output2.txt', 'w')
graph = Graph()
test_cases = int(infile.readline())
for i in range(test_cases):
(n, d) = infile.readline().split()
n_points, degree = int(n), int(d)
points = []
for j in range(n_points):
(x, y) = infile.readline().split()
points.append(Object(float(x), float(y)))
x_axis = [i.x for i in points]
y_axis = [i.y for i in points]
graph.update_org(x_axis, y_axis)
population = create_population(degree)
max_val, max_chromosome, count = 0.0, [], 0
for y in range(max_generations):
(population, value, chromosome) = genetic_algorithm(population, points, y)
count += 1
if value > max_val:
count = 0
max_val, max_chromosome = value, chromosome
y_calculated = ff.calculate_y(max_chromosome, points)
graph.update_pred(x_axis, y_calculated)
# print(max_chromosome)
print('%s / %s: %s' % (y, max_generations, max_chromosome), end='\r')
# if count == 250:
# break
# print(" value: %d" % max_val)
print('%s / %s: %s' % (y, max_generations, max_chromosome))
outfile.write('Case: %d \n' % (i+1))
for f in max_chromosome:
outfile.write(str(f) + ' ')
outfile.write(" value: %f \n" % max_val)
print(" value: %f" % max_val)
x_axis = [i.x for i in points]
y_axis = [i.y for i in points]
plt.plot(x_axis, y_axis, 'ro')
# outfile2.write('case: %d \n' % (i+1))
y_calculated = ff.calculate_y(max_chromosome, points)
# for z in range(len(points)):
# outfile2.write('%f %f\n' % (points[z].x , y_calculated[z]))
plt.plot(x_axis, y_calculated, linestyle='-.')
plt.show()
outfile.close()
