def test_solution():
matrix = [[0, 1, 0, 0, 0, 1], [4, 0, 0, 3, 2, 0], [0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]
expected = [0, 3, 2, 9, 14]
result = solution(matrix)
assert expected == result
def test_solution():
f = open('samples', 'r')
data = f.read()
for i in data.split("<="):
if i:
actual, expected = map(lambda x : x.replace("\n", ""), i.split("=>"))
print "Actual : ", actual
print "Expected : ", expected
result = solution(actual)
print "Solution : ", result
assert expected == result
def test_solution():
f = open('samples', 'r')
data = f.read()
for i in data.split("<="):
if i:
actual, expected = map(lambda x: x.replace("\n", ""),
i.split("=>"))
print "Actual : ", actual
print "Expected : ", expected
result = solution(actual)
print "Solution : ", result
assert expected == result
def read_test(line):
input_lines = []
output_lines = []
if line == "<=":
line = next_line()
while line != "=>":
input_lines.append(line)
line = next_line()
line = next_line()
while line != "<=" and line != "END":
output_lines.append(line)
line = next_line()
result = solution(input_lines[0], input_lines[1])
assert output_lines[0] == result
return line
def init_population(number, N): # number指AP数量，N是种群规模
"""
初始化
"""
solutions = []
for k in range(N):
pos = []
for i in range(number):
while True:
row = random.randint(0, 49)
col = random.randint(0, 119)
if env.structure[row][col] and ([col, row] in pos):
continue
break
pos.append([col, row])
solutions.append(solution(pos))
return solutions
def test_medium_range(self):
A = [x + 1 for x in range(-1000, 1000)]
self.assertEqual(solution(A), 999000000)
def test_example1(self):
A = [-3, 1, 2, -2, 5, 6]
self.assertEqual(solution(A), 60, msg="example test")
import solution
# Not confusing at all.
solution = solution.solution
# Test Time!
assert (solution('4') == 2)
assert (solution('15') == 5)
# My Tests
assert (solution('1') == 0)
assert (solution('2') == 1)
assert (solution('3') == 2)
assert (solution('4') == 2)
assert (solution('8') == 3)
assert (solution('16') == 4)
assert (solution('32') == 5)
assert (solution('32') == 5)
for k in range(1100):
assert (solution(str(2**k)) == k)
print(f'2**{k} == {2 ** k} PASSED')
def test_basic(self):
""" Test from the task """
self.assertEqual(solution(""""""), 0)
from solution import *
P = np.array([[45, 25, 35, 20], [30, 20, 20, 25], [8, 16, 20, 24],
[32, 16, 14, 12]])
C = np.array([[10, 8, 10, 12], [20, 7, 12, 10], [3, 5, 8, 10], [10, 8, 10, 5]])
a = np.array([[25, 20, 50, 50], [20, 12, 15, 45], [15, 10, 20, 40],
[10, 40, 10, 25]])
A = [500, 200, 100, 1000]
b = [50, 80, 30, 45]
task = "pessimist"
alpha = 1
if __name__ == "__main__":
solution(P=P, C=C, a=a, A=A, b=b, task=task, alpha=alpha)
def test_1(m, f, result):
assert solution(m, f) == result
while count <= 1000:
#根据需要也可以改动
average = 0
population_After_selection = selection(init_sol)
# print("1111", len(population_After_selection))
population_After_cross = cross(population_After_selection, 0.8)
# print("2222", len(population_After_cross))
population_After_mutation = mutation(population_After_cross,
0.001)
pos = []
init_sol = []
for gene in population_After_mutation:
pos = gene2pos(gene)
if pos is None:
continue
solution_new = solution(pos)
results_in_background_new = solution_new.fitness(env)
solution_new.judgePopulation(results_in_background_new)
init_sol.append(solution_new)
quick_sort(init_sol, 0, len(init_sol) - 1)
for k in range(0, 5):
average = average + init_sol[len(init_sol) - 1 -
k].fit_number
average = average // 5
optimal_solutions.append(average)
if -0.01 < (optimal_solutions[count] -
optimal_solutions[count - 1]) / optimal_solutions[
count - 1] < 0.01 and count > 200:
#print(optimal_solutions[count])
#print(optimal_solutions[count - 1])
break
from solution import *
from code import P, C, a, A, b
y1, y2 = [], []
alpha_list = np.arange(0.49, 1, 0.01).tolist()
alpha_list.append(1)
task = "pessimist"
for alpha in alpha_list:
y1.append(solution(P=P, C=C, a=a, A=A, b=b, task=task, alpha=alpha))
task = "optimist"
for alpha in alpha_list:
y2.append(solution(P=P, C=C, a=a, A=A, b=b, task=task, alpha=alpha))
plt.plot(alpha_list, y1, label="pessimist")
plt.plot(alpha_list, y2, label="optimist")
plt.xlabel('alpha')
plt.ylabel('F')
plt.legend()
plt.show()