我在列表中有一定数量的矩阵，当我尝试将矩阵附加到NumPy数组时，它只附加指定的行，当我尝试编辑代码以附加整个矩阵时，它一直返回以下错误:
Traceback (most recent call last):
File "so.py", line 129, in <module>
parents = selection(cal_fitness(t1, t2, t3, matlist, 300, arr), 2, matlist) #
File "so.py", line 124, in selection
parents[i, :] = population[max_fitness_idx[0][0]]
ValueError: could not broadcast input array from shape (3,5) into shape (5,)
错误相关函数:
def selection(fitness, num_parents, population):
fitness = list(fitness)
parents = numpy.empty((num_parents, len(population)))
for i in range(num_parents):
max_fitness_idx = numpy.where(fitness == numpy.max(fitness))
#parents[i, :] = population[max_fitness_idx[0][0]][2]
parents[i, :] = population[max_fitness_idx[0][0]]
fitness[max_fitness_idx[0][0]] = -999999
return parents.astype(int)
parents = selection(cal_fitness(t1, t2, t3, matlist, 300, arr), 2, matlist)
print(parents)
完整代码:
import numpy
import random
import pandas
wsn = numpy.arange(1, 6)
taskn = 3
t1 = numpy.random.randint(30, 200, size=len(wsn))
t2 = numpy.random.randint(30, 200, size=len(wsn))
t3 = numpy.random.randint(30, 200, size=len(wsn))
# print('nGenerated Data:tnnNumber t Task 1 t Task 2 t Task 3n')
ni = min(len(t1), len(t2), len(t3))
# for i in range(ni):
# print('t {0} t {1} t {2} tt {3}n'.format(wsn[i], t1[i], t2[i], t3[i]))
# print('nn')
qmin = 50
qmax = 140
for i in range(len(t1)):
if t1[i] <= qmin or t1[i] >= qmax:
# t1=numpy.delete(t1,i)
t1[i] = 0
for i in range(len(t2)):
if t2[i] <= qmin or t2[i] >= qmax:
# t2=numpy.delete(t2,i)
t2[i] = 0
for i in range(len(t3)):
if t3[i] <= qmin or t3[i] >= qmax:
# t3=numpy.delete(t3,i)
t3[i] = 0
i = 0
m = max(len(t1), len(t2), len(t3))
if t1[i] == 0 and t2[i] == 0 and t3[i] == 0:
t1 = numpy.delete(t1, i)
t2 = numpy.delete(t2, i)
t3 = numpy.delete(t3, i)
i += 1
solperpop = len(wsn)
gen = 20
j = 0
pop_size = (taskn, solperpop)
# print('population size: {}'.format(pop_size))
# for j in range(ni):
# pop_size=list(solperpop,taskn)
matlist = list()
# print('nn')
i = 0
k = 0
nbrofindv = 5
arr = []
for i in range(nbrofindv):
init_pop = numpy.zeros(pop_size, dtype=int)
init_pop = init_pop.astype(int)
k = 0
l = 0
for k in range(taskn):
l = random.randrange(solperpop - 1)
init_pop[k][l] = 1
arr.append(l)
matlist.append(init_pop)
pandas.set_option('display.max_columns', None)
pandas.set_option('display.width', None)
zipped = pandas.DataFrame(list(zip(*matlist)))
# , columns=['Individual 1', 'Individual 2', 'Individual 3', 'Individual 4', 'Individual 5'])
print(zipped)
print('nn')
i = 0
for i in range(len(wsn)):
if t1[i] == 0:
if init_pop[0][i] != 0:
init_pop[0][i] == 0
if t2[i] == 0:
if init_pop[1][i] != 0:
init_pop[1][i] == 0
if t3[i] == 0:
if init_pop[2][i] != 0:
init_pop[2][i] == 0
def cal_fitness(task1, task2, task3, matix, mmax, array):
fitness = numpy.empty(len(matix))
S1 = numpy.empty(len(matix), dtype=int)
z = 0
for i in range(len(matix)):
S1[i] = task1[array[0 + z]] + task2[array[1 + z]] + task3[array[2 + z]]
z += 3
if S1[i] <= mmax:
fitness[i] = S1[i]
else:
fitness[i] = 0
return fitness.astype(int)
fitness = cal_fitness(t1, t2, t3, matlist, 300, arr)
def selection(fitness, num_parents, population):
fitness = list(fitness)
parents = numpy.empty((num_parents, len(population)))
for i in range(num_parents):
max_fitness_idx = numpy.where(fitness == numpy.max(fitness))
#parents[i, :] = population[max_fitness_idx[0][0]][2]
parents[i, :] = population[max_fitness_idx[0][0]]
fitness[max_fitness_idx[0][0]] = -999999
return parents.astype(int)
parents = selection(cal_fitness(t1, t2, t3, matlist, 300, arr), 2, matlist)
print(parents)
print('nn')
def crossover(parents, num_offsprings):
offsprings = numpy.empty((num_offsprings, parents.shape[1]))
crossover_point = int(parents.shape[1] / 2)
crossover_rate = 0.5
i = 0
while parents.shape[0] < num_offsprings:
parent1_index = i % parents.shape[0]
parent2_index = (i + 1) % parents.shape[0]
x = random.random()
if x > crossover_rate:
continue
parent1_index = i % parents.shape[0]
parent2_index = (i + 1) % parents.shape[0]
offsprings[i, 0:crossover_point] = parents[parent1_index, 0:crossover_point]
offsprings[i, crossover_point:] = parents[parent2_index, crossover_point:]
i += 1 # <== modified
return offsprings # <== modified
print(crossover(parents, 2)) # <== modified
这个问题与parents指定的尺寸有关，现在是2d，所以必须修改为3d。因此，IIUC，您可以通过以下方式实现此目标:
def selection(fitness, num_parents, population):
fitness = list(fitness)
# parents shape is modified (a new axis with the needed length is added)
parents = numpy.empty((num_parents, len(population[0]), len(population[0][0]))) # <==
for i in range(num_parents):
max_fitness_idx = numpy.where(fitness == numpy.max(fitness))
parents[i, :] = population[max_fitness_idx[0][0]]
fitness[max_fitness_idx[0][0]] = -999999
return parents.astype(int)