Am trying to append a matrix to a numpy array from a 3d list but It only append first row and when I-CodePudding

I have a certain number of matrix in a list and when I try to append a matrix to the NumPy array It only appends the specified row and when I try to edit the code to append the whole matrix It keep returning the following error:

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,)

The error related function:

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)

The full code:

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:\t\n\nNumber   \t Task 1  \t   Task 2  \t   Task 3\n')
ni = min(len(t1), len(t2), len(t3))
# for i in range(ni):
#     print('\t {0}    \t   {1}    \t    {2}  \t\t {3}\n'.format(wsn[i], t1[i], t2[i], t3[i]))
# print('\n\n')
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('\n\n')
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('\n\n')
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('\n\n')

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

CodePudding user response：

This issue is related to dimensions that specified for parents, which is 2d now so must be modified to 3d. So, IIUC, you can achieve this aim by:

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)