如何在 python 中正确使用 numpy.zeros答案

【问题标题】：how to properly use numpy.zeros in python如何在 python 中正确使用 numpy.zeros
【发布时间】：2017-10-16 20:30:50
【问题描述】：

我正在学习机器学习，我在 github 上找到了这段代码，但是我遇到了一些问题来让它正常工作，而且我也没有使用 python 的经验，这并没有让事情变得更容易哈哈哈

filhos = np.zeros( (n_filhos, n_vars) ) 返回此错误：

Traceback（最近一次调用最后一次）：文件 "D:\GitHub\evoman_framework\optimization_individualevolution_demo.py", 第 272 行，在 filhos = cruzamento(pop) # 交叉文件 "D:\GitHub\evoman_framework\optimization_individualevolution_demo.py", 171 号线，克鲁萨门托 filhos = np.zeros( (n_filhos, n_vars) ) 类型错误：只有整数标量数组可以转换为标量索引

############################################################################### 
# EvoMan FrameWork - V1.0 2016                                    #
# DEMO : Neuroevolution - Genetic Algorithm with perceptron neural network.   #
# Author: Karine Miras                                                #
# karine.smiras@gmail.com                                         #
############################################################################### 

# imports framework
import sys
sys.path.insert(0, 'evoman') 
from environment import Environment
from controller import Controller

# imports other libs 
import time
import numpy as np
from math import fabs,sqrt
import glob, os

# genetic algorithm params

run_mode = 'train' # train or test
stateread = None # 'state_1' 
statesave = 'state_1'
n_vars = (env.get_num_sensors()+1)*5  # perceptron
#n_vars = (env.get_num_sensors()+1)*10 + 11*5  # multilayer with 10 neurons
#n_vars = (env.get_num_sensors()+1)*50 + 51*5 # multilayer with 50 neurons
dom_u = 1
dom_l = -1
npop = 100
gens = 30
mutacao = 0.2
last_best = 0

# crossover
def cruzamento(pop):

    total_filhos = np.zeros((0,n_vars))


    for p in range(0,pop.shape[0], 2):       
        p1 = torneio(pop)
        p2 = torneio(pop)

        n_filhos =   np.random.randint(1,3+1, 1) 
        filhos =  np.zeros( (n_filhos, n_vars) )

        for f in range(0,n_filhos): 

            cross_prop = np.random.uniform(0,1)
            filhos[f] = p1*cross_prop+p2*(1-cross_prop)

            # mutation 
            for i in filhos[f]:
                if np.random.uniform(0 ,1)<=mutacao:
                    filhos[f][i] =   filhos[f][i]+np.random.normal(dom_l, dom_u)

            filhos[f] = np.array(map(lambda y: limites(y), filhos[f]))           

            total_filhos = np.vstack((total_filhos, filhos[f]))

    return total_filhos

【问题讨论】：

代码太多，请减少到minimal reproducible example。
减少了，感谢您的提示，现在尽量减少

标签： python python-2.7 numpy

【解决方案1】：

您收到此错误是因为您的 n_filhos 或 n_vars 类型不是整数。我只能单独运行第一个变量并返回数组。

>>> n_filhos = np.random.randint(1,3+1, 1) 
>>> n_filhos
array([3])

在运行前部分检查它们的类型。

【讨论】：

【解决方案2】：

np.random.randint(1,3+1, 1) 返回一个数组，而不是整数。维度规范需要整数元组。相反，有一个 numpy 数组和一个整数的元组：

>>> np.random.randint(1,3+1, 1)
array([2])

【讨论】：