我正在学习机器学习,我在github中找到了这个代码,但是我遇到了一些问题才能使它正常工作,而且我也没有使用python的经验,这使得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的\ evoman_framework \ optimization_individualevolution_demo.py&#34 ;, 第272行,in filhos = cruzamento(pop)#crossover文件" D:\ GitHub \ evoman_framework \ optimization_individualevolution_demo.py", 第171行,在cruzamento filhos = np.zeros((n_filhos,n_vars))TypeError:只能将整数标量数组转换为标量索引
###############################################################################
# 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
答案 0 :(得分:0)
您收到此错误是因为您的n_filhos
或n_vars
类型不是整数。我可以单独运行第一个变量,然后返回数组。
>>> n_filhos = np.random.randint(1,3+1, 1)
>>> n_filhos
array([3])
在跑步之前检查它们的类型。
答案 1 :(得分:0)
np.random.randint(1,3+1, 1)
返回一个数组,而不是整数。维度规范期望整数元组。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])