当您存储一个没有足够的内存分配的numpy阵列时会发生什么？
我只是在与NP.array及其内存分配一起玩，我希望如果您尝试存储一个对于内存分配太大的数组，您只会遇到错误，或者只会存储第一个X数字数字。相反，我只是回来了似乎是随机的数字。幕后发生了什么？
import numpy as np
def create_new_array(num_list):
new_array = np.array(num_list,np.int8)
return print(new_array)
create_new_array([31112 , 32321, 24567,456,324,789])
输出： [-120，65，-9，-56，68，21]
更改输入值稍微给出了完全不同的输出，我对此非常好奇。
I was just playing around with np.array and its memory allocation, I expected that if you tried to store an array that was too big for the memory allocation you would just get an error or would just store the first x number of digits. Instead I just got back seemingly random numbers. What is going on behind the scenes here?
import numpy as np
def create_new_array(num_list):
new_array = np.array(num_list,np.int8)
return print(new_array)
create_new_array([31112 , 32321, 24567,456,324,789])
output:
[-120, 65, -9, -56, 68, 21]
Changing the input values slightly gives completly differnt outputs and I'm very curiouse as to why this is.
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数字不是随机的；它们是补充。对于
int8，它可以表示的最大正数为127，在此上面返回了数字的补充。例如：The numbers are not random; they are the complements. For
int8, the maximum positive number it can represent is 127, above which the numbers' complements are returned. For example: