【不报错但却写不进文件】
- 这段目的是使用文件追加扩增数据，数据采样范围在(mean-STDEV, mean+STDEV)。东拼西凑了代码运行后没有报错但却无法写入文件，恳求帮助我改一下代码
import csv
import random
import numpy as np
import pandas as pd
# load the total data CSV file into a list
data_file_1 = open('RSDS_AI_10.csv', 'rb')
data_list_1 = data_file_1.readlines()[1:0]
data_file_1.close()
# augment training datasets
for record in data_list_1:
# split the record by the ',' commas
record = record.decode()
type(record)
array = record.split(',')
all_values = record.split(',')
arr_std1 = np.std(float(all_values[1])) #标准差
arr_std2 = np.std(float(all_values[2]))
arr_std3 = np.std(float(all_values[3]))
arr_std4 = np.std(float(all_values[4]))
arr_std5 = np.std(float(all_values[5]))
arr_std6 = np.std(float(all_values[6]))
arr_std7 = np.std(float(all_values[7]))
arr_std8 = np.std(float(all_values[8]))
arr_std9 = np.std(float(all_values[9]))
arr_std10 = np.std(float(all_values[10]))
arr_std11 = np.std(float(all_values[11]))
arr_std12 = np.std(float(all_values[12]))
arr_std13 = np.std(float(all_values[13]))
arr_std14 = np.std(float(all_values[14]))
arr_std15 = np.std(float(all_values[15]))
arr_std16 = np.std(float(all_values[16]))
arr_std17 = np.std(float(all_values[17]))
arr_std18 = np.std(float(all_values[18]))
arr_std19 = np.std(float(all_values[19]))
arr_std20 = np.std(float(all_values[20]))
arr_std21 = np.std(float(all_values[21]))
arr_std22 = np.std(float(all_values[22]))
arr_std23 = np.std(float(all_values[23]))
arr_std24 = np.std(float(all_values[24]))
arr_std25 = np.std(float(all_values[25]))
arr_std26 = np.std(float(all_values[26]))
arr_std27 = np.std(float(all_values[27]))
arr_std28 = np.std(float(all_values[28]))
arr_std29 = np.std(float(all_values[29]))
arr_std30 = np.std(float(all_values[30]))
arr_std31 = np.std(float(all_values[31]))
arr_std32 = np.std(float(all_values[32]))
arr_std33 = np.std(float(all_values[33]))
arr_std34 = np.std(float(all_values[34]))
arr_mean1 = np.mean(float(all_values[1])) #平均值
arr_mean2 = np.mean(float(all_values[2]))
arr_mean3 = np.mean(float(all_values[3]))
arr_mean4 = np.mean(float(all_values[4]))
arr_mean5 = np.mean(float(all_values[5]))
arr_mean6 = np.mean(float(all_values[6]))
arr_mean7 = np.mean(float(all_values[7]))
arr_mean8 = np.mean(float(all_values[8]))
arr_mean9 = np.mean(float(all_values[9]))
arr_mean10 = np.mean(float(all_values[10]))
arr_mean11 = np.mean(float(all_values[11]))
arr_mean12 = np.mean(float(all_values[12]))
arr_mean13 = np.mean(float(all_values[13]))
arr_mean14 = np.mean(float(all_values[14]))
arr_mean15 = np.mean(float(all_values[15]))
arr_mean16 = np.mean(float(all_values[16]))
arr_mean17 = np.mean(float(all_values[17]))
arr_mean18 = np.mean(float(all_values[18]))
arr_mean19 = np.mean(float(all_values[19]))
arr_mean20 = np.mean(float(all_values[20]))
arr_mean21 = np.mean(float(all_values[21]))
arr_mean22 = np.mean(float(all_values[22]))
arr_mean23 = np.mean(float(all_values[23]))
arr_mean24 = np.mean(float(all_values[24]))
arr_mean25 = np.mean(float(all_values[25]))
arr_mean26 = np.mean(float(all_values[26]))
arr_mean27 = np.mean(float(all_values[27]))
arr_mean28 = np.mean(float(all_values[28]))
arr_mean29 = np.mean(float(all_values[29]))
arr_mean30 = np.mean(float(all_values[30]))
arr_mean31 = np.mean(float(all_values[31]))
arr_mean32 = np.mean(float(all_values[32]))
arr_mean33 = np.mean(float(all_values[33]))
arr_mean34 = np.mean(float(all_values[34]))
# build new CSV file to save new data
p = open('D:fansile.csv', 'ab', encoding='utf-8', newline='')
csv_writer = csv.writer(p)
for i in range(10): # generate 10 times training data
input1 = np.random.uniform(arr_mean1 - arr_std1, arr_mean1 + arr_std1) # randomly sampling from (mean-STDEV, mean+STDEV)
input2 = np.random.uniform(arr_mean2 - arr_std2, arr_mean2 + arr_std2)
input3 = np.random.uniform(arr_mean3 - arr_std3, arr_mean3 + arr_std3)
input4 = np.random.uniform(arr_mean4 - arr_std4, arr_mean4 + arr_std4)
input5 = np.random.uniform(arr_mean5 - arr_std5, arr_mean5 + arr_std5)
input6 = np.random.uniform(arr_mean6 - arr_std6, arr_mean6 + arr_std6)
input7 = np.random.uniform(arr_mean7 - arr_std7, arr_mean7 + arr_std7)
input8 = np.random.uniform(arr_mean8 - arr_std8, arr_mean8 + arr_std8)
input9 = np.random.uniform(arr_mean9 - arr_std9, arr_mean9 + arr_std9)
input10 = np.random.uniform(arr_mean10 - arr_std10, arr_mean10 + arr_std10)
input11 = np.random.uniform(arr_mean11 - arr_std11, arr_mean11 + arr_std11)
input12 = np.random.uniform(arr_mean12 - arr_std12, arr_mean12 + arr_std12)
input13 = np.random.uniform(arr_mean13 - arr_std13, arr_mean13 + arr_std13)
input14 = np.random.uniform(arr_mean14 - arr_std14, arr_mean14 + arr_std14)
input15 = np.random.uniform(arr_mean15 - arr_std15, arr_mean15 + arr_std15)
input16 = np.random.uniform(arr_mean16 - arr_std16, arr_mean16 + arr_std16)
input17 = np.random.uniform(arr_mean17 - arr_std17, arr_mean17 + arr_std17)
input18 = np.random.uniform(arr_mean18 - arr_std18, arr_mean18 + arr_std18)
input19 = np.random.uniform(arr_mean19 - arr_std19, arr_mean19 + arr_std19)
input20 = np.random.uniform(arr_mean20 - arr_std20, arr_mean20 + arr_std20)
input21 = np.random.uniform(arr_mean21 - arr_std21, arr_mean21 + arr_std21)
input22 = np.random.uniform(arr_mean22 - arr_std22, arr_mean22 + arr_std22)
input23 = np.random.uniform(arr_mean23 - arr_std23, arr_mean23 + arr_std23)
input24 = np.random.uniform(arr_mean24 - arr_std24, arr_mean24 + arr_std24)
input25 = np.random.uniform(arr_mean25 - arr_std25, arr_mean25 + arr_std25)
input26 = np.random.uniform(arr_mean26 - arr_std26, arr_mean26 + arr_std26)
input27 = np.random.uniform(arr_mean27 - arr_std27, arr_mean27 + arr_std27)
input28 = np.random.uniform(arr_mean28 - arr_std28, arr_mean28 + arr_std28)
input29 = np.random.uniform(arr_mean29 - arr_std29, arr_mean29 + arr_std29)
input30 = np.random.uniform(arr_mean30 - arr_std30, arr_mean30 + arr_std30)
input31 = np.random.uniform(arr_mean31 - arr_std31, arr_mean31 + arr_std31)
input32 = np.random.uniform(arr_mean32 - arr_std32, arr_mean32 + arr_std32)
input33 = np.random.uniform(arr_mean33 - arr_std33, arr_mean33 + arr_std33)
input34 = np.random.uniform(arr_mean34 - arr_std34, arr_mean34 + arr_std34)
csv_writer.writerow([input1, input2, input3, input4, input5, input6, input7, input8, input9, input10,input11,input12,
input13,input14,input15,input16,input17,input18,input19,input20,input21,input22,input23,input24
,input25,input26,input27,input28,input29,input30,input31,input32,input33,input34]) # decided by your structure of dataset
p.close()
- 无报错但写不进去
- 问了一个人他说我是把一个文件用两个权限不同的指针去指，每个指针有着不同的操作，不能这样写，需要借助临时文件，但具体怎么做没有说
- 最终想要数据扩增迭代10次，并追加到csv文件中
