课程与功能 -
我不确定我做错了什么，因为我几乎可以肯定地确定我已经引用了变量，并且都是正确的。课程与功能 -
我对使用函数还不太熟悉，并且刚刚开始学习如何在一天前使用Python类。
所以，当我运行代码，我得到这个错误信息：
line 37, in pathlist while self.no_of_files > 0: #self.number_of_files
AttributeError: 'int' object has no attribute 'no_of_files'
我猜它是与我的代码顺序步骤，或者是因为我已经转换输入到代码第20行中的int（）的numfiles。
我附上我的代码如下。请帮我在此先感谢:)
import csv import numpy as np
''' DEFINING MAIN CONTROL'''
def main():
no_of_files # = number_of_files()
a = Calculate_RMSE_Assess_Models()
a.no_of_files() # = no_of_files
a.pathlist()
a.out_path()
a.open_read_write_files()
''' DEFINING CLASS OF ALL '''
class Calculate_RMSE_Assess_Models:
def __init__(self, no_of_files):
self.no_of_files = no_of_files
def number_of_files():
numfiles = input("Enter the number of files to iterate through: ")
numfilesnumber = int(numfiles)
return numfilesnumber
no_of_files = number_of_files()
def pathlist(self):
filepathlist = []
while self.no_of_files > 0: #self.number_of_files
path = input("Enter the filepath of the input file: ")
filepathlist.append(path)
no_of_files = no_of_files - 1
return filepathlist
list_filepath = pathlist(no_of_files)
def out_path():
path = input("Enter the file path of output path: ")
return path
file_out_path = outpath()
def open_read_write_files():
with open('{d[0]}'.format(d=list_filepath), 'r') as csvinput, open('{d[1]}'.format(d=list_filepath), 'r') as csvinput2, open('d{[2]}'.format(d=list_filepath), 'r') as csvinput3, open('{d}'.format(d=file_out_path), 'w') as csvoutput:
reader, reader2, reader3 = csv.reader(csvinput, csvinput2, csvinput3) #1: Decision Forest, 2: Boosted Decision Tree, 3: ANN
writer = csv.DictWriter(csvoutput, lineterminator='\n', fieldnames = ['oldRMSE', 'Decision Forest Regression RMSE', 'Boosted Decision Tree Regression RMSE', 'Neural Network Regression RMSE', 'Old Accurate Predictions', 'Old Inaccurate Predictions', 'Decision Forest Accurate Predictions', 'Decision Forest Inaccurate Predictions', 'Boosted Decision Tree Accurate Predictions', 'Boosted Decision Tree Inaccurate Predictions', 'Neural Network Accurate Predictions', 'Neural Network Inaccurate Predictions'])
#######################################
#For Decision Forest Predictions
headerline = next(reader)
emptyl=[]
for row in reader:
emptyl.append(row)
#Calculate RMSE
DecFSqResidSum = 0
for row in emptyl:
for cell in row:
if cell == row[-3]:
DecFSqResidSum = float(cell) + DecFSqResidSum
DecFSqResidAvg = DecFSqResidSum/len(emptyl)
DecForest_RMSE = np.sqrt(DecFSqResidAvg)
#Constructing No. of Correct/Incorrect Predictions
DecisionForest_Accurate = 0
DecisionForest_Inaccurate = 0
Old_Accurate = 0
Old_Inaccurate = 0
for row in emptyl:
for cell in row:
if cell == row[-2] and 'Accurate' in cell:
Old_Accurate += 1
else:
Old_Inaccurate += 1
if cell == row[-1] and 'Accurate' in cell:
DecisionForest_Accurate += 1
else:
DecisionForest_Inaccurate += 1
######################################
#For Boosted Decision Tree
headerline2 = next(reader2)
emptyl2=[] #make new csv file(list) from csv reader
for row in reader2:
emptyl2.append(row)
#Calculate RMSE
OldSqResidSum = 0
BoostDTSqResidSum = 0
for row in emptyl2: #make Sum of Squared Residuals
for cell in row:
if cell == row[-4]:
OldSqResidSum = float(cell) + OldSqResidSum
if cell == row[-3]:
BoostDTSqResidSum = float(cell) + BoostDTSqResidSum
OldSqResidAvg = OldSqResidSum/len(emptyl2) #divide by N to get average
BoostDTResidAvg = BoostDTSqResidSum/len(emptyl2)
oldRMSE = np.sqrt(OldSqResidAvg) #calculate RMSE of ESTARRTIME & Boosted Decision Tree
BoostedDecTree_RMSE = np.sqrt(BoostDTResidAvg)
#Constructing Correct/Incorrect Predictions
BoostedDT_Accurate = 0
BoostedDT_Inaccurate = 0
for row in emptyl2:
if cell == row[-1] and 'Accurate' in cell:
BoostedDT_Accurate += 1
else:
BoostedDT_Inaccurate += 1
######################################
#For Artificial Neural Network (ANN) Predictions
headerline3 = next(reader3)
emptyl3=[]
for row in reader3:
emptyl3.append(row)
#Calculate RMSE
ANNSqResidSum = 0
for row in emptyl3:
for cell in row:
if cell == row[-3]:
ANNSqResidSum = float(cell) + ANNSqResidSum
ANNSqResidAvg = ANNSqResidSum/len(emptyl3)
ANN_RMSE = np.sqrt(ANNSqResidAvg)
#Constructing Correct/Incorrect Predictions
ANN_Accurate = 0
ANN_Inaccurate = 0
for row in emptyl3:
for cell in row:
if cell == row[-1] and 'Accurate' in cell:
ANN_Accurate += 1
else:
ANN_Inaccurate += 1
#Compile the Error Measures
finalcsv = []
finalcsv.append(oldRMSE)
finalcsv.append(DecForest_RMSE)
finalcsv.append(BoostedDecTree_RMSE)
finalcsv.append(ANN_RMSE)
finalcsv.append(Old_Accurate)
finalcsv.append(Old_Inaccurate)
finalcsv.append(DecisionForest_Accurate)
finalcsv.append(DecisionForest_Inaccurate)
finalcsv.append(BoostedDT_Accurate)
finalcsv.append(BoostedDT_Inaccurate)
finalcsv.append(ANN_Accurate)
finalcsv.append(ANN_Inaccurate)
#Write the Final Comparison file
writer.writeheader()
writer.writerows({'oldRMSE': row[0], 'Decision Forest Regression RMSE': row[1], 'Boosted Decision Tree Regression RMSE': row[2], 'Neural Network Regression RMSE': row[3], 'Old Accurate Predictions': row[4], 'Old Inaccurate Predictions': row[5], 'Decision Forest Accurate Predictions': row[6], 'Decision Forest Inaccurate Predictions': row[7], 'Boosted Decision Tree Accurate Predictions': row[8], 'Boosted Decision Tree Inaccurate Predictions': row[9], 'Neural Network Accurate Predictions': row[10], 'Neural Network Inaccurate Predictions': row[11]} for row in np.nditer(finalcsv))
main()
回答：
你应该给一个no_of_files PARAMS通过调用高清初始化（个体经营，no_of_files）创建一个 实例Calculate_RMSE_Assess_Models时。
回答：
您需要添加self到number_of_files()，out_path()签名，并open_read_write_file()：
class Calculate_RMSE_Assess_Models: def __init__(self, no_of_files):
self.no_of_files = no_of_files
def number_of_files():
numfiles = input("Enter the number of files to iterate through: ")
numfilesnumber = int(numfiles)
return numfilesnumber
def pathlist(self):
filepathlist = []
while self.no_of_files > 0: #self.number_of_files
path = input("Enter the filepath of the input file: ")
filepathlist.append(path)
no_of_files = no_of_files - 1
return filepathlist
def out_path(self):
path = input("Enter the file path of output path: ")
return path
def open_read_write_files(self):
pass
但是，如果您希望保留类中的一个函数的性质，可以使用classmethod装饰：
class Calculate_RMSE_Assess_Models: def __init__(self, no_of_files):
self.no_of_files = no_of_files
@classmethod
def number_of_files(cls):
numfiles = input("Enter the number of files to iterate through: ")
numfilesnumber = int(numfiles)
return numfilesnumber
def pathlist(self):
filepathlist = []
while self.no_of_files > 0: #self.number_of_files
path = input("Enter the filepath of the input file: ")
filepathlist.append(path)
no_of_files = no_of_files - 1
return filepathlist
@classmethod
def out_path(cls):
path = input("Enter the file path of output path: ")
return path
@classmethod
def open_read_write_files(cls):
pass
回答：
在你的班级定义中，你有list_filepath = pathlist(no_of_files)。这称为pathlist，no_of_files为self。 no_of_files是int，因此while self.no_of_files > 0:正试图访问int的no_of_files属性。
完整的回溯显示了这一点。在查看这样的问题时发布完整的Traceback是有帮助的。
Traceback (most recent call last): File "redacted", line 17, in <module>
class Calculate_RMSE_Assess_Models:
File "redacted", line 38, in Calculate_RMSE_Assess_Models
list_filepath = pathlist(no_of_files)
File "redacted", line 32, in pathlist
while self.no_of_files > 0: #self.number_of_files
AttributeError: 'int' object has no attribute 'no_of_files'
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