我得到的错误是第32行，在 dates.append（[int（date.split（'-'）[2]）]） IndexError：列表索引超出范围
我不知道该如何解决。我只是python和金融的初学者，所以我只是在做实验。我期望的输出是带有RBF模型，线性模型，多项式模型和数据的图形。此外，第31天的预测价格。任何帮助将不胜感激。谢谢大家。
#import the packages
import pandas as pd
import numpy as np
from sklearn.svm import SVR
import matplotlib.pyplot as plt
#Load the data
#from google.colab import files # Use to load data on Google Colab
#uploaded = files.upload() # Use to load data on Google Colab
df = pd.read_csv('AAPL.csv')
df.head(7)
#Create the lists / X and y data set
dates = []
prices = []
#Get the number of rows and columns in the data set
df.shape
#Print the last row of data (this will be the that we test on)
df.tail(1)
#Get all of the data except for the last row
df = df.head(len(df)-1)
print(df.shape)
df_dates = df.loc[:,'Date'] # Get all of the rows from the Date column
df_open = df.loc[:,'Open'] #Get all of the rows from the Open column
#Create the independent data set 'X' as dates
for date in df_dates:
dates.append( [int(date.split('-')[2])] )
#Create the dependent data set 'y' as prices
for open_price in df_open:
prices.append(float(open_price))
#See what days were recoreded in teh data set
print(dates)
#Function to make predictions using 3 different support vector regression models with 3
different kernals
def predict_prices(dates, prices, x):
#Create 3 Support Vector Regression Models
svr_lin = SVR(kernel='linear', C=1e3)
svr_poly = SVR(kernel='poly', C=1e3, degree=2)
svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
#Train the models on the dates and prices
svr_lin.fit(dates,prices)
svr_poly.fit(dates, prices)
svr_rbf.fit(dates, prices)
#Plot the models on a graph to see which has the best fit
plt.scatter(dates, prices, color = 'black', label='Data')
plt.plot(dates, svr_rbf.predict(dates), color = 'red', label='RBF model')
plt.plot(dates, svr_lin.predict(dates), color = 'green', label='Linear model')
plt.plot(dates, svr_poly.predict(dates), color = 'blue', label='Polynomial model')
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('Support Vector Regression')
plt.legend()
plt.show()
#return all three model predictions
return svr_rbf.predict(x)[0], svr_lin.predict(x)[0], svr_poly.predict(x)[0]
#Predict the price of FB on day 31
predicted_price = predict_prices(dates, prices, [[31]])
print(predicted_price)