square.append(square)什么意思
时间: 2023-09-17 08:14:41 浏览: 56
这段代码中,"square"是一个列表(List)的变量名。列表是Python中用于储存多个元素的数据结构。".append()"是列表对象的一个方法,用于在列表的末尾添加一个元素。
在这个代码行中,".append(square)"的作用是将变量"square"本身添加到列表"square"的末尾。这意味着,如果列表"square"之前有元素[1, 4, 9],执行此代码后,列表"square"的值将变为[1, 4, 9, [1, 4, 9]]。也就是说,列表"square"的最后一个元素是它自己。
相关问题
用pandas函数代替下列语句:def RMSE_MAPE(ydata,y_hatdata,indexlist): y=[] y_hat=[] for i in indexlist: y.append(ydata.iat[i,0]) y.append(ydata.iat[i,1]) y.append(ydata.iat[i,2]) y_hat.append(y_hatdata.iat[i,0]) y_hat.append(y_hatdata.iat[i,1]) y_hat.append(y_hatdata.iat[i,2]) x1=0 for i in range(len(indexlist)): x1=x1+np.square(y[i] - y_hat[i]) x2=0 for i in range(len(indexlist)): x2=x2+np.abs((y[i] - y_hat[i]) / y[i]) RMSE = np.sqrt(x1/len(indexlist)) MAPE = x2*100/len(indexlist) print('RMSE:'+str(RMSE)+' MAPE:'+str(MAPE)) return
import pandas as pd
def RMSE_MAPE(ydata, y_hatdata, indexlist):
y = pd.concat([ydata.loc[indexlist, 'col1'], ydata.loc[indexlist, 'col2'], ydata.loc[indexlist, 'col3']], ignore_index=True)
y_hat = pd.concat([y_hatdata.loc[indexlist, 'col1'], y_hatdata.loc[indexlist, 'col2'], y_hatdata.loc[indexlist, 'col3']], ignore_index=True)
x1 = np.square(y - y_hat).sum()
x2 = np.abs((y - y_hat) / y).sum()
RMSE = np.sqrt(x1 / len(y))
MAPE = x2 * 100 / len(y)
print('RMSE:', RMSE, 'MAPE:', MAPE)
return
用pandas函数替代该函数:def RMSE_MAPE(ydata,y_hatdata,indexlist): y=[] y_hat=[] for i in indexlist: y.append(ydata.iat[i,0]) y.append(ydata.iat[i,1]) y.append(ydata.iat[i,2]) y_hat.append(y_hatdata.iat[i,0]) y_hat.append(y_hatdata.iat[i,1]) y_hat.append(y_hatdata.iat[i,2]) x1=0 for i in range(len(indexlist)): x1=x1+np.square(y[i] - y_hat[i]) x2=0 for i in range(len(indexlist)): x2=x2+np.abs((y[i] - y_hat[i]) / y[i]) RMSE = np.sqrt(x1/len(indexlist)) MAPE = x2*100/len(indexlist) print('RMSE:'+str(RMSE)+' MAPE:'+str(MAPE)) return
可以使用以下的pandas函数替代该函数:
def RMSE_MAPE(ydata, y_hatdata, indexlist):
y = ydata.iloc[indexlist, :].values.flatten()
y_hat = y_hatdata.iloc[indexlist, :].values.flatten()
x1 = np.sum(np.square(y - y_hat))
x2 = np.sum(np.abs((y - y_hat) / y)) * 100
RMSE = np.sqrt(x1/len(y))
MAPE = x2/len(y)
print('RMSE:', RMSE, 'MAPE:', MAPE)
return RMSE, MAPE
这个函数首先使用iloc函数来筛选出在indexlist中的行,并将其展开为一维数组y和y_hat。然后求出x1和x2,并根据它们的值计算RMSE和MAPE。最后返回RMSE和MAPE。