Check_Data[SOCKET][0] |= pel[i]<<(i);定义
时间: 2023-09-21 22:06:23 浏览: 22
这是一个位运算的语句。其中,pel[i]表示一个8位二进制数的第i位(从右往左数),Check_Data[SOCKET][0]是一个32位的整型数组元素,表示一个32位的二进制数。这个语句的作用是将pel数组中的8位二进制数,从右往左依次放入Check_Data[SOCKET][0]的低8位中,即将pel[0]放入Check_Data[SOCKET][0]的第0位,将pel[1]放入Check_Data[SOCKET][0]的第1位,以此类推。
相关问题
factor_out = 'PE' datafit = result[['yearmonth','Ret', 'Rfree', 'ExRet', 'PE','PEL1']].copy(deep=True) n_in = np.sum(datafit['yearmonth'] <= 20080101) n_out = np.sum(datafit['yearmonth'] > 20080101) rout = np.zeros(n_out) rmean = np.zeros(n_out) rreal = np.zeros(n_out) rfree = np.zeros(n_out) volt2 = np.zeros(n_out) for i in range(n_out): model = smf.ols('ExRet ~ PEL1', data=datafit[['ExRet', 'PEL1']].iloc[:(n_in+i),:]) results = model.fit() b = results.params['Intercept'] k = results.params['PEL1'] f = datafit['PE'].iloc[n_in+i-1] rreal[i] = datafit['ExRet'].iloc[n_in+i] rfree[i] = datafit['Rfree'].iloc[n_in+i] rout[i] = k*f+b rmean[i] = np.mean(datafit['ExRet'].iloc[:(n_in+i)].values) volt2[i] = np.sum(datafit['Ret'].iloc[(n_in+i-9):(n_in+i)].values**2) print('Out-of-sample tests for one factor model with OLS:') print('Predictor: {:s}'.format(factor_out)) R2os, MFSEadj, pvalue_MFSEadj = myfun_stat_gains(rout, rmean, rreal) Uout, Umean, DeltaU = myfun_econ_gains(rout, rmean, rreal, rfree, volt2, gmm=5) del datafit修改代码
这段代码的功能是进行一个单因子模型的外样本测试,使用OLS回归模型进行预测。具体来说,它对于给定的因子('PE'),使用历史数据中的'PEL1'值作为预测变量,对历史数据中的'ExRet'进行回归,得到截距项b和系数k,然后使用未来一个月的'PE'值f,带入回归模型,得到对未来一个月的'ExRet'的预测值rout。然后,它计算了一些统计指标(如R方、平均方差误差等)来评估预测性能,并计算了一些经济指标(如收益、风险等)来评估投资效果。最后,它输出了这些指标的值。如果需要修改代码,需要根据具体需求进行修改。
Traceback (most recent call last): File "D:\kelly\PycharmProjects\pythonProject8\大作业.py", line 145, in <module> model = smf.ols('ExRet ~ PEL1', data=datafit[['ExRet', 'PEL1']].iloc[:(n_in+i),:]) File "D:\python3.10\lib\site-packages\statsmodels\base\model.py", line 226, in from_formula mod = cls(endog, exog, *args, **kwargs) File "D:\python3.10\lib\site-packages\statsmodels\regression\linear_model.py", line 906, in __init__ super(OLS, self).__init__(endog, exog, missing=missing, File "D:\python3.10\lib\site-packages\statsmodels\regression\linear_model.py", line 733, in __init__ super(WLS, self).__init__(endog, exog, missing=missing, File "D:\python3.10\lib\site-packages\statsmodels\regression\linear_model.py", line 190, in __init__ super(RegressionModel, self).__init__(endog, exog, **kwargs) File "D:\python3.10\lib\site-packages\statsmodels\base\model.py", line 267, in __init__ super().__init__(endog, exog, **kwargs) File "D:\python3.10\lib\site-packages\statsmodels\base\model.py", line 92, in __init__ self.data = self._handle_data(endog, exog, missing, hasconst, File "D:\python3.10\lib\site-packages\statsmodels\base\model.py", line 132, in _handle_data data = handle_data(endog, exog, missing, hasconst, **kwargs) File "D:\python3.10\lib\site-packages\statsmodels\base\data.py", line 700, in handle_data return klass(endog, exog=exog, missing=missing, hasconst=hasconst, File "D:\python3.10\lib\site-packages\statsmodels\base\data.py", line 88, in __init__ self._handle_constant(hasconst) File "D:\python3.10\lib\site-packages\statsmodels\base\data.py", line 132, in _handle_constant exog_max = np.max(self.exog, axis=0) File "<__array_function__ internals>", line 180, in amax File "D:\python3.10\lib\site-packages\numpy\core\fromnumeric.py", line 2793, in amax return _wrapreduction(a, np.maximum, 'max', axis, None, out, File "D:\python3.10\lib\site-packages\numpy\core\fromnumeric.py", line 86, in _wrapreduction return ufunc.reduce(obj, axis, dtype, out, **passkwargs) ValueError: zero-size array to reduction operation maximum which has no identity报错如何四u该
根据报错信息,很可能是因为数据集中的某些列中没有数据,导致使用`np.max()`函数时出现了"zero-size array"的错误。可以在使用`np.max()`函数之前先检查数据集中是否存在空值或缺失值,或者可以使用`np.nanmax()`函数来处理数据,该函数可以忽略缺失值并返回最大值。同时,也可以检查一下数据的维度是否符合要求。如果无法解决问题,可以将报错信息和相关代码提供给更有经验的人士进行调试。
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)