当我从numpy 1.9升级到1.10时，我开始看到以下回归模型在具有相同硬件配置的不同机器上给出不同的结果：
fitted_model = pd.ols(y=lhs_unpickled, x=rhs_unpickled, intercept=False)
print fitted_model.beta
lhs_unpickled和rhs_unpickled如下所示：
> lhs_unpickled[1:5]
2008-04-24 00:18:00+00:00 -0.465517
2008-04-24 00:33:00+00:00 -0.519584
2008-04-24 00:48:00+00:00 -0.607410
2008-04-24 01:03:00+00:00 -0.705983
Freq: 15T, Name: AI_Index, dtype: float64
> rhs_unpickled[1:5]
CPM XQH FOD EX
2008-04-24 00:18:00+00:00 -0.301556 0.148582 0.079320 -0.707586
2008-04-24 00:33:00+00:00 -0.274421 0.071747 0.130182 -0.659409
2008-04-24 00:48:00+00:00 -0.273960 -0.001447 0.148643 -0.703215
2008-04-24 01:03:00+00:00 -0.238426 -0.008732 0.130801 -0.698489
在使用numpy 1.10时，是否有关于此pd.ols()函数的特定内容会导致这种不一致的行为？