rescale values?
时间: 2024-05-24 09:12:10 浏览: 6
Rescaling values refers to the process of changing the scale or range of values in a dataset. This can be done to normalize the data, make it more comparable to other datasets, or to make it easier to interpret. The most common techniques for rescaling values include normalization, standardization, and min-max scaling. Normalization involves scaling the values to a range between 0 and 1, while standardization involves transforming the values to have a mean of 0 and a standard deviation of 1. Min-max scaling involves scaling the values to a specific range, such as between 0 and 100.
相关问题
Traceback (most recent call last): File "D:\kelly\PycharmProjects\pythonProject7\7.2上证.py", line 33, in <module> am_arch = arch_model(r, mean='constant', vol='arch', p=1, q=0, dist='normal') File "D:\python3.10\lib\site-packages\arch\univariate\mean.py", line 1981, in arch_model am = ConstantMean(y, hold_back=hold_back, rescale=rescale) File "D:\python3.10\lib\site-packages\arch\univariate\mean.py", line 1118, in __init__ super().__init__( File "D:\python3.10\lib\site-packages\arch\univariate\mean.py", line 266, in __init__ super().__init__( File "D:\python3.10\lib\site-packages\arch\univariate\base.py", line 198, in __init__ raise ValueError( ValueError: NaN or inf values found in y. y must contains only finite values.怎么改错
这个错误提示是因为你的数据 `r` 中存在非数值或无穷大的值,需要先处理一下数据,将其中的非数值或无穷大的值去掉或替换为其他数值。你可以使用 Pandas 库中的 `fillna()` 方法将非数值或无穷大的值替换为平均值或中位数,具体操作如下:
```python
import pandas as pd
# 将 r 中的非数值或无穷大的值替换为平均值
r = pd.Series(r)
r = r.fillna(r.mean())
```
或者,如果你想将非数值或无穷大的值直接去掉,可以使用 Pandas 库中的 `dropna()` 方法,具体操作如下:
```python
import pandas as pd
# 去掉 r 中的非数值或无穷大的值
r = pd.Series(r)
r = r.dropna()
```
处理完数据后,再调用 `arch_model()` 函数就不会出现上述错误了。
Traceback (most recent call last): File "D:\kelly\PycharmProjects\pythonProject8\大作业.py", line 39, in <module> am_ar_garch = arch_model(r[:i], mean='ar', lags=1, vol='garch', dist='normal', p=2, q=2) File "D:\python3.10\lib\site-packages\arch\univariate\mean.py", line 1977, in arch_model am = ARX(y, None, lags, hold_back=hold_back, rescale=rescale) File "D:\python3.10\lib\site-packages\arch\univariate\mean.py", line 1455, in __init__ super().__init__( File "D:\python3.10\lib\site-packages\arch\univariate\mean.py", line 266, in __init__ super().__init__( File "D:\python3.10\lib\site-packages\arch\univariate\base.py", line 198, in __init__ raise ValueError( ValueError: NaN or inf values found in y. y must contains only finite values.如何修改报错
这个报错信息告诉我们在使用arch_model函数时,y中含有NaN或inf值,需要将这些非有限值删除或替换,可以使用pandas库中的dropna()函数将NaN值删除,使用numpy库中的isfinite()函数将非有限值替换为NaN或其他值。具体操作可以参考下面的代码示例:
```python
import pandas as pd
import numpy as np
from arch import arch_model
# 加载数据
data = pd.read_csv('data.csv', index_col=0, parse_dates=True)
# 删除NaN值
data = data.dropna()
# 将非有限值替换为NaN
data[~np.isfinite(data)] = np.nan
# 拟合模型
am = arch_model(data, mean='AR', lags=1, vol='GARCH', p=2, q=2, dist='Normal')
res = am.fit()
# 输出结果
print(res.summary())
```
在这个示例中,我们首先使用pandas库的dropna()函数删除了数据中的NaN值,然后使用numpy库的isfinite()函数将非有限值替换为NaN。接下来,我们使用arch_model函数拟合了一个AR-GARCH模型,并使用fit()函数拟合模型并输出结果。