运行结果出现如下错误,Input contains NaN, infinity or a value too large for dtype('float32').
时间: 2024-04-28 09:19:50 浏览: 28
这个错误通常是由于输入数据中包含了 NaN、无穷大或过大的值,而这些值无法被 float32 类型的数据承载。你需要进行数据清洗或对数据进行缩放处理,以确保输入数据的值域在 float32 能够承载的范围内。你可以使用 numpy 库中的 isnan() 和 isinf() 函数来检测数据中是否含有 NaN 和无穷大的值,然后对这些值进行处理或替换。另外你可以使用 sklearn.preprocessing 库中的 Scaler 对数据进行缩放处理。
相关问题
Input contains NaN, infinity or a value too large for dtype('float32').
这个错误通常是因为输入数据中包含了 NaN、无穷大或者超出了 float32 数据类型的范围的值。你需要检查输入数据,确保数据的格式和范围正确。如果数据中确实包含了 NaN 或者无穷大的值,可以考虑使用数据清洗的方法进行处理,比如将这些值替换为其他合理的值。如果数据超出了 float32 数据类型的范围,可以尝试使用更高精度的数据类型,比如 float64。
Input contains NaN, infinity or a value too large for dtype('float64').
As an AI language model, I cannot see the input that you are referring to. However, this error message usually occurs when the input data contains invalid values such as NaN (Not a Number), infinity, or a value that is too large to be represented as a float64 data type.
To fix this error, you can try the following steps:
1. Check your input data for any NaN or infinity values using the `np.isnan()` and `np.isinf()` functions from the NumPy library.
2. Replace any NaN or infinity values with valid values or remove them from your dataset.
3. If the input value is too large to be represented as a float64 data type, you can try using a higher precision data type such as float128 or decimal.
4. If you are working with a large dataset, you can also try using a chunking approach to process the data in smaller batches and avoid memory overflow errors.
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