数据没问题的情况下，显示array must not contain infs or NaNs

这个错误通常是由于数组中包含了无穷大（inf）或不是数字（NaN）的值而引起的。您需要检查数据是否包含这些无效的数值，并对其进行处理。可以使用以下代码来检查数据中是否存在inf或NaN：

import numpy as np

# example data
data = np.array([1.0, 2.0, 3.0, np.inf, np.nan])

# check for infs and NaNs
if np.any(np.isnan(data)) or np.any(np.isinf(data)):
    print("Data contains NaNs or infs.")
else:
    print("Data is valid.")

如果数据中确实存在inf或NaN，则可以使用以下代码将其替换为有效值，例如0或平均值：

# replace infs with 0 and NaNs with mean
data[np.isinf(data)] = 0
mean = np.mean(data[~np.isnan(data)])
data[np.isnan(data)] = mean

这将把inf替换为0，NaN替换为数据的平均值，从而使数据有效。

相关问题

array must not contain infs or NaNs

As an AI language model, I'm not sure what context you are referring to, but in general, when working with arrays, it is important to ensure that they do not contain any infinite (inf) or Not-a-Number (NaN) values. This is because inf and NaN values can cause numerical calculations to fail or produce unexpected results.

To check if an array contains inf or NaN values, you can use the numpy functions `numpy.isinf()` and `numpy.isnan()`. If either function returns True for any element of the array, it means that the array contains inf or NaN values.

Here's an example:

import numpy as np

a = np.array([1, 2, np.inf, 4])
b = np.array([1, 2, np.nan, 4])

print(np.isinf(a))  # [False False  True False]
print(np.isnan(b))  # [False False  True False]

To remove inf or NaN values from an array, you can use the `numpy.isfinite()` function, which returns a boolean mask indicating which elements of an array are finite:

a = np.array([1, 2, np.inf, 4])
a = a[np.isfinite(a)]
print(a)  # [1 2 4]

This will remove all inf values from the array and leave only the finite values.

Array must not contain infs or NaNs

这是一个编程类的问题，可能是在使用某个函数或算法时出现的错误提示。通常情况下，这意味着数组中包含了无穷大或不是数字的元素，需要对数组进行处理或筛选，以确保其中不包含这些非法元素。具体的处理方法取决于具体的情况和使用的编程语言或工具。