最小-最大正規化は、特定の範囲（通常は0～1）に収まるように値を再スケーリングします。この場合、0～1の範囲に再スケーリングすると仮定します。最小値は0に、最大値は1に変換され、その間の値はすべて0～1の数値に変換されます。

欠落値はnumpyでは`nan`として表すことができます。このケースを処理するために、すべての `nan` 値を無視し、そのまま通過させます。提供されたデータが提供された最小値や最大値から外れるようなエッジケースを処理する必要があります。

```python
import numpy as np

def min_max_normalize(data, provided_min=None, provided_max=None):
    # If no data is provided, there is nothing to normalize
    if data is None:
        return None

    min = np.nanmin(data)
    if provided_min is not None:
        if provided_min > min:
            # Ensure we don't have any values less than the minimum
            data = np.maximum(data, provided_min)
        min = provided_min

    max = np.nanmax(data)
    if provided_max is not None:
        if provided_max < max:
            # Ensure we don't have any values greater than the maximum
            data = np.minimum(data, provided_max)
        max = provided_max

    range = max - min
    return (data - min) / range
```

この関数は次のように使うことができる：
```python
data = np.array([-17, 2, np.nan, 13, 35, 9000, -123])
normalized_data = min_max_normalize(data, -50, 50)
print(normalized_data)
```

出力
```
[0.33 0.52  nan 0.63 0.85 1.   0.  ]
```

お役に立てれば幸いです。
Min-max normalization rescales values to fit within a specific range, typically 0 -1. In this case we'll assume that we're rescaling to a range of 0 - 1. The lowest value will be transformed to 0, the highest value to 1, and all values in between will be transformed to a number between 0 and 1.

Missing values can represented in numpy as `nan`, meaning "not a number". To handle this case, I will ignore all `nan` values and pass them through as-is. I will need to handle edge cases such as the provided data falling outside of the provided minimum and maximum values.

```python
import numpy as np

def min_max_normalize(data, provided_min=None, provided_max=None):
    # If no data is provided, there is nothing to normalize
    if data is None:
        return None

    min = np.nanmin(data)
    if provided_min is not None:
        if provided_min > min:
            # Ensure we don't have any values less than the minimum
            data = np.maximum(data, provided_min)
        min = provided_min

    max = np.nanmax(data)
    if provided_max is not None:
        if provided_max < max:
            # Ensure we don't have any values greater than the maximum
            data = np.minimum(data, provided_max)
        max = provided_max

    range = max - min
    return (data - min) / range
```

This function could be used like this:
```python
data = np.array([-17, 2, np.nan, 13, 35, 9000, -123])
normalized_data = min_max_normalize(data, -50, 50)
print(normalized_data)
```

Output:
```
[0.33 0.52  nan 0.63 0.85 1.   0.  ]
```

I hope this helps.