Data scaling: row vs. column and naming of methods

[elcorto]

The DataScaler docs say (from mala/datahandling/data_scaler.py)

    Parameters
    ----------
    typestring :  string
        Specifies how scaling should be performed.
        Options:

        - "None": No normalization is applied.
        - "standard": Standardization (Scale to mean 0,
          standard deviation 1)
        - "normal": Min-Max scaling (Scale to be in range 0...1)
        - "feature-wise-standard": Row Standardization (Scale to mean 0,
          standard deviation 1)
        - "feature-wise-normal": Row Min-Max scaling (Scale to be in range
          0...1)

1. The "Row" parts of feature-wise-* suggest that inputs $X$ (e.g. bispectrum descriptors) and outputs $Y$ (LDOS) have shape (n_features, n_samples), while the data in https://github.com/mala-project/test-data has (18, 18, 27, 94) ($X$) and (18, 18, 27, 11) ($Y$) and is, in test/scaling_test.py, reshaped to (18*18*27, n_features). Further, the DataScaler code does torch.mean(unscaled, 0, ...), so operates on columns (features, axis 0).

2. It should be clearly stated that the "normal" and "standard" modes operate on the whole array, rather than along the opposite axis of the one that the feature-wise- modes use, which users may assume.

3. Maybe "normal" should be renamed to something like "minmax", since "Normalization" is usually something different.