`gaitmap.data_transform`.AbsMaxScaler#

class gaitmap.data_transform.AbsMaxScaler(out_max: float = 1)[source]#

Scale data by its absolute maximum.

The data y after the transform is calculated as

y = x * out_max / max(abs(x))

If all values in the input are zero, the input is returned unchanged.

Note that the maximum over all columns is calculated. I.e. Only a single global scaling factor is applied to all the columns.

Parameters:

out_max: The value the maximum will be scaled to. After scaling the absolute maximum in the data will be equal to this value. Note that if the absolute maximum corresponds to a minimum in the data, this minimum will be scaled to -out_max.

Other Parameters:

Attributes:

Methods

`clone`()	Create a new instance of the class with all parameters copied over.
`from_json`(json_str)	Import an gaitmap object from its json representation.
`get_params`([deep])	Get parameters for this algorithm.
`set_params`(**params)	Set the parameters of this Algorithm.
`to_json`()	Export the current object parameters as json.
`transform`(data, **_)	Scale the data.

clone() → Self[source]#

Create a new instance of the class with all parameters copied over.

This will create a new instance of the class itself and all nested objects

classmethod from_json(json_str: str) → Self[source]#

Import an gaitmap object from its json representation.

For details have a look at the this example.

You can use the to_json method of a class to export it as a compatible json string.

Parameters:

get_params(deep: bool = True) → dict[str, Any][source]#

Get parameters for this algorithm.

Parameters:

deep: Only relevant if object contains nested algorithm objects. If this is the case and deep is True, the params of these nested objects are included in the output using a prefix like nested_object_name__ (Note the two “_” at the end)

Returns: