gaitmap.data_transform.TrainableStandardScaler

class gaitmap.data_transform.TrainableStandardScaler(mean: float | None = None, std: float | None = None, ddof: int = 1)

Apply a standard scaling to the data.

The transformed data y is calculated as:

Parameters:

mean

The mean of the training data. The value can either be set manually or automatically calculated from the training data using self_optimize.

std

The standard deviation of the training data. The value can either be set manually or automatically calculated from the training data using self_optimize.

ddof

The degree of freedom used in the standard deviation calculation.

Other Parameters:

data

The data passed to the transform method.

Attributes:

transformed_data_

The transformed data.

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.
self_optimize(data, **_)	Learn the parameters of the transformer based on provided data.
set_params(**params)	Set the parameters of this Algorithm.
to_json()	Export the current object parameters as json.
transform(data, **_)	Scale the data.

__init__(mean: float | None = None, std: float | None = None, ddof: int = 1) → None

clone() → Self

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

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:

json_str

json formatted string

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

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:

params

Parameter names mapped to their values.

self_optimize(data: Sequence[DataFrame], **_) → Self

Learn the parameters of the transformer based on provided data.

Parameters:

data

A sequence of dataframes, each representing single-sensor data.

kwargs

Optional keword arguments

Returns:

self

The trained instance of the transformer

set_params(**params: Any) → Self

Set the parameters of this Algorithm.

To set parameters of nested objects use nested_object_name__para_name=.

to_json() → str

Export the current object parameters as json.

For details have a look at the this example.

You can use the from_json method of any gaitmap algorithm to load the object again.

Warning

This will only export the Parameters of the instance, but not any results!

transform(data: DataFrame, **_) → Self

Scale the data.

Parameters:

data

A dataframe representing single sensor data.

Returns:

self

The instance of the transformer with the results attached

Examples using gaitmap.data_transform.TrainableStandardScaler

BarthDtw stride segmentation with Custom Template

BarthDtw stride segmentation with Custom Template