gaitmap.data_transform.StandardScaler

class gaitmap.data_transform.StandardScaler(ddof: int = 1)

Apply a standard scaling to the data.

The transformed data y is calculated as:

y = (x - x.mean()) / x.std(ddof)

Note

Only a single mean and std are calculated over the entire data (i.e. not per column).

Parameters:

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.
set_params(**params)	Set the parameters of this Algorithm.
to_json()	Export the current object parameters as json.
transform(data, **_)	Scale the data.

__init__(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.

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.StandardScaler

BarthDtw stride segmentation with Custom Template

BarthDtw stride segmentation with Custom Template