gaitmap.base.BaseStrideSegmentation#
- class gaitmap.base.BaseStrideSegmentation[source]#
Base class for all stride segmentation algorithms.
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.
segment(data, sampling_rate_hz, **kwargs)Find stride candidates in data.
set_params(**params)Set the parameters of this Algorithm.
to_json()Export the current object parameters as json.
- __init__(*args, **kwargs)#
- 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_jsonmethod 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][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:
- params
Parameter names mapped to their values.
- segment(data: DataFrame | dict[Union[collections.abc.Hashable, str], pandas.core.frame.DataFrame], sampling_rate_hz: float, **kwargs) Self[source]#
Find stride candidates in data.
Examples using gaitmap.base.BaseStrideSegmentation#
HMM stride segmentation - Prediction with pre-trained model
