gemseo.mlearning.regression.algos.thin_plate_spline_settings module#

Settings of the thin plate spline (TPS) regressor.

Settings TPSRegressor_Settings(*, transformer=<factory>, parameters=<factory>, input_names=(), output_names=(), function=RBF.THIN_PLATE, der_function=None, epsilon=None, smooth=0.0, norm='euclidean')[source]#

Bases: RBFRegressor_Settings

The settings of the thin plate spline (TPS) regressor.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

  • transformer (Mapping[str, Any]) --

    By default it is set to <factory>.

  • parameters (Mapping[str, Any]) --

    By default it is set to <factory>.

  • input_names (Sequence[str]) --

    By default it is set to ().

  • output_names (Sequence[str]) --

    By default it is set to ().

  • function (Literal[RBF.THIN_PLATE]) --

    By default it is set to "thin_plate".

  • der_function (Annotated[Callable[[_NDArrayPydantic[Any, dtype[_ScalarType_co]]], _NDArrayPydantic[Any, dtype[_ScalarType_co]]], WithJsonSchema(json_schema={}, mode=None)] | None)

  • epsilon (float | None)

  • smooth (float) --

    By default it is set to 0.0.

  • norm (str | Annotated[Callable[[_NDArrayPydantic[Any, dtype[_ScalarType_co]], _NDArrayPydantic[Any, dtype[_ScalarType_co]]], float], WithJsonSchema(json_schema={}, mode=None)]) --

    By default it is set to "euclidean".

Return type:

None

function: Literal[RBF.THIN_PLATE] = RBF.THIN_PLATE#

The thin plate radial basis function for \(r^2\log(r)\).

model_post_init(context, /)#

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that's what pydantic-core passes when calling it.

Parameters:

  • self (BaseModel) -- The BaseModel instance.

  • context (Any) -- The context.

Return type:

None