`folie.functions`.Linear¶

class folie.functions.Linear(domain=None, output_shape=(), coefficients=None)[source]¶

The linear function f(x) = c x

Makes a deep copy of this function.

Returns:

copy: A new copy of this model.

fit(x, *args, y=None, estimator=LinearRegression(copy_X=False, fit_intercept=False), sample_weight=None, **kwargs)[source]¶

Fit coefficients of the function using linear regression. Use as features the derivative of the function with respect to the coefficients

Parameters:

X{array-like} of shape (n_samples, dim)
Point of evaluation of the training data
yarray-like of shape (n_samples,) or (n_samples, n_targets)
Target values. Will be cast to X’s dtype if necessary.
estimator: sklearn compatible estimator
Defaut to sklearn.linear_model.LinearRegression(copy_X=False, fit_intercept=False) but any compatible estimator can be used.
Estimator should have a coef_ attibutes after fitting

fit_transform(X, y=None, **fit_params)[source]¶

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features): Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_params(deep=False)[source]¶

Get the parameters.

Returns:

paramsmapping of string to any: Parameter names mapped to their values.

grad_coeffs(x, *args, **kwargs)[source]¶

Gradient of the function with respect to the coefficients.

Parameters:

xarray_like: Input data.

Returns:

transformedarray_like: The gradient

resize(new_shape)[source]¶

Change the output shape of the function.

Parameters:

new_shapetuple, array-like: The new output shape of the function

set_output(*, transform=None)[source]¶

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

selfestimator instance: Estimator instance.

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: