- class aydin.regression.linear.LinearRegressor(mode: str = 'huber', max_num_iterations: int = 512, alpha: float = 1, beta: float = 0.0001, **kwargs)
The Linear Regressor is the simplest of all repressors, and in general performs poorly. However, it is also very fast and can be advantageous in some ‘simple’ situations.
- fit(x_train, y_train, x_valid=None, y_valid=None, regressor_callback=None)
Fits function y=f(x) given training pairs (x_train, y_train). Stops when performance stops improving on the test dataset: (x_test, y_test). The target y_train can have multiple ‘channels’. This will cause multiple regressors to be instanciated internally to be able to predict these channels from the input features.
x training values
y training values
x validation values
y validation values
- static load(path: str)
Returns an ‘all-batteries-included’ regressor from a given path (folder).
path to load from.
- predict(x, models_to_use=None)
Predicts y given x by applying the learned function f: y=f(x) If the regressor is trained on multiple ouput channels, this will return the corresponding number of channels…
inferred y values
- recommended_max_num_datapoints() int
Recommended maximum number of datapoints
- save(path: str)
Saves an ‘all-batteries-included’ regressor at a given path (folder).
path to save to
- frozenEncoded JSON object
Stops training (can be called by another thread)