uf3.regression.least_squares.BasicLinearModel

class BasicLinearModel(regularizer: Optional[numpy.ndarray] = None)

Bases: object

Base class for linear regression.

Parameters

regularizer (np.ndarray) – regularization matrix.

Methods

fit	Direct solution to linear least squares with LU decomposition.
predict	Predict using fit coefficients.
score	Evaluate score (negative error metric).

fit(x: numpy.ndarray, y: numpy.ndarray, ridge_penalty: float = 1e-08)

Direct solution to linear least squares with LU decomposition.

Parameters

• x (np.ndarray) – input matrix of shape (n_samples, n_features).

• y (np.ndarray) – output vector of length n_samples.

• ridge_penalty (float) – magnitude of ridge penalty. Ignored if self.regularizer is set at initialization.

predict(x: numpy.ndarray)

Predict using fit coefficients.

Parameters

x (np.ndarray) – input matrix of shape (n_samples, n_features).

Returns

vector of predictions.

Return type

predictions (np.ndarray)

score(x, y, weights=None, normalize=True)

Evaluate score (negative error metric).

Parameters

• x (np.ndarray) – input matrix of shape (n_samples, n_features).

• y (np.ndarray) – output vector of length n_samples.

• weights (np.ndarray) – sample weights (optional).

• normalize (bool) – whether to normalize by the std of y.

Returns

negative weighted root-mean-square-error.

Return type

score (float)