adelie.diagnostic.predict#

adelie.diagnostic.predict(X: ndarray | MatrixNaiveBase32 | MatrixNaiveBase64, betas: ndarray | csr_matrix, intercepts: ndarray, *, offsets: ndarray | None = None, n_threads: int = 1)[source]#

Computes the linear predictions.

The single-response linear prediction is given by

$$\begin{array}{r}\hat{\eta }=X\beta +{\beta }_0\mathbf{1}+{\eta }^0\end{array}$$

The multi-response linear prediction is given by

$$\begin{array}{r}\hat{\eta }=(X\otimes I_K)\beta +(\mathbf{1}\otimes I_k){\beta }_0+{\eta }^0\end{array}$$

The single or multi-response is detected based on the shape of intercepts. If intercepts one-dimensional, we assume single-response. Otherwise, we assume multi-response.

Parameters:

X(n, p) Union[ndarray, MatrixNaiveBase32, MatrixNaiveBase64]

Feature matrix. It is typically one of the matrices defined in adelie.matrix submodule or numpy.ndarray.

betas(L, p) or (L, p*K) Union[ndarray, csr_matrix]

Coefficient vectors $\beta$.

intercepts(L,) or (L, K) ndarray

Intercepts ${\beta }_0$.

offsets(n,) or (n, K) ndarray, optional

Observation offsets ${\eta }^0$. Default is None, in which case, it is set to np.zeros(n) if y is single-response and np.zeros((n, K)) if multi-response.

n_threadsint, optional

Number of threads. Default is 1.

Returns:

linear_preds(L, n) or (L, n, K) ndarray

Linear predictions.