ML::Solver::Lars Namespace Reference

Functions
template<typename math_t , typename idx_t >
void	larsFit (const raft::handle_t &handle, math_t *X, idx_t n_rows, idx_t n_cols, const math_t *y, math_t *beta, idx_t *active_idx, math_t *alphas, idx_t *n_active, math_t *Gram, int max_iter, math_t *coef_path, rapids_logger::level_enum verbosity, idx_t ld_X, idx_t ld_G, math_t eps)
	Train a regressor using LARS method. More...
template<typename math_t , typename idx_t >
void	larsPredict (const raft::handle_t &handle, const math_t *X, idx_t n_rows, idx_t n_cols, idx_t ld_X, const math_t *beta, idx_t n_active, idx_t *active_idx, math_t intercept, math_t *preds)
	Predict with LARS regressor. More...

Function Documentation

larsFit()

template<typename math_t , typename idx_t >

void ML::Solver::Lars::larsFit	(	const raft::handle_t &	handle,
		math_t *	X,
		idx_t	n_rows,
		idx_t	n_cols,
		const math_t *	y,
		math_t *	beta,
		idx_t *	active_idx,
		math_t *	alphas,
		idx_t *	n_active,
		math_t *	Gram,
		int	max_iter,
		math_t *	coef_path,
		rapids_logger::level_enum	verbosity,
		idx_t	ld_X,
		idx_t	ld_G,
		math_t	eps
	)

Train a regressor using LARS method.

Parameters

handle	RAFT handle
X	device array of training vectors in column major format, size [n_rows * n_cols]. Note that the columns of X will be permuted if the Gram matrix is not specified. It is expected that X is normalized so that each column has zero mean and unit variance.
n_rows	number of training samples
n_cols	number of feature columns
y	device array of the regression targets, size [n_rows]. y should be normalized to have zero mean.
beta	device array of regression coefficients, has to be allocated on entry, size [max_iter]
active_idx	device array containing the indices of active variables. Must be allocated on entry. Size [max_iter]
alphas	the maximum correlation along the regularization path are returned here. Must be a device array allocated on entry Size [max_iter].
n_active	host pointer to return the number of active elements, scalar.
Gram	device array containing Gram matrix (X.T * X). Can be nullptr. Size [n_cols * ld_G]
max_iter	maximum number of iterations, this equals with the maximum number of coefficients returned. max_iter <= n_cols.
coef_path	coefficients along the regularization path are returned here. Must be nullptr, or a device array already allocated on entry. Size [max_iter * max_iter].
verbosity	verbosity level
ld_X	leading dimension of X (stride of columns, ld_X >= n_rows).
ld_G	leading dimesion of G (ld_G >= n_cols)
eps	numeric parameter for Cholesky rank one update

larsPredict()

template<typename math_t , typename idx_t >

void ML::Solver::Lars::larsPredict	(	const raft::handle_t &	handle,
		const math_t *	X,
		idx_t	n_rows,
		idx_t	n_cols,
		idx_t	ld_X,
		const math_t *	beta,
		idx_t	n_active,
		idx_t *	active_idx,
		math_t	intercept,
		math_t *	preds
	)

Predict with LARS regressor.

Parameters

handle	RAFT handle
X	device array of training vectors in column major format, size [n_rows * n_cols].
n_rows	number of training samples
n_cols	number of feature columns
ld_X	leading dimension of X (stride of columns)
beta	device array of regression coefficients, size [n_active]
n_active	the number of regression coefficients
active_idx	device array containing the indices of active variables. Only these columns of X will be used for prediction, size [n_active].
intercept
preds	device array to store the predictions, size [n_rows]. Must be allocated on entry.