Namespaces
	opg

Functions
void	olsFit (const raft::handle_t &handle, float input, size_t n_rows, size_t n_cols, float labels, float coef, float intercept, bool fit_intercept, bool normalize, int algo=0, float *sample_weight=nullptr)

void	olsFit (const raft::handle_t &handle, double input, size_t n_rows, size_t n_cols, double labels, double coef, double intercept, bool fit_intercept, bool normalize, int algo=0, double *sample_weight=nullptr)

void	ridgeFit (const raft::handle_t &handle, float input, size_t n_rows, size_t n_cols, float labels, float alpha, int n_alpha, float coef, float intercept, bool fit_intercept, bool normalize, int algo=0, float sample_weight=nullptr)

void	ridgeFit (const raft::handle_t &handle, double input, size_t n_rows, size_t n_cols, double labels, double alpha, int n_alpha, double coef, double intercept, bool fit_intercept, bool normalize, int algo=0, double sample_weight=nullptr)

void	gemmPredict (const raft::handle_t &handle, const float input, size_t n_rows, size_t n_cols, const float coef, float intercept, float *preds)

void	gemmPredict (const raft::handle_t &handle, const double input, size_t n_rows, size_t n_cols, const double coef, double intercept, double *preds)

template<typename T , typename I = int>
void	qnFit (const raft::handle_t &cuml_handle, const qn_params &params, T X, bool X_col_major, T y, I N, I D, I C, T w0, T f, int num_iters, T sample_weight=nullptr, T svr_eps=0)
	Fit a GLM using quasi newton methods. More...

template<typename T , typename I = int>
void	qnFitSparse (const raft::handle_t &cuml_handle, const qn_params &params, T X_values, I X_cols, I X_row_ids, I X_nnz, T y, I N, I D, I C, T w0, T f, int num_iters, T sample_weight=nullptr, T svr_eps=0)
	Fit a GLM using quasi newton methods. More...

template<typename T , typename I = int>
void	qnDecisionFunction (const raft::handle_t &cuml_handle, const qn_params &params, T X, bool X_col_major, I N, I D, I C, T coefs, T *scores)
	Obtain the confidence scores of samples. More...

template<typename T , typename I = int>
void	qnDecisionFunctionSparse (const raft::handle_t &cuml_handle, const qn_params &params, T X_values, I X_cols, I X_row_ids, I X_nnz, I N, I D, I C, T coefs, T *scores)
	Obtain the confidence scores of samples. More...

template<typename T , typename I = int>
void	qnPredict (const raft::handle_t &cuml_handle, const qn_params &params, T X, bool X_col_major, I N, I D, I C, T coefs, T *preds)
	Predict a GLM using quasi newton methods. More...

template<typename T , typename I = int>
void	qnPredictSparse (const raft::handle_t &cuml_handle, const qn_params &params, T X_values, I X_cols, I X_row_ids, I X_nnz, I N, I D, I C, T coefs, T *preds)
	Predict a GLM using quasi newton methods. More...

cumlError_t	cumlSpQnFit (cumlHandle_t cuml_handle, const qn_params pams, float X, float y, int N, int D, int C, float w0, float f, int num_iters, bool X_col_major)

cumlError_t	cumlDpQnFit (cumlHandle_t cuml_handle, const qn_params pams, double X, double y, int N, int D, int C, double w0, double f, int num_iters, bool X_col_major)

Function Documentation

◆ cumlDpQnFit()

cumlError_t ML::GLM::cumlDpQnFit	(	cumlHandle_t	cuml_handle,
		const qn_params *	pams,
		double *	X,
		double *	y,
		int	N,
		int	D,
		int	C,
		double *	w0,
		double *	f,
		int *	num_iters,
		bool	X_col_major
	)

◆ cumlSpQnFit()

cumlError_t ML::GLM::cumlSpQnFit	(	cumlHandle_t	cuml_handle,
		const qn_params *	pams,
		float *	X,
		float *	y,
		int	N,
		int	D,
		int	C,
		float *	w0,
		float *	f,
		int *	num_iters,
		bool	X_col_major
	)

◆ qnDecisionFunction()

template<typename T , typename I = int>

void ML::GLM::qnDecisionFunction	(	const raft::handle_t &	cuml_handle,
		const qn_params &	params,
		T *	X,
		bool	X_col_major,
		I	N,
		I	D,
		I	C,
		T *	coefs,
		T *	scores
	)

Obtain the confidence scores of samples.

Parameters

cuml_handle	reference to raft::handle_t object
params	model parameters
X	device pointer to a contiguous feature matrix of dimension [N, D]
X_col_major	true if X is stored column-major
N	number of examples
D	number of features
C	number of outputs (number of classes or `1` for regression)
coefs	device pointer to model coefficients. Length D if fit_intercept == false else D+1
scores	device pointer to confidence scores of length N (for binary logistic: [0,1], for multinomial: [0,...,C-1])

◆ qnDecisionFunctionSparse()

template<typename T , typename I = int>

void ML::GLM::qnDecisionFunctionSparse	(	const raft::handle_t &	cuml_handle,
		const qn_params &	params,
		T *	X_values,
		I *	X_cols,
		I *	X_row_ids,
		I	X_nnz,
		I	N,
		I	D,
		I	C,
		T *	coefs,
		T *	scores
	)

Obtain the confidence scores of samples.

Parameters

cuml_handle	reference to raft::handle_t object
params	model parameters
X_values	feature matrix values (CSR format), length = X_nnz
X_cols	feature matrix columns (CSR format), length = X_nnz, range = [0, ... D-1]
X_row_ids	feature matrix compressed row ids (CSR format), length = N + 1, range = [0, ... X_nnz]
X_nnz	number of non-zero entries in the feature matrix (CSR format)
N	number of examples
D	number of features
C	number of outputs (number of classes or `1` for regression)
coefs	device pointer to model coefficients. Length D if fit_intercept == false else D+1
scores	device pointer to confidence scores of length N (for binary logistic: [0,1], for multinomial: [0,...,C-1])

◆ qnFit()

template<typename T , typename I = int>

void ML::GLM::qnFit	(	const raft::handle_t &	cuml_handle,
		const qn_params &	params,
		T *	X,
		bool	X_col_major,
		T *	y,
		I	N,
		I	D,
		I	C,
		T *	w0,
		T *	f,
		int *	num_iters,
		T *	sample_weight = `nullptr`,
		T	svr_eps = `0`
	)

Fit a GLM using quasi newton methods.

Parameters

cuml_handle	reference to raft::handle_t object
params	model parameters
X	device pointer to a contiguous feature matrix of dimension [N, D]
X_col_major	true if X is stored column-major
y	device pointer to label vector of length N
N	number of examples
D	number of features
C	number of outputs (number of classes or `1` for regression)
w0	device pointer of size (D + (fit_intercept ? 1 : 0)) * C with initial point, overwritten by final result.
f	host pointer holding the final objective value
num_iters	host pointer holding the actual number of iterations taken
sample_weight	device pointer to sample weight vector of length n_rows (nullptr for uniform weights)
svr_eps	epsilon parameter for svr

◆ qnFitSparse()

template<typename T , typename I = int>

void ML::GLM::qnFitSparse	(	const raft::handle_t &	cuml_handle,
		const qn_params &	params,
		T *	X_values,
		I *	X_cols,
		I *	X_row_ids,
		I	X_nnz,
		T *	y,
		I	N,
		I	D,
		I	C,
		T *	w0,
		T *	f,
		int *	num_iters,
		T *	sample_weight = `nullptr`,
		T	svr_eps = `0`
	)

Fit a GLM using quasi newton methods.

Parameters

cuml_handle	reference to raft::handle_t object
params	model parameters
X_values	feature matrix values (CSR format), length = X_nnz
X_cols	feature matrix columns (CSR format), length = X_nnz, range = [0, ... D-1]
X_row_ids	feature matrix compressed row ids (CSR format), length = N + 1, range = [0, ... X_nnz]
X_nnz	number of non-zero entries in the feature matrix (CSR format)
y	device pointer to label vector of length N
N	number of examples
D	number of features
C	number of outputs (number of classes or `1` for regression)
w0	device pointer of size (D + (fit_intercept ? 1 : 0)) * C with initial point, overwritten by final result.
f	host pointer holding the final objective value
num_iters	host pointer holding the actual number of iterations taken
sample_weight	device pointer to sample weight vector of length n_rows (nullptr for uniform weights)
svr_eps	epsilon parameter for svr

◆ qnPredict()

template<typename T , typename I = int>

void ML::GLM::qnPredict	(	const raft::handle_t &	cuml_handle,
		const qn_params &	params,
		T *	X,
		bool	X_col_major,
		I	N,
		I	D,
		I	C,
		T *	coefs,
		T *	preds
	)

Predict a GLM using quasi newton methods.

Parameters

cuml_handle	reference to raft::handle_t object
params	model parameters
X	device pointer to a contiguous feature matrix of dimension [N, D]
X_col_major	true if X is stored column-major
N	number of examples
D	number of features
C	number of outputs (number of classes or `1` for regression)
coefs	device pointer to model coefficients. Length D if fit_intercept == false else D+1
preds	device pointer to predictions of length N (for binary logistic: [0,1], for multinomial: [0,...,C-1])

◆ qnPredictSparse()

template<typename T , typename I = int>

void ML::GLM::qnPredictSparse	(	const raft::handle_t &	cuml_handle,
		const qn_params &	params,
		T *	X_values,
		I *	X_cols,
		I *	X_row_ids,
		I	X_nnz,
		I	N,
		I	D,
		I	C,
		T *	coefs,
		T *	preds
	)

Predict a GLM using quasi newton methods.

Parameters

cuml_handle	reference to raft::handle_t object
params	model parameters
X_values	feature matrix values (CSR format), length = X_nnz
X_cols	feature matrix columns (CSR format), length = X_nnz, range = [0, ... D-1]
X_row_ids	feature matrix compressed row ids (CSR format), length = N + 1, range = [0, ... X_nnz]
X_nnz	number of non-zero entries in the feature matrix (CSR format)
N	number of examples
D	number of features
C	number of outputs (number of classes or `1` for regression)
coefs	device pointer to model coefficients. Length D if fit_intercept == false else D+1
preds	device pointer to predictions of length N (for binary logistic: [0,1], for multinomial: [0,...,C-1])

Namespaces

Functions

Function Documentation

◆ cumlDpQnFit()

◆ cumlSpQnFit()

◆ qnDecisionFunction()

◆ qnDecisionFunctionSparse()

◆ qnFit()

◆ qnFitSparse()

◆ qnPredict()

◆ qnPredictSparse()