Functions
cumlError_t	cumlSpSvcFit (cumlHandle_t handle, float input, int n_rows, int n_cols, float labels, float C, float cache_size, int max_iter, int nochange_steps, float tol, int verbosity, cumlSvmKernelType kernel, int degree, float gamma, float coef0, int n_support, float b, float dual_coefs, float x_support, int *support_idx, int n_classes, float **unique_labels)

cumlError_t	cumlDpSvcFit (cumlHandle_t handle, double input, int n_rows, int n_cols, double labels, double C, double cache_size, int max_iter, int nochange_steps, double tol, int verbosity, cumlSvmKernelType kernel, int degree, double gamma, double coef0, int n_support, double b, double dual_coefs, double x_support, int *support_idx, int n_classes, double **unique_labels)

cumlError_t	cumlSpSvcPredict (cumlHandle_t handle, float input, int n_rows, int n_cols, cumlSvmKernelType kernel, int degree, float gamma, float coef0, int n_support, float b, float dual_coefs, float x_support, int n_classes, float unique_labels, float *preds, float buffer_size, int predict_class)

cumlError_t	cumlDpSvcPredict (cumlHandle_t handle, double input, int n_rows, int n_cols, cumlSvmKernelType kernel, int degree, double gamma, double coef0, int n_support, double b, double dual_coefs, double x_support, int n_classes, double unique_labels, double *preds, double buffer_size, int predict_class)

Detailed Description

The output device buffers shall be unallocated on entry. Note that n_support, n_classes and b are host scalars, all other output pointers are device pointers.

Parameters

[in]	handle	the cuML handle
[in]	input	device pointer for the input data in column major format. Size n_rows x n_cols.
[in]	n_rows	number of rows
[in]	n_cols	number of columns
[in]	labels	device pointer for the labels. Size n_rows.
[in]	C	penalty term
[in]	cache_size	size of kernel cache in device memory (MiB)
[in]	max_iter	maximum number of outer iterations in SmoSolver
[in]	nochange_steps	max number of outer iterations without change in convergence
[in]	tol	tolerance to stop fitting
[in]	verbosity	Fine grained control over logging of useful information as algorithm executes. Currently passing anything greater than or equal to CUML_LEVEL_INFO will make it execute quietly
[in]	kernel	type of kernel (LINEAR, POLYNOMIAL, RBF or TANH)
[in]	degree	of polynomial kernel (ignored by others)
[in]	gamma	multiplier in the RBF, POLYNOMIAL and TANH kernels
[in]	coef0	additive constant in poly and tanh kernels
[out]	n_support	number of support vectors
[out]	b	constant used in the decision function
[out]	dual_coefs	non-zero dual coefficients, size [n_support].
[out]	x_support	support vectors in column major format. Size [n_support x n_cols].
[out]	support_idx	indices (from the traning set) of the support vectors, size [n_support].
[out]	n_classes	number of classes found in the input labels
[out]	unique_labels	device pointer for the unique classes, size [n_classes]

Returns: CUML_SUCCESS on success and other corresponding flags upon any failures.

The output preds array shall be allocated on entry.

Parameters

[in]	handle	the cuML handle
[in]	input	device pointer for the input data in column major format. Size n_rows x n_cols.
[in]	n_rows	number of rows
[in]	n_cols	number of columns
[in]	kernel	type of kernel (LINEAR, POLYNOMIAL, RBF or TANH)
[in]	degree	of polynomial kernel (ignored by others)
[in]	gamma	multiplier in the RBF, POLYNOMIAL and TANH kernels
[in]	coef0	additive constant in poly and tanh kernels
[in]	n_support	number of support vectors
[in]	b	constant used in the decision function
[in]	dual_coefs	non-zero dual coefficients, size [n_support].
[in]	x_support	support vectors in column major format. Size [n_support x n_cols].
[in]	n_classes	number of classes found in the input labels
[in]	unique_labels	device pointer for the unique classes, size [n_classes]
[out]	preds	device pointer for the predictions. Size [n_rows].
[in]	buffer_size	size of temporary buffer in MiB
[in]	predict_class	whether to predict class label (true), or just return the decision function value (false)

Returns: CUML_SUCCESS on success and other corresponding flags upon any failures.

Function Documentation

◆ cumlDpSvcFit()

cumlError_t cumlDpSvcFit	(	cumlHandle_t	handle,
		double *	input,
		int	n_rows,
		int	n_cols,
		double *	labels,
		double	C,
		double	cache_size,
		int	max_iter,
		int	nochange_steps,
		double	tol,
		int	verbosity,
		cumlSvmKernelType	kernel,
		int	degree,
		double	gamma,
		double	coef0,
		int *	n_support,
		double *	b,
		double **	dual_coefs,
		double **	x_support,
		int **	support_idx,
		int *	n_classes,
		double **	unique_labels
	)

◆ cumlDpSvcPredict()

cumlError_t cumlDpSvcPredict	(	cumlHandle_t	handle,
		double *	input,
		int	n_rows,
		int	n_cols,
		cumlSvmKernelType	kernel,
		int	degree,
		double	gamma,
		double	coef0,
		int	n_support,
		double	b,
		double *	dual_coefs,
		double *	x_support,
		int	n_classes,
		double *	unique_labels,
		double *	preds,
		double	buffer_size,
		int	predict_class
	)

◆ cumlSpSvcFit()

cumlError_t cumlSpSvcFit	(	cumlHandle_t	handle,
		float *	input,
		int	n_rows,
		int	n_cols,
		float *	labels,
		float	C,
		float	cache_size,
		int	max_iter,
		int	nochange_steps,
		float	tol,
		int	verbosity,
		cumlSvmKernelType	kernel,
		int	degree,
		float	gamma,
		float	coef0,
		int *	n_support,
		float *	b,
		float **	dual_coefs,
		float **	x_support,
		int **	support_idx,
		int *	n_classes,
		float **	unique_labels
	)

◆ cumlSpSvcPredict()