online_sv.sql_in File Reference

SQL functions for support vector machines. More...

Functions
float8	svm_dot (float8[] x, float8[] y)
	Dot product kernel function. More...
float8	svm_dot (float8[] x, float8[] y, float8 placeholder)
float8	svm_polynomial (float8[] x, float8[] y, float8 degree)
	Polynomial kernel function. More...
float8	svm_gaussian (float8[] x, float8[] y, float8 gamma)
	Gaussian kernel function. More...
float8	svm_predict_sub (int, int, float8[], float8[], float8[], text, float8)
float8	svm_predict (svm_model_rec svs, float8[] ind, text kernel, float8 kernel_param)
svm_model_rec	svm_reg_update (svm_model_rec svs, float8[] ind, float8 label, text kernel, float8 eta, float8 nu, float8 slambda, float8 kernel_param)
aggregate svm_model_rec	svm_reg_agg (float8[], float8, text, float8, float8, float8, float8)
svm_model_rec	svm_cls_update (svm_model_rec svs, float8[] ind, float8 label, text kernel, float8 eta, float8 nu, float8 kernel_param)
aggregate svm_model_rec	svm_cls_agg (float8[], float8, text, float8, float8, float8)
svm_model_rec	svm_nd_update (svm_model_rec svs, float8[] ind, text kernel, float8 eta, float8 nu, float8 kernel_param)
aggregate svm_model_rec	svm_nd_agg (float8[], text, float8, float8, float8)
void	svm_store_model (text model_temp_table, text model_name, text model_table)
void	svm_drop_model (text model_table)
	Drops all tables pertaining to a model. More...
set< svm_reg_result >	svm_regression (text input_table, text model_table, bool parallel, text kernel_func)
	This is the support vector regression function. More...
set< svm_reg_result >	svm_regression (text input_table, text model_table, bool parallel, text kernel_func, bool verbose, float8 eta, float8 nu, float8 slambda, float8 kernel_param)
	This is the support vector regression function. More...
set< svm_reg_result >	svm_regression (text input_table, text model_table, bool parallel, text kernel_func, bool verbose, float8 eta, float8 nu, float8 slambda)
	This is the support vector regression function. More...
set< svm_cls_result >	svm_classification (text input_table, text model_table, bool parallel, text kernel_func, float8 kernel_param)
	This is the support vector classification function. More...
set< svm_cls_result >	svm_classification (text input_table, text model_table, bool parallel, text kernel_func)
set< svm_cls_result >	svm_classification (text input_table, text model_table, bool parallel, text kernel_func, bool verbose, float8 eta, float8 nu, float8 kernel_param)
	This is the support vector classification function. More...
set< svm_cls_result >	svm_classification (text input_table, text model_table, bool parallel, text kernel_func, bool verbose, float8 eta, float8 nu)
set< svm_nd_result >	svm_novelty_detection (text input_table, text model_table, bool parallel, text kernel_func)
	This is the support vector novelty detection function. More...
set< svm_nd_result >	svm_novelty_detection (text input_table, text model_table, bool parallel, text kernel_func, bool verbose, float8 eta, float8 nu, float8 kernel_param)
	This is the support vector novelty detection function. More...
set< svm_nd_result >	svm_novelty_detection (text input_table, text model_table, bool parallel, text kernel_func, bool verbose, float8 eta, float8 nu)
	This is the support vector novelty detection function. More...
text	svm_predict_batch (text input_table, text data_col, text id_col, text model_table, text output_table, bool parallel)
	Scores the data points stored in a table using a learned support-vector model. More...
float8[]	__svm_random_ind (int d)
float8[]	__svm_random_ind2 (int d)
void	svm_generate_reg_data (text output_table, int num, int dim)
float8	__svm_target_reg_func (float8[] ind)
void	svm_generate_cls_data (text output_table, int num, int dim)
float8	__svm_target_cl_func (float8[] ind)
void	svm_generate_nd_data (text output_table, int num, int dim)
void	svm_data_normalization (text input_table)
	Normalizes the data stored in a table, and save the normalized data in a new table. More...

Detailed Description

See also

For an introduction to Support vector machines (SVMs) and related kernel methods, see the module description Support Vector Machines.

Function Documentation

float8 [] __svm_random_ind

(

int

d

)

float8 [] __svm_random_ind2

(

int

d

)

float8 __svm_target_cl_func

(

float8[]

ind

)

float8 __svm_target_reg_func

(

float8[]

ind

)

set<svm_cls_result> svm_classification	(	text	input_table,
		text	model_table,
		bool	parallel,
		text	kernel_func,
		float8	kernel_param
	)

Parameters

input_table	The name of the table/view with the training data
model_table	The name of the table under which we want to store the learned model
parallel	A flag indicating whether the system should learn multiple models in parallel
kernel_func	Kernel function

Returns

A summary of the learning process

set<svm_cls_result> svm_classification	(	text	input_table,
		text	model_table,
		bool	parallel,
		text	kernel_func
	)

set<svm_cls_result> svm_classification	(	text	input_table,
		text	model_table,
		bool	parallel,
		text	kernel_func,
		bool	verbose,
		float8	eta,
		float8	nu,
		float8	kernel_param
	)

Parameters

input_table	The name of the table/view with the training data
model_table	The name of the table under which we want to store the learned model
parallel	A flag indicating whether the system should learn multiple models in parallel
kernel_func	Kernel function
verbose	Verbosity of reporting
eta	Learning rate in (0,1]
nu	Compression parameter in (0,1] associated with the fraction of training data that will become support vectors
kernel_param	Default parameter for kernel function

Returns

A summary of the learning process

set<svm_cls_result> svm_classification	(	text	input_table,
		text	model_table,
		bool	parallel,
		text	kernel_func,
		bool	verbose,
		float8	eta,
		float8	nu
	)

aggregate svm_model_rec svm_cls_agg	(	float8	[],
		float8	,
		text	,
		float8	,
		float8	,
		float8
	)

svm_model_rec svm_cls_update	(	svm_model_rec	svs,
		float8[]	ind,
		float8	label,
		text	kernel,
		float8	eta,
		float8	nu,
		float8	kernel_param
	)

void svm_data_normalization

(

text

input_table

)

Parameters

input_table

Name of table/view containing the data points to be scored

float8 svm_dot	(	float8[]	x,
		float8[]	y
	)

Parameters

x	The data point \( \boldsymbol x \)
y	The data point \( \boldsymbol y \)

Returns

Returns dot product of the two data points.

float8 svm_dot	(	float8[]	x,
		float8[]	y,
		float8	placeholder
	)

void svm_drop_model

(

text

model_table

)

Parameters

model_table

The table to be dropped.

float8 svm_gaussian	(	float8[]	x,
		float8[]	y,
		float8	gamma
	)

Parameters

x	The data point \( \boldsymbol x \)
y	The data point \( \boldsymbol y \)
gamma	The spread \( \gamma \)

Returns

Returns \( K(\boldsymbol x,\boldsymbol y)=exp(-\gamma || \boldsymbol x \cdot \boldsymbol y ||^2 ) \)

void svm_generate_cls_data	(	text	output_table,
		int	num,
		int	dim
	)

void svm_generate_nd_data	(	text	output_table,
		int	num,
		int	dim
	)

void svm_generate_reg_data	(	text	output_table,
		int	num,
		int	dim
	)

aggregate svm_model_rec svm_nd_agg	(	float8	[],
		text	,
		float8	,
		float8	,
		float8
	)

svm_model_rec svm_nd_update	(	svm_model_rec	svs,
		float8[]	ind,
		text	kernel,
		float8	eta,
		float8	nu,
		float8	kernel_param
	)

set<svm_nd_result> svm_novelty_detection	(	text	input_table,
		text	model_table,
		bool	parallel,
		text	kernel_func
	)

Parameters

input_table	The name of the table/view with the training data
model_table	The name of the table under which we want to store the learned model
parallel	A flag indicating whether the system should learn multiple models in parallel
kernel_func	Kernel function

Returns

A summary of the learning process

set<svm_nd_result> svm_novelty_detection	(	text	input_table,
		text	model_table,
		bool	parallel,
		text	kernel_func,
		bool	verbose,
		float8	eta,
		float8	nu,
		float8	kernel_param
	)

Parameters

input_table	The name of the table/view with the training data
model_table	The name of the table under which we want to store the learned model
parallel	A flag indicating whether the system should learn multiple models in parallel
kernel_func	Kernel function
verbose	Verbosity of reporting
eta	Learning rate in (0,1]
nu	Compression parameter in (0,1] associated with the fraction of training data that will become support vectors
kernel_param	Default parameter for kernel function
kernel_param

Returns

A summary of the learning process

set<svm_nd_result> svm_novelty_detection	(	text	input_table,
		text	model_table,
		bool	parallel,
		text	kernel_func,
		bool	verbose,
		float8	eta,
		float8	nu
	)

Parameters

input_table	The name of the table/view with the training data
model_table	The name of the table under which we want to store the learned model
parallel	A flag indicating whether the system should learn multiple models in parallel
kernel_func	Kernel function
verbose	Verbosity of reporting
eta	Learning rate in (0,1]
nu	Compression parameter in (0,1] associated with the fraction of training data that will become support vectors
kernel_param	Default parameter for kernel function
kernel_param

Returns

A summary of the learning process

float8 svm_polynomial	(	float8[]	x,
		float8[]	y,
		float8	degree
	)

Parameters

x	The data point \( \boldsymbol x \)
y	The data point \( \boldsymbol y \)
degree	The degree \( d \)

Returns

Returns \( K(\boldsymbol x,\boldsymbol y)=(\boldsymbol x \cdot \boldsymbol y)^d \)

float8 svm_predict	(	svm_model_rec	svs,
		float8[]	ind,
		text	kernel,
		float8	kernel_param
	)

text svm_predict_batch	(	text	input_table,
		text	data_col,
		text	id_col,
		text	model_table,
		text	output_table,
		bool	parallel
	)

Parameters

input_table	Name of table/view containing the data points to be scored
data_col	Name of column in input_table containing the data points
id_col	Name of column in input_table containing the integer identifier of data points
model_table	Name of table where the learned model to be used is stored
output_table	Name of table to store the results
parallel	A flag indicating whether the model to be used was learned in parallel

Returns

Textual summary of the algorithm run

float8 svm_predict_sub	(	int	,
		int	,
		float8	[],
		float8	[],
		float8	[],
		text	,
		float8
	)

aggregate svm_model_rec svm_reg_agg	(	float8	[],
		float8	,
		text	,
		float8	,
		float8	,
		float8	,
		float8
	)

svm_model_rec svm_reg_update	(	svm_model_rec	svs,
		float8[]	ind,
		float8	label,
		text	kernel,
		float8	eta,
		float8	nu,
		float8	slambda,
		float8	kernel_param
	)

set<svm_reg_result> svm_regression	(	text	input_table,
		text	model_table,
		bool	parallel,
		text	kernel_func
	)

Parameters

input_table	The name of the table/view with the training data
model_table	The name of the table under which we want to store the learned model
parallel	A flag indicating whether the system should learn multiple models in parallel
kernel_func	Kernel function

Returns

A summary of the learning process

set<svm_reg_result> svm_regression	(	text	input_table,
		text	model_table,
		bool	parallel,
		text	kernel_func,
		bool	verbose,
		float8	eta,
		float8	nu,
		float8	slambda,
		float8	kernel_param
	)

Parameters

input_table	The name of the table/view with the training data
model_table	The name of the table under which we want to store the learned model
parallel	A flag indicating whether the system should learn multiple models in parallel
kernel_func	Kernel function
verbose	Verbosity of reporting
eta	Learning rate in (0,1]
nu	Compression parameter in (0,1] associated with the fraction of training data that will become support vectors
slambda	Regularisation parameter
kernel_param	Default parameter for kernel function

Returns

A summary of the learning process

set<svm_reg_result> svm_regression	(	text	input_table,
		text	model_table,
		bool	parallel,
		text	kernel_func,
		bool	verbose,
		float8	eta,
		float8	nu,
		float8	slambda
	)

Parameters

input_table	The name of the table/view with the training data
model_table	The name of the table under which we want to store the learned model
parallel	A flag indicating whether the system should learn multiple models in parallel
kernel_func	Kernel function
verbose	Verbosity of reporting
eta	Learning rate in (0,1]
nu	Compression parameter in (0,1] associated with the fraction of training data that will become support vectors
slambda	Regularisation parameter
kernel_param	Default parameter for kernel function

Returns

A summary of the learning process

void svm_store_model	(	text	model_temp_table,
		text	model_name,
		text	model_table
	)