SQL functions for linear support vector machines. More...

Functions
float8[]	linear_svm_igd_transition (float8[] state, float8[] ind_var, boolean dep_var, float8[] previous_state, integer dimension, float8 stepsize, float8 reg)

float8[]	linear_svm_igd_merge (float8[] state1, float8[] state2)

float8[]	linear_svm_igd_final (float8[] state)

aggregate float8[]	linear_svm_igd_step_serial (float8[] ind_var, boolean dep_var, float8[] previous_state, integer dimension, float8 stepsize, float8 reg)

aggregate float8[]	linear_svm_igd_step (float8[] ind_var, boolean dep_var, float8[] previous_state, integer dimension, float8 stepsize, float8 reg)

float8	internal_linear_svm_igd_distance (float8[] state1, float8[] state2)

linear_svm_result	internal_linear_svm_igd_result (float8[] state)

set< lsvm_sgd_result >	lsvm_classification (text input_table, text model_table, bool parallel, bool verbose, float8 eta, float8 reg, int max_iter)
	This is the linear support vector classification function. More...

set< lsvm_sgd_result >	lsvm_classification (text input_table, text model_table, bool parallel, bool verbose, float8 eta, float8 reg)
	This is the linear support vector classification function. More...

set< lsvm_sgd_result >	lsvm_classification (text input_table, text model_table, bool parallel)
	This is the linear support vector classification function. More...

set< lsvm_sgd_result >	lsvm_classification (text input_table, text model_table)
	This is the linear support vector classification function. More...

text	lsvm_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 linear support-vector model. More...

text	lsvm_predict_batch (text input_table, text data_col, text id_col, text model_table, text output_table)
	Scores the data points stored in a table using a learned linear support-vector model. More...

float8	lsvm_predict (float8[] coefficients, float8[] ind_var)
	Evaluates a linear support-vector model on a given data point. 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 internal_linear_svm_igd_distance	(	float8[]	state1,
		float8[]	state2
	)

linear_svm_result internal_linear_svm_igd_result ( float8[] state )

float8 [] linear_svm_igd_final ( float8[] state )

float8 [] linear_svm_igd_merge	(	float8[]	state1,
		float8[]	state2
	)

aggregate float8 [] linear_svm_igd_step	(	float8[]	ind_var,
		boolean	dep_var,
		float8[]	previous_state,
		integer	dimension,
		float8	stepsize,
		float8	reg
	)

aggregate float8 [] linear_svm_igd_step_serial	(	float8[]	ind_var,
		boolean	dep_var,
		float8[]	previous_state,
		integer	dimension,
		float8	stepsize,
		float8	reg
	)

float8 [] linear_svm_igd_transition	(	float8[]	state,
		float8[]	ind_var,
		boolean	dep_var,
		float8[]	previous_state,
		integer	dimension,
		float8	stepsize,
		float8	reg
	)

set<lsvm_sgd_result> lsvm_classification	(	text	input_table,
		text	model_table,
		bool	parallel,
		bool	verbose,
		float8	eta,
		float8	reg,
		int	max_iter
	)

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 (Defult: False)
verbose	Verbosity of reporting (Default: False)
eta	Initial learning rate in (0,1] (Default: 0.1)
reg	Regularization parameter, often chosen by cross-validation (Default: 0.001)
max_iter	Maximum number of iterations to run learning algorithm (Default: 100)

Returns: A summary of the learning process

set<lsvm_sgd_result> lsvm_classification	(	text	input_table,
		text	model_table,
		bool	parallel,
		bool	verbose,
		float8	eta,
		float8	reg
	)

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 (Defult: False)
verbose	Verbosity of reporting (Default: False)
eta	Initial learning rate in (0,1] (Default: 0.1)
reg	Regularization parameter, often chosen by cross-validation (Default: 0.001)

Returns: A summary of the learning process

set<lsvm_sgd_result> lsvm_classification	(	text	input_table,
		text	model_table,
		bool	parallel
	)

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 (Default: false)

Returns: A summary of the learning process

set<lsvm_sgd_result> lsvm_classification	(	text	input_table,
		text	model_table
	)

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

Returns: A summary of the learning process

float8 lsvm_predict	(	float8[]	coefficients,
		float8[]	ind_var
	)

Parameters

coefficients	The learned model coefficients
ind_var	The independent variables of the instance to be predicted

Returns: This function returns 1 or -1

text lsvm_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	Deprecated and ignored boolean flag. (Default: NULL)

Returns: Textual summary of the algorithm run

text lsvm_predict_batch	(	text	input_table,
		text	data_col,
		text	id_col,
		text	model_table,
		text	output_table
	)

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

Returns: Textual summary of the algorithm run

Functions

Detailed Description

Function Documentation