svm.c File Reference

Support Vector Machines (SVM) - Implementation. More...

#include "svm.h"
#include "mathop.h"
#include <string.h>

Include dependency graph for svm.c:

Go to the source code of this file.

Classes
struct	VlSvm_
Defines
#define	SETLOSS(x, y)
Functions
void	_vl_svm_evaluate (VlSvm *self)
void	_vl_svm_sdca_train (VlSvm *self)
void	_vl_svm_sgd_train (VlSvm *self)
void	_vl_svm_update_statistics (VlSvm *self)
void	vl_svm_delete (VlSvm *self)
	Delete object.
double	vl_svm_get_bias (VlSvm const *self)
	Get the value of the bias.
double	vl_svm_get_bias_learning_rate (VlSvm const *self)
	Get the bias leraning rate.
double	vl_svm_get_bias_multiplier (VlSvm const *self)
	Get the bias multiplier.
vl_size	vl_svm_get_diagnostic_frequency (VlSvm const *self)
	Get the diagnostic frequency.
vl_size	vl_svm_get_dimension (VlSvm *self)
	Get the model dimenison.
double	vl_svm_get_epsilon (VlSvm const *self)
	Get the convergence threshold.
vl_size	vl_svm_get_iteration_number (VlSvm const *self)
	Get the current iteration number.
double	vl_svm_get_lambda (VlSvm const *self)
	Get the regularizer parameter lambda.
vl_size	vl_svm_get_max_num_iterations (VlSvm const *self)
	Get the maximum number of iterations.
double const *	vl_svm_get_model (VlSvm const *self)
	Get the SVM model.
vl_size	vl_svm_get_num_data (VlSvm *self)
	Get the number of data samples.
double const *	vl_svm_get_scores (VlSvm const *self)
	Get the scores of the data points.
VlSvmSolverType	vl_svm_get_solver (VlSvm const *self)
	Get the SVM solver type.
VlSvmStatistics const *	vl_svm_get_statistics (VlSvm const *self)
	Get the solver statistics.
double const *	vl_svm_get_weights (VlSvm const *self)
	Get the data weights.
double	vl_svm_hinge2_conjugate_loss (double u, double label)
	SVM square hinge loss conjugate.
double	vl_svm_hinge2_dca_update (double alpha, double inner, double norm2, double label)
	SVM square hinge loss DCA update.
double	vl_svm_hinge2_loss (double inner, double label)
	SVM square hinge loss.
double	vl_svm_hinge2_loss_derivative (double inner, double label)
	SVM square hinge loss derivative.
double	vl_svm_hinge_conjugate_loss (double u, double label)
	SVM hinge loss conjugate.
double	vl_svm_hinge_dca_update (double alpha, double inner, double norm2, double label)
	SVM hinge loss DCA update.
double	vl_svm_hinge_loss (double inner, double label)
	SVM hinge loss.
double	vl_svm_hinge_loss_derivative (double inner, double label)
	SVM hinge loss derivative.
double	vl_svm_l1_conjugate_loss (double u, double label)
	SVM l1 loss conjugate.
double	vl_svm_l1_dca_update (double alpha, double inner, double norm2, double label)
	SVM l1 loss DCA update.
double	vl_svm_l1_loss (double inner, double label)
	SVM l1 loss.
double	vl_svm_l1_loss_derivative (double inner, double label)
	SVM l1 loss derivative.
double	vl_svm_l2_conjugate_loss (double u, double label)
	SVM l2 loss conjugate.
double	vl_svm_l2_dca_update (double alpha, double inner, double norm2, double label)
	SVM l2 loss DCA update.
double	vl_svm_l2_loss (double inner, double label)
	SVM l2 loss.
double	vl_svm_l2_loss_derivative (double inner, double label)
	SVM l2 loss derivative.
double	vl_svm_logistic_conjugate_loss (double u, double label)
	SVM l2 loss conjugate.
double	vl_svm_logistic_dca_update (double alpha, double inner, double norm2, double label)
	SVM l2 loss DCA update.
double	vl_svm_logistic_loss (double inner, double label)
	SVM l2 loss.
double	vl_svm_logistic_loss_derivative (double inner, double label)
	SVM l2 loss derivative.
VlSvm *	vl_svm_new (VlSvmSolverType type, double const *data, vl_size dimension, vl_size numData, double const *labels, double lambda)
	Create a new object with plain data.
VlSvm *	vl_svm_new_with_abstract_data (VlSvmSolverType solver, void *data, vl_size dimension, vl_size numData, double const *labels, double lambda)
	Create a new object with abstract data.
VlSvm *	vl_svm_new_with_dataset (VlSvmSolverType solver, VlSvmDataset *dataset, double const *labels, double lambda)
	Create a new object with a dataset.
void	vl_svm_set_bias (VlSvm *self, double b)
	Set the SVM bias.
void	vl_svm_set_bias_learning_rate (VlSvm *self, double rate)
	Set the bias learning rate.
void	vl_svm_set_bias_multiplier (VlSvm *self, double b)
	Set the bias multiplier.
void	vl_svm_set_conjugate_loss_function (VlSvm *self, VlSvmLossFunction f)
	Set the conjugate loss function callback.
void	vl_svm_set_data_functions (VlSvm *self, VlSvmInnerProductFunction inner, VlSvmAccumulateFunction acc)
	Set the data functions.
void	vl_svm_set_dca_update_function (VlSvm *self, VlSvmDcaUpdateFunction f)
	Set the DCA update function callback.
void	vl_svm_set_diagnostic_frequency (VlSvm *self, vl_size f)
	Set the diagnostic frequency.
void	vl_svm_set_diagnostic_function (VlSvm *self, VlSvmDiagnosticFunction f, void *data)
	Set the diagnostic function callback.
void	vl_svm_set_epsilon (VlSvm *self, double epsilon)
	Set the convergence threshold.
void	vl_svm_set_iteration_number (VlSvm *self, vl_uindex n)
	Set the current iteratio number.
void	vl_svm_set_lambda (VlSvm *self, double lambda)
	Set the regularizer parameter lambda.
void	vl_svm_set_loss (VlSvm *self, VlSvmLossType loss)
	Set the loss function to one of the default types.
void	vl_svm_set_loss_derivative_function (VlSvm *self, VlSvmLossFunction f)
	Set the loss derivative function callback.
void	vl_svm_set_loss_function (VlSvm *self, VlSvmLossFunction f)
	Set the loss function callback.
void	vl_svm_set_max_num_iterations (VlSvm *self, vl_size n)
	Set the maximum number of iterations.
void	vl_svm_set_model (VlSvm *self, double const *model)
	Set the SVM model.
void	vl_svm_set_weights (VlSvm *self, double const *weights)
	Set the data weights.
void	vl_svm_train (VlSvm *self)
	Run the SVM solver.
VL_INLINE double	xlogx (double x)

---

Detailed Description

Support Vector Machines (SVM) - Implementation.

Author:

Milan Sulc

Daniele Perrone

Andrea Vedaldi

Definition in file svm.c.

---

Define Documentation

#define SETLOSS	(		x,
			y
	)

Value:

case VlSvmLoss ## x: \
  vl_svm_set_loss_function(self, vl_svm_ ## y ## _loss) ; \
  vl_svm_set_loss_derivative_function(self, vl_svm_ ## y ## _loss_derivative) ; \
  vl_svm_set_conjugate_loss_function(self, vl_svm_ ## y ## _conjugate_loss) ; \
  vl_svm_set_dca_update_function(self, vl_svm_ ## y ## _dca_update) ; \
  break;

---

Function Documentation

void _vl_svm_evaluate

(

VlSvm *

self

)

Definition at line 1911 of file svm.c.

void _vl_svm_sdca_train

(

VlSvm *

self

)

Definition at line 1931 of file svm.c.

void _vl_svm_sgd_train

(

VlSvm *

self

)

Definition at line 2018 of file svm.c.

void _vl_svm_update_statistics

(

VlSvm *

self

)

Definition at line 1871 of file svm.c.

void vl_svm_delete

(

VlSvm *

self

)

Delete object.

Parameters:

self

object.

See also:

vl_svm_new

Definition at line 1097 of file svm.c.

double vl_svm_get_bias

(

VlSvm const *

self

)

Get the value of the bias.

Parameters:

self

object.

Returns:

bias $b$.

The value of the bias returned already include the effect of bias mutliplier.

Definition at line 1422 of file svm.c.

double vl_svm_get_bias_learning_rate

(

VlSvm const *

self

)

Get the bias leraning rate.

Parameters:

self

object

Returns:

bias learning rate.

Definition at line 1160 of file svm.c.

double vl_svm_get_bias_multiplier

(

VlSvm const *

self

)

Get the bias multiplier.

Parameters:

self

object.

Returns:

bias multiplier.

Definition at line 1186 of file svm.c.

vl_size vl_svm_get_diagnostic_frequency

(

VlSvm const *

self

)

Get the diagnostic frequency.

Parameters:

self

object.

Returns:

diagnostic frequency.

Definition at line 1260 of file svm.c.

vl_size vl_svm_get_dimension

(

VlSvm *

self

)

Get the model dimenison.

Parameters:

self

object.

Returns:

model dimension.

This is the dimensionality of the weight vector $$.

Definition at line 1348 of file svm.c.

double vl_svm_get_epsilon

(

VlSvm const *

self

)

Get the convergence threshold.

Parameters:

self

object

Returns:

epsilon threshold.

Definition at line 1135 of file svm.c.

vl_size vl_svm_get_iteration_number

(

VlSvm const *

self

)

Get the current iteration number.

Parameters:

self

object.

Returns:

current iteration number.

Definition at line 1212 of file svm.c.

double vl_svm_get_lambda

(

VlSvm const *

self

)

Get the regularizer parameter lambda.

Parameters:

self

object.

Returns:

diagnostic frequency.

Definition at line 1300 of file svm.c.

vl_size vl_svm_get_max_num_iterations

(

VlSvm const *

self

)

Get the maximum number of iterations.

Parameters:

self

object.

Returns:

maximum number of iterations.

Definition at line 1234 of file svm.c.

double const* vl_svm_get_model

(

VlSvm const *

self

)

Get the SVM model.

Parameters:

self

object.

Returns:

model.

This is the weight vector $$.

Definition at line 1374 of file svm.c.

vl_size vl_svm_get_num_data

(

VlSvm *

self

)

Get the number of data samples.

Parameters:

self

object.

Returns:

model number of data samples

This is the dimensionality of the weight vector $$.

Definition at line 1361 of file svm.c.

double const* vl_svm_get_scores

(

VlSvm const *

self

)

Get the scores of the data points.

Parameters:

self

object.

Returns:

vector of scores.

After training or during the diagnostic callback, this function can be used to retrieve the scores of the points, i.e. $ , + b$.

Definition at line 1448 of file svm.c.

VlSvmSolverType vl_svm_get_solver

(

VlSvm const *

self

)

Get the SVM solver type.

Parameters:

self

object.

Returns:

SVM solver type.

Definition at line 1271 of file svm.c.

VlSvmStatistics const* vl_svm_get_statistics

(

VlSvm const *

self

)

Get the solver statistics.

Parameters:

self

object.

Returns:

statistics.

Definition at line 1433 of file svm.c.

double const* vl_svm_get_weights

(

VlSvm const *

self

)

Get the data weights.

Parameters:

self

object.

Returns:

data weights.

Definition at line 1331 of file svm.c.

double vl_svm_hinge2_conjugate_loss	(	double	u,
		double	label
	)

SVM square hinge loss conjugate.

Parameters:

u	dual variable.
label	label value.

Returns:

conjugate loss.

Definition at line 1667 of file svm.c.

double vl_svm_hinge2_dca_update	(	double	alpha,
		double	inner,
		double	norm2,
		double	label
	)

SVM square hinge loss DCA update.

Parameters:

alpha	current value of the dual variable.
inner	inner product $^ $ of the sample with the SVM model.
norm2	normalization factor $\|\|^2/ n$.
label	label $y$ of the sample.

Returns:

incremental update $$ of the dual variable.

See also:

Stochastic Dual Coordinate Ascent

Definition at line 1678 of file svm.c.

double vl_svm_hinge2_loss	(	double	inner,
		double	label
	)

SVM square hinge loss.

Parameters:

inner	inner product between sample and model $^ $.
label	sample label $y$.

Returns:

value of the loss.

The interface is the same for a loss function, its derivative, or the conjugate loss.

See also:

SVM fundamentals

Definition at line 1646 of file svm.c.

double vl_svm_hinge2_loss_derivative	(	double	inner,
		double	label
	)

SVM square hinge loss derivative.

Parameters:

inner	inner product between sample and model $^ $.
label	sample label $y$.

Returns:

value of the loss.

The interface is the same for a loss function, its derivative, or the conjugate loss.

See also:

SVM fundamentals

Definition at line 1655 of file svm.c.

double vl_svm_hinge_conjugate_loss	(	double	u,
		double	label
	)

SVM hinge loss conjugate.

Parameters:

u	dual variable.
label	label value.

Returns:

conjugate loss.

Definition at line 1626 of file svm.c.

double vl_svm_hinge_dca_update	(	double	alpha,
		double	inner,
		double	norm2,
		double	label
	)

SVM hinge loss DCA update.

Parameters:

alpha	current value of the dual variable.
inner	inner product $^ $ of the sample with the SVM model.
norm2	normalization factor $\|\|^2/ n$.
label	label $y$ of the sample.

Returns:

incremental update $$ of the dual variable.

See also:

Stochastic Dual Coordinate Ascent

Definition at line 1638 of file svm.c.

double vl_svm_hinge_loss	(	double	inner,
		double	label
	)

SVM hinge loss.

Parameters:

inner	inner product between sample and model $^ $.
label	sample label $y$.

Returns:

value of the loss.

The interface is the same for a loss function, its derivative, or the conjugate loss.

See also:

SVM fundamentals

Definition at line 1603 of file svm.c.

double vl_svm_hinge_loss_derivative	(	double	inner,
		double	label
	)

SVM hinge loss derivative.

Parameters:

inner	inner product between sample and model $^ $.
label	sample label $y$.

Returns:

value of the loss.

The interface is the same for a loss function, its derivative, or the conjugate loss.

See also:

SVM fundamentals

Definition at line 1611 of file svm.c.

double vl_svm_l1_conjugate_loss	(	double	u,
		double	label
	)

SVM l1 loss conjugate.

Parameters:

u	dual variable.
label	label value.

Returns:

conjugate loss.

Definition at line 1706 of file svm.c.

double vl_svm_l1_dca_update	(	double	alpha,
		double	inner,
		double	norm2,
		double	label
	)

SVM l1 loss DCA update.

Parameters:

alpha	current value of the dual variable.
inner	inner product $^ $ of the sample with the SVM model.
norm2	normalization factor $\|\|^2/ n$.
label	label $y$ of the sample.

Returns:

incremental update $$ of the dual variable.

See also:

Stochastic Dual Coordinate Ascent

Definition at line 1717 of file svm.c.

double vl_svm_l1_loss	(	double	inner,
		double	label
	)

SVM l1 loss.

Parameters:

inner	inner product between sample and model $^ $.
label	sample label $y$.

Returns:

value of the loss.

The interface is the same for a loss function, its derivative, or the conjugate loss.

See also:

SVM fundamentals

Definition at line 1686 of file svm.c.

double vl_svm_l1_loss_derivative	(	double	inner,
		double	label
	)

SVM l1 loss derivative.

Parameters:

inner	inner product between sample and model $^ $.
label	sample label $y$.

Returns:

value of the loss.

The interface is the same for a loss function, its derivative, or the conjugate loss.

See also:

SVM fundamentals

Definition at line 1694 of file svm.c.

double vl_svm_l2_conjugate_loss	(	double	u,
		double	label
	)

SVM l2 loss conjugate.

Parameters:

u	dual variable.
label	label value.

Returns:

conjugate loss.

Definition at line 1746 of file svm.c.

double vl_svm_l2_dca_update	(	double	alpha,
		double	inner,
		double	norm2,
		double	label
	)

SVM l2 loss DCA update.

Parameters:

alpha	current value of the dual variable.
inner	inner product $^ $ of the sample with the SVM model.
norm2	normalization factor $\|\|^2/ n$.
label	label $y$ of the sample.

Returns:

incremental update $$ of the dual variable.

See also:

Stochastic Dual Coordinate Ascent

Definition at line 1753 of file svm.c.

double vl_svm_l2_loss	(	double	inner,
		double	label
	)

SVM l2 loss.

Parameters:

inner	inner product between sample and model $^ $.
label	sample label $y$.

Returns:

value of the loss.

The interface is the same for a loss function, its derivative, or the conjugate loss.

See also:

SVM fundamentals

Definition at line 1729 of file svm.c.

double vl_svm_l2_loss_derivative	(	double	inner,
		double	label
	)

SVM l2 loss derivative.

Parameters:

inner	inner product between sample and model $^ $.
label	sample label $y$.

Returns:

value of the loss.

The interface is the same for a loss function, its derivative, or the conjugate loss.

See also:

SVM fundamentals

Definition at line 1738 of file svm.c.

double vl_svm_logistic_conjugate_loss	(	double	u,
		double	label
	)

SVM l2 loss conjugate.

Parameters:

u	dual variable.
label	label value.

Returns:

conjugate loss.

Definition at line 1789 of file svm.c.

double vl_svm_logistic_dca_update	(	double	alpha,
		double	inner,
		double	norm2,
		double	label
	)

SVM l2 loss DCA update.

Parameters:

alpha	current value of the dual variable.
inner	inner product $^ $ of the sample with the SVM model.
norm2	normalization factor $\|\|^2/ n$.
label	label $y$ of the sample.

Returns:

incremental update $$ of the dual variable.

See also:

Stochastic Dual Coordinate Ascent

Definition at line 1801 of file svm.c.

double vl_svm_logistic_loss	(	double	inner,
		double	label
	)

SVM l2 loss.

Parameters:

inner	inner product between sample and model $^ $.
label	sample label $y$.

Returns:

value of the loss.

The interface is the same for a loss function, its derivative, or the conjugate loss.

See also:

SVM fundamentals

Definition at line 1760 of file svm.c.

double vl_svm_logistic_loss_derivative	(	double	inner,
		double	label
	)

SVM l2 loss derivative.

Parameters:

inner	inner product between sample and model $^ $.
label	sample label $y$.

Returns:

value of the loss.

The interface is the same for a loss function, its derivative, or the conjugate loss.

See also:

SVM fundamentals

Definition at line 1773 of file svm.c.

VlSvm* vl_svm_new	(	VlSvmSolverType	type,
		double const *	data,
		vl_size	dimension,
		vl_size	numData,
		double const *	labels,
		double	lambda
	)

Create a new object with plain data.

Parameters:

type	type of SMV solver.
data	a pointer to a matrix of data.
dimension	dimension of the SVM model.
numData	number of training samples.
labels	training labels.
lambda	regularizer parameter.

Returns:

the new object.

data has one column per sample, in double format. More advanced inputs can be used with vl_svm_new_with_dataset and vl_svm_new_with_abstract_data.

See also:

vl_svm_delete

Definition at line 957 of file svm.c.

VlSvm* vl_svm_new_with_abstract_data	(	VlSvmSolverType	solver,
		void *	data,
		vl_size	dimension,
		vl_size	numData,
		double const *	labels,
		double	lambda
	)

Create a new object with abstract data.

Parameters:

solver	type of SMV solver.
data	pointer to the data.
dimension	dimension of the SVM model.
numData	num training samples.
labels	training samples labels.
lambda	regularizer parameter.

Returns:

the new object.

After calling this function, vl_svm_set_data_functions *must* be used to setup suitable callbacks for the inner product and accumulation operations (

See also:

svm-data-abstraction).

vl_svm_delete

Definition at line 1014 of file svm.c.

VlSvm* vl_svm_new_with_dataset	(	VlSvmSolverType	solver,
		VlSvmDataset *	dataset,
		double const *	labels,
		double	lambda
	)

Create a new object with a dataset.

Parameters:

solver	type of SMV solver.
dataset	SVM dataset object
labels	training samples labels.
lambda	regularizer parameter.

Returns:

the new object.

See also:

vl_svm_delete

Definition at line 980 of file svm.c.

void vl_svm_set_bias	(	VlSvm *	self,
		double	b
	)

Set the SVM bias.

Parameters:

self	object.
b	bias.

The function set the internal representation of the SVM bias to be equal to b (the bias multiplier is applied). The same remark that applies to vl_svm_set_model applies here too.

Definition at line 1406 of file svm.c.

void vl_svm_set_bias_learning_rate	(	VlSvm *	self,
		double	rate
	)

Set the bias learning rate.

Parameters:

self	object
rate	bias learning rate (positive).

This parameter applies only to the SGD solver.

Definition at line 1148 of file svm.c.

void vl_svm_set_bias_multiplier	(	VlSvm *	self,
		double	b
	)

Set the bias multiplier.

Parameters:

self	object
b	bias multiplier.

The *bias multiplier* is the value of the constant feature appended to the data vectors to implement the bias (Adding a bias).

Definition at line 1174 of file svm.c.

void vl_svm_set_conjugate_loss_function	(	VlSvm *	self,
		VlSvmLossFunction	f
	)

Set the conjugate loss function callback.

Parameters:

self	object.
f	loss function callback.

Note that setting up a loss requires specifying more than just one callback. See Loss functions for details.

.

Definition at line 1529 of file svm.c.

void vl_svm_set_data_functions	(	VlSvm *	self,
		VlSvmInnerProductFunction	inner,
		VlSvmAccumulateFunction	acc
	)

Set the data functions.

Parameters:

self	object.
inner	inner product function.
acc	accumulate function.

See svm-data-abstraction.

Definition at line 1492 of file svm.c.

void vl_svm_set_dca_update_function	(	VlSvm *	self,
		VlSvmDcaUpdateFunction	f
	)

Set the DCA update function callback.

Parameters:

self	object.
f	loss function callback.

Note that setting up a loss requires specifying more than just one callback. See Loss functions for details.

.

Definition at line 1539 of file svm.c.

void vl_svm_set_diagnostic_frequency	(	VlSvm *	self,
		vl_size	f
	)

Set the diagnostic frequency.

Parameters:

self	object.
f	diagnostic frequency (>= 1).

A diagnostic round (to test for convergence and to printout information) is performed every f iterations.

Definition at line 1248 of file svm.c.

void vl_svm_set_diagnostic_function	(	VlSvm *	self,
		VlSvmDiagnosticFunction	f,
		void *	data
	)

Set the diagnostic function callback.

Parameters:

self	object.
f	diagnostic function pointer.
data	pointer to data used by the diagnostic function.

Definition at line 1479 of file svm.c.

void vl_svm_set_epsilon	(	VlSvm *	self,
		double	epsilon
	)

Set the convergence threshold.

Parameters:

self	object
epsilon	threshold (non-negative).

Definition at line 1123 of file svm.c.

void vl_svm_set_iteration_number	(	VlSvm *	self,
		vl_uindex	n
	)

Set the current iteratio number.

Parameters:

self	object.
n	iteration number.

If called before training, this can be used with SGD for a warm start, as the net effect is to slow down the learning rate.

Definition at line 1201 of file svm.c.

void vl_svm_set_lambda	(	VlSvm *	self,
		double	lambda
	)

Set the regularizer parameter lambda.

Parameters:

self	object.
lambda	regularizer parameter.

Note that lambda is usually set when calling a constructor for VlSvm as certain parameters, such as the maximum number of iterations, are tuned accordingly. This tuning is not performed when lambda is changed using this function.

Definition at line 1288 of file svm.c.

void vl_svm_set_loss	(	VlSvm *	self,
		VlSvmLossType	loss
	)

Set the loss function to one of the default types.

Parameters:

self	object.
loss	type of loss function.

See also:

Loss functions.

Definition at line 1552 of file svm.c.

void vl_svm_set_loss_derivative_function	(	VlSvm *	self,
		VlSvmLossFunction	f
	)

Set the loss derivative function callback.

Parameters:

self	object.
f	loss function callback.

Note that setting up a loss requires specifying more than just one callback. See Loss functions for details.

.

Definition at line 1519 of file svm.c.

void vl_svm_set_loss_function	(	VlSvm *	self,
		VlSvmLossFunction	f
	)

Set the loss function callback.

Parameters:

self	object.
f	loss function callback.

Note that setting up a loss requires specifying more than just one callback. See Loss functions for details.

Definition at line 1509 of file svm.c.

void vl_svm_set_max_num_iterations	(	VlSvm *	self,
		vl_size	n
	)

Set the maximum number of iterations.

Parameters:

self	object.
n	maximum number of iterations.

Definition at line 1223 of file svm.c.

void vl_svm_set_model	(	VlSvm *	self,
		double const *	model
	)

Set the SVM model.

Parameters:

self	object.
model	model.

The function *copies* the content of the vector model to the internal model buffer. This operation can be used for warm start with the SGD algorithm, but has undefined effect with the SDCA algorithm.

Definition at line 1389 of file svm.c.

void vl_svm_set_weights	(	VlSvm *	self,
		double const *	weights
	)

Set the data weights.

Parameters:

self	object.
weights	data weights.

weights must be an array of non-negative weights. The loss of each data point is multiplied by the corresponding weight.

Set weights to NULL to weight the data uniformly by 1 (default).

Note that the weights array is *not* copied and must be valid througout the object lifetime (unless it is replaced).

Definition at line 1320 of file svm.c.

void vl_svm_train

(

VlSvm *

self

)

Run the SVM solver.

Parameters:

self

object.

The data on which the SVM operates is passed upon the cration of the VlSvm object. This function runs a solver to learn a corresponding model. See Getting started.

Definition at line 2156 of file svm.c.

VL_INLINE double xlogx

(

double

x

)

Definition at line 1780 of file svm.c.