Support Vector Machines (Support Vector Machines (SVM)) More...
Data Structures | |
struct | VlSvmStatistics |
SVM statistics This structure contains statistics characterising the state of the SVM solver, such as the current value of the objective function. More... | |
Typedefs | |
typedef OPAQUE | VlSvm |
SVM solver. This object implements VLFeat SVM solvers (see svm.h). | |
Enumerations | |
enum | VlSvmSolverType { VlSvmSolverNone = 0, VlSvmSolverSgd = 1, VlSvmSolverSdca } |
Type of SVM solver. More... | |
enum | VlSvmLossType { VlSvmLossHinge = 0, VlSvmLossHinge2 = 1, VlSvmLossL1, VlSvmLossL2, VlSvmLossLogistic } |
Type of SVM loss. More... | |
enum | VlSvmSolverStatus { VlSvmStatusTraining = 1, VlSvmStatusConverged, VlSvmStatusMaxNumIterationsReached } |
Solver status. More... | |
Functions | |
Create and destroy | |
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. More... | |
VlSvm * | vl_svm_new_with_dataset (VlSvmSolverType type, VlSvmDataset *dataset, double const *labels, double lambda) |
Create a new object with a dataset. More... | |
VlSvm * | vl_svm_new_with_abstract_data (VlSvmSolverType type, void *data, vl_size dimension, vl_size numData, double const *labels, double lambda) |
Create a new object with abstract data. More... | |
void | vl_svm_delete (VlSvm *self) |
Delete object. More... | |
Retrieve parameters and data | |
VlSvmStatistics const * | vl_svm_get_statistics (VlSvm const *self) |
Get the solver statistics. More... | |
double const * | vl_svm_get_model (VlSvm const *self) |
Get the SVM model. More... | |
double | vl_svm_get_bias (VlSvm const *self) |
Get the value of the bias. More... | |
vl_size | vl_svm_get_dimension (VlSvm *self) |
Get the model dimenison. More... | |
vl_size | vl_svm_get_num_data (VlSvm *self) |
Get the number of data samples. More... | |
double | vl_svm_get_epsilon (VlSvm const *self) |
Get the convergence threshold. More... | |
double | vl_svm_get_bias_learning_rate (VlSvm const *self) |
Get the bias leraning rate. More... | |
vl_size | vl_svm_get_max_num_iterations (VlSvm const *self) |
Get the maximum number of iterations. More... | |
vl_size | vl_svm_get_diagnostic_frequency (VlSvm const *self) |
Get the diagnostic frequency. More... | |
VlSvmSolverType | vl_svm_get_solver (VlSvm const *self) |
Get the SVM solver type. More... | |
double | vl_svm_get_bias_multiplier (VlSvm const *self) |
Get the bias multiplier. More... | |
double | vl_svm_get_lambda (VlSvm const *self) |
Get the regularizer parameter lambda. More... | |
vl_size | vl_svm_get_iteration_number (VlSvm const *self) |
Get the current iteration number. More... | |
double const * | vl_svm_get_scores (VlSvm const *self) |
Get the scores of the data points. More... | |
double const * | vl_svm_get_weights (VlSvm const *self) |
Get the data weights. More... | |
Set parameters | |
void | vl_svm_set_epsilon (VlSvm *self, double epsilon) |
Set the convergence threshold. More... | |
void | vl_svm_set_bias_learning_rate (VlSvm *self, double rate) |
Set the bias learning rate. More... | |
void | vl_svm_set_max_num_iterations (VlSvm *self, vl_size maxNumIterations) |
Set the maximum number of iterations. More... | |
void | vl_svm_set_diagnostic_frequency (VlSvm *self, vl_size f) |
Set the diagnostic frequency. More... | |
void | vl_svm_set_bias_multiplier (VlSvm *self, double b) |
Set the bias multiplier. More... | |
void | vl_svm_set_model (VlSvm *self, double const *model) |
Set the SVM model. More... | |
void | vl_svm_set_bias (VlSvm *self, double b) |
Set the SVM bias. More... | |
void | vl_svm_set_iteration_number (VlSvm *self, vl_uindex n) |
Set the current iteratio number. More... | |
void | vl_svm_set_weights (VlSvm *self, double const *weights) |
Set the data weights. More... | |
void | vl_svm_set_diagnostic_function (VlSvm *self, VlSvmDiagnosticFunction f, void *data) |
Set the diagnostic function callback. More... | |
void | vl_svm_set_loss_function (VlSvm *self, VlSvmLossFunction f) |
Set the loss function callback. More... | |
void | vl_svm_set_loss_derivative_function (VlSvm *self, VlSvmLossFunction f) |
Set the loss derivative function callback. More... | |
void | vl_svm_set_conjugate_loss_function (VlSvm *self, VlSvmLossFunction f) |
Set the conjugate loss function callback. More... | |
void | vl_svm_set_dca_update_function (VlSvm *self, VlSvmDcaUpdateFunction f) |
Set the DCA update function callback. More... | |
void | vl_svm_set_data_functions (VlSvm *self, VlSvmInnerProductFunction inner, VlSvmAccumulateFunction acc) |
Set the data functions. More... | |
void | vl_svm_set_loss (VlSvm *self, VlSvmLossType loss) |
Set the loss function to one of the default types. More... | |
Process data | |
void | vl_svm_train (VlSvm *self) |
Run the SVM solver. More... | |
Loss functions | |
| |
double | vl_svm_hinge_loss (double label, double inner) |
SVM hinge loss. More... | |
double | vl_svm_hinge_loss_derivative (double label, double inner) |
SVM hinge loss derivative. More... | |
double | vl_svm_hinge_conjugate_loss (double label, double u) |
SVM hinge loss conjugate. More... | |
double | vl_svm_hinge_dca_update (double alpha, double inner, double norm2, double label) |
SVM hinge loss DCA update. More... | |
double | vl_svm_hinge2_loss (double label, double inner) |
SVM square hinge loss. More... | |
double | vl_svm_hinge2_loss_derivative (double label, double inner) |
SVM square hinge loss derivative. More... | |
double | vl_svm_hinge2_conjugate_loss (double label, double u) |
SVM square hinge loss conjugate. More... | |
double | vl_svm_hinge2_dca_update (double alpha, double inner, double norm2, double label) |
SVM square hinge loss DCA update. More... | |
double | vl_svm_l1_loss (double label, double inner) |
SVM l1 loss. More... | |
double | vl_svm_l1_loss_derivative (double label, double inner) |
SVM l1 loss derivative. More... | |
double | vl_svm_l1_conjugate_loss (double label, double u) |
SVM l1 loss conjugate. More... | |
double | vl_svm_l1_dca_update (double alpha, double inner, double norm2, double label) |
SVM l1 loss DCA update. More... | |
double | vl_svm_l2_loss (double label, double inner) |
SVM l2 loss. More... | |
double | vl_svm_l2_loss_derivative (double label, double inner) |
SVM l2 loss derivative. More... | |
double | vl_svm_l2_conjugate_loss (double label, double u) |
SVM l2 loss conjugate. More... | |
double | vl_svm_l2_dca_update (double alpha, double inner, double norm2, double label) |
SVM l2 loss DCA update. More... | |
double | vl_svm_logistic_loss (double label, double inner) |
SVM l2 loss. More... | |
double | vl_svm_logistic_loss_derivative (double label, double inner) |
SVM l2 loss derivative. More... | |
double | vl_svm_logistic_conjugate_loss (double label, double u) |
SVM l2 loss conjugate. More... | |
double | vl_svm_logistic_dca_update (double alpha, double inner, double norm2, double label) |
SVM l2 loss DCA update. More... | |
Detailed Description
- See also
- Support Vector Machines (SVM).
Enumeration Type Documentation
◆ VlSvmLossType
enum VlSvmLossType |
Default SVM loss types. The loss can be set by using vl_svm_set_loss. Note that custom losses can be used too by using vl_svm_set_loss_function, vl_svm_set_loss_derivative_function, etc.
- See also
- svm-loss-functions
Enumerator | |
---|---|
VlSvmLossHinge | Standard hinge loss. |
VlSvmLossHinge2 | Hinge loss squared. |
VlSvmLossL1 | L1 loss. |
VlSvmLossL2 | L2 loss. |
VlSvmLossLogistic | Logistic loss. |
◆ VlSvmSolverStatus
enum VlSvmSolverStatus |
◆ VlSvmSolverType
enum VlSvmSolverType |
Enumerator | |
---|---|
VlSvmSolverNone | No solver (used to evaluate an SVM). |
VlSvmSolverSgd | SGD algorithm (Stochastic Gradient Descent). |
VlSvmSolverSdca | SDCA algorithm (Stochastic Dual Coordinate Ascent). |
Function Documentation
◆ vl_svm_delete()
void vl_svm_delete | ( | VlSvm * | self | ) |
- Parameters
-
self object.
- See also
- vl_svm_new
◆ vl_svm_get_bias()
double vl_svm_get_bias | ( | VlSvm const * | self | ) |
- Parameters
-
self object.
- Returns
- bias \(b\).
The value of the bias returned already include the effect of bias mutliplier.
◆ vl_svm_get_bias_learning_rate()
double vl_svm_get_bias_learning_rate | ( | VlSvm const * | self | ) |
- Parameters
-
self object
- Returns
- bias learning rate.
◆ vl_svm_get_bias_multiplier()
double vl_svm_get_bias_multiplier | ( | VlSvm const * | self | ) |
- Parameters
-
self object.
- Returns
- bias multiplier.
◆ vl_svm_get_diagnostic_frequency()
- Parameters
-
self object.
- Returns
- diagnostic frequency.
◆ vl_svm_get_dimension()
- Parameters
-
self object.
- Returns
- model dimension.
This is the dimensionality of the weight vector \(\bw\).
◆ vl_svm_get_epsilon()
double vl_svm_get_epsilon | ( | VlSvm const * | self | ) |
- Parameters
-
self object
- Returns
- epsilon threshold.
◆ vl_svm_get_iteration_number()
- Parameters
-
self object.
- Returns
- current iteration number.
◆ vl_svm_get_lambda()
double vl_svm_get_lambda | ( | VlSvm const * | self | ) |
- Parameters
-
self object.
- Returns
- diagnostic frequency.
◆ vl_svm_get_max_num_iterations()
- Parameters
-
self object.
- Returns
- maximum number of iterations.
◆ vl_svm_get_model()
double const* vl_svm_get_model | ( | VlSvm const * | self | ) |
- Parameters
-
self object.
- Returns
- model.
This is the weight vector \(\bw\).
◆ vl_svm_get_num_data()
- Parameters
-
self object.
- Returns
- model number of data samples
This is the dimensionality of the weight vector \(\bw\).
◆ vl_svm_get_scores()
double const* vl_svm_get_scores | ( | VlSvm const * | self | ) |
- 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. \(\langle \bx_i, \bw \rangle + b\).
◆ vl_svm_get_solver()
VlSvmSolverType vl_svm_get_solver | ( | VlSvm const * | self | ) |
- Parameters
-
self object.
- Returns
- SVM solver type.
◆ vl_svm_get_statistics()
VlSvmStatistics const* vl_svm_get_statistics | ( | VlSvm const * | self | ) |
- Parameters
-
self object.
- Returns
- statistics.
◆ vl_svm_get_weights()
double const* vl_svm_get_weights | ( | VlSvm const * | self | ) |
- Parameters
-
self object.
- Returns
- data weights.
◆ vl_svm_hinge2_conjugate_loss()
double vl_svm_hinge2_conjugate_loss | ( | double | u, |
double | label | ||
) |
- Parameters
-
u dual variable. label label value.
- Returns
- conjugate loss.
◆ vl_svm_hinge2_dca_update()
double vl_svm_hinge2_dca_update | ( | double | alpha, |
double | inner, | ||
double | norm2, | ||
double | label | ||
) |
- Parameters
-
alpha current value of the dual variable. inner inner product \(\bw^\top \bx\) of the sample with the SVM model. norm2 normalization factor \(\|\bx\|^2/\lambda n\). label label \(y\) of the sample.
- Returns
- incremental update \(\Delta\alpha\) of the dual variable.
◆ vl_svm_hinge2_loss()
double vl_svm_hinge2_loss | ( | double | inner, |
double | label | ||
) |
- Parameters
-
inner inner product between sample and model \(\bw^\top \bx\). 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
◆ vl_svm_hinge2_loss_derivative()
double vl_svm_hinge2_loss_derivative | ( | double | inner, |
double | label | ||
) |
- Parameters
-
inner inner product between sample and model \(\bw^\top \bx\). 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
◆ vl_svm_hinge_conjugate_loss()
double vl_svm_hinge_conjugate_loss | ( | double | u, |
double | label | ||
) |
- Parameters
-
u dual variable. label label value.
- Returns
- conjugate loss.
◆ vl_svm_hinge_dca_update()
double vl_svm_hinge_dca_update | ( | double | alpha, |
double | inner, | ||
double | norm2, | ||
double | label | ||
) |
- Parameters
-
alpha current value of the dual variable. inner inner product \(\bw^\top \bx\) of the sample with the SVM model. norm2 normalization factor \(\|\bx\|^2/\lambda n\). label label \(y\) of the sample.
- Returns
- incremental update \(\Delta\alpha\) of the dual variable.
◆ vl_svm_hinge_loss()
double vl_svm_hinge_loss | ( | double | inner, |
double | label | ||
) |
- Parameters
-
inner inner product between sample and model \(\bw^\top \bx\). 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
◆ vl_svm_hinge_loss_derivative()
double vl_svm_hinge_loss_derivative | ( | double | inner, |
double | label | ||
) |
- Parameters
-
inner inner product between sample and model \(\bw^\top \bx\). 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
◆ vl_svm_l1_conjugate_loss()
double vl_svm_l1_conjugate_loss | ( | double | u, |
double | label | ||
) |
- Parameters
-
u dual variable. label label value.
- Returns
- conjugate loss.
◆ vl_svm_l1_dca_update()
double vl_svm_l1_dca_update | ( | double | alpha, |
double | inner, | ||
double | norm2, | ||
double | label | ||
) |
- Parameters
-
alpha current value of the dual variable. inner inner product \(\bw^\top \bx\) of the sample with the SVM model. norm2 normalization factor \(\|\bx\|^2/\lambda n\). label label \(y\) of the sample.
- Returns
- incremental update \(\Delta\alpha\) of the dual variable.
◆ vl_svm_l1_loss()
double vl_svm_l1_loss | ( | double | inner, |
double | label | ||
) |
- Parameters
-
inner inner product between sample and model \(\bw^\top \bx\). 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
◆ vl_svm_l1_loss_derivative()
double vl_svm_l1_loss_derivative | ( | double | inner, |
double | label | ||
) |
- Parameters
-
inner inner product between sample and model \(\bw^\top \bx\). 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
◆ vl_svm_l2_conjugate_loss()
double vl_svm_l2_conjugate_loss | ( | double | u, |
double | label | ||
) |
- Parameters
-
u dual variable. label label value.
- Returns
- conjugate loss.
◆ vl_svm_l2_dca_update()
double vl_svm_l2_dca_update | ( | double | alpha, |
double | inner, | ||
double | norm2, | ||
double | label | ||
) |
- Parameters
-
alpha current value of the dual variable. inner inner product \(\bw^\top \bx\) of the sample with the SVM model. norm2 normalization factor \(\|\bx\|^2/\lambda n\). label label \(y\) of the sample.
- Returns
- incremental update \(\Delta\alpha\) of the dual variable.
◆ vl_svm_l2_loss()
double vl_svm_l2_loss | ( | double | inner, |
double | label | ||
) |
- Parameters
-
inner inner product between sample and model \(\bw^\top \bx\). 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
◆ vl_svm_l2_loss_derivative()
double vl_svm_l2_loss_derivative | ( | double | inner, |
double | label | ||
) |
- Parameters
-
inner inner product between sample and model \(\bw^\top \bx\). 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
◆ vl_svm_logistic_conjugate_loss()
double vl_svm_logistic_conjugate_loss | ( | double | u, |
double | label | ||
) |
- Parameters
-
u dual variable. label label value.
- Returns
- conjugate loss.
◆ vl_svm_logistic_dca_update()
double vl_svm_logistic_dca_update | ( | double | alpha, |
double | inner, | ||
double | norm2, | ||
double | label | ||
) |
- Parameters
-
alpha current value of the dual variable. inner inner product \(\bw^\top \bx\) of the sample with the SVM model. norm2 normalization factor \(\|\bx\|^2/\lambda n\). label label \(y\) of the sample.
- Returns
- incremental update \(\Delta\alpha\) of the dual variable.
◆ vl_svm_logistic_loss()
double vl_svm_logistic_loss | ( | double | inner, |
double | label | ||
) |
- Parameters
-
inner inner product between sample and model \(\bw^\top \bx\). 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
◆ vl_svm_logistic_loss_derivative()
double vl_svm_logistic_loss_derivative | ( | double | inner, |
double | label | ||
) |
- Parameters
-
inner inner product between sample and model \(\bw^\top \bx\). 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
◆ vl_svm_new()
VlSvm* vl_svm_new | ( | VlSvmSolverType | type, |
double const * | data, | ||
vl_size | dimension, | ||
vl_size | numData, | ||
double const * | labels, | ||
double | lambda | ||
) |
- 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
◆ vl_svm_new_with_abstract_data()
VlSvm* vl_svm_new_with_abstract_data | ( | VlSvmSolverType | solver, |
void * | data, | ||
vl_size | dimension, | ||
vl_size | numData, | ||
double const * | labels, | ||
double | lambda | ||
) |
- 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
◆ vl_svm_new_with_dataset()
VlSvm* vl_svm_new_with_dataset | ( | VlSvmSolverType | solver, |
VlSvmDataset * | dataset, | ||
double const * | labels, | ||
double | lambda | ||
) |
- 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
◆ vl_svm_set_bias()
void vl_svm_set_bias | ( | VlSvm * | self, |
double | b | ||
) |
- 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.
◆ vl_svm_set_bias_learning_rate()
void vl_svm_set_bias_learning_rate | ( | VlSvm * | self, |
double | rate | ||
) |
- Parameters
-
self object rate bias learning rate (positive).
This parameter applies only to the SGD solver.
◆ vl_svm_set_bias_multiplier()
void vl_svm_set_bias_multiplier | ( | VlSvm * | self, |
double | b | ||
) |
- 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).
◆ vl_svm_set_conjugate_loss_function()
void vl_svm_set_conjugate_loss_function | ( | VlSvm * | self, |
VlSvmLossFunction | f | ||
) |
◆ vl_svm_set_data_functions()
void vl_svm_set_data_functions | ( | VlSvm * | self, |
VlSvmInnerProductFunction | inner, | ||
VlSvmAccumulateFunction | acc | ||
) |
- Parameters
-
self object. inner inner product function. acc accumulate function.
◆ vl_svm_set_dca_update_function()
void vl_svm_set_dca_update_function | ( | VlSvm * | self, |
VlSvmDcaUpdateFunction | f | ||
) |
◆ vl_svm_set_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.
◆ vl_svm_set_diagnostic_function()
void vl_svm_set_diagnostic_function | ( | VlSvm * | self, |
VlSvmDiagnosticFunction | f, | ||
void * | data | ||
) |
- Parameters
-
self object. f diagnostic function pointer. data pointer to data used by the diagnostic function.
◆ vl_svm_set_epsilon()
void vl_svm_set_epsilon | ( | VlSvm * | self, |
double | epsilon | ||
) |
- Parameters
-
self object epsilon threshold (non-negative).
◆ vl_svm_set_iteration_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.
◆ vl_svm_set_loss()
void vl_svm_set_loss | ( | VlSvm * | self, |
VlSvmLossType | loss | ||
) |
- Parameters
-
self object. loss type of loss function.
- See also
- Loss functions.
◆ vl_svm_set_loss_derivative_function()
void vl_svm_set_loss_derivative_function | ( | VlSvm * | self, |
VlSvmLossFunction | f | ||
) |
◆ vl_svm_set_loss_function()
void vl_svm_set_loss_function | ( | VlSvm * | self, |
VlSvmLossFunction | f | ||
) |
- 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.
◆ vl_svm_set_max_num_iterations()
- Parameters
-
self object. n maximum number of iterations.
◆ vl_svm_set_model()
void vl_svm_set_model | ( | VlSvm * | self, |
double const * | 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.
◆ vl_svm_set_weights()
void vl_svm_set_weights | ( | VlSvm * | self, |
double const * | 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).
◆ vl_svm_train()
void vl_svm_train | ( | VlSvm * | self | ) |
- 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.