35 void CMultitaskL12LogisticRegression::init()
68 for (int32_t i=0; i<y.vlen; i++)
71 malsar_options options = malsar_options::default_options();
76 options.tasks_indices = tasks;
84 SG_WARNING(
"Please install Eigen3 to use MultitaskL12LogisticRegression\n")
102 for (int32_t i=0; i<y.vlen; i++)
105 malsar_options options = malsar_options::default_options();
119 SG_WARNING(
"Please install Eigen3 to use MultitaskL12LogisticRegression\n")
124 SG_FREE(options.tasks_indices);
void set_rho1(float64_t rho1)
virtual ~CMultitaskL12LogisticRegression()
virtual int32_t get_num_labels() const =0
CMultitaskL12LogisticRegression()
virtual bool train_machine(CFeatures *data=NULL)
virtual bool train_locked_implementation(SGVector< index_t > *tasks)
class TaskGroup used to represent a group of tasks. Tasks in group do not overlap.
Features that support dot products among other operations.
float64_t get_rho2() const
class Multitask Logistic Regression used to solve classification problems with a few tasks related vi...
CTaskRelation * m_task_relation
void set_rho2(float64_t rho2)
float64_t get_rho1() const
virtual void set_features(CDotFeatures *feat)
SGMatrix< float64_t > m_tasks_w
malsar_result_t malsar_joint_feature_learning(CDotFeatures *features, double *y, double rho1, double rho2, const malsar_options &options)
The class Features is the base class of all feature objects.
Binary Labels for binary classification.
SGVector< float64_t > m_tasks_c
used to represent tasks in multitask learning