15 using namespace shogun;
41 SG_ERROR(
"Specified features are not of type CDotFeatures\n")
59 for (int32_t i=0; i<num_feat; i++)
67 for (int32_t i=0; i<num_vec; i++)
71 if (CMath::sign<float64_t>(output[i]) != train_labels.
vector[i])
80 for (int32_t j=0; j<num_feat; j++)
88 SG_INFO(
"Averaged Perceptron algorithm converged after %d iterations.\n", iter)
90 SG_WARNING(
"Averaged Perceptron algorithm did not converge after %d iterations.\n",
max_iter)
93 for (int32_t i=0; i<num_feat; i++)
94 w[i]=tmp_w[i]/(num_vec*iter);
95 bias=tmp_bias/(num_vec*iter);
virtual ELabelType get_label_type() const =0
The class Labels models labels, i.e. class assignments of objects.
virtual ~CAveragedPerceptron()
virtual int32_t get_num_vectors() const =0
virtual void add_to_dense_vec(float64_t alpha, int32_t vec_idx1, float64_t *vec2, int32_t vec2_len, bool abs_val=false)=0
Features that support dot products among other operations.
virtual int32_t get_dim_feature_space() const =0
virtual float64_t apply_one(int32_t vec_idx)
virtual void set_features(CDotFeatures *feat)
Class LinearMachine is a generic interface for all kinds of linear machines like classifiers.
The class Features is the base class of all feature objects.
virtual bool train_machine(CFeatures *data=NULL)
Binary Labels for binary classification.
bool has_property(EFeatureProperty p) const
virtual void set_labels(CLabels *lab)