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.
all of classes and functions are contained in the shogun namespace
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)