90 for (
index_t idx=0; idx<n; idx++)
virtual const char * get_name() const =0
virtual void set_inducing_features(CFeatures *feat)
SGVector< float64_t > get_variance_vector(CFeatures *data)
void set_int_labels(SGVector< int32_t > labels)
static int32_t arg_max(T *vec, int32_t inc, int32_t len, T *maxv_ptr=NULL)
A base class for Gaussian Processes.
CGaussianProcessClassification()
virtual EInferenceType get_inference_type() const
SGVector< float64_t > get_posterior_variances(CFeatures *data)
virtual bool supports_binary() const
virtual int32_t get_num_vectors() const =0
#define SG_NOTIMPLEMENTED
SGVector< float64_t > get_posterior_means(CFeatures *data)
virtual SGVector< float64_t > get_predictive_variances(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const =0
virtual ~CGaussianProcessClassification()
virtual CLabels * get_labels()
virtual CFeatures * get_features()
static CSingleFITCLaplaceInferenceMethod * obtain_from_generic(CInference *inference)
SGVector< float64_t > get_mean_vector(CFeatures *data)
Multiclass Labels for multi-class classification.
SGVector< float64_t > get_probabilities(CFeatures *data)
virtual SGVector< float64_t > get_predictive_log_probabilities(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL)
virtual CBinaryLabels * apply_binary(CFeatures *data=NULL)
virtual CFeatures * get_inducing_features()
CLikelihoodModel * get_model()
virtual bool train_machine(CFeatures *data=NULL)
virtual void set_features(CFeatures *feat)
all of classes and functions are contained in the shogun namespace
The Inference Method base class.
The class Features is the base class of all feature objects.
static float64_t exp(float64_t x)
virtual SGVector< float64_t > get_predictive_means(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const =0
Binary Labels for binary classification.
virtual CMulticlassLabels * apply_multiclass(CFeatures *data=NULL)
The FITC approximation inference method class for regression and binary Classification. Note that the number of inducing points (m) is usually far less than the number of input points (n). (the time complexity is computed based on the assumption m < n)
virtual bool supports_multiclass() const
The Likelihood model base class.