39 #include <shogun/lib/config.h> 90 for (
index_t idx=0; idx<n; idx++)
virtual void set_inducing_features(CFeatures *feat)
SGVector< float64_t > get_variance_vector(CFeatures *data)
virtual SGVector< float64_t > get_predictive_means(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const =0
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
#define SG_NOTIMPLEMENTED
SGVector< float64_t > get_posterior_means(CFeatures *data)
virtual ~CGaussianProcessClassification()
virtual CLabels * get_labels()
virtual SGVector< float64_t > get_predictive_variances(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const =0
virtual CFeatures * get_features()
static CSingleFITCLaplaceInferenceMethod * obtain_from_generic(CInference *inference)
std::enable_if<!std::is_same< T, complex128_t >::value, float64_t >::type mean(const Container< T > &a)
SGVector< float64_t > get_mean_vector(CFeatures *data)
Multiclass Labels for multi-class classification.
SGVector< float64_t > get_probabilities(CFeatures *data)
virtual const char * get_name() const =0
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)
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.
virtual int32_t get_num_vectors() const =0