19#ifndef LM_LINEARGPRLEARNER__ 
   20#define LM_LINEARGPRLEARNER__ 
   25#include <yarp/sig/Matrix.h> 
   31namespace learningmachine {
 
   94    LinearGPRLearner(
unsigned int dom = 1, 
unsigned int cod = 1, 
double sigma = 1.0);
 
  114    virtual void feedSample(
const yarp::sig::Vector& input, 
const yarp::sig::Vector& output);
 
  119    virtual void train();
 
  156    virtual void readBottle(yarp::os::Bottle& bot);
 
  186    virtual bool configure(yarp::os::Searchable& config);
 
 
An generalized interface for a learning machine with a fixed domain and codomain size.
 
Standard linear Bayesian regression or, equivalently, Gaussian Process Regression with a linear covar...
 
virtual Prediction predict(const yarp::sig::Vector &input)
Ask the learning machine to predict the output for a given input.
 
virtual ~LinearGPRLearner()
Destructor.
 
virtual void readBottle(yarp::os::Bottle &bot)
Unserializes a machine from a bottle.
 
virtual std::string getConfigHelp()
Asks the learning machine to return a string containing the list of configuration options that it sup...
 
virtual std::string getInfo()
Asks the learning machine to return a string containing information on its operation so far.
 
LinearGPRLearner & operator=(const LinearGPRLearner &other)
Assignment operator.
 
double getSigma()
Accessor for the signal noise \sigma.
 
void setSigma(double s)
Sets the signal noise \sigma to a specified value.
 
virtual bool configure(yarp::os::Searchable &config)
Change parameters.
 
LinearGPRLearner * clone()
Asks the learning machine to return a clone of its type.
 
virtual void train()
Train the learning machine on the examples that have been supplied so far.
 
virtual void writeBottle(yarp::os::Bottle &bot)
 
void setCoDomainSize(unsigned int size)
Mutator for the codomain size.
 
void setDomainSize(unsigned int size)
Mutator for the domain size.
 
virtual void feedSample(const yarp::sig::Vector &input, const yarp::sig::Vector &output)
Provide the learning machine with an example of the desired mapping.
 
void reset()
Forget everything and start over.
 
A class that represents a prediction result.
 
This file contains the definition of unique IDs for the body parts and the skin parts of the robot.