LWPR (Locally Weighted Projection Regression) function approximator. More...

#include <FunctionApproximatorLWPR.hpp>

Inheritance diagram for FunctionApproximatorLWPR:

Collaboration diagram for FunctionApproximatorLWPR:

Public Member Functions
	FunctionApproximatorLWPR (const MetaParametersLWPR const meta_parameters, const ModelParametersLWPR const model_parameters=NULL)
	Initialize a function approximator with meta- and model-parameters. More...

	FunctionApproximatorLWPR (const ModelParametersLWPR *const model_parameters)
	Initialize a function approximator with model parameters. More...

FunctionApproximator *	clone (void) const
	Return a pointer to a deep copy of the FunctionApproximator object. More...

void	train (const Eigen::Ref< const Eigen::MatrixXd > &inputs, const Eigen::Ref< const Eigen::MatrixXd > &targets)
	Train the function approximator with corresponding input and target examples. More...

void	predict (const Eigen::Ref< const Eigen::MatrixXd > &inputs, Eigen::MatrixXd &output)
	Query the function approximator to make a prediction. More...

std::string	getName (void) const
	Get the name of this function approximator. More...

void	set_print_training_progress (bool print_training_progress)
	Print some output to cout during training. More...

Public Member Functions inherited from FunctionApproximator
	FunctionApproximator (const MetaParameters const meta_parameters, const ModelParameters const model_parameters=NULL)
	Initialize a function approximator with meta- and optionally model-parameters. More...

	FunctionApproximator (const ModelParameters *const model_parameters)
	Initialize a function approximator with model-parameters. More...

void	train (const Eigen::Ref< const Eigen::MatrixXd > &inputs, const Eigen::Ref< const Eigen::MatrixXd > &targets, std::string save_directory, bool overwrite=false)
	Train the function approximator with corresponding input and target examples (and write results to file). More...

void	reTrain (const Eigen::Ref< const Eigen::MatrixXd > &inputs, const Eigen::Ref< const Eigen::MatrixXd > &targets)
	Re-train the function approximator with corresponding input and target examples. More...

void	reTrain (const Eigen::Ref< const Eigen::MatrixXd > &inputs, const Eigen::Ref< const Eigen::MatrixXd > &targets, std::string save_directory, bool overwrite=false)
	Re-train the function approximator with corresponding input and target examples (and write results to file). More...

virtual void	predict (const Eigen::Ref< const Eigen::MatrixXd > &inputs, Eigen::MatrixXd &outputs, Eigen::MatrixXd &variances)
	Query the function approximator to make a prediction, and also to predict its variance. More...

virtual void	predict (const Eigen::Ref< const Eigen::MatrixXd > &inputs, Eigen::MatrixXd &outputs, std::vector< Eigen::MatrixXd > &variances)
	Query the function approximator to make a prediction, and also to predict its variance. More...

virtual void	predictVariance (const Eigen::Ref< const Eigen::MatrixXd > &inputs, Eigen::MatrixXd &variances)
	Query the function approximator to get the variance of a prediction This function is not implemented by all function approximators. More...

bool	isTrained (void) const
	Determine whether the function approximator has already been trained with data or not. More...

int	getExpectedInputDim (void) const
	The expected dimensionality of the input data. More...

int	getExpectedOutputDim (void) const
	The expected dimensionality of the output data. More...

void	getSelectableParameters (std::set< std::string > &selected_values_labels) const
	Return all the names of the parameter types that can be selected. More...

void	setSelectedParameters (const std::set< std::string > &selected_values_labels)
	Determine which subset of parameters is represented in the vector returned by Parameterizable::getParameterVectorSelected. More...

void	getParameterVectorSelectedMinMax (Eigen::VectorXd &min, Eigen::VectorXd &max) const
	Get the minimum and maximum of the selected parameters in one vector. More...

int	getParameterVectorSelectedSize (void) const
	Get the size of the vector of selected parameters, as returned by getParameterVectorSelected(. More...

void	setParameterVectorSelected (const Eigen::VectorXd &values, bool normalized=false)
	Set all the values of the selected parameters with one vector. More...

void	getParameterVectorSelected (Eigen::VectorXd &values, bool normalized=false) const
	Get the values of the selected parameters in one vector. More...

void	getParameterVectorMask (const std::set< std::string > selected_values_labels, Eigen::VectorXi &selected_mask) const
	Get a mask for selecting parameters. More...

int	getParameterVectorAllSize (void) const
	Get the size of the parameter values vector when it contains all available parameter values. More...

void	getParameterVectorAll (Eigen::VectorXd &values) const
	Return a vector that returns all available parameter values. More...

void	setParameterVectorAll (const Eigen::VectorXd &values)
	Set all available parameter values with one vector. More...

UnifiedModel *	getUnifiedModel (void) const
	Return a representation of this function approximator's model as a unified model. More...

std::string	toString (void) const
	Returns a string representation of the object. More...

const MetaParameters *	getMetaParameters (void) const
	Accessor for FunctionApproximator::meta_parameters_. More...

const ModelParameters *	getModelParameters (void) const
	Accessor for FunctionApproximator::model_parameters_. More...

void	setParameterVectorModifierPrivate (std::string modifier, bool new_value)
	Turn certain modifiers on or off, see Parameterizable::setParameterVectorModifier(). More...

virtual bool	saveGridData (const Eigen::VectorXd &min, const Eigen::VectorXd &max, const Eigen::VectorXi &n_samples_per_dim, std::string directory, bool overwrite=false) const
	Generate a grid of inputs, and output the response of the basis functions and line segments for these inputs. More...

Public Member Functions inherited from Parameterizable
virtual	~Parameterizable (void)
	Destructor.

virtual void	getParameterVectorSelectedNormalized (Eigen::VectorXd &values) const
	Get the normalized values of the selected parameters in one vector. More...

void	getParameterVectorSelectedMinMax (Eigen::VectorXd &min, Eigen::VectorXd &max) const
	Get the minimum and maximum of the selected parameters in one vector. More...

void	getParameterVectorAllMinMax (Eigen::VectorXd &min, Eigen::VectorXd &max) const
	Get the minimum and maximum values of the current parameter vector. More...

void	getParameterVectorSelectedRanges (Eigen::VectorXd &ranges) const
	Get the ranges of the selected parameters, i.e. More...

virtual void	setParameterVectorSelectedNormalized (const Eigen::VectorXd &values)
	Set all the values of the selected parameters with one vector of normalized values. More...

void	setSelectedParametersOne (std::string selected)
	Set the parameters that are currently selected. More...

void	setParameterVectorModifier (std::string modifier, bool new_value)
	Turn certain modifiers on or off. More...

void	setVectorLengthsPerDimension (const Eigen::VectorXi &lengths_per_dimension)
	The vector (VectorXd) with parameter values can be split into different parts (as vector<VectorXd>; this function specifices the length of each sub-vector. More...

Eigen::VectorXi	getVectorLengthsPerDimension (void) const
	Get the specified length of each vector in each dimension. More...

void	getParameterVectorSelected (std::vector< Eigen::VectorXd > &values, bool normalized=false) const
	Get the values of the selected parameters in one vector. More...

void	setParameterVectorSelected (const std::vector< Eigen::VectorXd > &values, bool normalized=false)
	Set all the values of the selected parameters with a vector of vectors. More...

Friends
class	boost::serialization::access
	Give boost serialization access to private members. More...

Additional Inherited Members
Static Public Member Functions inherited from FunctionApproximator
static void	generateInputsGrid (const Eigen::VectorXd &min, const Eigen::VectorXd &max, const Eigen::VectorXi &n_samples_per_dim, Eigen::MatrixXd &inputs_grid)
	Generate a input samples that lie on a grid (much like Matlab's meshgrid) For instance, if min = [2 6], and max = [3 8], and n_samples_per_dim = [3 5] then this function first makes linearly spaces samples along each dimension, e.g. More...

Protected Member Functions inherited from FunctionApproximator
void	setModelParameters (ModelParameters *model_parameters)
	Accessor for FunctionApproximator::model_parameters_. More...

	FunctionApproximator (void)
	Default constructor. More...

Detailed Description

LWPR (Locally Weighted Projection Regression) function approximator.

Definition at line 44 of file FunctionApproximatorLWPR.hpp.

Constructor & Destructor Documentation

FunctionApproximatorLWPR	(	const MetaParametersLWPR *const	meta_parameters,
		const ModelParametersLWPR *const	model_parameters = `NULL`
	)

Initialize a function approximator with meta- and model-parameters.

Parameters

[in]	meta_parameters	The training algorithm meta-parameters
[in]	model_parameters	The parameters of the trained model. If this parameter is not passed, the function approximator is initialized as untrained. In this case, you must call FunctionApproximator::train() before being able to call FunctionApproximator::predict(). Either meta_parameters XOR model-parameters can passed as NULL, but not both.

Definition at line 48 of file FunctionApproximatorLWPR.cpp.

 :
   FunctionApproximator(meta_parameters,model_parameters),
   print_training_progress_(false)
 {
 }

FunctionApproximatorLWPR ( const ModelParametersLWPR *const model_parameters )

Initialize a function approximator with model parameters.

Parameters

[in] model_parameters The parameters of the (previously) trained model.

Definition at line 55 of file FunctionApproximatorLWPR.cpp.

 :
   FunctionApproximator(model_parameters),
   print_training_progress_(false)
 {
 }

Member Function Documentation

FunctionApproximator * clone ( void ) const

virtual

Return a pointer to a deep copy of the FunctionApproximator object.

Returns: Pointer to a deep copy

Implements FunctionApproximator.

Definition at line 62 of file FunctionApproximatorLWPR.cpp.

                                                                 {
   // All error checking and cloning is left to the FunctionApproximator constructor.
   FunctionApproximatorLWPR* fa_lwpr = new FunctionApproximatorLWPR(
     dynamic_cast<const MetaParametersLWPR*>(getMetaParameters()),
     dynamic_cast<const ModelParametersLWPR*>(getModelParameters())
     );
   fa_lwpr->set_print_training_progress(print_training_progress_);
   return fa_lwpr;
 };

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void train	(	const Eigen::Ref< const Eigen::MatrixXd > &	inputs,
		const Eigen::Ref< const Eigen::MatrixXd > &	targets
	)

virtual

Train the function approximator with corresponding input and target examples.

Parameters

[in]	inputs	Input values of the training examples
[in]	targets	Target values of the training examples

Implements FunctionApproximator.

Definition at line 72 of file FunctionApproximatorLWPR.cpp.

 {
   if (isTrained())  
   {
     cerr << "WARNING: You may not call FunctionApproximatorLWPR::train more than once. Doing nothing." << endl;
     cerr << "   (if you really want to retrain, call reTrain function instead)" << endl;
     return;
   }
   
   assert(inputs.rows() == targets.rows()); // Must have same number of examples
   assert(inputs.cols()==getExpectedInputDim());
   
   const MetaParametersLWPR* meta_parameters_lwpr = dynamic_cast<const MetaParametersLWPR*>(getMetaParameters());
   
   int n_in =inputs.cols();
   int n_out=targets.cols();
  
   LWPR_Object* lwpr_object = new LWPR_Object(n_in, n_out);
         lwpr_object->setInitD(    meta_parameters_lwpr->init_D_[0]); // todo Fix this
         lwpr_object->wGen(        meta_parameters_lwpr->w_gen_);
   lwpr_object->wPrune(      meta_parameters_lwpr->w_prune_);
   lwpr_object->updateD(     meta_parameters_lwpr->update_D_);
   lwpr_object->setInitAlpha(meta_parameters_lwpr->init_alpha_);
   lwpr_object->penalty(     meta_parameters_lwpr->penalty_);
         lwpr_object->diagOnly(    meta_parameters_lwpr->diag_only_);
   lwpr_object->useMeta(     meta_parameters_lwpr->use_meta_);
   lwpr_object->metaRate(    meta_parameters_lwpr->meta_rate_);
   lwpr_object->kernel(      meta_parameters_lwpr->kernel_name_.c_str());
    
   
   vector<double> input_vector(n_in);
   vector<double> target_vector(n_out);
   int n_input_samples = inputs.rows();
   
   //http://stackoverflow.com/questions/15858569/randomly-permute-rows-columns-of-a-matrix-with-eigen
   PermutationMatrix<Dynamic,Dynamic> permute(n_input_samples);  
   permute.setIdentity();
   VectorXi shuffled_indices = VectorXi::LinSpaced(n_input_samples,0,n_input_samples-1);
   MatrixXd outputs;
   for (int iterations=0; iterations<50; iterations++)
   {
     random_shuffle(permute.indices().data(), permute.indices().data()+permute.indices().size());
     shuffled_indices = (shuffled_indices.transpose()*permute).transpose();
 
     for (int ii=0; ii<n_input_samples; ii++)
     {
       // Eigen::VectorXd -> std::vector
       Map<VectorXd>(input_vector.data(),n_in) = inputs.row(shuffled_indices[ii]);  
       Map<VectorXd>(target_vector.data(),n_out) = targets.row(shuffled_indices[ii]);
       // update model
       lwpr_object->update(input_vector, target_vector);
     }
 
     if (print_training_progress_)
     {
       if (iterations%5==0)
       {
         FunctionApproximatorLWPR* fa_tmp = new FunctionApproximatorLWPR(new ModelParametersLWPR(new LWPR_Object(*lwpr_object)));
         fa_tmp->predict(inputs, outputs);
         delete fa_tmp;
         MatrixXd abs_error = (targets.array()-outputs.array()).abs();
         VectorXd mean_abs_error_per_output_dim = abs_error.colwise().mean();
         cout << "Iteration " << iterations << " MEA=" << mean_abs_error_per_output_dim << endl;
       }
     }
     
   }
   setModelParameters(new ModelParametersLWPR(lwpr_object));
   
 }

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void predict	(	const Eigen::Ref< const Eigen::MatrixXd > &	inputs,
		Eigen::MatrixXd &	outputs
	)

virtual

Query the function approximator to make a prediction.

Parameters

[in]	inputs	Input values of the query
[out]	outputs	Predicted output values

Remarks: This method should be const. But third party functions which is called in this function have not always been implemented as const (Examples: LWPRObject::predict or RRRFF::predict ). Therefore, this function cannot be const.

Implements FunctionApproximator.

Definition at line 144 of file FunctionApproximatorLWPR.cpp.

 {
   if (!isTrained())  
   {
     cerr << "WARNING: You may not call FunctionApproximatorLWPR::predict if you have not trained yet. Doing nothing." << endl;
     return;
   }
 
   const ModelParametersLWPR* model_parameters_lwpr = static_cast<const ModelParametersLWPR*>(getModelParameters());
 
   int n_in  = model_parameters_lwpr->lwpr_object_->model.nIn;
   assert(inputs.cols()==n_in);
   
   int n_input_samples = inputs.rows();
   int n_out = model_parameters_lwpr->lwpr_object_->model.nOut;
 
   outputs.resize(n_input_samples,n_out);
   
   // Allocate memory for the temporary vectors for LWPR_Object::predict
   vector<double> input_vector(n_in);
   vector<double> output_vector(n_out);
 
   // Do prediction for each sample  
   for (int ii=0; ii<n_input_samples; ii++)
   {
     // LWPR_Object::predict uses std::vector, so do some conversions here.
     Map<VectorXd>(input_vector.data(),n_in) = inputs.row(ii);  // Eigen::VectorXd -> std::vector
     output_vector = model_parameters_lwpr->lwpr_object_->predict(input_vector);
     outputs.row(ii) = Map<VectorXd>(&output_vector[0], n_out); // std::vector -> Eigen::VectorXd
   }
   
 }

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std::string getName ( void ) const

inlinevirtual

Get the name of this function approximator.

Returns: Name of this function approximator

Implements FunctionApproximator.

Definition at line 68 of file FunctionApproximatorLWPR.hpp.

                                       {
     return std::string("LWPR");  
   };

void set_print_training_progress ( bool print_training_progress )

inline

Print some output to cout during training.

This can be useful to see if the error is decreasing consistently.

Parameters

[in] print_training_progress true: print to cout, false: do nothing.

Definition at line 77 of file FunctionApproximatorLWPR.hpp.

   {
     print_training_progress_ = print_training_progress;
   }

Friends And Related Function Documentation

friend class boost::serialization::access

friend

Give boost serialization access to private members.

Definition at line 91 of file FunctionApproximatorLWPR.hpp.

The documentation for this class was generated from the following files:

Public Member Functions

Friends

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Friends And Related Function Documentation