Drake
EvaluatorBase Class Referenceabstract

Detailed Description

Provides an abstract interface to represent an expression, mapping a fixed or dynamic number of inputs to a fixed number of outputs, that may be evaluated on a scalar type of double or AutoDiffXd.

These objects, and its derivatives, are meant to be bound to a given set of variables using the Binding<> class.

#include <drake/solvers/evaluator_base.h>

Public Member Functions

virtual ~EvaluatorBase ()
 
void Eval (const Eigen::Ref< const Eigen::VectorXd > &x, Eigen::VectorXd *y) const
 Evaluates the expression. More...
 
void Eval (const Eigen::Ref< const AutoDiffVecXd > &x, AutoDiffVecXd *y) const
 Evaluates the expression. More...
 
void Eval (const Eigen::Ref< const VectorX< symbolic::Variable >> &x, VectorX< symbolic::Expression > *y) const
 Evaluates the expression. More...
 
void set_description (const std::string &description)
 Set a human-friendly description for the evaluator. More...
 
const std::string & get_description () const
 Getter for a human-friendly description for the evaluator. More...
 
int num_vars () const
 Getter for the number of variables, namely the number of rows in x, as used in Eval(x, y). More...
 
int num_outputs () const
 Getter for the number of outputs, namely the number of rows in y, as used in Eval(x, y). More...
 
void SetGradientSparsityPattern (const std::vector< std::pair< int, int >> &gradient_sparsity_pattern)
 Set the sparsity pattern of the gradient matrix ∂y/∂x (the gradient of y value in Eval, w.r.t x in Eval) . More...
 
const optional< std::vector< std::pair< int, int > > > & gradient_sparsity_pattern () const
 Returns the vector of (row_index, col_index) that contains all the entries in the gradient of Eval function (∂y/∂x) whose value could be non-zero, namely if ∂yᵢ/∂xⱼ could be non-zero, then the pair (i, j) is in gradient_sparsity_pattern. More...
 
Does not allow copy, move, or assignment
 EvaluatorBase (const EvaluatorBase &)=delete
 
EvaluatorBaseoperator= (const EvaluatorBase &)=delete
 
 EvaluatorBase (EvaluatorBase &&)=delete
 
EvaluatorBaseoperator= (EvaluatorBase &&)=delete
 

Protected Member Functions

 EvaluatorBase (int num_outputs, int num_vars, const std::string &description="")
 Constructs a evaluator. More...
 
virtual void DoEval (const Eigen::Ref< const Eigen::VectorXd > &x, Eigen::VectorXd *y) const =0
 Implements expression evaluation for scalar type double. More...
 
virtual void DoEval (const Eigen::Ref< const AutoDiffVecXd > &x, AutoDiffVecXd *y) const =0
 Implements expression evaluation for scalar type AutoDiffXd. More...
 
virtual void DoEval (const Eigen::Ref< const VectorX< symbolic::Variable >> &x, VectorX< symbolic::Expression > *y) const =0
 Implements expression evaluation for scalar type symbolic::Expression. More...
 
void set_num_outputs (int num_outputs)
 

Constructor & Destructor Documentation

◆ EvaluatorBase() [1/3]

EvaluatorBase ( const EvaluatorBase )
delete

◆ EvaluatorBase() [2/3]

EvaluatorBase ( EvaluatorBase &&  )
delete

◆ ~EvaluatorBase()

virtual ~EvaluatorBase ( )
virtual

◆ EvaluatorBase() [3/3]

EvaluatorBase ( int  num_outputs,
int  num_vars,
const std::string &  description = "" 
)
protected

Constructs a evaluator.

Parameters
num_outputs.The number of rows in the output.
num_vars.The number of rows in the input. If the input dimension is not known, then set num_vars to Eigen::Dynamic.
descriptionA human-friendly description.
See also
Eval(...)

Member Function Documentation

◆ DoEval() [1/3]

virtual void DoEval ( const Eigen::Ref< const Eigen::VectorXd > &  x,
Eigen::VectorXd *  y 
) const
protectedpure virtual

◆ DoEval() [2/3]

virtual void DoEval ( const Eigen::Ref< const AutoDiffVecXd > &  x,
AutoDiffVecXd y 
) const
protectedpure virtual

◆ DoEval() [3/3]

virtual void DoEval ( const Eigen::Ref< const VectorX< symbolic::Variable >> &  x,
VectorX< symbolic::Expression > *  y 
) const
protectedpure virtual

◆ Eval() [1/3]

void Eval ( const Eigen::Ref< const Eigen::VectorXd > &  x,
Eigen::VectorXd *  y 
) const

Evaluates the expression.

Parameters
[in]xA num_vars x 1 input vector.
[out]yA num_outputs x 1 output vector.

◆ Eval() [2/3]

void Eval ( const Eigen::Ref< const AutoDiffVecXd > &  x,
AutoDiffVecXd y 
) const

Evaluates the expression.

Parameters
[in]xA num_vars x 1 input vector.
[out]yA num_outputs x 1 output vector.

◆ Eval() [3/3]

void Eval ( const Eigen::Ref< const VectorX< symbolic::Variable >> &  x,
VectorX< symbolic::Expression > *  y 
) const

Evaluates the expression.

Parameters
[in]xA num_vars x 1 input vector.
[out]yA num_outputs x 1 output vector.

◆ get_description()

const std::string& get_description ( ) const

Getter for a human-friendly description for the evaluator.

◆ gradient_sparsity_pattern()

const optional<std::vector<std::pair<int, int> > >& gradient_sparsity_pattern ( ) const

Returns the vector of (row_index, col_index) that contains all the entries in the gradient of Eval function (∂y/∂x) whose value could be non-zero, namely if ∂yᵢ/∂xⱼ could be non-zero, then the pair (i, j) is in gradient_sparsity_pattern.

Return values
gradient_sparsity_patternIf nullopt, then we regard all entries of the gradient as potentially non-zero.

◆ num_outputs()

int num_outputs ( ) const

Getter for the number of outputs, namely the number of rows in y, as used in Eval(x, y).

◆ num_vars()

int num_vars ( ) const

Getter for the number of variables, namely the number of rows in x, as used in Eval(x, y).

◆ operator=() [1/2]

EvaluatorBase& operator= ( EvaluatorBase &&  )
delete

◆ operator=() [2/2]

EvaluatorBase& operator= ( const EvaluatorBase )
delete

◆ set_description()

void set_description ( const std::string &  description)

Set a human-friendly description for the evaluator.

◆ set_num_outputs()

void set_num_outputs ( int  num_outputs)
protected

◆ SetGradientSparsityPattern()

void SetGradientSparsityPattern ( const std::vector< std::pair< int, int >> &  gradient_sparsity_pattern)

Set the sparsity pattern of the gradient matrix ∂y/∂x (the gradient of y value in Eval, w.r.t x in Eval) .

gradient_sparsity_pattern contains all the pairs of (row_index, col_index) for which the corresponding entries could have non-zero value in the gradient matrix ∂y/∂x.


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