Fred Class Reference

Wrapper around vector demonstrating interface necessary. More...

Inheritance diagram for Fred:

Collaboration diagram for Fred:

Public Member Functions
	Fred (const Fred &f)
	Fred (const Tensor< double > f, const Tensor< double > x, const Tensor< double > w, const double mu, const double p)
	Fred (double a, double b, double c)
string	a (const string &input) const
string	b (const string &input) const
double	get (int i) const
Tensor< double >	gradient (const Tensor< double > &expnt)
	Should return the derivative of the function. More...
Fred	operator* (const double &d) const
Fred &	operator+= (const Fred &f)
Fred &	operator= (const Fred &f)
bool	provides_gradient () const
	Override this to return true if the derivative is implemented. More...
void	set (int i, double a)
double	value (const Tensor< double > &expnt)
	Should return the value of the objective function. More...
void	value_and_gradient (const Tensor< double > &expnt, double &value, Tensor< double > &gradient)
	Reimplement if more efficient to evaluate both value and gradient in one call. More...
Public Member Functions inherited from madness::OptimizationTargetInterface
virtual	~OptimizationTargetInterface ()
double	test_gradient (Tensor< double > &x, double value_precision, bool doprint=true)
	Numerical test of the derivative ... optionally prints to stdout, returns max abs error. More...

Private Member Functions
	Fred ()
double	dpenalty (double expnt) const
Tensor< double >	make_g (const Tensor< double > &expnt)
	Makes the matrix of Gaussians g(i,j) = myexp(-expnt[j]*x[i]x[i]) More...
double	penalty (double expnt) const

Private Attributes
const double	alpha
const Tensor< double >	f
const double	mu
const long	nx
const double	p
std::vector< double >	v
const Tensor< double >	w
const Tensor< double >	x

Detailed Description

Wrapper around vector demonstrating interface necessary.

Constructor & Destructor Documentation

Fred() [1/4]

Fred::Fred ( )

private

Fred() [2/4]

Fred::Fred ( double  a, double  b, double  c  )

inline

References a, b, c, and v.

Fred() [3/4]

Fred::Fred ( const Fred &  f )

inline

Fred() [4/4]

Fred::Fred ( const Tensor< double >  f, const Tensor< double >  x, const Tensor< double >  w, const double  mu, const double  p  )

inline

Member Function Documentation

a()

string Fred::a ( const string &  input ) const

inline

Referenced by main().

b()

string Fred::b ( const string &  input ) const

inline

Referenced by main().

dpenalty()

double Fred::dpenalty ( double  expnt ) const

inlineprivate

References alpha.

get()

double Fred::get ( int  i ) const

inline

References v.

Referenced by test1().

gradient()

Tensor<double> Fred::gradient ( const Tensor< double > &  x )

inlinevirtual

Should return the derivative of the function.

Reimplemented from madness::OptimizationTargetInterface.

References errsq(), and madness::g.

make_g()

Tensor<double> Fred::make_g ( const Tensor< double > &  expnt )

inlineprivate

Makes the matrix of Gaussians g(i,j) = myexp(-expnt[j]*x[i]x[i])

References madness::g, myexp(), and madness::BaseTensor::size().

operator*()

Fred Fred::operator* ( const double &  d ) const

inline

References d(), and madness::f.

operator+=()

Fred& Fred::operator+= ( const Fred &  f )

inline

References madness::f, and v.

operator=()

Fred& Fred::operator= ( const Fred &  f )

inline

References madness::f, and v.

penalty()

double Fred::penalty ( double  expnt ) const

inlineprivate

References alpha.

provides_gradient()

bool Fred::provides_gradient ( ) const

inlinevirtual

Override this to return true if the derivative is implemented.

Reimplemented from madness::OptimizationTargetInterface.

set()

void Fred::set ( int  i, double  a  )

inline

References a, and v.

Referenced by expL(), and N().

value()

double Fred::value ( const Tensor< double > &  x )

inlinevirtual

Should return the value of the objective function.

Implements madness::OptimizationTargetInterface.

References errsq().

value_and_gradient()

void Fred::value_and_gradient ( const Tensor< double > &  x, double &  value, Tensor< double > &  gradient  )

inlinevirtual

Reimplement if more efficient to evaluate both value and gradient in one call.

Reimplemented from madness::OptimizationTargetInterface.

References c, e(), errsq(), madness::f, madness::g, mu, optimize_coeffs(), p(), pow(), madness::BaseTensor::size(), v, and w().

Member Data Documentation

alpha

const double Fred::alpha

private

f

const Tensor<double> Fred::f

private

mu

const double Fred::mu

private

nx

const long Fred::nx

private

p

const double Fred::p

private

v

std::vector<double> Fred::v

private

w

const Tensor<double> Fred::w

private

x

const Tensor<double> Fred::x

private

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

• testspectralprop.cc
• gfit.cc
• test_future.cc