contrib/mul/pdf1d/pdf1d_exponential.h

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// This is mul/pdf1d/pdf1d_exponential.h
#ifndef pdf1d_exponential_h
#define pdf1d_exponential_h

//:
// \file
// \brief Univariate exponential PDF
// \author Tim Cootes

#include <pdf1d/pdf1d_pdf.h>
#include <vcl_iosfwd.h>

//: Class for exponential distributions
//  $p(x)=\lambda exp(-\lambda x)$
class pdf1d_exponential : public pdf1d_pdf
{
  double lambda_;
  double log_lambda_;

 public:
  //: Dflt ctor (creates exponential distribution in range [0,1])
  pdf1d_exponential();

  //: Dflt ctor (creates exponential distribution in range [lo,hi])
  pdf1d_exponential(double lambda);

  //: Destructor
  virtual ~pdf1d_exponential();

  //: Return standard deviation
  double sd() const { return 1.0/lambda_; }

  //: Creates exponential distribution with given lambda
  void set_lambda(double lambda);

  //: Value of lambda
  double lambda() const { return lambda_; }

  //: Create a sampler object on the heap
  // Caller is responsible for deletion.
  virtual pdf1d_sampler* new_sampler() const;

  //: Probability density at x
  virtual double operator()(double x) const;

  //: Log of probability density at x
  // This value is also the Normalised Mahalanobis distance
  // from the centroid to the given vector.
  virtual double log_p(double x) const;

  //: Cumulative Probability (P(x'<x) for x' drawn from the distribution)
  //  Returns 1-exp(-x/lambda)
  virtual double cdf(double x) const;

  //: Return true if cdf() uses an analytic implementation
  virtual bool cdf_is_analytic() const;

  //: Gradient of PDF at x
  virtual double gradient(double x, double& p) const;

  //: Compute threshold for PDF to pass a given proportion
  virtual double log_prob_thresh(double pass_proportion) const;

  //: Compute nearest point to x which has a density above a threshold
  //  If log_p(x)>log_p_min then x returned unchanged.  Otherwise x is moved
  //  (typically up the gradient) until log_p(x)>=log_p_min.
  // \param x This may be modified to the nearest plausible position.
  // \param log_p_min lower threshold for log_p(x)
  virtual double nearest_plausible(double x, double log_p_min) const;


  //: Version number for I/O
  short version_no() const;

  //: Name of the class
  virtual vcl_string is_a() const;

  //: Does the name of the class match the argument?
  virtual bool is_class(vcl_string const& s) const;

  //: Create a copy on the heap and return base class pointer
  virtual pdf1d_pdf* clone() const;

  //: Print class to os
  virtual void print_summary(vcl_ostream& os) const;

  //: Save class to binary file stream
  virtual void b_write(vsl_b_ostream& bfs) const;


  //: Load class from binary file stream
  virtual void b_read(vsl_b_istream& bfs);
};

#endif // pdf1d_exponential_h