Class for univariate flat distributions: p(x)=1/(hi-lo) in [lo,hi]. More...
#include <pdf1d_flat.h>
Public Member Functions | |
| pdf1d_flat () | |
| Dflt ctor (creates flat distribution in range [0,1]). | |
| pdf1d_flat (double lo, double hi) | |
| Dflt ctor (creates flat distribution in range [lo,hi]). | |
| virtual | ~pdf1d_flat () |
| Destructor. | |
| double | sd () const |
| Return standard deviation. | |
| void | set (double lo, double hi) |
| Creates flat distribution in range [lo,hi]. | |
| double | lo () const |
| Lower limit of range. | |
| double | hi () const |
| Upper limit of range. | |
| virtual pdf1d_sampler * | new_sampler () const |
| Create a sampler object on the heap. | |
| virtual double | operator() (double x) const |
| Probability density at x. | |
| virtual double | log_p (double x) const |
| Log of probability density at x. | |
| virtual double | cdf (double x) const |
| Cumulative Probability (P(x'<x) for x' drawn from the distribution). | |
| virtual bool | cdf_is_analytic () const |
| Return true if cdf() uses an analytic implementation. | |
| virtual double | gradient (double x, double &p) const |
| Gradient of PDF at x. | |
| virtual double | log_prob_thresh (double pass_proportion) const |
| Compute threshold for PDF to pass a given proportion. | |
| virtual double | nearest_plausible (double x, double log_p_min) const |
| Compute nearest point to x which has a density above a threshold. | |
| short | version_no () const |
| Version number for I/O. | |
| virtual vcl_string | is_a () const |
| Name of the class. | |
| virtual bool | is_class (vcl_string const &s) const |
| Does the name of the class match the argument?. | |
| virtual pdf1d_pdf * | clone () const |
| Create a copy on the heap and return base class pointer. | |
| virtual void | print_summary (vcl_ostream &os) const |
| Print class to os. | |
| virtual void | b_write (vsl_b_ostream &bfs) const |
| Save class to binary file stream. | |
| virtual void | b_read (vsl_b_istream &bfs) |
| Load class from binary file stream. | |
| double | mean () const |
| Mean of distribution. | |
| double | variance () const |
| Variance of each dimension. | |
| virtual int | n_peaks () const |
| Number of peaks of distribution. | |
| virtual double | peak (int) const |
| Position of the i'th peak. | |
| virtual double | inverse_cdf (double P) const |
| The inverse cdf. | |
| virtual bool | is_valid_pdf () const |
| Return true if the object represents a valid PDF. | |
| void | get_samples (vnl_vector< double > &x) const |
| Fill x with samples drawn from distribution. | |
| bool | write_plot_file (const vcl_string &plot_file, double min_x, double max_x, int n) const |
| Write values (x,p(x)) to text file suitable for plotting. | |
Protected Member Functions | |
| void | set_mean (double m) |
| void | set_variance (double v) |
Private Attributes | |
| double | lo_ |
| double | hi_ |
| double | p_ |
| double | log_p_ |
Detailed Description
Class for univariate flat distributions: p(x)=1/(hi-lo) in [lo,hi].
In range [lo,hi] p(x)=1/(hi-lo)
Definition at line 16 of file pdf1d_flat.h.
Constructor & Destructor Documentation
| pdf1d_flat::pdf1d_flat | ( | ) |
Dflt ctor (creates flat distribution in range [0,1]).
Definition at line 19 of file pdf1d_flat.cxx.
| pdf1d_flat::pdf1d_flat | ( | double | lo, |
| double | hi | ||
| ) |
Dflt ctor (creates flat distribution in range [lo,hi]).
Definition at line 24 of file pdf1d_flat.cxx.
| pdf1d_flat::~pdf1d_flat | ( | ) | [virtual] |
Destructor.
Definition at line 31 of file pdf1d_flat.cxx.
Member Function Documentation
| void pdf1d_flat::b_read | ( | vsl_b_istream & | bfs | ) | [virtual] |
Load class from binary file stream.
Implements pdf1d_pdf.
Definition at line 185 of file pdf1d_flat.cxx.
| void pdf1d_flat::b_write | ( | vsl_b_ostream & | bfs | ) | const [virtual] |
Save class to binary file stream.
Implements pdf1d_pdf.
Definition at line 173 of file pdf1d_flat.cxx.
| double pdf1d_flat::cdf | ( | double | x | ) | const [virtual] |
Cumulative Probability (P(x'<x) for x' drawn from the distribution).
Reimplemented from pdf1d_pdf.
Definition at line 79 of file pdf1d_flat.cxx.
| bool pdf1d_flat::cdf_is_analytic | ( | ) | const [virtual] |
Return true if cdf() uses an analytic implementation.
Default is false, as the base implementation is to draw samples from the distribution randomly to estimate cdf(x)
Reimplemented from pdf1d_pdf.
Definition at line 89 of file pdf1d_flat.cxx.
| pdf1d_pdf * pdf1d_flat::clone | ( | ) | const [virtual] |
Create a copy on the heap and return base class pointer.
Implements pdf1d_pdf.
Definition at line 154 of file pdf1d_flat.cxx.
| void pdf1d_pdf::get_samples | ( | vnl_vector< double > & | x | ) | const [inherited] |
Fill x with samples drawn from distribution.
Utility function. This calls new_sampler() to do the work, then deletes the sampler again. If you intend calling this repeatedly, create a sampler yourself.
Definition at line 132 of file pdf1d_pdf.cxx.
| double pdf1d_flat::gradient | ( | double | x, |
| double & | p | ||
| ) | const [virtual] |
| double pdf1d_flat::hi | ( | ) | const [inline] |
Upper limit of range.
Definition at line 42 of file pdf1d_flat.h.
| double pdf1d_pdf::inverse_cdf | ( | double | P | ) | const [virtual, inherited] |
The inverse cdf.
The inverse cumulative distribution function.
The value of x: P(x'<x) = P for x' drawn from distribution pdf. The default version of this algorithm uses sampling if !cdf_is_analytic(), and Newton-Raphson root finding otherwise.
The value of x: P(x'<x) = P for x' drawn from distribution pdf.
Reimplemented in pdf1d_kernel_pdf.
Definition at line 288 of file pdf1d_pdf.cxx.
| vcl_string pdf1d_flat::is_a | ( | ) | const [virtual] |
| bool pdf1d_flat::is_class | ( | vcl_string const & | s | ) | const [virtual] |
Does the name of the class match the argument?.
Reimplemented from pdf1d_pdf.
Definition at line 136 of file pdf1d_flat.cxx.
| bool pdf1d_pdf::is_valid_pdf | ( | ) | const [virtual, inherited] |
Return true if the object represents a valid PDF.
This will return false, if n_dims() is 0, for example just ofter default construction.
Reimplemented in pdf1d_mixture.
Definition at line 126 of file pdf1d_pdf.cxx.
| double pdf1d_flat::lo | ( | ) | const [inline] |
Lower limit of range.
Definition at line 39 of file pdf1d_flat.h.
| double pdf1d_flat::log_p | ( | double | x | ) | const [virtual] |
Log of probability density at x.
This value is also the Normalised Mahalanobis distance from the centroid to the given vector.
Implements pdf1d_pdf.
Definition at line 72 of file pdf1d_flat.cxx.
| double pdf1d_flat::log_prob_thresh | ( | double | pass_proportion | ) | const [virtual] |
Compute threshold for PDF to pass a given proportion.
Reimplemented from pdf1d_pdf.
Definition at line 108 of file pdf1d_flat.cxx.
| double pdf1d_pdf::mean | ( | ) | const [inline, inherited] |
Mean of distribution.
Definition at line 42 of file pdf1d_pdf.h.
| virtual int pdf1d_pdf::n_peaks | ( | ) | const [inline, virtual, inherited] |
Number of peaks of distribution.
Definition at line 48 of file pdf1d_pdf.h.
| double pdf1d_flat::nearest_plausible | ( | double | x, |
| double | log_p_min | ||
| ) | const [virtual] |
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.
- Parameters:
-
x This may be modified to the nearest plausible position. log_p_min lower threshold for log_p(x)
Implements pdf1d_pdf.
Definition at line 115 of file pdf1d_flat.cxx.
| pdf1d_sampler * pdf1d_flat::new_sampler | ( | ) | const [virtual] |
Create a sampler object on the heap.
Caller is responsible for deletion.
Implements pdf1d_pdf.
Definition at line 55 of file pdf1d_flat.cxx.
| double pdf1d_flat::operator() | ( | double | x | ) | const [virtual] |
Probability density at x.
Reimplemented from pdf1d_pdf.
Definition at line 64 of file pdf1d_flat.cxx.
| virtual double pdf1d_pdf::peak | ( | int | ) | const [inline, virtual, inherited] |
Position of the i'th peak.
Definition at line 51 of file pdf1d_pdf.h.
| void pdf1d_flat::print_summary | ( | vcl_ostream & | os | ) | const [virtual] |
| double pdf1d_flat::sd | ( | ) | const [inline] |
Return standard deviation.
Definition at line 33 of file pdf1d_flat.h.
| void pdf1d_flat::set | ( | double | lo, |
| double | hi | ||
| ) |
Creates flat distribution in range [lo,hi].
Initialise.
Definition at line 38 of file pdf1d_flat.cxx.
| void pdf1d_pdf::set_mean | ( | double | m | ) | [inline, protected, inherited] |
Reimplemented in pdf1d_gaussian.
Definition at line 31 of file pdf1d_pdf.h.
| void pdf1d_pdf::set_variance | ( | double | v | ) | [inline, protected, inherited] |
Definition at line 32 of file pdf1d_pdf.h.
| double pdf1d_pdf::variance | ( | ) | const [inline, inherited] |
Variance of each dimension.
Definition at line 45 of file pdf1d_pdf.h.
| short pdf1d_flat::version_no | ( | ) | const |
Version number for I/O.
Reimplemented from pdf1d_pdf.
Definition at line 145 of file pdf1d_flat.cxx.
| bool pdf1d_pdf::write_plot_file | ( | const vcl_string & | plot_file, |
| double | min_x, | ||
| double | max_x, | ||
| int | n | ||
| ) | const [inherited] |
Write values (x,p(x)) to text file suitable for plotting.
Evaluate pdf at n points in range [min_x,max_x] and write a text file, each line of which is {x p(x)}, suitable for plotting with many graph packages
Definition at line 142 of file pdf1d_pdf.cxx.
Member Data Documentation
double pdf1d_flat::hi_ [private] |
Definition at line 18 of file pdf1d_flat.h.
double pdf1d_flat::lo_ [private] |
Definition at line 18 of file pdf1d_flat.h.
double pdf1d_flat::log_p_ [private] |
Definition at line 20 of file pdf1d_flat.h.
double pdf1d_flat::p_ [private] |
Definition at line 19 of file pdf1d_flat.h.
The documentation for this class was generated from the following files:
- contrib/mul/pdf1d/pdf1d_flat.h
- contrib/mul/pdf1d/pdf1d_flat.cxx
