Class for univariate kernel based PDFs. More...
#include <pdf1d_kernel_pdf.h>
Public Member Functions | |
| pdf1d_kernel_pdf () | |
| Dflt ctor. | |
| virtual | ~pdf1d_kernel_pdf () |
| Destructor. | |
| virtual void | set_centres (const vnl_vector< double > &x, double width) |
| Initialise so all kernels have the same width. | |
| virtual void | set_centres (const vnl_vector< double > &x, const vnl_vector< double > &width) |
| Initialise so all kernels have given width. | |
| const vnl_vector< double > & | centre () const |
| Position of kernel centres. | |
| const vnl_vector< double > & | width () const |
| Width of each kernel. | |
| bool | all_same_width () const |
| True if all kernels have the same width. | |
| virtual double | inverse_cdf (double P) const |
| The inverse cdf. | |
| 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 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 | log_p (double x) const =0 |
| Log of probability density at x. | |
| virtual double | operator() (double x) const |
| 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 =0 |
| Gradient and value of PDF at x. | |
| virtual pdf1d_sampler * | new_sampler () const =0 |
| Create a sampler object on the heap. | |
| 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 =0 |
| Compute nearest point to x which has a density above a threshold. | |
| 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. | |
| virtual pdf1d_pdf * | clone () const =0 |
| Create a copy on the heap and return base class pointer. | |
| 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) |
Protected Attributes | |
| vnl_vector< double > | x_ |
| Position of kernel centres. | |
| vnl_vector< double > | width_ |
| Width of each kernel. | |
| vcl_vector< int > | index_ |
| Workspace. | |
| bool | all_same_width_ |
| True if all kernels have the same width. | |
Detailed Description
Class for univariate kernel based PDFs.
Distribution is the sum of a set of kernel functions placed on the training samples.
Essentially a special case of a mixture model. It differs from the former in that a) every component has the same shape, though possibly a different width b) every component has the same weight c) Usually one uses different training algorithms. Mixture models are more general.
Definition at line 24 of file pdf1d_kernel_pdf.h.
Constructor & Destructor Documentation
| pdf1d_kernel_pdf::pdf1d_kernel_pdf | ( | ) |
Dflt ctor.
Definition at line 23 of file pdf1d_kernel_pdf.cxx.
| pdf1d_kernel_pdf::~pdf1d_kernel_pdf | ( | ) | [virtual] |
Destructor.
Definition at line 29 of file pdf1d_kernel_pdf.cxx.
Member Function Documentation
| bool pdf1d_kernel_pdf::all_same_width | ( | ) | const [inline] |
True if all kernels have the same width.
Definition at line 61 of file pdf1d_kernel_pdf.h.
| void pdf1d_kernel_pdf::b_read | ( | vsl_b_istream & | bfs | ) | [virtual] |
Load class from binary file stream.
Implements pdf1d_pdf.
Reimplemented in pdf1d_weighted_kernel_pdf, pdf1d_epanech_kernel_pdf, and pdf1d_gaussian_kernel_pdf.
Definition at line 206 of file pdf1d_kernel_pdf.cxx.
| void pdf1d_kernel_pdf::b_write | ( | vsl_b_ostream & | bfs | ) | const [virtual] |
Save class to binary file stream.
Implements pdf1d_pdf.
Reimplemented in pdf1d_weighted_kernel_pdf, pdf1d_epanech_kernel_pdf, and pdf1d_gaussian_kernel_pdf.
Definition at line 195 of file pdf1d_kernel_pdf.cxx.
| double pdf1d_pdf::cdf | ( | double | x0 | ) | const [virtual, inherited] |
Cumulative Probability (P(x'<x) for x' drawn from the distribution.
Cumulative Probability (P(x'<x) for x' drawn from the distribution).
By default this can be calculated by drawing random samples from the distribution and computing the number less than x.
Reimplemented in pdf1d_flat, pdf1d_gaussian, pdf1d_exponential, pdf1d_mixture, pdf1d_epanech_kernel_pdf, pdf1d_gaussian_kernel_pdf, and pdf1d_weighted_epanech_kernel_pdf.
Definition at line 45 of file pdf1d_pdf.cxx.
| bool pdf1d_pdf::cdf_is_analytic | ( | ) | const [virtual, inherited] |
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 in pdf1d_flat, pdf1d_gaussian, pdf1d_exponential, pdf1d_mixture, pdf1d_epanech_kernel_pdf, pdf1d_gaussian_kernel_pdf, and pdf1d_weighted_epanech_kernel_pdf.
Definition at line 62 of file pdf1d_pdf.cxx.
| const vnl_vector<double>& pdf1d_kernel_pdf::centre | ( | ) | const [inline] |
Position of kernel centres.
Definition at line 55 of file pdf1d_kernel_pdf.h.
| virtual pdf1d_pdf* pdf1d_pdf::clone | ( | ) | const [pure virtual, inherited] |
Create a copy on the heap and return base class pointer.
Implemented in pdf1d_mixture, pdf1d_flat, pdf1d_gaussian, pdf1d_exponential, pdf1d_epanech_kernel_pdf, pdf1d_gaussian_kernel_pdf, and pdf1d_weighted_epanech_kernel_pdf.
| 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.
| virtual double pdf1d_pdf::gradient | ( | double | x, |
| double & | p | ||
| ) | const [pure virtual, inherited] |
Gradient and value of PDF at x.
Computes gradient of PDF at x, and returns the prob at x in p
Implemented in pdf1d_flat, pdf1d_gaussian, pdf1d_exponential, pdf1d_mixture, pdf1d_epanech_kernel_pdf, pdf1d_gaussian_kernel_pdf, and pdf1d_weighted_epanech_kernel_pdf.
| double pdf1d_kernel_pdf::inverse_cdf | ( | double | P | ) | const [virtual] |
The inverse cdf.
The value of x: P(x'<x) = P for x' drawn from distribution pdf. Uses Newton-Raphson.
The value of x: P(x'<x) = P for x' drawn from distribution pdf.
Reimplemented from pdf1d_pdf.
Definition at line 69 of file pdf1d_kernel_pdf.cxx.
| vcl_string pdf1d_kernel_pdf::is_a | ( | ) | const [virtual] |
Name of the class.
Reimplemented from pdf1d_pdf.
Reimplemented in pdf1d_weighted_kernel_pdf, pdf1d_epanech_kernel_pdf, pdf1d_gaussian_kernel_pdf, and pdf1d_weighted_epanech_kernel_pdf.
Definition at line 165 of file pdf1d_kernel_pdf.cxx.
| bool pdf1d_kernel_pdf::is_class | ( | vcl_string const & | s | ) | const [virtual] |
Does the name of the class match the argument?.
Reimplemented from pdf1d_pdf.
Reimplemented in pdf1d_weighted_kernel_pdf, pdf1d_epanech_kernel_pdf, pdf1d_gaussian_kernel_pdf, and pdf1d_weighted_epanech_kernel_pdf.
Definition at line 173 of file pdf1d_kernel_pdf.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.
| virtual double pdf1d_pdf::log_p | ( | double | x | ) | const [pure virtual, inherited] |
Log of probability density at x.
Implemented in pdf1d_flat, pdf1d_gaussian, pdf1d_exponential, pdf1d_mixture, pdf1d_epanech_kernel_pdf, pdf1d_gaussian_kernel_pdf, and pdf1d_weighted_epanech_kernel_pdf.
| double pdf1d_pdf::log_prob_thresh | ( | double | pass_proportion | ) | const [virtual, inherited] |
Compute threshold for PDF to pass a given proportion.
Reimplemented in pdf1d_flat, pdf1d_gaussian, and pdf1d_exponential.
Definition at line 68 of file pdf1d_pdf.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.
| virtual double pdf1d_pdf::nearest_plausible | ( | double | x, |
| double | log_p_min | ||
| ) | const [pure virtual, inherited] |
Compute nearest point to x which has a density above a threshold.
If log_p(x)>log_p_min then x returned unchanged. Otherwise move (typically up the gradient) until log_p(x)>=log_p_min.
Implemented in pdf1d_flat, pdf1d_gaussian, pdf1d_exponential, pdf1d_mixture, pdf1d_epanech_kernel_pdf, pdf1d_gaussian_kernel_pdf, and pdf1d_weighted_epanech_kernel_pdf.
| virtual pdf1d_sampler* pdf1d_pdf::new_sampler | ( | ) | const [pure virtual, inherited] |
Create a sampler object on the heap.
Caller is responsible for deletion.
Implemented in pdf1d_mixture, pdf1d_flat, pdf1d_gaussian, pdf1d_exponential, pdf1d_epanech_kernel_pdf, pdf1d_gaussian_kernel_pdf, and pdf1d_weighted_epanech_kernel_pdf.
| double pdf1d_pdf::operator() | ( | double | x | ) | const [virtual, inherited] |
Probability density at x.
Reimplemented in pdf1d_flat, pdf1d_exponential, pdf1d_mixture, pdf1d_epanech_kernel_pdf, pdf1d_gaussian_kernel_pdf, and pdf1d_weighted_epanech_kernel_pdf.
Definition at line 37 of file pdf1d_pdf.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_kernel_pdf::print_summary | ( | vcl_ostream & | os | ) | const [virtual] |
Print class to os.
Implements pdf1d_pdf.
Reimplemented in pdf1d_weighted_kernel_pdf, pdf1d_epanech_kernel_pdf, and pdf1d_gaussian_kernel_pdf.
Definition at line 187 of file pdf1d_kernel_pdf.cxx.
| void pdf1d_kernel_pdf::set_centres | ( | const vnl_vector< double > & | x, |
| double | width | ||
| ) | [virtual] |
Initialise so all kernels have the same width.
Reimplemented in pdf1d_weighted_kernel_pdf.
Definition at line 35 of file pdf1d_kernel_pdf.cxx.
| void pdf1d_kernel_pdf::set_centres | ( | const vnl_vector< double > & | x, |
| const vnl_vector< double > & | width | ||
| ) | [virtual] |
Initialise so all kernels have given width.
Reimplemented in pdf1d_weighted_kernel_pdf.
Definition at line 50 of file pdf1d_kernel_pdf.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_kernel_pdf::version_no | ( | ) | const |
Version number for I/O.
Reimplemented from pdf1d_pdf.
Reimplemented in pdf1d_weighted_kernel_pdf, pdf1d_epanech_kernel_pdf, pdf1d_gaussian_kernel_pdf, and pdf1d_weighted_epanech_kernel_pdf.
Definition at line 180 of file pdf1d_kernel_pdf.cxx.
| const vnl_vector<double>& pdf1d_kernel_pdf::width | ( | ) | const [inline] |
Width of each kernel.
Definition at line 58 of file pdf1d_kernel_pdf.h.
| 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
bool pdf1d_kernel_pdf::all_same_width_ [protected] |
True if all kernels have the same width.
Definition at line 38 of file pdf1d_kernel_pdf.h.
vcl_vector<int> pdf1d_kernel_pdf::index_ [mutable, protected] |
Workspace.
If non-empty x_(index[i]) <= x_(index[i+1])
Definition at line 35 of file pdf1d_kernel_pdf.h.
vnl_vector<double> pdf1d_kernel_pdf::width_ [protected] |
Width of each kernel.
Definition at line 31 of file pdf1d_kernel_pdf.h.
vnl_vector<double> pdf1d_kernel_pdf::x_ [protected] |
Position of kernel centres.
Definition at line 28 of file pdf1d_kernel_pdf.h.
The documentation for this class was generated from the following files:
- contrib/mul/pdf1d/pdf1d_kernel_pdf.h
- contrib/mul/pdf1d/pdf1d_kernel_pdf.cxx
