The base class for all probability distributions. More...
#include <vpdl_distribution.h>
Public Types | |
| typedef vpdt_field_default< T, n >::type | field_type |
| the data type used for vectors. More... | |
| typedef vpdt_field_default< T, n >::type | vector |
| the data type used for vectors. More... | |
| typedef vpdt_field_traits< field_type >::matrix_type | matrix |
| the data type used for matrices. More... | |
Public Member Functions | |
| virtual | ~vpdl_distribution () |
| virtual unsigned int | dimension () const =0 |
Return the run time dimension, which does not equal n when n==0. More... | |
| virtual vpdl_distribution< T, n > * | clone () const =0 |
| Create a copy on the heap and return base class pointer. More... | |
| virtual T | density (const vector &pt) const =0 |
| Evaluate the unnormalized density at a point. More... | |
| virtual T | prob_density (const vector &pt) const |
| Evaluate the probability density at a point. More... | |
| virtual T | log_prob_density (const vector &pt) const |
| Evaluate the log probability density at a point. More... | |
| virtual T | gradient_density (const vector &pt, vector &g) const =0 |
| Compute the gradient of the unnormalized density at a point. More... | |
| virtual T | norm_const () const =0 |
| The normalization constant for the density. More... | |
| virtual T | cumulative_prob (const vector &pt) const =0 |
| Evaluate the cumulative distribution function at a point. More... | |
| virtual vector | inverse_cdf (const T &p) const |
| Compute the inverse of the cumulative_prob() function. More... | |
| virtual T | box_prob (const vector &min_pt, const vector &max_pt) const |
| The probability of being in an axis-aligned box. More... | |
| virtual void | compute_mean (vector &mean) const =0 |
| Compute the mean of the distribution. More... | |
| virtual void | compute_covar (matrix &covar) const =0 |
| Compute the covariance of the distribution. More... | |
Detailed Description
template<class T, unsigned int n = 0>
class vpdl_distribution< T, n >
The base class for all probability distributions.
There is a distinct polymorphic class hierarchy for each choice of template parameters. The vector and matrix data types vary with both T and n.
- Template Parameters
-
T is the scalar type use for numerical calculations (generally double or float) n is the fixed dimension of the space with special case 0 (the default) indicating dynamic dimension set at run time. - For n > 1 the data types are vnl_vector_fixed<T,n> and vnl_matrix_fixed<T,n,n>
- For n == 1 the data types are T and T
- For n == 0 the data types are vnl_vector<T> and vnl_matrix<T>
Definition at line 33 of file vpdl_distribution.h.
Member Typedef Documentation
◆ field_type
| typedef vpdt_field_default<T,n>::type vpdl_distribution< T, n >::field_type |
the data type used for vectors.
Definition at line 39 of file vpdl_distribution.h.
◆ matrix
| typedef vpdt_field_traits<field_type>::matrix_type vpdl_distribution< T, n >::matrix |
the data type used for matrices.
Definition at line 44 of file vpdl_distribution.h.
◆ vector
| typedef vpdt_field_default<T,n>::type vpdl_distribution< T, n >::vector |
the data type used for vectors.
Definition at line 42 of file vpdl_distribution.h.
Constructor & Destructor Documentation
◆ ~vpdl_distribution()
|
inlinevirtual |
Definition at line 36 of file vpdl_distribution.h.
Member Function Documentation
◆ box_prob()
|
virtual |
The probability of being in an axis-aligned box.
The box is defined by two points, the minimum and maximum. Implemented in terms of cumulative_prob() by default.
Reimplemented in vpdl_mixture< T, n >, vpdl_kernel_gaussian_sfbw< T, n >, and vpdl_gaussian_sphere< T, n >.
Definition at line 89 of file vpdl_distribution.hxx.
◆ clone()
|
pure virtual |
Create a copy on the heap and return base class pointer.
Implemented in vpdl_mixture< T, n >, vpdl_mixture_of< dist_t >, vpdl_gaussian_sphere< T, n >, vpdl_gaussian_indep< T, n >, vpdl_gaussian< T, n >, and vpdl_kernel_gaussian_sfbw< T, n >.
◆ compute_covar()
|
pure virtual |
Compute the covariance of the distribution.
This may be trivial for distributions like Gaussians, but actually involves computation for others.
Implemented in vpdl_mixture< T, n >, vpdl_kernel_gaussian_sfbw< T, n >, vpdl_gaussian_sphere< T, n >, vpdl_gaussian< T, n >, vpdl_gaussian_indep< T, n >, and vpdl_mixture_of< dist_t >.
◆ compute_mean()
|
pure virtual |
Compute the mean of the distribution.
This may be trivial for distributions like Gaussians, but actually involves computation for others.
Implemented in vpdl_mixture< T, n >, vpdl_gaussian_sphere< T, n >, vpdl_gaussian_indep< T, n >, vpdl_gaussian< T, n >, and vpdl_kernel_base< T, n >.
◆ cumulative_prob()
|
pure virtual |
Evaluate the cumulative distribution function at a point.
This is the integral of the density function from negative infinity (in all dimensions) to the point in question
- Note
- It is not possible to compute this value for all functions in closed form. In some cases, numerical integration may be used. If no good solutions exists the function should return a quiet NaN.
Implemented in vpdl_mixture< T, n >, vpdl_kernel_gaussian_sfbw< T, n >, vpdl_gaussian_sphere< T, n >, vpdl_gaussian_indep< T, n >, and vpdl_gaussian< T, n >.
◆ density()
|
pure virtual |
Evaluate the unnormalized density at a point.
- Note
- This is not a probability density. To make this a probability multiply by norm_const()
- See also
- prob_density
Implemented in vpdl_mixture< T, n >, vpdl_gaussian_sphere< T, n >, vpdl_gaussian_indep< T, n >, vpdl_gaussian< T, n >, and vpdl_kernel_gaussian_sfbw< T, n >.
◆ dimension()
|
pure virtual |
Return the run time dimension, which does not equal n when n==0.
Implemented in vpdl_mixture< T, n >, vpdl_mixture_of< dist_t >, vpdl_gaussian_sphere< T, n >, vpdl_gaussian_indep< T, n >, vpdl_gaussian< T, n >, and vpdl_kernel_base< T, n >.
◆ gradient_density()
|
pure virtual |
Compute the gradient of the unnormalized density at a point.
- Returns
- the density at pt since it is usually needed as well, and is often trivial to compute while computing gradient
- Return values
-
g the gradient vector
Implemented in vpdl_mixture< T, n >, vpdl_kernel_gaussian_sfbw< T, n >, vpdl_gaussian_sphere< T, n >, vpdl_gaussian_indep< T, n >, and vpdl_gaussian< T, n >.
◆ inverse_cdf()
|
virtual |
Compute the inverse of the cumulative_prob() function.
The value of x: P(x'<x) = P for x' drawn from the distribution.
- Note
- This is only valid for univariate distributions multivariate distributions will return a quiet NaN
The value of x: P(x'<x) = P for x' drawn from the distribution. This is only valid for univariate distributions multivariate distributions will return -infinity
Definition at line 78 of file vpdl_distribution.hxx.
◆ log_prob_density()
|
inlinevirtual |
Evaluate the log probability density at a point.
Reimplemented in vpdl_gaussian_sphere< T, n >, vpdl_gaussian_indep< T, n >, and vpdl_gaussian< T, n >.
Definition at line 65 of file vpdl_distribution.h.
◆ norm_const()
|
pure virtual |
The normalization constant for the density.
When density() is multiplied by this value it becomes prob_density norm_const() is reciprocal of the integral of density over the entire field
Implemented in vpdl_mixture< T, n >, vpdl_kernel_fbw_base< T, n >, vpdl_mixture_of< dist_t >, vpdl_gaussian_sphere< T, n >, vpdl_gaussian_indep< T, n >, and vpdl_gaussian< T, n >.
◆ prob_density()
|
inlinevirtual |
Evaluate the probability density at a point.
Reimplemented in vpdl_mixture< T, n >, vpdl_kernel_gaussian_sfbw< T, n >, vpdl_gaussian_sphere< T, n >, vpdl_gaussian_indep< T, n >, and vpdl_gaussian< T, n >.
Definition at line 59 of file vpdl_distribution.h.
The documentation for this class was generated from the following files:
- core/vpdl/vpdl_distribution.h
- core/vpdl/vpdl_distribution.hxx
