Module to select an weighted sampler
The idea is that it contains the list of indexes up to a size n,
and each index has an weight
The sampling is done proportionally to the weights.
It depends on the rndgen-fortran module, found at https://github.com/wcota/rndgen-fortran
Each sampler algorithm is implemented separately.
!> Example:
```fortran
program example_sampler
use datastructs_fortran
use kinds_mod
use rndgen_mod
implicit none
type(rndgen) :: gen
class(sampler_base_t), allocatable :: my_sampler
integer(kind=i4) :: i
real(kind=dp), parameter :: weights(*) = [100.0_dp, 50.0_dp, 200.0_dp, 25.0_dp]
integer(kind=i4), allocatable :: count_times(:)
integer(kind=i4) :: selected_index
integer(kind=i4), parameter :: n_samples = 10000
! choose the sampler algorithm
call choose_sampler(my_sampler, 'btree')
call my_sampler%init(size(weights))
call my_sampler%set_weight_array(weights)
! initialize the random generator with a seed
call gen%init(12345)
! count the number of times each element was selected
allocate(count_times(size(weights)))
count_times = 0
! Sample from the distribution
do i = 1, n_samples
selected_index = my_sampler%sample(gen)
!write(*, fmt_general) "Selected index:", selected_index
count_times(selected_index) = count_times(selected_index) + 1
end do
! Print the result
write(*, fmt_general) "For n_samples = ", n_samples
do i = 1, size(weights)
write(*, fmt_general) "@ Index:", i, " Weight:", weights(i), "Count:", count_times(i), "Prob:", 1.0_dp * count_times(i) / sum(count_times), "Expected prob:", weights(i) / sum(weights)
end do
end program example_sampler
Todo
Document the rest of the internal modules ```
| Type | Visibility | Attributes | Name | Initial | |||
|---|---|---|---|---|---|---|---|
| character(len=*), | public, | parameter | :: | sampler_choices | = | 'btree,rejection,rejection_two_classes,rejection_maxheap,rejection_maxheap_two_classes,rejection_maxheap_composition' |
List of available sampler algorithms |
Subroutine to allocate a class(sampler_base_t) to its respective object
| Type | Intent | Optional | Attributes | Name | ||
|---|---|---|---|---|---|---|
| class(sampler_base_t), | intent(out), | allocatable | :: | weighted_sampler | ||
| character(len=*), | intent(in) | :: | selected_algorithm |