stratified_sampling

<vector>
v: a numeric candidate v from which draw sample.</vector>

<integer>
k: selection sample size.</integer>

<vector>
g: stratify structure g - info on grouping of v so that the selected sample is stratified across groups.</vector>


select k elements from vector v w.r.t stratify structure group g.
 TODO - implementing via template so v is flexible as vec or uvec.


Algorithms for solving selective k-means problem,
which is defined as finding k rows in an m x n matrix such that
the sum of each column minimal is minimized.
In the scenario when m == n and each cell value in matrix is a
valid distance metric, this is equivalent to a k-means problem.
The selective k-means extends the k-means problem in the sense
that it is possible to have m != n, often the case m < n which
implies the search is limited within a small subset of rows.
Also, the selective k-means extends the k-means problem in the
sense that the instance in row set can be instance not seen in
the column set, e.g., select 2 from 3 internet service provider
(row) for 5 houses (column) such that minimize the overall cost
(cell value) - overall cost is the sum of the column minimal of
the selected 2 service provider.

Guang Yang

Selective k-Means

stratified_sampling function

<vector>
v: a numeric candidate v from which draw sample.

<vector>
g: stratify structure g - info on grouping of v so that the selected sample is stratified across groups.

stratified_sampling: stratified_sampling

Description

Usage

Arguments

Value