RAUtil Class Reference

## Static Public Member Functions

static size_t MinimumSamplesReqd (const size_t n, const size_t k, const double tau, const double alpha)
Compute the minimum number of samples required to guarantee the given rank-approximation and success probability. More...

static void ObtainDistinctSamples (const size_t numSamples, const size_t rangeUpperBound, arma::uvec &distinctSamples)
Pick up desired number of samples (with replacement) from a given range of integers so that only the distinct samples are returned from the range [0 - specified upper bound) More...

static double SuccessProbability (const size_t n, const size_t k, const size_t m, const size_t t)
Compute the success probability of obtaining 'k'-neighbors from a set of size 'n' within the top 't' neighbors if 'm' samples are made. More...

## Detailed Description

Definition at line 21 of file ra_util.hpp.

## ◆ MinimumSamplesReqd()

 static size_t MinimumSamplesReqd ( const size_t n, const size_t k, const double tau, const double alpha )
static

Compute the minimum number of samples required to guarantee the given rank-approximation and success probability.

Parameters
 n Size of the set to be sampled from. k The number of neighbors required within the rank-approximation. tau The rank-approximation in percentile of the data. alpha The success probability desired.

## ◆ ObtainDistinctSamples()

 static void ObtainDistinctSamples ( const size_t numSamples, const size_t rangeUpperBound, arma::uvec & distinctSamples )
static

Pick up desired number of samples (with replacement) from a given range of integers so that only the distinct samples are returned from the range [0 - specified upper bound)

Parameters
 numSamples Number of random samples. rangeUpperBound The upper bound on the range of integers. distinctSamples The list of the distinct samples.

## ◆ SuccessProbability()

 static double SuccessProbability ( const size_t n, const size_t k, const size_t m, const size_t t )
static

Compute the success probability of obtaining 'k'-neighbors from a set of size 'n' within the top 't' neighbors if 'm' samples are made.

Parameters
 n Size of the set being sampled from. k The number of neighbors required within the rank-approximation. m The number of random samples. t The desired rank-approximation.

The documentation for this class was generated from the following file:
• /home/jenkins-mlpack/mlpack.org/_src/mlpack-git/src/mlpack/methods/rann/ra_util.hpp