#include <UniformHBE.h>
 | 
|   | UniformHBE (shared_ptr< MatrixXd > X, int M, double w, int k, shared_ptr< Kernel > ker, int subsample) | 
|   | 
| double  | query (VectorXd q, double lb, int m) | 
|   | 
◆ UniformHBE()
      
        
          | UniformHBE::UniformHBE  | 
          ( | 
          shared_ptr< MatrixXd >  | 
          X,  | 
        
        
           | 
           | 
          int  | 
          M,  | 
        
        
           | 
           | 
          double  | 
          w,  | 
        
        
           | 
           | 
          int  | 
          k,  | 
        
        
           | 
           | 
          shared_ptr< Kernel >  | 
          ker,  | 
        
        
           | 
           | 
          int  | 
          subsample  | 
        
        
           | 
          ) | 
           |  | 
        
      
 
- Parameters
 - 
  
    | X | dataset  | 
    | M | number of samples  | 
    | w | bin width  | 
    | k | number of hash functions  | 
    | ker | kernel function  | 
    | subsample | build table on a random subsample number of points from the original dataset  | 
  
   
 
 
◆ evaluateQuery()
  
  
      
        
          | double UniformHBE::evaluateQuery  | 
          ( | 
          VectorXd  | 
          query | ) | 
           | 
         
       
   | 
  
protected   | 
  
 
Take a biased sample from a hash table via HBE. 
- Parameters
 - 
  
  
 
- Returns
 - normalized contribution of the biased sample 
 
 
 
◆ MoM()
  
  
      
        
          | vector< double > UniformHBE::MoM  | 
          ( | 
          VectorXd  | 
          q,  | 
         
        
           | 
           | 
          int  | 
          L,  | 
         
        
           | 
           | 
          int  | 
          m  | 
         
        
           | 
          ) | 
           |  | 
         
       
   | 
  
protectedvirtual   | 
  
 
- Parameters
 - 
  
    | q | query  | 
    | L | median of L means  | 
    | m | means of m samples  | 
  
   
- Returns
 - : a vector of L elements, where each element is a sum of m samples 
 
Implements MoMEstimator.
 
 
◆ tables
A collection of hash tables for HBE. 
 
 
The documentation for this class was generated from the following files: