LIN_V.m

function [Wq, V] = LIN_V( X, S, K )
% Given a binary similarity matrix S, the bit length K and feature vectors X, this function returns Wq to be used as linear hash functions for query and V as the codes for database objects such that the hamming distance of binary codes between query and databased objects corresponds to the similarity matrix.
% 
% Input:
% S:              The similarity matrix which is an N*N sign matrix
% K:              Number of bits in the factorization
% X:              A d*N matrix of features where each column is the set of features for an object
%
% Output:
% Wq:             A K*d matrix we use to generate the binary codes for queries where sign(Wq*X) will be the binary codes for all objects in the training set as queries.
% V:              A K*N matrix that the binary codes for all objects in the training set as database objects.


  param.beta = 0.7;             % Weight on the positive pairs. 1-betta is the weight on negative pairs
  param.conv = 0.5;             % The convexity of loss function. If conv==1 the loss function is convex but for smaller values of conv the loss function is tighter
  param.epsilon = 0.001;        % The precision under which we optimize loss
  param.windowsize = 3;         % The number of updates over all bits and ubjects without improvement of loss as the exit condition
  param.blocksize = 1000;       % The size of random block for updates
  param.nsamples = 1000000;     % The number of random samples we use to collect statistics
  param.batchsize = 100;        % Batch size in SGD
  param.nbatches = 100;         % Number of batches in SGD
  param.nepoch = 10;            % Nubmer of epoches in SGD
  verbose = 1;                  % If verbose=1 then the program outputs precision and recall at each step
  % calles the main function with the defined parameteres
  [Wq, V] = LIN_V_logistic( X, S, K, param, verbose );

end