gistfile1.txt

function m = SVMq(X, y, m, lr, num_range)

  n_examples = size(X, 1);
  n_features = size(X, 2);
  
  num_ranges = {};
  for j=1:n_features
     num_ranges{j} = num_range; 
  end
  X1 = quantize_minmaxfull(X, num_ranges);
  
  n_fetchfull = 0;
  for i=1:n_examples
      label = y(i);
      dot = X1(i, :) * m'; % Quantized Version
      
      weight = abs(1-dot* label);
      prob   = weight/(weight + 1);
      
      if rand() < prob     % Importance Sampling -- 
                           % Give those near boundary a 
                           % smaller probability to be
                           % choosen
                           
          upper = dot + sum(m(find(m>0)))/num_range;
          lower = dot + sum(m(find(m<0)))/num_range;

          if (1-upper*label) * (1-lower*label) < 0
                           % If we are not sure about
                           % whether quantization is
                           % safe
            n_fetchfull = n_fetchfull + 1;
            dot = X(i, :) * m';
                           % Fetch original data 
          end

          if 1 - dot * label > 0
              m = m + lr * label * X1(i, :) / prob;
          end  
      end
  end
  
  n_fetchfull

end