% implements Param_Est, returns the number of eigenvectors needed to
% explain 90% of the variance in the data, and displays a plot where the
% x-axis is the number of eigenvectors and the y-axis is the percentage of
% variance explained.
function [neigenvectors] = ProportionOfVariance(training_data)

   % stack data
   data = vertcat(training_data);

  % TODO: perform PCA

  % TODO: compute proportion of variance explained

  % TODO: show figure of proportion of variance explained where the x-axis is the number of eigenvectors and the y-axis is the percentage of
  % variance explained
 
end % Function end