function [features1 features2] = simZipf(dim, s)
% Generate feature matrix based on zipf distribution
% Inputs:
% 1. dim- number of features
% 2. s- constant 

% P(ith feature = 1) = ( 1/i^s ) / (sum(n=1 to N) 1/n^s )

% Output:
% 1. features1 - feature matrix using class1
% 2. features2 - feature matrix using class2
classOne = zeros(1, dim);
har = 0;
for n= 1:dim
    har = har + 1/(n.^s);
end
for i= 1:dim
    classOne(i) = (1/(i.^s)) ./har;
    % classOne(i) = (1/(i.^s))./0.99;
    classTwo(dim-i+1) = classOne(i);
end

% populating features1 and features2
features1 = single(rand(1,dim)<classOne);
features2 = single(rand(1,dim)<classTwo);