# Figure 7.1: Logistic regression

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    % Section 7.1.1
    % Boyd & Vandenberghe, "Convex Optimization"
    % Original by Lieven Vandenberghe
    % Adapted for CVX by Argyris Zymnis - 01/31/06
    %
    % We consider a binary random variable y with prob(y=1) = p and
    % prob(y=0) = 1-p. We assume that that y depends on a vector of
    % explanatory variables u in R^n. The logistic model has the form
    % p = exp(a'*u+b)/(1+exp(a'*u+b)), where a and b are the model parameters.
    % We have m data points (u_1,y_1),...,(u_m,y_m).
    % We can reorder the data so that for u_1,..,u_q the outcome is y = 1
    % and for u_(q+1),...,u_m the outcome is y = 0. Then it can be shown
    % that the ML estimate of a and b can be found by solving
    %
    % minimize sum_{i=1,..,q}(a'*u_i+b) - sum_i(log(1+exp(a'*u_i+b)))
    %
    % In this case we have m = 100 and the u_i are just scalars.
    % The figure shows the data as well as the function
    % f(x) = exp(aml*x+bml)/(1+exp(aml*x+bml)) where aml and bml are the
    % ML estimates of a and b.

    randn('state',0);
    rand('state',0);

    % Generate data
    a =  1;
    b = -5 ;
    m= 100;

    u = 10*rand(m,1);
    y = (rand(m,1) < exp(a*u+b)./(1+exp(a*u+b)));
    plot(u,y,'o')
    axis([-1,11,-0.1, 1.1]);

    % Solve problem
    %
    % minimize  -(sum_(y_i=1) ui)*a - b + sum log (1+exp(a*ui+b)

    U = [ones(m,1) u];
    cvx_expert true
    cvx_begin
        variables x(2)
        maximize(y'*U*x-sum(log_sum_exp([zeros(1,m); x'*U'])))
    cvx_end

    % Plot results and logistic function

    ind1 = find(y==1);
    ind2 = find(y==0);

    aml = x(2);  bml = x(1);
    us = linspace(-1,11,1000)';
    ps = exp(aml*us + bml)./(1+exp(aml*us+bml));

    dots = plot(us,ps,'-', u(ind1),y(ind1),'o',...
         u(ind2),y(ind2),'o');

    axis([-1, 11,-0.1,1.1]);
    xlabel('x');
    ylabel('y');

    Successive approximation method to be employed.
       For improved efficiency, SDPT3 is solving the dual problem.
       SDPT3 will be called several times to refine the solution.
       Original size: 600 variables, 202 equality constraints
       200 exponentials add 1600 variables, 1000 equality constraints
    -----------------------------------------------------------------
     Cones  |             Errors              |
    Mov/Act | Centering  Exp cone   Poly cone | Status
    --------+---------------------------------+---------
    200/200 | 6.970e+00  2.483e+00  0.000e+00 | Solved
    200/200 | 9.608e-01  7.319e-02  0.000e+00 | Solved
    199/200 | 1.067e-01  8.487e-04  0.000e+00 | Solved
    200/200 | 9.296e-03  6.366e-06  0.000e+00 | Solved
      0/127 | 3.203e-04  6.392e-09  0.000e+00 | Solved
    -----------------------------------------------------------------
    Status: Solved
    Optimal value (cvx_optval): -32.9797