%%% learns logistic regression weights using the NN toolbox 
%%% *************************************************************
%%% Milos Hauskrecht
%%% CS2750 Machine Learning, University of Pittsburgh
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%% load the train and test data (both are normalized)
load pima_train.txt;
load pima_test.txt;
tr_data = pima_train;
test_data = pima_test;

data_col= size(tr_data,2)
n_features = data_col - 1

%%% create x
x = tr_data(:,1:n_features);
%% create y vector
y=tr_data(:,data_col);
 
%% builds x for the the test set
x_test = test_data(:,1:n_features);
%% builds y vector for the test set
y_test=test_data(:,data_col);

%%% builds a one layer neural network with logistic function output
%%% to be trained with the gradient method
net=newff(minmax(x'),[1],{'logsig'},'traingd');

%% sets the parameters of the NN model
%% see the NN toolbox documentation
net.trainParam.epochs = 2000;
net.trainParam.show = 10;
net.trainParam.max_fail=5;

TV.P=x_test';
TV.T=y_test';
%%% training of the neural net
%% supplying the test set allows to see curves for train and test LMS errors
[net, tr] = train(net,x',y',[],[],[],TV);

%% simulates learned network on inputs in x
%% that is, it computes outputs for all inputs in x and current set of weights
res=sim(net,x');

%%% 'Mean training error (logistic)'
error = mean_misclass_error(round(res'),y);

%% 'Mean training error (logistic)'
error = mean_misclass_error(round(res'),y);