function get_pdfs(data)

% data = [id_num (class member {0,1}) (membership belief [0..1])]

id = data(:,1);
T = data(:,2);
P = data(:,3);

% seperate into meteorite and rock sample classifier outputs
[metsamples, rocksamples] = seperate(T,P);

% compute pdf of each using parzen estimation
x = 0:0.001:1;
pm = parzen_pdf(x,metsamples,0.05);
pr = parzen_pdf(x,rocksamples,0.01);

% plot estimated pdf's
figure(1)
plot(x,pm,'r-',x,pr,'b-',x,pm,'r.',x,pr,'b.');
legend('meteorites','rocks');
axis([0 1 0 13]);
title('Bayes Network Meteorite Classifier Output');
xlabel('probability of being a meteorite');
ylabel('distribution');
drawnow

% compute error rates
[false_pos, false_neg] = classification_error(x, pm, pr);
figure(2)
plot(x,false_pos,'b-',x,false_neg,'r-',x,false_pos,'b.',x,false_neg,'r.');
grid on
title('Meteorite Classification Error Rates');
xlabel('decision threshold');
ylabel('probability of misclassification');
legend('false positive', 'false negative');
drawnow

% [false_pos, false_neg] = actual_error_rate(x,T,P);
% hold on
% plot(x,false_pos,'g.',x,false_neg,'r.');

