plot(neg[0,:], neg[1,:], "b.")
grid(True)
title('Data',size=10)
# plot PRC for SVM
subplot(223)
PRC_evaluation=PRCEvaluation()
PRC_evaluation.evaluate(svm.classify(),labels)
PRC = PRC_evaluation.get_PRC()
plot(PRC[:,0], PRC[:,1])
fill_between(PRC[:,0],PRC[:,1],0,alpha=0.1)
text(0.55,mean(PRC[:,1])/3,'auPRC = %.5f' % PRC_evaluation.get_auPRC())
grid(True)
xlabel('Precision')
ylabel('Recall')
title('LibSVM (Gaussian kernel, C=%.3f) PRC curve' % svm.get_C1(),size=10)
# plot PRC for LDA
subplot(224)
PRC_evaluation.evaluate(lda.classify(),labels)
PRC = PRC_evaluation.get_PRC()
plot(PRC[:,0], PRC[:,1])
fill_between(PRC[:,0],PRC[:,1],0,alpha=0.1)
text(0.55,mean(PRC[:,1])/3,'auPRC = %.5f' % PRC_evaluation.get_auPRC())
grid(True)
xlabel('Precision')
ylabel('Recall')
title('LDA (gamma=%.3f) PRC curve' % lda.get_gamma(),size=10)
connect('key_press_event', util.quit)
show()