import util util.set_title('PRC example') util.DISTANCE=0.5 subplots_adjust(hspace=0.3) pos=util.get_realdata(True) neg=util.get_realdata(False) features=util.get_realfeatures(pos, neg) labels=util.get_labels() # classifiers gk=GaussianKernel(features, features, 1.0) svm = LibSVM(1000.0, gk, labels) svm.train() lda=LDA(1,features,labels) lda.train() ## plot points subplot(211) plot(pos[0,:], pos[1,:], "r.") 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])
def classifier_lda_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_twoclass=label_traindat,gamma=3,num_threads=1): from shogun.Features import RealFeatures, Labels from shogun.Classifier import LDA feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) labels=Labels(label_train_twoclass) lda=LDA(gamma, feats_train, labels) lda.train() lda.get_bias() lda.get_w() lda.set_features(feats_test) lda.apply().get_labels() return lda,lda.apply().get_labels()
def classifier_lda_modular(fm_train_real=traindat, fm_test_real=testdat, label_train_twoclass=label_traindat, gamma=3, num_threads=1): from shogun.Features import RealFeatures, Labels from shogun.Classifier import LDA feats_train = RealFeatures(fm_train_real) feats_test = RealFeatures(fm_test_real) labels = Labels(label_train_twoclass) lda = LDA(gamma, feats_train, labels) lda.train() lda.get_bias() lda.get_w() lda.set_features(feats_test) lda.classify().get_labels() return lda, lda.classify().get_labels()
import util util.set_title('ROC example') util.DISTANCE = 0.5 subplots_adjust(hspace=0.3) pos = util.get_realdata(True) neg = util.get_realdata(False) features = util.get_realfeatures(pos, neg) labels = util.get_labels() # classifiers gk = GaussianKernel(features, features, 1.0) svm = LibSVM(1000.0, gk, labels) svm.train() lda = LDA(1, features, labels) lda.train() ## plot points subplot(211) plot(pos[0, :], pos[1, :], "r.") plot(neg[0, :], neg[1, :], "b.") grid(True) title('Data', size=10) # plot ROC for SVM subplot(223) ROC_evaluation = ROCEvaluation() ROC_evaluation.evaluate(svm.apply(), labels) roc = ROC_evaluation.get_ROC() print roc
import util util.set_title('ROC example') util.DISTANCE=0.5 subplots_adjust(hspace=0.3) pos=util.get_realdata(True) neg=util.get_realdata(False) features=util.get_realfeatures(pos, neg) labels=util.get_labels() # classifiers gk=GaussianKernel(features, features, 1.0) svm = LibSVM(1000.0, gk, labels) svm.train() lda=LDA(1,features,labels) lda.train() ## plot points subplot(211) plot(pos[0,:], pos[1,:], "r.") plot(neg[0,:], neg[1,:], "b.") grid(True) title('Data',size=10) # plot ROC for SVM subplot(223) ROC_evaluation=ROCEvaluation() ROC_evaluation.evaluate(svm.apply(),labels) roc = ROC_evaluation.get_ROC() print roc
def lda (): print 'LDA' from shogun.Features import RealFeatures, Labels from shogun.Classifier import LDA feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) gamma=3 num_threads=1 labels=Labels(label_train_twoclass) lda=LDA(gamma, feats_train, labels) lda.train() lda.get_bias() lda.get_w() #lda.set_features(feats_train) result = lda.classify() prediction_labels = result.get_labels() print prediction_labels>0 lda.set_features(feats_test) result = lda.classify() prediction_labels = result.get_labels() print prediction_labels>0