Exemplos de LDA.apply em Python

Exemplo n.º 1

Arquivo: classifier_lda_modular.py Projeto: 42MachineLearning/shogun

def classifier_lda_modular (train_fname=traindat,test_fname=testdat,label_fname=label_traindat,gamma=3,num_threads=1):
	from modshogun import RealFeatures, BinaryLabels, LDA, CSVFile

	feats_train=RealFeatures(CSVFile(train_fname))
	feats_test=RealFeatures(CSVFile(test_fname))
	labels=BinaryLabels(CSVFile(label_fname))

	lda=LDA(gamma, feats_train, labels)
	lda.train()

	bias=lda.get_bias()
	w=lda.get_w()
	predictions = lda.apply(feats_test).get_labels()
	return lda,predictions

Exemplo n.º 2

def classifier_lda_modular(train_fname=traindat,
                           test_fname=testdat,
                           label_fname=label_traindat,
                           gamma=3,
                           num_threads=1):
    from modshogun import RealFeatures, BinaryLabels, LDA, CSVFile

    feats_train = RealFeatures(CSVFile(train_fname))
    feats_test = RealFeatures(CSVFile(test_fname))
    labels = BinaryLabels(CSVFile(label_fname))

    lda = LDA(gamma, feats_train, labels)
    lda.train()

    bias = lda.get_bias()
    w = lda.get_w()
    predictions = lda.apply(feats_test).get_labels()
    return lda, predictions

Exemplo n.º 3

Arquivo: prc.py Projeto: 42MachineLearning/shogun

title('Data',size=10)

# plot PRC for SVM
subplot(223)
PRC_evaluation=PRCEvaluation()
PRC_evaluation.evaluate(svm.apply(),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.apply(),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()

Exemplo n.º 4

# plot ROC for SVM
subplot(223)
ROC_evaluation=ROCEvaluation()
ROC_evaluation.evaluate(svm.apply(),labels)
roc = ROC_evaluation.get_ROC()
print roc
plot(roc[0], roc[1])
fill_between(roc[0],roc[1],0,alpha=0.1)
text(mean(roc[0])/2,mean(roc[1])/2,'auROC = %.5f' % ROC_evaluation.get_auROC())
grid(True)
xlabel('FPR')
ylabel('TPR')
title('LibSVM (Gaussian kernel, C=%.3f) ROC curve' % svm.get_C1(),size=10)

# plot ROC for LDA
subplot(224)
ROC_evaluation.evaluate(lda.apply(),labels)
roc = ROC_evaluation.get_ROC()
plot(roc[0], roc[1])
fill_between(roc[0],roc[1],0,alpha=0.1)
text(mean(roc[0])/2,mean(roc[1])/2,'auROC = %.5f' % ROC_evaluation.get_auROC())
grid(True)
xlabel('FPR')
ylabel('TPR')
title('LDA (gamma=%.3f) ROC curve' % lda.get_gamma(),size=10)

connect('key_press_event', util.quit)
show()