def diagonal_lmnn(features, labels, k=3, max_iter=10000): from modshogun import LMNN, MSG_DEBUG import numpy lmnn = LMNN(features, labels, k) # lmnn.io.set_loglevel(MSG_DEBUG) lmnn.set_diagonal(True) lmnn.set_maxiter(max_iter) lmnn.train(numpy.eye(features.get_num_features())) return lmnn
def diagonal_lmnn(features,labels,k=3,max_iter=10000): from modshogun import LMNN, MSG_DEBUG import numpy lmnn = LMNN(features,labels,k) # lmnn.io.set_loglevel(MSG_DEBUG) lmnn.set_diagonal(True) lmnn.set_maxiter(max_iter) lmnn.train(numpy.eye(features.get_num_features())) return lmnn
def lmnn_diagonal(train_features, train_labels, test_features, test_labels, k=1): from modshogun import LMNN, KNN, MSG_DEBUG, MulticlassAccuracy import numpy lmnn = LMNN(train_features, train_labels, k) lmnn.set_diagonal(True) lmnn.train() distance = lmnn.get_distance() knn = KNN(k, distance, train_labels) knn.train() train_output = knn.apply() test_output = knn.apply(test_features) evaluator = MulticlassAccuracy() print 'LMNN-diagonal training error is %.4f' % ((1-evaluator.evaluate(train_output, train_labels))*100) print 'LMNN-diagonal test error is %.4f' % ((1-evaluator.evaluate(test_output, test_labels))*100)
def lmnn_diagonal(train_features, train_labels, test_features, test_labels, k=1): from modshogun import LMNN, KNN, MSG_DEBUG, MulticlassAccuracy import numpy lmnn = LMNN(train_features, train_labels, k) lmnn.set_diagonal(True) lmnn.train() distance = lmnn.get_distance() knn = KNN(k, distance, train_labels) knn.train() train_output = knn.apply() test_output = knn.apply(test_features) evaluator = MulticlassAccuracy() print 'LMNN-diagonal training error is %.4f' % ( (1 - evaluator.evaluate(train_output, train_labels)) * 100) print 'LMNN-diagonal test error is %.4f' % ( (1 - evaluator.evaluate(test_output, test_labels)) * 100)
#!/usr/bin/python from scipy import io data_dict = io.loadmat('../data/NBData20_train_preprocessed.mat') xt = data_dict['xt'] yt = data_dict['yt'] import numpy from modshogun import RealFeatures, MulticlassLabels, LMNN, MSG_DEBUG features = RealFeatures(xt.T) labels = MulticlassLabels(numpy.squeeze(yt)) k = 6 lmnn = LMNN(features, labels, k) lmnn.io.set_loglevel(MSG_DEBUG) lmnn.set_diagonal(True) lmnn.set_maxiter(10000) lmnn.train(numpy.eye(features.get_num_features()))
#!/usr/bin/python from scipy import io data_dict = io.loadmat('../data/NBData20_train_preprocessed.mat') xt = data_dict['xt'] yt = data_dict['yt'] import numpy from modshogun import RealFeatures,MulticlassLabels,LMNN,MSG_DEBUG features = RealFeatures(xt.T) labels = MulticlassLabels(numpy.squeeze(yt)) k = 6 lmnn = LMNN(features,labels,k) lmnn.io.set_loglevel(MSG_DEBUG) lmnn.set_diagonal(True) lmnn.set_maxiter(10000) lmnn.train(numpy.eye(features.get_num_features()))