def converter_multidimensionalscaling_modular(data_fname):
try:
import numpy
from modshogun import RealFeatures, MultidimensionalScaling, EuclideanDistance, CSVFile
features = RealFeatures(CSVFile(data_fname))
distance_before = EuclideanDistance()
distance_before.init(features, features)
converter = MultidimensionalScaling()
converter.set_target_dim(2)
converter.set_landmark(False)
embedding = converter.apply(features)
distance_after = EuclideanDistance()
distance_after.init(embedding, embedding)
distance_matrix_after = distance_after.get_distance_matrix()
distance_matrix_before = distance_before.get_distance_matrix()
return numpy.linalg.norm(distance_matrix_after -
distance_matrix_before) / numpy.linalg.norm(
distance_matrix_before) < 1e-6
except ImportError:
print('No Eigen3 available')
def distance_normsquared_modular(train_fname=traindat, test_fname=testdat):
from modshogun import RealFeatures, EuclideanDistance, CSVFile
feats_train = RealFeatures(CSVFile(train_fname))
feats_test = RealFeatures(CSVFile(test_fname))
distance = EuclideanDistance(feats_train, feats_train)
distance.set_disable_sqrt(True)
dm_train = distance.get_distance_matrix()
distance.init(feats_train, feats_test)
dm_test = distance.get_distance_matrix()
return distance, dm_train, dm_test
def distance_euclidean_modular(train_fname=traindat,test_fname=testdat): from modshogun import RealFeatures, EuclideanDistance, CSVFile feats_train=RealFeatures(CSVFile(train_fname)) feats_test=RealFeatures(CSVFile(test_fname)) distance=EuclideanDistance(feats_train, feats_train) dm_train=distance.get_distance_matrix() distance.init(feats_train, feats_test) dm_test=distance.get_distance_matrix() return distance,dm_train,dm_test
def distance_director_euclidean_modular(fm_train_real=traindat,
fm_test_real=testdat,
scale=1.2):
try:
from modshogun import DirectorDistance
except ImportError:
print("recompile shogun with --enable-swig-directors")
return
class DirectorEuclideanDistance(DirectorDistance):
def __init__(self):
DirectorDistance.__init__(self, True)
def distance_function(self, idx_a, idx_b):
seq1 = self.get_lhs().get_feature_vector(idx_a)
seq2 = self.get_rhs().get_feature_vector(idx_b)
return numpy.linalg.norm(seq1 - seq2)
from modshogun import EuclideanDistance
from modshogun import Time
feats_train = RealFeatures(fm_train_real)
#feats_train.io.set_loglevel(MSG_DEBUG)
feats_train.parallel.set_num_threads(1)
feats_test = RealFeatures(fm_test_real)
distance = EuclideanDistance()
distance.init(feats_train, feats_test)
ddistance = DirectorEuclideanDistance()
ddistance.init(feats_train, feats_test)
#print "dm_train"
t = Time()
dm_train = distance.get_distance_matrix()
#t1=t.cur_time_diff(True)
#print "ddm_train"
t = Time()
ddm_train = ddistance.get_distance_matrix()
#t2=t.cur_time_diff(True)
#print "dm_train", dm_train
#print "ddm_train", ddm_train
return dm_train, ddm_train
def distance_director_euclidean_modular(fm_train_real=traindat, fm_test_real=testdat, scale=1.2):
try:
from modshogun import DirectorDistance
except ImportError:
print("recompile shogun with --enable-swig-directors")
return
class DirectorEuclideanDistance(DirectorDistance):
def __init__(self):
DirectorDistance.__init__(self, True)
def distance_function(self, idx_a, idx_b):
seq1 = self.get_lhs().get_feature_vector(idx_a)
seq2 = self.get_rhs().get_feature_vector(idx_b)
return numpy.linalg.norm(seq1 - seq2)
from modshogun import EuclideanDistance
from modshogun import Time
feats_train = RealFeatures(fm_train_real)
# feats_train.io.set_loglevel(MSG_DEBUG)
feats_train.parallel.set_num_threads(1)
feats_test = RealFeatures(fm_test_real)
distance = EuclideanDistance()
distance.init(feats_train, feats_test)
ddistance = DirectorEuclideanDistance()
ddistance.init(feats_train, feats_test)
# print "dm_train"
t = Time()
dm_train = distance.get_distance_matrix()
# t1=t.cur_time_diff(True)
# print "ddm_train"
t = Time()
ddm_train = ddistance.get_distance_matrix()
# t2=t.cur_time_diff(True)
# print "dm_train", dm_train
# print "ddm_train", ddm_train
return dm_train, ddm_train
def converter_multidimensionalscaling_modular (data_fname):
try:
import numpy
from modshogun import RealFeatures, MultidimensionalScaling, EuclideanDistance, CSVFile
features = RealFeatures(CSVFile(data_fname))
distance_before = EuclideanDistance()
distance_before.init(features,features)
converter = MultidimensionalScaling()
converter.set_target_dim(2)
converter.set_landmark(False)
embedding = converter.apply(features)
distance_after = EuclideanDistance()
distance_after.init(embedding,embedding)
distance_matrix_after = distance_after.get_distance_matrix()
distance_matrix_before = distance_before.get_distance_matrix()
return numpy.linalg.norm(distance_matrix_after-distance_matrix_before)/numpy.linalg.norm(distance_matrix_before) < 1e-6
except ImportError:
print('No Eigen3 available')
