def distance_director_euclidian_modular (fm_train_real=traindat,fm_test_real=testdat,scale=1.2): from shogun.Distance import EuclidianDistance from modshogun import Time feats_train=RealFeatures(fm_train_real) feats_train.io.set_loglevel(0) feats_train.parallel.set_num_threads(1) feats_test=RealFeatures(fm_test_real) distance=EuclidianDistance() distance.init(feats_train, feats_test) ddistance=DirectorEuclidianDistance() 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 norm_squared_distance (): from shogun.Features import RealFeatures from shogun.Distance import EuclidianDistance print 'EuclidianDistance - NormSquared' feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) distance=EuclidianDistance(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()
def euclidian_distance (): print 'EuclidianDistance' from shogun.Features import RealFeatures from shogun.Distance import EuclidianDistance feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) distance=EuclidianDistance(feats_train, feats_train) dm_train=distance.get_distance_matrix() distance.init(feats_train, feats_test) dm_test=distance.get_distance_matrix()
def distance_euclidian_modular (fm_train_real=traindat,fm_test_real=testdat): from shogun.Features import RealFeatures from shogun.Distance import EuclidianDistance feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) distance=EuclidianDistance(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_euclidian_modular(fm_train_real=traindat, fm_test_real=testdat):
from shogun.Features import RealFeatures
from shogun.Distance import EuclidianDistance
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
distance = EuclidianDistance(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 converter_multidimensionalscaling_modular(data):
from shogun.Features import RealFeatures
from shogun.Converter import MultidimensionalScaling
from shogun.Distance import EuclidianDistance
features = RealFeatures(data)
distance_before = EuclidianDistance()
distance_before.init(features, features)
converter = MultidimensionalScaling()
converter.set_target_dim(2)
converter.set_landmark(False)
embedding = converter.apply(features)
distance_after = EuclidianDistance()
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)
def converter_multidimensionalscaling_modular(data): from shogun.Features import RealFeatures from shogun.Converter import MultidimensionalScaling from shogun.Distance import EuclidianDistance features = RealFeatures(data) distance_before = EuclidianDistance() distance_before.init(features,features) converter = MultidimensionalScaling() converter.set_target_dim(2) converter.set_landmark(False) embedding =converter.apply(features) distance_after = EuclidianDistance() 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)
def classify_input(points, cdata, transpose):
clusteredpoints = list()
for i in range(len(cdata[0])):
clusteredpoints.append(list())
if transpose == 1:
from shogun.Distance import EuclidianDistance
from shogun.Features import RealFeatures
from numpy import min, where
obs = RealFeatures(points)
centroids = RealFeatures(cdata)
distance = EuclidianDistance(obs, centroids)
# out1 = distance.get_distance_matrix()
distance.init(obs, centroids)
out = distance.get_distance_matrix()
labels = list()
for i in range(len(out)):
# dist = distances[:len(points),i]
dist = zip(out)[i]
minimum = min(dist)
itemindex=where(dist==minimum)
labels.append(itemindex[1][0])
print len(points)
print len(points[0])
for i in range(len(clusteredpoints)):
for types in range(len(points)):
clusteredpoints[i].append(list())
for index in range(len(labels)):
for item in range(len(points)):
clusteredpoints[labels[index]][item].append(points[item][index])
return clusteredpoints, labels
