def converter_multidimensionalscaling_modular(data):
try:
from shogun.Features import RealFeatures
from shogun.Converter import MultidimensionalScaling
from shogun.Distance import EuclideanDistance
features = RealFeatures(data)
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)
except ImportError:
print('No Eigen3 available')
def converter_multidimensionalscaling_modular (data):
try:
from shogun.Features import RealFeatures
from shogun.Converter import MultidimensionalScaling
from shogun.Distance import EuclideanDistance
features = RealFeatures(data)
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')
import numpy
numpy.random.seed(40)
tt = numpy.genfromtxt('../../data/toy/swissroll_color.dat', unpack=True).T
X = numpy.genfromtxt('../../data/toy/swissroll.dat', unpack=True).T
N = X.shape[1]
converters = []
from shogun.Converter import LocallyLinearEmbedding
lle = LocallyLinearEmbedding()
lle.set_k(9)
converters.append((lle, "LLE with k=%d" % lle.get_k()))
from shogun.Converter import MultidimensionalScaling
mds = MultidimensionalScaling()
converters.append((mds, "Classic MDS"))
lmds = MultidimensionalScaling()
lmds.set_landmark(True)
lmds.set_landmark_number(20)
converters.append(
(lmds, "Landmark MDS with %d landmarks" % lmds.get_landmark_number()))
from shogun.Converter import Isomap
cisomap = Isomap()
cisomap.set_k(9)
converters.append((cisomap, "Isomap with k=%d" % cisomap.get_k()))
from shogun.Converter import DiffusionMaps
from shogun.Kernel import GaussianKernel
dm = DiffusionMaps()
dm.set_t(2)
numpy.random.seed(40)
tt = numpy.genfromtxt('../../../../data/toy/swissroll_color.dat',unpack=True).T
X = numpy.genfromtxt('../../../../data/toy/swissroll.dat',unpack=True).T
N = X.shape[1]
converters = []
from shogun.Converter import LocallyLinearEmbedding
lle = LocallyLinearEmbedding()
lle.set_k(9)
converters.append((lle, "LLE with k=%d" % lle.get_k()))
from shogun.Converter import MultidimensionalScaling
mds = MultidimensionalScaling()
converters.append((mds, "Classic MDS"))
lmds = MultidimensionalScaling()
lmds.set_landmark(True)
lmds.set_landmark_number(20)
converters.append((lmds,"Landmark MDS with %d landmarks" % lmds.get_landmark_number()))
from shogun.Converter import Isomap
cisomap = Isomap()
cisomap.set_k(9)
converters.append((cisomap,"Isomap with k=%d" % cisomap.get_k()))
from shogun.Converter import DiffusionMaps
from shogun.Kernel import GaussianKernel
dm = DiffusionMaps()
dm.set_t(20)
kernel = GaussianKernel(100,1.0)
dm.set_kernel(kernel)
