def preprocessor_dimensionreductionpreprocessor_modular(data, k):
from shogun.Features import RealFeatures
from shogun.Preprocessor import DimensionReductionPreprocessor
from shogun.Converter import LocallyLinearEmbedding
features = RealFeatures(data)
converter = LocallyLinearEmbedding()
converter.set_k(k)
preprocessor = DimensionReductionPreprocessor(converter)
preprocessor.init(features)
preprocessor.apply_to_feature_matrix(features)
return features
def preprocessor_dimensionreductionpreprocessor_modular(data, k): from shogun.Features import RealFeatures from shogun.Preprocessor import DimensionReductionPreprocessor from shogun.Converter import LocallyLinearEmbedding features = RealFeatures(data) converter = LocallyLinearEmbedding() converter.set_k(k) preprocessor = DimensionReductionPreprocessor(converter) preprocessor.init(features) preprocessor.apply_to_feature_matrix(features) return features
def converter_locallylinearembedding_modular(data, k):
from shogun.Features import RealFeatures
from shogun.Converter import LocallyLinearEmbedding
features = RealFeatures(data)
converter = LocallyLinearEmbedding()
converter.set_target_dim(1)
converter.set_k(k)
converter.apply(features)
return features
def converter_locallylinearembedding_modular(data, k):
try:
from shogun.Features import RealFeatures
from shogun.Converter import LocallyLinearEmbedding
features = RealFeatures(data)
converter = LocallyLinearEmbedding()
converter.set_target_dim(1)
converter.set_k(k)
converter.apply(features)
return features
except ImportError:
print('No Eigen3 available')
def converter_locallylinearembedding_modular(data,k): from shogun.Features import RealFeatures from shogun.Converter import LocallyLinearEmbedding features = RealFeatures(data) converter = LocallyLinearEmbedding() converter.set_target_dim(1) converter.set_k(k) converter.apply(features) return features
def converter_locallylinearembedding_modular (data,k):
try:
from shogun.Features import RealFeatures
from shogun.Converter import LocallyLinearEmbedding
features = RealFeatures(data)
converter = LocallyLinearEmbedding()
converter.set_target_dim(1)
converter.set_k(k)
converter.apply(features)
return features
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)
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(20)
kernel = GaussianKernel(100,1.0)
