Python Isomap Examples

Example #1

File: converter_isomap_modular.py Project: theoryno3/shogun

def converter_isomap_modular(data):
    try:
        from shogun.Features import RealFeatures
        from shogun.Converter import Isomap

        features = RealFeatures(data)

        converter = Isomap()
        converter.set_k(20)
        converter.set_target_dim(1)
        converter.apply(features)

        return features
    except ImportError:
        print('No Eigen3 available')

Example #2

File: converter_isomap_modular.py Project: behollis/muViewBranch

def converter_isomap_modular(data):
    try:
        from shogun.Features import RealFeatures
        from shogun.Converter import Isomap

        features = RealFeatures(data)

        converter = Isomap()
        converter.set_k(20)
        converter.set_target_dim(1)
        converter.apply(features)

        return features
    except ImportError:
        print("No Eigen3 available")

Example #3

embedding = converter.embed(features)

X = embedding.get_feature_matrix()

#fig.add_subplot(3, 1, 2)
fig = pylab.figure()

pylab.plot(X[0, y1], X[1, y1], 'rx')
pylab.plot(X[0, y2], X[1, y2], 'go')

pylab.title('Stochastic Proximity Embedding with global strategy')
pylab.xlabel('x')
pylab.ylabel('y')

# Compute Isomap embedding (for comparison)
converter = Isomap()
converter.set_target_dim(2)
converter.set_k(6)

embedding = converter.embed(features)

X = embedding.get_feature_matrix()

#fig.add_subplot(3, 1, 3)
fig = pylab.figure()

pylab.plot(X[0, y1], X[1, y1], 'rx')
pylab.plot(X[0, y2], X[1, y2], 'go')

pylab.title('Isomap')
pylab.xlabel('x')

Example #4

File: converter_isomap_modular.py Project: behollis/muViewBranch

def converter_isomap_modular(data):
	from shogun.Features import RealFeatures
	from shogun.Converter import Isomap
	
	features = RealFeatures(data)
		
	converter = Isomap()
	converter.set_landmark(True)
	converter.set_landmark_number(5)
	converter.set_k(6)
	converter.set_target_dim(1)
	converter.apply(features)

	return features

Example #5

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)
dm.set_width(1000.0)
converters.append(
    (dm, "Diffusion Maps with t=%d, sigma=%f" % (dm.get_t(), dm.get_width())))

from shogun.Converter import HessianLocallyLinearEmbedding
hlle = HessianLocallyLinearEmbedding()
hlle.set_k(6)
converters.append((hlle, "Hessian LLE with k=%d" % (hlle.get_k())))

Example #6

def converter_isomap_modular(data):
    from shogun.Features import RealFeatures
    from shogun.Converter import Isomap

    features = RealFeatures(data)

    converter = Isomap()
    converter.set_landmark(True)
    converter.set_landmark_number(5)
    converter.set_k(6)
    converter.set_target_dim(1)
    converter.apply(features)

    return features

Example #7

File: swissroll.py Project: axitkhurana/shogun

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)
converters.append((dm,"Diffusion Maps with t=%d, sigma=%f" % (dm.get_t(),kernel.get_width())))

from shogun.Converter import HessianLocallyLinearEmbedding
hlle = HessianLocallyLinearEmbedding()
hlle.set_k(6)
converters.append((hlle,"Hessian LLE with k=%d" % (hlle.get_k())))