Ejemplos de MultidimensionalScaling.get_landmark_number en Python

Lenguaje de programación: Python

Namespace/Package Name: shogun.Converter

Método / Función: get_landmark_number

Ejemplos en hotexamples.com: 3

Python MultidimensionalScaling.get_landmark_number - 3 ejemplos encontrados. Estos son los ejemplos en Python del mundo real mejor valorados de shogun.Converter.MultidimensionalScaling.get_landmark_number extraídos de proyectos de código abierto. Puedes valorar ejemplos para ayudarnos a mejorar la calidad de los ejemplos.

Métodos usados con frecuencia

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MultidimensionalScaling(2)

get_landmark_number(2)

set_landmark(2)

apply(1)

set_landmark_number(1)

set_target_dim(1)

Ejemplo n.º 1

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Archivo: swissroll.py Proyecto: axitkhurana/shogun

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

from shogun.Converter import HessianLocallyLinearEmbedding

Ejemplo n.º 2

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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)
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

Ejemplo n.º 3

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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(1)
kernel = GaussianKernel(100,10.0)
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