Python HessianLocallyLinearEmbedding.set_k 예제들

예제 #1

def converter_hessianlocallylinearembedding_modular(data,k):
	from shogun.Features import RealFeatures
	from shogun.Converter import HessianLocallyLinearEmbedding
	
	features = RealFeatures(data)
		
	converter = HessianLocallyLinearEmbedding()
	converter.set_target_dim(1)
	converter.set_k(k)
	converter.apply(features)

	return features

예제 #2

파일: converter_hessianlocallylinearembedding_modular.py 프로젝트: theoryno3/shogun

def converter_hessianlocallylinearembedding_modular(data, k):
    try:
        from shogun.Features import RealFeatures
        from shogun.Converter import HessianLocallyLinearEmbedding

        features = RealFeatures(data)

        converter = HessianLocallyLinearEmbedding()
        converter.set_target_dim(1)
        converter.set_k(k)
        converter.apply(features)

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

예제 #3

파일: converter_hessianlocallylinearembedding_modular.py 프로젝트: AlexBinder/shogun

def converter_hessianlocallylinearembedding_modular (data,k):
	try:
		from shogun.Features import RealFeatures
		from shogun.Converter import HessianLocallyLinearEmbedding
		
		features = RealFeatures(data)
			
		converter = HessianLocallyLinearEmbedding()
		converter.set_target_dim(1)
		converter.set_k(k)
		converter.apply(features)

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

예제 #4

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())))

from shogun.Converter import LocalTangentSpaceAlignment
ltsa = LocalTangentSpaceAlignment()
ltsa.set_k(6)
converters.append((ltsa, "LTSA with k=%d" % (ltsa.get_k())))

from shogun.Converter import LaplacianEigenmaps
le = LaplacianEigenmaps()
le.set_k(20)
le.set_tau(100.0)
converters.append(
    (le, "Laplacian Eigenmaps with k=%d, tau=%d" % (le.get_k(), le.get_tau())))

import matplotlib

예제 #5

파일: swissroll.py 프로젝트: axitkhurana/shogun

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())))

from shogun.Converter import LocalTangentSpaceAlignment
ltsa = LocalTangentSpaceAlignment()
ltsa.set_k(6)
converters.append((ltsa,"LTSA with k=%d" % (ltsa.get_k())))

from shogun.Converter import LaplacianEigenmaps
le = LaplacianEigenmaps()
le.set_k(15)
le.set_tau(25.0)
converters.append((le,"Laplacian Eigenmaps with k=%d, tau=%d" % (le.get_k(),le.get_tau())))

import matplotlib
import matplotlib.pyplot as plt