Python LocallyLinearEmbedding.set_kの例

コード例 #1

ファイル: converter_locallylinearembedding_modular.py プロジェクト: Anshul-Bansal/gsoc

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

コード例 #2

ファイル: converter_locallylinearembedding_modular.py プロジェクト: vinodrajendran001/ASP

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

コード例 #3

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

コード例 #4

ファイル: preprocessor_dimensionreductionpreprocessor_modular.py プロジェクト: ashish-sadh/shogun

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

コード例 #5

ファイル: converter_locallylinearembedding_modular.py プロジェクト: theoryno3/shogun

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')

コード例 #6

ファイル: converter_locallylinearembedding_modular.py プロジェクト: AlexBinder/shogun

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')

コード例 #7

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)

コード例 #8

ファイル: swissroll.py プロジェクト: axitkhurana/shogun

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)