def converter_laplacianeigenmaps_modular(data, k): from shogun.Features import RealFeatures from shogun.Converter import LaplacianEigenmaps features = RealFeatures(data) converter = LaplacianEigenmaps() converter.set_target_dim(1) converter.set_k(k) converter.set_tau(2.0) converter.apply(features) return features
def converter_laplacianeigenmaps_modular(data, k): try: from shogun.Features import RealFeatures from shogun.Converter import LaplacianEigenmaps features = RealFeatures(data) converter = LaplacianEigenmaps() converter.set_target_dim(1) converter.set_k(k) converter.set_tau(2.0) converter.apply(features) return features except ImportError: print('No Eigen3 available')
def converter_laplacianeigenmaps_modular(data,k): from shogun.Features import RealFeatures from shogun.Converter import LaplacianEigenmaps features = RealFeatures(data) converter = LaplacianEigenmaps() converter.set_target_dim(1) converter.set_k(k) converter.set_tau(2.0) converter.apply(features) return features
def converter_laplacianeigenmaps_modular (data,k): try: from shogun.Features import RealFeatures from shogun.Converter import LaplacianEigenmaps features = RealFeatures(data) converter = LaplacianEigenmaps() converter.set_target_dim(1) converter.set_k(k) converter.set_tau(20.0) converter.apply(features) return features except ImportError: print('No Eigen3 available')
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 import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D fig = plt.figure() new_mpl = False try: swiss_roll_fig = fig.add_subplot(3, 3, 1, projection='3d')
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 from mpl_toolkits.mplot3d import Axes3D fig = plt.figure() new_mpl = False try: swiss_roll_fig = fig.add_subplot(3,3,1, projection='3d') new_mpl = True
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 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 from mpl_toolkits.mplot3d import Axes3D fig = plt.figure() new_mpl = False try: swiss_roll_fig = fig.add_subplot(3,3,1, projection='3d') new_mpl = True