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
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')
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")
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())))
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())))