def preprocessor_kernelpca_modular(data, threshold, width): from shogun.Features import RealFeatures from shogun.Preprocessor import KernelPCA from shogun.Kernel import GaussianKernel features = RealFeatures(data) kernel = GaussianKernel(features, features, width) preprocessor = KernelPCA(kernel) preprocessor.init(features) preprocessor.set_target_dim(2) #X=preprocessor.get_transformation_matrix() X2 = preprocessor.apply_to_feature_matrix(features) lx0 = len(X2) modified_d1 = zeros((lx0, number_of_points_for_circle1)) modified_d2 = zeros((lx0, number_of_points_for_circle2)) modified_d1 = [X2[i][0:number_of_points_for_circle1] for i in range(lx0)] modified_d2 = [ X2[i][number_of_points_for_circle1:(number_of_points_for_circle1 + number_of_points_for_circle2)] for i in range(lx0) ] p.plot(modified_d1[0][:], modified_d1[1][:], 'o', modified_d2[0][:], modified_d2[1][:], 'x') p.title('final data') p.show() return features
def preprocessor_kernelpca_modular(data, threshold, width): from shogun.Features import RealFeatures from shogun.Preprocessor import KernelPCA from shogun.Kernel import GaussianKernel features = RealFeatures(data) kernel = GaussianKernel(features, features, width) preprocessor = KernelPCA(kernel) preprocessor.init(features) preprocessor.apply_to_feature_matrix(features) return features