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
