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
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
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) print 'apply to feature matrix=%d, ',n.shape(X2) p.plot(X2,'o') p.title('apply_to_feature_matrix') p.show() #sys.exit(0) print 'type of features=%',(type(X2)) print 'le X=\n',len(X2[0]) print 'size of the original data is= ,and the returned matrix is',n.shape(data),n.shape(X2) lx0=len(X2) lx1=len(X2[0]) modified_d1=[[0 for x in xrange(len(d[0]))] for x in xrange(lx0)] modified_d2=[[0 for x in xrange(int(len(d2[0])))] for x in xrange(lx0)] for i in range(lx0): for j in range(len(d[0])): modified_d1[i][j]=X2[i][j] for i in range(lx0): for j in range(lx1-len(d[0])): modified_d2[i][j]=X2[i][j+len(d[0])] print 'size of new datasets are =',n.shape(d),n.shape(d2) p.plot(modified_d1[0][:],modified_d1[1][:],'o',modified_d2[0][:],modified_d2[1][:],'x') p.title('final data') p.show() print 'new d1=',modified_d1 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.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.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