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)
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)
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)
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)
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
