def preprocessor_normone_modular(fm_train_real=traindat,
fm_test_real=testdat,
width=1.4,
size_cache=10):
from modshogun import Chi2Kernel
from modshogun import RealFeatures
from modshogun import NormOne
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
preprocessor = NormOne()
preprocessor.init(feats_train)
feats_train.add_preprocessor(preprocessor)
feats_train.apply_preprocessor()
feats_test.add_preprocessor(preprocessor)
feats_test.apply_preprocessor()
kernel = Chi2Kernel(feats_train, feats_train, width, size_cache)
km_train = kernel.get_kernel_matrix()
kernel.init(feats_train, feats_test)
km_test = kernel.get_kernel_matrix()
return km_train, km_test, kernel
def preprocessor_randomfouriergausspreproc_modular(fm_train_real=traindat,
fm_test_real=testdat,
width=1.4,
size_cache=10):
from modshogun import Chi2Kernel
from modshogun import RealFeatures
from modshogun import RandomFourierGaussPreproc
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
preproc = RandomFourierGaussPreproc()
preproc.init(feats_train)
feats_train.add_preprocessor(preproc)
feats_train.apply_preprocessor()
feats_test.add_preprocessor(preproc)
feats_test.apply_preprocessor()
kernel = Chi2Kernel(feats_train, feats_train, width, size_cache)
km_train = kernel.get_kernel_matrix()
kernel.init(feats_train, feats_test)
km_test = kernel.get_kernel_matrix()
return km_train, km_test, kernel
def preprocessor_randomfouriergausspreproc_modular (fm_train_real=traindat,fm_test_real=testdat,width=1.4,size_cache=10): from modshogun import Chi2Kernel from modshogun import RealFeatures from modshogun import RandomFourierGaussPreproc feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) preproc=RandomFourierGaussPreproc() preproc.init(feats_train) feats_train.add_preprocessor(preproc) feats_train.apply_preprocessor() feats_test.add_preprocessor(preproc) feats_test.apply_preprocessor() kernel=Chi2Kernel(feats_train, feats_train, width, size_cache) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def preprocessor_prunevarsubmean_modular (fm_train_real=traindat,fm_test_real=testdat,width=1.4,size_cache=10): from modshogun import Chi2Kernel from modshogun import RealFeatures from modshogun import PruneVarSubMean feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) preproc=PruneVarSubMean() preproc.init(feats_train) feats_train.add_preprocessor(preproc) feats_train.apply_preprocessor() feats_test.add_preprocessor(preproc) feats_test.apply_preprocessor() kernel=Chi2Kernel(feats_train, feats_train, width, size_cache) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def preprocessor_normone_modular (fm_train_real=traindat,fm_test_real=testdat,width=1.4,size_cache=10): from modshogun import Chi2Kernel from modshogun import RealFeatures from modshogun import NormOne feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) preprocessor=NormOne() preprocessor.init(feats_train) feats_train.add_preprocessor(preprocessor) feats_train.apply_preprocessor() feats_test.add_preprocessor(preprocessor) feats_test.apply_preprocessor() kernel=Chi2Kernel(feats_train, feats_train, width, size_cache) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
from modshogun import CSVFile, RealFeatures, RescaleFeatures
from scipy.linalg import solve_triangular, cholesky, sqrtm, inv
import matplotlib.pyplot as pyplot
import numpy
# load wine features
features = RealFeatures(CSVFile('../data/fm_wine.dat'))
print('%d vectors with %d features.' % (features.get_num_vectors(), features.get_num_features()))
print('original features mean = ' + str(numpy.mean(features, axis=1)))
# rescale the features to [0,1]
feature_rescaling = RescaleFeatures()
feature_rescaling.init(features)
features.add_preprocessor(feature_rescaling)
features.apply_preprocessor()
print('mean after rescaling = ' + str(numpy.mean(features, axis=1)))
# remove mean from data
data = features.get_feature_matrix()
data = data.T
data-= numpy.mean(data, axis=0)
print numpy.mean(data, axis=0)
fig, axarr = pyplot.subplots(1,2)
axarr[0].matshow(numpy.cov(data.T))
#### whiten data
''' this method to whiten the data didn't really work out
from scipy.linalg import solve_triangular, cholesky, sqrtm, inv
import matplotlib.pyplot as pyplot
import numpy
# load wine features
features = RealFeatures(CSVFile('../data/fm_wine.dat'))
print('%d vectors with %d features.' %
(features.get_num_vectors(), features.get_num_features()))
print('original features mean = ' + str(numpy.mean(features, axis=1)))
# rescale the features to [0,1]
feature_rescaling = RescaleFeatures()
feature_rescaling.init(features)
features.add_preprocessor(feature_rescaling)
features.apply_preprocessor()
print('mean after rescaling = ' + str(numpy.mean(features, axis=1)))
# remove mean from data
data = features.get_feature_matrix()
data = data.T
data -= numpy.mean(data, axis=0)
print numpy.mean(data, axis=0)
fig, axarr = pyplot.subplots(1, 2)
axarr[0].matshow(numpy.cov(data.T))
#### whiten data
''' this method to whiten the data didn't really work out
L = cholesky(numpy.cov(data.T))
