本文整理汇总了Python中modshogun.RealFeatures.apply_preprocessor方法的典型用法代码示例。如果您正苦于以下问题：Python RealFeatures.apply_preprocessor方法的具体用法？Python RealFeatures.apply_preprocessor怎么用？Python RealFeatures.apply_preprocessor使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类modshogun.RealFeatures的用法示例。
在下文中一共展示了RealFeatures.apply_preprocessor方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: preprocessor_randomfouriergausspreproc_modular
# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import apply_preprocessor [as 别名]
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示例2: preprocessor_prunevarsubmean_modular
# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import apply_preprocessor [as 别名]
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示例3: preprocessor_normone_modular
# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import apply_preprocessor [as 别名]
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示例4: RealFeatures
# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import apply_preprocessor [as 别名]
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