本文整理汇总了Python中shogun.Features.RealFeatures.add_preproc方法的典型用法代码示例。如果您正苦于以下问题：Python RealFeatures.add_preproc方法的具体用法？Python RealFeatures.add_preproc怎么用？Python RealFeatures.add_preproc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shogun.Features.RealFeatures的用法示例。
在下文中一共展示了RealFeatures.add_preproc方法的5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: norm_one
# 需要导入模块: from shogun.Features import RealFeatures [as 别名]
# 或者: from shogun.Features.RealFeatures import add_preproc [as 别名]
def norm_one ():
print 'NormOne'
from shogun.Kernel import Chi2Kernel
from shogun.Features import RealFeatures
from shogun.PreProc import NormOne
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
preproc=NormOne()
preproc.init(feats_train)
feats_train.add_preproc(preproc)
feats_train.apply_preproc()
feats_test.add_preproc(preproc)
feats_test.apply_preproc()
width=1.4
size_cache=10
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()示例2: prune_var_sub_mean
# 需要导入模块: from shogun.Features import RealFeatures [as 别名]
# 或者: from shogun.Features.RealFeatures import add_preproc [as 别名]
def prune_var_sub_mean ():
print 'PruneVarSubMean'
from shogun.Kernel import Chi2Kernel
from shogun.Features import RealFeatures
from shogun.PreProc import PruneVarSubMean
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
preproc=PruneVarSubMean()
preproc.init(feats_train)
feats_train.add_preproc(preproc)
feats_train.apply_preproc()
feats_test.add_preproc(preproc)
feats_test.apply_preproc()
width=1.4
size_cache=10
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()示例3: preproc_prunevarsubmean_modular
# 需要导入模块: from shogun.Features import RealFeatures [as 别名]
# 或者: from shogun.Features.RealFeatures import add_preproc [as 别名]
def preproc_prunevarsubmean_modular(fm_train_real=traindat, fm_test_real=testdat, width=1.4, size_cache=10):
from shogun.Kernel import Chi2Kernel
from shogun.Features import RealFeatures
from shogun.PreProc import PruneVarSubMean
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
preproc = PruneVarSubMean()
preproc.init(feats_train)
feats_train.add_preproc(preproc)
feats_train.apply_preproc()
feats_test.add_preproc(preproc)
feats_test.apply_preproc()
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: preproc_normone_modular
# 需要导入模块: from shogun.Features import RealFeatures [as 别名]
# 或者: from shogun.Features.RealFeatures import add_preproc [as 别名]
def preproc_normone_modular (fm_train_real=traindat,fm_test_real=testdat,width=1.4,size_cache=10):
from shogun.Kernel import Chi2Kernel
from shogun.Features import RealFeatures
from shogun.Preprocessor import NormOne
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
preproc=NormOne()
preproc.init(feats_train)
feats_train.add_preproc(preproc)
feats_train.apply_preproc()
feats_test.add_preproc(preproc)
feats_test.apply_preproc()
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5: serialization_complex_example
# 需要导入模块: from shogun.Features import RealFeatures [as 别名]
# 或者: from shogun.Features.RealFeatures import add_preproc [as 别名]
def serialization_complex_example(num=5, dist=1, dim=10, C=2.0, width=10):
import os
from numpy import concatenate, zeros, ones
from numpy.random import randn, seed
from shogun.Features import RealFeatures, Labels
from shogun.Classifier import GMNPSVM
from shogun.Kernel import GaussianKernel
from shogun.Library import SerializableHdf5File,SerializableAsciiFile, \
SerializableJsonFile,SerializableXmlFile,MSG_DEBUG
from shogun.PreProc import NormOne, LogPlusOne
seed(17)
data=concatenate((randn(dim, num), randn(dim, num) + dist,
randn(dim, num) + 2*dist,
randn(dim, num) + 3*dist), axis=1)
lab=concatenate((zeros(num), ones(num), 2*ones(num), 3*ones(num)))
feats=RealFeatures(data)
#feats.io.set_loglevel(MSG_DEBUG)
kernel=GaussianKernel(feats, feats, width)
labels=Labels(lab)
svm = GMNPSVM(C, kernel, labels)
feats.add_preproc(NormOne())
feats.add_preproc(LogPlusOne())
feats.set_preprocessed(1)
svm.train(feats)
#svm.print_serializable()
fstream = SerializableHdf5File("blaah.h5", "w")
status = svm.save_serializable(fstream)
check_status(status)
fstream = SerializableAsciiFile("blaah.asc", "w")
status = svm.save_serializable(fstream)
check_status(status)
fstream = SerializableJsonFile("blaah.json", "w")
status = svm.save_serializable(fstream)
check_status(status)
fstream = SerializableXmlFile("blaah.xml", "w")
status = svm.save_serializable(fstream)
check_status(status)
fstream = SerializableHdf5File("blaah.h5", "r")
new_svm=GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status)
new_svm.train()
fstream = SerializableAsciiFile("blaah.asc", "r")
new_svm=GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status)
new_svm.train()
fstream = SerializableJsonFile("blaah.json", "r")
new_svm=GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status)
new_svm.train()
fstream = SerializableXmlFile("blaah.xml", "r")
new_svm=GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status)
new_svm.train()
os.unlink("blaah.h5")
os.unlink("blaah.asc")
os.unlink("blaah.json")
os.unlink("blaah.xml")
return svm,new_svm