本文整理汇总了Python中modshogun.RealFeatures.set_preprocessed方法的典型用法代码示例。如果您正苦于以下问题：Python RealFeatures.set_preprocessed方法的具体用法？Python RealFeatures.set_preprocessed怎么用？Python RealFeatures.set_preprocessed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.set_preprocessed方法的1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: serialization_complex_example
# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import set_preprocessed [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 modshogun import RealFeatures, MulticlassLabels
from modshogun import GMNPSVM
from modshogun import GaussianKernel
try:
from modshogun import SerializableHdf5File,SerializableAsciiFile, \
SerializableJsonFile,SerializableXmlFile,MSG_DEBUG
except ImportError:
return
from modshogun 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)
#feats.io.enable_file_and_line()
kernel=GaussianKernel(feats, feats, width)
labels=MulticlassLabels(lab)
svm = GMNPSVM(C, kernel, labels)
feats.add_preprocessor(NormOne())
feats.add_preprocessor(LogPlusOne())
feats.set_preprocessed(1)
svm.train(feats)
bias_ref = svm.get_svm(0).get_bias()
#svm.print_serializable()
fstream = SerializableHdf5File("blaah.h5", "w")
status = svm.save_serializable(fstream)
check_status(status,'h5')
fstream = SerializableAsciiFile("blaah.asc", "w")
status = svm.save_serializable(fstream)
check_status(status,'asc')
fstream = SerializableJsonFile("blaah.json", "w")
status = svm.save_serializable(fstream)
check_status(status,'json')
fstream = SerializableXmlFile("blaah.xml", "w")
status = svm.save_serializable(fstream)
check_status(status,'xml')
fstream = SerializableHdf5File("blaah.h5", "r")
new_svm=GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status,'h5')
new_svm.train()
bias_h5 = new_svm.get_svm(0).get_bias()
fstream = SerializableAsciiFile("blaah.asc", "r")
new_svm=GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status,'asc')
new_svm.train()
bias_asc = new_svm.get_svm(0).get_bias()
fstream = SerializableJsonFile("blaah.json", "r")
new_svm=GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status,'json')
new_svm.train()
bias_json = new_svm.get_svm(0).get_bias()
fstream = SerializableXmlFile("blaah.xml", "r")
new_svm=GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status,'xml')
new_svm.train()
bias_xml = new_svm.get_svm(0).get_bias()
os.unlink("blaah.h5")
os.unlink("blaah.asc")
os.unlink("blaah.json")
os.unlink("blaah.xml")
return svm,new_svm, bias_ref, bias_h5, bias_asc, bias_json, bias_xml