Python RealFeatures.add_preprocessor方法代码示例

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import add_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

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import add_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

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import add_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

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import add_preprocessor [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

# 需要导入模块: from modshogun import RealFeatures [as 别名]
# 或者: from modshogun.RealFeatures import add_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