def classifier_gmnpsvm_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5): from shogun.Features import RealFeatures, Labels from shogun.Kernel import GaussianKernel from shogun.Classifier import GMNPSVM feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) kernel=GaussianKernel(feats_train, feats_train, width) labels=Labels(label_train_multiclass) svm=GMNPSVM(C, kernel, labels) svm.set_epsilon(epsilon) svm.train(feats_train) kernel.init(feats_train, feats_test) out=svm.apply(feats_test).get_labels() return out,kernel
def classifier_gmnpsvm_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5): from shogun.Features import RealFeatures, MulticlassLabels from shogun.Kernel import GaussianKernel from shogun.Classifier import GMNPSVM feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) kernel=GaussianKernel(feats_train, feats_train, width) labels=MulticlassLabels(label_train_multiclass) svm=GMNPSVM(C, kernel, labels) svm.set_epsilon(epsilon) svm.train(feats_train) kernel.init(feats_train, feats_test) out=svm.apply(feats_test).get_labels() return out,kernel
def gmnpsvm (): print 'GMNPSVM' from shogun.Features import RealFeatures, Labels from shogun.Kernel import GaussianKernel from shogun.Classifier import GMNPSVM feats_train=RealFeatures(fm_train_real) feats_test=RealFeatures(fm_test_real) width=2.1 kernel=GaussianKernel(feats_train, feats_train, width) C=1 epsilon=1e-5 labels=Labels(label_train_multiclass) svm=GMNPSVM(C, kernel, labels) svm.set_epsilon(epsilon) svm.train(feats_train) #kernel.init(feats_train, feats_test) out=svm.classify(feats_test).get_labels()
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, MulticlassLabels
from shogun.Classifier import GMNPSVM
from shogun.Kernel import GaussianKernel
from shogun.IO import SerializableHdf5File,SerializableAsciiFile, \
SerializableJsonFile,SerializableXmlFile,MSG_DEBUG
from shogun.Preprocessor 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 = MulticlassLabels(lab)
svm = GMNPSVM(C, kernel, labels)
feats.add_preprocessor(NormOne())
feats.add_preprocessor(LogPlusOne())
feats.set_preprocessed(1)
svm.train(feats)
#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()
fstream = SerializableAsciiFile("blaah.asc", "r")
new_svm = GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status, 'asc')
new_svm.train()
fstream = SerializableJsonFile("blaah.json", "r")
new_svm = GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status, 'json')
new_svm.train()
fstream = SerializableXmlFile("blaah.xml", "r")
new_svm = GMNPSVM()
status = new_svm.load_serializable(fstream)
check_status(status, 'xml')
new_svm.train()
os.unlink("blaah.h5")
os.unlink("blaah.asc")
os.unlink("blaah.json")
os.unlink("blaah.xml")
return svm, new_svm
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.IO import SerializableHdf5File,SerializableAsciiFile, \
SerializableJsonFile,SerializableXmlFile,MSG_DEBUG
from shogun.Preprocessor 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_preprocessor(NormOne())
feats.add_preprocessor(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
