def svm_train(kernel, labels, C1, C2=None):
"""Trains a SVM with the given kernel"""
num_threads = 1
kernel.io.disable_progress()
svm = LibSVM(C1, kernel, labels)
if C2:
svm.set_C(C1, C2)
svm.parallel.set_num_threads(num_threads)
svm.io.disable_progress()
svm.train()
return svm
def svm_train(kernel, labels, C1, C2=None):
"""Trains a SVM with the given kernel"""
num_threads = 1
kernel.io.disable_progress()
svm = LibSVM(C1, kernel, labels)
if C2:
svm.set_C(C1, C2)
svm.parallel.set_num_threads(num_threads)
svm.io.disable_progress()
svm.train()
return svm
from numpy import random
from shogun.Classifier import LibSVM
from shogun.Features import RealFeatures, Labels
from shogun.Kernel import LinearKernel
num_feats = 23
num_vec = 42
scale = 2.1
size_cache = 10
C = 0.017
epsilon = 1e-5
tube_epsilon = 1e-2
svm = LibSVM()
svm.set_C(C, C)
svm.set_epsilon(epsilon)
svm.set_tube_epsilon(tube_epsilon)
for i in xrange(3):
data_train = random.rand(num_feats, num_vec)
data_test = random.rand(num_feats, num_vec)
feats_train = RealFeatures(data_train)
feats_test = RealFeatures(data_test)
labels = Labels(random.rand(num_vec).round() * 2 - 1)
svm.set_kernel(LinearKernel(size_cache, scale))
svm.set_labels(labels)
kernel = svm.get_kernel()
print "kernel cache size: %s" % (kernel.get_cache_size())
from shogun.Classifier import LibSVM from shogun.Features import RealFeatures, Labels from shogun.Kernel import LinearKernel num_feats=23 num_vec=42 scale=2.1 size_cache=10 C=0.017 epsilon=1e-5 tube_epsilon=1e-2 svm=LibSVM() svm.set_C(C, C) svm.set_epsilon(epsilon) svm.set_tube_epsilon(tube_epsilon) for i in xrange(3): data_train=random.rand(num_feats, num_vec) data_test=random.rand(num_feats, num_vec) feats_train=RealFeatures(data_train) feats_test=RealFeatures(data_test) labels=Labels(random.rand(num_vec).round()*2-1) svm.set_kernel(LinearKernel(size_cache, scale)) svm.set_labels(labels) kernel=svm.get_kernel() print "kernel cache size: %s" % (kernel.get_cache_size())
