def kernel_linear_modular(train_fname=traindat, test_fname=testdat, scale=1.2):
from modshogun import RealFeatures, LinearKernel, AvgDiagKernelNormalizer, CSVFile
feats_train = RealFeatures(CSVFile(train_fname))
feats_test = RealFeatures(CSVFile(test_fname))
kernel = LinearKernel()
kernel.set_normalizer(AvgDiagKernelNormalizer(scale))
kernel.init(feats_train, feats_train)
km_train = kernel.get_kernel_matrix()
kernel.init(feats_train, feats_test)
km_test = kernel.get_kernel_matrix()
return km_train, km_test, kernel
def kernel_linear_modular (train_fname=traindat,test_fname=testdat,scale=1.2): from modshogun import RealFeatures, LinearKernel, AvgDiagKernelNormalizer, CSVFile feats_train=RealFeatures(CSVFile(train_fname)) feats_test=RealFeatures(CSVFile(test_fname)) kernel=LinearKernel() kernel.set_normalizer(AvgDiagKernelNormalizer(scale)) kernel.init(feats_train, feats_train) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def kernel_sparse_linear_modular (fm_train_real=traindat,fm_test_real=testdat,scale=1.1): from modshogun import SparseRealFeatures from modshogun import LinearKernel, AvgDiagKernelNormalizer feats_train=SparseRealFeatures(fm_train_real) feats_test=SparseRealFeatures(fm_test_real) kernel=LinearKernel() kernel.set_normalizer(AvgDiagKernelNormalizer(scale)) kernel.init(feats_train, feats_train) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def kernel_director_linear_modular(fm_train_real=traindat,
fm_test_real=testdat,
scale=1.2):
try:
from modshogun import DirectorKernel
except ImportError:
print("recompile shogun with --enable-swig-directors")
return
class DirectorLinearKernel(DirectorKernel):
def __init__(self):
DirectorKernel.__init__(self, True)
def kernel_function(self, idx_a, idx_b):
seq1 = self.get_lhs().get_feature_vector(idx_a)
seq2 = self.get_rhs().get_feature_vector(idx_b)
return numpy.dot(seq1, seq2)
from modshogun import LinearKernel, AvgDiagKernelNormalizer
from modshogun import Time
feats_train = RealFeatures(fm_train_real)
#feats_train.io.set_loglevel(MSG_DEBUG)
feats_train.parallel.set_num_threads(1)
feats_test = RealFeatures(fm_test_real)
kernel = LinearKernel()
kernel.set_normalizer(AvgDiagKernelNormalizer(scale))
kernel.init(feats_train, feats_train)
dkernel = DirectorLinearKernel()
dkernel.set_normalizer(AvgDiagKernelNormalizer(scale))
dkernel.init(feats_train, feats_train)
#print "km_train"
t = Time()
km_train = kernel.get_kernel_matrix()
#t1=t.cur_time_diff(True)
#print "dkm_train"
t = Time()
dkm_train = dkernel.get_kernel_matrix()
#t2=t.cur_time_diff(True)
#print "km_train", km_train
#print "dkm_train", dkm_train
return km_train, dkm_train
def kernel_linear_word_modular (fm_train_word=traindat,fm_test_word=testdat,scale=1.2): from modshogun import LinearKernel, AvgDiagKernelNormalizer from modshogun import WordFeatures feats_train=WordFeatures(fm_train_word) feats_test=WordFeatures(fm_test_word) kernel=LinearKernel(feats_train, feats_train) kernel.set_normalizer(AvgDiagKernelNormalizer(scale)) kernel.init(feats_train, feats_train) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return kernel
def kernel_sparse_linear_modular(fm_train_real=traindat,
fm_test_real=testdat,
scale=1.1):
from modshogun import SparseRealFeatures
from modshogun import LinearKernel, AvgDiagKernelNormalizer
feats_train = SparseRealFeatures(fm_train_real)
feats_test = SparseRealFeatures(fm_test_real)
kernel = LinearKernel()
kernel.set_normalizer(AvgDiagKernelNormalizer(scale))
kernel.init(feats_train, feats_train)
km_train = kernel.get_kernel_matrix()
kernel.init(feats_train, feats_test)
km_test = kernel.get_kernel_matrix()
return km_train, km_test, kernel
def kernel_linear_word_modular(fm_train_word=traindat,
fm_test_word=testdat,
scale=1.2):
from modshogun import LinearKernel, AvgDiagKernelNormalizer
from modshogun import WordFeatures
feats_train = WordFeatures(fm_train_word)
feats_test = WordFeatures(fm_test_word)
kernel = LinearKernel(feats_train, feats_train)
kernel.set_normalizer(AvgDiagKernelNormalizer(scale))
kernel.init(feats_train, feats_train)
km_train = kernel.get_kernel_matrix()
kernel.init(feats_train, feats_test)
km_test = kernel.get_kernel_matrix()
return kernel
