本文整理汇总了Python中shogun.Kernel.GaussianKernel.get_kernel_matrix方法的典型用法代码示例。如果您正苦于以下问题：Python GaussianKernel.get_kernel_matrix方法的具体用法？Python GaussianKernel.get_kernel_matrix怎么用？Python GaussianKernel.get_kernel_matrix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shogun.Kernel.GaussianKernel的用法示例。
在下文中一共展示了GaussianKernel.get_kernel_matrix方法的4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: kernel_io_modular
# 需要导入模块: from shogun.Kernel import GaussianKernel [as 别名]
# 或者: from shogun.Kernel.GaussianKernel import get_kernel_matrix [as 别名]
def kernel_io_modular (fm_train_real=traindat,fm_test_real=testdat,width=1.9):
from shogun.Features import RealFeatures
from shogun.Kernel import GaussianKernel
from shogun.Library import AsciiFile, BinaryFile
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
kernel=GaussianKernel(feats_train, feats_train, width)
km_train=kernel.get_kernel_matrix()
f=AsciiFile("gaussian_train.ascii","w")
kernel.save(f)
del f
kernel.init(feats_train, feats_test)
km_test=kernel.get_kernel_matrix()
f=AsciiFile("gaussian_test.ascii","w")
kernel.save(f)
del f
#clean up
import os
os.unlink("gaussian_test.ascii")
os.unlink("gaussian_train.ascii")
return km_train, km_test, kernel示例2: mkl_binclass_modular
# 需要导入模块: from shogun.Kernel import GaussianKernel [as 别名]
# 或者: from shogun.Kernel.GaussianKernel import get_kernel_matrix [as 别名]
def mkl_binclass_modular (train_data, testdata, train_labels, test_labels, d1, d2):
# create some Gaussian train/test matrix
tfeats = RealFeatures(train_data)
tkernel = GaussianKernel(128, d1)
tkernel.init(tfeats, tfeats)
K_train = tkernel.get_kernel_matrix()
pfeats = RealFeatures(test_data)
tkernel.init(tfeats, pfeats)
K_test = tkernel.get_kernel_matrix()
# create combined train features
feats_train = CombinedFeatures()
feats_train.append_feature_obj(RealFeatures(train_data))
# and corresponding combined kernel
kernel = CombinedKernel()
kernel.append_kernel(CustomKernel(K_train))
kernel.append_kernel(GaussianKernel(128, d2))
kernel.init(feats_train, feats_train)
# train mkl
labels = Labels(train_labels)
mkl = MKLClassification()
# not to use svmlight
mkl.set_interleaved_optimization_enabled(0)
# which norm to use for MKL
mkl.set_mkl_norm(2)
# set cost (neg, pos)
mkl.set_C(1, 1)
# set kernel and labels
mkl.set_kernel(kernel)
mkl.set_labels(labels)
# train
mkl.train()
# test
# create combined test features
feats_pred = CombinedFeatures()
feats_pred.append_feature_obj(RealFeatures(test_data))
# and corresponding combined kernel
kernel = CombinedKernel()
kernel.append_kernel(CustomKernel(K_test))
kernel.append_kernel(GaussianKernel(128, d2))
kernel.init(feats_train, feats_pred)
# and classify
mkl.set_kernel(kernel)
output = mkl.apply().get_labels()
output = [1.0 if i>0 else -1.0 for i in output]
accu = len(where(output == test_labels)[0]) / float(len(output))
return accu示例3: kernel_gaussian_modular
# 需要导入模块: from shogun.Kernel import GaussianKernel [as 别名]
# 或者: from shogun.Kernel.GaussianKernel import get_kernel_matrix [as 别名]
def kernel_gaussian_modular (fm_train_real=traindat,fm_test_real=testdat, width=1.3):
from shogun.Features import RealFeatures
from shogun.Kernel import GaussianKernel
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
kernel=GaussianKernel(feats_train, feats_train, width)
km_train=kernel.get_kernel_matrix()
kernel.init(feats_train, feats_test)
km_test=kernel.get_kernel_matrix()
return km_train,km_test,kernel示例4: gaussian
# 需要导入模块: from shogun.Kernel import GaussianKernel [as 别名]
# 或者: from shogun.Kernel.GaussianKernel import get_kernel_matrix [as 别名]
def gaussian ():
print 'Gaussian'
from shogun.Features import RealFeatures
from shogun.Kernel import GaussianKernel
feats_train=RealFeatures(fm_train_real)
feats_test=RealFeatures(fm_test_real)
width=1.9
kernel=GaussianKernel(feats_train, feats_train, width)
km_train=kernel.get_kernel_matrix()
kernel.init(feats_train, feats_test)
km_test=kernel.get_kernel_matrix()