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src/o/r/Orange-2.7.2/docs/reference/rst/code/svm-custom-kernel.py   Orange(Download)
iris = data.Table("iris.tab")
l1 = SVMLearner()
l1.kernel_func = kernels.RBFKernelWrapper(Euclidean(iris), gamma=0.5)
l1.kernel_type = SVMLearner.Custom
l1.probability = True
c1 = l1(iris)
l1.name = "SVM - RBF(Euclidean)"
 
l2 = SVMLearner()
l2.kernel_func = kernels.RBFKernelWrapper(Hamming(iris), gamma=0.5)
l3 = SVMLearner()
l3.kernel_func = kernels.CompositeKernelWrapper(
    kernels.RBFKernelWrapper(Euclidean(iris), gamma=0.5),
    kernels.RBFKernelWrapper(Hamming(iris), gamma=0.5), l=0.5)
l3.kernel_type = SVMLearner.Custom

src/o/r/Orange-2.7.2/Orange/testing/unit/tests/test_svm.py   Orange(Download)
                            example_weighted_sum
from Orange.classification import svm                            
from Orange.classification.svm.kernels import BagOfWords, RBFKernelWrapper
from Orange.testing import testing
from Orange.testing.testing import datasets_driven, test_on_datasets, test_on_data
            dist = orange.ExamplesDistanceConstructor_Hamming(data)
        self.learner = self.LEARNER(kernel_type=SVMLearner.Custom,
                                    kernel_func=RBFKernelWrapper(dist, gamma=0.5))
 
        testing.LearnerTestCase.test_learner_on(self, data)