def test_sparse_pps(self):
with self.data.unlocked():
self.data.X = csr_matrix(self.data.X)
out = AdaptiveNormalize()(self.data)
true_out = Scale(center=Scale.NoCentering, scale=Scale.Span)(self.data)
np.testing.assert_array_equal(out, true_out)
self.data = self.data.X.toarray()
class OneClassSVMLearner(_OutlierLearner):
name = "One class SVM"
__wraps__ = OneClassSVM
preprocessors = SklLearner.preprocessors + [AdaptiveNormalize()]
def __init__(self, kernel='rbf', degree=3, gamma="auto", coef0=0.0,
tol=0.001, nu=0.5, shrinking=True, cache_size=200,
max_iter=-1, preprocessors=None):
super().__init__(preprocessors=preprocessors)
self.params = vars()
def test_dense_pps(self):
true_out = Normalize()(self.data)
out = AdaptiveNormalize()(self.data)
np.testing.assert_array_equal(out, true_out)
import sklearn.svm as skl_svm
from Orange.classification import SklLearner
from Orange.preprocess import AdaptiveNormalize
__all__ = ["SVMLearner", "LinearSVMLearner", "NuSVMLearner"]
svm_pps = SklLearner.preprocessors + [AdaptiveNormalize()]
class SVMLearner(SklLearner):
__wraps__ = skl_svm.SVC
preprocessors = svm_pps
def __init__(self,
C=1.0,
kernel='rbf',
degree=3,
gamma="auto",
coef0=0.0,
shrinking=True,
probability=False,
tol=0.001,
cache_size=200,
max_iter=-1,
preprocessors=None):
super().__init__(preprocessors=preprocessors)
self.params = vars()
class LinearSVMLearner(SklLearner):