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from model import LibSvmModel
import libsvm
__all__ = [
'LibSvmOneClassModel',
'LibSvmOneClassResults'
]
class LibSvmOneClassResults:
def __init__(self, model, traindataset, kernel, PredictorType):
modelc = model.contents
if modelc.param.svm_type != libsvm.ONE_CLASS:
raise TypeError, '%s is for one-class problems' % \
str(self.__class__)
self.rho = modelc.rho[0]
self.sv_coef = modelc.sv_coef[0][:modelc.l]
self.predictor = PredictorType(model, traindataset, kernel)
def predict(self, dataset):
"""
This function returns a list of boolean values which indicate
whether the test vectors form part of the distribution.
"""
return [self.predictor.predict(x) > 0 for x in dataset]
def predict_values(self, dataset):
"""
This function returns a list of floating point values which
indicate whether the test vectors form part of the
distribution.
A positive value indicates that the test vector is part of the
distribution, while a non-positive value indicates that is is
not.
"""
return [self.predictor.predict_values(x, 1) for x in dataset]
def compact(self):
self.predictor.compact()
class LibSvmOneClassModel(LibSvmModel):
"""
A model for distribution estimation (one-class SVM).
See also: Scholkopf, et al. Estimating the Support of a
High-Dimensional Distribution.
"""
ResultsType = LibSvmOneClassResults
def __init__(self, kernel, nu=0.5, **kwargs):
"""
Parameters:
- `nu`: XXX
"""
LibSvmModel.__init__(self, kernel, **kwargs)
self.nu = nu
self.param.svm_type = libsvm.ONE_CLASS
self.param.nu = nu
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