def __call__(self, data, weight_id=None):
"""
Return a classifier trained on the `data` (`weight_id` is ignored).
:param Orange.data.Table data:
Training data set.
:param int weight_id:
Ignored.
:rval: Orange.core.LinearClassifier
.. note::
The :class:`Orange.core.LinearClassifier` is same class as
:class:`Orange.classification.svm.LinearClassifier`.
"""
if not isinstance(data.domain.class_var, Orange.feature.Discrete):
raise TypeError("Can only learn a discrete class.")
if data.domain.has_discrete_attributes(False) or self.normalization:
dc = DomainContinuizer()
dc.multinomial_treatment = self.multinomial_treatment
dc.class_treatment = dc.Ignore
dc.continuous_treatment = \
dc.NormalizeByVariance if self.normalization else dc.Leave
c_domain = dc(data)
data = data.translate(c_domain)
return super(LibLinearLogRegLearner, self).__call__(data, weight_id)
def __call__(self, data, weight_id=None):
"""
Return a classifier trained on the `data` (`weight_id` is ignored).
:param Orange.data.Table data:
Training data set.
:param int weight_id:
Ignored.
:rval: Orange.core.LinearClassifier
.. note::
The :class:`Orange.core.LinearClassifier` is same class as
:class:`Orange.classification.svm.LinearClassifier`.
"""
if not isinstance(data.domain.class_var, Orange.feature.Discrete):
raise TypeError("Can only learn a discrete class.")
if data.domain.has_discrete_attributes(False) or self.normalization:
dc = DomainContinuizer()
dc.multinomial_treatment = self.multinomial_treatment
dc.class_treatment = dc.Ignore
dc.continuous_treatment = \
dc.NormalizeByVariance if self.normalization else dc.Leave
c_domain = dc(data)
data = data.translate(c_domain)
return super(LibLinearLogRegLearner, self).__call__(data, weight_id)
def _normalize(self, data):
dc = DomainContinuizer()
dc.class_treatment = DomainContinuizer.Ignore
dc.continuous_treatment = DomainContinuizer.NormalizeBySpan
dc.multinomial_treatment = DomainContinuizer.AsNormalizedOrdinal
newdomain = dc(data)
return data.translate(newdomain)
def __call__(self, data, weight_id=None):
if not isinstance(data.domain.class_var, Orange.feature.Discrete):
raise TypeError("Can only learn a discrete class.")
if data.domain.has_discrete_attributes(False) or self.normalization:
dc = DomainContinuizer()
dc.multinomial_treatment = self.multinomial_treatment
dc.class_treatment = dc.Ignore
dc.continuous_treatment = dc.NormalizeByVariance if self.normalization else dc.Leave
c_domain = dc(data)
data = data.translate(c_domain)
return super(LibLinearLogRegLearner, self).__call__(data, weight_id)
def __call__(self, data, weight_id=None):
if not isinstance(data.domain.class_var, Orange.feature.Discrete):
raise TypeError("Can only learn a discrete class.")
if data.domain.has_discrete_attributes(False) or self.normalization:
dc = DomainContinuizer()
dc.multinomial_treatment = self.multinomial_treatment
dc.class_treatment = dc.Ignore
dc.continuous_treatment = \
dc.NormalizeByVariance if self.normalization else dc.Leave
c_domain = dc(data)
data = data.translate(c_domain)
return super(LibLinearLogRegLearner, self).__call__(data, weight_id)