(self, data)
return normalizer(data)
Compute and apply normalization of the given data. Returns a new
        data table.

        Parameters
        ----------
        data : Orange.data.Table
            A data table to be normalized.

        Returns
        -------
        data : Orange.data.Table
            Normalized data table.
Compute and apply normalization of the given data. Returns a new
        data table.
Compute and apply normalization of the given data . Returns a new data table .
def __call__(self, data):
        """
        Compute and apply normalization of the given data. Returns a new
        data table.

        Parameters
        ----------
        data : Orange.data.Table
            A data table to be normalized.

        Returns
        -------
        data : Orange.data.Table
            Normalized data table.
        """
        from . import normalize

        if all(a.attributes.get('skip-normalization', False)
               for a in data.domain.attributes if a.is_continuous):
            # Skip normalization for datasets where all features are marked as already normalized.
            # Required for SVMs (with normalizer as their default preprocessor) on sparse data to
            # retain sparse structure. Normalizing sparse data would otherwise result in a dense
            # matrix, which requires too much memory. For example, this is used for Bag of Words
            # models where normalization is not really needed.
            return data

        normalizer = normalize.Normalizer(
            zero_based=self.zero_based,
            norm_type=self.norm_type,
            transform_class=self.transform_class,
            center=self.center,
            normalize_datetime=self.normalize_datetime
        )
        return normalizer(data)
def __call__ ( self , data ) : from . import normalize if all ( a . attributes . get ( 'skip-normalization' , False ) for a in data . domain . attributes if a . is_continuous ) : # Skip normalization for datasets where all features are marked as already normalized. # Required for SVMs (with normalizer as their default preprocessor) on sparse data to # retain sparse structure. Normalizing sparse data would otherwise result in a dense # matrix, which requires too much memory. For example, this is used for Bag of Words # models where normalization is not really needed. return data normalizer = normalize . Normalizer ( zero_based = self . zero_based , norm_type = self . norm_type , transform_class = self . transform_class , center = self . center , normalize_datetime = self . normalize_datetime ) return normalizer ( data )