pub(crate) fn field_dims(&self) -> Vec<u32> {
        match self.variant {
            EncodingDictionaryVariant::FieldAwareFrequencyTruncation(ref dictionary) => {
                dictionary.field_dims()
            }
            EncodingDictionaryVariant::Identity(ref dictionary) => dictionary.field_dims(),
            _ => {
                let encoder = self.new_encoder();
                vec![1; encoder.dense_offset()]
            }
        }
    }
/// Returns the field dimensions up to the dense inputs. This is the number of features
/// encoded in the cateogircal column, if any are present (columns with only 1 feature
/// must be 1-hot or numeric).
Returns the field dimensions up to the dense inputs. This is the number of features
encoded in the cateogircal column, if any are present (columns with only 1 feature
must be 1-hot or numeric).
Returns the field dimensions up to the dense inputs . This is the number of features encoded in the cateogircal column if any are present ( columns with only 1 feature must be 1 - hot or numeric ) .
pub(crate) fn field_dims(&self) -> Vec<u32> {
        match self.variant {
            EncodingDictionaryVariant::FieldAwareFrequencyTruncation(ref dictionary) => {
                dictionary.field_dims()
            }
            EncodingDictionaryVariant::Identity(ref dictionary) => dictionary.field_dims(),
            _ => {
                let encoder = self.new_encoder();
                vec![1; encoder.dense_offset()]
            }
        }
    }
pub ( crate ) fn field_dims ( & self ) -> Vec < u32 > { match self . variant { EncodingDictionaryVariant :: FieldAwareFrequencyTruncation ( ref dictionary ) => { dictionary . field_dims ( ) } EncodingDictionaryVariant :: Identity ( ref dictionary ) => dictionary . field_dims ( ) , _ => { let encoder = self . new_encoder ( ) ; vec ! [ 1 encoder dense_offset ( ) ] } } }
Returns the field dimensions up to the dense inputs.
Returns the field dimensions up to the dense inputs .
&self
&self