Trait sample_consensus::Consensus

pub trait Consensus<E, Data> where
    E: Estimator<Data>, {
    type Inliers: IntoIterator<Item = usize>;
    fn model<I>(&mut self, estimator: &E, data: I) -> Option<E::Model>
    where
        I: Iterator<Item = Data> + Clone;
    fn model_inliers<I>(
        &mut self, 
        estimator: &E, 
        data: I
    ) -> Option<(E::Model, Self::Inliers)>
    where
        I: Iterator<Item = Data> + Clone;
}

A consensus algorithm extracts a consensus from an underlying model of data. This consensus includes a model of the data and which datapoints fit the model.

Note that all the consensus methods take a &mut self. This allows the consensus to store state such as an RNG or pre-allocated memory. This means multiple threads will be forced to create their own Consensus instance, which is most efficient.

Associated Types

type Inliers: IntoIterator<Item = usize>

Iterator over the indices of the inliers in the clonable iterator.

Required methods

fn model<I>(&mut self, estimator: &E, data: I) -> Option<E::Model> where
    I: Iterator<Item = Data> + Clone, 

Takes a slice over the data and an estimator instance. It returns None if no valid model could be found for the data and Some if a model was found.

Make sure to shuffle your data before calling this. You can use SliceRandom::shuffle.

fn model_inliers<I>(
    &mut self,
    estimator: &E,
    data: I
) -> Option<(E::Model, Self::Inliers)> where
    I: Iterator<Item = Data> + Clone, 

Takes a slice over the data and an estimator instance. It returns None if no valid model could be found for the data and Some if a model was found. It includes the inliers consistent with the model.

Make sure to shuffle your data before calling this. You can use SliceRandom::shuffle.

Implementors