[−][src]Trait sample_consensus::Consensus
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,
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.
fn model_inliers<I>(
&mut self,
estimator: &E,
data: I
) -> Option<(E::Model, Self::Inliers)> where
I: Iterator<Item = Data> + Clone,
&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.