[−][src]Crate sample_consensus

Traits

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.
Estimator	An `Estimator` is able to create a model that best fits a set of data. It is also able to determine the residual error each data point contributes in relation to the model.
Model	A model is a best-fit of at least some of the underlying data. You can compute residuals in respect to the model.
MultiConsensus	See `Consensus`. A multi-consensus can handle situations where different subsets of the data are consistent with different models. This kind of consensus also considers whether a point is part of another orthoganal model that is known before assuming it is a true outlier. In this situation there are inliers of different models and then true outliers that are actual erroneous data that should be filtered out.

Type Definitions