[][src]Crate sample_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.


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.


A model is a best-fit of at least some of the underlying data. You can compute residuals in respect to the model.


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 orthogonal 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.