Package org.ddogleg.fitting.modelset.distance

Class StatisticalDistanceModelMatcher<Model,Point>

java.lang.Object
org.ddogleg.fitting.modelset.distance.StatisticalDistanceModelMatcher<Model,Point>
All Implemented Interfaces:
ModelMatcher<Model,Point>
public class StatisticalDistanceModelMatcher<Model,Point> extends Object implements ModelMatcher<Model,Point>

Outliers are removed by first fitting a model to all the data points. Points which deviate the most are removed and the best fit parameters are recomputed. This is done until some error metric changes very little or the maximum number of iterations has been exceeded. Works well when the inlier set is much greater than the noise.

Anyone have a good reference paper for this approach?

  • Field Details

    • param

      protected Model param

    • currParam

      protected Model currParam

    • oldCenter

      protected double oldCenter

    • centerError

      protected double centerError

  • Constructor Details

    • StatisticalDistanceModelMatcher

      public StatisticalDistanceModelMatcher(int maxIterations, double minChange, double exitCenterError, double failError, int minFitPoints, StatisticalDistance statistics, double pruneThreshold, ModelManager<Model> modelManager, ModelGenerator<Model,Point> modelFitter, DistanceFromModel<Model,Point> modelError, ModelCodec<Model> codec)
      Creates a new model matcher. The type of statistics it uses is specified by "statistics". 0 for mean and standard deviation and 1 for median and percentile. If statistics=0 is set then the threshold correspond to the number of standard deviations a point can be away from the mean before it is pruned. If statistics=1 then points which have a percentile error more than that value are pruned.
      Parameters:
      maxIterations - The maximum number of iterations it will perform.
      minChange - It will stop iterating of the change in error is less than this amount.
      exitCenterError - If the error is less than this value it will stop iterating.
      failError - If the final error is more than this amount it failed.
      minFitPoints - If fewer than this number of points remain, then it failed.
      statistics - 0 = mean statistics and 1 = percentile statistics
      pruneThreshold - Points which exceed this statistic are pruned. See StatisticalDistance for details.
      modelFitter - Fits a model to a set of points
      modelError - Computes the error between a point and the model.

  • Method Details

    • process

      public boolean process(List<Point> dataSet)
      Description copied from interface: ModelMatcher
      Finds a set of points from the provided list that are a good fit for the internal model and computes the fit parameters for the model.
      Specified by:
      process in interface ModelMatcher<Model,Point>
      Parameters:
      dataSet - Set of points (with noise) that are to be fit.
      Returns:
      true if it successfully found a solution or false if not.

    • computeDiff

      protected double computeDiff(Model modelA, Model modelB)
      Computes the difference between the two parameters.

    • getModelParameters

      public Model getModelParameters()
      Description copied from interface: ModelMatcher
      Model for the match set
      Specified by:
      getModelParameters in interface ModelMatcher<Model,Point>
      Returns:
      model.

    • getMatchSet

      public List<Point> getMatchSet()
      Description copied from interface: ModelMatcher
      A set of points which match the provided parameters.
      Specified by:
      getMatchSet in interface ModelMatcher<Model,Point>
      Returns:
      List of points in the match set.

    • getInputIndex

      public int getInputIndex(int matchIndex)
      Description copied from interface: ModelMatcher
      For an item in the match set, return the index of the item in the original input set.
      Specified by:
      getInputIndex in interface ModelMatcher<Model,Point>
      Parameters:
      matchIndex - Index of an element in the match set.
      Returns:
      Index of the same element in the original input list.

    • getFitQuality

      public double getFitQuality()
      Description copied from interface: ModelMatcher
      Returns the metric used to evaluate the quality of fit. Meaning is implementation specific. Larger or smaller values could be preferred depending on implementation..
      Specified by:
      getFitQuality in interface ModelMatcher<Model,Point>
      Returns:
      Quality of fit to matched set of points

    • getMinimumSize

      public int getMinimumSize()
      Description copied from interface: ModelMatcher
      This is the minimum number of observations which can be input and produce a valid model.
      Specified by:
      getMinimumSize in interface ModelMatcher<Model,Point>
      Returns:
      Minimum number of sample points

    • reset

      public void reset()
      Description copied from interface: ModelMatcher
      Resets the model matcher to its original state. This means that given identical inputs it would produce the same outputs
      Specified by:
      reset in interface ModelMatcher<Model,Point>

    • getPointType

      public Class<Point> getPointType()
      Description copied from interface: ModelMatcher
      Returns a class for the input point object
      Specified by:
      getPointType in interface ModelMatcher<Model,Point>

    • getModelType

      public Class<Model> getModelType()
      Description copied from interface: ModelMatcher
      Returns a class for the input point object
      Specified by:
      getModelType in interface ModelMatcher<Model,Point>