SimLoop: Loop Over and Test Trajectories With Different Translations
In SimilarityMeasures: Trajectory Similarity Measures

Description Usage Arguments Details Value Author(s) See Also Examples

Function to loop over and test the trajectories using the different translations in each dimension. This is used by the LCSS function to test all of the n dimensional translations. Do not call this function directly.

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SimLoop(traj1, traj2, pointSpacing, pointDistance, spacing, 
        similarity, translations, dimensions, 
        dimLeft=dimensions, currentTrans=rep(0, dimensions))

`traj1`	An m x n matrix containing trajectory1. Here m is the number of points and n is the dimension of the points.
`traj2`	A k x n matrix containing trajectory2. Here k is the number of points and n is the dimension of the points. The two trajectories are not required to have the same number of points.
`pointSpacing`	An integer value of the maximum index difference between trajectory1 and trajectory2 allowed in the calculation.
`pointDistance`	A floating point number representing the maximum distance in each dimension allowed for points to be considered equivalent.
`spacing`	The integer spacing between each translation that will be tested.
`similarity`	A vector containing the current best similarity and translations calculated.
`translations`	A list of vectors containing the translations in each dimension.
`dimensions`	An integer representing the number of dimensions being used for the calculation.
`dimLeft`	An integer number of dimensions which have not been looped over yet.
`currentTrans`	A vector containing the current translation being tested.

This function is used to loop over the n dimensions for the LCSS function. This function should not be called directly.

Returns the current best LCSS value and the translations that created this as a vector.

Kevin Toohey

LCSS, LCSSRatio, LCSSRatioCalc, LCSSTranslation, LCSSCalc

# Creating two trajectories.
path1 <- matrix(c(0, 1, 2, 3, 0, 1, 2, 3), 4)
path2 <- matrix(c(0, 1, 2, 3, 4, 5, 6, 7), 4)

# Running the LCSS algorithm on the trajectories.
LCSS(path1, path2, 2, 2, 0.5)