David Shorten
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ec01683592
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Incorporated JL code from 21/11/2018
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2018-12-10 20:22:15 +11:00 |
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jlizier
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16015e0f12
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Nearest neighbour searchers now return number of points found for findPointsWithinR as well as marking them in input arrays. Also added new method sumDistanceAboveThresholdForPointsWithinRs() for the univariate searcher (to be used in spiking TE estimator under development)
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2018-11-24 21:16:36 +11:00 |
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jlizier
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1bb67f0840
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Adding neighbour search utilities in preparation for attempting fast surrogate generation (performing multiple neighbour searches at once where we can); in particular adding a countPointsWithinR with a remapping of the point's indices. Unit test added as well for this.
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2017-11-22 20:48:30 +11:00 |
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jlizier
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9a7ff306c8
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Adding utilities for fast nearest neighbour searchers to handle calculations on new samples and prepare for conditional MI fast surrogates computation
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2016-10-20 09:26:12 +11:00 |
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joseph.lizier
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687978103c
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Added auto-embedding with Ragwitz criteria to AIS Kraskov calculator, partially addressing Issue 38. Also adds getProperty() method to AIS calculators and MI calculators.
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2015-06-04 03:48:55 +00:00 |
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joseph.lizier
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733da8265d
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Dynamic correlation exclusion, or Theiler window, added to all Kraskov estimators, i.e. Mutual Info, Conditional MI, Multi-info, and thereby the derived classes including transfer entropy. Fixes issue 35
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2014-11-25 12:31:44 +00:00 |
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joseph.lizier
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57fafd1551
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Adding more methods to nearest neighbour search classes to return collections of points within ranges via arrays, and to use additional criteria in searching or take arguments for one variable that has already been tested. Utilising these new searches within Conditional MI Kraskov estimator algorithm 1
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2014-11-03 04:57:24 +00:00 |
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joseph.lizier
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32c062f880
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Inlining norms in Nearest Neighbour search classes, and adding methods to return Collections of points within ranges
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2014-10-31 12:02:54 +00:00 |
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joseph.lizier
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6ade6d4311
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Adding fast nearest neighbour searcher for univariate data (uses only a sorted array rather than whole k-d tree). Adds an abstract nearest neighbour searcher so calling classes don't necessarily need to know whether they're dealing with uni or multi-variates. MI and conditional MI Kraskov altered to use this where possible.
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2014-10-30 04:04:16 +00:00 |