2a18cd0e74
Adding property to allow used to set random seed for noise addition to data in MI, CMI and wrapped estimators. Closes issue #99
2024-04-16 14:08:04 +10:00
5f87e2018b
MI common estimators utilising underlying nearest neighbour searchers holding different data sets separately (for dynamic correlation exclusion)
2023-10-09 13:14:29 +11:00
b02760ddd3
Adding overloads on setObservations and addObservations for MI and TE calculators to handle one variable univariate and one variable multivariate
2021-11-15 10:58:59 +11:00
2ab3ac01bb
Adding non-overloaded method signatures for adding 1D vs 2D observations for common class for continuous MI and CMI estimators, since JPype 0.7 does not seem able to resolve overloaded 1D vs 2D arrays any longer
2019-07-25 23:11:44 +10:00
d9db7a15ab
Further clarifications for including NORMALISE and NOISE_LEVEL_TO_ADD properties to all mutual information calculators, and handling linear redundancy sub-cases
2019-04-03 00:07:05 +11:00
b1437452e4
Adding NORMALISE and NOISE_LEVEL_TO_ADD properties to all mutual information calculators for continuous-valued data (extends this capability from only KSG to Gaussian and kernel as well, the latter already had NORMALISE)
2019-04-02 12:38:17 +11:00
3aa1493d7f
Added addObservations() method with sourceValid and destValid boolean arrays to TransferEntropyCalculatorViacondMutualInfo class
2018-09-21 10:34:01 +10:00
46a9785da2
In surrogates for MI and CMI also turning adding noise off since this will have been done in the initial calculation already, and will just broaden the noise here.
2018-05-12 22:35:54 +10:00
28ba409ff6
Turning off normalisation when computing surrogates in Common CMI and MI calculators, because normalisation has already been carried out (and if one variable had no variance, this will inflate the values back to original scale)
2018-05-12 21:30:39 +10:00
d77a9f274e
Added interface ChannelCalculator for MutualInfoCalculatorMultiVariate; this should have always been there but not all calculators implemented the univariate methods. It previously only implemented ChannelCalculatorMultiVariate. Added those methods into MutualInfoMultiVariateCommon, which provides the base implementation for all required calculators, so all are now taken care of. This means MI and TE can both be fully used in one interface with the set/addObservations methods.
2017-08-18 13:54:10 +10:00
687978103c
Added auto-embedding with Ragwitz criteria to AIS Kraskov calculator, partially addressing Issue 38. Also adds getProperty() method to AIS calculators and MI calculators.
2015-06-04 03:48:55 +00:00
79db42274c
Added methods for computing MI/TE etc in Multivariate calculators on only univariate time series (double[]) when the calculators have been initialised for dimension 1 in source and destination
2015-01-23 03:11:35 +00:00
733da8265d
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
2014-11-25 12:31:44 +00:00
6ade6d4311
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.
2014-10-30 04:04:16 +00:00
edbe595032
Javadocs made release-ready in the continuous package. Also added computeLocalUsingPreviousObservations into ChannelCalculator and ChannelCalculatorMultiVariate (removed from MutualInfoMultivarate), and made the ViaCondMutualInfo classes not be abstract anymore, and moved embedding properties up into the main TransferEntropyCalculator interface
2014-08-13 02:18:33 +00:00
98d5373d62
Adding GPLv3 license statement to all code headers in the infodymamics.measures.continuous package (for starters)
2014-08-06 05:31:37 +00:00
eef14a901b
More info on error messages for wrong dimensions of data provided.
2014-04-30 01:30:22 +00:00
83f7de5472
Added interface for ConditionalTransferEntropy. Added abstract implementation ConditionalTransferEntropyCalculatorViaCondMutualInfo, and child classes for Kraskov and Gaussian implementations.
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Added associated embedding method to MatrixUtils, and fixed a lot of header comments here. Minor fixes to comments and which methods are specified (e.g. setProperties) for TransferEntropy and Entropy calculators.
2014-04-17 05:58:13 +00:00
12fc61d396
Moved Kraskov mutual info calculator multivariate to inherit from the common MI multivariate class, so as to share code for adding observations, statistical tests, etc. Passed unit testing.
2014-03-26 05:08:46 +00:00
2571c3ae9e
Rearchitected Active Info Storage calculators to use a common parent class for data collection, and underlying mutual information classes for the implementation specifics. Includes adding Kraskov and Gaussian Active Info Storage calculators, and implementing embedding delay for the past history.
2014-03-26 04:05:20 +00:00
b4645584fe
Added new method generateRandomPerturbations(int, int) to RandomGenerator, and switched all computeSignificance methods to use this (since there's no need for checking for distinct perturbations really, and this is much faster). Required adding sortIndices(double[]) method to MatrixUtils
2013-10-18 00:42:12 +00:00
b17ba9b05c
Minor alterations to utilities, including adding exceptions and adding to-do notes.
2013-03-18 05:55:43 +00:00
07ce2d0e24
Added determinant computation via Cholesky decomposition for symmetric matrices (more efficient)
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Utilising determinant computation via Cholesky decomposition in Gaussian entropy and MI calculators
Allowed ChannelCalculatorCommon.finaliseObservations() to throw Exceptions so that child classes can do so (in particular Gaussian MI needs to do so if it finds the variables are linearly dependent)
2012-08-17 07:28:50 +00:00
c828445784
Pulled common functionality for MutualInfoMultiVariate Kernel and Gaussian into MutualInfoMultiVariateCommon (primarily adding multiple observations and computing statistical significance).
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MutualInfoMultiVariateKernel now computes statistical significance based on shuffling the first variable (the source) in line with the description in ChannelCalculator.
This adds ability to add multiple observations for a MutualInfoMultiVariateKernel calculator also.
2012-08-07 07:32:52 +00:00
d19045014a
Adding extra matrix utility functions to allow multivariate normal PDF to be calculated (including Cholesky decomposition, inversion of symmetric matrices), extra matrix multiplcation routines, etc.
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Added routines in MathsUtils for uni- and multi-variate normal PDF and CDF (univariate only).
Implemented local MI for Gaussian MI. Pulled many routines into the MutualInfoMultiVariateCommon
2012-08-06 07:44:23 +00:00
Joseph Lizier