Skip to content

CHANGELOG.md

Latest commit

 

History

History
100 lines (58 loc) · 5.08 KB

CHANGELOG.md

File metadata and controls

100 lines (58 loc) · 5.08 KB
Raw

Changelog

Release v1.8

  • New function cmi, which computes conditional mutual information.

Release v1.7

  • Bug fix in mutualinfo for naive estimators from Entropies.jl.

Release v1.1.4

  • Release process updated.

Release v1.1.3

  • Fixed typo in documentation

Release v1.1.2

  • Documentation improvements.

Release v1.1.1

  • Documentation improvements.

Release v1.1.0

  • Internal API changes. Not affecting end user.
  • Documentation update.

Release v1.0.0

  • Completely new API. See documentation for details.

Release v0.4.3

New functionality

Added convenience methods to compute regular transfer entropy directly from time series. The following methods work:

  1. transferentropy(source, driver; dim = 3, τ = 1, η = 1): compute transfer entropy from source to target using a dim-dimensional delay reconstruction with prediction lag η and embedding lag τ, inferring appropriate bin sizes from time series length. Extra dimensions are assigned to the T_{pp} component of the delay reconstruction. Returns the result over those binsizes.

  2. transferentropy(source, driver, Union{::RectangularBinning, AbstractArray{::RectangularBinning}}, dim = 3, τ = 1, η = 1): compute transfer entropy from source to target using a dim-dimensional delay reconstruction with prediction lag η and embedding lag τ, specifying the partition(s) explicitly. Extra dimensions are assigned to the T_{pp} component of the delay reconstruction. Returns one transfer entropy estimate per partition.

  3. transferentropy(source, driver, k::Int, l::Int, m::Int; τ = 1, η = 1): compute transfer entropy from source to target using a dim-dimensional delay reconstruction with k being the dimension of the T_f component, l the dimension of the T_{pp} component and m the dimension of the S_{pp} component, using prediction lag η and embedding lag τ, inferring appropriate bin sizes from time series length. Returns the result over those binsizes.

  4. transferentropy(source, driver, k::Int, l::Int, m::Int; τ = 1, η = 1): compute transfer entropy from source to target using a dim-dimensional delay reconstruction with k being the dimension of the T_f component, l the dimension of the T_{pp} component and m the dimension of the S_{pp} component, using prediction lag η and embedding lag τ, specifying the partition(s) explicitly. Returns one transfer entropy estimate per partition.

The same works for computing conditional transfer entropy with three time series, but an extra time series and embedding component must be added, i.e. transferentropy(source, driver, cond; dim = 4, τ = 1, η = 1) or transferentropy(source, driver, cond; k::Int, l::Int, m::Int, n::Int; τ = 1, η = 1, where m is the dimension of the C_{pp} component of the delay reconstruction.

An optional estimator keyword may be provided. The default is estimator = VisitationFrequency(), but estimator = TransferOperatorGrid() also works. For triangulation-based estimators, you still need to compute the invariant measure manually first, then compute transfer entropy as usual.

Release v0.4.2

New functionality

  • Added te_embed function that for regular and conditional transfer entropy analyses constructs appropriate delay reconstruction points and corresponding TEVars instances that instructs the transferentropy methods how to compute the marginals.

Release v0.4.1

  • Update documentation with more examples.
  • Fix bug in triangulation estimator where multiple points at the origin was used to sample simplices. This bug only affected the new methods with updates syntax from v0.4.0.

Release v0.4.0

New syntax for the different estimators.

Rectangular binnings

  • transferentropy(pts, v::TEVars, binning_scheme::RectangularBinning, VisitationFrequency()) uses a regular visitation frequency estimator.

  • transferentropy(pts, v::TEVars, binning_scheme::RectangularBinning, TransferOperatorGrid()) uses the transfer operator grid estimator.

Triangulation binnings

For computing transfer entropy from triangulations, first compute the invariant measure over the triangulation, then superimpose a rectangular grid and compute the transfer entropy over that grid. For a precomputed invariant meausre, the syntax is:

  • transferentropy(μ::AbstractTriangulationInvariantMeasure, vars::TEVars, binning_scheme::RectangularBinning; n::Int = 10000).

For example:

μapprox = invariantmeasure(pts, TriangulationBinning(), ApproximateIntersection())
μexact = invariantmeasure(pts, TriangulationBinning(), ExactIntersection())

# Compute transfer entropy at single bin size
transferentropy(μapprox, vars, RectangularBinning(0.2))
transferentropy(μexact, vars, RectangularBinning(0.2))

# Compute transfer entropy from the invariant measure over multiple 
# bin sizes. This is fast, because the measure has been precomputed.
binsizes = [0.2, 0.3, 0.5, 0.7]
[transferentropy(μapprox, vars, RectangularBinning(bs)) for bs in binsizes]
[transferentropy(μexact, vars, RectangularBinning(bs)) for bs in binsizes]