From 7e2780f365fc06ab58582231905936a8d5ffeb61 Mon Sep 17 00:00:00 2001 From: jmoo2880 Date: Fri, 27 Dec 2024 06:00:04 +1100 Subject: [PATCH] re-arrange docs pages --- docs/make.jl | 2 +- docs/src/{tutorial.md => classification.md} | 5 +---- docs/src/imputation.md | 1 + docs/src/tools.md | 5 ++++- 4 files changed, 7 insertions(+), 6 deletions(-) rename docs/src/{tutorial.md => classification.md} (99%) diff --git a/docs/make.jl b/docs/make.jl index 4894ae7..a2b75bd 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -14,7 +14,7 @@ makedocs( plugins = [bib], pages = [ "Introduction" => "index.md", - "Tutorial: Classification" => "tutorial.md", + "Classification" => "classification.md", "Imputation" => "imputation.md", "Synthetic Data Generation" => "synthdatagen.md", "Encodings" => "encodings.md", diff --git a/docs/src/tutorial.md b/docs/src/classification.md similarity index 99% rename from docs/src/tutorial.md rename to docs/src/classification.md index c39f5bf..71a239f 100644 --- a/docs/src/tutorial.md +++ b/docs/src/classification.md @@ -148,9 +148,6 @@ julia> mean(predictions .== y_test) 0.925 ``` -## Imputation -See [Imputation](@ref Imputation_top) - ## Training with a custom basis To train with a custom basis, first, declare a custom basis with [`function_basis`](@ref), and pass it in as the last argument to [`fitMPS`](@ref). For this to work, the encoding hyperparameter must be set to `:Custom` in `MPSOptions` @@ -166,4 +163,4 @@ fitMPS(::Matrix, ::Vector, ::Matrix, ::Vector, ::MPSOptions, ::Nothing) sweep_summary(info) get_training_summary(mps::TrainedMPS, test_states::EncodedTimeSeriesSet) print_opts -``` \ No newline at end of file +``` diff --git a/docs/src/imputation.md b/docs/src/imputation.md index 15a9aec..b7e7329 100644 --- a/docs/src/imputation.md +++ b/docs/src/imputation.md @@ -106,6 +106,7 @@ julia> using Plots julia> plot(plots...) ``` ![](./figures/median_impute.svg) + The solid orange line depicts the "ground-truth" (observed) time-series values, the dotted blue line is the MPS-imputed data points and the dotted red line is the 1-NNI baseline. The blue shading indicates the uncertainty due to encoding error. diff --git a/docs/src/tools.md b/docs/src/tools.md index d457915..7cc4e86 100644 --- a/docs/src/tools.md +++ b/docs/src/tools.md @@ -21,7 +21,10 @@ where ``\rho_i`` is the reduced density matrix (rdm) at site ``i``, obtained by and ``\ket{\psi}`` is the MPS. Using the 1D spin-chain as an illustrative example, the SEE between a single site (dark blue) and the rest of the system (light blue) can be depicted as: -![](./figures/tools/see.svg) +

+ +

+ #### (2) Bipartite entanglement entropy (BEE) The bipartite entanglement entropy (BEE) quantifies the quantum entanglement between two complementary subsystems of a matrix product state (MPS). For an MPS with ``N`` sites, we can create a bipartition by splitting the system at any bond ``l``, resulting in region ``A`` (sites ``1`` to ``l``) and region ``B`` (sites ``l+1`` to ``N``).