re-arrange docs pages

joshuabmoore · Dec 26, 2024 · 7e2780f · 7e2780f
1 parent e815243
commit 7e2780f
Show file tree

Hide file tree

Showing 4 changed files with 7 additions and 6 deletions.
diff --git 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 → docs/src/classification.md b/docs/src/tutorial.md → 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
-```
+```
diff --git 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
@@ -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)
+<p align="center" width="100%">
+    <img src=./figures/tools/see.svg>
+</p>
+
 #### (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``).