Kermit-UGent · cvigilv · Mar 24, 2026 · Mar 24, 2026 · Mar 24, 2026 · Mar 24, 2026
diff --git a/Project.toml b/Project.toml
@@ -4,7 +4,6 @@ authors = ["Carlos Vigil-Vásquez", "Dimi Boeckaerts", "Michiel Stock", "Steff T
 version = "0.1.0"
 
 [deps]
-Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"

diff --git a/README.md b/README.md
@@ -4,7 +4,7 @@ Hyperdimensional computing in Julia
 
 [![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://michielstock.github.io/HyperdimensionalComputing.jl/stable)
 [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://michielstock.github.io/HyperdimensionalComputing.jl/dev)
-[![Build Status](https://github.com/dimiboeckaerts/HyperdimensionalComputing.jl/workflows/CI/badge.svg)](https://github.com/dimiboeckaerts/HyperdimensionalComputing.jl/actions)
+[![Build Status](https://github.com/MichielStock/HyperdimensionalComputing.jl/workflows/CI/badge.svg)](https://github.com/MichielStock/HyperdimensionalComputing.jl/actions)
 [![code style: runic](https://img.shields.io/badge/code_style-%E1%9A%B1%E1%9A%A2%E1%9A%BE%E1%9B%81%E1%9A%B2-black)](https://github.com/fredrikekre/Runic.jl)
 
 This package implements special types of vectors and associated methods for hyperdimensional

diff --git a/docs/src/examples/introduction-to-hdc.jl b/docs/src/examples/introduction-to-hdc.jl
@@ -1,5 +1,3 @@
-using Handcalcs #hide
-
 # # Introduction
 #
 # Hyperdimensional Computing (HDC) is a brain-inspired computational paradigm that represents
@@ -64,7 +62,7 @@ h₃ = BipolarHV(; D = 8);
 # ## Bundling
 #
 # Bundling (also known as superposition) combines multiple hypervectors to create a new hypervector
-# that is similar to it's constituyents.
+# that is similar to it's constituents.
 #
 # $$u = [h_1 + h_2 + h_3]$$
 #
@@ -86,7 +84,7 @@ bundle([h₁, h₂, h₃])
 
 h₁ + h₂ + h₃
 
-# This operation generates a hypervector that is similar to all it's contituyent hypervectors,
+# This operation generates a hypervector that is similar to all it's constituent hypervectors,
 # such that
 #
 # $$h₁ \sim u, h₂ \sim u, h₃ \sim u$$
@@ -97,7 +95,7 @@ h₁ + h₂ + h₃
 # ## Binding
 #
 # Binding combines multiple hypervectors to create a new hypervector that is dissimilar to it's
-# constituyents, such that:
+# constituents, such that:
 #
 # $$v = [h₁ \times h₂ \times h₃]$$
 #
@@ -111,7 +109,7 @@ bind([h₁, h₂, h₃])
 
 h₁ * h₂ * h₃
 
-# This operation generates a hypervector that is similar to all it's contituyent hypervectors,
+# This operation generates a hypervector that is similar to all it's constituent hypervectors,
 # such that
 #
 # $$h₁ \nsim v, h₂ \nsim v, h₃ \nsim v$$

diff --git a/docs/src/index.md b/docs/src/index.md
@@ -4,11 +4,11 @@ CurrentModule = HyperdimensionalComputing
 
 # HyperdimensionalComputing.jl
 
-Documentation for [HyperdimensionalComputing](https://github.com/dimiboeckaerts/HyperdimensionalComputing.jl).
+Documentation for [HyperdimensionalComputing](https://github.com/MichielStock/HyperdimensionalComputing.jl).
 
 ## Overview
 
-HyperdimensionalComputing.jl provides...
+HyperdimensionalComputing.jl provides types and operations for hyperdimensional computing (HDC), a brain-inspired computational paradigm that represents and manipulates information using high-dimensional vectors. The package supports multiple vector symbolic architectures (BSC, MAP, FHRR, graded) with composable encoding strategies for sets, sequences, key-value pairs, graphs, and numeric levels.
 
 ## Manual
 

diff --git a/ext/UnicodePlotting.jl b/ext/UnicodePlotting.jl
@@ -24,10 +24,8 @@ function Base.show(io::IO, mime::MIME"text/plain", hv::AbstractHV)
 end
 
 function Base.show(io::IO, mime::MIME"text/plain", hv::Union{BinaryHV, BipolarHV})
-    counts = Dict(e => count(==(e), hv) for e in unique(hv))
-    #n = hv isa BinaryHV ? 0 : -1  # negative element
+    counts = Dict(string(e) => count(==(e), hv) for e in unique(hv))
     println(io, "$(length(hv))-element $(typeof(hv))")
-    #println(io, "1 / $n : $(count(hv.v)) / $(length(hv) - count(hv.v))")
     println(io, barplot(counts))
     return println(io, unicodeheatmap(hv))
 end

diff --git a/scripts/concept.jl b/scripts/concept.jl
diff --git a/src/HyperdimensionalComputing.jl b/src/HyperdimensionalComputing.jl
@@ -1,6 +1,6 @@
 module HyperdimensionalComputing
 
-using Distances, Random, Distributions, LinearAlgebra
+using Random, Distributions, LinearAlgebra
 
 include("types.jl")
 export AbstractHV,
@@ -45,6 +45,4 @@ export similarity,
     nearest_neighbor
 
 
-#include("learning.jl")
-
 end
diff --git a/src/encoding.jl b/src/encoding.jl
@@ -466,12 +466,12 @@ This encoding is based on the following mathematical notation:
 
 *Undirected graphs*
 ```math
-\\otimes_{i=1}^{m} S_i \\otimes T_i
+\\oplus_{i=1}^{m} S_i \\otimes T_i
 ```
 
 *Directed graphs*
 ```math
-\\otimes_{i=1}^{m} S_i \\otimes \\Pi(T_i)
+\\oplus_{i=1}^{m} S_i \\otimes \\Pi(T_i)
 ```
 
 where `K` and `V` are the key and value hypervector collections, `m` is the size of the
@@ -598,12 +598,12 @@ decodelevel(HV, numvalues; testbound = false) = decodelevel(level(HV, length(num
     convertlevel(hvlevels, numvals..., kwargs...)
     convertlevel(HV::AbstractHV, numvals..., kwargs...)
 
-Creates the `encoder` and `decoder` for a level incoding in one step. See `encodelevel`
+Creates the `encoder` and `decoder` for a level encoding in one step. See `encodelevel`
 and `decodelevel` for their respective documentations.
 """
-convertlevel(hvlevels, numvals...; kwargs...) = encodelevel(hvlevels, numvals...; kwargs...), decodelevel(hvlevels, numvals..., kwargs...)
+convertlevel(hvlevels, numvals...; kwargs...) = encodelevel(hvlevels, numvals...; kwargs...), decodelevel(hvlevels, numvals...; kwargs...)
 
-convertlevel(hv::AbstractHV, numvals...; kwargs...) = encodelevel(hv, numvals...; kwargs...), decodelevel(hv, numvals..., kwargs...)
+convertlevel(hv::AbstractHV, numvals...; kwargs...) = encodelevel(hv, numvals...; kwargs...), decodelevel(hv, numvals...; kwargs...)
 
 
 # levels using FHRR

diff --git a/src/learning.jl b/src/learning.jl
diff --git a/src/operations.jl b/src/operations.jl
@@ -2,8 +2,6 @@
 operations.jl; This file implements operations that can be done on hypervectors to enable them to encode text-based data.
 =#
 
-# Remark: use element-wise reduce, maybe using LazyArrays?
-
 #=
 
 | Operation            | symbol | remark                                                                                                          |
@@ -219,7 +217,7 @@ Base.isequal(v::AbstractHV, u::AbstractHV) = v.v == u.v
 """
     Base.isapprox(u::AbstractHV, v::AbstractHV, atol=length(u)/100, ptol=0.01)
 
-Measurures when two hypervectors are similar (have more elements in common than expected
+Measures when two hypervectors are similar (have more elements in common than expected
 by chance).
 
 One can specify either:
@@ -239,12 +237,12 @@ end
 """
     Base.isapprox(u::AbstractHV, v::AbstractHV, atol=length(u)/100, ptol=0.01)
 
-Measurures when two hypervectors are similar (have more elements in common than expected
+Measures when two hypervectors are similar (have more elements in common than expected
 by chance) using the Hamming distance. Uses a bootstrap to construct a null distribution.
 
 One can specify either:
 - `ptol=1e-10` threshold for seeing that many matches due to chance
-- `N_bootstap=200` number of samples for bootstrapping
+- `N_bootstrap=200` number of samples for bootstrapping
 """
 function Base.isapprox(u::T, v::T; ptol = 1.0e-10, N_bootstrap = 500) where {T <: AbstractHV}
     @assert length(u) == length(v) "Vectors have to be of equal length"

diff --git a/src/predictors.jl b/src/predictors.jl