https://github.com/mcabbott/weightedarrays.jl
Matrices with column weights
https://github.com/mcabbott/weightedarrays.jl
Last synced: 6 months ago
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Matrices with column weights
- Host: GitHub
- URL: https://github.com/mcabbott/weightedarrays.jl
- Owner: mcabbott
- License: other
- Created: 2018-08-17T16:05:23.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2022-02-28T01:27:14.000Z (over 4 years ago)
- Last Synced: 2024-10-13T19:28:29.520Z (over 1 year ago)
- Language: Julia
- Size: 216 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Weighted Arrays .jl
[](https://travis-ci.org/mcabbott/WeightedArrays.jl)
This simple package defines a `WeightedMatrix`, a struct with vector of weights corresponding to the columns of a matrix. By default the `weights(x)` add up to 1. The `array(x)` values may have a box constraint:
```julia
julia> Weighted(randn(3,5))
Weighted 3×5 Array{Float64,2}, of unclamped θ:
-0.264476 -1.83297 0.0669732 -0.340433 -1.87672
0.0461253 -0.330401 0.0215189 2.3129 -1.78839
0.461376 0.00486523 -0.819182 -1.43221 -0.855756
with normalised weights p(θ), 5-element Array{Float64,1}:
0.2 0.2 0.2 0.2 0.2
julia> Weighted(rand(2,4), ones(4), 0, 1)
Weighted 2×4 Array{Float64,2}, clamped 0.0 ≦ θ ≦ 1.0:
0.7842 0.257179 0.483388 0.780996
0.138967 0.748165 0.387104 0.167825
with normalised weights p(θ), 4-element Array{Float64,1}:
0.25 0.25 0.25 0.25
```
These examples are roughly `wrandn(3,5)` and `wrand(2,4)`, there are also sub-random `sobol(3,7)` and regular `wgrid(2, 0:0.1:1)`.
Their values are mutable, `clamp!(x)` will enforce the box constraint, and `normalise!(x)` (with an s) the weights.
They are not subtypes of `AbstractArray`, but many functions will work.
For instance `x[1:2, :]` keeps only the first two rows (and the weights),
`hcat(x,y)` will concatenate the weights,
and `mapslices(f,x)` will act with `f` on columns & then restore weights.
`sort(x)` re-arranges columns to order by the weights, `sortcols(x)` orders by the array instead,
`unique(x)` will accumulate the weights of identical columns.
A few functions like `log(x)` and `tanh(x)` act element-wise but update the box constraints appropriately.
Most of this will work for any N-dimensional Array, not just a Matrix. The weights then belong to the last dimension.
Plot recipes are defined, in which the area of points indicating weight.
The example shown is a grid plus a bivariate sub-random normal distribution:
```julia
julia> using Plots
julia> plot(wgrid(2, -5:5), m=:+)
julia> plot!(soboln(2, 2000), m=:diamond, c=:red)
```
With more than three rows e.g. `plot(wrandn(4,50))`, it will plot the first two principal components (and attempt to scale these correctly).
There is a function `pplot(x)` which saves the PCA function (see help for `wPCA(x)`) in a global variable, so that `pplot!(t)` can add more points on the same axes.