caseykneale

rmsrosa
 — 
10/27/2021
I essentially followed the instructions here, which is a pretty recent post: https://www.linkedin.com/pulse/running-julia-my-android-phone-paresh-mathur, except I didn't install Ubuntu, but Fedora, for which I used https://github.com/nmilosev/termux-fedora.

1. Install Termux on your Android phone from the F-Droid (don't use the one from the App Store, it is outdated and not maintained). See https://termux.com/

2. Update Termux just in case: pkg update && pkg upgrade

3. Install a linux distro. I used Fedora, but can be anything:

caseykneale

function NNLS_CD(X, Y; ϵ = 1e-9, max_iters = 300)
    rows,vars = size(X)
    XTX,XTY = transpose(X) * X, transpose(X) * Y
    x  = zeros(vars)
    μ = -XTY
    Hxf = similar(XTY)
    @inbounds for iter in 1:max_iters
        Hxf = XTX * x - XTY
        all(>=(-ϵ), Hxf) && break
        for v in 1:vars

caseykneale

# uses Flux.v11
using Plots, Flux

function update!(opt, x, x̄)
  x[:] .-= apply!(opt, x, x̄)[:]
end

function update!(opt, xs::Flux.Params, gs)
  for x in xs
    (gs[x] === nothing) && continue

caseykneale

using GeometryBasics

x(a::AbstractVector) = first(a)
y(a::AbstractVector) = last(a)
x(a::AbstractMatrix) = a[ :, 1 ]
y(a::AbstractMatrix) = a[ :, 2 ]

function orientation(p::AbstractVector, q::AbstractVector, r::AbstractVector)::Int
	val = ( y(q) - y(p) ) * ( x(r) - x(q) ) - ( x(q) - x(p) ) * ( y(r) - y(q) ) 
    return (val ≈ 0) ? 0 : ( (val > 0) ? 1 : 2 )

caseykneale

using GeometryBasics

x(a::Point{2, T}) where { T <: Number } = first(a)
y(a::Point{2, T}) where { T <: Number } = last(a)
#Ewy
Matrix(z::Vector{Point{2, T}}) where { T <: Number } = hcat( z .|> x, z .|> y )
to_points(z) = [ Point(z[:,r][:]...) for r in 1:size(z,2)]

function orientation(p::Point{2, T}, q::Point{2, T}, r::Point{2, T})::Int where {T <: Number }
	val = ( y(q) - y(p) ) * ( x(r) - x(q) ) - ( x(q) - x(p) ) * ( y(r) - y(q) ) 

caseykneale

import DSP: conv
using LinearAlgebra

function boundaries( ::Val{:zeros}, x::Vector, half_width::Int )
    return (padded = true, z = vcat( zeros(half_width), x, zeros(half_width) ) )
end
function boundaries( ::Val{:repeating}, x::Vector, half_width::Int )
    return (padded = true, z = vcat( repeat( [ first(x) ], half_width), x, repeat( [ last(x) ], half_width) ) )
end
function boundaries( ::Val{:mirroring}, x::Vector, half_width::Int )

caseykneale

kasemiir <- as.data.frame(read.csv2("/mydata2.csv"))
#i batches(BatchID), j variables and k time(Time)
	
batch_part <- split(kasemiir, f=kasemiir$BatchID)
#now we have a list of dataframes split over batchid 
#z[[2]] #is batch2 etc...
#make sure each subdf is ordered by time and convert to matrix
for( subdf in batch_part){
  subdf <- data.matrix( subdf[order("Time")] )
}

caseykneale

using DiffEqFlux, OrdinaryDiffEq, Flux, MLDataUtils, NNlib
using Flux: logitcrossentropy
using MLDatasets: MNIST

function loadmnist(batchsize = bs)
	# Use MLDataUtils LabelEnc for natural onehot conversion
    onehot(labels_raw) = convertlabel(LabelEnc.OneOfK, labels_raw, LabelEnc.NativeLabels(collect(0:9)))
	# Load MNIST
	imgs, labels_raw = MNIST.traindata();
	# Process images into (H,W,C,BS) batches

caseykneale

using Gtk, Plots, Cairo
using DataStructures

function replaceBiggest!(a,b)
    selinds = abs.(a) .< abs.(b)
    a[ selinds ] .= b[ selinds ]
end

mutable struct ClickablePlot