Robert Feldt robertfeldt

## got_sw3_generate_text_example.jl
# Setup step 0: Build PyCall.jl for right Python binary
#
# Might be needed to ensure the right python is used, here for my homebrew
# installed python3 on a M1 MacBook Pro:
# ENV["PYTHON"] = "/opt/homebrew/bin/python3"
# using Pkg
# Pkg.build("PyCall")

# Setup step 1: Ensuring you have access to GPT-SW3 by setting up a token on Hugging Face
#  a. login on HF: https://huggingface.co/login

## boxes_puzzle.jl
# Checking the "Boxes puzzle" solution strategy via stochastic simulation.
# robert.feldt@gmail.com 2022

using Random # We need randperm below

# Fill N boxes by getting a random permutation of length N.
fillboxes(N) = randperm(N)

# Given a strategy, simulate its use by N people and return true iff strategy
# worked for them all.

## speeding_up_qgram_distances_with_precalculation_sorted_array_version_faster.jl
using StringDistances

function countdict(qgrams)
    d = Dict{eltype(qgrams), Int32}()
    for qg in qgrams
        index = Base.ht_keyindex2!(d, qg)
		if index > 0
			d.age += 1
			@inbounds d.keys[index] = qg
			@inbounds d.vals[index] = d.vals[index][1] + 1

## speeding_up_qgram_distances_with_precalculation_sorted_array_version.jl.jl
using StringDistances

function countdict(qgrams)
    d = Dict{eltype(qgrams), Int32}()
    for qg in qgrams
        index = Base.ht_keyindex2!(d, qg)
		if index > 0
			d.age += 1
			@inbounds d.keys[index] = qg
			@inbounds d.vals[index] = d.vals[index][1] + 1

## speeding_up_qgram_distances_with_precalculation.jl
using StringDistances

function countdict(qgrams)
    d = Dict{eltype(qgrams), Int32}()
    for qg in qgrams
        index = Base.ht_keyindex2!(d, qg)
		if index > 0
			d.age += 1
			@inbounds d.keys[index] = qg
			@inbounds d.vals[index] = d.vals[index][1] + 1

## dynamically_updating_VegaLite_from_Julia.jl
using HTTP, JSON

const VegaLiteWebsocketFrontEndTemplate = """
<!DOCTYPE html>
<head>
  <meta charset="utf-8">
  <script src="https://cdn.jsdelivr.net/npm/vega@3"></script>
	<script src="https://cdn.jsdelivr.net/npm/vega-lite@2"></script>
	<script src="https://cdn.jsdelivr.net/npm/vega-embed@3"></script>
</head>

## multithreaded_optimization.jl
if Threads.nthreads() < 2
    exit("Multiple threads NOT found! Makes no sense to test this without them... Start as, for example: JULIA_NUM_THREADS=4 julia multithreaded_optimization.jl")
end

using BlackBoxOptim

# Functions to optimize. Should be thread-safe.
function rosenbrock(x)
    sleep(0.01) # So that there is some benefit in the thread switching...
    sum(i -> 100*abs2(x[i+1] - x[i]^2) + abs2(x[i] - 1), Base.OneTo(length(x)-1))

## fake_emsej_review_data.json
{
    "nodes": [
      {
        "name": "Submissions",
        "color": "#C0C0C0"
      },
      {
        "name": "Desk rejected",
        "color": "#FF3333"
      },

## icse19_attendees_per_capita.jl
# Output when running this script:
# ICSE 2019 attendees per million capita:
# 1. Luxembourg, 15, 25.126
# 2. Canada, 330, 8.852
# 3. Sweden, 41, 4.078
# 4. Ireland, 16, 3.301
# 5. Switzerland, 27, 3.137
# 6. Singapore, 17, 2.897
# 7. Norway, 15, 2.777
# 8. Netherlands, 32, 1.868

## emse_april17_paper_downloads.jl
using HypothesisTests

# Checked num downloads from each papers page in SpringerLink 2017-04-26 08:05 CET
OpenAccessPaperDownloads = [2400, 589] # says 2.4k downloads, approximates with 2400
NonOAPaperDownloads = [134, 126, 85, 104, 56, 228, 112]

println(pvalue(MannWhitneyUTest(OpenAccessPaperDownloads, NonOAPaperDownloads)))
# p-value is 0.055
# Executed 2017-04-26 08:10 CET on MacBook Pro 13" 2015 running Julia 0.5.1 with HypothesisTests version 0.5.1
	# Setup step 0: Build PyCall.jl for right Python binary
	#
	# Might be needed to ensure the right python is used, here for my homebrew
	# installed python3 on a M1 MacBook Pro:
	# ENV["PYTHON"] = "/opt/homebrew/bin/python3"
	# using Pkg
	# Pkg.build("PyCall")

	# Setup step 1: Ensuring you have access to GPT-SW3 by setting up a token on Hugging Face
	# a. login on HF: https://huggingface.co/login
	# Checking the "Boxes puzzle" solution strategy via stochastic simulation.
	# robert.feldt@gmail.com 2022

	using Random # We need randperm below

	# Fill N boxes by getting a random permutation of length N.
	fillboxes(N) = randperm(N)

	# Given a strategy, simulate its use by N people and return true iff strategy
	# worked for them all.
	using StringDistances

	function countdict(qgrams)
	d = Dict{eltype(qgrams), Int32}()
	for qg in qgrams
	index = Base.ht_keyindex2!(d, qg)
	if index > 0
	d.age += 1
	@inbounds d.keys[index] = qg
	@inbounds d.vals[index] = d.vals[index][1] + 1
	using HTTP, JSON

	const VegaLiteWebsocketFrontEndTemplate = """
	<!DOCTYPE html>
	<head>
	<meta charset="utf-8">
	<script src="https://cdn.jsdelivr.net/npm/vega@3"></script>
	<script src="https://cdn.jsdelivr.net/npm/vega-lite@2"></script>
	<script src="https://cdn.jsdelivr.net/npm/vega-embed@3"></script>
	</head>
	if Threads.nthreads() < 2
	exit("Multiple threads NOT found! Makes no sense to test this without them... Start as, for example: JULIA_NUM_THREADS=4 julia multithreaded_optimization.jl")
	end

	using BlackBoxOptim

	# Functions to optimize. Should be thread-safe.
	function rosenbrock(x)
	sleep(0.01) # So that there is some benefit in the thread switching...
	sum(i -> 100*abs2(x[i+1] - x[i]^2) + abs2(x[i] - 1), Base.OneTo(length(x)-1))
	{
	"nodes": [
	{
	"name": "Submissions",
	"color": "#C0C0C0"
	},
	{
	"name": "Desk rejected",
	"color": "#FF3333"
	},
	# Output when running this script:
	# ICSE 2019 attendees per million capita:
	# 1. Luxembourg, 15, 25.126
	# 2. Canada, 330, 8.852
	# 3. Sweden, 41, 4.078
	# 4. Ireland, 16, 3.301
	# 5. Switzerland, 27, 3.137
	# 6. Singapore, 17, 2.897
	# 7. Norway, 15, 2.777
	# 8. Netherlands, 32, 1.868
	using HypothesisTests

	# Checked num downloads from each papers page in SpringerLink 2017-04-26 08:05 CET
	OpenAccessPaperDownloads = [2400, 589] # says 2.4k downloads, approximates with 2400
	NonOAPaperDownloads = [134, 126, 85, 104, 56, 228, 112]

	println(pvalue(MannWhitneyUTest(OpenAccessPaperDownloads, NonOAPaperDownloads)))
	# p-value is 0.055
	# Executed 2017-04-26 08:10 CET on MacBook Pro 13" 2015 running Julia 0.5.1 with HypothesisTests version 0.5.1