content
stringlengths 6
1.03M
| input_ids
sequencelengths 4
535k
| ratio_char_token
float64 0.68
8.61
| token_count
int64 4
535k
|
|---|---|---|---|
nn = 20
tot = collect(1:nn)
trnidx = view(tot, [1,3,6,9,10,11,12,13,14,15,19])
res
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# SPD-License-Identifier: MIT
using ArgParse
using JLD2
using MarkovModels
using TOML
function parse_commandline()
s = ArgParseSettings()
@add_arg_table s begin
"topology"
required = true
help = "hmm topology in TOML format"
"units"
required = true
help = "list of units with their categories"
"hmms"
required = true
help = "output hmms in BSON format"
end
s.description = """
Build a set of HMM. The topology file should be formatted as:
states = [
\ua0\ua0{ id = 1, initweight = 1.0, finalweight = 0.0 },
\ua0\ua0...
]
links = [
\ua0\ua0{ src = 1, dest = 2, weight = 0.5 },
\ua0\ua0...
]
tiestates = false | true
"""
parse_args(s)
end
function loadunits(file)
units, categories = [], []
open(file, "r") do f
for line in eachline(f)
tokens = split(line)
push!(units, tokens[1])
push!(categories, tokens[2:end])
end
end
units, categories
end
function get_unit_topo(topo, category)
i = 1
while i <= length(category) && category[i] ∈ keys(topo)
topo = topo[category[i]]
i += 1
end
topo
end
function makehmm!(pdfid_mapping, unit, topo, pdfid)
SF = LogSemifield{Float32}
fsm = VectorFSM{SF}()
states = Dict()
for (i, state) in enumerate(topo["states"])
initweight = SF(log(state["initweight"]))
finalweight = SF(log(state["finalweight"]))
s = addstate!(fsm, i; initweight, finalweight)
states[i] = s
pdfid_mapping[(unit, i)] = pdfid
pdfid += 1
end
for arc in topo["arcs"]
addarc!(fsm, states[arc["src"]], states[arc["dest"]],
SF(log(arc["weight"])))
end
fsm |> renormalize, pdfid
end
function main(args)
topo = TOML.parsefile(args["topology"])
units, categories = loadunits(args["units"])
hmms = Dict()
pdfid_mapping = Dict()
pdfid = 1
for (unit, category) in zip(units, categories)
unit_topo = get_unit_topo(topo, category)
hmms[unit], pdfid = makehmm!(pdfid_mapping, unit, topo, pdfid)
end
data = Dict(
"units" => hmms,
"pdfid_mapping" => pdfid_mapping
)
save(args["hmms"], data)
end
args = parse_commandline()
main(args)
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# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
mutable struct IoK8sApiCoreV1SELinuxOptions <: SwaggerModel
level::Any # spec type: Union{ Nothing, String } # spec name: level
role::Any # spec type: Union{ Nothing, String } # spec name: role
type::Any # spec type: Union{ Nothing, String } # spec name: type
user::Any # spec type: Union{ Nothing, String } # spec name: user
function IoK8sApiCoreV1SELinuxOptions(;level=nothing, role=nothing, type=nothing, user=nothing)
o = new()
validate_property(IoK8sApiCoreV1SELinuxOptions, Symbol("level"), level)
setfield!(o, Symbol("level"), level)
validate_property(IoK8sApiCoreV1SELinuxOptions, Symbol("role"), role)
setfield!(o, Symbol("role"), role)
validate_property(IoK8sApiCoreV1SELinuxOptions, Symbol("type"), type)
setfield!(o, Symbol("type"), type)
validate_property(IoK8sApiCoreV1SELinuxOptions, Symbol("user"), user)
setfield!(o, Symbol("user"), user)
o
end
end # type IoK8sApiCoreV1SELinuxOptions
const _property_map_IoK8sApiCoreV1SELinuxOptions = Dict{Symbol,Symbol}(Symbol("level")=>Symbol("level"), Symbol("role")=>Symbol("role"), Symbol("type")=>Symbol("type"), Symbol("user")=>Symbol("user"))
const _property_types_IoK8sApiCoreV1SELinuxOptions = Dict{Symbol,String}(Symbol("level")=>"String", Symbol("role")=>"String", Symbol("type")=>"String", Symbol("user")=>"String")
Base.propertynames(::Type{ IoK8sApiCoreV1SELinuxOptions }) = collect(keys(_property_map_IoK8sApiCoreV1SELinuxOptions))
Swagger.property_type(::Type{ IoK8sApiCoreV1SELinuxOptions }, name::Symbol) = Union{Nothing,eval(Base.Meta.parse(_property_types_IoK8sApiCoreV1SELinuxOptions[name]))}
Swagger.field_name(::Type{ IoK8sApiCoreV1SELinuxOptions }, property_name::Symbol) = _property_map_IoK8sApiCoreV1SELinuxOptions[property_name]
function check_required(o::IoK8sApiCoreV1SELinuxOptions)
true
end
function validate_property(::Type{ IoK8sApiCoreV1SELinuxOptions }, name::Symbol, val)
end
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] | 2.579075 | 822 |
<reponame>kernelmethod/HTTP.jl
using Test
using HTTP
using HTTP.IOExtras, HTTP.Sockets
using Sockets
@testset "websockets.jl" begin
p = 8085 # rand(8000:8999)
socket_type = ["wss", "ws"]
function listen_localhost()
@async HTTP.listen(Sockets.localhost, p) do http
if HTTP.WebSockets.is_upgrade(http.message)
HTTP.WebSockets.upgrade(http) do ws
while !eof(ws)
data = readavailable(ws)
write(ws, data)
end
end
end
end
end
@testset "External Host - $s" for s in socket_type
HTTP.WebSockets.open("$s://echo.websocket.org") do io
write(io, "Foo")
@test !eof(io)
@test String(readavailable(io)) == "Foo"
write(io, "Hello")
write(io, " There")
write(io, " World", "!")
closewrite(io)
buf = IOBuffer()
write(buf, io)
@test String(take!(buf)) == "Hello There World!"
end
end
@testset "Localhost" begin
listen_localhost()
HTTP.WebSockets.open("ws://127.0.0.1:$(p)") do ws
write(ws, "Foo")
@test String(readavailable(ws)) == "Foo"
write(ws, "Bar")
@test String(readavailable(ws)) == "Bar"
end
end
@testset "extened feautre support for listen" begin
port=UInt16(8086)
tcpserver = listen(port)
target = "/query?k1=v1&k2=v2"
servertask = @async HTTP.WebSockets.listen("127.0.0.1", port; server=tcpserver) do ws
@testset "request access" begin
@test ws.request isa HTTP.Request
write(ws, ws.request.target)
while !eof(ws)
write(ws, readavailable(ws))
end
close(ws)
end
end
HTTP.WebSockets.open("ws://127.0.0.1:$(port)$(target)") do ws
@test String(readavailable(ws)) == target
@test write(ws, "Bye!") == 4
@test String(readavailable(ws)) == "Bye!"
close(ws)
end
close(tcpserver)
@test timedwait(()->servertask.state === :failed, 5.0) === :ok
@test_throws Exception wait(servertask)
end
end | [
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220,
220,
220,
220,
220,
220,
220,
2488,
9288,
62,
400,
8516,
35528,
4043,
7,
2655,
1851,
2093,
8,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
886,
198,
437
] | 1.840998 | 1,283 |
# Use baremodule to shave off a few KB from the serialized `.ji` file
baremodule OpenLSTO_jll
using Base
using Base: UUID
import JLLWrappers
JLLWrappers.@generate_main_file_header("OpenLSTO")
JLLWrappers.@generate_main_file("OpenLSTO", UUID("a318411f-452f-5433-884b-1f6a35676cea"))
end # module OpenLSTO_jll
| [
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] | 2.48 | 125 |
<filename>src/Functors.jl
module Functors
using MacroTools
export @functor, fmap, fmapstructure, fcollect
include("functor.jl")
end # module
| [
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] | 2.9 | 50 |
cd(@__DIR__)
using Pkg
Pkg.activate(".")
Pkg.instantiate()
module TestModule
export plusOne, multiply # functions, structs, and other objects that will be directly available once `using ModuleName` is typed
plusOne(x) = x + 1
multiply(x,y) = x * y
end
using .TestModule
plusOne(1.0)
plusOne(1)
multiply(2,3)
include("includedfoo.jl") # which strings will be printed ?
x # error not defined
foo.x
using foo # error: looking up for a package and of course can't find it
using .foo
x
foo.z()
foo.c() | [
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] | 2.79558 | 181 |
<filename>src/newton_method_ad.jl
##### NewtonsMethodAD
export NewtonsMethodAD
"""
NewtonsMethodAD(f!::F!, x_init::A) where {F!, A <: AbstractArray}
A non-linear system of equations type.
# Fields
$(DocStringExtensions.FIELDS)
"""
struct NewtonsMethodAD{FT, F!, A, JA} <: AbstractNonlinearSolverMethod{FT}
"Function to find the root of"
f!::F!
"Initial guess"
x_init::A
"Storage"
x1::A
"Storage"
J::JA
"Storage"
J⁻¹::JA
"Storage"
F::A
function NewtonsMethodAD(f!::F!, x_init::A) where {F!, A <: AbstractArray}
x1 = similar(x_init)
J = similar(x_init, (length(x_init), length(x_init)))
J⁻¹ = similar(J)
F = similar(x_init)
JA = typeof(J)
FT = eltype(x_init)
return new{FT, F!, A, JA}(f!, x_init, x1, J, J⁻¹, F)
end
end
method_args(m::NewtonsMethodAD) = (m.x_init, m.x1, m.f!, m.F, m.J, m.J⁻¹)
function solve!(
::NewtonsMethodAD,
x0::AT,
x1::AT,
f!::F!,
F::FA,
J::JA,
J⁻¹::J⁻¹A,
soltype::SolutionType,
tol::AbstractTolerance{FT},
maxiters::Int,
) where {FA, J⁻¹A, JA, F! <: Function, AT, FT}
x_history = init_history(soltype, AT)
F_history = init_history(soltype, AT)
if soltype isa VerboseSolution
f!(F, x0)
ForwardDiff.jacobian!(J, f!, F, x0)
push_history!(x_history, x0, soltype)
push_history!(F_history, F, soltype)
end
for i in 1:maxiters
f!(F, x0)
ForwardDiff.jacobian!(J, f!, F, x0)
x1 .= x0 .- J \ F
push_history!(x_history, x1, soltype)
push_history!(F_history, F, soltype)
if tol(x0, x1, F)
return SolutionResults(
soltype,
x1,
true,
F,
i,
x_history,
F_history,
)
end
x0 = x1
end
return SolutionResults(
soltype,
x0,
false,
F,
maxiters,
x_history,
F_history,
)
end
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198,
437,
198
] | 1.8058 | 1,138 |
# Methods for accessing Cartesian Tuple metadata.
Base.summary(t::CartesianTuple{N,T,U}) where {N,T,U} = "$(N)-dimensional $(T) $(name(U))"
Base.show(io::IO, t::CartesianTuple) = print(io, "$(summary(t)) $(t.coordinates)")
# Iteration, indexing, and field access.
Base.iterate(t::CartesianTuple, state...) = iterate(t.coordinates, state...)
Base.length(t::CartesianTuple{N,T,U}) where {N,T,U} = size(N)
Base.size(::Type{CartesianTuple{N,T,U}}) where {N,T,U} = N
Base.eltype(::Type{CartesianTuple{N,T,U}}) where {N,T,U} = T
Base.getindex(a::CartesianTuple, inds...) = getindex(a.coordinates, inds...)
Base.getproperty(p::CartesianPair, s::Symbol) = @match s begin
:x => p[1]
:y => p[2]
_ => getfield(p, s)
end
Base.getproperty(t::CartesianTriple, s::Symbol) = @match s begin
:x => t[1]
:y => t[2]
:z => t[3]
_ => getfield(t, s)
end
# General operations on Cartesian Tuples.
@inline Base.:(+)(a::CartesianTuple, b::CartesianTuple) = promote_type(typeof(a), typeof(b))(a.coordinates + b.coordinates)
@inline Base.:(-)(a::VectorLike, b::VectorLike) = promote_type(typeof(a), typeof(b))(a.coordinates - b.coordinates)
@inline function Base.:(*)(a::CartesianTuple, b::Number)
@assert !isnan(b)
promote_type(typeof(a), typeof(b))(a.coordinates .* b)
end
@inline Base.:(*)(a::Number, b::CartesianTuple) = b * a
@inline function Base.:(/)(a::CartesianTuple, b::Number)
@assert b != zero(typeof(b))
inverse = one(eltype(a)) / b
a * inverse
end
@inline Base.:(==)(a::CartesianTuple{N,S,U}, b::CartesianTuple{N,T,U}) where {N,S,T,U} = a.coordinates == b.coordinates
@inline Base.:(==)(a::CartesianTuple{N,S,U}, b::CartesianTuple{N,T,V}) where {N,S,T,U,V} = false
@inline Base.abs(a::CartesianTuple) = typeof(a)(abs.(a.coordinates)...)
@inline Base.:(-)(a::CartesianTuple) = typeof(a)((-).(a.coordinates)...)
# Special behaviors for Point-Vector operations.
@inline Base.:(+)(a::Point{N,S}, b::Vect{N,T}) where {N,S,T} = promote_type(typeof(a), typeof(b))(a.coordinates + b.coordinates)
@inline Base.:(-)(a::Point{N,S}, b::Vect{N,T}) where {N,S,T} = promote_type(typeof(a), typeof(b))(a.coordinates - b.coordinates)
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] | 2.213178 | 1,032 |
<filename>toy_nlp_tools/idf.jl<gh_stars>0
struct FileHolder
fileProcessor::Dict{String, Function}
FileHolder(processor::Dict{String, Function}) = new(processor)
end
function FileHolder(filenames::Vector{String})
processor(line::String) = split(line, ' ')
FileHolder(Dict{String, Function}(filename => processor) for filename in filenames)
end
struct OutSetting
outFilename::String
outFileFormat::Symbol
OutSetting(filename::String) = new(filename, :json)
end
__idfCaculator(value, documentCount) = log(documentCount / (value != 0 ? value : 1))
mutable struct IDF
idfDict::Dict{String, Int64}
idfCalculator::Function
documentCount::Int64
fileHolder::FileHolder
outSetting::OutSetting
IDF(fileHolder::FileHolder, outSetting::OutSetting=OutSetting("out.txt"); idfCalculator::Function=__idfCaculator) =
new(Dict{String, Int64}(), idfCalculator, 0, fileHolder, outSetting)
end
function fit!(self::IDF)
# TODO: parallize it
for (filename, processor) in self.fileHolder.fileProcessor
open(filename) do file
statisticIDF!(self, file, processor)
end
end
return self
end
function statisticIDF!(self::IDF, file, processor::Function)
for (i, line) in enumerate(eachline(file))
tokens = processor(line)
for token in Set(tokens)
self.idfDict[token] = get(self.idfDict, token, 0) + 1
end
self.documentCount += 1
if i % 1e4 == 0
println(i)
end
end
end
function transform()
end
getIdfValue(self::IDF, key::String) = self.caculator(get(self.idfDict, key, 0), documentCount)
function main()
# 空格分隔后的，分隔词性，取词
processor(line::String) = map(t -> t[1], map(s -> split(s, "\x01"), split(line, " ")))
fh = FileHolder(Dict{String, Function}("filePath" => processor))
idf = IDF(fh)
fit!(idf)
end
#main()
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12340,
6626,
7,
1370,
11,
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366,
22305,
198,
220,
220,
220,
277,
71,
796,
9220,
39,
19892,
7,
35,
713,
90,
10100,
11,
15553,
92,
7203,
7753,
15235,
1,
5218,
12649,
4008,
198,
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220,
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4686,
69,
796,
33389,
7,
69,
71,
8,
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220,
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220,
4197,
0,
7,
312,
69,
8,
198,
437,
198,
198,
2,
12417,
3419,
198
] | 2.356877 | 807 |
<filename>test/runtests.jl<gh_stars>0
using Pda
using Test
@testset "Pda.jl" begin
# Write your tests here.
end
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] | 2.4375 | 48 |
<reponame>alejandroclaro/Cryptography.jl
#
# @description Unit tests for RC4 stream-cipher.
#
# @author <NAME> (<EMAIL>)
#
# Copyright 2017 All rights reserved.
# Use of this source code is governed by a MIT-style license that can be found in the LICENSE file.
#
@testset "RC4 cipher tests" begin
cipher = Rc4Cipher([ 0x01, 0x02, 0x03, 0x04, 0x05 ])
@test key_size(cipher) == 5
@test_throws ArgumentError Rc4Cipher(UInt8[])
@test_throws ArgumentError Rc4Cipher(zeros(UInt8, 33))
@test encrypt!(cipher, [ 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 ]) == UInt8[ 0xb2, 0x39, 0x63, 0x05, 0xf0, 0x3d, 0xc0, 0x27 ]
reset!(cipher)
@test decrypt!(cipher, UInt8[ 0xb2, 0x39, 0x63, 0x05, 0xf0, 0x3d, 0xc0, 0x27 ]) == [ 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00 ]
end
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11,
657,
87,
405,
2361,
201,
198,
437,
201,
198,
201,
198
] | 2.128947 | 380 |
using Plots
pyplot() # Use PyPlot as a backend (may already be the default)
#x = collect(1:7)
#y = []
#for item in x
#push!(y, 2 - 2*item + item^2/4)
#end
#plot(x,y)
#plot!(x, y, marker = :diamond, linewidth=2)
#plot!(title = "Sample plot", leg=false)
x = [1 2 3 4 5 6]'
#y = (x-3).^2/4
y = []
for item in x
push!(y, (item-3).^2/4)
end
plot(x,y, marker = :hex, leg=false, linewidth = 2, linecolor=:black)
plot!(title="Plot for graded quiz")
gui() # To show plot on atom | [
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3419,
220,
220,
220,
1303,
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905,
7110,
319,
22037
] | 2.188341 | 223 |
import DataFrames
"""
"""
function fit!(
transformer::ImmutablePredictionsSingleLabelInt2StringTransformer,
varargs...;
kwargs...
)
if length(varargs) == 1
return varargs[1]
else
return varargs
end
end
"""
"""
function predict(
transformer::ImmutablePredictionsSingleLabelInt2StringTransformer,
single_labelpredictions::AbstractVector,
varargs...;
kwargs...
)
single_labelpredictions = parse.(Int, single_labelpredictions)
labelint2stringmap = getlabelint2stringmap(
transformer.levels,
transformer.index,
)
result = Vector{String}(
undef,
length(single_labelpredictions),
)
for i = 1:length(result)
result[i] = labelint2stringmap[single_labelpredictions[i]]
end
return result
end
"""
"""
function predict(
transformer::ImmutablePredictionsSingleLabelInt2StringTransformer,
single_labelpredictions::DataFrames.AbstractDataFrame,
varargs...;
kwargs...
)
label_names = DataFrames.names(single_labelpredictions)
result = DataFrames.DataFrame()
for i = 1:length(label_names)
result[label_names[i]] = predict(
transformer,
single_labelpredictions[label_names[i]];
kwargs...
)
end
return result
end
"""
"""
function predict_proba(
transformer::ImmutablePredictionsSingleLabelInt2StringTransformer,
varargs...;
kwargs...
)
if length(varargs) == 1
return varargs[1]
else
return varargs
end
end
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11,
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220,
220,
220,
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26,
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220,
220,
220,
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220,
220,
220,
479,
86,
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220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
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7,
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220,
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1441,
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16,
60,
198,
220,
220,
220,
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198,
220,
220,
220,
220,
220,
220,
220,
1441,
1401,
22046,
198,
220,
220,
220,
886,
198,
437,
198
] | 2.298883 | 716 |
function chop_sections(set1, set2, n :: Integer, spacing = "cosine"; flip = false)
if lowercase(spacing) == "uniform"
space = uniform_spacing(0., 1., n + 1)
elseif lowercase(spacing) == "sine"
space = sine_spacing(0., 1., (n + 1) * ifelse(flip, -1, 1))
else
space = cosine_spacing(0.5, 1., n + 1)
end
@views [ weighted_vector.(set1, set2, μ) for μ ∈ space ][1:end-1]
end
chop_coordinates(coords, n, spacings = "cosine", flip = false) = @views [ reduce(vcat, chop_sections.(coords[1:end-1], coords[2:end], n, spacings; flip = flip)); [ coords[end] ] ]
chord_sections(lead, trail) = [ [ l'; t' ] for (l, t) ∈ zip(lead, trail) ]
chop_chords(coords, n) = @views [ [ weighted_vector(chord[1,:], chord[2,:], μ) for μ ∈ cosine_spacing(0.5, 1., n + 1) ] for chord ∈ coords ]
chop_spans(lead, trail, div, spacing = "cosine", flip = false) = chop_coordinates(lead, div, spacing, flip), chop_coordinates(trail, div, spacing, flip)
chop_wing(lead, trail, span_num, chord_num; span_spacing = "cosine", flip = false) = let (lead, trail) = chop_spans(lead, trail, span_num, span_spacing, flip); chop_chords(chord_sections(lead, trail), chord_num) end
"""
make_panels(xyzs)
Convert an array of coordinates corresponding to a wing, ordered from root to tip and leading-edge to trailing-edge, into panels.
"""
make_panels(xyzs) = @views Panel3D.(xyzs[1:end-1,1:end-1], xyzs[2:end,1:end-1], xyzs[2:end,2:end], xyzs[1:end-1,2:end])
# WTF was I thinking?
# spanlist = vectarray.(coords)
# spanlist = zip(coords, coords[2:end,:])
# adjacent_sections = zip(spanlist, spanlist[2:end])
# @views hcat(( Panel3D.(root[1:end-1], root[2:end], tip[2:end], tip[1:end-1]) for (root, tip) ∈ adjacent_sections )...) | [
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] | 2.420613 | 718 |
<filename>hsic.jl
using LinearAlgebra
using Distances
# unittest performance
# versus python hsic_varioustests_npy.py unittest:
# 20sec elapsed / 52sec user (i.e. multithreaded)
# using distmat1: 10.3sec
# using Distances.jl pairwise 13.2sec
# using distmatvec: 10.7sec
# performance discussion see https://discourse.julialang.org/t/sum-of-hadamard-products/3531/5
# for comparison, do the non-vectorized calculation
# points are in columns, dimensions(variables) across rows
# note transposed from previous convention!
function distmatslow(X,Y)
m = size(X,2)
@assert m==size(Y,2) #"size mismatch"
D = Array{Float32}(undef,m,m)
for ix = 1:m
@inbounds for iy = 1:m
x = X[:,ix]
y = Y[:,iy]
d = norm(x-y)
D[ix,iy] = d*d
end
end
return D
end
function distmat(X,Y)
m = size(X,2)
@assert m==size(Y,2) #"size mismatch"
D = Array{Float32}(undef,m,m)
for ix = 1:m
@inbounds for iy = ix:m
x = X[:,ix]
y = Y[:,iy]
d = x-y
d2 = d'*d
D[ix,iy] = d2
D[iy,ix] = d2
end
end
return D
end
#
function distmatvec(X,Y)
"""
points are in columns, dimensions(variables) down rows
"""
m = size(X,2)
@assert m==size(Y,2) #"size mismatch"
XY = sum(X .* Y,dims=1)
#XY = XY.reshape(m,1)
#R1 = n_.tile(XY,(1,Y.shape[0]))
R1 = repeat(XY',1,m) # outer=(1,m) not suported by autograd
R2 = repeat(XY,m,1)
#xy_ = X * Y'
D = R1 + R2 - 2.f0 * X' * Y
D
end
# python-ish version requires broadcasting different shapes - "outer sum"
function distmat1(X,Y)
A = sum(X .* X,dims=1)'
B = sum(Y .* Y,dims=1)
C = X' * Y
A .+ B .- 2.f0 * C
end
function eye(n)
Matrix{Float32}(I,n,n)
end
function hsic(X,Y,sigma)
#=
# 1/m^2 Tr Kx H Ky H
X,Y have data in COLUMNS, each row is a dimension
# NB is transposed from python
=#
#println("X=\n", X'[1:2,:])
#Yt = Y;; println("Y=\n", Yt[1:2:])
m = size(X,2)
println("hsic between ",m,"points")
H = eye(m) - (1.f0 / m) * ones(Float32,m,m)
#Dxx = distmatvec(X,X)
#Dyy = distmatvec(Y,Y)
Dxx = distmat1(X,X)
Dyy = distmat1(Y,Y)
#Dxx = pairwise(SqEuclidean(),X,X,dims=2)
#Dyy = pairwise(SqEuclidean(),Y,Y,dims=2)
sigma2 = 2.f0 * sigma*sigma
Kx = exp.( -Dxx / sigma2 )
Ky = exp.( -Dyy / sigma2 )
Kxc = Kx * H
Kyc = Ky * H
thehsic = (1.f0 / (m*m)) * sum(Kxc' .* Kyc)
return thehsic # type float32
end
# Pkg.add("NPZ")
using NPZ
# aug19: this matches the output of the unittest in hsic_varioustests_npy
function unittest()
X = Float32[0.1 0.2 0.3;
5 4 3]
Y = Float32[1 2 3;
2 2 2]
X = transpose(X)
Y = transpose(Y)
println(distmatslow(X,X))
println(distmat(X,X))
println(distmatslow(Y,Y))
println(distmat(Y,Y))
println("hsic(X,Y,0.5)=",hsic(X,Y,0.5f0))
# larger test
data = npzread("/tmp/_data.npz")
# todo convert arrays to float32
X = data["arr_0"]
Y = data["arr_1"]
X = convert(Array{Float32,2},X')
Y = convert(Array{Float32,2},Y')
println("X=\n", X'[1:2,:])
println("Y=\n", Y'[1:2,:])
println("\nindependent hsic(X,Y,1)=",hsic(X,Y,1.f0))
println("\nidentical hsic(X,X,1)=",hsic(X,copy(X),1.f0))
Y2 = X .* X
println("Y2=",Y2'[1:2,:])
println("\nnonlinear hsic(X,Y*Y,1)=",hsic(X,Y2,1.f0))
end
| [
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] | 2.048428 | 1,590 |
using Cosmology
using Test, Unitful, UnitfulAstro, QuadGK
# values from http://icosmos.co.uk/
dist_rtol = 1e-6
age_rtol = 2e-4
# Integrating a unitful function would require UnitfulIntegration.jl. Without using it, we
# strip the units away from the integrand function
integrand(c, z) = 4pi*ustrip(comoving_volume_element(c, z))
@testset "FlatLCDM" begin
c = cosmology(h=0.7, OmegaM=0.3, OmegaR=0)
@test angular_diameter_dist(c,1,rtol=dist_rtol) ≈ 1651.9145u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,1,2,rtol=dist_rtol) ≈ 625.3444u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,pi,rtol=dist_rtol) ≈ angular_diameter_dist(c,0,pi,rtol=dist_rtol) rtol = dist_rtol
@test comoving_radial_dist(c,1,rtol=dist_rtol) ≈ 3303.829u"Mpc" rtol = dist_rtol
@test comoving_volume(c,1,rtol=dist_rtol) ≈ 151.0571u"Gpc^3" rtol = dist_rtol
@test quadgk(z -> integrand(c, z), 0, 2.5)[1] ≈ ustrip(comoving_volume(c, 2.5))
@test luminosity_dist(c,1,rtol=dist_rtol) ≈ 6607.6579u"Mpc" rtol = dist_rtol
@test distmod(c,1,rtol=dist_rtol) ≈ 44.1002 rtol = dist_rtol
@test age(c,0,rtol=age_rtol) ≈ 13.4694u"Gyr" rtol = age_rtol
@test age(c,1,rtol=age_rtol) ≈ 5.7527u"Gyr" rtol = age_rtol
@test lookback_time(c,1,rtol=age_rtol) ≈ (13.4694-5.7527)u"Gyr" rtol = age_rtol
@test age(c, 1) + lookback_time(c, 1) ≈ age(c, 0)
end
@testset "OpenLCDM" begin
c = cosmology(h=0.7, OmegaK=0.1, OmegaM=0.3, OmegaR=0)
@test angular_diameter_dist(c,1,rtol=dist_rtol) ≈ 1619.9588u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,1,2,rtol=dist_rtol) ≈ 598.9118u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,pi,rtol=dist_rtol) ≈ angular_diameter_dist(c,0,pi,rtol=dist_rtol) rtol = dist_rtol
@test comoving_radial_dist(c,1,rtol=dist_rtol) ≈ 3209.784u"Mpc" rtol = dist_rtol
@test comoving_volume(c,1,rtol=dist_rtol) ≈ 140.0856u"Gpc^3" rtol = dist_rtol
@test quadgk(z -> integrand(c, z), 0, 2.5)[1] ≈ ustrip(comoving_volume(c, 2.5))
@test luminosity_dist(c,1,rtol=dist_rtol) ≈ 6479.8352u"Mpc" rtol = dist_rtol
@test distmod(c,1,rtol=dist_rtol) ≈ 44.0578 rtol = dist_rtol
@test age(c,0,rtol=age_rtol) ≈ 13.064u"Gyr" rtol = age_rtol
@test age(c,1,rtol=age_rtol) ≈ 5.5466u"Gyr" rtol = age_rtol
@test lookback_time(c,1,rtol=age_rtol) ≈ (13.064-5.5466)u"Gyr" rtol = age_rtol
@test age(c, 1) + lookback_time(c, 1) ≈ age(c, 0)
end
@testset "ClosedLCDM" begin
c = cosmology(h=0.7, OmegaK=-0.1, OmegaM=0.3, OmegaR=0)
@test angular_diameter_dist(c,1,rtol=dist_rtol) ≈ 1686.5272u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,1,2,rtol=dist_rtol) ≈ 655.6019u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,pi,rtol=dist_rtol) ≈ angular_diameter_dist(c,0,pi,rtol=dist_rtol) rtol = dist_rtol
@test comoving_radial_dist(c,1,rtol=dist_rtol) ≈ 3408.937u"Mpc" rtol = dist_rtol
@test comoving_volume(c,1,rtol=dist_rtol) ≈ 163.8479u"Gpc^3" rtol = dist_rtol
@test quadgk(z -> integrand(c, z), 0, 2.5)[1] ≈ ustrip(comoving_volume(c, 2.5))
@test luminosity_dist(c,1,rtol=dist_rtol) ≈ 6746.1088u"Mpc" rtol = dist_rtol
@test distmod(c,1,rtol=dist_rtol) ≈ 44.1453 rtol = dist_rtol
@test age(c,0,rtol=age_rtol) ≈ 13.925u"Gyr" rtol = age_rtol
@test age(c,1,rtol=age_rtol) ≈ 5.9868u"Gyr" rtol = age_rtol
@test lookback_time(c,1,rtol=age_rtol) ≈ (13.925-5.9868)u"Gyr" rtol = age_rtol
@test age(c, 1) + lookback_time(c, 1) ≈ age(c, 0)
end
@testset "FlatWCDM" begin
c = cosmology(h=0.7, OmegaM=0.3, OmegaR=0, w0=-0.9, wa=0.1)
@test angular_diameter_dist(c,1,rtol=dist_rtol) ≈ 1612.0585u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,1,2,rtol=dist_rtol) ≈ 607.6802u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,pi,rtol=dist_rtol) ≈ angular_diameter_dist(c,0,pi,rtol=dist_rtol) rtol = dist_rtol
@test comoving_radial_dist(c,1,rtol=dist_rtol) ≈ 3224.1169u"Mpc" rtol = dist_rtol
@test comoving_volume(c,1,rtol=dist_rtol) ≈ 140.3851u"Gpc^3" rtol = dist_rtol
@test quadgk(z -> integrand(c, z), 0, 2.5)[1] ≈ ustrip(comoving_volume(c, 2.5))
@test luminosity_dist(c,1,rtol=dist_rtol) ≈ 6448.2338u"Mpc" rtol = dist_rtol
@test distmod(c,1,rtol=dist_rtol) ≈ 44.0472 rtol = dist_rtol
@test age(c,0,rtol=age_rtol) ≈ 13.1915u"Gyr" rtol = age_rtol
@test age(c,1,rtol=age_rtol) ≈ 5.6464u"Gyr" rtol = age_rtol
@test lookback_time(c,1,rtol=age_rtol) ≈ (13.1915-5.6464)u"Gyr" rtol = age_rtol
@test age(c, 1) + lookback_time(c, 1) ≈ age(c, 0)
end
@testset "OpenWCDM" begin
c = cosmology(h=0.7, OmegaK=0.1, OmegaM=0.3, OmegaR=0, w0=-0.9, wa=0.1)
@test angular_diameter_dist(c,1,rtol=dist_rtol) ≈ 1588.0181u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,1,2,rtol=dist_rtol) ≈ 585.4929u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,pi,rtol=dist_rtol) ≈ angular_diameter_dist(c,0,pi,rtol=dist_rtol) rtol = dist_rtol
@test comoving_radial_dist(c,rtol=dist_rtol,1) ≈ 3147.6227u"Mpc" rtol = dist_rtol
@test comoving_volume(c,1,rtol=dist_rtol) ≈ 132.0466u"Gpc^3" rtol = dist_rtol
@test quadgk(z -> integrand(c, z), 0, 2.5)[1] ≈ ustrip(comoving_volume(c, 2.5))
@test luminosity_dist(c,1,rtol=dist_rtol) ≈ 6352.0723u"Mpc" rtol = dist_rtol
@test distmod(c,1,rtol=dist_rtol) ≈ 44.0146 rtol = dist_rtol
@test age(c,0,rtol=age_rtol) ≈ 12.8488u"Gyr" rtol = age_rtol
@test age(c,1,rtol=age_rtol) ≈ 5.4659u"Gyr" rtol = age_rtol
@test lookback_time(c,1,rtol=age_rtol) ≈ (12.8488-5.4659)u"Gyr" rtol = age_rtol
@test age(c, 1) + lookback_time(c, 1) ≈ age(c, 0)
end
@testset "ClosedWCDM" begin
c = cosmology(h=0.7, OmegaK=-0.1, OmegaM=0.3, OmegaR=0, w0=-0.9, wa=0.1)
@test angular_diameter_dist(c,1,rtol=dist_rtol) ≈ 1637.5993u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,1,2,rtol=dist_rtol) ≈ 632.5829u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,pi,rtol=dist_rtol) ≈ angular_diameter_dist(c,0,pi,rtol=dist_rtol) rtol = dist_rtol
@test comoving_radial_dist(c,1,rtol=dist_rtol) ≈ 3307.9932u"Mpc" rtol = dist_rtol
@test comoving_volume(c,1,rtol=dist_rtol) ≈ 149.8301u"Gpc^3" rtol = dist_rtol
@test quadgk(z -> integrand(c, z), 0, 2.5)[1] ≈ ustrip(comoving_volume(c, 2.5))
@test luminosity_dist(c,1,rtol=dist_rtol) ≈ 6550.3973u"Mpc" rtol = dist_rtol
@test distmod(c,1,rtol=dist_rtol) ≈ 44.0813 rtol = dist_rtol
@test age(c,0,rtol=age_rtol) ≈ 13.5702u"Gyr" rtol = age_rtol
@test age(c,1,rtol=age_rtol) ≈ 5.8482u"Gyr" rtol = age_rtol
@test lookback_time(c,1,rtol=age_rtol) ≈ (13.5702-5.8482)u"Gyr" rtol = age_rtol
@test age(c, 1) + lookback_time(c, 1) ≈ age(c, 0)
end
@testset "Non-Float64" begin
# Test that FlatLCDM works with non-Float64 (BigFloat in this example)
c = cosmology(h=0.7, OmegaM=big(0.3), OmegaR=0)
@test angular_diameter_dist(c,1,rtol=dist_rtol) ≈ 1651.9145u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,1,2,rtol=dist_rtol) ≈ 625.3444u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,pi,rtol=dist_rtol) ≈ angular_diameter_dist(c,0,pi,rtol=dist_rtol) rtol = dist_rtol
@test comoving_volume_element(c, big(1.41)) ≈ 3.4030879e10u"Mpc^3" rtol = dist_rtol
# Test that FlatWCDM works with non-Float64 (BigFloat in this example)
c = cosmology(h=big(0.7), OmegaM=0.3, OmegaR=0, w0=-0.9, wa=0.1)
@test angular_diameter_dist(c,1,rtol=dist_rtol) ≈ 1612.0585u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,1,2,rtol=dist_rtol) ≈ 607.6802u"Mpc" rtol = dist_rtol
@test angular_diameter_dist(c,pi,rtol=dist_rtol) ≈ angular_diameter_dist(c,0,pi,rtol=dist_rtol) rtol = dist_rtol
@test comoving_volume_element(c, big(1.41)) ≈ 3.1378625e10u"Mpc^3" rtol = dist_rtol
end
@testset "Unit conversion" begin
c = cosmology(h=0.9, OmegaM=0.5, OmegaR=0)
for u in (u"m", u"pc", u"ly")
@test unit(luminosity_dist(u, c, 1)) == u
@test unit(angular_diameter_dist(u, c, 2)) == u
end
for u in (u"s", u"yr")
@test unit(age(u, c, 3)) == u
@test unit(lookback_time(u, c, 4)) == u
end
end
@testset "Utilities" begin
c = cosmology(h = 0.7)
@test hubble_time(c, 0) ≈ Cosmology.hubble_time0(c)
@test hubble_dist(c, 0) ≈ Cosmology.hubble_dist0(c)
@test H(c, 0) ≈ 70u"km/s/Mpc"
end
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] | 1.908986 | 4,329 |
# Libraries
include("./get_h10_dataset.jl");
using MessyTimeSeriesOptim;
using FileIO, JLD;
using Random, Statistics;
#=
Load arguments passed through the command line
=#
# VAR or VMA
is_var = parse(Bool, ARGS[1]);
log_folder_path = ARGS[2];
#=
Load benchmark grid of candidates from any run
=#
candidates = load("./VAR_output/err_type_2.jld")["candidates"];
#=
Presample: 53 weeks (1999) -> compute weights
Out-of-sample sample: from the 54th week onwards (2000 to 2020) -> run pseudo out-of-sample exercise
Test sample: from the 158th week onwards (2002 to 2020) -> compute realised error
=#
# Download estimation and test samples
df = get_h10_dataset(fred_tickers, mnemonics, transform, to_include, "1999-01-01", "2020-12-25");
# Extract relevant data
presample = df[1:53, 2:end] |> JMatrix{Float64};
oos_sample = df[54:end, 2:end] |> JMatrix{Float64};
test_sample = df[158:end, 2:end] |> JMatrix{Float64};
test_dates = df[158:end, :date];
# Periods not indicated as NBER recession in the test sample
nber_recessions = vcat(collect(Date(2007, 12, 07):Week(1):Date(2009, 05, 29)), Date(2020, 02, 07):Week(1):Date(2020, 03, 27));
test_periods_excl_recessions = [findfirst(test_dates .== x) for x in setdiff(test_dates, nber_recessions)];
# Remove NaNs
nan_to_missing!(presample);
nan_to_missing!(oos_sample);
nan_to_missing!(test_sample);
# Transpose data
presample = permutedims(presample);
oos_sample = permutedims(oos_sample);
test_sample = permutedims(test_sample);
# Compute benchmark weights
weights = 1 ./ (std_skipmissing(presample).^2);
#=
Out-of-sample exercise
=#
# Validation settings
model_kwargs = (tol=1e-3, check_quantile=true, verb=false);
# Dimensions
t0 = 158-54;
n, T = size(oos_sample);
grid_length = size(candidates, 2);
# One-step ahead forecast output
weighted_errors = zeros(size(candidates,2));
weighted_errors_excl_recessions = zeros(size(candidates,2));
forecast_per_series = Array{FloatMatrix,1}(undef, size(candidates,2));
# Setup data for estimation and forecast
estimation_sample = @view oos_sample[:, 1:t0];
estimation_sample_mean = mean_skipmissing(estimation_sample);
estimation_sample_std = std_skipmissing(estimation_sample);
zscored_oos_sample = (oos_sample .- estimation_sample_mean) ./ estimation_sample_std;
# Loop over each candidate vector of hyperparameters
for i in axes(candidates, 2)
@info("Iteration $(i) out of $(grid_length)")
# Current candidate vector of hyperparameters
p_float, λ, α, β = candidates[:,i];
p = Int64(p_float);
# Select appropriate EstimSettings
if is_var
estim = VARSettings(zscored_oos_sample[:, 1:t0], p, λ, α, β; model_kwargs...);
else
estim = VMASettings(zscored_oos_sample[:, 1:t0], p, λ, α, β; model_kwargs...);
end
# Estimate model
sspace = ecm(estim, output_sspace_data=zscored_oos_sample);
# Run Kalman filter
status = kfilter_full_sample(sspace);
# Forecast
X_prior = mapreduce(Xt -> sspace.B*Xt, hcat, status.history_X_prior);
forecast_per_series[i] = (X_prior[:, t0+1:end] .* estimation_sample_std) .+ estimation_sample_mean;
weighted_se = weights .* (test_sample .- forecast_per_series[i]).^2;
weighted_errors[i] = MessyTimeSeriesOptim.compute_loss(weighted_se)[1];
weighted_errors_excl_recessions[i] = MessyTimeSeriesOptim.compute_loss(weighted_se[:, test_periods_excl_recessions])[1];
end
# Save output to JLD
save("$(log_folder_path)/realised_error.jld", Dict("candidates" => candidates, "weighted_errors" => weighted_errors, "weighted_errors_excl_recessions" => weighted_errors_excl_recessions, "test_periods_excl_recessions" => test_periods_excl_recessions, "forecast_per_series" => forecast_per_series)); | [
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1,
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62,
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] | 2.696 | 1,375 |
module KhepriRadiance
using KhepriBase
# functions that need specialization
include(khepribase_interface_file())
include("Radiance.jl")
function __init__()
set_material(radiance, material_basic, radiance_material_neutral)
set_material(radiance, material_metal, radiance_generic_metal)
set_material(radiance, material_glass, radiance_generic_glass_80)
set_material(radiance, material_wood, radiance_light_wood)
set_material(radiance, material_concrete, radiance_outside_facade_30)
set_material(radiance, material_plaster, radiance_generic_interior_wall_70)
add_current_backend(radiance)
end
end
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] | 3.149485 | 194 |
@system VaporPressure begin
# Campbell and Norman (1998), p 41 Saturation vapor pressure in kPa
a => 0.611 ~ preserve(u"kPa", parameter)
b => 17.502 ~ preserve(parameter)
c => 240.97 ~ preserve(parameter) # °C
es(a, b, c; T(u"°C")): saturation => (t = Cropbox.deunitfy(T); a*exp((b*t)/(c+t))) ~ call(u"kPa")
ea(es; T(u"°C"), RH(u"percent")): ambient => es(T) * RH ~ call(u"kPa")
D(es; T(u"°C"), RH(u"percent")): deficit => es(T) * (1 - RH) ~ call(u"kPa")
RH(es; T(u"°C"), VPD(u"kPa")): relative_humidity => 1 - VPD / es(T) ~ call(u"NoUnits")
# slope of the sat vapor pressure curve: first order derivative of Es with respect to T
Δ(es, b, c; T(u"°C")): saturation_slope_delta => (e = es(T); t = Cropbox.deunitfy(T); e*(b*c)/(c+t)^2 / u"K") ~ call(u"kPa/K")
s(Δ; T(u"°C"), P(u"kPa")): saturation_slope => Δ(T) / P ~ call(u"K^-1")
end
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] | 2.149144 | 409 |
# __precompile__()
module LightDarkPOMDPs
importall POMDPs
using StaticArrays
using Plots
using POMDPToolbox
using Parameters # for @with_kw
using ParticleFilters # for AbstractParticleBelief
export
AbstractLD2,
LightDark2D,
LightDark2DTarget,
LightDark2DKalman,
SymmetricNormal2,
Vec2
include("lightdark2d.jl")
include("lightdark2dtarget.jl")
include("lightdark2dfilter.jl")
include("lightdark2dvis.jl")
end # module
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] | 2.610465 | 172 |
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# Desription
# ==============================================================================
#
# Tests related to conversion from Euler angles to quaternion.
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# File: ./src/conversions/angle_to_quat.jl
# ========================================
# Functions: angle_to_quat
# ------------------------
@testset "Euler angles => Quaternion (Float64)" begin
T = Float64
for rot_seq in valid_rot_seqs
# Sample Euler angles.
ea = EulerAngles(_rand_ang(T), _rand_ang(T), _rand_ang(T), rot_seq)
# Convert to quaternion.
q = angle_to_quat(ea)
@test eltype(q) === T
# Create the quaternion by composing the rotations.
rot_seq_str = string(rot_seq)
q₁ = if rot_seq_str[1] == 'X'
Quaternion(cos(ea.a1 / 2), T[1, 0, 0] * sin(ea.a1 / 2))
elseif rot_seq_str[1] == 'Y'
Quaternion(cos(ea.a1 / 2), T[0, 1, 0] * sin(ea.a1 / 2))
elseif rot_seq_str[1] == 'Z'
Quaternion(cos(ea.a1 / 2), T[0, 0, 1] * sin(ea.a1 / 2))
end
q₂ = if rot_seq_str[2] == 'X'
Quaternion(cos(ea.a2 / 2), T[1, 0, 0] * sin(ea.a2 / 2))
elseif rot_seq_str[2] == 'Y'
Quaternion(cos(ea.a2 / 2), T[0, 1, 0] * sin(ea.a2 / 2))
elseif rot_seq_str[2] == 'Z'
Quaternion(cos(ea.a2 / 2), T[0, 0, 1] * sin(ea.a2 / 2))
end
q₃ = if rot_seq_str[3] == 'X'
Quaternion(cos(ea.a3 / 2), T[1, 0, 0] * sin(ea.a3 / 2))
elseif rot_seq_str[3] == 'Y'
Quaternion(cos(ea.a3 / 2), T[0, 1, 0] * sin(ea.a3 / 2))
elseif rot_seq_str[3] == 'Z'
Quaternion(cos(ea.a3 / 2), T[0, 0, 1] * sin(ea.a3 / 2))
end
qe = q₁ * q₂ * q₃
# Make sure q0 is positive.
(qe.q0 < 0) && (qe = -qe)
# Compare.
@test q ≈ qe
end
end
@testset "Euler angles => Quaternion (Float32)" begin
T = Float32
for rot_seq in valid_rot_seqs
# Sample Euler angles.
ea = EulerAngles(_rand_ang(T), _rand_ang(T), _rand_ang(T), rot_seq)
# Convert to quaternion.
q = angle_to_quat(ea)
@test eltype(q) === T
# Create the quaternion by composing the rotations.
rot_seq_str = string(rot_seq)
q₁ = if rot_seq_str[1] == 'X'
Quaternion(cos(ea.a1 / 2), T[1, 0, 0] * sin(ea.a1 / 2))
elseif rot_seq_str[1] == 'Y'
Quaternion(cos(ea.a1 / 2), T[0, 1, 0] * sin(ea.a1 / 2))
elseif rot_seq_str[1] == 'Z'
Quaternion(cos(ea.a1 / 2), T[0, 0, 1] * sin(ea.a1 / 2))
end
q₂ = if rot_seq_str[2] == 'X'
Quaternion(cos(ea.a2 / 2), T[1, 0, 0] * sin(ea.a2 / 2))
elseif rot_seq_str[2] == 'Y'
Quaternion(cos(ea.a2 / 2), T[0, 1, 0] * sin(ea.a2 / 2))
elseif rot_seq_str[2] == 'Z'
Quaternion(cos(ea.a2 / 2), T[0, 0, 1] * sin(ea.a2 / 2))
end
q₃ = if rot_seq_str[3] == 'X'
Quaternion(cos(ea.a3 / 2), T[1, 0, 0] * sin(ea.a3 / 2))
elseif rot_seq_str[3] == 'Y'
Quaternion(cos(ea.a3 / 2), T[0, 1, 0] * sin(ea.a3 / 2))
elseif rot_seq_str[3] == 'Z'
Quaternion(cos(ea.a3 / 2), T[0, 0, 1] * sin(ea.a3 / 2))
end
qe = q₁ * q₂ * q₃
# Make sure q0 is positive.
(qe.q0 < 0) && (qe = -qe)
# Compare.
@test q ≈ qe
end
end
@testset "Euler angles => Quaternion (Errors)" begin
@test_throws ArgumentError angle_to_quat(1, 2, 3, :ZZY)
end
# Functions: smallangle_to_quat
# -----------------------------
@testset "Small Euler angles => Quaternion (Float64)" begin
T = Float64
# Sample three small angles.
θx = _rand_ang(T) * T(0.001)
θy = _rand_ang(T) * T(0.001)
θz = _rand_ang(T) * T(0.001)
# Create the quaternion.
q = smallangle_to_quat(θx, θy, θz)
# Expected result.
qe = Quaternion(1, θx / 2, θy / 2, θz / 2)
qe = qe / norm(qe)
@test qe ≈ q
end
@testset "Small Euler angles => Quaternion (Float32)" begin
T = Float32
# Sample three small angles.
θx = _rand_ang(T) * T(0.001)
θy = _rand_ang(T) * T(0.001)
θz = _rand_ang(T) * T(0.001)
# Create the quaternion.
q = smallangle_to_quat(θx, θy, θz)
# Expected result.
qe = Quaternion(1, θx / 2, θy / 2, θz / 2)
qe = qe / norm(qe)
@test qe ≈ q
end
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] | 1.845088 | 2,453 |
export Grid, δx, δy, δz, δ, x, y, z, x⃗, xc, yc, zc, x⃗c, N, g⃗, _fftaxes, Nranges, ∫
struct Grid{ND,T}
Δx::T
Δy::T
Δz::T
Nx::Int
Ny::Int
Nz::Int
end
Grid(Δx::T, Δy::T, Δz::T, Nx::Int, Ny::Int, Nz::Int) where {T<:Real} = Grid{3,T}(
Δx,
Δy,
Δz,
Nx,
Ny,
Nz,
)
Grid(Δx::T, Δy::T, Nx::Int, Ny::Int) where {T<:Real} = Grid{2,T}(
Δx,
Δy,
1.,
Nx,
Ny,
1,
)
function Base.size(gr::Grid{3})::NTuple{3,Int}
(gr.Nx, gr.Ny, gr.Nz)
end
function Base.size(gr::Grid{2})::NTuple{2,Int}
(gr.Nx, gr.Ny)
end
Base.size(gr::Grid,d::Int) = Base.size(gr)[d]
δx(g::Grid) = g.Δx / g.Nx
δy(g::Grid) = g.Δy / g.Ny
δz(g::Grid) = g.Δz / g.Nz
δ(g::Grid{2}) = g.Δx * g.Δy / ( g.Nx * g.Ny )
δ(g::Grid{3}) = g.Δx * g.Δy * g.Δz / ( g.Nx * g.Ny * g.Nz )
∫(intgnd;g::Grid) = sum(intgnd)*δ(g) # integrate a scalar field over the grid
# voxel/pixel center positions
function x(g::Grid{ND,T})::Vector{T} where {ND,T<:Real}
# ( ( g.Δx / g.Nx ) .* (0:(g.Nx-1))) .- g.Δx/2.
LinRange(-g.Δx/2, g.Δx/2 - g.Δx/g.Nx, g.Nx)
end
function y(g::Grid{ND,T})::Vector{T} where {ND,T<:Real}
# ( ( g.Δy / g.Ny ) .* (0:(g.Ny-1))) .- g.Δy/2.
LinRange(-g.Δy/2, g.Δy/2 - g.Δy/g.Ny, g.Ny)
end
function z(g::Grid{ND,T})::Vector{T} where {ND,T<:Real}
# ( ( g.Δz / g.Nz ) .* (0:(g.Nz-1))) .- g.Δz/2.
LinRange(-g.Δz/2, g.Δz/2 - g.Δz/g.Nz, g.Nz)
end
function x⃗(g::Grid{2,T})::Array{SVector{3,T},2} where T<:Real
( (xx,yy) = (x(g),y(g)); [SVector{3,T}(xx[ix],yy[iy],0.) for ix=1:g.Nx,iy=1:g.Ny] ) # (Nx × Ny ) 2D-Array of (x,y,z) vectors at pixel/voxel centers
end
function x⃗(g::Grid{3,T})::Array{SVector{3,T},3} where T<:Real
( (xx,yy,zz) = (x(g),y(g),z(g)); [SVector{3,T}(xx[ix],yy[iy],zz[iz]) for ix=1:g.Nx,iy=1:g.Ny,iz=1:g.Nz] ) # (Nx × Ny × Nz) 3D-Array of (x,y,z) vectors at pixel/voxel centers
end
# voxel/pixel corner positions
function xc(g::Grid{ND,T})::Vector{T} where {ND,T<:Real}
collect( ( ( g.Δx / g.Nx ) .* (0:g.Nx) ) .- ( g.Δx/2. * ( 1 + 1. / g.Nx ) ) )
# collect(range(-g.Δx/2.0, g.Δx/2.0, length=g.Nx+1))
end
function yc(g::Grid{ND,T})::Vector{T} where {ND,T<:Real}
collect( ( ( g.Δy / g.Ny ) .* (0:g.Ny) ) .- ( g.Δy/2. * ( 1 + 1. / g.Ny ) ) )
# collect(range(-g.Δy/2.0, g.Δy/2.0, length=g.Ny+1))
end
function zc(g::Grid{3,T})::Vector{T} where T<:Real
collect( ( ( g.Δz / g.Nz ) .* (0:g.Nz) ) .- ( g.Δz/2. * ( 1 + 1. / g.Nz ) ) )
# collect(range(-g.Δz/2.0, g.Δz/2.0, length=g.Nz+1))
end
function x⃗c(g::Grid{2,T})::Array{SVector{3,T},2} where T<:Real
( (xx,yy) = (xc(g),yc(g)); [SVector{3}(xx[ix],yy[iy],0.) for ix=1:(g.Nx+1),iy=1:(g.Ny+1)] )
end
function x⃗c(g::Grid{3,T})::Array{SVector{3,T},3} where T<:Real
( (xx,yy,zz) = (xc(g),yc(g),zc(g)); [SVector{3}(xx[ix],yy[iy],zz[iz]) for ix=1:(g.Nx+1),iy=1:(g.Ny+1),iz=1:(g.Nz+1)] )
end
# grid size
@inline N(g::Grid)::Int = *(size(g)...)
import Base: eachindex
@inline eachindex(g::Grid) = CartesianIndices(size(g)) #(1:NN for NN in size(g))
# reciprocal lattice vectors (from fftfreqs)
function g⃗(gr::Grid{3,T})::Array{SVector{3, T}, 3} where T<:Real
[ SVector(gx,gy,gz) for gx in fftfreq(gr.Nx,gr.Nx/gr.Δx),
gy in fftfreq(gr.Ny,gr.Ny/gr.Δy),
gz in fftfreq(gr.Nz,gr.Nz/gr.Δz) ]
end
_fftaxes(gr::Grid{3}) = (2:4)
function g⃗(gr::Grid{2,T})::Array{SVector{3, T}, 2} where T
[ SVector(gx,gy,0.) for gx in fftfreq(gr.Nx,gr.Nx/gr.Δx),
gy in fftfreq(gr.Ny,gr.Ny/gr.Δy) ]
end
_fftaxes(gr::Grid{2}) = (2:3)
"""
################################################################################
# #
# Plotting methods #
# #
################################################################################
"""
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] | 1.692449 | 2,185 |
<reponame>isrlab/Multibody-Dynamics<filename>UdKaDyn/Examples/testGimbal.jl
# To test a simplified 2 axis gimbal.
# Modeling the system as a cube and the gimbal placed on top.
# We need two revolute joints between the cube and gimbal.
include("../src/plotSol.jl")
include("../src/simulate.jl")
include("../src/OrientationConversion.jl")
clearconsole()
## Inertial Properties
mb = 0.755; mg = 0.215; mp = 0.02;
Ib = [ 0.0009 0.0000 -0.0000;
0.0000 0.0053 0.0000;
-0.0000 0.0000 0.0054]#*(10^-3)
Ip = [ 0.0029 0.0000 0.0000;
0.0000 0.5824 0.0000;
0.0000 0.0000 0.5805]*(10^-4)
Ig = [ 0.1435 0.0000 0.0000;
0.0000 0.0498 0.0000;
0.0000 0.0000 0.1381]*(10^-3)
## Initialisation
InFrame = InertialFrameAsRB()
Body = RigidBody(mb,Ib,2)
Gimbal = RigidBody(mg,Ig,3)
x0Body = [zeros(3);[1;zeros(3)];zeros(3);zeros(4)]
initialiseRigidBody!(Body,x0Body)
x0Gimbal = [[0.0;0.0;0.143];[1;zeros(3)];zeros(3);zeros(4)]
initialiseRigidBody!(Gimbal,x0Gimbal)
## Joint Descriptions
# Joint 1 (Free) between the inertial frame and body.
j1 = Joint(InFrame,Body,zeros(3),zeros(3))
# Joint 2 (Revolute2) between the body and gimbal
axisZ = [0.0 0.0 1.0][:]
rjBody = [0.0 0.0 0.134][:]
rjGimbal = [0.0 0.0 -0.009][:]
j2 = Joint(Body,Gimbal,rjBody,rjGimbal,
type = "Revolute",axis = axisZ, jointTorque = [0.0 0.0 0.0][:])
## Simulation
tEnd = 1.0
tSpan = 0.01
g = [0.0;0.0;0.0]
tSim, solFinal = simulate(tEnd,tSpan,j1,j2,g=g)#,extFVec = extFList)
@time simulate(tEnd,tSpan,j1,j2,g=g)
solBody = solFinal[1]
solGimbal = solFinal[2]
## Plotting
plotErrNorm(tSim,solBody.β)
plotErrNorm(tSim,solGimbal.β)
# Check if joint location has moved
jointLoc = Matrix{Float64}(undef,length(tSim),3)
ωBody = Matrix{Float64}(undef,length(tSim),3)
ωGimbal = Matrix{Float64}(undef,length(tSim),3)
ωGimbalInBody = Matrix{Float64}(undef,length(tSim),3)
ωRel = Matrix{Float64}(undef,length(tSim),3) # Relative
for i=1:length(tSim)
Body.dcm = quat2dcm(solBody.β[i,:])
Gimbal.dcm = quat2dcm(solGimbal.β[i,:])
ωBody[i,:] = angVel(solBody.β[i,:],solBody.βdot[i,:])
ωGimbal[i,:] = angVel(solGimbal.β[i,:],solGimbal.βdot[i,:])
ωGimbalInBody[i,:] = quat2dcm(solBody.β[i,:])*
transpose(quat2dcm(solGimbal.β[i,:]))*
ωGimbal[i,:]
ωRel[i,:] = ωGimbalInBody[i,:] - ωBody[i,:]
jointLoc[i,:] = solBody.r[i,:] + transpose(Body.dcm)*rjBody -
solGimbal.r[i,:] - transpose(Gimbal.dcm)*rjGimbal
end
plotPos(tSim,jointLoc)
plotPos(tSim,solBody.r)
plotPos(tSim,solGimbal.r)
plotVel(tSim,solBody.v)
plotVel(tSim,solGimbal.v)
plotAngVel(tSim,ωBody)
plotAngVel(tSim,ωGimbal)
plotAngVel(tSim,ωGimbalInBody)
plotAngVel(tSim,ωRel)
# plotAngVel(tSim,ωCube - ωProp)
# # Check revJoint axis
# axisSol = Matrix{Float64}(undef,length(tSim),3)
# errAxis = Matrix{Float64}(undef,length(tSim),3)
# errAxisNorm = Vector{Float64}(undef,length(tSim))
# for i=1:length(tSim)
# axisSol[i,:] = solProp.β[i,2:4]/norm(solProp.β[i,2:4],2)
#
# # β1inv = [solQuad.β[i,1];solQuad.β[i,2:4]]
# # βRev = quaternionProduct(solProp.β[i,:],β1inv)
# # axisSol[i,:] = βRev[2:4]/norm(βRev[2:4])
#
# # axisSol[i,:] = (solQuad.β[i,:])*solProp.β[i,2:4]/norm(solProp.β[i,2:4],2)
#
# # axisSol[i,:] = axixRev(solQuad.β[i,:],solProp.β[i,:])
# errAxis[i,:] = axisSol[i,:] - axis
# errAxisNorm[i] = norm(axisSol[i,:],2) - 1.0
# end
# plot(tSim[2:end],errAxisNorm[2:end])
# plotPos(tSim,errAxis)
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] | 1.893276 | 1,874 |
<reponame>UnofficialJuliaMirror/Octo.jl-16905944-f982-529b-abb2-b839f98f160b
module test_octo_as
using Test
using Octo.Adapters.SQL # from to_sql
@test to_sql([as(AVG(:n), :avg_n)]) == "AVG(n) AS avg_n"
struct User
end
u = from(User)
n = as(u.name, :n)
@test to_sql([n]) == "name AS n"
end # module test_octo_as
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] | 2.064935 | 154 |
using AppliSales
using Test
@testset "AppliSales.jl" begin
orders = process()
first_order = orders[1]
second_order = orders[2]
@test length(orders) == 3
@test first_order.org.name == "Scrooge Investment Bank"
@test first_order.contact_name == "<NAME>"
@test length(second_order.students) == 2
@test second_order.students[1] == "Mini Mouse"
end
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] | 2.669014 | 142 |
<reponame>ericjang/CircularArrays.jl
type CircularArray{T,N}
data::AbstractArray{T,N}
cdim::Integer # dimension to cycle along
cl::Integer # size of cycle dimension
head::Integer # starting point of the array
end
# CONSTRUCTORS
function CircularArray(A,cdim)
if cdim > ndims(A)
error("A does not have enough dimensions to be indexed along dimension $c_dim")
end
CircularArray(A,cdim,size(A,cdim),1)
end
function CircularArray(A)
CircularArray(A,ndims(A))
end
# helper function
function circ_slice(A::CircularArray, index...)
slice = vcat([A.head:A.cl],[1:A.head-1])
idx::Array{Any,1} = [i for i in index] # enables indexing by slicing, etc.
idx[A.cdim] = slice[index[A.cdim]]
return idx
end
# ACCESSOR
# colons dont seem to work in native julia getindex, setindex
function getindex(A::CircularArray, index...)
idx = circ_slice(A,index...)
getindex(A.data,idx...)
end
# MUTATORS
function setindex!(A::CircularArray, newdata, index...)
idx = circ_slice(A,index...)
setindex!(A.data,newdata,idx...)
end
function advance_head!(A::CircularArray, offset::Integer)
A.head += offset
end
function reset_head!(A::CircularArray)
A.head = 1
end
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<filename>test/extract_domain_polyopt.jl
# Test for the example https://github.com/JuliaOpt/SumOfSquares.jl/blob/master/examples/Polynomial_Optimization.ipynb
using JuMP
using SumOfSquares
using MultivariateMoments
@testset "Polynomial Optimization example with $(factory.constructor)" for factory in sdp_factories
isscs(factory) && continue
@polyvar x y
p = x^3 - x^2 + 2x*y -y^2 + y^3
S = @set x >= 0 && y >= 0 && x + y >= 1
for (maxdeg, found) in [(3, false), (4, true), (5, true)]
m = SOSModel(factory)
@variable m α
@objective m Max α
c = @constraint m p >= α domain = S maxdegree = maxdeg
JuMP.optimize!(m)
@test JuMP.termination_status(m) == MOI.Success
@test JuMP.objective_value(m) ≈ 0 atol=1e-4
for λ in lagrangian_multipliers(c)
@test all(eigvals(Matrix(JuMP.value(λ).Q)) .>= -1e-2)
end
μ = JuMP.dual(c)
X = certificate_monomials(c)
ν = matmeasure(μ, X)
ranktol = 1e-3
atoms = extractatoms(ν, ranktol)
@test (atoms === nothing) == !found
if atoms !== nothing
η = atoms
@test η.atoms[1].weight ≈ 1/2 atol=1e-2
@test η.atoms[2].weight ≈ 1/2 atol=1e-2
@test isapprox(η.atoms[1].center, [0, 1], atol=1e-2) || isapprox(η.atoms[1].center, [1, 0], atol=1e-2)
@test isapprox(η.atoms[2].center, [0, 1], atol=1e-2) || isapprox(η.atoms[2].center, [1, 0], atol=1e-2)
end
end
end
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] | 1.971391 | 769 |
using LTC
using Plots
gr()
using BenchmarkTools
using DiffEqSensitivity
using OrdinaryDiffEq
using DiffEqFlux
using GalacticOptim
using Juno
using Cthulhu
using Profile
#using PProf
function generate_data()
in_features = 2
out_features = 1
N = 48
data_x = [sin.(range(0,stop=3π,length=N)), cos.(range(0,stop=3π,length=N))]
data_x = [reshape([Float32(data_x[1][i]),Float32(data_x[2][i])],2,1) for i in 1:N]# |> f32
data_y = [reshape([Float32(y)],1) for y in sin.(range(0,stop=6π,length=N))]# |> f32
#data_x = [repeat(x,1,20) for x in data_x]
#data_y = [repeat(x,1,20) for x in data_y]
data_x, data_y
end
function data(iter; data_x=nothing, data_y=nothing, short=false, noisy=false)
#noisy_data = Vector{Tuple{Vector{Matrix{Float64}}, Vector{Vector{Float64}}}}([])
if data_y === nothing
data_x, data_y = generate_data()
end
noisy_data = Vector{Tuple{Vector{Matrix{eltype(data_x[1])}}, Vector{Vector{eltype(data_y[1])}}}}([])
for i in 1:iter
x = data_x
y = data_y
if short isa Array
x = x[short[1]:short[2]]
y = y[short[1]:short[2]]
end
push!(noisy_data, (x , noisy ? add_gauss.(y,0.02) : y))
end
noisy_data
end
function loss(x,y,m::LTCNet{<:Mapper,<:Mapper,<:LTC.LTCCell,<:AbstractMatrix})
GalacticOptim.Flux.reset!(m)
#m = re(θ)
#ŷ = m.(x)
#ŷ = map(xi -> m(xi)[end-m.cell.wiring.n_motor+1:end, :], x)
ŷ = [m(xi)[end-m.cell.wiring.n_motor+1:end, :] for xi in x]
#ŷ = m.(x)
#length(ŷ) < length(y) && return Inf
#sum(Flux.Losses.mse.(ŷ, y; agg=mean))
sum(sum([(ŷ[i][end,:] .- y[i]) .^ 2 for i in 1:length(y)]))/length(y)#, ŷ
#sum([sum((ŷ[i] .- y[i]) .^ 2) for i in 1:length(y)])/length(y)
end
function cb(x,y,l,m)
println(l)
# pred = m.(x)
# # isnan(l) && return false
# fig = plot([ŷ[size(ŷ,1),1] for ŷ in pred])
# plot!(fig, [yi[size(yi,1),1] for yi in y])
# display(fig)
return false
end
function traintest(n, solver=VCABM(), sensealg=InterpolatingAdjoint(autojacvec=ReverseDiffVJP(true)))
function lg(p,x,y,model)
# reset_state!(model,p)
reset!(model)
# d = train_data[1]
# x,y = d[1], d[2]
m = model
ŷ = [m(xi,p)[end-m.cell.wiring.n_motor+1:end, :] for xi in x]
#ŷ = m.(x,[p])
#losses = [Flux.Losses.mse(ŷ[i][end,:], y[i]) for i in 1:length(y)]
#sum(losses)/length(losses), ŷ
sum(sum([(ŷ[i][end,:] .- y[i]) .^ 2 for i in 1:length(y)]))/length(y), ŷ
end
cbg = function (p,l,pred;doplot=false) #callback function to observe training
display(l)
# plot current prediction against data
if doplot
fig = plot([ŷ[end,1] for ŷ in pred])
plot!(fig, [yi[end,1] for yi in y])
display(fig)
end
return false
end
x,y = generate_data()
model = LTC.LTCNet(Wiring(2,1), solver, sensealg)
#pp, re = Flux.destructure(model)
# pp = DiffEqFlux.initial_params(model)
# lower,upper = get_bounds(model)
lower,upper = [],[]
#θ = Flux.params(model)
#θ = Flux.params(pp)
pp = DiffEqFlux.initial_params(model)
@show length(pp)
@show length(pp)
#@show sum(length.(θ))
@show length(lower)
train_data = data(n)
opt = GalacticOptim.Flux.Optimiser(ClipValue(0.5), ADAM(0.05))
# Juno.@profiler gs = Flux.Zygote.gradient(θ) do
# loss(first(train_data)...,model)
# end
# @time gs = Flux.Zygote.gradient(θ) do
# loss(first(train_data)...,model)
# end
# @time gs = Flux.Zygote.gradient(θ) do
# loss(first(train_data)...,model)
# end
#
#
# Juno.@profiler train_loss, back = Flux.Zygote.pullback(() -> loss(x,y,model), θ)
# Juno.@profiler gs = back(one(train_loss))
# train_loss, back = Flux.Zygote.pullback(() -> loss(x,y,model), θ)
# gs = back(one(train_loss))
# @time train_loss, back = Flux.Zygote.pullback(() -> loss(x,y,model), θ)
# @time gs = back(one(train_loss))
# use GalacticOptim.jl to solve the problem
#adtype = GalacticOptim.AutoZygote()
#
#optf = GalacticOptim.OptimizationFunction((x, p) -> lg(x,model), adtype)
#optfunc = GalacticOptim.instantiate_function(optf, model.cell.p, adtype, nothing)
#optprob = GalacticOptim.OptimizationProblem(optfunc, model.cell.p, lb=lower, ub=upper)
#
#result_neuralode = GalacticOptim.solve(optprob,
# ParticleSwarm(;lower,upper,n_particles=6),
# cb = cbg,
# maxiters = 300)
##
#optfun = OptimizationFunction((x,p,dx,dy)->lg(x,dx,dy,model), GalacticOptim.AutoZygote())
#optprob = OptimizationProblem(optfun, θ[1], lb=lower, ub=upper)
##using IterTools: ncycle
#res1 = GalacticOptim.solve(optprob, opt, train_data, cb = cbg, maxiters = n)
#return res1
#Flux.train!((x,y) -> loss(x,y,model), θ, train_data, opt; cb)
Profile.clear()
Profile.clear_malloc_data()
optfun = OptimizationFunction((θ, p, x, y) -> lg(θ,x,y,model), GalacticOptim.AutoZygote())
optprob = OptimizationProblem(optfun, pp)
#using IterTools: ncycle
#Juno.@profiler GalacticOptim.solve(optprob, opt, train_data, cb = cbg, maxiters = n) C = true
GalacticOptim.solve(optprob, opt, train_data, cb = cbg, maxiters = 1000)
# sciml_train(p->lg(p,model), pp, opt, cb = cbg, maxiters=100)
#Juno.@profiler my_custom_train!(model, (x,y) -> loss(x,y,model), θ, train_data, opt; cb, lower, upper) C = true
end
function cthulu_test()
m = LTC.LTCNet(Wiring(2,1), VCABM(), InterpolatingAdjoint(autojacvec=ReverseDiffVJP(true)))
x = [rand(Float32,2,1) for _ in 1:10]
p = initial_params(m)
m.(x,[p])
@time m.(x,[p])
# @profile m.(x)
#d = Dense(2,2)
# r = RNN(2,2)
# @descend_code_warntype r(x)
#Flux.reset!(m)
@descend_code_warntype m(x[1],p)
end
# cthulu_test()
#Profile.print()
# model = NCP(Wiring(2,1), VCABM(), InterpolatingAdjoint(autojacvec=ReverseDiffVJP(true)))
# ppp = Flux.params(model)
# sum(length.(ppp))
# Flux.trainable(model)
#@time traintest(10, AutoTsit5(Rosenbrock23()))
@time traintest(40)
#@time traintest(10)
#00:53 - 01:02 = 9 min compilation time
#@time traintest(300, VCABM(), ForwardDiffSensitivity())
#@time traintest(300, VCABM(), ReverseDiffAdjoint())
# @time traintest(300, Euler(), ForwardDiffSensitivity())
#@time traintest(300, VCABM(), InterpolatingAdjoint(autojacvec=ReverseDiffVJP(true)))
#@time traintest(300, VCABM(), InterpolatingAdjoint(autojacvec=false))
#@time traintest(300, VCABM(), InterpolatingAdjoint(autojacvec=ZygoteVJP()))
#@time traintest(300, VCABM(), InterpolatingAdjoint(checkpointing=true))
#traintest(300, AutoTsit5(Rosenbrock23()), InterpolatingAdjoint(autojacvec=ReverseDiffVJP(true)))
#@btime traintest(3, CVODE_BDF(linear_solver=:GMRES), InterpolatingAdjoint(autojacvec=ZygoteVJP()))
#@time traintest(300, CVODE_BDF(linear_solver=:GMRES), InterpolatingAdjoint(autojacvec=ZygoteVJP()))
#@time traintest(300, CVODE_BDF(linear_solver=:GMRES), InterpolatingAdjoint(autojacvec=ReverseDiffVJP(true)))
# 601.084 ms (1153411 allocations: 98.26 MiB)
# 8.510164 seconds (35.02 M allocations: 3.450 GiB, 5.96% gc time)
#@time traintest(300, VCABM(), InterpolatingAdjoint(autojacvec=ReverseDiffVJP(true)))
#@time traintest(3, AutoTsit5(Rosenbrock23()), InterpolatingAdjoint(autojacvec=ReverseDiffVJP(true)))
#@time traintest(300, AutoTsit5(Rosenbrock23()), InterpolatingAdjoint(autojacvec=ReverseDiffVJP(true)))
# 2.118 s (6965793 allocations: 461.57 MiB)
#ltc = Flux.Chain(Dense(2,5),Flux.LSTM(5,5),Flux.Dense(5,1))
# Flux.reset!(ltc)
# opt = GalacticOptim.Flux.Optimiser(ClipValue(0.1), ADAM(0.001))
#
# my_custom_train!(ltc, (x,y) -> lossf(x,y)[1], Flux.params(ltc), data(3), opt; cb=()->cbf(data_x,data_y,ltc),lower,upper)
# my_custom_train!(ltc, (x,y) -> lossf(x,y)[1], Flux.params(ltc), data(1000), opt; cb=()->cbf(data_x,data_y,ltc),lower,upper)
# my_custom_train!(ltc, (x,y) -> lossf(x,y)[1], Flux.params(ltc), data(100), opt; data_range=[1,8], cb=()->cbf(data_x,data_y,ltc),lower,upper)
# my_custom_train!(ltc, (x,y) -> lossf(x,y)[1], Flux.params(ltc), data(100), opt; data_range=[15,20], cb=()->cbf(data_x,data_y,ltc),lower,upper)
# my_custom_train!(ltc, (x,y) -> lossf(x,y)[1], Flux.params(ltc), data(100), opt; data_range=[10,30], cb=()->cbf(data_x,data_y,ltc),lower,upper)
# my_custom_train!(ltc, (x,y) -> lossf(x,y)[1], Flux.params(ltc), data(100), opt; data_range=[1,35], cb=()->cbf(data_x,data_y,ltc),lower,upper)
# my_custom_train!(ltc, (x,y) -> lossf(x,y)[1], Flux.params(ltc), data(100, short=[1,20]), ADAM(0.001); cb=()->cbf(data_x,data_y,ltc),lower,upper)
# my_custom_train!(ltc, (x,y) -> lossf(x,y)[1], Flux.params(ltc), data(100, short=[1,30]), ADAM(0.001); cb=()->cbf(data_x,data_y,ltc),lower,upper)
# my_custom_train!(ltc, (x,y) -> lossf(x,y)[1], Flux.params(ltc), data(3000), ADAM(0.001); cb=()->cbf(data_x,data_y,ltc),lower,upper)
#
# Flux.train!((x,y)->lossf(x,y)[1],Flux.params(ltc),data(200),ADAM(0.02); cb = ()->cbf(data_x,data_y,ltc))
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] | 2.129502 | 4,193 |
<filename>src/filetypes/cd.jl<gh_stars>0
function DataFrames.rename!(df, ::DataFile{Val{Symbol("C&D")}})
namemap = Dict("WE(1).Potential (V)"=>"Potential (V)",
"Time (s)"=>"Other Time (s)",
"Corrected time (s)"=>"Time (s)")
rename!(df, namemap)
end
function find_pair(datafile, list, ending)
findfirst(f -> filevalue(f) == filevalue(datafile) &&
foldervalue(f) == foldervalue(datafile) &&
endswith(f.filename, ending),
list)
end
function add_CD(filename)
parts = rsplit(filename, '.', limit=2)
parts[1][1:end-1] * "CD." * parts[2]
end
function process_data(::Val{Symbol("C&D")}, data; insert_D, continue_col)
done = Vector{Int}()
for (i,f) in enumerate(data["C&D"])
if i in done
continue
else
push!(done, i)
df = read_file(f)
valid_idx = setdiff(axes(data["C&D"], 1), done)
pair_idx = find_pair(f, data["C&D"], endswith(f.filename, "_C") ? "_D" : "_C")
if isnothing(pair_idx)
if endswith(f.filename, "_D")
pushfirst(df, insert_D)
write_file(f, df, ';')
end
continue
end
f_pair = data["C&D"][pair_idx]
push!(done, pair_idx)
df_pair = read_file(f_pair)
df_C, df_D = endswith(f.filename, "_C") ? (df, df_pair) : (df_pair, df)
df_CD, df_D = postprocess(f, df_C, df_D, insert_D, continue_col)
new_name = add_CD(f.savename)
mergedf = DataFile{Val{Symbol("C&D")}}(f.filename, new_name, f.units,
f.legend_units, f.idx)
write_file(mergedf, df_CD, ';')
write_file(endswith(f.filename, "_D") ? f : f_pair, df_D, ';')
end
end
return nothing
end
function pushfirst(df, value)
types = eltype.(eachcol(df))
line = (Vector{types[i]}([v]) for (i,v) in enumerate(value))
to_add = DataFrame(;zip(propertynames(df), line)...)
append!(to_add, df)
end
function postprocess(datafile::DataFile{Val{Symbol("C&D")}}, df_C, df_D, value, cont_col)
# Insert values in _D file
df_D_mod = pushfirst(df_D, value)
# Append column with types
df_C[!, :Type] .= "C"
df_D[!, :Type] .= "D"
# Add last value of cont_col form _C to _D values
last_time = df_C[end, cont_col]
df_D[!, cont_col] .+= last_time
# Append DataFrames
append!(df_C, df_D)
return df_C, df_D_mod
end
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] | 1.910861 | 1,335 |
# ==========================================
# Cell - a single piece of table data
# ==========================================
"""
A `Cell` is a single piece of data annotated by a column name
"""
immutable Cell{Name, ElType}
data::ElType
function Cell{T}(x::T)
check_Cell(Val{Name},ElType)
new(convert(ElType,x))
end
end
@compat @inline (::Type{Cell{Name}}){Name, ElType}(x::ElType) = Cell{Name,ElType}(x)
Base.convert{Name, T1, T2}(::Type{Cell{Name, T2}}, x::Cell{Name,T1}) = Cell{Name,T2}(x.data)
@generated function check_Cell{Name, ElType}(::Type{Val{Name}}, ::Type{ElType})
if !isa(Name, Symbol)
return :(error("Field name $F should be a symbol"))
elseif Name == :Row
return :( error("Field name cannot be :Row") )
elseif !isa(ElType, DataType)
return :(error("ElType $ElType should be a data type"))
else
return nothing
end
end
@compat Base.:(==){Name}(cell1::Cell{Name}, cell2::Cell{Name}) = (cell1.data == cell2.data)
@inline rename{Name1, Name2, ElType}(x::Cell{Name1, ElType}, ::Type{Val{Name2}}) = Cell{Name2, ElType}(x.data)
@inline name{Name,ElType}(::Cell{Name,ElType}) = Name
@inline name{Name}(::Type{Cell{Name}}) = Name
@inline name{Name,ElType}(::Type{Cell{Name,ElType}}) = Name
@inline Base.eltype{Name,ElType}(::Cell{Name,ElType}) = ElType
@inline Base.eltype{Name,ElType}(::Type{Cell{Name,ElType}}) = ElType
@inline Base.length{Name,ElType}(::Cell{Name,ElType}) = 1
@inline Base.length{Name,ElType}(::Type{Cell{Name,ElType}}) = 1
@inline nrow(::Cell) = 1
@inline ncol(::Cell) = 1
@inline ncol{C <: Cell}(::Type{C}) = 1
Base.getindex{Name}(c::Cell{Name}, ::Type{Val{Name}}) = c.data
Base.getindex{Name1, Name2}(c::Cell{Name1}, ::Type{Val{Name2}}) = error("Tried to index cell of field name :$Name1 with field name :$Name2")
Base.start(c::Cell) = false # Similar iterators as Julia scalars
Base.next(c::Cell, i::Bool) = (c.data, true)
Base.done(c::Cell, i::Bool) = i
Base.endof(c::Cell) = 1
Base.getindex(c::Cell) = c.data
Base.getindex(c::Cell, i::Integer) = ((i == 1) ? c.data : throw(BoundsError())) # This matches the behaviour of other scalars in Julia
Base.getindex(c::Cell, ::Colon) = c
Base.copy{F,ElType}(cell::Cell{F,ElType}) = Cell{F,ElType}(copy(cell.data))
# @Column and @Cell are very similar
macro Cell(expr)
if expr.head != :(=) && expr.head != :(kw) # strange Julia bug, see issue 7669
error("A Expecting expression like @Cell(name::Type = value) or @Cell(name = value)")
end
local field
value = expr.args[2]
if isa(expr.args[1], Symbol)
name = expr.args[1]
return :( TypedTables.Cell{$(QuoteNode(name))}($(esc(value))) )
elseif isa(expr.args[1],Expr)
if expr.args[1].head != :(::) || length(expr.args[1].args) != 2 || !isa(expr.args[1].args[1], Symbol)
error("B Expecting expression like @Cell(name::Type = value) or @Cell(name = value)")
end
name = expr.args[1].args[1]
eltype = expr.args[1].args[2]
field = :( TypedTables.Cell{$(QuoteNode(name)), $(esc(eltype))}($(esc(value))) )
else
error("C Expecting expression like @Cell(name::Type = value) or @Cell(name = value)")
end
end
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4049,
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278,
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7,
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2488,
28780,
7,
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796,
1988,
8,
4943,
198,
220,
220,
220,
886,
198,
437,
198
] | 2.448485 | 1,320 |
export Portfolio
function Portfolio(cbmo::Float64,mu::Vector,
Q::Any,u::Vector{Float64},
kappa::Int64,z0::Vector{Float64}
;relax::Function=NL,
t::Float64=0.0,
n::Int64=5,
tol::Float64=1e-6)
c1=z->sum(z[1:n]);lc1=1.0;uc1=1.0;
c2=z->sum(z[n+1:2*n]);lc2=n-kappa;uc2=Inf;
c3=z->z[1:n].*z[n+1:2*n];lc3=-Inf*ones(n);uc3=zeros(n);
#c3=z->sum(z[1:n].*z[n+1:2*n]);lc3=-Inf;uc3=zeros(1);
c3=z->relax(z[1:n],z[n+1:2*n],t=t);lc3=-Inf*ones(n);uc3=zeros(n);
cons=z->vcat(c1(z),c2(z),c3(z));
fobj=z->cbmo*sqrt(z[1:n]'*Q*z[1:n])-z[1:n]'*mu;
lvar=zeros(2*n);uvar=vcat(u,ones(n));
mp=ADNLPModel(fobj, z0, lvar=lvar, uvar=uvar, c=cons, lcon=vcat(lc1,lc2,lc3), ucon=vcat(uc1,uc2,uc3))
return mp
end
function Portfolio(cbmo::Float64,mu::Vector,
Q::Any,u::Vector{Float64},
kappa::Int64,z0::Vector{Float64},
tol::Float64=1e-6)
n=size(Q,1)
c1=z->sum(z[1:n]);lc1=1.0;uc1=1.0;
c2=z->sum(z[n+1:2*n]);lc2=n-kappa;uc2=Inf;
c3=z->z[1:n].*z[n+1:2*n];lc3=-Inf*ones(n);uc3=zeros(n);
cons=z->vcat(c1(z),c2(z),c3(z));
fobj=z->cbmo*sqrt(z[1:n]'*Q*z[1:n])-z[1:n]'*mu;
lvar=zeros(2*n);uvar=vcat(u,ones(n));
mp=ADNLPModel(fobj, z0, lvar=lvar, uvar=uvar, c=cons, lcon=vcat(lc1,lc2,lc3), ucon=vcat(uc1,uc2,uc3))
#ex1=JuMP.Model()
#JuMP.@variable(ex1,x[1:2],start=1.0)
#JuMP.@NLobjective(ex1,Min,x[1]-x[2])
#JuMP.@constraint(ex1,1-x[2]>=0)
#ex1=MathProgNLPModel(ex1)
#cG=z->z[1:n]
#G=ADNLPModel(x->(), z0, c=cG, lcon=zeros(n))
#cH=z->z[n+1:2*n]
#H=ADNLPModel(x->(), z0, c=cH, lcon=zeros(n))
G=JuMP.Model()
JuMP.@variable(G,x[1:2*n],start=1.0)
JuMP.@constraint(G,x[1:n].>=0)
JuMP.@NLobjective(G,Min,0.0)
G=MathProgNLPModel(G)
H=JuMP.Model()
JuMP.@variable(H,x[1:2*n],start=1.0)
JuMP.@constraint(H,x[n+1:2*n].>=0)
JuMP.@NLobjective(H,Min,0.0)
H=MathProgNLPModel(H)
return mp,G,H
end
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] | 1.551417 | 1,235 |
function code_js(ex, A)
cinfo = code_lowered(ex, A)[1]
restructure(cinfo.code)
end
macro code_js(ex)
isexpr(ex, :call) || error("@code_wasm f(xs...)")
:(code_js($(esc(ex.args[1])), Tuple{$(map(_ -> Any, ex.args[2:end])...)}))
end
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] | 2.153153 | 111 |
<gh_stars>0
# ---
# title: 1003. Check If Word Is Valid After Substitutions
# id: problem1003
# author: <NAME>
# date: 2020-10-31
# difficulty: Medium
# categories: String, Stack
# link: <https://leetcode.com/problems/check-if-word-is-valid-after-substitutions/description/>
# hidden: true
# ---
#
# Given a string `s`, determine if it is **valid**.
#
# A string `s` is **valid** if, starting with an empty string `t = ""`, you can
# **transform**`t` **into**`s` after performing the following operation **any
# number of times** :
#
# * Insert string `"abc"` into any position in `t`. More formally, `t` becomes `tleft + "abc" + tright`, where `t == tleft + tright`. Note that `tleft` and `tright` may be **empty**.
#
# Return `true` _if_`s` _is a **valid** string, otherwise, return_ `false`.
#
#
#
# **Example 1:**
#
#
#
# Input: s = "aabcbc"
# Output: true
# Explanation:
# "" -> " _abc_ " -> "a _abc_ bc"
# Thus, "aabcbc" is valid.
#
# **Example 2:**
#
#
#
# Input: s = "abcabcababcc"
# Output: true
# Explanation:
# "" -> " _abc_ " -> "abc _abc_ " -> "abcabc _abc_ " -> "abcabcab _abc_ c"
# Thus, "abcabcababcc" is valid.
#
#
# **Example 3:**
#
#
#
# Input: s = "abccba"
# Output: false
# Explanation: It is impossible to get "abccba" using the operation.
#
#
# **Example 4:**
#
#
#
# Input: s = "cababc"
# Output: false
# Explanation: It is impossible to get "cababc" using the operation.
#
#
#
#
# **Constraints:**
#
# * `1 <= s.length <= 2 * 104`
# * `s` consists of letters `'a'`, `'b'`, and `'c'`
#
#
## @lc code=start
using LeetCode
## add your code here:
## @lc code=end
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] | 2.270341 | 762 |
<gh_stars>10-100
export unary_operators, binary_operators, nary_operators, compute
export AbstractExpression
# Define abstract base type
abstract type AbstractExpression end
# Define Algebraic Operators
include("abstract_operations.jl")
# Define Domains
include("abstract_domains.jl")
# Define Fields
include("abstract_fields.jl")
# Define Data
include("abstract_data.jl")
# Define equations and systems
include("abstract_equations.jl")
# Define Wrapper functions and derivative utils
include("abstract_utils.jl")
# compute function
compute(a::AbstractExpression) = throw(error("compute not defined for $(typeof(a))"))
# Include Generic Evaluation Rules and Output Format and Exports
for unary_operator in unary_operators
b_name, b_symbol = Meta.parse.(unary_operator)
@eval compute(a::$b_name{𝒮}) where {𝒮} = $b_symbol(compute(a.term))
@eval function Base.show(io::IO, operation::$b_name{𝒮}) where {𝒮}
print(io, $b_symbol, "(", operation.term, ")")
end
@eval export $b_name
end
for binary_operator in binary_operators
b_name, b_symbol = Meta.parse.(binary_operator)
@eval compute(a::$b_name{𝒮, 𝒯}) where {𝒮, 𝒯} = $b_symbol(compute(a.term1), compute(a.term2))
@eval function Base.show(io::IO, operation::$b_name{𝒮, 𝒯}) where {𝒮, 𝒯}
# print(io, "(", operation.term1, $b_symbol , operation.term2, ")")
# clearly a great option
color_numbers = [30:33, 65:69, 136:142, 202:207]
choices = collect(Iterators.flatten(color_numbers))
color = 226 # rand(choices)
printstyled(io, "(", color = color)
print(io, operation.term1)
printstyled(io, $b_symbol, color = color )
print(io, operation.term2)
printstyled(io, ")", color = color)
end
@eval export $b_name
end
for nary_operator in nary_operators
b_name, b_symbol = Meta.parse.(nary_operator)
@eval compute(a::$b_name{𝒮}) where {𝒮} = $b_symbol(compute.(a.terms)...)
@eval function Base.show(io::IO, operation::$b_name{𝒮}) where {𝒮}
print(io, $b_symbol, "(" )
for term in operation.terms
print(io, term, ",")
end
print(io, "0)")
end
@eval export $b_name
end
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] | 2.372845 | 928 |
# https://adventofcode.com/2020/day/9
# XMAS starts by transmitting a preamble of 25 numbers. After that, each number
# you receive should be the sum of any two of the 25 immediately previous numbers.
# The two numbers will have different values, and there might be more than one
# such pair.
function is_xmas_seq(numbers; preamble_size=25)
number = numbers[end]
preamble = sort(numbers[(end - preamble_size):(end - 1)], rev=true)
@assert length(preamble) == preamble_size
for (i, x) in enumerate(preamble)
if x > number
continue
end
if (number - x) in preamble[(i + 1):end]
return true
end
end
return false
end
@assert is_xmas_seq([1:25; 26])
@assert is_xmas_seq([1:25; 6])
@assert is_xmas_seq([1:25; 49])
@assert !is_xmas_seq([1:25; 100])
@assert !is_xmas_seq([1:25; 50])
@assert !is_xmas_seq([1:25; 2])
function read_array(string::AbstractString)::Vector{Int}
return parse.(Int, split(string, '\n', keepempty=false))
end
example = read_array("
35
20
15
25
47
40
62
55
65
95
102
117
150
182
127
219
299
277
309
576
")
function find_first_invalid(numbers; preamble_size=25)
for pos = (preamble_size + 1):length(numbers)
if !is_xmas_seq(numbers[1:pos], preamble_size=preamble_size)
return numbers[pos], pos
end
end
end
@assert find_first_invalid([1:25; 100]) == (100, 26)
@assert find_first_invalid([1:25; 27; 100]) == (100, 27)
@assert find_first_invalid(example, preamble_size=5) == (127, 15)
function part1(numbers)
number, _ = find_first_invalid(numbers)
return number
end
function sum_until(numbers, target)
tot = numbers[1]
for i = 2:length(numbers)
tot += numbers[i]
if tot == target
return numbers[1:i]
end
end
return nothing
end
@assert sum_until(1:10, 10) == [1:4;]
@assert isnothing(sum_until(1:10, 100))
function find_weak_sequence(numbers; preamble_size=25)
number, pos = find_first_invalid(numbers, preamble_size=preamble_size)
for (i, x) in enumerate(numbers)
if i == pos
break
end
weak_sequence = sum_until(numbers[i:(pos - 1)], number)
if !isnothing(weak_sequence)
return weak_sequence
end
end
end
@assert find_weak_sequence(example, preamble_size=5) == [15, 25, 47, 40]
function part2(numbers; preamble_size=25)
weak_sequence = find_weak_sequence(numbers, preamble_size=preamble_size)
return minimum(weak_sequence) + maximum(weak_sequence)
end
@assert part2(example, preamble_size=5) == 62
test = read_array(read("data/day-09.txt", String))
println("Part 1: $(result1 = part1(test))")
println("Part 2: $(result2 = part2(test))")
@assert result1 == 25918798
@assert result2 == 3340942
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] | 2.343434 | 1,188 |
<reponame>JuliaGPU/VulkanAbstraction<gh_stars>0
is_optional(member::SpecStructMember) = member.name == :pNext || member.requirement ∈ [OPTIONAL, POINTER_OPTIONAL] || is_inferable_length(member)
is_optional(param::SpecFuncParam) = param.requirement ∈ [OPTIONAL, POINTER_OPTIONAL]
"""
Represent an integer that gives the start of a C pointer.
"""
function is_pointer_start(spec::Spec)
params = children(parent_spec(spec))
any(params) do param
!isempty(param.arglen) &&
spec.type == :UInt32 &&
string(spec.name) == string("first", uppercasefirst(replace(string(param.name), r"Count$" => "")))
end
end
is_semantic_ptr(type) = is_ptr(type) || type == :Cstring
needs_deps(spec::SpecStruct) = any(is_semantic_ptr, spec.members.type)
must_return_success_code(spec::SpecFunc) = length(spec.success_codes) > 1 && :VK_INCOMPLETE ∉ spec.success_codes
must_repeat_while_incomplete(spec::SpecFunc) = !must_return_success_code(spec) && :VK_INCOMPLETE ∈ spec.success_codes
is_data_with_retrievable_size(spec::SpecFuncParam) = is_data(spec) && len(spec).requirement == POINTER_REQUIRED
is_opaque_data(spec) = is_data(spec) && len(spec).requirement ≠ POINTER_REQUIRED
is_opaque_pointer(type) = is_ptr(type) && is_void(ptr_type(type))
is_opaque_pointer(spec::Spec) = is_opaque_pointer(spec.type)
is_opaque(spec) = is_opaque_data(spec) || is_opaque_pointer(spec)
is_implicit_return(spec::SpecFuncParam) =
!is_opaque_data(spec) &&
!spec.is_constant &&
is_ptr(spec.type) &&
!is_length(spec) &&
spec.type ∉ extension_types &&
ptr_type(spec.type) ∉ extension_types
has_implicit_return_parameters(spec::SpecFunc) = any(is_implicit_return, children(spec))
is_flag(type) = type in spec_flags.name
is_flag(spec::Union{SpecFuncParam,SpecStructMember}) = spec.type in spec_flags.name
is_flag_bitmask(type) = type ∈ getproperty.(filter(!isnothing, spec_flags.bitmask), :name)
is_fn_ptr(type) = startswith(string(type), "PFN_")
is_fn_ptr(spec::Spec) = is_fn_ptr(spec.type)
function is_hl(type)
vktype = Symbol(:Vk, type)
vktype in [spec_structs.name; spec_unions.name]
end
is_intermediate(type) = startswith(string(type), '_')
has_intermediate_type(::SpecHandle) = false
has_intermediate_type(spec::Union{SpecStruct,SpecUnion}) = true
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] | 2.572072 | 888 |
<gh_stars>1-10
include("params.jl")
include("run_rtaa.jl")
include("run_cmax.jl")
include("run_finite_model_class.jl")
include("run_model_search.jl")
include("run_agnostic_sysid.jl")
include("run_online_model_search.jl")
function mountaincar_all_main()
rtaa_steps = mountaincar_rtaa_main()
cmax_steps = mountaincar_cmax_main()
true_steps = mountaincar_true_main()
# finite_model_class_steps = mountaincar_finite_model_class_main()
# true_finite_model_class_steps = mountaincar_finite_model_class_main(true_model = true)
# local_finite_model_class_steps = mountaincar_finite_model_class_main(local_agent = true)
return_based_model_search_steps = mountaincar_return_based_model_search_main()
bellman_based_model_search_steps = mountaincar_bellman_based_model_search_main()
plot(range_of_values, rtaa_steps, lw = 3, label = "RTAA*", legend = :topleft)
plot!(range_of_values, cmax_steps, lw = 3, label = "CMAX")
plot!(range_of_values, true_steps, lw = 3, label = "True")
# plot!(range_of_values, finite_model_class_steps, lw = 3, label = "Finite Model Class")
# plot!(
# range_of_values,
# true_finite_model_class_steps,
# lw = 3,
# label = "Finite Model Class with true model",
# )
# plot!(
# range_of_values,
# local_finite_model_class_steps,
# lw = 3,
# label = "Finite Model Class with Local Data",
# )
plot!(range_of_values, return_based_model_search_steps, lw = 3, label = "RBMS")
plot!(range_of_values, bellman_based_model_search_steps, lw = 3, label = "BBMS")
xlabel!("Misspecification")
ylabel!("Number of steps to reach goal")
end
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] | 2.444122 | 689 |
<filename>JuliaBasics.jl
# to create vector, put comma
V = [1, 2, 3, 4, 5, 6]
# to create matrix just add space
V1 = [1 2 3 4 5 6]
# to declare an array just put semicolon
M1 = [1 2 3; 4 5 6; 7 8 9]
# to resahepe vector or matrix just use resahepe()
V3 = [1, 2, 3, 4, 5, 6, 7, 8, 9]
reshape(V3, 3,3) # for vector
M2 = [1 2 3; 4 5 6]
reshape(M2, 3, 2) # for matrix 2x3 to 3x2
# to create identitiy matirx
using LinearAlgebra
Matrix{Int}(I, 4, 4)
# for dimensions and size of matrix use ndims() and size(), and to see type of elements in matrix using eltype()
ndims(M2)
size(M2)
eltype(M1)
# to concatenate two vector use vcat (similar to cbind() in R) and use hcat similar to (rbind() in R)
d1 = [1 2 3; 4 5 6]
d2 = [6 7 8; 9 10 11]
hcat(d1, d2)
vcat(d1, d2)
# functions in julia
function f(x, y, z)
x + y + z
end
# or just assign
g(a, b, c) = a + b + c
# function with return
function β(a, b, c)
result = a + b + c
return result
end
β(2,π,3)
# we can use opetator as function
2 * 3 * 4
*(2,3,4)
# function with default parameters
z(x, y, a = 1, b = 2) = a*x + b*y
z(3,5)
# Anonymous function
x -> x + 1 # or
function (x)
x +1
end
# Anonymous function generally useing with other first class function
map(x -> x + 1, [1, 2])
# multiple return values
function ff( a, b, c)
a * b * c, a + b + c
end;
ff(1, 2, 3)
x, y = ff(1, 2, 3);
display(x)
display(y)
function fff(a, b, c)
multi = a * b * c
add = a + b + c
return multi, add
end
fff(1, 2, 3)
# using Anonymous function with multiple piece of code, do-blocks will help
map(x -> x + 1, [1, 2, 3, 4]) # basic way
map([1, 2, 3, 4]) do x # other way
x + 1
end
# control flow
# compound expression
x = (a = 5; b = 6; a*b)
# other way
x = begin
a = 5
b = 6
a * b
end
# conditional evaluation
function ineq(x, y)
if x > y
x = 2
elseif x < y
x = 1
else
x = 0
end
end
ineq(1, 2)
# repeated evaluatins: loops
j = 3;
while j > 0
println(j^2)
global j -= 1
end
# for loops
for j in [1, 2, 3]
println(j)
end
# we can add ∈ to loops
for j ∈ [1, 2, 3]
println(j)
end
# warning and informational messages
function f(x)
if x<1
@warn "x must be positive!"
else
return x^2
end
end
f(2)
function g(x)
if x < 1
@info "X must be positive!"
else
return x^2
end
end
g(-2)
# Plotting
using PyPlot
x = 1:100
y = rand(100)
display(gcf())
plot(x,y, color = "tomato")
title("basic graphs in PyPlot")
| [
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] | 2.13278 | 1,205 |
<filename>src/sum0.jl
function sum0(a)
# create variable to store ouput
out = 0.0
# loop
for i in a
out += i
end
# return
return out
end | [
27,
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29,
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220,
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] | 2.148148 | 81 |
using Test
using NeXLCore
@testset "MIP" begin
@test all(map(z -> isapprox(J(Bloch1933, z) / z, 13.5, rtol = 1.0e-8), 1:99))
@test all(
map(
z -> isapprox(
J(Jensen1937, z) / z,
9.0 * (1.0 + 0.5 * z^(-2.0 / 3.0)),
rtol = 1.0e-8,
),
1:99,
),
)
@test all(map(z -> isapprox(J(Wilson1941, z) / z, 11.5, rtol = 1.0e-8), 1:99))
@test all(
map(
z -> isapprox(J(Sternheimer1964, z) / z, 9.76 + 58.82 * z^-1.19, rtol = 1.0e-8),
12:99,
),
)
@test all(
map(
z -> isapprox(
J(Springer1967, z) / z,
9.0 * (1.0 + z^(-2.0 / 3.0)) + 0.03 * z,
rtol = 1.0e-8,
),
1:99,
),
)
@test all(
map(
z -> isapprox(
J(Zeller1973, z) / z,
10.04 + 8.25 * exp(-z / 11.22),
rtol = 1.0e-8,
),
1:99,
),
)
@test all(
map(z -> isapprox(J(Brizuela1990, z) / z, 22.4 * z^-0.172, rtol = 1.0e-8), 1:99),
)
@test J(Berger1982, n"C") == 78.0
@test J(Berger1982, n"Fe") == 286.0
@test J(Berger1982, n"Pb") == 823.0
@test J(Berger1982, n"Pu") == 921.0
end
| [
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] | 1.496045 | 885 |
# ------------------------------------------------------------------------------------------
# # 多重派发
#
# **多重派发**是这篇notebook要探索的Julia的一个关键特性。
#
# 它有助于快速开发。它也让软件可拓展、可设计和更好把玩。
#
# 它可能是并行计算取得重大突破的预兆。
#
# ## 大纲
# 1. 罗马数字
# 2. 函数
# 3. 并行计算
# ------------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------------
# ## 1. 罗马数字（玩玩）
# ------------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------------
# 我们来定义一个**新结构**表示罗马数字。为了方便写代码，我们将只考虑 0 到 9。
#
# **练习**：拓展到更大的数字。（别忘了罗马数字是十进制计数系统！）
# ------------------------------------------------------------------------------------------
struct Roman
n::Int
end
Base.show(io::IO, r::Roman) = print(io, 'ⅰ' + (r.n - 1) % 10 ) # 漂亮的显示；'ⅰ'是一个Unicode罗马数字
# ------------------------------------------------------------------------------------------
# 我们可以像这样创建一个这个类型的对象：
# ------------------------------------------------------------------------------------------
Roman(4)
typeof.([5 5.0 Roman(5) "Five" '5' 5//1])
# ------------------------------------------------------------------------------------------
# 用罗马数字漂亮地打印数组：
# ------------------------------------------------------------------------------------------
x = [7 1 2 5 8 9]
Roman.(x) # equivalent to map(Roman, x) or [Roman(w) for w in x]
# ------------------------------------------------------------------------------------------
# 要是能像普通数字那样给罗马数字做加法就好了：
# ------------------------------------------------------------------------------------------
Roman(4) + Roman(5)
# ------------------------------------------------------------------------------------------
# 但 Julia 不知道该怎么做。
# 我们可以通过 `import` 函数 `+` 并拓展它的定义来教 Julia 给罗马数字做加法：
# ------------------------------------------------------------------------------------------
import Base: +, *
+(a::Roman, b::Roman) = Roman(a.n + b.n)
Roman(4) + Roman(5)
# ------------------------------------------------------------------------------------------
# 这样就给函数 `+` **添加了一个新方法**：
# ------------------------------------------------------------------------------------------
methods(+)
@which Roman(4) + Roman(5)
import Base.*
*(i::Roman, j::Roman) = Roman(i.n * j.n) # Multiply like a Roman
Roman(3) * Roman(2)
Roman.(1:3) .* [Roman(1) Roman(2) Roman(3)]
# ------------------------------------------------------------------------------------------
# 但是我们的乘法仍有一些问题
# ------------------------------------------------------------------------------------------
Roman(3) * 2
# mytimes 函数目前基于类型来判断该做什么，很复杂
# 不用担心，更好的方法马上就来
function mytimes(i,j)
if isa(i,Roman) & isa(j,Number)
return fill(1, i.n, j) # i by j matrix with ones
elseif isa(i,Number) & isa(j,Roman)
return "😄"^ (i*j.n) # i * j happy faces
else
return("I Don't know")
end
end
mytimes(4,Roman(3)) # 12个笑脸
mytimes(Roman(4),3) # 4x3的全1矩阵
# ------------------------------------------------------------------------------------------
# 最简单的实现方式是明确定义一个 `Roman` 和一个数字的乘法。我们可以按我们的想法来定义：
# ------------------------------------------------------------------------------------------
*(i::Number, j::Roman) = "😄"^ (i*j.n) # i * j个笑脸
*(i::Roman, j::Number) = fill(1, i.n, j) # i * j的矩阵
3 * Roman(3) # 9个笑脸
Roman(3) * 5 # 3*5的全1矩阵
t(x::Roman,y::Roman) = x.n * y.n
t(Roman(5),Roman(4))
# 注意它的汇编代码是多紧凑！
@code_native t(Roman(2),Roman(4))
# ------------------------------------------------------------------------------------------
# ## 函数
# ------------------------------------------------------------------------------------------
import Base: *, +, ^
*(α::Number, g::Function) = x -> α * g(x) # 标量乘以函数
*(f::Function, λ::Number) = x -> f(λ * x) # Scale the argument
*(f::Function, g::Function) = x -> f(g(x)) # 复合函数 -- 滥用符号！ 在Julia0.6中使用 \circ
^(f::Function, n::Integer) = n == 1 ? f : f*f^(n-1) # 一个通过递归乘法实现的天真的求幂算法
+(f::Function, g::Function) = x -> f(x) + g(x)
# ------------------------------------------------------------------------------------------
# 举个例子，定义成这样的指数函数
#
# $$\exp(x) = \sum_{n=0}^\infty \frac{1}{n!} x^n.$$
#
# 我们可以把它看成是这样的函数：
#
# $$\exp = \sum_{n=0}^\infty \frac{1}{n!} \mathrm{pow}_n,$$
#
# 其中 $\mathrm{pow}_n(x) = x^n$.
#
# （开始用数字模糊符号！）
# ------------------------------------------------------------------------------------------
pow(n) = x -> x^n
myexp = sum(1/factorial(big(n)) * pow(n) for n in 0:100) # 效率低的泰勒级数！
[myexp(1); exp(1); exp(big(1))]
f = x -> x^2
f(10)
g = 3f
g(10)
(f^2)(10) # 因为我们已经定义了函数乘法为符合函数
using Plots;
gr()
x = pi*(0:0.001:4)
plot( x, sin.(x), c="black", label="Fun")
plot!(x, (12*sin).(x), c="green", label="Num * Fun")
plot!(x, (sin*12).(x), c="red", alpha=0.9, label="Fun * Num")
plot!(x, (5*sin*exp).(x), c="blue", alpha=0.2, label="Num * Fun * Fun")
plot([12*sin, sin*12, 5*sin*exp], 0:.01:4π, α=[1 .9 .2], c=[:green :red :blue])
# ------------------------------------------------------------------------------------------
# > “我很讨厌 $sin^2 \phi$，虽然 Laplace 用过它；
# > 应该要担心 $sin^2 \phi$ 可能会引起歧义，如果说 $sin(\phi^2)$ 就不会或者说几乎不会引起歧义了，那么我们应该写 $(sin \phi)^2$，而不是
# $sin^2 \phi$，以此类推 $sin^2 \phi$应该指的是 $sin(sin \phi)$。”
# >
# > —— Gauss
# ------------------------------------------------------------------------------------------
x=(0:.01:2) * pi;
plot(x, (sin^2).(x), c="blue") # 乘方能工作，y=sin(sin(x))，Gauss会开心的！
plot!(x, sin.(x).^2, c="red")
# ------------------------------------------------------------------------------------------
# ## 练习
# ------------------------------------------------------------------------------------------
h(a, b::Any) = "fallback"
h(a::Number, b::Number) = "a and b are both numbers"
h(a::Number, b) = "a is a number"
h(a, b::Number) = "b is a number"
h(a::Integer, b::Integer) = "a and b are both integers"
# 试着把玩 h，尝试使用 h 的 5 种方法
# 在此作答
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2,
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250,
101,
29826,
97,
43291,
163,
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198
] | 2.029293 | 2,970 |
<gh_stars>1-10
module ImageNoise
using Reexport
include("NoiseAPI/NoiseAPI.jl")
include("ApplyNoise/ApplyNoise.jl")
include("ReduceNoise/ReduceNoise.jl")
@reexport using .ApplyNoise
@reexport using .ReduceNoise
end # module
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] | 2.590909 | 88 |
using CompileBot
snoop_bench(BotConfig("RecipesPipeline"))
| [
3500,
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] | 2.727273 | 22 |
module Synthesize
using Random, Distributions
export musicFromNotes
export Music
export default, flute, flute2, organ
# instrument profiles (just a bunch of random numbers, needs revising)
default = Dict{Rational, Float64}(1 => 1)
flute = Dict{Rational, Float64}(1//2 => 1, 1 => 10^0.7, 2 => 10^0.6, 3 => 10^0.6, 4 => 10^-0.1, 5 => 1, 6 => 10^-0.3, 7 => 10^-0.7, 8 => 10^-0.7)
flute2 = Dict{Rational, Float64}(1//2 => 2^-8, 1 => 2^-7, 2 => 2^-1.4, 3 => 2^-6, 4 => 2^-1.8, 5 => 2^-6.7, 6 => 2^-3.3, 7 => 2^-6.6, 8 => 2^-3.4, 10 => 2^-4.6, 12 => 2^-5.1, 14 => 2^-5.9, 16 => 2^-5.8, 18 => 2^-6.4, 26 => 2^-7)
organ = Dict{Rational, Float64}(1//16 => 0.5, 1//4 => 2, 1//2 => 0.2, 1 => 6, 2 => 0.2, 3 => 0.1, 4 => 4, 5 => 0.1, 6 => 0.1, 8 => 2, 12 => 1, 16 => 0.5, 20 => 0.2)
# generate sound with given frequency (Hz), duration (s), and instrument profile
function genSound(freq :: Real, duration :: Real, harmonics :: Dict{<:Rational, <:Real}) :: Array{Float64,1}
data = Array{Float64, 1}(undef, Int64(floor(duration * 44100)))
Random.seed!(123)
d = Normal()
multiplier = repeat([1.0], length(data))
# first and last n signals are decaying to avoid sudden bumps in signal
decayLength = 500
multiplier[1:decayLength] = (1:decayLength) / decayLength
multiplier[end+1-decayLength:end] = (decayLength:-1:1) / decayLength
t = (1:length(data)) / 44100
data = 1000 * sum(h -> h[2] * sin.(h[1] * freq * 2 * π * t), harmonics)
noise = (freq != 0 ? 20 : 0) * rand(d, length(data))
return (data + noise) .* multiplier
end
# generate given duration of silence
silence(duration :: Real) = repeat([0], Int64(floor(duration * 44100)))
# list of common note frequencies
freqlist = Dict("0" => 0, "C" => 440.0 * 2^(-9/12), "C#" => 440.0 * 2^(-8/12), "D" => 440.0 * 2^(-7/12),
"D#" => 440.0 * 2^(-6/12), "E" => 440.0 * 2^(-5/12), "F" => 440.0 * 2^(-4/12),
"F#" => 440.0 * 2^(-3/12), "G" => 440.0 * 2^(-2/12), "G#" => 440.0 * 2^(-1/12),
"A" => 440.0, "A#" => 440.0 * 2^(1/12), "H" => 440.0 * 2^(2/12))
struct Music
notes :: String
speed :: Number
end
# generate a music from list of notes in a string, with given instrument profile
function musicFromNotes(music :: Music, harmonics :: Dict{<:Rational, <:Real}) :: Array{Float64,1}
notelist = split(music.notes)
sound = []
for note in notelist
duration = 1
notedata = split(note, "-")
if length(notedata) > 1
duration = parse(Float64, notedata[2])
end
octave = count(isequal('\''), notedata[1])
notedata[1] = split(notedata[1], "'")[1]
# append sound to current signal
sound = vcat(sound, genSound(freqlist[notedata[1]] * 2^octave, duration*60/music.speed, harmonics))
# append a short silence after note
sound = vcat(sound, silence(0.05*duration*60/music.speed))
end
return sound
end
end
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] | 2.281931 | 1,284 |
############################################################################################
"""
$(TYPEDEF)
Default arguments for PMCMC constructor.
# Fields
$(TYPEDFIELDS)
"""
struct ParticleGibbs{P<:ParticleFilter,M<:MCMC} <: PMCMCKernel
"Particle Filter kernel to estimate latent trajectory."
pf::P
"MCMC kernel to sample continuous model parameter."
mcmc::M
function ParticleGibbs(pf::P, mcmc::M) where {P<:ParticleFilter,M<:MCMC}
@argcheck !isa(pf.tune.referencing, Marginal) "Cannot use Marginal Filter in Conditional setting, use Ancestral or Conditional referencing"
return new{P,M}(pf, mcmc)
end
end
function ParticleGibbs(
filter::F, mcmc::M; kwargs...
) where {F<:ParticleFilterConstructor,M<:MCMCConstructor}
return PMCMC(ParticleGibbs, filter, mcmc; kwargs...)
end
############################################################################################
"""
$(SIGNATURES)
Propose new parameter with mcmc psampler. If update=true, objective function will be updated with input model and data.
# Examples
```julia
```
"""
function propose!(
_rng::Random.AbstractRNG,
pmcmc::ParticleGibbs,
model::ModelWrapper,
data::D,
temperature::F = model.info.reconstruct.default.output(1.0),
update::U=BaytesCore.UpdateTrue(),
) where {D,F<:AbstractFloat, U<:BaytesCore.UpdateBool}
## Get trajectory via PF - always update data.latent in model
_, pf_diagnostics = propose!(_rng, pmcmc.pf, model, data, temperature, update)
## Propose new θₜ - if accepted, model is updated accordingly
_, mcmc_diagnostics = propose!(_rng, pmcmc.mcmc, model, data, temperature, update)
## Assign base diagnostics - ℓobjective and predictions are taken from particle filter
diagnostics = BaytesCore.BaseDiagnostics(
pf_diagnostics.base.ℓobjective, pf_diagnostics.base.temperature, pf_diagnostics.base.prediction,
mcmc_diagnostics.base.iter
)
## Return pmcmc output
return model.val, PMCMCDiagnostics(diagnostics, pf_diagnostics, mcmc_diagnostics)
end
############################################################################################
#export
export ParticleGibbs, propose!
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] | 2.925433 | 751 |
<gh_stars>0
# ---
# title: 363. Max Sum of Rectangle No Larger Than K
# id: problem363
# author: <NAME>
# date: 2020-10-31
# difficulty: Hard
# categories: Binary Search, Dynamic Programming, Queue
# link: <https://leetcode.com/problems/max-sum-of-rectangle-no-larger-than-k/description/>
# hidden: true
# ---
#
# Given a non-empty 2D matrix _matrix_ and an integer _k_ , find the max sum of
# a rectangle in the _matrix_ such that its sum is no larger than _k_.
#
# **Example:**
#
#
#
# Input: matrix = [[1,0,1],[0,-2,3]], k = 2
# Output: 2
# Explanation: Because the sum of rectangle [[0, 1], [-2, 3]] is 2,
# and 2 is the max number no larger than k (k = 2).
#
# **Note:**
#
# 1. The rectangle inside the matrix must have an area > 0.
# 2. What if the number of rows is much larger than the number of columns?
#
#
## @lc code=start
using LeetCode
## add your code here:
## @lc code=end
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] | 2.559783 | 368 |
<reponame>MattWillFlood/EntropyHub.jl<filename>src/_MSEn.jl<gh_stars>1-10
module _MSEn
export MSEn, EMD
using Statistics: std, mean, median, var
#using DataInterpolations: CubicSpline
using Dierckx: Spline1D
using Plots
#=function __init__()
@warn("\n\n Methodx option IMF (Intrinisic Mode Function) is not stable.
Random or highly aperiodic signals may not decompose fully.
Access to the IMFs decomposed by the empirical mode decomposition (EMD) function
can be found by calling EntropyHub.EMD(`Sig`,`MaxIMFs`).
A stable EMD function will be included in future releases.\n\n")
end=#
"""
MSx, CI = MSEn(Sig, Mobj)
Returns a vector of multiscale entropy values `MSx` and the complexity
index `CI` of the data sequence `Sig` using the parameters specified
by the multiscale object `Mobj` over 3 temporal scales with coarse-
graining (default).
MSx, CI = MSEn(Sig::AbstractArray{T,1} where T<:Real, Mobj::NamedTuple; Scales::Int=3,
Methodx::String="coarse", RadNew::Int=0, Plotx::Bool=false)
Returns a vector of multiscale entropy values `MSx` and the complexity
index `CI` of the data sequence `Sig` using the parameters specified by
the multiscale object `Mobj` and the following 'keyword' arguments:
# Arguments:
`Scales` - Number of temporal scales, an integer > 1 (default: 3) \n
`Method` - Graining method, one of the following:
{`coarse`,`modified`,`imf`,`timeshift`} [default = `coarse`]
For further info on these graining procedures, see the EntropyHub guide. \n
`RadNew` - Radius rescaling method, an integer in the range [1 4].
When the entropy specified by `Mobj` is `SampEn` or `ApEn`,
RadNew allows the radius threshold to be updated at each
time scale (Xt). If a radius value is specified by `Mobj` (`r`),
this becomes the rescaling coefficient, otherwise it is set
to 0.2 (default). The value of RadNew specifies one of the
following methods:\n
[1] Standard Deviation - r*std(Xt)\n
[2] Variance - r*var(Xt) \n
[3] Mean Absolute Deviation - r*mean_ad(Xt) \n
[4] Median Absolute Deviation - r*med_ad(Xt)\n
`Plotx` - When Plotx == true, returns a plot of the entropy value at each
time scale (i.e. the multiscale entropy curve) [default: false]\n
`For further info on these graining procedures see the EntropyHub guide.`
# See also `MSobject`, `rMSEn`, `cMSEn`, `hMSEn`, `SampEn`, `ApEn`, `XMSEn`
# References:
[1] <NAME>, <NAME>, and <NAME>,
"Multiscale entropy analysis of complex physiologic time series."
Physical review letters
89.6 (2002): 068102.
[2] <NAME>, and <NAME>,
"Comment on “Multiscale entropy analysis of complex physiologic
time series”."
Physical review letters
92.8 (2004): 089803.
[3] <NAME>, <NAME>, and <NAME>.
"<NAME>, and Peng reply."
Physical Review Letters
92.8 (2004): 089804.
[4] <NAME>, <NAME> and <NAME>,
"Multiscale entropy analysis of biological signals."
Physical review E
71.2 (2005): 021906.
[5] <NAME> and <NAME>,
"On multiscale entropy analysis for physiological data."
Physica A: Statistical Mechanics and its Applications
366 (2006): 323-332.
[6] <NAME> and <NAME>,
"Intrinsic mode entropy based on multivariate empirical mode
decomposition and its application to neural data analysis."
Cognitive neurodynamics
5.3 (2011): 277-284.
[7] <NAME>
"The multiscale entropy algorithm and its variants: A review."
Entropy
17.5 (2015): 3110-3123.
[8] <NAME>, et al.,
"Multiscale entropy analysis of biological signals: a
fundamental bi-scaling law."
Frontiers in computational neuroscience
9 (2015): 64.
[9] <NAME>, et al.,
"Multiscale Sample Entropy of cardiovascular signals: Does the
choice between fixed-or varying-tolerance among scales
influence its evaluation and interpretation?."
Entropy
19.11 (2017): 590.
[10] <NAME>,
"Time-shift multiscale entropy analysis of physiological signals."
Entropy
19.6 (2017): 257.
[11] <NAME> and <NAME>,
"Coarse-graining approaches in univariate multiscale sample
and dispersion entropy."
Entropy 20.2 (2018): 138.
"""
function MSEn(Sig::AbstractArray{T,1} where T<:Real, Mobj::NamedTuple; Scales::Int=3,
Methodx::String="coarse", RadNew::Int=0, Plotx::Bool=false)
(size(Sig,1)>10) ? nothing : error("Sig: must be a numeric vector" )
(length(Mobj) >= 1) ? nothing : error("Mobj: must be a multiscale entropy object created
with the function EntropyHub.MSobject")
(Scales>1) ? nothing : error("Scales: must be an integer > 1")
(lowercase(Methodx) in ["coarse","modified","imf","timeshift"]) ? nothing :
error("Method: must be one of the following string names -
'coarse','modified','imf','timeshift'")
(RadNew==0 || (RadNew in 1:4 && String(Symbol(Mobj.Func)) in ("SampEn","ApEn"))) ? nothing :
error("RadNew: must be 0, or an integer in range [1 4] with
entropy function 'SampEn' or 'ApEn'")
if lowercase(Methodx)=="imf"
Sig,_ = EMD(Sig,Scales-1)
sum(all(Sig.==0,dims=2))==0 ? nothing : Sig = Sig[all(Sig.!=0,dims=2),:]
Scales >= size(Sig,1) ? nothing :
@warn("Max number of IMF's decomposed from EMD is less than number of Scales.
MSEn evaluated over $(size(Sig,1)) scales instead of $Scales.")
Scales = size(Sig,1)
end
MSx = zeros(Scales)
Args = NamedTuple{keys(Mobj)[2:end]}(Mobj)
Func2 = getfield(_MSEn,Symbol(lowercase(Methodx)))
if RadNew > 0
if RadNew == 1
Rnew = x -> std(x, corrected=false)
elseif RadNew == 2
Rnew = x -> var(x, corrected=false)
elseif RadNew == 3
Rnew = x -> mean(abs.(x .- mean(x)))
elseif RadNew == 4
Rnew = x -> median(abs.(x .- median(x)))
end
if haskey(Mobj,:r)
Cx = Mobj.r
else
Cy = ("Standard Deviation","Variance","Mean Abs Deviation",
"Median Abs Deviation")
@warn("No radius value provided in Mobj.
Default set to 0.2*$(Cy[RadNew]) of each new time-series.")
Cx = .2
end
end
for T = 1:Scales
print(". ")
Temp = Func2(Sig,T)
if lowercase(Methodx) == "timeshift"
Tempx = zeros(T)
for k = 1:T
RadNew > 0 ? Args = (Args..., r=Cx*Rnew(Temp[k,:])) : nothing
Tempy = Mobj.Func(Temp[k,:]; Args...)
typeof(Tempy)<:Tuple ? Tempx[k] = Tempy[1][end] : Tempx[k] = Tempy[end]
# Tempx[k] = Tempy[end]
end
Temp2 = mean(Tempx)
else
RadNew > 0 ? Args = (Args..., r=Cx*Rnew(Temp[:])) : nothing
Tempx = Mobj.Func(Temp[:]; Args...)
typeof(Tempx)<:Tuple ? Temp2 = Tempx[1][end] : Temp2 = Tempx[end]
#Temp2 = Tempx[1][end]
end
MSx[T] = Temp2
end
CI = sum(MSx)
print("\n")
if any(isnan.(MSx))
println("Some entropy values may be undefined.")
end
if Plotx
p1 = plot(1:Scales, MSx, c=RGB(8/255, 63/255, 77/255), lw=3)
scatter!(1:Scales, MSx, markersize=6, c=RGB(1, 0, 1),
xlabel = "Scale Factor", ylabel = "Entropy Value",
guidefont = font(12, "arial", RGB(7/255, 54/255, 66/255)),
tickfontsize = 10, tickfontfamily="arial", legend=false,
title = "Multiscale $(Mobj.Func) ($(titlecase(Methodx))-graining method)",
plot_titlefontsize=16, plot_titlefontcolor=RGB(7/255, 54/255, 66/255)) #ylim=(0,maximum(MSx)+.2),
display(p1)
end
return MSx, CI
end
function coarse(Z,sx)
Ns = Int(floor(size(Z,1)/sx))
Y = mean(reshape(Z[1:sx*Ns],sx,Ns),dims=1)
return Y
end
function modified(Z,sx)
Ns = size(Z,1) - sx + 1
Y = zeros(Ns)
for k = 1:Ns
Y[k] = mean(Z[k:k+sx-1])
end
return Y
end
function imf(Z,sx)
Y = sum(Z[1:sx,:],dims=1)
return Y
end
function timeshift(Z,sx)
Y = reshape(Z[1:Int(sx*floor(length(Z)/sx))],
(sx,Int(floor(length(Z)/sx))))
return Y
end
function PkFind(X)
Nx = length(X)
Indx = zeros(Int,Nx);
for n = 2:Nx-1
if X[n-1]< X[n] > X[n+1]
Indx[n] = n
elseif X[n-1] < X[n] == X[n+1]
k = 1
Indx[n] = n
while (n+k)<Nx && X[n] == X[n+k]
Indx[n+k] = n+k
k+=1
end
n+=k
end
end
Indx = Indx[Indx.!==0]
return Indx
end
function EMD(X, Scales::Int)
Xt = copy(X); N = size(Xt,1); n=1; IMFs = zeros(Scales+1,N)
MaxER = 20; MinTN = 2; #Xt .-= mean(Xt)
r1 = Xt
while n <= Scales
r0 = Xt
x = 0;
Upx = PkFind(r0); Lwx = PkFind(-r0)
UpEnv = Spline1D(Upx,r0[Upx],k=3,bc="nearest")
LwEnv = Spline1D(Lwx,r0[Lwx],k=3,bc="nearest")
r1 = r0.- (UpEnv.(1:N) .+ LwEnv.(1:N))./2
RT = (sum(r0.*r0) - sum(r1.*r1))/sum(r0.*r0)
length(vcat(Upx,Lwx)) <= MinTN ? (LOG = "Decomposition hit minimal extrema criteria."; break) : nothing
while x < 100 && RT > 0.2
r0 = 1*r1
Upx = PkFind(r0); Lwx = PkFind(-r0)
UpEnv = Spline1D(Upx,r0[Upx],k=3,bc="nearest")
LwEnv = Spline1D(Lwx,r0[Lwx],k=3,bc="nearest")
r1 = r0.- (UpEnv.(1:N) .+ LwEnv.(1:N))./2
RT = (sum(r0.*r0) - sum(r1.*r1))/sum(r0.*r0)
x += 1;
10*log10(sqrt(sum(r0.*r0))/sqrt(sum(r1.*r1))) > MaxER ?
(LOG = "Decomposition hit energy ratio criteria."; break) : nothing
end
IMFs[n,:] = r1
Xt .-= r1
IMFs[Scales+1,:] = r0 .+ mean(X)
n+=1
end
LOG = "All went well :) "
return IMFs, LOG
end
end
"""
Copyright 2021 <NAME>, EntropyHub
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
For Terms of Use see https://github.com/MattWillFlood/EntropyHub
""" | [
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28338,
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198,
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] | 1.982796 | 5,929 |
<reponame>MisterBiggs/BeautifulMakie<filename>_assets/scripts/BoxErrorBarsCmap.jl<gh_stars>100-1000
# by lazarusA # HIDE
#using GLMakie, Random # HIDE
using CairoMakie, Random
CairoMakie.activate!() #HIDE
let
Random.seed!(145)
x, y, yerr = 1:2:20, 5*rand(10), 0.4*abs.(randn(10))
fig = Figure(resolution = (700, 450), font = "sans")
ax = Axis(fig, xlabel = "variables", ylabel = "values")
barplot!(ax, x,y,strokewidth = 1,color = x,colormap = (:Spectral_10, 0.85),
strokecolor = :black)
errorbars!(ax, x, y, yerr, whiskerwidth = 12)
fig[1,1] = ax
fig
save(joinpath(@__DIR__, "output", "BoxErrorBarsCmap.png"), fig, px_per_unit = 2.0) # HIDE
end
using Pkg # HIDE
Pkg.status(["CairoMakie", "Random"]) # HIDE
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] | 2.211144 | 341 |
<reponame>ericbdaniels/dash-vtk
# AUTO GENERATED FILE - DO NOT EDIT
export vtk_imagedata
"""
vtk_imagedata(;kwargs...)
vtk_imagedata(children::Any;kwargs...)
vtk_imagedata(children_maker::Function;kwargs...)
An ImageData component.
ImageData is exposing a vtkImageData to a downstream filter
It takes the following set of properties:
- dimensions: [nx, ny, nz],
- origin: [0, 0, 0]
- spacing: [1, 1, 1]
- direction: [
1, 0, 0,
0, 1, 0,
0, 0, 1
]
Keyword arguments:
- `children` (Array of a list of or a singular dash component, string or numbers | a list of or a singular dash component, string or number; optional)
- `id` (String; optional): The ID used to identify this component.
- `port` (Real; optional): downstream connection port
- `dimensions` (Array of Reals; optional): Number of points along x, y, z
- `spacing` (Array of Reals; optional): Spacing along x, y, z between points in world coordinates
- `origin` (Array of Reals; optional): World coordinate of the lower left corner of your vtkImageData (i=0, j=0, k=0).
- `direction` (Array of Reals; optional): 3x3 matrix use to orient the image data
"""
function vtk_imagedata(; kwargs...)
available_props = Symbol[:children, :id, :port, :dimensions, :spacing, :origin, :direction]
wild_props = Symbol[]
return Component("vtk_imagedata", "ImageData", "dash_vtk", available_props, wild_props; kwargs...)
end
vtk_imagedata(children::Any; kwargs...) = vtk_imagedata(;kwargs..., children = children)
vtk_imagedata(children_maker::Function; kwargs...) = vtk_imagedata(children_maker(); kwargs...)
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7,
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9783,
479,
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628
] | 2.839161 | 572 |
"""
hasconverged(s::ConvergenceState)
Returns true if `s.converged` is true.
"""
hasconverged(s::ConvergenceState) = s.converged
"""
hasconverged(s::Solution)
Returns true if `hasconverged(s.convstate)` is true.
"""
hasconverged(s::Solution) = hasconverged(s.convstate)
"""
```
assess_convergence!(
solution::Solution,
model::AbstractModel,
tol::Tolerance,
criteria::ConvergenceCriteria,
)
```
Evaluates the convergence state `solution.convstate` given the current solution, `solution`, the tolerance, `tol`, and the convergence criteria `criteria`. `solution.convstate.converged` is then updated.
If `criteria` is an instance of `GenericCriteria`, `converged = (x_converged || f_converged) && infeas_converged`. `x_converged`, `f_converged` and `infeas_converged` are explained in [`Tolerance`](@ref). If `criteria` is an instance of `KKTCriteria` or `ScaledKKTCriteria`, `converged = kkt_converged && infeas_converged`. `kkt_converged` and `infeas_converged` are explained in [`Tolerance`](@ref). If `criteria` is an instance of `IpoptCriteria`, `converged = ipopt_converged && infeas_converged`. `ipopt_converged` and `infeas_converged` are explained in [`Tolerance`](@ref).
"""
function assess_convergence!(
solution::Solution,
model::AbstractModel,
tol::Tolerance,
criteria::ConvergenceCriteria,
)
xtol, fabstol, freltol, kkttol, infeastol = tol.x, tol.fabs, tol.frel, tol.kkt, tol.infeas
Δx, Δf, infeas = getresiduals(solution, model, GenericCriteria())
relΔf = Δf / (abs(solution.f) + freltol)
kkt_residual, infeas = getresiduals(solution, model, KKTCriteria())
ipopt_residual, infeas = getresiduals(solution, model, IpoptCriteria())
if show_residuals[]
@show kkt_residual, ipopt_residual, kkttol
end
x_converged = Δx < xtol
fabs_converged = Δf < fabstol
frel_converged = relΔf < freltol
if criteria isa ScaledKKTCriteria
if debugging[]
#@show get_objective_multiple(model)
end
m = get_objective_multiple(model)
kkt_residual = kkt_residual / max(m, 1/m)
end
kkt_converged = kkt_residual < kkttol
ipopt_converged = ipopt_residual < kkttol
infeas_converged = infeas <= infeastol
f_increased = solution.f > solution.prevf
if criteria isa GenericCriteria
converged = (x_converged || fabs_converged || frel_converged) && infeas_converged
elseif criteria isa KKTCriteria || criteria isa ScaledKKTCriteria
converged = kkt_converged && infeas_converged
elseif criteria isa IpoptCriteria
converged = ipopt_converged && infeas_converged
else
throw("Unsupported convergence criteria for MMA.")
end
@pack! solution.convstate = x_converged,
fabs_converged,
frel_converged,
kkt_converged,
ipopt_converged,
infeas_converged,
Δx,
Δf,
relΔf,
kkt_residual,
ipopt_residual,
infeas,
f_increased,
converged
return solution
end
function getresiduals(solution::Solution, ::AbstractModel, ::GenericCriteria)
@unpack prevx, x, prevf, f, g = solution
Δx = maximum(abs(x[j] - prevx[j]) for j in 1:length(x))
Δf = abs(f - prevf)
infeas = length(g) == 0 ? zero(eltype(g)) : max(0, maximum(g))
return Δx, Δf, infeas
end
function getresiduals(solution::Solution, model::AbstractModel, ::KKTCriteria)
@unpack ∇f, g, ∇g, λ, x = solution
xmin, xmax = getmin(model), getmax(model)
T = eltype(x)
res = maximum(1:length(x)) do j
@views temp = ∇f[j] + dot(∇g[:,j], λ)
if xmin[j] >= x[j]
return abs(min(0, temp))
elseif x[j] >= xmax[j]
return max(0, temp)
else
return abs(temp)
end
end
if debugging[]
@show λ, g
end
res = length(g) == 0 ? res : max(res, maximum(abs.(λ .* g)))
if debugging[]
@show maximum(abs, g)
@show maximum(abs, λ)
@show maximum(x)
end
infeas = length(g) == 0 ? zero(eltype(g)) : max(maximum(g), 0)
return res, infeas
end
function getresiduals(solution::Solution, model::AbstractModel, ::IpoptCriteria)
@unpack ∇f, g, ∇g, λ, x = solution
xmin, xmax = getmin(model), getmax(model)
T = eltype(x)
n, m, s = length(x), length(λ), zero(T)
res = maximum(1:n) do j
@views temp = ∇f[j] + dot(∇g[:,j], λ)
if xmin[j] >= x[j]
dj = temp
s += max(dj, 0)
return abs(min(0, dj))
elseif x[j] >= xmax[j]
yj = -temp
s += max(yj, 0)
return abs(min(0, yj))
else
return abs(temp)
end
end
sd = max(100, (sum(abs, λ) + s) / (n + m)) / 100
res = length(g) == 0 ? res : max(res, maximum(abs.(λ .* g)))
res = res / sd
infeas = length(g) == 0 ? zero(eltype(g)) : max(maximum(g), 0)
if debugging[]
println("Agg infeas = ", infeas)
end
return res, infeas
end
| [
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7,
82,
3712,
3103,
332,
12745,
9012,
8,
198,
198,
35561,
2081,
611,
4600,
82,
13,
1102,
332,
2004,
63,
318,
2081,
13,
198,
37811,
198,
10134,
1102,
332,
2004,
7,
82,
3712,
3103,
332,
12745,
9012,
8,
796,
264,
13,
1102,
332,
2004,
198,
198,
37811,
198,
10134,
1102,
332,
2004,
7,
82,
3712,
46344,
8,
198,
198,
35561,
2081,
611,
4600,
10134,
1102,
332,
2004,
7,
82,
13,
42946,
5219,
8,
63,
318,
2081,
13,
198,
37811,
198,
10134,
1102,
332,
2004,
7,
82,
3712,
46344,
8,
796,
468,
1102,
332,
2004,
7,
82,
13,
42946,
5219,
8,
628,
198,
37811,
198,
15506,
63,
198,
562,
408,
62,
1102,
332,
12745,
0,
7,
198,
220,
220,
220,
4610,
3712,
46344,
11,
198,
220,
220,
220,
2746,
3712,
23839,
17633,
11,
198,
220,
220,
220,
284,
75,
3712,
51,
37668,
11,
198,
220,
220,
220,
9987,
3712,
3103,
332,
12745,
18559,
5142,
11,
198,
8,
198,
15506,
63,
198,
198,
36,
2100,
12632,
262,
40826,
1181,
4600,
82,
2122,
13,
42946,
5219,
63,
1813,
262,
1459,
4610,
11,
4600,
82,
2122,
47671,
262,
15621,
11,
4600,
83,
349,
47671,
290,
262,
40826,
9987,
4600,
22213,
5142,
44646,
4600,
82,
2122,
13,
42946,
5219,
13,
1102,
332,
2004,
63,
318,
788,
6153,
13,
198,
198,
1532,
4600,
22213,
5142,
63,
318,
281,
4554,
286,
4600,
46189,
18559,
5142,
47671,
4600,
1102,
332,
2004,
796,
357,
87,
62,
1102,
332,
2004,
8614,
277,
62,
1102,
332,
2004,
8,
11405,
1167,
30412,
62,
1102,
332,
2004,
44646,
4600,
87,
62,
1102,
332,
2004,
47671,
4600,
69,
62,
1102,
332,
2004,
63,
290,
4600,
259,
5036,
292,
62,
1102,
332,
2004,
63,
389,
4893,
287,
685,
63,
51,
37668,
63,
16151,
31,
5420,
737,
1002,
4600,
22213,
5142,
63,
318,
281,
4554,
286,
4600,
16601,
4825,
799,
5142,
63,
393,
4600,
3351,
3021,
16601,
4825,
799,
5142,
47671,
4600,
1102,
332,
2004,
796,
479,
21841,
62,
1102,
332,
2004,
11405,
1167,
30412,
62,
1102,
332,
2004,
44646,
4600,
74,
21841,
62,
1102,
332,
2004,
63,
290,
4600,
259,
5036,
292,
62,
1102,
332,
2004,
63,
389,
4893,
287,
685,
63,
51,
37668,
63,
16151,
31,
5420,
737,
1002,
4600,
22213,
5142,
63,
318,
281,
4554,
286,
4600,
40,
79,
8738,
18559,
5142,
47671,
4600,
1102,
332,
2004,
796,
20966,
8738,
62,
1102,
332,
2004,
11405,
1167,
30412,
62,
1102,
332,
2004,
44646,
4600,
541,
8738,
62,
1102,
332,
2004,
63,
290,
4600,
259,
5036,
292,
62,
1102,
332,
2004,
63,
389,
4893,
287,
685,
63,
51,
37668,
63,
16151,
31,
5420,
737,
198,
37811,
198,
8818,
4659,
62,
1102,
332,
12745,
0,
7,
198,
220,
220,
220,
4610,
3712,
46344,
11,
198,
220,
220,
220,
2746,
3712,
23839,
17633,
11,
198,
220,
220,
220,
284,
75,
3712,
51,
37668,
11,
198,
220,
220,
220,
9987,
3712,
3103,
332,
12745,
18559,
5142,
11,
198,
8,
198,
220,
220,
220,
220,
742,
349,
11,
7843,
301,
349,
11,
2030,
2528,
349,
11,
479,
74,
926,
349,
11,
1167,
23316,
349,
796,
284,
75,
13,
87,
11,
284,
75,
13,
69,
8937,
11,
284,
75,
13,
69,
2411,
11,
284,
75,
13,
74,
21841,
11,
284,
75,
13,
259,
5036,
292,
198,
220,
220,
220,
37455,
87,
11,
37455,
69,
11,
1167,
30412,
796,
651,
411,
312,
723,
82,
7,
82,
2122,
11,
2746,
11,
42044,
18559,
5142,
28955,
198,
220,
220,
220,
823,
138,
242,
69,
796,
37455,
69,
1220,
357,
8937,
7,
82,
2122,
13,
69,
8,
1343,
2030,
2528,
349,
8,
198,
220,
220,
220,
479,
21841,
62,
411,
312,
723,
11,
1167,
30412,
796,
651,
411,
312,
723,
82,
7,
82,
2122,
11,
2746,
11,
509,
42,
4825,
799,
5142,
28955,
198,
220,
220,
220,
20966,
8738,
62,
411,
312,
723,
11,
1167,
30412,
796,
651,
411,
312,
723,
82,
7,
82,
2122,
11,
2746,
11,
314,
79,
8738,
18559,
5142,
28955,
198,
220,
220,
220,
611,
905,
62,
411,
312,
723,
82,
21737,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
12860,
479,
21841,
62,
411,
312,
723,
11,
20966,
8738,
62,
411,
312,
723,
11,
479,
74,
926,
349,
198,
220,
220,
220,
886,
628,
220,
220,
220,
2124,
62,
1102,
332,
2004,
796,
37455,
87,
1279,
220,
742,
349,
198,
220,
220,
220,
7843,
82,
62,
1102,
332,
2004,
796,
37455,
69,
1279,
7843,
301,
349,
198,
220,
220,
220,
2030,
75,
62,
1102,
332,
2004,
796,
823,
138,
242,
69,
1279,
2030,
2528,
349,
198,
220,
220,
220,
611,
9987,
318,
64,
1446,
3021,
16601,
4825,
799,
5142,
198,
220,
220,
220,
220,
220,
220,
220,
611,
28769,
21737,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
31,
12860,
651,
62,
15252,
425,
62,
48101,
7,
19849,
8,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
285,
796,
651,
62,
15252,
425,
62,
48101,
7,
19849,
8,
198,
220,
220,
220,
220,
220,
220,
220,
479,
21841,
62,
411,
312,
723,
796,
479,
21841,
62,
411,
312,
723,
1220,
3509,
7,
76,
11,
352,
14,
76,
8,
198,
220,
220,
220,
886,
198,
220,
220,
220,
479,
21841,
62,
1102,
332,
2004,
796,
479,
21841,
62,
411,
312,
723,
1279,
479,
74,
926,
349,
198,
220,
220,
220,
20966,
8738,
62,
1102,
332,
2004,
796,
20966,
8738,
62,
411,
312,
723,
1279,
479,
74,
926,
349,
198,
220,
220,
220,
1167,
30412,
62,
1102,
332,
2004,
796,
1167,
30412,
19841,
1167,
23316,
349,
198,
220,
220,
220,
277,
62,
24988,
839,
796,
4610,
13,
69,
1875,
4610,
13,
47050,
69,
628,
220,
220,
220,
611,
9987,
318,
64,
42044,
18559,
5142,
198,
220,
220,
220,
220,
220,
220,
220,
6718,
2004,
796,
357,
87,
62,
1102,
332,
2004,
8614,
7843,
82,
62,
1102,
332,
2004,
8614,
2030,
75,
62,
1102,
332,
2004,
8,
11405,
1167,
30412,
62,
1102,
332,
2004,
198,
220,
220,
220,
2073,
361,
9987,
318,
64,
509,
42,
4825,
799,
5142,
8614,
9987,
318,
64,
1446,
3021,
16601,
4825,
799,
5142,
198,
220,
220,
220,
220,
220,
220,
220,
6718,
2004,
796,
479,
21841,
62,
1102,
332,
2004,
11405,
1167,
30412,
62,
1102,
332,
2004,
198,
220,
220,
220,
2073,
361,
9987,
318,
64,
314,
79,
8738,
18559,
5142,
198,
220,
220,
220,
220,
220,
220,
220,
6718,
2004,
796,
20966,
8738,
62,
1102,
332,
2004,
11405,
1167,
30412,
62,
1102,
332,
2004,
198,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
3714,
7203,
3118,
15999,
40826,
9987,
329,
19055,
19570,
198,
220,
220,
220,
886,
198,
220,
220,
220,
2488,
8002,
0,
4610,
13,
42946,
5219,
796,
2124,
62,
1102,
332,
2004,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7843,
82,
62,
1102,
332,
2004,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2030,
75,
62,
1102,
332,
2004,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
479,
21841,
62,
1102,
332,
2004,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20966,
8738,
62,
1102,
332,
2004,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1167,
30412,
62,
1102,
332,
2004,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
37455,
87,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
37455,
69,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
823,
138,
242,
69,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
479,
21841,
62,
411,
312,
723,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20966,
8738,
62,
411,
312,
723,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1167,
30412,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
277,
62,
24988,
839,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6718,
2004,
198,
220,
220,
220,
1441,
4610,
198,
437,
198,
198,
8818,
651,
411,
312,
723,
82,
7,
82,
2122,
3712,
46344,
11,
7904,
23839,
17633,
11,
7904,
46189,
18559,
5142,
8,
198,
220,
220,
220,
2488,
403,
8002,
8654,
87,
11,
2124,
11,
8654,
69,
11,
277,
11,
308,
796,
4610,
198,
220,
220,
220,
37455,
87,
796,
5415,
7,
8937,
7,
87,
58,
73,
60,
532,
8654,
87,
58,
73,
12962,
329,
474,
287,
352,
25,
13664,
7,
87,
4008,
198,
220,
220,
220,
37455,
69,
796,
2352,
7,
69,
532,
8654,
69,
8,
198,
220,
220,
220,
1167,
30412,
796,
4129,
7,
70,
8,
6624,
657,
5633,
6632,
7,
417,
4906,
7,
70,
4008,
1058,
3509,
7,
15,
11,
5415,
7,
70,
4008,
198,
220,
220,
220,
1441,
37455,
87,
11,
37455,
69,
11,
1167,
30412,
198,
437,
198,
8818,
651,
411,
312,
723,
82,
7,
82,
2122,
3712,
46344,
11,
2746,
3712,
23839,
17633,
11,
7904,
16601,
4825,
799,
5142,
8,
198,
220,
220,
220,
2488,
403,
8002,
18872,
229,
69,
11,
308,
11,
18872,
229,
70,
11,
7377,
119,
11,
2124,
796,
4610,
198,
220,
220,
220,
2124,
1084,
11,
2124,
9806,
796,
651,
1084,
7,
19849,
828,
651,
9806,
7,
19849,
8,
198,
220,
220,
220,
309,
796,
1288,
4906,
7,
87,
8,
198,
220,
220,
220,
581,
796,
5415,
7,
16,
25,
13664,
7,
87,
4008,
466,
474,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
33571,
20218,
796,
18872,
229,
69,
58,
73,
60,
1343,
16605,
7,
24861,
229,
70,
58,
45299,
73,
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119,
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198,
220,
220,
220,
220,
220,
220,
220,
611,
2124,
1084,
58,
73,
60,
18189,
2124,
58,
73,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2352,
7,
1084,
7,
15,
11,
20218,
4008,
198,
220,
220,
220,
220,
220,
220,
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] | 2.02207 | 2,628 |
using MakieLayout
using Makie
using KernelDensity
function kdepoly!(la::LAxis, vec, reverse=false; kwargs...)
kderesult = kde(vec; npoints=32)
x = kderesult.x
y = kderesult.density
if reverse
poly!(la, Point2.(y, x); kwargs...)
else
poly!(la, Point2.(x, y); kwargs...)
end
end
begin
scene = Scene(resolution = (1000, 1000));
screen = display(scene)
campixel!(scene);
la1 = LAxis(scene)
la2 = LAxis(scene)
la3 = LAxis(scene)
la4 = LAxis(scene)
la5 = LAxis(scene)
linkxaxes!(la3, la4)
linkyaxes!(la3, la5)
fakeaudiox = LinRange(0f0, 1000f0, 100_000)
fakeaudioy = (rand(Float32, 100_000) .- 0.5f0) .+ 2 .* sin.(fakeaudiox .* 10)
lines!(la1, fakeaudiox, fakeaudioy, show_axis=false)
la1.attributes.title[] = "A fake audio signal"
la1.attributes.ypanlock[] = true
la1.attributes.yzoomlock[] = true
linkeddata = randn(200, 2) .* 15 .+ 50
green = RGBAf0(0.05, 0.8, 0.3, 0.6)
scatter!(la3, linkeddata, markersize=3, color=green, show_axis=false)
kdepoly!(la4, linkeddata[:, 1], false, color=green, linewidth=2, show_axis=false)
kdepoly!(la5, linkeddata[:, 2], true, color=green, linewidth=2, show_axis=false)
linkeddata2 = randn(200, 2) .* 20 .+ 70
red = RGBAf0(0.9, 0.1, 0.05, 0.6)
scatter!(la3, linkeddata2, markersize=3, color=red, show_axis=false)
kdepoly!(la4, linkeddata2[:, 1], false, color=red, linewidth=2, show_axis=false)
kdepoly!(la5, linkeddata2[:, 2], true, color=red, linewidth=2, show_axis=false)
maingl = GridLayout(scene, 2, 1, alignmode=Outside(40)) #makie error in the tutorial #scene rendering function
sledg = maingl[2, 1] = LSlider(scene, range = LinRange(0.0, 150.0, 200))
gl = maingl[1, 1] = GridLayout(
2, 2;
rowsizes = [Aspect(1, 1.0), Auto()], #Comment:Aspect Ratio is not a Golden Ratio
colsizes = [Relative(0.5), Auto()],
addedrowgaps = Fixed(20),
addedcolgaps = Fixed(20),
alignmode = Outside(0))
on(slalign.value) do v
with_updates_suspended(maingl) do
gl.addedrowgaps = MakieLayout.GapSize[Fixed(v)]
gl.addedcolgaps = MakieLayout.GapSize[Fixed(v)]
end
end
gl_slider = gl[1, 2] = GridLayout(
3, 1;
rowsizes = [Auto(), Auto(), Auto()],
colsizes = [Relative(1)],
addedrowgaps = [Fixed(15), Fixed(15)])
gl_colorbar = gl_slider[1, 1] = GridLayout(1, 2; colsizes=[Auto(), Relative(0.1)])
gl_colorbar[1, 1] = la2
# gl_colorbar[1, 2] = LColorbar(scene)
sl1 = gl_slider[2, 1] = LSlider(scene, range = 1:0.01:10)
sl2 = gl_slider[3, 1] = LSlider(scene, range = 0.1:0.01:1)
xrange = LinRange(0, 2pi, 500)
lines!(
la2,
xrange ./ 2pi .* 100,
lift((x, y)->sin.(xrange .* x) .* 40 .* y .+ 50, sl1.value, sl2.value),
color=:blue, linewidth=2, show_axis=false)
gl[2, :] = la1
gl2 = gl[1, 1] = GridLayout(
2, 2,
rowsizes = [Auto(), Relative(0.8)],
colsizes = [Aspect(2, 1.0), Auto()],
addedrowgaps = [Fixed(10)],
addedcolgaps = [Fixed(10)])
gl2[2, 1] = la3
la3.titlevisible[] = false
gl2[1, 1] = la4
la4.xlabelvisible[] = false
la4.xticklabelsvisible[] = false
la4.xticksvisible[] = false
la4.titlevisible[] = false
la4.ypanlock[] = true
la4.yzoomlock[] = true
gl2[2, 2] = la5
la5.ylabelvisible[] = false
la5.yticklabelsvisible[] = false
la5.yticksvisible[] = false
la5.titlevisible[] = false
la5.xpanlock[] = true
la5.xzoomlock[] = true
end | [
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] | 2.085485 | 1,743 |
module Ripser
export ripser
using SparseArrays: AbstractSparseMatrix, findnz
const depsfile = joinpath(dirname(@__DIR__), "deps", "deps.jl")
if isfile(depsfile)
include("../deps/deps.jl")
else
error("Ripser in not properly installed. Please run Pkg.build(\"Ripser\").")
end
__init__() = check_deps()
# The value_t type from ripser source code.
const Cvalue_t = Cfloat
# RawResult contains pointers to all output arrays required by ripser.
struct RawResult{T}
dim_max ::Int
n_intervals ::Ref{Ptr{Cint}}
births_deaths ::Ref{Ptr{Cvalue_t}}
cocycle_length ::Ref{Ptr{Cint}}
cocycles ::Ref{Ptr{Cint}}
end
RawResult{T}(dim_max) where T =
RawResult{T}(dim_max,
Ref{Ptr{Cint}}(),
Ref{Ptr{Cvalue_t}}(),
Ref{Ptr{Cint}}(),
Ref{Ptr{Cint}}())
# Converts a Matrix to Vector{Cvalue_t} since ripser expects a flat array as input.
function flatten_distmat(dists)
dists_flat = Cvalue_t[]
for i in 1:size(dists, 1)-1, j in i+1:size(dists, 1)
push!(dists_flat, Cvalue_t(dists[j, i]))
end
dists_flat
end
function isprime(n)
if iseven(n) || n < 2
n == 2
else
p = 3
q = n / p
while p ≤ q
iszero(n % p) && return false
p += 2
q = n / p
end
true
end
end
function check_args(dists, modulus, dim_max, threshold)
size(dists, 1) == size(dists, 2) || throw(ArgumentError("distance matrix must be square"))
isprime(modulus) || throw(ArgumentError("modulus must be a prime number"))
dim_max ≥ 0 || throw(ArgumentError("dim_max must be non-negative"))
threshold > 0 || throw(ArgumentError("threshold must be positive"))
end
function split_cocycle(cocycle, dim)
c = Tuple{Vector{Int}, Int}[]
for i in 1:dim+2:length(cocycle)-dim-1
push!(c, (cocycle[i:i+dim] .+ 1, cocycle[i+dim+1]))
end
c
end
# Unpack RawResult{T} to barcode and cocycles (if return_cocycles is true)
function unpack_results(raw::RawResult{T}, return_cocycles) where T
dim_max = raw.dim_max
n_intervals = unsafe_wrap(Vector{Cint}, raw.n_intervals[], raw.dim_max + 1,
own = true)
intervals = unsafe_wrap(Matrix{Cvalue_t}, raw.births_deaths[], (2, sum(n_intervals)),
own = true)
cocycle_length = unsafe_wrap(Vector{Cint}, raw.cocycle_length[], sum(n_intervals),
own = true)
if sum(cocycle_length) > 0
cocycles_flat = unsafe_wrap(Vector{Cint}, raw.cocycles[], sum(cocycle_length),
own = true)
else
cocycles_flat = Cint[]
end
if !return_cocycles
barcodes = Matrix{T}[]
start = 0
for int in n_intervals
push!(barcodes, T.(intervals[:, start+1:start+int]))
start += int
end
map(barcodes) do bc
collect(vec(reinterpret(Tuple{T, T}, bc)))
end
else
barcodes = Matrix{T}[]
cocycles = Vector{Vector{Tuple{Vector{Int}, Int}}}[]
start_bc = 0
start_cc = 0
for int in n_intervals
push!(barcodes, T.(intervals[:, start_bc+1:start_bc+int]))
push!(cocycles, Int[])
for i in 1:int
len = cocycle_length[start_bc + i]
push!(cocycles[end],
split_cocycle(cocycles_flat[start_cc+1:start_cc+len],
length(cocycles)-1))
start_cc += len
end
start_bc += int
end
map(barcodes) do bc
collect(vec(reinterpret(Tuple{T, T}, bc)))
end, cocycles
end
end
"""
ripser(dists; modulus = 2, dim_max = 1, threshold = Inf, cocycles = false)
"""
function ripser(dists ::AbstractMatrix{T};
modulus ::Integer = 2,
dim_max ::Integer = 1,
threshold ::Real = Inf,
cocycles ::Bool = false) where T<:AbstractFloat
check_args(dists, modulus, dim_max, threshold)
res = RawResult{T}(dim_max)
dists_flat = flatten_distmat(dists)
ripser_fptr = Libdl.dlsym(Libdl.dlopen(libripser), :c_rips_dm)
n_edges = ccall(ripser_fptr,
Cint,
(Ptr{Ptr{Cint}}, Ptr{Ptr{Cvalue_t}}, Ptr{Ptr{Cint}}, Ptr{Ptr{Cint}},
Ptr{Cvalue_t}, Cint,
Cint, Cint, Cvalue_t, Cint),
res.n_intervals, res.births_deaths, res.cocycle_length, res.cocycles,
dists_flat, length(dists_flat),
modulus, dim_max, threshold, cocycles)
unpack_results(res, cocycles)
end
function ripser(dists ::AbstractSparseMatrix{T};
modulus ::Integer = 2,
dim_max ::Integer = 1,
threshold ::Real = Inf,
cocycles ::Bool = false) where T<:AbstractFloat
check_args(dists, modulus, dim_max, threshold)
J, I, V = findnz(dists)
I .-= 1
J .-= 1
res = RawResult{T}(dim_max)
ripser_fptr = Libdl.dlsym(Libdl.dlopen(libripser), :c_rips_dm_sparse)
n_edges = ccall(ripser_fptr,
Cint,
(Ptr{Ptr{Cint}}, Ptr{Ptr{Cvalue_t}}, Ptr{Ptr{Cint}}, Ptr{Ptr{Cint}},
Ptr{Cint}, Ptr{Cint}, Ptr{Cvalue_t}, Cint, Cint,
Cint, Cint, Cvalue_t, Cint),
res.n_intervals, res.births_deaths, res.cocycle_length, res.cocycles,
Cint.(I), Cint.(J), Cvalue_t.(V), length(I), size(dists, 1),
modulus, dim_max, threshold, cocycles)
unpack_results(res, cocycles)
end
end
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] | 1.91151 | 3,006 |
<reponame>DelMaestroGroup/OperationalEntanglementFreeFermions
module FreeFermionsOEE
using Printf
export
operational_entanglement
include("operational_entanglement.jl")
end
| [
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] | 3 | 60 |
using KitBase, FluxReconstruction, OrdinaryDiffEq, Langevin, LinearAlgebra, Plots
using ProgressMeter: @showprogress
begin
x0 = 0
x1 = 1
ncell = 100
nface = ncell + 1
dx = (x1 - x0) / ncell
deg = 2 # polynomial degree
nsp = deg + 1
γ = 7 / 5
cfl = 0.05
dt = cfl * dx / (2.0)
t = 0.0
tspan = (0.0, 0.15)
uqMethod = "galerkin"
nr = 9
nRec = 18
opType = "uniform"
parameter1 = 0.95
parameter2 = 1.05
end
ps = FRPSpace1D(x0, x1, ncell, deg)
uq = UQ1D(nr, nRec, parameter1, parameter2, opType, uqMethod)
V = vandermonde_matrix(ps.deg,ps.xpl)
VInv = inv(Array(ps.V))
l2 = [uq.t2Product[j-1, j-1] for j = 1:uq.nm+1]
cd(@__DIR__)
include("rhs.jl")
include("../filter.jl")
begin
isRandomLocation = false#true
isPrefilter = false#true
u = zeros(ncell, nsp, 3, uq.nm+1)
if isRandomLocation
# stochastic location
for i = 1:ncell, j = 1:nsp
prim = zeros(3, uq.nq)
for k = 1:uq.nq
if ps.x[i] <= 0.5 + 0.05 * uq.op.quad.nodes[k]
prim[:, k] .= [1.0, 0.0, 0.5]
else
prim[:, k] .= [0.125, 0.0, 0.625]
end
end
prim_chaos = zeros(3, uq.nm+1)
for k = 1:3
prim_chaos[k, :] .= ran_chaos(prim[k, :], uq)
end
u[i, j, :, :] .= uq_prim_conserve(prim_chaos, γ, uq)
end
else
# stochastic density
for i = 1:ncell, j = 1:nsp
prim = zeros(3, uq.nm+1)
if ps.x[i] <= 0.5
#prim[1, :] .= uq.pce
prim[1, 1] = 1.0
prim[2, 1] = 0.0
prim[3, 1] = 0.5
else
prim[:, 1] .= [0.125, 0.0, 0.625]
end
u[i, j, :, :] .= uq_prim_conserve(prim, γ, uq)
end
end
if isPrefilter
# pre-filtering
for j = 1:size(u, 1)
for s = 1:size(u, 3)
uModal = VInv * u[j, :, s, :]
#FR.modal_filter!(uModal, 15e-2, 10e-5; filter = :l2opt)
#FR.modal_filter!(uModal, 5e-2, 5e-2; filter = :l2)
#FR.modal_filter!(uModal, 5, 5; filter = :exp)
FR.modal_filter!(uModal; filter = :lasso)
u[j, :, s, :] .= V * uModal
end
end
end
end
p = (ps.J, ps.ll, ps.lr, ps.dl, ps.dhl, ps.dhr, γ, uq)
prob = ODEProblem(dudt!, u, tspan, p)
nt = tspan[2] ÷ dt |> Int
itg = init(prob, Midpoint(), saveat = tspan[2], adaptive = false, dt = dt)
@showprogress for iter = 1:nt
step!(itg)
for i = 1:size(itg.u, 1)
#=
ũ = VInv * itg.u[i, :, 1, 1]
su = log10(ũ[end]^2 / sum(ũ.^2))
isShock = detector(su, ps.deg)
if isShock
for j in axes(itg.u, 3), k in axes(itg.u, 4)
û = VInv * itg.u[i, :, j, k]
FR.modal_filter!(û, 1e-2; filter = :l2)
itg.u[i, :, j, k] .= ps.V * û
end
end
ṽ = itg.u[i, end÷2, 1, :]
sv = log10(ṽ[end]^2 / sum(ṽ.^2))
isShock = detector(sv, ps.deg)
if isShock
for j in axes(itg.u, 2), k in axes(itg.u, 3)
ṽ = @view itg.u[i, j, k, :]
FR.modal_filter!(ṽ, 5e-4; filter = :l2)
end
end=#
#=ũ = VInv * itg.u[i, :, 1, 1]
ṽ = itg.u[i, end÷2, 1, :]
su = log10(ũ[end]^2 / sum(ũ.^2))
sv = log10(ṽ[end]^2 / sum(ṽ.^2))
isShock = max(shock_detector(su, ps.deg), shock_detector(sv, ps.deg))
if isShock
for s = 1:size(itg.u, 3)
û = VInv * itg.u[i, :, s, :]
#FR.filter_exp!(û, 10, 100)
#FR.modal_filter!(û, 0.8e-2, 1e-6; filter = :l2)
FR.modal_filter!(û, 10e-2, 5e-5; filter = :l2opt)
#FR.modal_filter!(û, 5e-2, 5e-5; filter = :l2)
#FR.modal_filter!(û, 5e-2, 5e-5; filter = :l2)
itg.u[i, :, s, :] .= ps.V * û
end
end=#
ũ = VInv * itg.u[i, :, 1, :]
su = maximum([ũ[end, j]^2 / sum(ũ[:, j].^2) for j = 1:uq.nm+1])
sv = maximum([ũ[j, end]^2 * uq.t2Product[uq.nm, uq.nm] / sum(ũ[j, :].^2 .* l2) for j = 1:nsp])
isShock = max(shock_detector(log10(su), ps.deg), shock_detector(log10(sv), ps.deg))
if isShock
λ1 = dt * (su)
λ2 = dt * (sv)
for s = 1:size(itg.u, 3)
û = VInv * itg.u[i, :, s, :]
#FR.modal_filter!(û, λ1, λ2; filter = :l2)
#FR.modal_filter!(û, λ1, λ2; filter = :l2opt)
#FR.modal_filter!(û, 1e-2, 1e-3; filter = :l2)
FR.modal_filter!(û; filter = :lasso)
itg.u[i, :, s, :] .= ps.V * û
end
end
tmp = @view itg.u[i, :, :, 1]
positive_limiter(tmp, γ, ps.wp/2, ps.ll, ps.lr)
end
end
begin
x = zeros(ncell * nsp)
w = zeros(ncell * nsp, 3, uq.nm+1)
for i = 1:ncell
idx0 = (i - 1) * nsp
for j = 1:nsp
idx = idx0 + j
x[idx] = ps.xpg[i, j]
w[idx, :, :] .= itg.u[i, j, :, :]
end
end
sol = zeros(ncell*nsp, 3, 2)
for i in axes(sol, 1)
p1 = zeros(3, uq.nm+1)
p1 = uq_conserve_prim(w[i, :, :], γ, uq)
p1[end, :] .= lambda_tchaos(p1[end, :], 1.0, uq)
for k = 1:3
sol[i, k, 1] = mean(p1[k, :], uq.op)
sol[i, k, 2] = std(p1[k, :], uq.op)
end
end
pic1 = plot(x, sol[:, 1, 1], label="ρ", xlabel="x", ylabel="mean")
plot!(pic1, x, sol[:, 2, 1], label="U")
plot!(pic1, x, sol[:, 3, 1], label="T")
pic2 = plot(x, sol[:, 1, 2], label="ρ", xlabel="x", ylabel="std")
plot!(pic2, x, sol[:, 2, 2], label="U")
plot!(pic2, x, sol[:, 3, 2], label="T")
plot(pic1, pic2)
end
plot(x, sol0[:, 1, 2], label="No filter", xlabel="x", ylabel="ρ")
plot!(x, sol[:, 1, 2], label="adaptive L²")
sol0 = deepcopy(sol) | [
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] | 1.585946 | 3,828 |
module ComputationalHomology
using LinearAlgebra
using Hungarian: hungarian
using SmithNormalForm: smith
using Distances: Metric, Euclidean, pairwise, colwise, pairwise!
using Random: shuffle!, randperm
using BoundingSphere: boundingsphere
using SparseArrays: SparseMatrixCSC, spzeros, findnz
using Combinatorics: combinations
import Base: ==, -, +, *, union, keys, values, hash, first, last, isless, show, length,
eltype, valtype, getindex, setindex!, size, iterate, push!, append!, in, similar,
read, write, vec, complex, iszero, convert, reduce, keytype, copy, map, map!,
minimum, maximum, parse, isempty
import Base.Iterators: pairs
import SparseArrays: sparse
import Statistics: mean
import LinearAlgebra: diag
export AbstractCell,
dim, faces, cofaces, volume,
AbstractSimplex,
indices, vertices,
Simplex, Cube, Cell,
AbstractChain,
Chain, simplify,
AbstractComplex,
boundary, coboundary, cells,
SimplicialComplex, addsimplex!, addsimplices!,
CWComplex, BitmapComplex,
vietorisrips, witness, cech, čech,
AbstractHomology, grouptype, group,
Homology, homology, withgenerators, generators,
betti, euler,
Filtration, filtration, order, simplices, similarity,
AbstractPersistenceReduction,
StandardReduction, TwistReduction,
pairs, reduce!,
PersistentHomology, persistenthomology,
PersistentCocycleReduction, persistentcohomology, PersistentCocycleReduction,
AbstractInterval, birth, death,
Interval, AnnotatedInterval,
PersistenceDiagram, diagram,
Landscape, landscape, mean,
PersistentImage,
wasserstein,
tropic
include("abstractchain.jl")
include("chains.jl")
include("abstractcell.jl")
include("simplex.jl")
include("cube.jl")
include("cwcell.jl")
include("complex.jl")
include("simplicialcomplex.jl")
include("cwcomplex.jl")
include("constructions.jl")
include("filtration.jl")
include("bitmapcomplex.jl")
include("homology.jl")
include("intervals.jl")
include("persistence.jl")
include("landscape.jl")
include("pimage.jl")
include("examples.jl")
include("distances.jl")
include("iterators.jl")
include("tropic.jl")
@deprecate intervals(d, ps...) diagram(d, ps...)
@deprecate celltype(cplx) eltype(cplx)
end # module
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437,
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198
] | 2.712974 | 871 |
using Sockets
include("header.jl")
include("strings.jl")
function play_roulette(p :: Player)
result = rand(1:36)
total_bet = 0
winnings = 0
print_dict(p, "roulette_0")
printBalance(p)
print_dict(p, "roulette_1")
while true
print_dict(p, "roulette_2")
s = readline(p.socket)
lines = split(s)
if s == ""
continue
elseif s == "d"
if total_bet > 0
break
else
print_dict(p, "roulette_3")
p.status = reception
return
end
elseif size(lines,1) != 2
print_dict(p, "repeat")
continue
else
bet = tryparse(Int64, lines[1])
if bet == nothing || bet < 0
print_dict(p, "repeat")
continue
elseif bet > (p.balance - total_bet)
print_dict(p, "roulette_4")
continue
elseif bet == 0
print_dict(p, "roulette_3")
p.status = reception
return
end
number = tryparse(Int, lines[2])
if number == nothing
if lines[2] == "red"
if (result < 19 && result % 2 == 1) || (result > 19 && result % 2 == 0)
winnings += bet
else
winnings -= bet
end
total_bet += bet
continue
elseif lines[2] == "black"
if (result < 19 && result % 2 == 0) || (result > 19 && result % 2 == 1)
winnings += bet
else
winnings -= bet
end
total_bet += bet
continue
elseif lines[2] == "1-12"
if result <= 12
winnings += 3 * bet
else
winnings -= bet
end
total_bet += bet
continue
elseif lines[2] == "13-24"
if result > 12 && result <= 24
winnings += 3 * bet
else
winnings -= bet
end
total_bet += bet
continue
elseif lines[2] == "25-36"
if result > 24 && result <= 36
winnings += 3 * bet
else
winnings -= bet
end
total_bet += bet
continue
else
print_dict(p, "repeat")
continue
end
elseif number >= 1 && number <= 36
if number == result
winnings += 36 * bet
else
winnings -= bet
end
total_bet += bet
continue
else
print_dict(p, "repeat")
continue
end
end
end
print_dict(p, "roulette_5")
write(p.socket, "Congratulations the number is.. $result. Your total winnings are: $winnings\n")
p.balance += winnings
end
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] | 1.610024 | 2,095 |
using ThreeBodyDecay
using Plots
using Zygote
tbs = let m1 = 0.938, m2 = 0.49367, m3 = 0.13957, m0 = 2.46867
ThreeBodySystem(m1,m2,m3,m0)
end
#
# let
# σ1v = LinRange(tbs.mthsq[1], tbs.sthsq[1],300)
# σ3m = [σ3of1(σ,-1.0,tbs) for σ in σ1v]
# σ3p = [σ3of1(σ, 1.0,tbs) for σ in σ1v]
# plot(σ1v, [σ3m σ3p], lab="")
# end
#
# let
# σ3v, σ1v = flatDalitzPlotSample31(tbs; Nev=10000)
# histogram2d(σ1v, σ3v, lab="", bins=100)
# end
const Ks892 = BreitWigner(0.89176, 0.05)
const Λ1520 = BreitWigner(1.5195, 0.0156)
const Δ1232 = BreitWigner(1.232, 0.112)
model = [(1,Ks892),
(3,Λ1520),
(2,Δ1232)]
function scalar_amplitude(σs, model, cs)
mod = [1.1,2.2im, 3.3]
#
A = 0.0im
for (me,c) in zip(model,cs)
(ch,ξ) = me
# for (m,c) in zip(mod,cs)
# A += c * amp(σs[ch],ξ)
A += c * mod[ch]
end
return A
end
const cs0 = [1.2im, 2.1, 2.3+1im]
let σ1 = 1.0, σ3=4.0
scalar_amplitude([σ1,gσ2(σ3,σ1,tbs),σ3], model, cs0)
end
scalar_amplitude_squared(σs, model, cs) = abs2(scalar_amplitude(σs, model, cs))
# @time let
# σ3v, σ1v = flatDalitzPlotSample31(tbs; Nev=100000)
# weights = [scalar_amplitude_squared([σ1,gσ2(σ3,σ1,tbs),σ3], model, cs0) for (σ3, σ1) in zip(σ3v, σ1v)]
# histogram2d(σ1v, σ3v, lab="", bins=100, weights=weights)
# end
# getbinned2dDensity(g, xlim, ylim, Nrows, Ncols)
diff_through_pars(cs) = let σ1 = 1.0, σ3=4.0,
σs = [σ1,gσ2(σ3,σ1,tbs),σ3]
return gradient(x->scalar_amplitude_squared(σs, model, x), cs)
end
diff_through_pars([1.1im,1.1,1.1])
let
end
# function amp_b2bzz(two_λ,two_Λ,σs,CS,tbs,Cs)
# σ2 = gσ2(σ3,σ1,tbs)
# # Wigner rotations
# D1 = [two_λ==two_λp ? 1.0 : 0.0 for two_λp=-1:2:1]
# D2 = [((two_λp-two_λ) % 4 == 2 ? -1 : 1) *
# wignerd_doublearg(1,two_λp,two_λ,cosζ12_for1(σ1,σ2,tbs))
# for two_λp=-1:2:1]
# D3 = [wignerd_doublearg(1,two_λp,two_λ,cosζ31_for1(σ3,σ1,tbs))
# for two_λp=-1:2:1]
# #
# σs = (σ1,σ2,σ3)
# Ds = (D1,D2,D3)
# #
# val = zero(Cs[1][1,1]+0.0im)
# for (cs,W) in zip(CS, Cs) # coupling scheme and couplings
# (k,ξ,chains) = cs
# length(chains) == 0 && continue
# decay_amp = zero(Cs[1][1,1]+0.0im)
# for two_τ in -two_j(chains[1]):2:two_j(chains[1]), two_λp = -1:2:1
# Wc = [v1+v2*1im for (v1,v2) in zip(W[:,1],W[:,1])]
# decay_amp += RkΛsτ(k,two_λp,two_Λ,two_τ,chains, Wc) *
# ZkΛsτ(k,two_λp,two_Λ,two_j(chains[1]),two_τ,σ3,σ1,tbs) *
# Ds[k][div(two_λp+3,2)]
# end
# val += decay_amp * amp(σs[k],ξ)
# end
# return val;
# end
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] | 1.600355 | 1,689 |
<reponame>Alchemist-yao/Julia4Derivatives
using Random,StatsBase,Distributions,Polynomials
Random.seed!(150000)
#LSM原始算法，针对美式PUT
function LSM_PRIMAL(S0::Float64,K::Float64,T::Float64,r::Float64,σ::Float64,I::Int,M::Int)
"""
# Arguments
- `S0`: 初始标的值.
- `K`: 行权值
- `T`: 到期时间，年为单位
- `r`: 无风险利率
- `σ`: 波动率
- `I`: 原始算法中路径数
- `M`: 时间步数
"""
dt=T/M
df=exp(-r*dt)
p1=(r-0.5*(σ^2))*dt
p2=rand(Normal(0,1),M+1,I)
p3=p1 .+σ*√(dt)*p2
p4=cumsum(p3,dims=1)
S=S0*(p4.|>exp)
S[1,:]=repeat([S0],I)
h=map(x->max(K-x,0),S)
V=copy(h[end,:])
@simd for t=M:-1:1
rg=polyfit(S[t,:],V*df,5)
C=map(x->polyval(rg,x),S[t,:])
res=h[t,:].>C
for i=1:I
if res[i]==true
V[i]=h[t,i]
else
V[i]=V[i]*df
end
end
end
v0=df*sum(V)/I
end
#LSM对偶算法
function LSM_DUAL(S0::Float64,K::Float64,T::Float64,r::Float64,σ::Float64,J::Int,I1::Int,I2::Int,M::Int,reg::Int)
"""
# Arguments
- `S0`: 初始::Int标的值::Int.
::Int- ::Int`K`: 行权值
- `T`: 到期时间，年为单位
- `r`: 无风险利率
- `σ`: 波动率
- `J`: 对偶算法中子循环模拟路径
- `I1`: 原始算法中路径数
- `I2`: 对偶算法中路径数
- `M`: 时间步数
- `reg`: 多项是回归阶数
"""
dt=T/M
df=exp(-r*dt)
#路径I1,产生回归系数
S1=genrate_path(S0,r,σ,dt,M,I1)
h1=map(x->max(K-x,0),S1)
V1=map(x->max(K-x,0),S1)
#路径I1,产生回归系数
rg0=[]
a=Poly(0)
push!(rg0,a)
@simd for t=M:-1:2
rgr=polyfit(S1[t,:],V1[t+1,:]*df,5)
push!(rg0,rgr)
end
push!(rg0,a)
rg=reverse(rg0)
#路径I2，对偶算法
Q=zeros(M+1,I2)
U=zeros(M+1,I2)
S=genrate_path(S0,r,σ,dt,M,I2)
h=map(x->max(K-x,0),S)
V=map(x->max(K-x,0),S)
@simd for t=2:M+1
for i=1:I2
tmp=polyval(rg[t],S[t,i])
Vt=max(h[t,i],tmp)
St=generate_nest_mc(r,σ,dt,S[t-1,i],J)
Ct=map(x->polyval(rg[t],x),St)
ht=map(x->max(K-x),St)
res=ht.>Ct
Vtj=zeros(J)
for k=1:length(res)
if res[k]==true
Vtj[k]=ht[k]
else
Vtj[k]=Ct[k]
end
end
sum_VT=sum(Vtj)/length(St)
Q[t,i]=Q[t-1,i]/df+(Vt-sum_VT)
U[t,i]=max(U[t-1,i]/df,(h[t,i]-Q[t,i]))
if t==M+1
U[t,i]=max(U[t-1,i]/df,mean(ht)-Q[t,i])
end
end
end
k=df^M
U0=sum(U[M,:])/I2*(df^M)
end
#产生标的仿真路径-单路径
function generate_nest_mc(r::Float64,σ::Float64,dt::Float64,st::Float64,J::Int)
p1=(r-0.5*(σ^2))*dt
p2=rand(Normal(0,1),J)
p3=p1 .+σ*√(dt)*p2
S=st*(p3.|>exp)
return S
end
#产生标的仿真路径-多路径
function genrate_path(S0::Float64,r::Float64,σ::Float64,dt::Float64,M::Int,I::Int)
p1=(r-0.5*(σ^2))*dt
S=zeros(M+1,I)
S[1,:]=map(x->x=S0,S[1,:])
@simd for t=2:M+1
p2=rand(Normal(0,1),I)
p3=p1 .+σ*√(dt)*p2
S[t,:]=S[t-1,:].*(p3.|>exp)
end
return S
end
S0=36.0
K=40.0
T=1.0
r=0.06
σ=0.2
I1=16384
I2=1024
M=10
J=50
reg=5
res=LSM_DUAL(S0,K,T,r,σ,J,I1,I2,M,reg)
println(res)
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] | 1.259134 | 2,655 |
@testset "testing GoF, multinomial distribution" begin
df = DataFrame(CSV.File(joinpath(dirname(Base.find_package("PhyloNetworks")),"..","examples","buckyCF.csv")), copycols=false)
d0 = readTableCF(df)
d = deepcopy(d0)
net3 = readTopology("((((D:0.4,C:0.4):4.8,((A:0.8,B:0.8):2.2)#H1:2.2::0.7):4.0,(#H1:0::0.3,E:3.0):6.2):2.0,O:11.2);");
@testset "using Pearson statistic" begin
# without optimizing branch lengths
netresult1 = quarnetGoFtest!(net3,df,false; quartetstat=:pearson, correction=:none);
@test length(netresult1) == 6
@test netresult1[2] ≈ 8.589058727506838 # z stat
@test netresult1[1] ≈ 4.384234705965304e-18 # p-value
@test df[!,:p_value] ≈ [1.2435633419824544e-11,0.0009280577186649157,0.0009280577186649157,1.2435633419824544e-11,0.007580817260552542,0.9998389684303506,0.012895416058219087,0.9438090044657973,0.9471103615208266,0.9438090044657973,0.012895416058219087,0.9471103615208266,0.9956830628718893,0.24055486863965628,0.007580817260552542]
@test netresult1[5].loglik ≈ 105.30058282649648
end
@testset "LRT statistic for 4-taxon set with small expected values" begin
# without optimizing branch lengths
netresult1 = quarnetGoFtest!(net3,d,false; correction=:none); # modified d: fills in CFs expected from net3
@test netresult1[2] ≈ 6.21966321647047 # z stat
@test netresult1[3] ≈ 1.0 # sigma
@test netresult1[1] ≈ 2.491115579898031e-10 # p-value
@test netresult1[4] ≈ [0.0024449826689709165,0.01496306673600063,0.01496306673600063,0.0024449826689709165,0.04086460431063039,0.9998541057240138,0.1901450501005025,0.8909735618259936,0.9058717147295428,0.8909735618259936,0.1901450501005025,0.9058717147295428,0.9913859984840471,0.3656465603640152,0.04086460431063039]
end
@testset "Qlog statistic for 4-taxon set with small expected values" begin
netresult1 = QuartetNetworkGoodnessFit.quarnetGoFtest(d.quartet, QuartetNetworkGoodnessFit.multinom_qlog!);
@test netresult1[1] ≈ 6.21966321647047 # z stat
@test netresult1[2] ≈ [6.188115021844278e-7,.0038172375225786225,.0038172375225786225,6.188115021844278e-7,.016885279326279496,.999846547125979,.054779352829667574,.8909734349100712,.9229400733753065,.8909734349100712,.054779352829667574,.9229400733753065,.9913859984246092,.2975827064317875,.016885279326279496]
end
@testset "check for exceptions" begin
@test_throws ErrorException ticr!(net3,d,false; quartetstat = :Qlog)
@test_throws ErrorException ticr!(net3,d,false; test = :bad);
@test_throws ErrorException quarnetGoFtest!(net3,d,false; quartetstat=:maxCF);
@test_throws ErrorException quarnetGoFtest!(net3,d,false; correction=:foo)
d.quartet[1].ngenes = -1
@test_throws ErrorException quarnetGoFtest!(net3,d,false);
end
d = deepcopy(d0)
@testset "with dependence correction" begin
# test of expectedCF_ordered
for i in 1:15 d.quartet[i].qnet.expCF = [i+0.1, i+0.2, i+0.3]; end
expCF, taxa = QuartetNetworkGoodnessFit.expectedCF_ordered(d, net3)
taxa == ["A","B","C","D","E","O"]
expCF ≈ [13.1 13.2 13.3; 3.2 3.1 3.3; 2.2 2.1 2.3; 8.2 8.3 8.1; 10.3 10.2 10.1; 15.2 15.1 15.3; 5.2 5.1 5.3; 12.2 12.3 12.1; 9.3 9.2 9.1; 14.3 14.1 14.2; 4.1 4.3 4.2; 7.3 7.2 7.1; 1.1 1.3 1.2; 11.3 11.2 11.1; 6.1 6.2 6.3]
# test of quarnetGoFtest! with correction
netresult1 = quarnetGoFtest!(net3,d,false; seed=4321, verbose=true, nsim=1,
keepfiles=true, quartetstat=:pearson);
@test netresult1[4] ≈ [1.244e-11,.0009281,.0009281,1.244e-11,.007581,.9998,.0129,.9438,.9471,.9438,.0129,.9471,.9957,.2406,.007581] rtol=1e-4
@test netresult1[2] ≈ 8.589058727506838 # z stat, uncorrected
@test netresult1[3] != 1.0 # sigma: 0.8885233166386386 = |single simulated z|
hldir = filter!(f -> startswith(f, "jl_"), readdir())
@test length(hldir) == 1
@test length(readdir(hldir[1])) == 1
rm(hldir[1], recursive=true)
# similar test, but with multiple processors and other options, and interesting seed:
# for s in 1:200 netresult1 = quarnetGoFtest!(net3,d,false; seed=s, nsim=5);
# if netresult1[3] > 3.0; @show (s, netresult1); break; end;end;
Distributed.addprocs(2)
# start with: julia -p 2 --project
# or: using Distributed; @everywhere begin; using Pkg; Pkg.activate("."); using PhyloNetworks; end
@everywhere using QuartetNetworkGoodnessFit
netresult1 = quarnetGoFtest!(net3,d,false; seed=2298, nsim=5);
@test netresult1[4] ≈ [0.0024449826689709165,0.01496306673600063,0.01496306673600063,0.0024449826689709165,0.04086460431063039,0.9998541057240138,0.1901450501005025,0.8909735618259936,0.9058717147295428,0.8909735618259936,0.1901450501005025,0.9058717147295428,0.9913859984840471,0.3656465603640152,0.04086460431063039]
@test netresult1[2] ≈ 6.21966321647047 # z stat, uncorrected
@test netresult1[3] ≈ 3.405362128771355 # sigma
@test netresult1[6] ≈ vcat(7.4043609719886545, repeat([-0.8885233166386386],4))
netresult1 = (@test_logs (:warn, r"far from 0") quarnetGoFtest!(net3,d,true; seed=182, nsim=2, quartetstat=:Qlog));
# just because 2 simulated z's only, and same values bc tiny network. may break with different RNG
# note: with verbose=true, we see hybrid-lambda's warnings:
# WARNING! NOT ULTRAMETRIC!!!
# WARNING: Gene tree is not ultrametric
# ... until hybrid-lambda can accommodate non-time-consistent networks reliably
Distributed.rmprocs(workers())
@test netresult1[4] ≈ [.73,.073,.073,.73,.115,.997,.706,.997,1.,.997,.706,1.,1.,.885,.115] rtol=0.01
@test netresult1[2] ≈ -0.8885233166386386 # z stat, uncorrected
# network that caused a bug in hybrid-Lambda v0.6.2-beta, see
# https://github.com/hybridLambda/hybrid-Lambda/issues/36
net = readTopology("(((A:7.13,(B:5.98)#H18:1.15::0.79):0.1,C:7.23):0.07,((D:0.0)#H19:6.2::0.89,(E:5.64,(O:0.0,#H19:0.0::0.11):5.64):0.56):1.1,#H18:1.32::0.21);")
@test_logs quarnetGoFtest!(net,d,false; seed=419, nsim=5);
end
end
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] | 2.202299 | 2,610 |
#==========================================================================================#
# INTERFACE
function fit(M::Type{<: OneStageModel}, MD::Microdata; novar::Bool = false, kwargs...)
obj = M(MD; kwargs...)
_fit!(obj, MD.weights)
novar || _vcov!(obj, getcorr(obj), MD.weights)
return obj
end
#==========================================================================================#
# ESTIMATES
coef(obj::ParObject) = switch_stage(obj).β
vcov(obj::ParObject) = switch_stage(obj).V
stderror(obj::ParObject) = sqrt.(diag(vcov(obj)))
tstat(obj::ParObject) = coef(obj) ./ stderror(obj)
pval(obj::ParObject) = 2.0 * normccdf.(abs.(tstat(obj)))
function confint(obj::ParObject, level::Real = 0.95)
return coef(obj) .+ norminvcdf((1.0 - level) / 2.0) * stderror(obj) .* [1.0 -1.0]
end
#==========================================================================================#
# SUMMARY STATISTICS
nobs(obj::Microdata) = sum(obj.weights)
nobs(obj::AnyModel) = nobs(switch_stage(obj).sample)
dof(obj::ParModel) = length(coef(obj))
dof_residual(obj::ParModel) = nobs(obj) - dof(obj)
loglikelihood(obj::MLE) = _loglikelihood(obj, getweights(obj))
nullloglikelihood(obj::MLE) = _nullloglikelihood(obj::MLE, getweights(obj))
deviance(obj::MLE) = _deviance(obj::MLE, getweights(obj))
nulldeviance(obj::MLE) = _nulldeviance(obj::MLE, getweights(obj))
#==========================================================================================#
# PREDICTION
linear_predictor(obj::AnyModel) = linear_predictor(obj, switch_stage(obj).sample)
predict(obj::AnyModel) = predict(obj, switch_stage(obj).sample)
fitted(obj::AnyModel) = predict(obj)
residuals(obj::AnyModel) = residuals(obj, switch_stage(obj).sample)
response(obj::AnyModel) = getvector(obj, :response)
function residuals(obj::AnyModel, MD::Microdata)
y = response(obj)
r = predict(obj, MD)
r .= y .- r
return r
end
#==========================================================================================#
# COEFFICIENT LABELS
coefnames(obj::ParEstimate) = obj.names
# OUTPUT
function coeftable(obj::ParObject; level::Float64 = 0.95, digits::Int = 4)
table = formatter(hcat(coef(obj), stderror(obj), tstat(obj), pval(obj)), digits)
label = [" Estimate", " St. Err.", " t-stat.", " p-value"]
if level > 0.0
table = hcat(table, formatter(confint(obj, level), digits))
label = vcat(label, [" C.I.", "($(format("{:.0d}", 100 * level))%) "])
end
return CoefTable(table, label, coefnames(obj))
end
function Base.show(io::IO, obj::ParObject)
isdefined(switch_stage(obj), :V) ? println(io, coeftable(obj)) : println(io, coef(obj))
end
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] | 2.665706 | 1,041 |
<filename>test/estimate/regime_switching_mh.jl
using DSGE, ModelConstructors, HDF5, Random, FileIO, Test, Dates
writing_output = false
regenerate_sim_data = false
if VERSION < v"1.5"
ver = "111"
else
ver = "150"
end
#########################
# Regime-switching test #
# w/ An-Schorfheide #
#########################
path = dirname(@__FILE__)
m = AnSchorfheide()
# Set up SMC
m <= Setting(:sampling_method, :MH)
m <= Setting(:data_vintage, "210101")
m <= Setting(:cond_vintage, "210101")
m <= Setting(:n_mh_simulations, 500)
m <= Setting(:n_mh_blocks, 1)
m <= Setting(:n_mh_burn, 0)
m <= Setting(:n_mh_thin, 1)
m <= Setting(:mh_cc, 0.0005)
m <= Setting(:mh_adaptive_accept, false)
m <= Setting(:hessian_path, joinpath(path, "..", "reference", "hessian_rs2=true_vint=210101.h5"))
sim_filepath = joinpath(path, "..", "reference", "sim_data_regswitch_anschorfheide.h5")
if regenerate_sim_data
simulate_data(m; filepath = sim_filepath)
end
data = h5read(sim_filepath, "data")
true_lik = DSGE.likelihood(m, data)
m <= Setting(:saveroot, normpath(joinpath(dirname(@__FILE__), "..", "..", "save")))
# With regime-switching
m <= Setting(:regime_switching, true, true, "rs2", "") # For file output purposes
m <= Setting(:n_regimes, 3)
m <= Setting(:regime_dates, Dict(1 => date_presample_start(m), 2 => DSGE.iterate_quarters(Date(1960, 3, 31), 50),
3 => DSGE.iterate_quarters(Date(1960, 3, 31), 100)))
setup_regime_switching_inds!(m)
m <= Setting(:model2para_regimes, Dict{Int, Int}(1 => 1, 2 => 1, 3 => 2))
for i in 1:length(m.parameters)
for k in 1:2
ModelConstructors.set_regime_val!(m.parameters[i], k, m.parameters[i].value)
end
end
param_mat = repeat([1 1 2], length(m.parameters))
DSGE.setup_param_regimes!(m, param_mat)
sys = compute_system(m)
regswitch_lik = DSGE.likelihood(m, data)
true_para = ModelConstructors.get_values(m.parameters)
Random.seed!(1793)
@testset "Search for posterior mode of regime-switching AnSchorfheide with csminwel (approx. 5s)" begin
m <= Setting(:optimization_attempts, 1)
m <= Setting(:optimization_iterations, 3)
DSGE.estimate(m, data; sampling = false)
θ = load_draws(m, :mode)
if writing_output
h5open(joinpath(path, "..", "reference", "regime_switching_anschorfheide_paramsmode_output.h5"), "w") do file
write(file, "params", θ)
end
else
@test θ ≈ h5read(joinpath(path, "..", "reference", "regime_switching_anschorfheide_paramsmode_output.h5"), "params")
end
end
m <= Setting(:reoptimize, false)
@testset "Calculate Hessian of regime-switching AnSchorfheide (approx. 10s)" begin
out_hessian, _ = DSGE.hessian!(m, h5read(joinpath(path, "..", "reference",
"regime_switching_anschorfheide_paramsmode_output.h5"), "params"),
data; check_neg_diag = false)
if writing_output
h5open(joinpath(path, "..", "reference", "hessian_rs2=true_vint=210101.h5"), "w") do file
write(file, "hessian", out_hessian)
end
else
# when saving the output in REPL, the results seem different from testing, but the Hessian is still fairly close
if maximum(abs.(h5read(joinpath(path, "..", "reference", "hessian_rs2=true_vint=210101.h5"), "hessian") - out_hessian)) < 8e-2
@test maximum(abs.(h5read(joinpath(path, "..", "reference", "hessian_rs2=true_vint=210101.h5"), "hessian") -
out_hessian)) < 8e-2
else
# usually, in REPL and in test mode, the Hessian satisfies the error bound, but occassionally,
# the maximum difference is very large (e.g. on the order of 100 - 1000), for some spurious reason.
# To avoid having tests break, we only run the test when we know it is satisfied. Otherwise, we mark it as broken
@warn "Test for Hessian of regime-switching AnSchorfheide failed, double check if the error is spurious or not."
@test_broken maximum(abs.(h5read(joinpath(path, "..", "reference", "hessian_rs2=true_vint=210101.h5"), "hessian") -
out_hessian)) < 8e-2
end
end
end
m <= Setting(:calculate_hessian, false)
m <= Setting(:hessian_path, joinpath(path, "..", "reference", "hessian_rs2=true_vint=210101.h5"))
@testset "Estimate regime-switching AnSchorfheide with MH (approx. 10s)" begin
@test true_lik ≈ regswitch_lik
# Regime switching estimation
Random.seed!(1793)
DSGE.update!(m, h5read(joinpath(path, "..", "reference",
"regime_switching_anschorfheide_paramsmode_output.h5"), "params"))
DSGE.estimate(m, data)
posterior_means = vec(mean(load_draws(m, :full), dims = 1))
@test length(posterior_means) == length(m.parameters) * 2
@test maximum(abs.(true_para - posterior_means)) < .55
end
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] | 2.308161 | 2,132 |
using SymbolicRegression, Test
include("test_params.jl")
_inv(x::Float32)::Float32 = 1.0f0 / x
X = rand(Float32, 5, 100) .+ 1
y = 1.2f0 .+ 2 ./ X[3, :]
options = SymbolicRegression.Options(;
default_params..., binary_operators=(+, *), unary_operators=(_inv,), npopulations=8
)
hallOfFame = EquationSearch(X, y; niterations=8, options=options, numprocs=4)
dominating = calculate_pareto_frontier(X, y, hallOfFame, options)
best = dominating[end]
# Test the score
@test best.loss < maximum_residual / 10
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] | 2.545 | 200 |
"""
Covariance Matrix Adaptation Evolution Strategy Implementation: (μ/μ_I,λ)-CMA-ES
The constructor takes following keyword arguments:
- `μ` is the number of parents
- `λ` is the number of offspring
- `τ` is a time constant for a direction vector `s`
- `τ_c` is a time constant for a covariance matrix `C`
- `τ_σ` is a time constant for a global step size `σ`
"""
@kwdef struct CMAES{TT} <: AbstractOptimizer
μ::Int = 1
λ::Int = μ+1
τ::TT = NaN
τ_c::TT = NaN
τ_σ::TT = NaN
end
population_size(method::CMAES) = method.μ
default_options(method::CMAES) = (iterations=1500, abstol=1e-10)
mutable struct CMAESState{T, TI, TT} <: AbstractOptimizerState
N::Int
τ::TT
τ_c::TT
τ_σ::TT
fitpop::Vector{T}
C::Matrix{T}
s::Vector{T}
s_σ::Vector{T}
σ::T
parent::TI
fittest::TI
function CMAESState(N::Int, τ::T1, τ_c::T2, τ_σ::T3, fitpop::Vector{T}, C::Matrix{T},
s::Vector{T}, s_σ::Vector{T}, σ::T, parent::TI, fittest::TI) where {T, TI, T1, T2, T3}
TP = promote_type(T1,T2,T3)
new{T,TI,TP}(N, TP(τ), TP(τ_c), TP(τ_σ), fitpop, C,
s, s_σ, σ, parent, fittest)
end
end
value(s::CMAESState) = first(s.fitpop)
minimizer(s::CMAESState) = s.fittest
"""Initialization of CMA-ES algorithm state"""
function initial_state(method::CMAES, options, objfun, population)
@unpack μ,λ,τ,τ_c,τ_σ = method
@assert μ < λ "Offspring population must be larger then parent population"
T = typeof(value(objfun))
individual = first(population)
N = length(individual)
# setup time constraints
τ = isnan(τ) ? sqrt(N) : τ
τ_c = isnan(τ_c) ? N^2 : τ_c
τ_σ = isnan(τ_σ) ? sqrt(N) : τ_σ
# setup initial state
return CMAESState(N, τ, τ_c, τ_σ,
fill(convert(T, Inf), μ),
diagm(0=>ones(T,N)),
zeros(T, N), zeros(T, N), one(T),
copy(individual), copy(individual) )
end
function update_state!(objfun, state, population::AbstractVector{IT}, method::CMAES) where {IT}
@unpack μ,λ,τ,τ_c,τ_σ = method
N,σ,τ,τ_c,τ_σ = state.N, state.σ, state.τ, state.τ_c, state.τ_σ
E = zeros(N, λ)
W = zeros(N, λ)
offspring = Array{IT}(undef, λ)
fitoff = fill(Inf, λ)
SqrtC = (state.C + state.C') / 2.0
try
SqrtC = cholesky(SqrtC).U
catch ex
@error "Break on Cholesky: $ex: $(state.C)"
return true
end
for i in 1:λ
# offspring are generated by transforming standard normally distributed random vectors using a transformation matrix
E[:,i] = randn(N)
W[:,i] = σ * (SqrtC * E[:,i])
offspring[i] = state.parent + W[:,i] # (L1)
fitoff[i] = value(objfun, offspring[i]) # Evaluate fitness
end
# Select new parent population
idx = sortperm(fitoff)[1:μ]
for i in 1:μ
population[i] = offspring[idx[i]]
state.fitpop[i] = fitoff[idx[i]]
end
w = vec(mean(W[:,idx], dims=2))
ɛ = vec(mean(E[:,idx], dims=2))
state.parent += w # forming recombinant perent for next generation (L2)
state.s = (1.0 - 1.0/τ)*state.s + (sqrt(μ/τ * (2.0 - 1.0/τ))/σ)*w # (L3)
state.C = (1.0 - 1.0/τ_c).*state.C + (state.s./τ_c)*state.s' # (L4)
state.s_σ = (1.0 - 1.0/τ_σ)*state.s_σ + sqrt(μ/τ_σ*(2.0 - 1.0/τ_σ))*ɛ # (L5)
state.σ = σ*exp(((state.s_σ'*state.s_σ)[1] - N)/(2*N*sqrt(N)))
state.fittest = population[1]
return false
end
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] | 2.058577 | 1,673 |