content stringlengths 6 1.03M | input_ids listlengths 4 535k | ratio_char_token float64 0.68 8.61 | token_count int64 4 535k |
|---|---|---|---|
<reponame>UnofficialJuliaMirrorSnapshots/Gridap.jl-56d4f2e9-7ea1-5844-9cf6-b9c51ca7ce8e<filename>test/CellValuesTests/ConstantCellValuesTests.jl
module ConstantCellValuesTests
using Test
using Gridap
using TensorValues
using ..MapsMocks
# Constructors
l = 10
sv = 1.0
scv = ConstantCellNumber(sv,l)
sv2 = 1.1
scv2 = ... | [
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... | 2.167121 | 1,101 |
<filename>examples/TwoD_block.jl
using WaterLily
using StaticArrays
function block(L=2^5;Re=250,U=1,amp=0,ϵ=0.5,thk=2ϵ+√2)
# Set viscosity
ν=U*L/Re
# Create dynamic block geometry
function sdf(x,t)
y = x .- SVector(0.,clamp(x[2],-L/2,L/2))
√sum(abs2,y)-thk/2
end
function map(x,... | [
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11,
139,... | 1.91055 | 436 |
using DifferentialEquations: ODEProblem, DifferentialEquations
@inline function __integrate(u::Function, domain::Tuple{T,T}; kwargs...) where T <: AbstractFloat
prob = ODEProblem( (f, p, τ) -> u(τ), zero(T), domain)
return DifferentialEquations.solve(prob; kwargs...)
end
@inline function __getNorm(u::Function, c::C... | [
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12... | 2.520149 | 1,613 |
## Day 22: Reactor Reboot ##
############################
struct Instr
is_on::Bool
x1::Int
x2::Int
y1::Int
y2::Int
z1::Int
z2::Int
end
function parse_line(line::String)::Instr
m = match(r"(on|off) x=(-?[0-9]+)..(-?[0-9]+),y=(-?[0-9]+)..(-?[0-9]+),z=(-?[0-9]+)..(-?[0-9]+)", line)
I... | [
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198,... | 2.094887 | 2,034 |
# Julia wrapper for header: /usr/include/scip/scip_dialog.h
# Automatically generated using Clang.jl wrap_c
function SCIPincludeDialog(scip, dialog, dialogcopy, dialogexec, dialogdesc, dialogfree, name, desc, issubmenu, dialogdata)
ccall((:SCIPincludeDialog, libscip), SCIP_RETCODE, (Ptr{SCIP_}, Ptr{Ptr{SCIP_DIALO... | [
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... | 2.452855 | 753 |
module TestBasic
using ConicHulls, ConicHulls.Common, ConicHulls.Dets, ConicHulls.Hulls
using ConicHulls.RefHull
using ConicHulls.Hulls.dominates
using ConicHulls.Insertion.find_dominated_facet
function printhull(hull::ConicHull)
println("ConicHull:")
for facet in hull.facets
println(" ", facet)
... | [
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... | 2.120732 | 820 |
<gh_stars>1-10
module HTM
using Hyperscript
export @htm_str
const UENDTAG = "<//>"
include("util.jl")
include("parse.jl")
"""
create_element(tag, attrs[, children...])
Create a DOM element.
This is an alternative syntax and (currently) serves as a rather trivial
abstraction layer inspired by
[`React.createEle... | [
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4943,... | 2.510691 | 2,011 |
using Printf
parse_line(line) = [c == '.' ? false : true for c in line]
function load(path)
# NOTE: I do not know why vcat doesn't work here, but this is just the input loader, so I don't
# care too much
hcat(map(parse_line, readlines(path))...)'
end
function check_slopes(data)
y = 1
# NOTE: We could do th... | [
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466,
407,
760,
1521,
410,
... | 2.355556 | 360 |
"""
Line(p1, p2)
Construct a `Line` geometric object (representing a segment) with given beginning and end
points, which are generally 2-vectors or 3-vectors.
"""
struct Line{N, T <: Real}
p1::SVector{N, T}
p2::SVector{N, T}
end
function Line(p1::StaticVector{N, T}, p2::StaticVector{N, T}) where {N, T <: ... | [
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... | 2.114921 | 1,079 |
<reponame>PallHaraldsson/Dash.jl
struct WatchState
filename::String
mtime ::Float64
WatchState(filename) = new(filename, mtime(filename))
end
function poll_until_changed(files::Set{String}; interval = 1.)
watched = WatchState[]
for f in files
!isfile(f) && return
push!(watched, Watch... | [
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8,... | 1.829762 | 840 |
<filename>examples/marine/rov/asfm/animateSonarSim.jl
addprocs(2)
# Visualize SONAR with Director
using TransformUtils
using KernelDensityEstimate
using IncrementalInference
using RoME
using Caesar
using DrakeVisualizer, GeometryTypes
using ColorTypes: RGBA
using MeshIO, FileIO
using CoordinateTransformations
using Ro... | [
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350... | 2.036443 | 2,058 |
<gh_stars>0
"""
constraints.jl
"""
using Altro
using LinearAlgebra
@inline bound_constraint_eval(x, u, x_max, x_min, u_max, u_min) = ([
x - x_max;
x_min - x;
u - u_max;
u_min - u;
])
function bound_constraint_jacobian(x, u, x_max, x_min, u_max, u_min)
n = length(x)
m = length(u)
p = 2 * n... | [
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7,
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11,
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11,
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... | 1.607973 | 903 |
using TensorOperations
using KrylovKit
using LinearAlgebra
using Random
function init_random_MPS(d,m,N)
MPS=Dict()
MPS[1] = im*rand(1,d,m)
for i in 2:N-1
MPS[i]= im*rand(m,d,m)
end
MPS[N] = im*rand(m,d,1)
return MPS
end
function Construct_Ising_MPO(h,J,N)
d=2
D=3
id = [ 1 ... | [
3500,
309,
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602,
198,
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7,
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11,
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11,
45,
8,
628,
220,
220,
220,
337,
3705,
28,
35,
713,
34... | 1.686456 | 1,078 |
<reponame>pkofod/StructuralEstimation.jl
using StructuralEstimation
srand(123)
# State 1
X1 = 0:1
F1 = [[1. 0.; 1. 0.], [0. 1.; 0. 1.]]
# State 2
X2 = 1:5
nX2 = length(X2)
F2 = 1./(1+abs(ones(length(X2),1)*X2'-X2*ones(1, length(X2))))
F2 = F2./sum(F2,1)'
# States
S = States(State(X1, F1), CommonState(X2, F2))
# Util... | [
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1... | 1.89698 | 563 |
# have Petsc report errors to Julia
function error_handler(comm::comm_type, line::Int32, func::Ptr{UInt8}, file::Ptr{UInt8}, n::PetscErrorCode, p::PetscErrorType, mess::Ptr{UInt8}, ctx::Ptr{Void})
# receives the error call from Petsc
func_string = bytestring(func)
file_string = bytestring(file)
if p == PETSC_ERROR_I... | [
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299,
... | 2.652755 | 599 |
<reponame>ven-k/LoopVectorization.jl<gh_stars>100-1000
function dot_simd(a::AbstractVector, b::AbstractVector)
s = zero(eltype(a))
@fastmath @inbounds @simd for i ∈ eachindex(a)
s += a[i]' * b[i]
end
s
end
function cdot_mat(ca::AbstractVector{Complex{T}}, cb::AbstractVector{Complex{T}}) where {T... | [
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8,
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220,
220,
220,
264,
... | 1.613071 | 7,880 |
function load(model::JuMP.Model, d::Data)
d.state = Loading
for variable in d.variables
load(model, variable)
end
for (index, constraint) in d.constraints
cref = load(model, constraint)
if cref !== nothing
d.transformed_constraints[index] = cref
end
end
... | [
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220,
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220,
220,
220,
220,
3440,
7,
... | 2.509333 | 375 |
isempty(ARGS) && error("Please pass a config file as command line argument.")
config_file = ARGS[1]
isfile(config_file) || error("Cannot read '$config_file'")
println("Config supplied: '$config_file'")
include(config_file)
using SVDD, OneClassActiveLearning, OneClassSampling
using JuMP
using Gurobi
using MLKernels
usi... | [
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110... | 2.357843 | 2,040 |
<filename>test/Perceptron_tests.jl
using Statistics
using Test
using DelimitedFiles
import MLJBase
const Mlj = MLJBase
using StableRNGs
using BetaML
#TESTRNG = FIXEDRNG # This could change...
TESTRNG = StableRNG(123)
println("*** Testing Perceptron algorithms...")
# ==================================
# TEST 1: Norma... | [
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4... | 2.038376 | 4,039 |
<filename>src/QueryStrategies/BatchQueryStrategies/EnumFilterHierarchicalBatchQs.jl
struct EnumFilterHierarchicalBatchQs <: MultiObjectiveBatchQs
model::SVDD.OCClassifier
inf_measure::SequentialPQs
rep_measure::Function
div_measure::Function
k::Int
function EnumFilterHierarchicalBatchQs(model::... | [
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... | 2.560256 | 780 |
<gh_stars>0
@testset "Discretization" begin
@testset "FanTriangulation" begin
pts = P2[(0.,0.), (1.,0.), (1.,1.), (0.75,1.5), (0.25,1.5), (0.,1.)]
tris = [Triangle([pts[1], pts[i], pts[i+1]]) for i in 2:length(pts)-1]
hex = Hexagon(pts)
mesh = discretize(hex, FanTriangulation())
@test nvertices(... | [
27,
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62,
30783,
29,
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31,
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2617,
366,
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1186,
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220,
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7,
15,
1539,
15,
12179,
357,
... | 1.982646 | 5,186 |
# estimate with MCMC (Turing) for conjugate prior
# compare with jlbayes_conjugate_regression.jl
using Turing
using Distributions
using Random
using LinearAlgebra
using StatsPlots
using Optim
# Generate moc data
n = 50
dim = 1
Random.seed!(99)
u = rand(Normal(0, 0.7), n) # true value σ² = 0.49
x = rand(Uniform(-sqrt(... | [
2,
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351,
13122,
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8,
329,
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1018,
378,
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2,
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351,
474,
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323,
274,
62,
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62,
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13,
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198,
198,
3500,
39141,
198,
3500,
46567,
507,
198,
3500,
14534,
198,
... | 2.525385 | 1,300 |
import Requires
function __init__()::Nothing
Requires.@require PyPlot="d330b81b-6aea-500a-939a-2ce795aea3ee" include("pyplot.jl")
Requires.@require Winston="bd07be1c-e76f-5ff0-9c0b-f51ef45303c6" include("winston.jl")
return nothing
end
| [
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198,
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15003,
834,
33529,
25,
18465,
198,
220,
220,
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31,
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65,
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12,
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64,
12,
24,
2670,
64,
12,
17,
344,
41544,
44705,
18,
... | 2.390476 | 105 |
using Printf
using Random
using GLFW
using ModernGL
using CSyntax
using CSyntax.CStatic
using CImGui
using CImGui.LibCImGui
using CImGui.GLFWBackend
using CImGui.OpenGLBackend
using ImPlot
import CImGui.LibCImGui: ImGuiCond_Always
using Formatting
import DataStructures.CircularBuffer
#using .W... | [
201,
198,
3500,
12578,
69,
201,
198,
3500,
14534,
201,
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3500,
9429,
33567,
897,
13,
34,
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201,
198,
201,
198,
3500,
327,
... | 1.831326 | 13,203 |
function free_energy_nopool(rbm::ConvRBM, v::AbstractArray; β::Real = true)
E = energy(visible(rbm), v)
I = inputs_v_to_h(rbm, v)
F = free_energy(hidden(rbm), I; β)
@assert size(E) == (vsizes(rbm, v).input_size..., vsizes(rbm, v).batch_size...)
@assert size(F) == (hsizes(rbm, I).output_size..., vsiz... | [
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7,
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20475,
3712,
3103,
85,
49,
12261,
11,
410,
3712,
23839,
19182,
26,
27169,
3712,
15633,
796,
2081,
8,
198,
220,
220,
220,
412,
796,
2568,
7,
23504,
7,
81,
20475,
828,
410,
8,
198,
2... | 2.255692 | 1,142 |
using SoftSquishyMatter # load simulation package
using Random # useful if you want to shuffle arrays
cd(dirname(@__FILE__)) # set directory to this file's folder
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Initialize empty simulation with a description for future reference
... | [
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611,
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765,
284,
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7,
31,
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25664,
834,
4008,
1303,
900,
8619,
284,
428,
2393,
338,
948... | 3.326258 | 1,649 |
export Experiment
using Dates
using ReinforcementLearningBase
using ReinforcementLearningCore
using .ReinforcementLearningEnvironments
using Flux
using BSON
using TensorBoardLogger
using Logging
function RLCore.Experiment(
::Val{:JuliaRL},
::Val{:BasicDQN},
::Val{:CartPole},
::Nothing;
save_dir = ... | [
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347,
11782,
198,
3500,
309,
22854,
... | 1.889073 | 16,858 |
<gh_stars>1-10
#!/usr/bin/env julia
module SCUTIWEN
import LightXML: parse_string, root,
child_elements, find_element,
content
import TextWrap: wrap
function main()
uri = "https://api.forismatic.com/api/1.0/?" *
"method=getQuote&format=xml&lang=en"
response = read(`curl $uri -s`, String)
le... | [
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29,
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62,
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11,
6808,
11,
198,
220,
220,
220,
1200,
62,
68,
3... | 1.823204 | 543 |
using SafeTestsets
@safetestset "Binary trend tests" begin include("binary_trend.jl") end
| [
3500,
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51,
3558,
1039,
198,
198,
31,
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1,
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62,
83,
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13,
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4943,
886,
198
] | 3.033333 | 30 |
<filename>test/UnitTests/sets.jl
# Unit Test for by default supported convex sets and their functions
using COSMO, Test, Random, LinearAlgebra
rng = Random.MersenneTwister(13131)
@testset "Convex Sets" begin
tol = 1e-4
@testset "Create and project" begin
# Zero Cone
zset = COSMO.ZeroSet(10)
x = ran... | [
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11,
6208,
11,
14534,
11,
44800,
2348,
29230,
198,
198,
81,
782,... | 2.022618 | 2,697 |
using Documenter, MixedModels, StatsBase
makedocs(
sitename = "MixedModels",
pages = ["index.md",
"constructors.md",
"optimization.md",
"GaussHermite.md",
"bootstrap.md",
"SimpleLMM.md",
"MultipleTerms.md",
"SingularCova... | [
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7,
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220,
220,
220,
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366,
44,
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5841,
1424,
1600,
198,
220,
220,
220,
5468,
796,
14631,
9630,
13,
9132,
1600,
198,
220,
... | 1.79845 | 258 |
<reponame>habemus-papadum/julia<gh_stars>0
# This file is a part of Julia. License is MIT: http://julialang.org/license
# TODO: optimize this
function Base.string(dt::DateTime)
y,m,d = yearmonthday(days(dt))
h,mi,s = hour(dt),minute(dt),second(dt)
yy = y < 0 ? @sprintf("%05i",y) : lpad(y,4,"0")
mm = lp... | [
27,
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261,
480,
29,
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385,
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79,
499,
324,
388,
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27,
456,
62,
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29,
15,
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2,
770,
2393,
318,
257,
636,
286,
22300,
13,
13789,
318,
17168,
25,
2638,
1378,
73,
377,
498,
648,
13,
2398,
14,
43... | 2.327866 | 6,219 |
<reponame>alejandromerchan/USDAQuickStats.jl
function get_nass(args...; format="json")
key = ENV["USDA_QUICK_SURVEY_KEY"]
header = string(usda_url, "/api/api_GET/?key=", key, "&format=$format")
query = ""
for i in args
arg = string("&", i)
query *= arg
end
req... | [
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220,
220,
220,
1994,
... | 2.098837 | 172 |
<reponame>UnofficialJuliaMirror/PlanOut.jl-307e1779-6603-555d-8172-ea684b6e5cfa<filename>src/ops/random.jl<gh_stars>1-10
LONG_SCALE = float(0xFFFFFFFFFFFFFFF)
maybeAppendUnit(unit::Any, appended_unit) = maybeAppendUnit([unit], appended_unit)
function maybeAppendUnit(unit::AbstractArray, appended_unit)
if length(appe... | [
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12,
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41580,
65,
21,
68,
20,
12993,
64,
27,
34345,
29,
10677,
1... | 2.55243 | 1,564 |
include("./exportData.jl")
using .exportData, DelimitedFiles
inds = [78, 88, 100, 106, 114]
output = Array{Any,2}(undef,(1002,length(inds)+1));
for (n,ind) in enumerate(inds)
t, data, BCL = getExpData(ind; tInds=1:1000);
if n==1
output[1,:] .= vcat("#ind", inds)
output[2,1] = "#BCLs"
output[3:end,1] .= t.-t[... | [
17256,
7,
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14,
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11,
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15690,
90,
7149,
11,
... | 1.977556 | 401 |
#-------------------------------------------------------------------------------
# File: sat_encoding.jl
# Description: This file contains all the fuctions needed to encode
# an SGP instance into an SAT model.
# Date: December 10, 2019
# Author: <NAME>, <NAME>,
# <NAME>, <NAME>, <NAME>
# TBH : This was mostly ... | [
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281,
29020,
2746,
13,
198,
2,
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25,
3426,
83... | 2.108302 | 2,096 |
using DiffEqFlux, Test
u0 = Float32[2.0; 0.0]
dudt2 = FastChain((x, p) -> x.^3,
FastDense(2, 50, tanh),
FastDense(50, 2))
p = initial_params(dudt2)
@test !DiffEqSensitivity.hasbranching(dudt2,u0,p)
dudt2 = FastChain((x, p) -> x.^3,
StaticDense(2, 4, tanh),
... | [
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8,
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13,
61,
18,
11,
198,
220,
220,
22... | 1.717742 | 248 |
<filename>test/test_cardinality.jl
using Infinities, Base64, Base.Checked, Test
@testset "InfiniteCardinal" begin
@testset "basics" begin
@test !isone(ℵ₀)
@test !iszero(ℵ₀)
@test sign(ℵ₀) ≡ 1 && !signbit(ℵ₀)
@test angle(ℵ₀) ≡ 0
@test Integer(∞) ≡ convert(Integer,∞) ≡ Integer... | [
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1,
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198,
220,
220,
220,
248... | 1.293287 | 3,754 |
<filename>plotlyjs_basics.jl<gh_stars>0
using PlotlyJS
using DataFrames, RDatasets
function linescatter()
trace1 = scatter(x=1:4, y=[10, 15, 13, 18])
plot(trace1)
end
linescatter()
function multiple_scatter_traces()
trace1 = scatter(;x=1:4, y=[10, 15, 13, 17], mode="markers", name="marker only")
trac... | [
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292,
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198,
198,
8818,
3951,
66,
1436,
3419,
198,
220,
220,
220,
... | 1.905099 | 2,687 |
using Test
using CLIMA.SurfaceFluxes
using CLIMA.SurfaceFluxes.Nishizawa2018
using CLIMA.SurfaceFluxes.Byun1990
using CLIMA.MoistThermodynamics
using RootSolvers
# FIXME: Use realistic values / test for correctness
# These tests have been run to ensure they do not fail,
# but they need further testing for correctness... | [
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7852,
3955,
32,
13,
14214,
2550,
37,
22564,
274,
13,
... | 1.653548 | 1,677 |
module AccurateArithmetic
export
four, two, negone, onehalf, onequarter,
fourx, twox, halfx, quarterx,
#=
These functions appear in the literature, named similarly.
=#
two_sum, two_diff, two_prod,
fast_two_sum, fast_two_diff,
ufp, ulp, splitting, extractscalar,
#=
Thes... | [
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15537,
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198,
220,
220,
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220,
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11,
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11,
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505,
11,
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11,
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220,
220,
220,
220,
220,
220,
1440,
87,
11,
734,
87,
11,
2063,
87,
... | 2.057661 | 607 |
<reponame>cbw124/Catlab.jl<gh_stars>0
module CategoricalAlgebra
using Reexport
include("FreeDiagrams.jl")
include("Limits.jl")
include("Sets.jl")
include("FinSets.jl")
include("Matrices.jl")
include("FinRelations.jl")
include("CSets.jl")
include("ACSetViews.jl")
include("GraphCategories.jl")
include("StructuredCospan... | [
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198,
198,
17256,
7203,
11146,
18683,
6713,
82,
13,
20362,
4943,
... | 2.679803 | 203 |
<filename>lib/YaoSym/src/symengine/blocks.jl
using YaoBlocks
using LuxurySparse
using LinearAlgebra
using ..SymEngine
using ..SymEngine: BasicType, BasicOp, BasicTrigFunction
op_types = [:Mul, :Add, :Pow]
const BiVarOp = Union{[SymEngine.BasicType{Val{i}} for i in op_types]...}
export @vars
simag = SymFunction("Im")... | [
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43094,
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198,
3500,
11485,
43... | 2.182932 | 2,367 |
using ForwardDiff
using ReverseDiff
using Measures
include("helper_functions.jl")
begin
λ1 = 0.4
λ2 = 1.5
α = 1
μ(α) = 1 / (1.5 * (α))
ν(α) = 1 / (0.7 * (α))
parameters = (λ1, λ2, α, μ, ν)
opponent_parameters = (λ2, λ1, α, μ, ν)
p, q = equilibrium_probability(parameters...)
step = 0... | [
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220,
220,
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7... | 2.083857 | 1,908 |
<reponame>omdowley/DynamicalBilliards.jl<filename>src/DynamicalBilliards.jl<gh_stars>10-100
__precompile__()
"""
A Julia package for dynamical billiard systems in two dimensions.
The goals of the package is to provide a flexible, easy-to-use
and intuitive framework for
fast implementation of billiard systems o... | [
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29,
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12,
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198,
834,
3866,
5589,
576,
83... | 2.749164 | 897 |
abstract type Model end
struct Zerodominant <: Model end #trait that highlights the regulation mechanism of a type
# struct Onedominant <: Model end
## future work: regulation network for conductances. Onedominant is not used for now.
## trait functions
model(::Type{<:RegIonChannel}) = Zerodominant()
#model(::Type{<... | [
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2003,
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25,
90... | 2.397974 | 691 |
<filename>src/write_outputs/dftranspose.jl<gh_stars>0
"""
GenX: An Configurable Capacity Expansion Model
Copyright (C) 2021, Massachusetts Institute of Technology
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software ... | [
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29,
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198,
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55,
25,
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11970,
29765,
25042,
9104,
198,
15269,
357,
34,
8,
33448,
11,
220,
10140,
... | 3.565674 | 571 |
<reponame>uzaynagme/LazySets.jl
# ========================
# Sampling from a LazySet
# ========================
"""
AbstractSampler
Abstract type for defining new sampling methods.
### Notes
All subtypes should implement a `sample!(D, X, ::Method)` method where the
first argument is the output (vector of vector... | [
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198,
2,
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1421,
18604,
198,
198,
37811,
198,
220,
220,
220,
27741... | 2.422516 | 5,143 |
<gh_stars>0
using ArgParse
s = ArgParseSettings()
@add_arg_table s begin
"--part2"
help = "Do part 2"
action = :store_true
"--test"
help = "Run test data"
action = :store_true
end
parsed_args = parse_args(ARGS, s)
mutable struct SNumber
value :: Union{UInt32, Tuple{SNumber, SNumber}}
end
... | [
27,
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62,
30783,
29,
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20559,
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31,
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220,
220,
220,
366,
438,
3911,
17,
1,
198,
220,
220,
220,
1037,
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... | 1.963799 | 2,127 |
module TestGraphvizWiringDiagrams
using Test
import JSON
using Catlab.Theories, Catlab.WiringDiagrams, Catlab.Graphics
import Catlab.Graphics: Graphviz
using Catlab.Graphics.WiringDiagramLayouts: position, normal
# Drawing
#########
function stmts(graph::Graphviz.Graph, type::Type)
[ stmt for stmt in graph.stmts ... | [
21412,
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37065,
85,
528,
54,
3428,
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6208,
198,
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198,
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5181,
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13,
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11,
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13,
54,
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18683,
6713,
82,
11,
5181,
23912,
13,
18172,
198,
11748,
5181,... | 2.570946 | 1,184 |
# Portions translated from SLICOT-Reference distribution
# Copyright (c) 2002-2020 NICONET e.V.
function run_mb04zd(datfile, io=stdout)
NIN = 5
NOUT = 6
NMAX = 20
LDA = NMAX
LDQG = NMAX
LDU = NMAX
LDWORK = ( NMAX+NMAX )*( NMAX+NMAX+1 )
ZERO = 0.0e0
ONE = 1.0e0
A = Array{Float64,2... | [
2,
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507,
14251,
422,
12419,
2149,
2394,
12,
26687,
6082,
198,
2,
15069,
357,
66,
8,
6244,
12,
42334,
45593,
1340,
2767,
304,
13,
53,
13,
198,
8818,
1057,
62,
2022,
3023,
89,
67,
7,
19608,
7753,
11,
33245,
28,
19282,
448,
8,
... | 1.755872 | 1,405 |
<filename>viaCheb.jl
module bycheb
using LinearAlgebra
include("cheb.jl")
using .cheb
export ring
EXTEND = 0.0 #0.5
"""
Σ is an even function of x
Φ is an odd function of x
U is an odd function of x
"""
mutable struct ring
N::Integer # number of nodes for full ring
nn::Integer
parmd::Dict{... | [
27,
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29,
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198,
39344,
5858,
198,
198,
13918,
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657,
... | 1.847994 | 1,421 |
#broad_pdf.jl
using JSON
function create_broad_pdf(n_x::Int, n_y::Int, n_z::Int, dependence::Float64=0.) :: Dict{Tuple{Int,Int,Int},BigFloat}
@assert n_x ≥ 2 "create_broad_pdf(): n_x >= 2 needed"
@assert n_y ≥ 2 "create_broad_pdf(): n_y >= 2 needed"
@assert n_z ≥ 2 "create_broad_pdf(): n_z >= 2 needed"
... | [
2,
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13,
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62,
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3712,
5317,
11,
21403,
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43879,
2414,
28,
15,
2014,
7904,
... | 1.870546 | 842 |
<reponame>FHoltorf/MomentClosure.jl<gh_stars>10-100
using MomentClosure
using MomentClosure: define_M, define_μ
using Test
using Catalyst
@parameters t, k_on, k_off, k_p, γ_p, b
@variables p(t), g(t)
vars = [g, p]
ps = [k_on, k_off, k_p, γ_p, b]
S = [1 -1 0 0;
0 0 b -1]
as = [k_on*(1-g), # 0 -> ... | [
27,
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29,
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198,
3500,
6208,... | 2.062063 | 1,144 |
<filename>examples/SkyTEM1D/gradientbased/03_make_model_fits_to_noise.jl
using PyPlot, DelimitedFiles, Random, Statistics, Revise,
transD_GP
## model fixed parts, i.e., air
Random.seed!(23)
zfixed = [-1e5]
ρfixed = [1e12]
nmax = 200
# Note that the receiver and transmitter need to be in layer 1
zstart = 0.0
e... | [
27,
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11,
4216,
320,
863,
25876,
11,
14534,
11,
14370,
11,
... | 1.72349 | 3,244 |
using Test
using Bridge, StaticArrays, Distributions
using Statistics, Random, LinearAlgebra
POSSIBLE_PARAMS = [:regular, :simpleAlter, :complexAlter, :simpleConjug,
:complexConjug]
SRC_DIR = joinpath(Base.source_dir(), "..", "src")
include("test_ODE_solver_change_pt.jl")
include("test_blocking.jl... | [
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25,
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11,
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36439,
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353,
11,
1058,
41887,
... | 2.669421 | 121 |
<gh_stars>1-10
"""
market_hours(market::AbstractString, date=today())
market_hours(market::Array, date=today())
Get hours of a market in the future:
https://developer.tdameritrade.com/market-hours/apis/get/marketdata/%7Bmarket%7D/hours
Get hours of multiple markets:
https://developer.tdameritrade.com/market-h... | [
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... | 2.542955 | 582 |
<filename>src/LinearEvolution.jl
"""
Linear evolution of the NP equations: this includes the
Teukolsky equation and the Metric reconstruction equations.
For more details see
Loutrel et. al. Phys.Rev.D 103 (2021) 10, 104017, arXiv:2008.11770
Ripley et. al. Phys.Rev.D 103 (2021) 1040180, arXiv:2010.00162
""... | [
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29,
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... | 1.434974 | 20,861 |
<filename>test/load.jl
try
using TestFunctionRunnerTests
true
catch
false
end || begin
let path = joinpath(@__DIR__, "TestFunctionRunnerTests")
path in LOAD_PATH || push!(LOAD_PATH, path)
end
using TestFunctionRunnerTests
end
| [
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220,
220,
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796,... | 2.58 | 100 |
# include("time_parse.jl")
# include("multifrequency-opf.jl")
# include("utilities.jl")
# function to run multifrequency_opf on one case within the given time range.
#
# parameters:
# month_st, day_st, period_st, month_en, day_en, period_en: the parameters specifying the period to plot
# folder: the directory containi... | [
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53... | 2.082713 | 10,470 |
<gh_stars>1-10
@testset "Term and Polynomial tests" begin
@testset "Term" begin
@polyvar x
# @test coefficienttype(1x) == Int
# @test coefficienttype(1.0x^2) == Float64
# @test coefficienttype(Term{true, Int}) == Int
@test zeroterm(Term{false, Int}).α == 0
@test one(Term{tr... | [
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220,
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220,
220,
2488,
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7785,
212... | 1.944291 | 1,813 |
<gh_stars>100-1000
using Flux
using Flux: @treelike
struct ResidualBlock{L,S}
layers::L
shortcut::S
end
struct ConvNorm{C, N}
conv::C
norm::N
end
(cn::ConvNorm)(value) = cn.norm(cn.conv(value))
@treelike ResidualBlock
@treelike ConvNorm
function ResidualBlock(filters, kernels::Array{Tuple{Int,Int}}, pads::A... | [
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30783,
29,
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25,
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198,
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11,
50,
92,
198,
220,
11685,
3712,
43,
198,
220,
29401,
3712,
50,
198... | 2.479543 | 1,051 |
<reponame>HatsuneMiku/Win32API.jl<gh_stars>0
# Win32API
VERSION >= v"0.4.0-dev+6521" && __precompile__()
module Win32API
export MessageBoxA, MessageBoxW
function MessageBoxA(
hwnd::Ptr{Void}, msg::AbstractString, title::AbstractString, opt::Int)
return ccall((:MessageBoxA, :user32), stdcall,
UInt, (Ptr{Void}... | [
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410,
1,
15,
13,
19,
13,
15,
12,
7959,
10,
2996,
2481,
1,
11405,... | 2.268116 | 276 |
<reponame>SimonTreillou/PlantBiophysics.jl
"""
struct to hold the parameters for Medlyn et al. (2011) stomatal
conductance model for CO₂.
# Arguments
- `g0`: intercept.
- `g1`: slope.
- `gs_min = 0.001`: residual conductance. We consider the residual conductance being different
from `g0` because in practice `g0` can... | [
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8,
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296,
10254,
198,
36495,
590,
2746,
329,
7... | 2.397041 | 1,622 |
using Sundials, Test
println("Test error handling")
f_error(u,p,t) = u/t
u0 = 1.0
prob = ODEProblem(f_error,u0,(0.0,1.0))
sol = solve(prob,CVODE_BDF())
sol = solve(prob,CVODE_BDF(),verbose=false)
f_error2(du,u,p,t) = u/t-1
u0 = 1.0; du0 = 1.0
prob = DAEProblem(f_error2,u0,du0,(0.0,1.0),differential_vars=[1])
sol = s... | [
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440,
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7,
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62,
... | 2.044554 | 202 |
<gh_stars>1-10
function nlp_cvx_206_010(
optimizer,
objective_tol,
primal_tol,
dual_tol,
termination_target = TERMINATION_TARGET_LOCAL,
primal_target = PRIMAL_TARGET_LOCAL,
)
# Test Goals:
# - linear objective
# - intersection convex quadratic constraints
# - power cones
# Va... | [
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62,
30783,
29,
16,
12,
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62,
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7,
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220,
220,
220,
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11,
198,
220,
220,
220,
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62,
83,
349,
11,
198,
220,
220,
220,
43750,
62,
83,
349,
11,
198... | 2.28046 | 435 |
export spgline
"""
Non-monotone linesearch
"""
function spgline(A::TA,
f::Tf,
d::AbstractVector{<:Number},
gtd_in::Number,
x::AbstractArray{ETx},
fMax::Tf,
funForward::Function,
funPenalty::Function,
... | [
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599,
70,
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15419,
12,
2144,
313,
505,
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198,
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599,
70,
1370,
7,
32,
3712,
5603,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
... | 1.788618 | 2,460 |
<gh_stars>0
sprites = [
[
0b11110000, # ****
0b10010000, # * *
0b10010000, # * *
0b10010000, # * *
0b11110000 # ****
]
[
0b00100000, # *
0b01100000, # **
0b00100000, # *
0b00100000, # *
0b01110000 # ***
]
[
0b11110000, # ****
0b00010000, # ... | [
27,
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65,
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220,
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198,
220,
220,
220,
... | 1.764398 | 1,146 |
# This file is auto-generated by AWSMetadata.jl
using AWS
using AWS.AWSServices: polly
using AWS.Compat
using AWS.UUIDs
"""
delete_lexicon(lexicon_name)
delete_lexicon(lexicon_name, params::Dict{String,<:Any})
Deletes the specified pronunciation lexicon stored in an AWS Region. A lexicon which has
been delete... | [
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... | 3.430259 | 4,402 |
function Newton_Spectral(
nlp::AbstractNLPModel,
x₀::AbstractVector;
τ₀::Float64 = 0.0005,
ϵ::Float64 = 1e-6,
maxiter::Int = 200,
)
x = copy(x₀)
iter = 0
f, g = obj(nlp, x), grad(nlp, x)
while (norm(g, Inf) > ϵ) && (iter <= maxiter)
H = Matrix(Symmetric(hess(nlp, x), :L))
Δ, O = eigen(H)
... | [
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45,
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158,
224,
222,
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26,
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220,
46651,
158,
224,
222,
3712,
43879,
2414,
796,
657,
13,
830,
20,
11,
198,
220,... | 1.929293 | 396 |
<filename>individual_implementations/btg julia/kernel.jl
using Random
using LinearAlgebra
using Distances
"""
Gaussian/RBF/Squared Exponential correlation function
"""
function rbf(x, y, θ=1.0)
1/sqrt(2*pi)*exp.(-θ*0.5*(norm(x .- y))^2)
end
"""
Gaussian/RBF/Squared Exponential correlation function
"""
function rb... | [
27,
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29,
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62,
320,
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602,
14,
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70,
474,
43640,
14,
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198,
3500,
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198,
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44800,
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198,
198,
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198,
35389,
31562,
14,
27912,
37,
14,
22266,
1144,
55... | 2.128583 | 1,221 |
<gh_stars>1-10
using Test, SeisModels
@testset "Conversion" begin
@testset "To LinearLayeredModel" begin
# PREMPolyModel
let m = LinearLayeredModel(PREM)
@test m isa LinearLayeredModel
# Default spacing
@test maximum(diff(m.r)) == 20
@test minimum(dif... | [
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62,
30783,
29,
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2617,
366,
2514,
44800,
23763,
1068,
17633,
1,
2221,
198,
220,
22... | 1.825683 | 1,939 |
# Autogenerated wrapper script for HDF5_jll for aarch64-apple-darwin
export libhdf5, libhdf5_hl
using Zlib_jll
using OpenSSL_jll
using LibCURL_jll
JLLWrappers.@generate_wrapper_header("HDF5")
JLLWrappers.@declare_library_product(libhdf5, "@rpath/libhdf5.200.dylib")
JLLWrappers.@declare_library_product(libhdf5_hl, "@rp... | [
2,
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20,
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1168,
8019,
62,
73,
297,
19... | 2.061828 | 372 |
<filename>src/ExampleSubmodule.jl
"""
This module is an example of how to create and import a submodule.
Functions can be exported using `export` and are then usable when the parent
module has a line like `using .ExampleSubmodule`.
"""
module ExampleSubmodule
export anexamplefunction
function anexamplefunction()
p... | [
27,
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29,
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14,
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7004,
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13,
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198,
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198,
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281,
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284,
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2733,
460,
307,
29050,
1262,
4600,
39344,
63,
290,
389,
788,
... | 3.909091 | 99 |
using Plots; pyplot(fmt = :png)
using SolidStateDetectors
using Unitful
T = Float32
simulation = Simulation{T}(SSD_examples[:InvertedCoax])
plot(simulation.detector, size=(700, 700))
apply_initial_state!(simulation, ElectricPotential) # optional
plot(
plot(simulation.electric_potential), # initial electric poten... | [
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26,
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796,
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51,
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1741,
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41798,
90,
51,
92,
7,
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35,
62,
1069,
12629... | 2.566062 | 772 |
<gh_stars>0
using SignatureGB
using Test
using Singular
SG = SignatureGB
include("./tests.jl")
| [
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7,
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13,
20362,
4943,
198
] | 2.852941 | 34 |
<filename>src/indexes/pivotselectiontables.jl<gh_stars>0
# Copyright 2016-2019 <NAME> <<EMAIL>>
#
# 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/lice... | [
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739,
... | 2.839844 | 512 |
using Dispersal, Test
init = [1.0 4.0 7.0;
2.0 5.0 8.0;
3.0 6.0 9.0]
output = ArrayOutput(init; tspan=1:2)
rule = AlleeExtinction(minfounders = 8.0)
sim!(output, rule)
@test output[1] == [1.0 4.0 7.0;
2.0 5.0 8.0;
3.0 6.0 9.0]
@test output[2] == [0.0 0.0 0.0;
... | [
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15,
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220,
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220,
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220,
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220... | 1.498054 | 257 |
include("../imports.jl")
@testset ExtendedTestSet "StopEarly" begin
learner = testlearner(Recorder(), Metrics(), EarlyStopping(1), ProgressPrinter(), coeff = 3, opt = Descent(0.1))
@test_throws CancelFittingException begin
@suppress fit!(learner, 100)
end
@test_nowarn print(Base.DevNull(), Ear... | [
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333... | 2.717949 | 156 |
<filename>scripts/JX/large_batch/large_batch.jl
using DrWatson, PyCall
@quickactivate "GCN_HM_GRN-Integration"
pushfirst!(PyVector(pyimport("sys")."path"), "");
run_sim = pyimport("large_batch").run_sim
allparams = Dict(
:layer => ["arma", "sage", "tag"],
:batch => [100, 200],
:cl => ["E116"]
)
dicts = ... | [
27,
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1... | 2.288952 | 353 |
"""
A rule contains all the information required to run a rule in a cellular
simulation, given an initial array. Rules can be chained together sequentially.
The output of the rule for an Rule is allways written to the current cell in the grid.
"""
abstract type Rule end
show(io::IO, rule::R) where R <: Rule = begin
... | [
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1... | 3.26335 | 824 |
<reponame>aerappa/Gridap.jl
"""
struct SubVector{T,A<:AbstractVector{T}} <: AbstractVector{T}
vector::A
pini::Int
pend::Int
end
"""
struct SubVector{T,A<:AbstractVector{T}} <: AbstractVector{T}
vector::A
pini::Int
pend::Int
end
Base.size(a::SubVector) = (1+a.pend-a.pini,)
@propagate_i... | [
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... | 2.238095 | 294 |
<reponame>nignatiadis/RegressionDiscontinuity.jl
using .Empirikos
using Zygote
using LinearFractional
using StatsFuns
abstract type AbstractRegressionDiscontinuityTarget end
abstract type TargetedRegressionDiscontinuityTarget <: AbstractRegressionDiscontinuityTarget end
struct ConstantTarget <: AbstractRegressionDisc... | [
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37,
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282,
198,
3500,
20595,
37,
13271... | 2.037315 | 11,604 |
<gh_stars>10-100
using Test, LinearAlgebra, StaticArrays
using DomainSets
using CompositeTypes.Indexing
include("test_common.jl")
include("test_maps.jl")
include("test_generic_domain.jl")
include("test_specific_domains.jl")
include("test_canonical.jl")
include("test_setoperations.jl")
include("test_applications.jl")
... | [
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198,
198,
17256,
7203,
9288,
62,
11321,
13,
20362,
4943,... | 2.957265 | 117 |
<gh_stars>1-10
using FactCheck, MarketData
FactCheck.setstyle(:compact)
FactCheck.onlystats(true)
facts("last methods takes the first observations") do
context("defaults to n=1") do
@fact last(cl).values[1] --> 21.9
end
context("takes other n values") do
@fact last(cl, 2).values[1] --> ... | [
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7,
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8,
198,
198,
37473,
7203,
12957,
5050,
2753,
262,
717,... | 2.474453 | 137 |
@testset "mesh" begin
eM = eMesh{Tri,Tet}()
push!(eM.point, SVector{3,Float64}(NaN, NaN, NaN))
@test_throws ErrorException verify_mesh(eM)
push!(eM.ϵ, NaN)
append!(eM, output_eMesh_half_plane())
mesh_remove_unused_points!(eM)
@test length(eM.point) == 4
append!(eM, output_eMesh_half_p... | [
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90,
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11,
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92,
7,
... | 1.841121 | 1,177 |
<reponame>karajan9/statisticalrethinking
using DrWatson
@quickactivate "StatReth"
# %%
using DataFrames
using CSV
using StatsBase
using Distributions
using StatsPlots
# %% 4.7 - 4.11
d = DataFrame(CSV.File(datadir("exp_raw/Howell_1.csv")))
# precis(d)
d.height
d2 = filter(row -> row.age >= 18, d) # either
d2 = d[... | [
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2,
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198,
3500,
6060,
35439,
198,
3500,
44189,
198,
3500,
20595,
... | 2.20603 | 995 |
"""
Provide an API for the interface defined in StatsBase.
cov(ce::CovarianceEstimator, X::AbstractMatrix, [w::AbstractWeights]; mean=nothing, dims::Int=1)
Compute the covariance matrix of the matrix `X` along dimension `dims`
using estimator `ce`. A weighting vector `w` can be specified.
The keyword argument `mean... | [
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23839,
1135,
2337,
11208,
1612,
... | 2.544693 | 716 |
# # Single degree-of-freedom
using ReachabilityAnalysis, StructuralDynamicsODESolvers
# ### Equations of motion
# Struct that holds a problem describing an harmonic oscillator with frequency ω:
# x''(t) + ω^2 x(t) = 0
#
# solution x(t) = Acos(ωt + B), v(t) = -ωAsin(ωt + B)
# x(0) = Acos(B)
# v(0) = -ωAsin(B)
#
# spe... | [
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2,
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257,
1917,
12059,
281,
492... | 2.010761 | 2,602 |
rel(path::AbstractString) = joinpath(splitdir(@__FILE__)[1], path)
facts("OHLC backtest with timearray input") do
# using quantstrat goldencross test
# details in teststrategy_goldencross.jl
ohlc_BA = TimeSeries.readtimearray(
rel("quantstrat/goldencross/data/OHLC_BA_2.csv"))
targetfun = TradingLogic.gold... | [
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5128,
4943,
466,
198,
220,
1303,
1262,
55... | 2.373357 | 2,207 |
# This file is a part of JuliaFEM.
# License is MIT: see https://github.com/JuliaFEM/FEMBasis.jl/blob/master/LICENSE
import Base: length, size
function length{T<:AbstractBasis}(B::T)
return length(T)
end
function size{T<:AbstractBasis}(B::T)
return size(T)
end
function eval_basis!{T<:AbstractBasis}(B::T, N,... | [
2,
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318,
257,
636,
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3620,
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271,
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14,
2436,
672,
14,
9866,
14,
43,
2149,
24290,
... | 1.74325 | 3,778 |
<reponame>nwh/QuasiDefinite.jl
using QuasiDefinite
using Base.Test
# generate random nxn spd matrix
function gen_spd(n,seed=0)
srand(seed)
A = rand(n,n)
A = A*A' + eye(n)
A
end
function test_qdtf2!(A,uplo::Char)
m, n = size(A)
LD = copy(A)
LD, info = QuasiDefinite.qdtf2!(uplo,LD)
@test... | [
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198,
8818,
2429,
62,
2777,
67,
7,
77,
... | 1.769231 | 442 |
<filename>test/helper/validate_sdd.jl
# helper test functions to check for SDD properties holding
function validate(sdd::Sdd)
for node in linearize(sdd) # linearize first so that callee can use bit field
validate_node(node)
end
#TODO make one of these for structured decomposability
@test isdecompos... | [
27,
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29,
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7,
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50,
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8,
198,
220,
220,
329,
10139,
287,
... | 2.376989 | 817 |
<gh_stars>10-100
# Test for infinite Bi-Lanczos
using NonlinearEigenproblemsTest
using NonlinearEigenproblems
using Test
using LinearAlgebra
@bench @testset "Infbilanczos σ=0" begin
nep=nep_gallery("qdep0");
nept=SPMF_NEP([copy(nep.A[1]'), copy(nep.A[2]'), copy(nep.A[3]')], nep.fi)
n=size(nep,1);
m=4... | [
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198,
2,
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198,
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36,
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198,
3500,
6208,
198,
3500,
44800,
... | 1.762781 | 1,467 |
<filename>test/conversions.jl<gh_stars>0
module TestConversions
using Base.Test
using DataArrays
@assert isequal(@data([1, 2, NA]),
convert(DataArray, @pdata([1, 2, NA])))
# Test vector() and matrix() conversion tools
dv = @data ones(5)
@assert isa(array(dv), Vector{Float64})
@assert isa(convert(... | [
27,
34345,
29,
9288,
14,
1102,
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13,
20362,
27,
456,
62,
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29,
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6208,
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198,
197,
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13,
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198,
197,
3500,
6060,
3163,
20477,
628,
197,
31,
30493,
318,
40496,
7,
31,
7890,
26933,
16,... | 2.44403 | 268 |
using Atum
using Atum.Euler
using PGFPlotsX
using StaticArrays: SVector
function sod(law, x⃗)
FT = eltype(law)
ρ = x⃗[1] < 1 // 2 ? 1 : 1 // 8
ρu⃗ = SVector(FT(0))
p = x⃗[1] < 1 // 2 ? 1 : 1 // 10
ρe = Euler.energy(law, ρ, ρu⃗, p)
SVector(ρ, ρu⃗..., ρe)
end
import Atum: boundarystate
boundarystate(law::E... | [
3500,
1629,
388,
198,
3500,
1629,
388,
13,
36,
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198,
198,
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3646,
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55,
198,
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25,
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198,
198,
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7,
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11,
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158,
225,
245,
8,
198,
220,
19446,
796,
1288... | 2.008526 | 821 |
# This file is a part of BAT.jl, licensed under the MIT License (MIT).
struct DensityWithDiff{D<:AbstractDensity,VJP<:DifferentiationAlgorithm} <: AbstractDensity
vjpalg::VJP
density::D
end
@inline Base.parent(density::DensityWithDiff) = density.density
vjp_algorithm(density::DensityWithDiff) = density.vjp... | [
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257,
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3920,
2348,
42289,
92,
... | 3.052885 | 624 |
<filename>src/laguerre.jl<gh_stars>1-10
"""
struct LaguerreBasis{P} <: AbstractMultipleOrthogonalBasis{P}
polynomials::Vector{P}
end
Orthogonal polynomial with respect to the univariate weight function ``w(x) = \\exp(-x)`` over the interval ``[0, \\infty]``.
"""
struct LaguerreBasis{P} <: AbstractMulti... | [
27,
34345,
29,
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263,
260,
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271,
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25,
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31217,
5574,
400,
519,
20996,
1552... | 2.365079 | 378 |
<gh_stars>10-100
## Example script for constant-strength source panel method
using LinearAlgebra
using Base.Iterators
using Seaborn
using AeroMDAO
## Airfoil
airfoil = Foil(naca4((0,0,1,2), 81; sharp_trailing_edge = true))
V, α = 1., 0.
ρ = 1.225
uniform = Uniform2D(V, α)
num_pans = 80
panels = paneller(airf... | [
27,
456,
62,
30783,
29,
940,
12,
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198,
2235,
17934,
4226,
329,
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198,
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2348,
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198,
3500,
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198,
3500,
1001,
397,
1211,
198,
3500,
44720,
44,
5631,
46,
198,
... | 2.559413 | 749 |
using Documenter, ParticleSwarmOptimizer
makedocs(
format = :html,
assets = ["assets/pso_animation_preview.gif"],
sitename = "ParticleSwarmOptimizer Documentation",
authors = "<NAME>",
pages = [
"Home" => "index.md",
"Settings" => Any[
"Parameter" => "parameter.md",
... | [
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16854,
263,
11,
2142,
1548,
10462,
1670,
27871,
320,
7509,
198,
198,
76,
4335,
420,
82,
7,
198,
220,
220,
220,
5794,
796,
1058,
6494,
11,
198,
220,
220,
220,
6798,
796,
14631,
19668,
14,
79,
568,
62,
11227,
341,
62,
3866,
11... | 2.139205 | 352 |
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