content stringlengths 6 1.03M | input_ids listlengths 4 535k | ratio_char_token float64 0.68 8.61 | token_count int64 4 535k |
|---|---|---|---|
<filename>test/parsemultipart.jl
using Test
using HTTP
import HTTP.MultiPartParsing: find_multipart_boundary, find_multipart_boundaries, find_header_boundary, parse_multipart_chunk, parse_multipart_body, parse_multipart_form
function generate_test_body()
Vector{UInt8}("----------------------------91807372115006157... | [
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... | 2.512783 | 2,073 |
#=
primal dual point
=#
mutable struct Point{T <: Real}
vec::Vector{T}
x::SubArray{T, 1, Vector{T}, Tuple{UnitRange{Int}}, true}
y::SubArray{T, 1, Vector{T}, Tuple{UnitRange{Int}}, true}
z::SubArray{T, 1, Vector{T}, Tuple{UnitRange{Int}}, true}
tau::SubArray{T, 0, Vector{T}, Tuple{Int}, true}
... | [
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2... | 2.106145 | 895 |
<filename>deps/build.jl
using BinDeps
@BinDeps.setup
xgboost = library_dependency("xgboost", aliases = ["libxgboost.so"])
if haskey(ENV, "XGBOOST_BUILD_VERSION") && ENV["XGBOOST_BUILD_VERSION"] == "master"
libcheckout = `git checkout master`
onload = "global const build_version = \"master\""
info("Using ... | [
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... | 2.111811 | 635 |
using Distributions
using Turing
using Test
# Define model
@model ad_test2(xs) = begin
s ~ InverseGamma(2,3)
m ~ Normal(0,sqrt.(s))
xs[1] ~ Normal(m, sqrt.(s))
xs[2] ~ Normal(m, sqrt.(s))
s, m
end
# Run HMC with chunk_size=1
chain = sample(ad_test2([1.5 2.0]), HMC(300, 0.1, 1))
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14... | 2.155556 | 135 |
# Optimized Product Quantization. Adapted from <NAME>'s code.
export train_opq, quantize_opq
"""
quantize_opq(X, R, C, V=false) -> B
Given data and PQ/OPQ codeboks, quantize.
# Arguments
- `X::Matrix{T}`: `d`-by-`n` data to quantize
- `R::Matrix{T}`: `d`-by-`d` rotation to apply to the data before quantizing
- ... | [
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... | 2.453968 | 2,520 |
using BinaryBuilder, Pkg
using Base.BinaryPlatforms: arch, os
include("../../fancy_toys.jl")
name = "CUDNN"
version = v"8.2.2"#.26
script = raw"""
mkdir -p ${libdir} ${prefix}/include
cd ${WORKSPACE}/srcdir
if [[ ${target} == powerpc64le-linux-gnu ]]; then
cd cuda/targets/ppc64le-linux
find .
install_l... | [
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9641... | 2.398907 | 732 |
<gh_stars>1-10
# The SteinChampionLenardMillsKernel is based on the reference
# "An introduction to abstract splines" written in 1996 by the
# aforementioned authors. The kernel is a kernel for the Sobolev
# space H^2([0,1]) subject to the extra condition that f(0) = f(1) = 0.
# The kernel is of the form
#
# k(x, y) = ... | [
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... | 2.417848 | 1,199 |
using Gadfly, DataArrays, RDatasets
set_default_plot_size(6inch, 3inch)
plot(dataset("car", "UN"), x=:GDP, y=:InfantMortality,
Geom.histogram2d, Scale.x_log10, Scale.y_log10)
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... | 2.192771 | 83 |
snd(x::String) = append!(played, registers[x])
set(x::String, y::String) = registers[x] = registers[y]
set(x::String, y::Int) = registers[x] = y
add(x::String, y::String) = registers[x] += registers[y]
add(x::String, y::Int) = registers[x] += y
mul(x::String, y::String) = registers[x] *= registers[y]
mul(x::String, y:... | [
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11,... | 1.86695 | 1,413 |
<reponame>OrchardLANL/DPFEHM.jl
ForwardDiff_gradient(x...) = ForwardDiff.gradient(x...)
function kr(psi, alpha, N)
if psi < 0
m = (N - 1) / N
denom = 1 + abs(alpha * psi) ^ N
numer = 1 - abs(alpha * psi) ^ (N - 1) * denom ^ (-m)
return numer ^ 2 / denom ^ (m / 2)
else
return one(psi)
end
end
kr(x::Abstra... | [
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8,... | 2.377928 | 2,220 |
<filename>src/ghost_exchange.jl
#=
Functions related to exchanging ghost data.
=#
"""
ExchangeGroup
A description of information that needs to be communicated between processes during
a ghost exchange. It includes the ID of the involved processes, the included elements,
and the variable and component indices. The... | [
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using BandedMatrices, FillArrays, Test
import LinearAlgebra: axpy!
import LazyArrays: DenseColumnMajor
import BandedMatrices: BandedColumns, bandeddata
@testset "BandedMatrix SubArray" begin
@testset "BandedMatrix SubArray interface" begin
A = brand(10,10,1,2)
V = view(A,2:4,3:6)
@test isba... | [
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... | 1.592324 | 2,762 |
using Xpress
mpb_path = ""
if VERSION < v"0.7"
mpb_path = Pkg.dir("MathProgBase")
else
import MathProgBase
mpb_path = joinpath(dirname(pathof(MathProgBase)),"..")
end
include(joinpath(mpb_path,"test","linproginterface.jl"))
println("Testing linproginterface with solver Xpress.XpressSolver")
linprogsolvert... | [
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1... | 2.565327 | 398 |
using Documenter, EconUtils
makedocs(
format = :html,
sitename = "EconUtils.jl",
pages = [
"index.md",
"GettingStarted.md",
"API.md",
"Examples.md",
"References.md"
]
)
deploydocs(
deps = Deps.pip("pygments", "mkdocs", "python-markdown-math"),
repo = "gi... | [
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54... | 1.95098 | 204 |
# These tests checks that varying μ0, μB results in the correct scale
# transformations in various energies / fields. Note:
# does not check that the actual absolute values are
# correct.
seed = 1111
μBs = [1, 2, 1, 4]
μ0s = [1, 1, 2, 3]
crystal = Sunny.diamond_crystal()
latsize = (4, 4, 4)
function collect_energy_a... | [
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198,... | 2.093101 | 1,203 |
<filename>src/IDLREPL.jl
import Base: LineEdit, REPL
function idl_repl()
# Setup idl prompt
prompt = LineEdit.Prompt("IDL> ";
prompt_prefix=Base.text_colors[:blue],
prompt_suffix=Base.text_colors[:white])
repl = Base.active_repl
promp... | [
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... | 1.889286 | 1,120 |
"""
ilc_weights(cij)
This function returns weights (a vector of the number of frequency channels) of the ILC method.
*Reference*: Equation (12) of Tegmark et al., Phys. Rev. D, 68, 123523 (2003)
# Arguments
- `cij::Array{<:AbstractFloat,2}`: symmetric covariance matrix with the dimention of `(nν, nν)` where `nν`... | [
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... | 1.975254 | 3,839 |
using Wanderer
using Test
DataSource1 = Dict(
:a => [1, 2, 3],
:b => [2, 3, 4],
:c => [3, 4, 5]
)
DataSource2 = Dict(
:a => ["a", "b", "c"],
:b => [2, 3, 4],
:c => [3, 4, 5]
)
@testset "Wanderer.jl" begin
@test (DataSource1 |> @select _.a + _.b + _.c => sum)[:sum] == [6, 9, 12]
@test ... | [
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220,... | 1.876289 | 582 |
if basename(pwd()) == "aoc"
cd("2017/11")
end
function hexdistance(path::AbstractString)
position = [0, 0]
for step in split(path, ',')
position .+= if step == "n"
[0, 2]
elseif step == "ne"
[1, 1]
elseif step == "se"
[1, -1]
elseif step =... | [
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1... | 1.831307 | 658 |
<reponame>lfsc507/MMI2<gh_stars>0
using ArgParse
using JLD2
parser = ArgParseSettings(allow_ambiguous_opts=false)
@add_arg_table! parser begin
"main"
required = true
"patch"
required = true
"--overwrite"
arg_type = Bool
default = true
end
args = parse_args(parser)
results =... | [
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... | 2.433898 | 295 |
#jl #! format: off
# # Bar and stack plots with [PowerGraphics.jl](github.com/nrel-siip/PowerGraphics.jl)
# PowerGraphics also provides some basic specifications for plotting `SimulationResults`.
# This example demonstrates some simple plotting capabilities using different Plots.julia
# backends.
#
# The plotting capa... | [
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6... | 3.642857 | 1,008 |
<gh_stars>1-10
function reset_distribution(p::LaserTagPOMDP, b::ParticleCollection, a, o)
# warn("Resetting Particle Filter Distribution")
rob = first(particles(b)).robot
nextrob = LaserTag.add_if_inside(p.floor, rob, LaserTag.ACTION_DIRS[a])
if o == LaserTag.C_SAME_LOC
return ParticleCollection... | [
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... | 2.398089 | 314 |
<gh_stars>0
using StableRNGs
@testset "Manifolds" begin
rng = StableRNG(213)
# Test case: find eigenbasis for first two eigenvalues of a symmetric matrix by minimizing the Rayleigh quotient under orthogonality constraints
n = 4
m = 2
A = Diagonal(range(1, stop=2, length=n))
fmanif(x) = real(dot... | [
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... | 2.025087 | 1,435 |
<gh_stars>0
using LinearAlgebra, MATLAB, ForwardDiff, SparseArrays, Printf
using Convex, Hypatia
const FD = ForwardDiff
include("/Users/kevintracy/.julia/dev/CPEG/src/qp_solver.jl")
function dynamics(x,u)
r = x[1:3]
v = x[4:6]
return [v;u[1:3] + [0;0;-9.81]]
end
function rk2(x,u)
Δt = u[4]
k1 = Δt... | [
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17256... | 1.675477 | 1,362 |
<gh_stars>1-10
export left_env
function LinearAlgebra.dot(ϕ::CuMPS, ψ::CuMPS)
C = CUDA.ones(eltype(ψ), 1, 1)
for i ∈ eachindex(ψ)
M = ψ[i]
M̃ = conj.(ϕ[i])
@cutensor C[x, y] := M̃[β, σ, x] * C[β, α] * M[α, σ, y] order = (α, β, σ)
end
return tr(C)
end
function left_env(ϕ::CuMPS... | [
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220,
220,
327,
796,
29369,
56... | 1.607666 | 887 |
using DimensionalData, Test, Unitful
using DimensionalData: X, Y, Z, Time, Start
a = [1 2; 3 4]
dimz = (X((143, 145)), Y((-38, -36)))
da = DimensionalArray(a, dimz)
@testset "getindex for single integers returns values" begin
@test da[X(1), Y(2)] == 2
@test da[X(2), Y(2)] == 4
end
@testset "getindex returns ... | [
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2... | 2.001524 | 2,624 |
<reponame>UnofficialJuliaMirror/TensorDecompositions.jl-04ed911b-6d5f-4088-a74e-60d2d5028204
"""
High-order singular value decomposition (HO-SVD).
"""
function hosvd(tensor::AbstractArray{T,N}, core_dims::NTuple{N, Int};
pad_zeros::Bool=false, compute_error::Bool=false) where {T,N}
pad_zeros || _chec... | [
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18... | 2.11753 | 502 |
using BenchmarkTools, JLD
# Define a parent BenchmarkGroup to contain our suite
#const suite = BenchmarkGroup()
#suite["psMethods"] = BenchmarkGroup(["integrationScheme","Nck"])
paramspath = joinpath(dirname(@__FILE__), "params.jld")
loadparams!(suite, BenchmarkTools.load(paramspath, "suite"), :evals, :samples);
res... | [
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2,
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862,
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8973,
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25187,
4102,
... | 2.963115 | 244 |
"""
Stores the results from running the benchmarks on a package.
The following (unexported) methods are defined on a `BenchmarkResults` (written below as `results`):
* `name(results)::String` - The commit of the package benchmarked
* `commit(results)::String` - The commit of the package benchmarked. If the package re... | [
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... | 2.446857 | 2,625 |
@enum mjtWarning begin # warning types
WARN_INERTIA = 0 # (near) singular inertia matrix
WARN_CONTACTFULL # too many contacts in contact list
WARN_CNSTRFULL # too many constraints
WARN_VGEOMFULL # too many visual geoms
WARN_BADQPOS ... | [
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... | 1.943022 | 2,773 |
##
import Optim: minimizer, optimize, minimum
mutable struct OptFun
opt
fun_idx::Int
end
string(opt::OptFun) = string(string(opt.opt),"\t", enumerate(BBOBFunction)[opt.fun_idx])
function benchmark(op::OptFun, run_lengths, Ntrials, dimensions, Δf)
f = enumerate(BBOBFunction)[op.fun_idx]
optimizer = o... | [
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246... | 2.109415 | 1,179 |
function factoredFastMarching3D(pEik::EikonalParam, mem::EikonalTempMemory)
kappaSquared = pEik.kappaSquared;
h = pEik.Mesh.h;
src = pEik.src;
n = pEik.Mesh.n.+1;
HO = pEik.HO;
N = prod(n);
if isempty(pEik.T1)
pEik.T1 = zeros(Float64,tuple(n...));
end
T = pEik.T1;
if isempty(pEik.order... | [
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... | 1.788672 | 1,642 |
# _____ _____ ____ _____ ____ ____ ____ ___ ___ ____
# |_ _| ____/ ___|_ _/ ___| | _ \ / ___|/ _ \ / _ \| _ \
# | | | _| \___ \ | | \___ \ _____| |_) |____| | _| | | | | | | | | |
# | | | |___ ___) || | ___) |_____| _ <_____| |_| | |_| | |_| | |_| |
# |_| |_____|____/ |_| |_... | [
2,
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... | 2.002387 | 6,704 |
<filename>src/rand/rocRAND.jl<gh_stars>100-1000
module rocRAND
using ..HSA
using ..AMDGPU
using ..AMDGPU: librocrand, mark!, wait!
using CEnum
export rand_logn!, rand_poisson!, rand_logn, rand_poisson
include("librocrand_common.jl")
include("error.jl")
include("librocrand.jl")
function version()
s = string(ROCR... | [
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... | 2.359477 | 306 |
using Logging
using PortfolioOpt
using JuMP
"""
worst_case_return(decision::Array{Float64,1}, formulation::PortfolioOpt.RobustBertsimas, solver)
Returns worst case return (WCR) in Bertsimas's uncertainty set ([`RobustBertsimas`](@ref)) for a defined decision:
$(PortfolioOpt._portfolio_return_latex_RobustBertsima... | [
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... | 2.468211 | 1,856 |
using MathepiaModels
using DifferentialEquations
using Plots
using Test
const DE = DifferentialEquations
@testset "SIRbasic" begin
u_0 = [1000, 0.1, 0]
p_data = (Λ = 0, d = 0, α = 0, N = 1000, β = 0.2, γ = 0.1)
tspan_data = (0.0, 100.0)
prob_data = DE.ODEProblem(SIRbasic, u_0, tspan_data, p_data)
da... | [
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3... | 2.072934 | 1,234 |
<gh_stars>10-100
@testset "GammaLikelihood" begin
for args in ((), (1.0,), (exp,), (ExpLink(),), (1.0, exp), (1.0, ExpLink()))
lik = GammaLikelihood(args...)
@test lik isa GammaLikelihood{Float64,ExpLink}
@test lik.α == 1
end
lik = GammaLikelihood(1.0)
test_interface(lik, SqExpo... | [
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357,
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11,
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357,
16870,
11280,
22784,
828,
357... | 2.261905 | 168 |
"""Train a decision tree using the ID algorithm."""
function id3(dataset::AbstractArray{T, 2}, target::Integer,
attributes::AbstractArray{String}) where {T}
targets = dataset[:, target]
classes = unique(targets)
nclasses = length(classes)
node = Node()
# Check if there is only one class... | [
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220... | 2.259494 | 790 |
using AcousticFWI,Seismic,PyPlot
function main()
ns = 4
ng = 250
nz = 150
nx = 250
nt = 1024
nf = 1024
dz = 0.01
dx = 0.01
dt = 0.002
f0 = 10.
fmin = 0.5
fmax = 30.
ext = 50
atten_max = 2.5
vp = 2.1*ones(nz,nx)
vp[61:100,:] = 3.0
vp[101:end,:] = 2.... | [
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... | 1.794473 | 1,158 |
@testset "ϵ-support" begin
a, b = ϵsupport(inv, 1)
@test a ≈ -1 atol = 1e-2
@test b ≈ +1 atol = 1e-2
end
@testset "Quad Rule" begin
q = adaptive_G_quad(identity)
@test q[1] ≈ 0 atol = 1e-14
q = adaptive_G_quad(identity; a = 0)
@test q[1] ≈ 1/2 atol = 1e-14
end | [
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... | 1.883117 | 154 |
"""
have_trace_compile
`true` if Julia is running with `--trace-compile`
"""
function have_trace_compile()
jloptstc = Base.JLOptions().trace_compile
jloptstc == C_NULL && return false
return true
end
"""
force_trace_compile(::String)
Force the trace compile to be enabled for the given file.
Note... | [
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220... | 2.600551 | 363 |
include("polyagamma_sampler.jl")
function _mcmc_iter_pg(
latent::AbstractLatent,
parameters::Vector{<:AbstractParameters},
responses_val::Vector{Float64},
W_val::Vector{Float64};
sampling = true
)
latent.posterior = __posterior(latent, parameters, responses_val, W_val)
_chain_a... | [
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2... | 2.450458 | 1,201 |
# This file is auto-generated by AWSMetadata.jl
using AWS
using AWS.AWSServices: gamelift
using AWS.Compat
using AWS.UUIDs
"""
accept_match(acceptance_type, player_ids, ticket_id)
accept_match(acceptance_type, player_ids, ticket_id, params::Dict{String,<:Any})
Registers a player's acceptance or rejection of a... | [
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... | 3.327148 | 81,807 |
<gh_stars>10-100
using KrylovMethods
using Test
using LinearOperators
include("getDivGrad.jl")
@testset "blockCG" begin
# small full system
A = [4.0 1; 1 4]
rhs = randn(2,2)
X,flag,relres,iter,resvec = blockCG(A,rhs,tol=1e-15,out=2,storeInterm=true)
@test norm(A*X[:,:,end]-rhs)/norm(rhs) <= 1e-14
# test message and f... | [
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2,... | 2.065728 | 426 |
"""
simdat_crossed([RNG], subj_n, item_n;
subj_btwn=nothing, item_btwn=nothing, both_win=nothing,
subj_prefix="S", item_prefix="I")
Return a row table with a design specified by the:
* number of subjects (`subj_n`),
* number of items (`item_n`)
* between-subject factors (`subj... | [
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220,
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... | 2.347709 | 1,484 |
<reponame>UnofficialJuliaMirror/AWSCore.jl-4f1ea46c-232b-54a6-9b17-cc2d0f3e6598
@testset "QueueURL" begin
expected = "http://queue.amazonaws.com/123456789012/testQueue"
xml = """
<CreateQueueResponse>
<CreateQueueResult>
<QueueUrl>
http://queue.amazonaws.... | [
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19... | 1.890258 | 2,515 |
<reponame>iliailmer/StructuralIdentifiability.jl
@testset "Power series matrix inverse" begin
T, t = Nemo.PowerSeriesRing(Nemo.GF(2^31 - 1), 50, "t"; model=:capped_absolute)
for d in 1:5
S = Nemo.MatrixSpace(T, d, d)
for case in 1:20
M = S([random_ps(T) for i in 1:d, j in 1:d])... | [
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... | 1.942771 | 332 |
isalnum(c) = isletter(c) || isnumeric(c)
is_url_char(c) = ((@assert UInt32(c) < 0x80); 'A' <= c <= '~' || '$' <= c <= '>' || c == '\f' || c == '\t')
is_mark(c) = (c == '-') || (c == '_') || (c == '.') || (c == '!') || (c == '~') ||
(c == '*') || (c == '\'') || (c == '(') || (c == ')')
is_userinfo_char(c)... | [
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19841... | 1.905884 | 6,237 |
<reponame>yebai/Turing.jl
using Turing
using DelimitedFiles
using BenchmarkHelper
if !haskey(BenchmarkSuite, "nuts")
BenchmarkSuite["nuts"] = BenchmarkGroup(["nuts"])
end
fname = joinpath(dirname(@__FILE__), "sv_nuts.data")
y, header = readdlm(fname, ',', header=true)
# Stochastic volatility (SV)
@model functio... | [
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494... | 2.11811 | 381 |
#jl #! format: off
# # Bar and stack plots with [PowerGraphics.jl](github.com/nrel-siip/PowerGraphics.jl)
# PowerGraphics also provides some basic specifications for plotting `SimulationResults`.
# This example demonstrates some simple plotting capabilities using different Plots.julia
# backends.
#
# The plotting capa... | [
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3769,
6... | 3.60539 | 1,039 |
<reponame>danielzhaotongliu/MALTrendsWeb<filename>backend/anime_data/snapshots_239.jl
{"score_count": 50668, "score": 8.17, "timestamp": 1580207201.0}
{"score_count": 49867, "score": 8.17, "timestamp": 1575130427.0}
{"score_count": 47758, "score": 8.2, "timestamp": 1564324308.0}
{"score_count": 49234, "score": 8.18, "t... | [
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358... | 2.364651 | 1,075 |
<filename>src/opt/vars.jl<gh_stars>1-10
"""
# The default options for [`WebsocketClient`](@ref)
!!! info "maxReceivedFrameSize"
`0x100000::Integer` 1MiB
The maximum frame size that the client will accept
!!! info "maxReceivedMessageSize"
`8 * 0x100000::Integer` 8MiB
The maximum assembled message size... | [
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31,
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8,
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10185,
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9806,
3041,
... | 2.910174 | 2,939 |
mutable struct ParamTable
m :: Matrix{String}
end
"""
$(SIGNATURES)
Holds symbol, name, description, value.
All as formatted text.
Used for constructing formatted parameter tables.
"""
ParamTable() = ParamTable(Matrix{String}(undef, 0, 4));
ParamTable(n :: Integer) = ParamTable(fill("", n, 4));
Base.length(p... | [
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11,
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11,
1988,
13,
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3237,
355,
... | 2.483871 | 713 |
"""
Farquhar–von Caemmerer–Berry (FvCB) model for C3 photosynthesis (Farquhar et al., 1980;
von Caemmerer and Farquhar, 1981) coupled with a conductance model.
The definition:
- `Tᵣ`: the reference temperature (°C) at which other parameters were measured
- `VcMaxRef`: maximum rate of Rubisco activity (``μmol\\ m^{-2... | [
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... | 2.449328 | 6,848 |
<reponame>antonior92/ParallelTrainingNN.jl
#*************************************************************************#
#
# Bias Type
#
#*************************************************************************#
"""
Bias(n)
Initialize `ParametricModel` representing bias operation `z = x + Θ`.
`n` is the input vecto... | [
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9,
2,
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198,
220,
220,
220,
34... | 2.724719 | 534 |
@testset "Dual Type" begin
h = LPHRepresentation(spzeros(Int, 2, 2), [1, 2], [3, 4], [4, 5], [6, 7])
p = @inferred polyhedron(h)
@test p isa SimplePolyhedron{2, Rational{BigInt}, LPHRepresentation{2, Rational{BigInt}, SparseMatrixCSC{Rational{BigInt},Int}}, Polyhedra.Hull{2, Rational{BigInt}, Vector{Rationa... | [
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685,
18,
11,
604,
4357,
685,
19,
11,
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4357,
685,
... | 2.356851 | 978 |
module FunRefsTest
using Comm, GIDs, FunRefs
using Base.Test
# Test basic operations
function test_basic(T::Type)
i = 1
ri = FunRef{T}(i)
@test ri[] == i
rn = FunRef{Union{}}()
global DONE = false
rexec(mod1(2, Comm.nprocs())) do
remote1(i, ri)
end
while !DONE yield() end
end
... | [
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7,
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8,
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220,
220,
220,
1312,
796,
... | 1.995201 | 1,667 |
# Copyright (c) 2017: <NAME> and contributors
# Copyright (c) 2017: Google Inc.
#
# Use of this source code is governed by an MIT-style license that can be found
# in the LICENSE.md file or at https://opensource.org/licenses/MIT.
# We fake the supertype to aid method dispatch
struct SolFileResults <: MOI.ModelLike
... | [
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318,
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416,
281,
17168,
12,
7635,
5964,
326,
460,
307,
1043,
198,... | 2.349036 | 5,604 |
module DGMethods
using MPI
using StaticArrays
using DocStringExtensions
using KernelAbstractions
using KernelAbstractions.Extras: @unroll
using ..MPIStateArrays
using ..Mesh.Grids
using ..Mesh.Topologies
using ..Mesh.Filters
using ..VariableTemplates
using ..Courant
using ..BalanceLaws:
BalanceLaw,
AbstractSta... | [
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7378,
... | 2.622739 | 774 |
# ---
# notebook: nothing
# ---
| [
2,
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2,
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198
] | 2.909091 | 11 |
# # Cells in Literate Julia
#
# The `{:cell}` syntax for [executable cells](#) is not limited to just
# markdown files. It will work the same across other source types as well.
# This page illustrates this using literate Julia.
#
# To use cells in a Julia file just add `{:cell}` at the end of a comment block
# before t... | [
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13,
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481,
670,
262,
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1973,
58... | 3.340087 | 691 |
<gh_stars>0
# module Ecto.Repo
module Schema
import ....Ecto
import ....Ecto: Changeset, InvalidChangesetError
## insert!
function insert!(repo::Base.Random.UUID, adapter, struct_or_changeset::Union{Ecto.Schema.t,Changeset.t}, opts::Dict)::Ecto.Schema.t
(isok, schema_or_changeset) = insert(repo, adapter, struct_... | [
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<filename>src/tuners/tuners.jl
### MCTunerState subtypes hold the samplers' temporary output used for tuning the sampler
abstract type MCTunerState end
mutable struct BasicMCTune <: MCTunerState
step::Real # Stepsize of MCMC iteration (for ex leapfrog in HMC or drift stepsize in MALA)
accepted::Integer # Number o... | [
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397,
8709,
2099,
337,
4177,
38886,... | 3.248047 | 512 |
<gh_stars>1-10
if abspath(PROGRAM_FILE) == @__FILE__
println("OK")
quit(1)
end
| [
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8,
198,
437,
198
] | 2 | 44 |
<reponame>p2t2/Scruff.jl<filename>src/sfuncs/conddist/discretecpt.jl
export
DiscreteCPT
"""
function DiscreteCPT(range::Vector{O}, paramdict::Dict{I, Vector{Float64}}) where {I <: Tuple, O}
Constructs an sfunc that represents a Discrete Conditional Probability table.
`DiscreteCPT`s are implemented as a `Tabl... | [
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... | 2.571233 | 730 |
<filename>src/snv.jl<gh_stars>0
function simulate_snv(seq, chr, start, profile_snv)
seqstr = seq.seq
for snv in profile_snv
if chr == snv["chrom"] && start <= snv["pos"] && start+length(seqstr) > snv["pos"]
seqstr = mutate(seqstr, start, snv)
# println(seqstr)
end
end... | [
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198... | 2.247525 | 303 |
<reponame>JuliaApproximation/BasisFunctions.jl<gh_stars>1-10
"""
A `WeightedDict` represents some function f(x) times an existing dictionary.
"""
struct WeightedDict{S,T} <: DerivedDict{S,T}
superdict :: Dictionary{S}
weightfun
end
WeightedDict(superdict::Dictionary{S,T},weightfun) where {S,T} = WeightedDi... | [
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... | 2.624375 | 2,601 |
<filename>src/game/tictactoe.jl
export TicTacToe
const BLANK = Int8(3)
struct TicTacToe
player::Int
board::Vector{Int8}
state::Int
end
TicTacToe() = TicTacToe(1, fill(BLANK,9), 100)
Base.isequal(x::TicTacToe, y::TicTacToe) = isequal(x.board, y.board)
Base.hash(x::TicTacToe) = hash(x.board)
function tra... | [
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220,
... | 1.923826 | 1,129 |
<filename>test/runtests.jl
using BHTsne
using Base.Test
@test 1 == 1
| [
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3558,
13,
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3500,
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13,
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198,
198,
31,
9288,
352,
6624,
352,
198
] | 2.413793 | 29 |
struct WikiCorpus{S}
path::S
end
WikiCorpus() = WikiCorpus(datadep"English WikiCorpus v1.0")
MultiResolutionIterators.levelname_map(::Type{WikiCorpus}) = [
:doc=>1, :document=>1,
:section=>2,
:para=>3, :line=>3, :paragraph=>3,
:sent=>4, :sentence=>4,
:word=>5, :token=>5,
:char=>6, :characte... | [
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198,
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4965,
... | 1.856201 | 1,669 |
<reponame>StephanSiemen/cfgrib.jl<filename>src/cfgrib.jl
module cfgrib
const cfgrib_jl_version = "0.0.0"
include("constants.jl")
include("cfmessage.jl")
include("indexing.jl")
include("dataset.jl")
end
| [
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13... | 2.440476 | 84 |
<filename>src/004.jl<gh_stars>0
function solve_004()
# Get all products of three digit numbers
prods = zeros(Int64, 900, 900)
for i=100:999, j=100:999
prods[i-99, j-99] = i*j
end
palindromes = filter(x -> x==reverse_int(x), prods)
return maximum(palindromes)
end | [
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... | 2.244275 | 131 |
<reponame>PhilipVinc/YaoBlocks.jl
using YaoBase
"""
content(x)
Returns the content of `x`.
"""
content(x::AbstractContainer) = x.content
"""
chcontent(x, blk)
Create a similar block of `x` and change its content to blk.
"""
chcontent(x::AbstractContainer, blk) = chsubblocks(x, blk)
subblocks(x::AbstractCo... | [
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... | 2.994019 | 836 |
struct Input{T}
year::Vector{Int}
month::Vector{Int}
day::Vector{Int}
hour::Vector{Int}
SW::Vector{T}
LW::Vector{T}
Sf::Vector{T}
Rf::Vector{T}
Ta::Vector{T}
RH::Vector{T}
Ua::Vector{T}
Ps::Vector{T}
end
Base.@kwdef struct Constants{T}
cp::T = 1005 # Speci... | [
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90,
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220,
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38469,
90,
5317... | 2.294211 | 3,144 |
module SandboxTests
import ..Pkg # ensure we are using the correct Pkg
using Test
using UUIDs
using Pkg
using ..Utils
test_test(fn, name; kwargs...) = Pkg.test(name; test_fn=fn, kwargs...)
test_test(fn; kwargs...) = Pkg.test(;test_fn=fn, kwargs...)
@testset "Basic `test` sandboxing" begin
# also indirectly... | [
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9... | 2.24247 | 664 |
<reponame>JuliaPlasma/ElectromagneticFields.jl<gh_stars>1-10
@doc raw"""
Penning trap with magnetic bottle in (x,y,z) coordinates.
Based on <NAME>, <NAME>, <NAME>, <NAME>, Study of adaptive symplectic methods for
simulating charged particle dynamics, Journal of Computational Dynamics 6, 429-448, 2019.
The covari... | [
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89... | 2.045936 | 1,415 |
WandererSymbol(x...) = Symbol(join(["Wanderer", x...], "."))
ARG = WandererSymbol("ARG") # we just need limited mangled symbols here.
TYPE_ROOT = WandererSymbol("TYPE_ROOT")
IN_TYPES = WandererSymbol("IN", "TYPES")
IN_FIELDS = WandererSymbol("IN", "FIELDS")
IN_SOURCE = WandererSymbol("IN", "SOURCE")
OUT_TYPES = Wand... | [
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7203,
1503,
38,
4943,
1303,
356,
655,
761,
3614,
... | 2.387309 | 457 |
<reponame>carterian8/CollectiveDynamics.jl<gh_stars>1-10
### ============== ### ============== ###
## Short and Long-range ##
## interactions models ##
## (topological short-range) ##
## <NAME> ##
## EXAMPLE SIMULATION SCRIPT ##
### ============== ### =====... | [
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... | 2.518382 | 1,360 |
using Test
include("cas_infer.jl")
ex1 = :(cos(1 + 3.0) + 4 + (4-4im))
ex2 = :("ciao" * 2)
ex3 = :("ciao" * " mondo")
@test ComplexF64 == infer(ex1)
@test_throws MethodError infer(ex2)
@test String == infer(ex3)
| [
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19,
12,
19,
320,
4008,
198,
1069,
17,
796,
1058,
7203,
66,
13481,
... | 2.18 | 100 |
function uncertainty_set_with_index(i, data, ϵ)
set = uncertainty_set(data[i, :], ϵ)
return collect(zip(fill(i, length(set)), set))
end
function combine(a::NTuple{K, Tuple{CartesianIndex{M}, Vector{Float64}}}, s::NTuple{N, Int})::Array{Float64, N} where {M, N, K}
@assert M == N - 1
p = zeros(s...)
... | [
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113,
8,
198,
220,
220,
220,
1441,
2824,
7,
13344,
7,
20797,
... | 2.25 | 384 |
using AbstractTrees: AbstractShadowTree
abstract type AbstractJLBoostTree <: AbstractShadowTree end
mutable struct JLBoostTreeModel
jlt::Vector
loss # this should be a function with deriv defined
target::Symbol
end
"""
trees(jlt::JLBoostTreeModel)
Return the trees from a tree-model
"""
trees(jlt::JLBoostTreeMod... | [
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198,
... | 2.320563 | 1,279 |
<gh_stars>1-10
#=
The robust control problem for a monopolist with adjustment costs. The
inverse demand curve is:
p_t = a_0 - a_1 y_t + d_t
where d_{t+1} = \rho d_t + \sigma_d w_{t+1} for w_t ~ N(0,1) and iid.
The period return function for the monopolist is
r_t = p_t y_t - gam (y_{t+1} - y_t)^2 / 2 - c y_t
... | [
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532,
... | 2.149527 | 1,585 |
<reponame>Balinus/DimensionalData.jl
basetype(x) = basetype(typeof(x))
basetype(t::Type) = t.name.wrapper
basetype(t::UnionAll) = t
f <| x = f(x)
# This shouldn't be hard coded, but it makes plots tolerable for now
shorten(x::AbstractFloat) = round(x, sigdigits=3)
shorten(x) = x
# Nothing doesn't string
getstring(:... | [
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2963,
431,
7,
83,
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6030,
8,
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256,
13,
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13,
48... | 2.5 | 144 |
const known_vargroups = Dict(
"Atmosphere"=>[
"aerosol_optical_thickness_1610",
"aerosol_optical_thickness_550",
"aerosol_optical_thickness_555",
"aerosol_optical_thickness_659",
"aerosol_optical_thickness_865",
"air_temperature_2m",
"o... | [
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7,
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1,
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1,
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58,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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349,
62,
8738,
605,
62,
400,
624,
1108,
62,
1433,
940,
1600,
... | 1.746799 | 1,406 |
for n in [2, 20, 200, 2000]
x = randn(Float32, n, n)
model = Dense(n, n)
println("CPU n=$n")
run_benchmark(model, x, cuda=false)
println("CUDA n=$n")
run_benchmark(model, x, cuda=true)
end
| [
1640,
299,
287,
685,
17,
11,
1160,
11,
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11,
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198,
220,
220,
220,
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77,
7,
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11,
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8,
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220,
220,
2746,
796,
360,
1072,
7,
77,
11,
299,
8,
198,
220,
220,
220,
44872,
... | 2.028037 | 107 |
## Example:
## DGP - Bivariate VAR(1) Model from Kilian, RESTAT, 1998
# B11 set to 0.01
using VectorAutoregressions
using Plots
plotly()
using Random, LinearAlgebra
using Statistics, GrowableArrays
const T,K = 1000,2
const H = 24
const nrep = 1000
const p = 1
const intercept = false
const burnin = 100
# Random.seed... | [
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198,
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16541,
382,
5914,
507,
198,
3500,
13... | 2.044872 | 780 |
<gh_stars>0
#= Copyright (c) 2016 Facebook
This program is free software; you can redistribute it and/or
modify it under the terms of version 2 of the GNU General Public
License as published by the Free Software Foundation.
Ported to Julia by <NAME>, 2021
=#
ccall(:jl_exit_on_sigint, Cvoid, (Cint,), 0)
... | [
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220,
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286,
2196,
362,
286,
262,... | 2.035063 | 3,565 |
<reponame>ziyiyin97/ADCME.jl<filename>deps/Plugin/MPITensor/test_mpi_tensor_solve.jl
include("ops.jl")
using Test
mpi_init()
A = Float64[1 0 2 0 3
0 0 1 2 3
0 3 4 1 0
0 4 2 1 0
1 1 2 0 1]
A = A * A'
B = SparseTensor(A)
rhs = rand(5)
ilower = 0
iupper = 4
solver = "GMRES"
printlevel = 2
rows, ncols,... | [
27,
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480,
29,
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62,
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13,
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198,
17256,
7203,
2840,... | 2.062077 | 886 |
<reponame>scuervo91/LinearSolvers.jl
function ThomasLU(A)
#A = the matrix of coefficients 'A' must be squared
m,n =size(A)
if m != n
error("Matrix must be squared")
end
e=zeros(m-1)
f=zeros(m)
g=zeros(m-1)
f[1]=A[1,1]
for i=2:m
e[i-1]=A[i,i-1]/f[i-1]
... | [
27,
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261,
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29,
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198,
220,
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32,
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262,
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286,
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705,
32,
6,
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307,
44345,
201,
198,
20... | 1.568254 | 315 |
include(normpath(joinpath(@__DIR__, "IOM", "src", "models", "EMOM", "EMOM.jl")))
using NCDatasets
using MPI
using Formatting
MPI.Init()
println("Processing data...")
using TOML
config = TOML.parsefile("data/config.toml")
init_POP_file = "hist/paper2021_POP2_CTL.pop.h.daily.0002-01-01.nc"
domain_file = config["MODEL... | [
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198,
3500... | 2.140221 | 813 |
<filename>src/algorithm.jl
@params struct AugLag2 <: AbstractOptimizer
primaloptimizer
dualoptimizer
end
function AugLag2(;
primaloptimizer = Optim.ConjugateGradient(linesearch=Optim.LineSearches.BackTracking(iterations = 10)),
dualoptimizer = Optim.GradientDescent(linesearch=Optim.LineSearches.BackTra... | [
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2... | 2.189443 | 3,410 |
<filename>src/batches/Batches.jl
module Batches
import ..Flux
include("batch.jl")
end
| [
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198,
437,
198
] | 2.617647 | 34 |
<filename>src/SolidStateDetectors.jl
# This file is a part of SolidStateDetectors.jl, licensed under the MIT License (MIT).
__precompile__(true)
module SolidStateDetectors
using LinearAlgebra
using Random
using Statistics
using ArraysOfArrays
using Interpolations
using IntervalSets
using JSON
using La... | [
27,
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29,
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14,
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669,
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357,
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737,
201,
198,
201,
198,
834,
3866,
5589,
576,
8... | 3.016706 | 838 |
module TensorDecompositions
using TensorOperations
using Distributions
using ProgressMeter
using Base.Cartesian
using StatsBase
using LinearAlgebra
export
# types
SparseArray,
TensorDecompositions, PARAFAC2, CANDECOMP, CUR, Tucker,
# TensorDecomposition methods
rel_res... | [
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220,
220,
220,
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220,
220,
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198,
220,
... | 2.265938 | 549 |
function plotbenchmarks(; write_results = OPTIONS["write_results"],
test = OPTIONS["test"],
blas_num_threads = OPTIONS["blas_num_threads"],
blocksparse_num_threads = OPTIONS["blocksparse_num_threads"],
maxdims = OPT... | [
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
... | 2.099211 | 2,409 |
<reponame>marcobonici/CosmoCentral.jl<filename>src/CosmologicalStructures.jl
abstract type AbstractCosmology end
abstract type AbstractCosmologicalGrid end
abstract type AbstractBackgroundQuantities end
abstract type BoltzmannSolverParams end
abstract type AbstractDensity end
abstract type AbstractConvolvedDensity end
... | [
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27741,
36734,
... | 2.554545 | 5,280 |
<reponame>Saransh-cpp/Calc.jl<filename>src/Subtract.jl
"""
subtract(x, y)
Perform subtraction between two floating point numbers.
# Arguments
- `a::Float64`: Number 1.
- `b::Float64`: Number 2.
# Returns
- `a - b::Float64`
# Examples
```julia-repl
julia> subtract(1.0, 2.0)
-1.0
```
"""
subtract(a::Float64, b::F... | [
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29,
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11,
331,
8,
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198,
5990,
687,
13284,
7861,
1022,
... | 2.27027 | 148 |
# v0.12 deprecations
@deprecate Dropout(p, dims) Dropout(p; dims=dims)
@deprecate InstanceNorm(λ, β, γ, μ, σ², ϵ, momentum) InstanceNorm(λ, β, γ, μ, σ², ϵ, momentum, true, true, nothing)
@deprecate BatchNorm(λ, β, γ, μ, σ², ϵ, momentum) BatchNorm(λ, β, γ, μ, σ², ϵ, momentum, true, true, nothing)
@deprecate GroupNorm(G,... | [
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15,
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31,
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8344,
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2262,
590,
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7,
39377,
11,
27169,
11,
737... | 2.509847 | 457 |
# Extract data from a 2D/3D DG solution and prepare it for visualization as a heatmap/contour plot.
#
# Returns a tuple with
# - x coordinates
# - y coordinates
# - nodal 2D data
# - x vertices for mesh visualization
# - y vertices for mesh visualization
#
# Note: This is a low-level function that is not considered as ... | [
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422,
257,
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2,
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198,
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532,
331,
22715,
19... | 2.650706 | 1,629 |
<gh_stars>0
# Create a special type for permutations. The real point here is to be able to
# unambiguously identify an RRPermArray (see below) so that we may "unwrap" in
# expressions like `channelview(colorview(C, A))`.
"""
ColorChanPerm(perm)
Construct a reordering permutation for the color channel.
This handles sw... | [
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62,
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29,
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2,
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257,
2041,
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8,
523,
326,
356,
743,... | 2.402634 | 5,392 |
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