content stringlengths 6 1.03M | input_ids listlengths 4 535k | ratio_char_token float64 0.68 8.61 | token_count int64 4 535k |
|---|---|---|---|
<reponame>sdk2k01/Discord.jl<gh_stars>100-1000
@testset "JSON" begin
io = IOBuffer()
val, e = readjson(io)
@test val === nothing
@test e isa Empty
io = IOBuffer("{bad]")
val, e = readjson(io)
@test val === nothing
@test e !== nothing
io = IOBuffer("[1,2,3]")
val, e = readjson(i... | [
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... | 2.222989 | 435 |
<reponame>stefano-meschiari/ColorSchemes.jl<gh_stars>100-1000
# websafe colors
# using Colors, ColorSchemes
# cs = ColorScheme([parse(RGB{Float64}, "#$(string.([r,g,b], base=16)...)") for r in 0x0:3:0xf for g in 0x0:3:0xf for b in 0x0:3:0xf])
loadcolorscheme(:websafe, [
RGB{Float64}(0.0,0.0,0.0),
RGB{F... | [
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5... | 1.369043 | 5,750 |
## Layer types
"""Union type for convolutional layers."""
const ConvLayer = Union{Conv} # TODO: DepthwiseConv, ConvTranspose, CrossCor
"""Union type for dropout layers."""
const DropoutLayer = Union{Dropout,typeof(Flux.dropout),AlphaDropout}
"""Union type for reshaping layers such as `flatten`."""
const ReshapingLaye... | [
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3... | 3.36409 | 401 |
<reponame>UnofficialJuliaMirrorSnapshots/BayesNets.jl-ba4760a4-c768-5bed-964b-cf806dc591cb
let
# A → C ← B
bn = BayesNet()
push!(bn, StaticCPD(:a, Categorical([1.0,0.0])))
push!(bn, StaticCPD(:b, Categorical([0.0,1.0])))
push!(bn, CategoricalCPD{Bernoulli}(:c, [:a, :b], [2,2],
[Bernoull... | [
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37988,
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48952,
21101,
198... | 1.755906 | 508 |
<gh_stars>0
using Roots
using UUIDs
using Observables
using CSV
using ScottishTaxBenefitModel
using .BCCalcs
using .Definitions
using .ExampleHelpers
using .FRSHouseholdGetter
using .GeneralTaxComponents
using .ModelHousehold
using .Monitor
using .Results
using .Runner
using .RunSettings
using .SimplePovertyCounts: G... | [
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50101,
19... | 2.744444 | 990 |
<filename>src/collective_dephasing_model.jl
using BlockDiagonalMatrices
using SparseArrays
using LinearAlgebra
using TimerOutputs
struct CollectiveDephasingModel <: Model
params::ModelParameters
Jx::SparseMatrixCSC
Jy::SparseMatrixCSC
Jz::SparseMatrixCSC
Jx2::SparseMatrixCSC
Jy2::SparseMatrixCS... | [
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198,... | 2.106447 | 2,001 |
# Note that this script can accept some limited command-line arguments, run
# `julia build_tarballs.jl --help` to see a usage message.
using BinaryBuilder, Pkg
name = "MPSolve"
version = v"3.2.1"
# Collection of sources required to complete build
sources = [
GitSource("https://github.com/robol/MPSolve.git", "65be... | [
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198,... | 2.876574 | 397 |
<gh_stars>100-1000
using Unitful
function unitful_testfunction(Vi)
if Vi ≤ 0.0u"V"
return 0.0u"V"
elseif Vi ≥ 1.0u"V"
return 1.0u"V"
else
return Vi
end
end
register_primitive(unitful_testfunction) # must be outside testset
@testset "Unitful" begin
@info "Testing Unitful"
... | [
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... | 1.634221 | 976 |
module Audios
using FileType
TypeMidi = FileType.Types("mid", MIME("audio/midi"))
TypeMp3 = FileType.Types("mp3", MIME("audio/mpeg"))
TypeM4a = FileType.Types("m4a", MIME("audio/m4a"))
TypeOgg = FileType.Types("ogg", MIME("audio/ogg"))
TypeFlac = FileType.Types("flac", MIME("audio/x-flac"))
TypeWav = FileType.Type... | [
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... | 2.036534 | 958 |
<reponame>bdhuddleston/DV3D.jl
# fast robust geometric predicates
# Converted into Julia from C
# Original functions written by
# <NAME>
# School of Computer Science
# Carnegie Mellon University
# 5000 Forbes Avenue
# Pittsburgh, Pennsylvania 15213-3891
# <EMAIL>
using GeometryTypes
"""
original comment:
/* W... | [
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327,
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198,
2,
220,
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198,
... | 2.150148 | 4,389 |
<filename>public/.julia/v0.5/JuPOT/src/core/assetscollection.jl
#=
AssetsCollection
================
A container to hold all information regarding the collection of assests to
be optimized
Methods:
-------
Author: <NAME>, <NAME>, <NAME>
Date: 01/23/2016
=#
type AssetsCollection{T1<:Real, T2<:Abstra... | [
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220,
317,
9290,
284,
1745,
477,
... | 2.559189 | 2,813 |
<reponame>jrklasen/trackRunning.jl
"""
`time_sec(run)` calculates the time in sec.
"""
function time_sec(run::Run)
cumsum(diff(run.date) / Base.Dates.Millisecond(1000))
end
"""
`dist_m(run, smoothing = true, λ = 500.0)` estimation of the distance in meters.
"""
function dist_m(run::Run;
smoothing::Bool... | [
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371... | 2.072973 | 740 |
<filename>src/02.VlasovAmpere.jl
ENV["GKSwstype"]="100" #src
# ## 1D1V Vlasov–Ampere system
#md #
#md # ```math
#md # \\frac{\\partial f}{\\partial t} + \\upsilon \\frac{\\partial f}{\\partial x} - E(t,x) \\frac{\\partial f}{\\partial \\upsilon} = 0
#md # ```
#md #
#md # ```math
#md # \\frac{\\partial E}{\\partial t} ... | [
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431... | 1.927621 | 2,404 |
import CLBLAS
import OpenCL
const cl = OpenCL
const clblas = CLBLAS
for platform in cl.platforms()
if platform[:name] == "Portable Computing Language"
warn("Portable Computing Language platform not yet supported")
continue
end
for device in cl.devices(platform)
@printf("==========... | [
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367... | 2.565378 | 543 |
"Import filter from file"
function readFilter(filename, skipstart = 2)
saved = readdlm(filename,',', skipstart = skipstart)
z1 = saved[1,:] + im*saved[2,:]
a1 = saved[3,:] + im*saved[4,:]
return z1,a1
end
"Import weight function from file"
function readWS(filename)
saved = readdlm(filename,',')
intv = saved[... | [
1,
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422,
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1,
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14267,
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8,
198,
197,
89,
16,
220,
796,
7448,
... | 2.437086 | 151 |
using VideoIO
using ImageInTerminal
ImageInTerminal.use_24bit()
f=opencamera()
img=read(f)
| [
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198,
3500,
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62,
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198,
69,
28,
9654,
25695,
3419,
198,
9600,
28,
961,
7,
69,
8,
628
] | 2.657143 | 35 |
module TSMLTypes
using DataFrames
export typerun
export Transformer,
TSLearner,
fit!,
transform!
abstract type Transformer end
abstract type TSLearner <: Transformer end
function transform!(tr::Transformer, instances::T) where {T<:Union{Vector,Matrix,DataFrame}}
error(typeof(tr)," not implemented yet: trans... | [
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198,
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0,
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397,... | 3.039326 | 178 |
<gh_stars>0
using Distributions, NLsolve, Statistics
function effectsize(xs::AbstractVector, ys::AbstractVector)
if length(xs) != length(ys)
throw(ArgumentError("samples must have the same number of observations"))
end
n, m = length(xs), length(ys)
x̄, ȳ = mean(xs), mean(ys)
σx², σy² = var... | [
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41... | 2.010274 | 876 |
<gh_stars>10-100
# Patches should allow using imported bindings in the body of the patch
@testset "imported binding in body" begin
@test_throws UndefVarError Minute
@test isdefined(Dates, :Minute)
using Dates: Minute, Hour
myminute(x::Integer) = Minute(x)
# Patches should work when referencing bin... | [
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851... | 2.910891 | 404 |
<gh_stars>1-10
using ParticleInCell
using LinearAlgebra
@testset "FDTD solver" begin
dimx, dimy = 2π, 2π
nx, ny = 128, 128
dt = 1e-4
nstep = 8
mesh = TwoDGrid(dimx, nx, dimy, ny)
ex = zeros(nx+1, ny+1)
ey = zeros(nx+1, ny+1)
bz = zeros(nx+1, ny+1)
jx = zeros(nx+1, ny+1)
jy =... | [
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1... | 1.747738 | 884 |
doc=
"""
usage:
stencil.jl <outputdir> <builddir> <refine> <scheme> <vtk>
builddir path Directory to write discretization.
outputdir path Directory to write output data to.
refine int Level of grid refinement to use. refine = 0 (no grid
... | [
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85,
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29,
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220,
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220,
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3108,
220,... | 1.74736 | 3,598 |
using Compat, Dates, DSP, HDF5, Logging, Printf, SeisIO, Test
using SeisIO.Quake, SeisIO.RandSeis, SeisIO.SeisHDF
import Dates: DateTime, Hour, now
import DelimitedFiles: readdlm
import Random: rand, randperm, randstring
import SeisIO: BUF, FDSN_sta_xml,
auto_coords, code2typ, typ2code,
bad_chars, checkbuf!, checkb... | [
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... | 2.06548 | 7,025 |
<reponame>JuliaTagBot/IntegralTransforms.jl
using Base.Test
#Better Julia practice:
#Write your tests as runnable scripts - you should be able to play all the file and know what went wrong
# write your own tests here
@test 42 == 42
| [
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149... | 3.477612 | 67 |
#####
##### `Mixer`
#####
struct Mixer{N,T<:NTuple{N,AbstractSynthComponent}} <: AbstractSynthComponent
inputs::T
end
Mixer(inputs::AbstractSynthComponent...) = Mixer(inputs)
next!(m::Mixer) = sum(next!, m.inputs)
Base.:+(inputs::AbstractSynthComponent...) = Mixer(inputs...)
output_type(::Type{Mixer{N,T}}) wher... | [
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1731... | 2.579505 | 283 |
"""Demo 3x7 test case"""
y = [0.62 0.73 0.71 1.50 1.17 0.43 1.08
0.62 1.73 0.95 1.46 1.60 1.16 0.38
0.90 0.32 -0.48 0.95 1.08 0.02 0.40]
function write_hpp(fname)
open(fname, "w") do io
println(io, "// automatically generated by `julia/test/demo3x7.jl`")
println(io, "#pragma once")
... | [
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... | 1.599589 | 487 |
struct SinkhornEpsilonScaling{A<:Sinkhorn,T<:Real} <: Sinkhorn
alg::A
factor::T
steps::Int
end
function Base.show(io::IO, alg::SinkhornEpsilonScaling)
return print(
io, alg.alg, " with ε-scaling (steps: ", alg.steps, ", factor: ", alg.factor, ")"
)
end
"""
SinkhornEpsilonScaling(algori... | [
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862,
33576,
3351,
4272,
90,
32,
27,
25,
50,
676,
25311,
11,
51,
27,
25,
15633,
92,
1279,
25,
311,
676,
25311,
198,
220,
220,
220,
435,
70,
3712,
32,
198,
220,
220,
220,
5766,
3712,
51,
198,
220,
220,
... | 2.263768 | 2,070 |
<filename>src/iterator.jl
import Base: start, next, done, iteratorsize, iteratoreltype, eltype, length
"""
KalmanFilter(y, M)
Kalman filter as iterator, iterating over `Gaussian`s representing
the filtered distribution of `x`. Arguments `y` iterates over signal values.
# Example
```
kf = KalmanFilter(Y, M) #
es... | [
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220,
220,
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7,
... | 2.291978 | 1,072 |
<filename>src/FeatureExtraction/utils.jl
"""
Parse fe variables
"""
function parse_fe_variables(fe_vars, expvars; depvar=nothing, is_pair=false)
valid_vars = copy(expvars)
if depvar != nothing
append!(valid_vars, [depvar])
end
selected_vars = []
if is_pair
vars = []
if isa... | [
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85,
945,
11,
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85,
945,
26,
1207,
7785,
28,
22366,... | 1.89799 | 4,578 |
function sidebar(session::Session, names::NamedTuple, colors::NamedTuple; port::Int = 3141)
############################################################# sortable legend
Legend = DOM.div( id = "legend", class = "legend", map( group ->
DOM.ul( id = group, DOM.h2( class = "legend-header", selecte... | [
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220,
2... | 1.886294 | 7,493 |
<reponame>attdona/Abaco.jl
using Abaco
using JSON3
using BenchmarkTools
function nop(s, ne, name, value, inputs)
end
function onresult(ts, ne, name, value, inputs)
@info "age [$ts]: scope: [$ne] $name = $value"
end
interval = 5
ages = 4
abaco = Abaco.init(nop, interval, ages)
formula(abaco, "y = x1 + x2")
func... | [
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8,
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437,... | 2.103152 | 349 |
<filename>src/util/PeriodicCartesianIndices.jl
module PeriodicIndexing
export PeriodicCartesianIndices
using Base: tail
import Base: getindex, length, size, IndexStyle, IndexCartesian, in, iterate, mod,
first, last
mod_tuple(c::NTuple{N,Int}, size::NTuple{N,Int}) where N =
ntuple(k->mod(c[k]-1,size[k])+1, ... | [
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198,
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7308,... | 2.334431 | 1,519 |
<filename>src/CanvasWebIO.jl
module CanvasWebIO
using WebIO, JSExpr
export Canvas, addmovable!, addclickable!, addstatic!
mutable struct Canvas
w::Scope
size
movables::Array
clickables::Array
static::Array
getter::Dict
selected::String #actually just the field for the selection
handle... | [
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11,
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76,
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12976,
540,
28265,
751... | 2.07232 | 3,844 |
using PyCall
torch = pyimport("torch")
np = pyimport("numpy")
"""
Note: the below code is a minimal test of using `cvxpylayers` and `torch` to
incorporate "differentiable convex programming".
Unfortunately, differentiable convex programming seems not realised in pure Julia packages.
"""
"""
Test for cvxpylayers
"""... | [
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257,
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1332,
286,
1262,
4600,
33967,
42... | 2.347066 | 801 |
function makeForcePlots3D()
dirvec = readdir()
if "forcePlots" in dirvec
rm("forcePlots", recursive=true)
end
mkdir("forcePlots")
mat, _ = DelimitedFiles.readdlm("resultsSummary", '\t', Float64, header=true)
nspan = (length(mat[1,:]) - 4)/8
t = mat[:,1]
len = length(t... | [
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787,
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18,
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220,
220,
220,
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220,
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220,
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1,
287,
26672,
35138,
198,
220,
220,
220,
220,
220,
220,
... | 1.758398 | 9,735 |
using MetaStrategist
using Documenter
DocMeta.setdocmeta!(MetaStrategist, :DocTestSetup, :(using MetaStrategist); recursive=true)
makedocs(;
modules=[MetaStrategist],
authors="<NAME>",
repo="https://github.com/JuliaConstraints/MetaStrategist.jl/blob/{commit}{path}#{line}",
sitename="MetaStrategist.jl"... | [
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11,
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30277,
13290,
2397,
396,
1776,
45115,
28,
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8,
198,... | 2.35 | 280 |
<filename>src/old/ATP/ATP.jl
using Catlab.WiringDiagrams
using Catlab.Present
using Catlab.Theories
using Catlab.CategoricalAlgebra.CSets
using Catlab.CategoricalAlgebra.StructuredCospans
using Catlab.Present: translate_generator
using Catlab.CategoricalAlgebra.FinSets
using Catlab.Theories: attr, adom
using Catlab.Cat... | [
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13,
34,
... | 2.153897 | 15,023 |
<gh_stars>100-1000
# GMM estimation for a sample from Chi^2(theta)
# compare to two method of moments estimators (see chi2mm.m)
using Econometrics, Random, LinearAlgebra, Distributions, Plots
function main()
n = 30
theta = 3.0
reps = 10000
results = zeros(reps)
W = eye(2)
y = zeros(n) # this is just a place holder to d... | [
27,
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8,
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3500,
412,
... | 2.517606 | 284 |
module HOOMD
# Signal Revise.jl that this module should be tracked as a package
__revise_mode__ = :eval
# Dependecies
using CUDA
using DLPack
using PyCall
using StaticArrays
DLExt = pyimport("hoomd.dlext")
# Types
struct ContextWrapper
context::PyObject
sysview::PyObject
synchronize::PyObject
fu... | [
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198,
2,
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721,
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198,
3500,
29369,
5631,
198,
35... | 2.390495 | 1,557 |
# Hermitian lattices
@attributes mutable struct HermLat{S, T, U, V, W} <: AbsLat{S}
space::HermSpace{S, T, U, W}
pmat::V
gram::U
rational_span::HermSpace{S, T, U, W}
base_algebra::S
involution::W
automorphism_group_generators::Vector{U}
automorphism_group_order::fmpz
generators
minimal_generators
... | [
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50,
11,
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11,
47... | 2.298969 | 194 |
<filename>src/economics/input_data/functions.jl
############################ Reads Data from Excel ########################
################################################################################
#**
function get_Cost_Data()
ks=cost_ks()
ks.FC_ac=5.8#fixed AC cost
ks.FC_dc=29#fixed DC cost
ks.... | [
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1372... | 2.050103 | 7,285 |
using PyPlot, Seismic
dt = 0.002
fn = 1/(2*dt)
# Ricker
w1 = Ricker(dt=dt)
nw = length(w1)
nc = floor(Int, nw/2)
t1 = dt*collect(-nc:1:nc)
nf = 8*nextpow2(nw)
df = 1/(nf*dt)
f1 = df*collect(0:1:nf-1)
wpad = cat(1,w1,zeros(nf-nw))
W1 = abs(fft(wpad))
W1 = W1/maximum(W1)
# Ormsby
w2 = Ormsby(dt=dt, f=[5.0, 10.0, 30.0,... | [
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7,
8... | 1.898449 | 709 |
<filename>MetaGen/scripts/test_gm.jl
using Revise
using MetaGen
using Gen
using Profile
using StatProfilerHTML
using GenRFS
#Profile.init(; n = 10^4, delay = 1e-5)
#GenRFS.modify_partition_ctx!(1000)
#call it
#@profilehtml gt_trace,_ = Gen.generate(metacog, (possible_objects,))
#@profilehtml gt_trace,_ = Gen.generat... | [
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5329,
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5215,
49,
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198,
198,
2,
37... | 2.547297 | 296 |
using Plots
using DelimitedFiles
include("options.jl")
print("Plotting results using $blas_num_threads BLAS thread")
blas_num_threads > 1 ? println("s") : println()
println("Plotting results using $omp_num_threads OpenBLAS threads")
println("Maximum DMRG bond dimension is set to $maxdim.")
seperator = "#"^70
times... | [
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62,
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82,
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198,
2436,
292,
62,
225... | 2.325377 | 796 |
<reponame>briochemc/Earth2014.jl
module Earth2014
using DataDeps, NCDatasets
function url_Earth2014(;res="5min")
res == "1min" ? "http://ddfe.curtin.edu.au/models/Earth2014/data_1min/GMT/Earth2014.BED2014.1min.geod.grd" :
res == "5min" ? "http://ddfe.curtin.edu.au/models/Earth2014/data_5min/GMT/Earth2014.BED2... | [
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62,
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7,
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20,
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49... | 2.255396 | 1,112 |
<reponame>p2t2/Scruff.jl<filename>src/sfuncs/score/softscore.jl
export SoftScore
"""
SoftScore(vs::Vector{I}, ss::Vector{Float64})
Return a `LogScore` of the log values in `ss` vector for
the associated keys in `vs`.
"""
function SoftScore(vs::Vector{I}, ss::Vector{Float64}) where I
return LogScore(vs, [log(... | [
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480,
29,
79,
17,
83,
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198,
198,
37811,
198,
220,
220,
220,
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26595,
7,
14... | 2.566964 | 224 |
using Polylogarithms
using SpecialFunctions
using Test
using DataFrames, CSV
import Base.MathConstants: π, pi, ℯ, e, γ, eulergamma, catalan, φ, golden
include("test_defs.jl")
include("../src/gamma_derivatives.jl")
@testset "Derivatives of the gamma function at 1.0" begin
@testset " throws errors" begin
... | [
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11,
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11,
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226,
107,
11,
304,
11,
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111,
11,
... | 1.941772 | 395 |
<gh_stars>0
using GaussianProcesses
using HDF5
# Load the possible parameter combinations
# Points at which to create images
parameters = readdlm("parameters.dat")
drop_out = parameters[end, :]
parameters = parameters[1:end-1, :]
# Column headers
# nu, mass, r_c, T_10, q, logM_gas, ksi, incl, PA = row
d = 2 # dime... | [
27,
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62,
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29,
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20,
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2,
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2,
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284,
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198,
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7307,
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1100,
25404,
76,
7203,
17143... | 2.295918 | 882 |
include("./Algos.jl")
using .Algos
using CSV
using DataFrames
using Plots
MAP_NAME = "map2"
TRAINING_EPS = 300000
EVALUATING_EPS = 100000
SAMPLING_RATE = 0.001
df = DataFrame(CSV.File("maps/$(MAP_NAME).csv"));
tiles = Matrix(df);
agent = Sarsa(tiles, learningRate = 0.1);
y1 = train(agent, TRAINING_EPS, samplingRate =... | [
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1,
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62,
... | 2.431034 | 638 |
module ROC
using ROCAnalysis, MLLabelUtils
using StatsBase: sample
export AbstractOP, ComplexOP, SimpleOP, findop, changeop!, simpleop,
AbstractPerfMetric, TPr, FPr, TNr, FNr
abstract type AbstractOP end
"""
Operating point object with additional information.
"""
mutable struct ComplexOP <: AbstractOP... | [
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11,
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404,
2826... | 1.959743 | 7,303 |
<filename>src/triangulationestimators/delaunay_triangulations/DelaunayTriangulations.jl
using Reexport
@reexport module DelaunayTriangulations
using Requires
function __init__()
@require Simplices="d5428e67-3037-59ba-9ab1-57a04f0a3b6a" begin
include("AbstractDelaunayTriangulation.jl")
... | [
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148... | 2.513043 | 230 |
#!/usr/bin/env julia
using Weber
using Lazy: @> # see https://github.com/MikeInnes/Lazy.jl
version = v"0.0.1"
sid,skip = @read_args("Gap Detection ($version).")
#===============================================================================
Experiment Settings
=======================================================... | [
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9641,
796,
410,
1,
15,
... | 2.660455 | 1,932 |
using VCFTools
using Test
using VariantCallFormat
using CodecZlib
using DelimitedFiles
# packages needed only for testing
using Random
using CSV
using DataFrames
using StatsBase
using LinearAlgebra
include("gtstats_test.jl")
include("conformgt_test.jl")
include("convert_test.jl")
include("filter_test.jl")
include("ai... | [
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3543... | 3.198198 | 111 |
<gh_stars>0
function process_likelihood_model(ρ_list::Vector,Eₘ_list::Vector)
# Generate the A matrix used to calculate likelihoods
# The A matrix depends on the input states and measurement operators
dimsmatch(ρ_list,Eₘ_list)
sum(abs,data(sum(Eₘ_list))-I)<1E-15 ||
throw(ArgumentError("Eₘ operat... | [
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1495... | 2.043169 | 2,108 |
<filename>tutorials/Tutorial_gridap_odes.jl<gh_stars>1-10
using Gridap
using ForwardDiff
using LinearAlgebra
using Test
using GridapODEs.ODETools
using GridapODEs.TransientFETools
using Gridap.FESpaces: get_algebraic_operator
import Gridap: ∇
import GridapODEs.TransientFETools: ∂t
∂
θ = 0.2
u(x,t) = (1.0-x[1])*x[1]*(... | [
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<gh_stars>10-100
using Base: @propagate_inbounds
"""
AugmentedKnots{T,k} <: AbstractVector{T}
Pads from both sides a vector of B-spline breakpoints, making sure that the
first and last values are repeated `k` times.
"""
struct AugmentedKnots{T, k, Breakpoints <: AbstractVector{T}} <: AbstractVector{T}
Nt :: I... | [
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<gh_stars>1-10
immutable TDist <: ContinuousUnivariateDistribution
df::Float64 # non-integer degrees of freedom allowed
function TDist(d::Real)
if d > 0.0
new(float64(d))
else
error("df must be positive")
end
end
end
@_jl_dist_1p TDist t
function entropy(d::TDist)
return ((d.df... | [
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220,
220,
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13320,... | 1.869748 | 476 |
push!(LOAD_PATH,"../src/")
using Firebase
using Documenter
DocMeta.setdocmeta!(Firebase, :DocTestSetup, :(using Firebase); recursive=true)
makedocs(;
modules=[Firebase],
authors="<NAME>",
repo="github.com/ashwani-rathee/Firebase.jl/blob/{commit}{path}#{line}",
sitename="Firebase.jl",
format=Docum... | [
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86... | 2.286822 | 387 |
# Unit tests for unit_convention.jl
import Unitful.𝐌, Unitful.𝐋, Unitful.𝐓, Unitful.𝚯
@testset "unit_convention.jl" begin
@test Atomistic.MASS_UNIT == aunit(𝐌)
@test Atomistic.LENGTH_UNIT == aunit(𝐋)
@test Atomistic.ENERGY_UNIT == aunit(𝐌 * 𝐋^2 / 𝐓^2)
@test Atomistic.TIME_UNIT == aunit(𝐓)
... | [
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9... | 1.876068 | 468 |
export replace_lines
"""
replace_lines(path::AbstractString)
replace_lines(file::File)
replace_lines(dir::Dir)
Replace part of the markdown file, which can be done an a per-line basis.
For instance you cannot do this with code blocks but you can do this with bullets
and section headings.
"""
replace_l... | [
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... | 1.998447 | 644 |
@testset "1103.distribute-candies-to-people.jl" begin
@test distribute_candies(7, 4) == [1, 2, 3, 1]
@test distribute_candies(10, 3) == [5, 2, 3]
@test distribute_candies(1958512, 40) == [
49050,
49100,
49150,
49200,
49250,
49300,
49350,
49400,... | [
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60,
... | 1.493554 | 543 |
# Copyright 2018 <NAME>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, softw... | [
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... | 2.683386 | 7,536 |
module SailRoute
include("uncertainty/discretization_error.jl")
include("route/domain.jl")
include("performance/polar.jl")
include("route/shortest_path.jl")
include("weather/load_weather.jl")
end # module
| [
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72... | 3.089552 | 67 |
<gh_stars>0
include("../src/Cosmology.jl")
using Cosmology
using Base.Test
function test_approx_eq_rtol(va, vb, rtol, astr, bstr)
diff = maximum(abs(va - vb))
tol = rtol*max(maximum(abs(va)), maximum(abs(vb)))
if diff > tol
sdiff = string("|", astr, " - ", bstr, "| <= ", tol)
error("asserti... | [
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... | 1.97585 | 2,236 |
<reponame>SMG2S/SMG2S.jl
using Smg2s, Test, SparseArrays
@testset "Nilpotent Matrix" begin
@testset "Nilp 1" begin
@test NilpMat(Nilp(2,8)) * NilpMat(Nilp(2,8)) * NilpMat(Nilp(2,8)) == spzeros(8, 8)
end
@testset "Nilp 2" begin
vec=[1; 1; 0; 1; 1; 1; 0]
nilp = Nilp(vec, 8)
@t... | [
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<reponame>efmanu/SubspaceInference.jl
#to plot uncertainities in neural networks
function plot_predictive(data, trajectories, xs; μ=0, σ=0, title=["Plot"], legend = false)
lt = length(trajectories)
if lt < 1
throw("Err: No data")
elseif lt == 1
μ = mean(trajectories["1"], dims=2)
σ = std(trajectories["1"], d... | [
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18... | 2.119469 | 3,842 |
<filename>src/radialmap/quantile.jl
export center_std_diag!, center_std_off!, center_std!
# Set ξ and σ for the diagonal entry, i.e. the last element of C
function center_std_diag!(C::RadialMapComponent, X::AbstractMatrix{Float64}, γ::Float64)
@get C (Nx, p)
@assert (p>0 && γ >0.0) || (p==0 && γ>=0.0) "Error ... | [
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<reponame>LCSB-BioCore/GigaScatter.jl
"""
mixableRaster(raster::Raster)::RasterMix
Convert a raster into a form that is suitable for combining with other rasters.
"""
function mixableRaster(raster::Raster)::RasterMix
colors = copy(raster[1:3, :, :])
colors[1, :, :] .*= raster[4, :, :]
colors[2, :, :] .... | [
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3... | 1.975177 | 564 |
<filename>src/Fretboards.jl
module Fretboards
using ArgCheck: @argcheck
using Crayons: Crayon
using DocStringExtensions: FIELDS, SIGNATURES, TYPEDEF
using OffsetArrays: OffsetMatrix
using UnPack: @unpack
include("pitch.jl")
include("fretboard.jl")
end # module
| [
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<gh_stars>1-10
#=
Copyright (c) 2018-2022 <NAME>, <NAME>, and contributors
This Julia package Hypatia.jl is released under the MIT license; see LICENSE
file in the root directory or at https://github.com/chriscoey/Hypatia.jl
=#
matrixcompletion_insts = [
[(k, d) for d in vcat(2, 5:5:max_d)] # includes compile run... | [
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... | 2.577093 | 227 |
#
# Copyright (c) 2021 <NAME>, <NAME>, <NAME>
# Licensed under the MIT license. See LICENSE file in the project root for details.
#
################################## INSTALLATION ####################################################
# (1) Enter Package Manager via ]
# (2) Install FMI via add FMI ... | [
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6808,
329,
3307,
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198,
2,
198,
198,
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40... | 2.478762 | 1,907 |
using CurveProximityQueries
using ConvexBodyProximityQueries
using Test
using LinearAlgebra
using StaticArrays
using IntervalArithmetic
using Random: seed!
using Unitful: nm, μm, mm, cm
import CurveProximityQueries: differentiate, integrate
@testset "CurveProximityQueries" begin
@testset "Constructors" begin
... | [
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14534,... | 1.930004 | 2,843 |
<reponame>blegat/PermutationGroups.jl<gh_stars>1-10
############################################################
# Naive Vector&Dict implementation (as fast as manual loop),
############################################################
struct Orbit1{T, S} <: AbstractOrbit{T,S}
elts::Vector{T}
vals::Dict{T, S}
en... | [
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828,
... | 2.341625 | 603 |
function UniformRealDistribution()
return UniformRealDistribution(())
end
function UniformRealDistribution(arg0::jdouble, arg1::jdouble)
return UniformRealDistribution((jdouble, jdouble), arg0, arg1)
end
function cumulative_probability(obj::UniformRealDistribution, arg0::jdouble)
return jcall(obj, "cumula... | [
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8,
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220,
220,
... | 2.990719 | 431 |
using ADCME
using PyPlot
using ProgressMeter
using Statistics
function f(x, σ)
ε = randn(size(x)...) * σ
return 10 * sin.(2π*x) + ε
end
batch_size = 32
noise = 1.0
X = reshape(LinRange(-0.5, 0.5, batch_size)|>Array, :, 1)
y = f(X, noise)
y_true = f(X, 0.0)
close("all")
scatter(X, y, marker="+", label="Trai... | [
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22... | 2.013818 | 1,375 |
using MIToS.MSA
using MIToS.Information
const msa_long = read("../data/PF00089_aligned.fasta", FASTA)
const msa_wide = read("../data/PF16957_aligned.fasta", FASTA)
mip(msa) = APC!(mapcolpairfreq!(mutual_information, msa, Counts{Float64,2,GappedAlphabet}(ContingencyTable(Float64,Val{2},GappedAlphabet()))))
mip(msa_lon... | [
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8,
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... | 2.317949 | 195 |
function _clear_cache(network::AbstractGibbsNetwork, sol::Solution)
i, j = node_from_index(network, length(first(sol.states))+1)
if j != network.ncols return end
delete!(memoize_cache(mps), (network, i))
delete!(memoize_cache(dressed_mps), (network, i))
delete!(memoize_cache(mpo), (network, i-1))
... | [
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1... | 1.935856 | 4,942 |
# ---
# title: 810. Chalkboard XOR Game
# id: problem810
# author: <NAME>
# date: 2020-10-31
# difficulty: Hard
# categories: Math
# link: <https://leetcode.com/problems/chalkboard-xor-game/description/>
# hidden: true
# ---
#
# We are given non-negative integers nums[i] which are written on a chalkboard.
# Alice and ... | [
2,
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2,
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2,
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25,
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198,
2,
1772,
25,
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29,
198,
2,
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25,
12131,
12,
940,
12,
3132,
198,
2,
8722,
25,
6912,
198,
2,
9376,
25... | 2.872302 | 556 |
import Random
import ALifeBenchmark
using Test
Random.seed!(1234)
include("analysis/neutral_networks_test.jl")
include("analysis/estimator_test.jl")
include("geb/inputs_tests.jl")
include("geb/actions_tests.jl")
include("geb/network_tests.jl")
include("geb/nodes_tests.jl")
include("geb/rules_tests.jl")
include("g... | [
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29531,
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28826,
0,
7,
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2682,
8,
628,
198,
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7203,
20930,
14,
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62,
3262,
5225,
62,
9288,
13,
20362,
4943,
198,
17256,
7203,
20930,
14,
... | 2.721649 | 194 |
<gh_stars>0
using Calculus
using Gadfly
using LaTeXStrings # for L"" strings
using Compat
using QuadGK
using SpecialFunctions
# WARNING: integrate(f,a,b) is deprecated, use (quadgk(f,a,b))[1] instead
# f(x) = integrate(z -> besselj(1, z), 0.0, x)
f(x) = QuadGK.quadgk(z -> besselj(1, z), 0.0, x)[1]
function my_draw(f... | [
27,
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62,
30783,
29,
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3500,
2199,
17576,
198,
3500,
20925,
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198,
3500,
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654,
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329,
406,
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198,
3500,
3082,
265,
198,
3500,
20648,
38,
42,
198,
3500,
6093,
24629,
2733,
198,
198,
2,
... | 2.047826 | 690 |
<reponame>khurrumsaleem/MOCNeutronTransport<gh_stars>0
# Bounding box
# ---------------------------------------------------------------------------------------------
# Bounding box of a vector of points
function boundingbox(points::Vector{<:Point2D})
xmin = ymin = typemax(T)
xmax = ymax = typemin(T)
for i =... | [
27,
7856,
261,
480,
29,
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333,
45241,
1000,
368,
14,
44,
4503,
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315,
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8291,
634,
27,
456,
62,
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29,
15,
198,
2,
347,
9969,
3091,
198,
2,
16529,
1783,
32501,
198,
2,
347,
9969,
3091,
286,
257,
15879,
286,
2173,
... | 1.777122 | 2,710 |
module TestPyVirtualenv
using PyVirtualenv: _leak, pycall_deps_jl, Py_IsInitialized, activate
@static if VERSION < v"0.7.0-DEV.2005"
using Base.Test
else
using Test
end
N = 10000
@testset "_leak(Cstring, ...)" begin
for i in 1:N
x = String(rand('A':'z', rand(1:1000)))
y = Base.unsafe_str... | [
21412,
6208,
20519,
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24330,
198,
198,
3500,
9485,
37725,
24330,
25,
4808,
293,
461,
11,
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62,
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82,
62,
20362,
11,
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62,
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11,
15155,
198,
198,
31,
12708,
611,
44156,
2849,
1279,
410,
1,
15,
13,
... | 2.11441 | 1,145 |
<gh_stars>10-100
# ---
# title: 1669. Merge In Between Linked Lists
# id: problem1669
# author: <NAME>
# date: 2020-10-31
# difficulty: Medium
# categories: Linked List
# link: <https://leetcode.com/problems/merge-in-between-linked-lists/description/>
# hidden: true
# ---
#
# You are given two linked lists: `list1` an... | [
27,
456,
62,
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29,
940,
12,
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198,
2,
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198,
2,
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25,
1467,
3388,
13,
39407,
554,
14307,
7502,
276,
44968,
198,
2,
4686,
25,
1917,
1433,
3388,
198,
2,
1772,
25,
1279,
20608,
29,
198,
2,
3128,
25,
12131,
12,
940... | 2.364023 | 706 |
<filename>dialogs.jl
function message(title::AbstractString, text::AbstractString)
ccall((:IupMessage, "libiup"), Void,
(AbstractString, AbstractString), title, text)
end
| [
27,
34345,
29,
38969,
18463,
13,
20362,
198,
8818,
3275,
7,
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23839,
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11,
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3712,
23839,
10100,
8,
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220,
220,
220,
269,
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25,
40,
929,
12837,
11,
366,
8019,
72,
929,
12340,
18331,
11,
198,
220,
22... | 2.890625 | 64 |
<filename>backend/anime_data/snapshots_5042.jl
{"score_count": 46482, "score": 7.44, "timestamp": 1458050585.0}
{"score_count": 43673, "score": 7.46, "timestamp": 1453815391.0}
{"score_count": 40125, "score": 7.49, "timestamp": 1448615503.0}
{"score_count": 104533, "score": 7.09, "timestamp": 1571753599.0}
{"score_coun... | [
27,
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29,
1891,
437,
14,
272,
524,
62,
7890,
14,
45380,
20910,
62,
1120,
3682,
13,
20362,
198,
4895,
26675,
62,
9127,
1298,
604,
2414,
6469,
11,
366,
26675,
1298,
767,
13,
2598,
11,
366,
16514,
27823,
1298,
20299,
1795,
1120,
3... | 2.336735 | 882 |
"Domain Specific Language for SEMs"
module SEMLang
# export @SEM, interpret, SEMSyntaxError
export SEM
using ..CausalCore: ExogenousVariable, EndogenousVariable
struct SEMSyntaxError <: Exception
msg
end
SEMSyntaxError() = SEMSyntaxError("")
"Parse exogenous variable `line`"
function parseexo(line)
new_var = li... | [
1,
43961,
17377,
15417,
329,
48603,
82,
1,
198,
21412,
48603,
43,
648,
198,
198,
2,
10784,
2488,
50,
3620,
11,
6179,
11,
48603,
13940,
41641,
12331,
198,
39344,
48603,
198,
3500,
11485,
24334,
6775,
14055,
25,
1475,
27897,
43015,
11,
... | 2.583333 | 372 |
export Constant
"""
Constant(x)
Constant sequence. Always returns `x`.
"""
struct Constant <: Sequence
x
end
sample(seq::Constant, t::AbstractVector) = fill(seq.x, length(t))
| [
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20217,
198,
198,
37811,
198,
220,
220,
220,
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7,
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8,
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198,
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25,
45835,
198,
220,
2124,
198,
437,
198,
198,
39873,
7,
41068,
3... | 2.920635 | 63 |
<reponame>mleprovost/TransportBasedInference.jl
# @testset "Verify log_pdf, grad_x_log_pdf and hess_x_log_pdf functions" begin
# atol = 1e-8
# Ne = 50
# m = 10
# Blist = [ProHermiteBasis(8); PhyHermiteBasis(8); CstProHermiteBasis(8)]#; CstPhyHermiteBasis(8)]#; CstLinProHermiteBasis(8); CstLinPhyHermiteB... | [
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455,
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198,
2,
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9288,
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366,
13414,
1958,
2604,
62,
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11,
3915,
62,
87,
62,
6404,
62,
12315,
290,
339,
824,
62,
87,
62,
6404,
62,
12... | 1.707772 | 8,158 |
"""
plot(arg1::Array; kwargs...)
reads (x,y) pairs from files [or standard input] and generates PostScript code that will plot lines,
polygons, or symbols at those locations on a map.
Full option list at [`psxy`](http://gmt.soest.hawaii.edu/doc/latest/psxy.html)
Parameters
----------
- **A** : **straight_lines** : ... | [
37811,
198,
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7,
853,
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26,
479,
86,
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357,
87,
11,
88,
8,
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422,
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685,
273,
3210,
5128,
60,
290,
18616,
2947,
7391,
2438,
326,
481,
7110,
3951,
11,
198,
35428,
70,
684,
1... | 2.588406 | 4,140 |
include("h_file.jl")
const EEPORT = 8000
function http_gatekeeper(req)
if req.method == "GET"
handle_file(req)
# Add a Pragma header (should not be stripped by proxies)?
# Or better, inject in document?
else
Response(501, "Unimplemented method on this server: $(req.method)")
... | [
17256,
7203,
71,
62,
7753,
13,
20362,
4943,
198,
9979,
412,
8905,
9863,
796,
38055,
198,
198,
8818,
2638,
62,
10494,
13884,
7,
42180,
8,
198,
220,
220,
220,
611,
43089,
13,
24396,
6624,
366,
18851,
1,
198,
220,
220,
220,
220,
220,
... | 2.621253 | 367 |
struct S a b c end
| [
7249,
311,
257,
275,
269,
886,
198
] | 2.714286 | 7 |
<reponame>wesselb/ConvCNP.jl
export DataGenerator, UniformUnion, Sawtooth, BayesianConvNP, Mixture
"""
DataGenerator
# Fields
- `process`: Something that can be called at inputs `x` and a noise level `noise` and gives
back a distribution that can be fed to `randn` to sample values corresponding to those
i... | [
27,
7856,
261,
480,
29,
86,
7878,
65,
14,
3103,
85,
34,
22182,
13,
20362,
198,
39344,
6060,
8645,
1352,
11,
35712,
38176,
11,
19882,
1462,
849,
11,
4696,
35610,
3103,
85,
22182,
11,
337,
9602,
198,
198,
37811,
198,
220,
220,
220,
... | 2.462276 | 3,128 |
# import Base:
# VersionNumber
# VERSION = VersionNumber(0,0,1,("git",),(0005,))
# module version
# end
## julia version info
# Include build number if we've got at least some distance from a tag (e.g. a release)
try
build_number = GIT_VERSION_INFO.build_number != 0 ? "+$(GIT_VERSION_INFO.build_number)" : ""... | [
2,
1330,
7308,
25,
198,
2,
220,
220,
220,
220,
10628,
15057,
198,
2,
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15057,
7,
15,
11,
15,
11,
16,
11,
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18300,
1600,
828,
7,
830,
20,
11,
4008,
198,
2,
8265,
2196,
198,
2,
886,
628,
198,
2235,
474... | 2.949438 | 178 |
<gh_stars>10-100
#================
Predictive
Learning
Models
================#
@doc """
| Model Type | Model Name |\n
|:---------- | ---------- |\n
| Baseline | MeanBaseline |\n
| Baseline | ClassBaseline |\n
| Continuous | LinearRegression |\n
| Continuous | LinearLeastSquare |\n
| Categorica... | [
27,
456,
62,
30783,
29,
940,
12,
3064,
198,
2,
4770,
198,
47,
17407,
425,
198,
220,
220,
220,
18252,
198,
220,
220,
220,
220,
220,
220,
220,
32329,
198,
4770,
2,
198,
31,
15390,
37227,
198,
91,
9104,
5994,
930,
9104,
6530,
930,
... | 3.236443 | 461 |
<reponame>cesmix-mit/PotentialLearning.jl<filename>src/Potentials/GaN.jl
"""
GaN Potential
See 10.1088/1361-648X/ab6cbe
"""
mutable struct GaN <: Potential
lj_Ga_Ga::LennardJones
lj_N_N::LennardJones
bm_Ga_N::BornMayer
c::Coulomb
no_Ga::Int64
no_N::Int64
end
function GaN(params::Dict)
... | [
27,
7856,
261,
480,
29,
728,
19816,
12,
2781,
14,
25396,
1843,
41730,
13,
20362,
27,
34345,
29,
10677,
14,
25396,
14817,
14,
35389,
45,
13,
20362,
198,
37811,
198,
220,
220,
220,
12822,
45,
32480,
198,
198,
6214,
838,
13,
940,
3459,... | 2.127986 | 586 |
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
mutable struct SecurityRuleDirection <: SwaggerModel
function SecurityRuleDirection(;)
o = new()
o
end
end # type SecurityRuleDirection
const _property_... | [
2,
770,
2393,
373,
7560,
416,
262,
22300,
2451,
7928,
6127,
35986,
198,
2,
2141,
407,
13096,
428,
2393,
3264,
13,
3401,
1958,
262,
1509,
7928,
20855,
2427,
13,
628,
198,
76,
18187,
2878,
4765,
31929,
35,
4154,
1279,
25,
2451,
7928,
... | 3.320819 | 293 |
<reponame>JuliaPackageMirrors/NamedArrays.jl<gh_stars>0
## (c) 2016 <NAME>
## Unit tests for ../src/arithmetic.jl
## This code is licensed under the MIT license
## See the file LICENSE.md in this distribution
## test arithmetic operations
print("arithmetic, ")
x = NamedArray(randn(5, 10))
@test (-x).array == -(x.ar... | [
27,
7856,
261,
480,
29,
16980,
544,
27813,
27453,
5965,
14,
45,
2434,
3163,
20477,
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20362,
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456,
62,
30783,
29,
15,
198,
2235,
357,
66,
8,
1584,
1279,
20608,
29,
198,
198,
2235,
11801,
5254,
329,
11485,
14,
10677,
14,
283,
2... | 2.178112 | 466 |
# x, y without type, this is a generic function
function f(x, y)
x + y
end
println(f(1, 2))
# 3
g = f # g is f
println(g(1, 2))
function with_return()
return "I have return"
end
println(with_return())
function hypot(x, y)
x = abs(x)
y = abs(y)
if x > y
r = y/x
return x*sqrt(1+r*r)... | [
2,
2124,
11,
331,
1231,
2099,
11,
428,
318,
257,
14276,
2163,
198,
8818,
277,
7,
87,
11,
331,
8,
198,
220,
220,
220,
2124,
1343,
331,
198,
437,
198,
198,
35235,
7,
69,
7,
16,
11,
362,
4008,
198,
2,
513,
198,
70,
796,
277,
13... | 1.984127 | 252 |
<gh_stars>0
module Move_AllVisualObjects
using Modia3D
filename = joinpath(Modia3D.path, "objects", "fish", "SiameseTiger0.3ds")
#filename = joinpath(Modia3D.path, "objects","engine", "crank", "crank.obj")
#file = FileShape.convexFile(filename; scaleFactor=MVector{3,Float64}(4.0,4.0,4.0))
# Material for Visualiza... | [
27,
456,
62,
30783,
29,
15,
198,
21412,
10028,
62,
3237,
36259,
10267,
82,
198,
198,
3500,
3401,
544,
18,
35,
628,
198,
198,
34345,
796,
4654,
6978,
7,
5841,
544,
18,
35,
13,
6978,
11,
366,
48205,
1600,
366,
11084,
1600,
366,
4280... | 2.048124 | 1,226 |
function MCTS.estimate_value(est::BasicPOMCP.SolvedFORollout,
bmdp::POMDPToolbox.GenerativeBeliefMDP,
belief,
d::Int64)
sim = RolloutSimulator(est.rng, Nullable{Any}(), Nullable{Float64}(), Nullable(d))
return simulate(sim, ... | [
8818,
337,
4177,
50,
13,
395,
1920,
62,
8367,
7,
395,
3712,
26416,
47,
2662,
8697,
13,
50,
5634,
13775,
692,
448,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
... | 1.907718 | 596 |
<gh_stars>1-10
# This file is a part of ValueShapes.jl, licensed under the MIT License (MIT).
using ValueShapes
using Test
@testset "functions" begin
shape = NamedTupleShape(
a = ArrayShape{Real}(3,2),
b = ArrayShape{Real}(2)
)
x_flat = rand(@inferred totalndof(shape))
x = @inferred ... | [
27,
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62,
30783,
29,
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12,
940,
198,
2,
770,
2393,
318,
257,
636,
286,
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2484,
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11,
11971,
739,
262,
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357,
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737,
198,
198,
3500,
11052,
2484,
7916,
198,
3500,
6208,
628,
198,
31,
9288,
... | 2.316901 | 284 |
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