content stringlengths 6 1.03M | input_ids listlengths 4 535k | ratio_char_token float64 0.68 8.61 | token_count int64 4 535k |
|---|---|---|---|
<filename>docs/make.jl<gh_stars>0
using Documenter, DiffEqOperators
makedocs(
sitename="DiffEqOperators.jl",
authors="<NAME> et al.",
clean=true,
doctest=false,
modules=[DiffEqOperators],
format=Documenter.HTML(assets=["assets/favicon.ico"],
canonical="https://diffeq... | [
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... | 2.285156 | 256 |
# # How-to Guide
using AeroMDAO # hide
using Plots # hide
gr(dpi = 300) # hide
using LaTeXStrings # hide
# ## Airfoil Geometry
# How to work with airfoil geometry.
# ### Import Coordinates File
# You can specify the path consisting of the foil's coordinates to the `read_foil` function. The format for the coordinates ... | [
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37... | 2.509627 | 5,090 |
<filename>src/BoundaryScalingFunction.jl
abstract type AbstractBoundaryScalingFunction <: AbstractScalingFunction end
struct LeftScalingFunction <: AbstractBoundaryScalingFunction
values::OffsetArrays.OffsetVector{Float64, Vector{Float64}}
support::Vector{DyadicRational}
vanishing_moments::Int64
index... | [
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56... | 2.403685 | 3,582 |
# LogLinear Transformation
"""
LogLinearFormula(df::Int64)
Returns GLM formula can be used in `glm` with `FreqTab` data frame.
`df`, is abbreviation for degrees of formula, represents degree of polynomial log-linear method.
This function works very slowly. If a fixed degree formula will be used repeatedly, define... | [
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63,... | 2.202617 | 2,981 |
<reponame>UnofficialJuliaMirrorSnapshots/ARules.jl-7cbe2057-1070-5a1a-9a20-8e476bfa53e1<filename>src/frequent_itemset_tree.jl
# This implements the first attempt at using bitarrays for
# storing and propagating the itemset information at each node
struct Node
id::Int16
item_ids::Array{Int16,1}
transaction... | [
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<filename>src/ast_walk.jl
#=
Copyright (c) 2015, Intel Corporation
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice,
this list of ... | [
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198,... | 2.552023 | 8,179 |
using MiniTB
using Test
using TBComponents
using CSV
using DataFrames
using StatsBase
using Utils
using BenchmarkTools
include("../src/web/web_model_libs.jl" )
print_test = false
BenchmarkTools.DEFAULT_PARAMETERS.seconds = 120
BenchmarkTools.DEFAULT_PARAMETERS.samples = 2
function basic_run( params, num_households... | [
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<reponame>mmider/BridgeSDEInference.jl<filename>src/deprecated/setup.jl<gh_stars>10-100
#=
-------------------------------------------------------------------------
Implements functionalities for setting up the Markov chain Monte Carlo
algorithm. The main object is `MCMCSetup` and its members comprise of
... | [
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<reponame>ascheinb/Oceananigans.jl<filename>src/TurbulenceClosures/viscous_dissipation_operators.jl
#####
##### Viscous fluxes
#####
@inline viscous_flux_ux(i, j, k, grid, clock, νᶜᶜᶜ::Number, u) = νᶜᶜᶜ * ℑxᶜᵃᵃ(i, j, k, grid, Axᵃᵃᶜ) * ∂xᶜᵃᵃ(i, j, k, grid, u)
@inline viscous_flux_uy(i, j, k, grid, clock, νᶠᶠᶜ::Number, ... | [
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@testset "Special cases" begin
T = Float64
f(l) = n -> gamma(l + 1 / 2) / gamma(2l) * gamma(n + 2l) / gamma(n + l + 1 / 2)
for (P, Q, fn) in (
(Gegenbauer{1 / 2,T}, Legendre{T}, n -> 1.0),
(Gegenbauer{1 / 4,T}, Jacobi{1 / 4 - 1 / 2,1 / 4 - 1 / 2,T}, f(1 / 4)),
(Gegenbauer{3 / 4,T}, ... | [
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134... | 1.769231 | 351 |
<reponame>physimatics/Tomography.jl
module Tomography
using Plots
using FFTW
using Interpolations
include("phantom.jl")
include("Radon.jl")
include("wave_forward.jl")
include("utils.jl")
#Radon Transform
export phantom
using Reexport
@reexport using
.Radon
#PAT(PhotoAcoustic Tomography)
#export wave_forward
e... | [
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models = (
SVMLinearClassifier,
SVMClassifier,
SVMNuClassifier,
)
fparams = (
SVMLinearClassifier=(:coef, :intercept, :classes),
SVMClassifier=(:support, :support_vectors, :n_support, :dual_coef, :coef,
:intercept, :fit_status, :classes),
SVMNuClassifier=(:support, :support_v... | [
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... | 2.042398 | 684 |
module ConjugateGradients
include("genericblas.jl")
include("reader.jl")
include("cg.jl")
include("bicgstab.jl")
end
| [
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... | 2.418182 | 55 |
# BSE kernel for the u channel
function compute_u_BSE!(
Λ :: Float64,
buff :: Matrix{Float64},
v :: Float64,
dv :: Float64,
u :: Float64,
vu :: Float64,
vup :: Float64,
r :: Reduced_lattice,
m :: Mesh,
a :: Action_su2,
temp :: Array{Float64, 3}
) ... | [
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... | 1.930348 | 804 |
export disassemble
export load
"""
load(filename) -> Vector{Int}
Loads machine code file and return as an integer array
"""
function load(filename::AbstractString)
parse.(Int, filter(!isempty, readlines(filename)))
end
"""
disassemble(filename::AbstractString)
Print out the disassembled contents of `f... | [
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3... | 2.71748 | 492 |
<reponame>UnofficialJuliaMirror/TSMLextra.jl-0c7047ce-818d-11e9-1109-0323cd70e08d
module TestCaret
using TSML
using TSMLextra
using Test
const IRIS = getiris()
const X = IRIS[:,1:4] |> Matrix
const Y = IRIS[:,5] |> Vector
const XX = IRIS[:,1:1] |> Matrix
const YY = IRIS[:,4] |> Vector
#const learners=["rf","treebag"... | [
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# Use baremodule to shave off a few KB from the serialized `.ji` file
baremodule libevdev_jll
using Base
using Base: UUID
import JLLWrappers
JLLWrappers.@generate_main_file_header("libevdev")
JLLWrappers.@generate_main_file("libevdev", UUID("2db6ffa8-e38f-5e21-84af-90c45d0032cc"))
end # module libevdev_jll
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export group_algebra, galois_module, group
################################################################################
#
# Basic field access
#
################################################################################
base_ring(A::AlgGrp{T}) where {T} = A.base_ring::parent_type(T)
Generic.dim(A::AlgGrp)... | [
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... | 2.196983 | 13,524 |
<gh_stars>10-100
@with_kw struct iLQSolver{TLM, TOM, TQM}
"The regularization term for the state cost quadraticization."
state_regularization::Float64 = 0.0
"The regularization term for the control cost quadraticization."
control_regularization::Float64 = 0.0
"The initial scaling of the feed-forward... | [
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<reponame>charlesap/Wut.jl
# included in module Wut
struct BitPat
b::BitArray{1}
BitPat(b) = new(BitArray(b))
end
export BitPat
function Base.show(io::IO, m::BitPat)
print(io,"[")
for (i,v) in enumerate(m.b)
v ? print(io," 1") : print(io," 0")
end
print(io," ]")
end
#end # module | [
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8,... | 2.120805 | 149 |
@enum(BRNGType,
VSL_BRNG_MCG31 = 0x00100000,
VSL_BRNG_R250 = 0x00200000,
VSL_BRNG_MRG32K3A = 0x00300000,
VSL_BRNG_MCG59 = 0x00400000,
VSL_BRNG_WH = 0x00500000,
VSL_BRNG_SOBOL = 0x00600000,
VSL_BRNG_NIEDERR = 0x00700000,
... | [
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86... | 1.913944 | 1,255 |
<reponame>shashi/Mjolnir.jl<gh_stars>0
arrayshape(::Type{Array{T,N}}, sz...) where {T,N} =
Partial{Array{T,N}}(convert(Array{Any}, fill(T, sz)))
arrayshape(T::Type, sz...) = arrayshape(Array{T,length(sz)}, sz...)
@abstract Basic getindex(xs::Const{<:Array}, i::Const...) =
Const(xs.value[map(i -> i.value, i)...])
... | [
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... | 2.514577 | 686 |
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
mutable struct ApplicationGatewayAvailableSslOptionsPropertiesFormat <: SwaggerModel
predefinedPolicies::Any # spec type: Union{ Nothing, Vector{SubResource} } # spec name: p... | [
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296... | 3.505507 | 908 |
<reponame>gabrieldansereau/SimpleSDMLayers.jl-dev
## Issue examples ####
# Dimensions should be 330 x 570
cd("assets/")
wcpath = joinpath(ENV["SDMLAYERS_PATH"], "WorldClim", "BioClim", "10", "wc2.1_10m_bio_1.tif")
tmpfile = tempname()
query = `gdalwarp -te -145.0 20.0 -50.0 75.0 $(wcpath) $(tmpfile)`
run(query)
usi... | [
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14,
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198... | 2.348297 | 646 |
<reponame>ranjanan/ModelingToolkit.jl
using ModelingToolkit
@variables x y
@parameters a b
loss = (a - x)^2 + b * (y - x^2)^2
sys1 = OptimizationSystem(loss,[x,y],[a,b],name=:sys1)
sys2 = OptimizationSystem(loss,[x,y],[a,b],name=:sys2)
@variables z
@parameters β
loss2 = sys1.x - sys2.y + z*β
combinedsys = Op... | [
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2... | 2.367424 | 264 |
module TestUnivariateFiniteMethods
using Test
using MLJBase
using CategoricalArrays
import Distributions:pdf, logpdf, support
import Distributions
using StableRNGs
import Random
rng = StableRNG(123)
v = categorical(collect("asqfasqffqsaaaa"), ordered=true)
V = categorical(collect("asqfasqffqsaaaa"))
a, s, q, f = v[1]... | [
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37,
9504,
46202,
198,
198,
3500,
6208,
198,
3500,
10373,
41,
14881,
198,
3500,
327,
2397,
12409,
3163,
20477,
198,
11748,
46567,
507,
25,
12315,
11,
2604,
12315,
11,
1104,
198,
11748,
46567,
507,
198,
3500,
520... | 1.883601 | 4,476 |
#Minimum cover EMS method
using JuMP,Gurobi
#define the model
m =Model(Gurobi.Optimizer)
#Let n represents the number of decision variables
n=10
#declaring variables
@variable(m, x[1:n], Bin) # binary constraint
#define the objective function
@objective(m , Min, sum(x[i] for i =1:n)) # objective is t... | [
2,
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3002,
41363,
2446,
201,
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7378,
11,
38,
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201,
198,
2,
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262,
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76,
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320,
7509,
8,
201,
198,
201,
198,
2,
5756,
299,
6870,
... | 2.034682 | 692 |
julia> collection = []
0-element Array{Any,1}
julia> push!(collection, 1,2,4,7)
4-element Array{Any,1}:
1
2
4
7
| [
73,
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352,
... | 2.207547 | 53 |
<filename>src/rep/voxels.jl<gh_stars>10-100
export VoxelGrid
"""
VoxelGrid
Initialize VoxelGrid representation.
`voxels` should be Array of size `(N, N, N, B)` where `N` is the number of
voxels features and `B` is the batch size of VoxelGrid.
### Fields:
- `voxels` - voxels features of VoxelGrid.
### Ava... | [
27,
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417,
41339,
198,
198,
24243,
1096,
28035,
417,
41339,
1... | 2.1616 | 625 |
<gh_stars>10-100
using Revise
using PyPlot
using Infiltrator
using LinearAlgebra
using Bem2d
"""
bemsolve(g, rho, lambda, mu)
Solve BEM with particular integral approach with gravitysquareparticular
"""
function bemsolve(els, nels, g, rho, lambda, mu, nu, x, y)
# Build BEM operator, TH
idx = getidxdict(e... | [
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29,
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67,
628,
198,
37811,
198,
220,
220,
220,
307,
907,
6442,
7,
... | 2.085679 | 1,599 |
module CompressingSolvers
using Base: Order
include("./domains.jl")
include("./basis_functions.jl")
include("./multicolor_ordering.jl")
include("./create_problems.jl")
include("./reconstruction.jl")
include("utils.jl")
# Write your package code here.
end
| [
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17256,
7,
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14,
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27045,
273,
62,
... | 2.88764 | 89 |
"""
AbstractDifference
Supertype for differences.
"""
abstract type AbstractDifference end
"""
VectorDifference{Tm<:AbstractVector,Ta<:AbstractVector,Tr<:AbstractVector} <: AbstractDifference
Vector difference.
"""
struct VectorDifference{Tm<:AbstractVector,Ta<:AbstractVector,Tr<:AbstractVector} <: AbstractD... | [
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38469,... | 2.686727 | 1,213 |
<gh_stars>1-10
#
# These functions set up the structures needed for the MendelSearch routines,
# including the Parameter data structure whose entries control optimization.
#
export Parameter
export optimization_keywords!, set_parameter_defaults
mutable struct Parameter
cases :: Int # number of cases in a least squar... | [
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4645,
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12784,
1630,
23989,
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198,
2,
198,
39344,
2513... | 3.200833 | 1,200 |
using sem, Arrow, ModelingToolkit, LinearAlgebra,
SparseArrays, DataFrames, Optim, LineSearches,
Statistics
cd("test")
## Observed Data
dat = DataFrame(Arrow.Table("comparisons/data_dem.arrow"))
par_ml = DataFrame(Arrow.Table("comparisons/par_dem_ml.arrow"))
par_ls = DataFrame(Arrow.Table("comparisons/par_de... | [
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11,
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220,
220,
220,
14370,
198,
198,
10210,
... | 1.723208 | 4,003 |
# function Mvz(vz_pml::SparseMatrixCSC{Float64,Int64}, rho::Array{Float64,2}, dz::Float64, dt::Float64, ext::Int64, iflag::Int64)
# rho = modExpand(rho, ext, iflag)
# (m,n) = size(rho)
# a1 = 9/8; a2 = -1/24;
# c1 = a1/dz; c2 = a2/dz;
# C3 = zeros(m*n)
# denum = zeros(m*n)
# for ix = 1 : n... | [
2,
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2414,
11,
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83,
3712,
43879,
... | 1.479915 | 1,892 |
<reponame>heliosdrm/RecurrenceAnalysis.jl
using RecurrenceAnalysis
using DynamicalSystemsBase, Random, Statistics, SparseArrays
using Graphs, LinearAlgebra
using Test
using DataStructures
RA = RecurrenceAnalysis
rng = Random.seed!(194)
# Trajectories of 200 points
# Examples of the Hénon map based on:
# <NAME> & <... | [
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3163,
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198,
3500,
29681,
82,
11,
44800,
... | 2.145583 | 2,830 |
<gh_stars>10-100
struct 𝕊{n} <: Manifold end
dim(::Type{𝕊{n}}) where n = n
embdim(::Type{𝕊{n}}) where n = n+1
# Creates aliases S1, TS1, etc.
for n = 1:3
@eval const $(Symbol("S$n")) = 𝕊{$n}
@eval const $(Symbol("TS$n")) = T{𝕊{$n}}
@eval export $(Symbol("S$n"))
@eval export $(Symbol("TS$n"))
end
... | [
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232,
90,
77,
11709,
8,
810,
299,
796,
299,
198,
368,
17457,
320,
... | 2.011102 | 1,171 |
module Benchmark
using Profile
using BSON
chars = [x for x in '0':'z']
strings = [String([rand(chars) for _ in 1:20]) for _ in 1:20]
rstr(n::Int)::String = rand(strings)[1:n]
struct Baz
going::String
deeper::String
end
Baz() = Baz(rstr(20), rstr(1))
struct Bar
level::Int64
bazes::Vector{Baz}
end
Bar() = B... | [
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25,
... | 2.428571 | 525 |
<reponame>bmoretz/Daily-Coding-Problem<filename>julia/problems/test/matrix_tests.jl
using Test
using LinearAlgebra
using problems.matrix
@testset "matrix rotation 1" begin
@testset "1x1" begin
mat = Int8[1;]
actual = rotate_matrix1(mat)
expected = Int8[1;]
@test actual == expecte... | [
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44800,
2348,
29230,
198,
198,
3500,
... | 1.573259 | 2,887 |
<gh_stars>0
### Some prime group generation algorithms. References:
# + https://crypto.stackexchange.com/questions/820/how-does-one-calculate-a-primitive-root-for-diffie-hellman
# + https://math.stackexchange.com/questions/124408/finding-a-primitive-root-of-a-prime-number
# Eventually will need to revisit this implmen... | [
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12,
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12,
505,
12,
9948,
3... | 2.132353 | 816 |
<reponame>zpeng2/Laguerre.jl<gh_stars>0
module Laguerre
using LinearAlgebra
include("abstract_laguerre.jl")
include("lpolynomial.jl")
include("lpolyprod.jl")
include("lfunction.jl")
include("quadratures.jl")
export LaguerrePolynomial, LaguerreFunction
export LGR,
LGRquad,
eval_laguerre_function,
... | [
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29,
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198,
198,
17256,
7203,
397,
8709,
62,
75,
11433,
263,
... | 2.267045 | 176 |
<reponame>bocklund/MkCell.jl<filename>benchmark/benchmarks.jl
using BenchmarkTools, MkCell
cellfcc = [-0.5 0 0.5; 0 0.5 0.5; -0.5 0.5 0];
cellcubic = [1.0 0 0; 0 1.0 0; 0 0 1.0];
const suite = BenchmarkGroup()
suite["integration"] = BenchmarkGroup()
suite["integration"]["fcc1"] = @benchmarkable MkCell.cellopt($cellf... | [
27,
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28780,
198,
198,
3846,
69,
535,
796,
25915,
15,
13,
20,
657,
... | 2.698068 | 414 |
######
# This file is part of the MomentArithmetic.jl package, for performing arithmetic operations
# between moments of uncertain numbers, when the moments and the dependencies are only
# partially known.
#
# Some usefull functions
#
# University of Liverpool,
# Institute for Risk and Unertainty
... | [
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198,
2,
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318,
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286,
262,
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3163,
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34768,
4560,
198,
2,
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286,
8627,
3146,
11,
618,
262,
7188,
290,
262,
20086,
389,
691,
198,
2,
12387,
1900,
13,
198,
... | 2.378 | 1,000 |
include("fbase.jl")
include("quadrature.jl")
include("utils.jl")
include("melem_rt0.jl")
using LinearAlgebra
function melemk0P2(verts::Array{Float64, 2})
#= Compute the A = ∫div(v) div(u) and the B = ∫v u element-matrices when the
approximation order is k=0. In that case, there are no interior degrees
... | [
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4943,
198,
17256,
7203,
26791,
13,
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198,
198,
17256,
7203,
1326,
10671,
62,
17034,
15,
13,
20362,
4943,
198,
198,
3500,
44800,
2348,
2923... | 1.905931 | 2,647 |
function Well19937c()
return Well19937c(())
end
function Well19937c(arg0::Vector{jint})
return Well19937c((Vector{jint},), arg0)
end
function Well19937c(arg0::jint)
return Well19937c((jint,), arg0)
end
function Well19937c(arg0::jlong)
return Well19937c((jlong,), arg0)
end
function next_int(obj::Well... | [
8818,
3894,
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2718,
66,
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220,
220,
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198,
437,
198,
198,
8818,
3894,
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2718,
66,
7,
853,
15,
3712,
38469,
90,
73,
600,
30072,
198,
220,
220,
220,
1441,
3894,
19104,
2718,... | 2.394904 | 157 |
using Pkg
Pkg.activate(@__DIR__)
using Conda
using PyCall
# check what is contained in the julia env
Pkg.status()
# check what is contained in the conda env
Conda.list()
# check which python pycall is using
@show pyimport("sys").executable
| [
3500,
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198,
47,
10025,
13,
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7,
31,
834,
34720,
834,
8,
198,
3500,
9724,
64,
198,
3500,
9485,
14134,
198,
198,
2,
2198,
644,
318,
7763,
287,
262,
474,
43640,
17365,
198,
47,
10025,
13,
13376,
3419,
198,
198,
2,
219... | 3.075949 | 79 |
using Test
using MPI
if get(ENV,"JULIA_MPI_TEST_ARRAYTYPE","") == "CuArray"
import CUDA
ArrayType = CUDA.CuArray
else
ArrayType = Array
end
MPI.Init()
comm = MPI.COMM_WORLD
size = MPI.Comm_size(comm)
rank = MPI.Comm_rank(comm)
dst = mod(rank+1, size)
src = mod(rank-1, size)
N = 32
send_mesg = ArrayTyp... | [
3500,
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198,
3500,
4904,
40,
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198,
361,
651,
7,
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53,
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3539,
62,
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40,
62,
51,
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62,
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366,
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19182,
1,
198,
220,
220,
220,
1330,
29369,
5631,
198,
220,
22... | 2.275155 | 1,610 |
struct BasicIdealParam <: EoSParam
end
abstract type BasicIdealModel <: IdealModel end
struct BasicIdeal <: BasicIdealModel
params::BasicIdealParam
end
export BasicIdeal
function BasicIdeal(components::Array{String,1}; userlocations::Array{String,1}=String[], verbose=false)
return BasicIdeal(BasicIdealParam(... | [
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2287,
17633,
198,
220,
220,... | 2.496753 | 308 |
<reponame>beffaCo/updateSqlite<filename>test/datastreams.jl
# DataFrames
FILE = joinpath(DSTESTDIR, "randoms_small.csv")
DF = readtable(FILE)
if typeof(DF[:hiredate]) <: NullableVector
DF[:hiredate] = NullableArray(Date[isnull(x) ? Date() : Date(get(x)) for x in DF[:hiredate]], [isnull(x) for x in DF[:hiredate]])
... | [
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7,
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34720,
11,
366,
25192,
315... | 2.289457 | 1,565 |
<gh_stars>0
# When at a leaf node, this function returns the values in an appropriate
# form. This differs for cases when the leaf node holds an array of elements,
# a single strings, or a single number (arrays of numbers not yet implemented).
function get_values(obj)
if length(obj) > 1
res = string(obj)... | [
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29,
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262,
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10139,
6622,
281,
7177,
286,
4847,
11,
198,
2,
257,
... | 2.29983 | 1,174 |
"Generic PDDL planning domain."
@kwdef mutable struct GenericDomain <: Domain
name::Symbol # Name of domain
requirements::Dict{Symbol,Bool} = Dict() # PDDL requirements used
typetree::Dict{Symbol,Vector{Symbol}} = Dict() # Types and their subtypes
datatypes::Dict{Symbol,Type} = Dict() # Non-object data ... | [
1,
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286,
7386,
198,
220,
220,
220,
5359,
3712,
35,
713,
90,
13940,
23... | 2.806829 | 1,025 |
function update_parameters!(net::Net{GPUBackend}, method::Nesterov, learning_rate,
last_momentum, momentum, param_blob, hist_blob, gradient, data_type)
# param_blob += -last_momentum* hist_blob (update with vt-1)
CuBLAS.axpy(net.backend.cublas_ctx, length(hist_blob), convert(data_type, -last_momentum), hist_bl... | [
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... | 2.425068 | 367 |
<reponame>o-jasper/Treekenize.jl
# Copyright (c) 2013 <NAME>, under the MIT license,
# see doc/mit.txt from the project directory.
module Treekenize
import Base.readline
export treekenize, StrExpr #Function for making trees itself.
export none_incorrect
#Each element needs these to know what to do.
export head_expr... | [
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... | 2.154268 | 3,468 |
<reponame>ronisbr/Crayons.jl
const FORCE_COLOR = Ref(false)
const FORCE_256_COLORS = Ref(false)
force_color(b::Bool) = FORCE_COLOR[] = b
force_256_colors(b::Bool) = FORCE_256_COLORS[] = b
_force_color() = FORCE_COLOR[] || haskey(ENV, "FORCE_COLOR")
_force_256_colors() = FORCE_256_COLORS[] || haskey(ENV, "FORCE_256_CO... | [
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... | 1.864799 | 5,318 |
# the following definition is used to compare sets of eigenvalues
function ≊(list1::AbstractVector, list2::AbstractVector)
length(list1) == length(list2) || return false
n = length(list1)
ind2 = collect(1:n)
p = sizehint!(Int[], n)
for i = 1:n
j = argmin(abs.(view(list2, ind2) .- list1[i]))
... | [
2,
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8,
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7,
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17,
... | 2.227979 | 193 |
"""
gmtinfo(cmd0::String="", arg1=[]; kwargs...)
Reads files and finds the extreme values in each of the columns.
Full option list at [`gmtinfo`](http://gmt.soest.hawaii.edu/doc/latest/gmtinfo.html)
Parameters
----------
- **A** : -- Str --
Specify how the range should be reported.
[`-A`](http://gmt.so... | [
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13,
198,
198,
13295,... | 2.406439 | 1,491 |
"""
Handle model specific keywords in kwargs argument in stan_run(model; kwargs...).
$(SIGNATURES)
"""
function handle_keywords!(m::T, kwrds) where { T <: CmdStanModels}
model_keywords = fieldnames(typeof(m))
excluded_model_keywords = [
:name, :model, :data, :init, :output_base, :tmpdir, :exec_path,... | [
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... | 2.016822 | 535 |
<reponame>JuliaPackageMirrors/Elly.jl
using Compat
using ProtoBuf
import ProtoBuf.meta
import Base: hash, isequal, ==
type UserInformationProto
effectiveUser::AbstractString
realUser::AbstractString
UserInformationProto() = (o=new(); fillunset(o); o)
end #type UserInformationProto
hash(v::UserInformationPr... | [
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... | 2.72967 | 455 |
module ShapeFromShading
using AlgebraicMultigrid
using Blink
using Distributions
using DSP
using FFTW
using Images
using Interact
using IterativeSolvers
using LinearAlgebra
using Makie
using Optim
using Parameters
using Preconditioners
using SparseArrays
using Statistics
abstract type AbstractSyntheticShape end
@with... | [
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... | 2.811681 | 2,671 |
<reponame>sloede/Trixi.jl
# Naive implementations of multiply_dimensionwise used to demonstrate the functionality
# without performance optimizations and for testing correctness of the optimized versions
# implemented below.
function multiply_dimensionwise_naive(matrix::AbstractMatrix, data_in::AbstractArray{<:Any, 2}... | [
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262,
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6300,... | 2.212763 | 7,459 |
<reponame>UnofficialJuliaMirror/CumulantsFeatures.jl-89efba0d-c40c-5510-8345-5c0ed49e5930<filename>test/outliers_detect/gendat4detection.jl
#!/usr/bin/env julia
using Distributed
using Random
using LinearAlgebra
procs_id = addprocs(8)
using DatagenCopulaBased
@everywhere using Distributions
@everywhere using Cumulants... | [
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480,
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20,
66,
15,
276,
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68,
3270,
1270,
27,... | 2.164778 | 1,147 |
import AbstractAlgebra.Generic: Perm, SymmetricGroup
# disambiguation
GroupsCore.order(::Type{I}, G::SymmetricGroup) where {I<:Integer} =
convert(I, factorial(G.n))
# disambiguation
GroupsCore.order(::Type{I}, g::Perm) where {I<:Integer} =
convert(I, foldl(lcm, length(c) for c in AbstractAlgebra.cycles(g)))
... | [
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8,
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1391,
4... | 2.251244 | 402 |
#EXAMPLE: broadcasting
using MPI
MPI.Init()
comm = MPI.COMM_WORLD
rank = MPI.Comm_rank(comm)
if rank == 0
data = [7.0,8.0,9.0,10.0]
else
data = Float64[]
end
data = MPI.bcast(data, 0, comm)
@show rank, data
MPI.Finalize() | [
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... | 2.097345 | 113 |
<reponame>zoemcc/Raytracing.jl
using Raytracing
using GeometryBasics
using Random
using CoordinateTransformations
using ColorTypes
using ColorVectorSpace
using StaticArrays
using Images
using ImageIO
using FileIO
using ImageMagick
using Test
@testset "Raytracing.jl" begin
T = Float64
origin = Point3{T}(0,0,1.5... | [
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198,
3500,
5315,
... | 2.309167 | 1,200 |
<filename>test/cuda/transformer.jl
@testset "Transformer" begin
import Flux: gpu
t = Transformer(10, 3, 15, 20) |> gpu
td = TransformerDecoder(10, 3, 15, 20) |> gpu
x = cu(randn(10, 7, 3))
y = cu(randn(10, 6, 3))
@test size(t(x)) == (10, 7, 3)
@test size(t(x[:, :, 2])) == (10, 7)
@test... | [
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256,
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7,
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11,
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11,
... | 1.951691 | 207 |
using Base.Test
using OptimTools
if !isdefined(:probs) || isempty(probs)
println("getting test problems")
include("getTestFunctions.jl")
end
his = []; flag = []; x0 = [];
secondOrderMethods = (dampedNewton,newton,modnewton,newtoncg)
# try stopping based on atol
for method in secondOrderMethods
println("testing $(... | [
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7,
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8,
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318,
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7,
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8,
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197,
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37210,
1332,
2761,
4943,
198,
197,
17256,
7203,
1136,
14402,
24629,
2733... | 2.084034 | 357 |
module FastMarching
include("libmsfm.jl")
# include("msfm2d.jl")
include("ndgrid.jl")
include("pointmin.jl")
include("rk4.jl")
include("s1.jl")
include("shortestpath.jl")
end # module
| [
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... | 2.547945 | 73 |
<filename>task_2_1-2.jl
function sortkey(key_values, a)
indperm=sortperm(key_values)
return a[indperm]
end
function sortkey(f::Function, a)
sortkey(f(a), a)
end | [
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1... | 2.276316 | 76 |
function optimize(je_mmle_model::JointEstimationMMLEModel)
local parameters = je_mmle_model.parameters
local latents = je_mmle_model.latents
local dist = je_mmle_model.dist
local n_index = je_mmle_model.n_index
local i_index = je_mmle_model.i_index
local responses_per_item = je_mmle_model.respon... | [
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220,
220,
220,
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796,
... | 1.903788 | 2,297 |
using Currencies
using Test
currencies = ((:USD, 2, 840, "US Dollar"),
(:EUR, 2, 978, "Euro"),
(:JPY, 0, 392, "Yen"),
(:JOD, 3, 400, "Jordanian Dinar"),
(:CNY, 2, 156, "<NAME>"))
# This just makes sure that the data was loaded and at least some basic values are ... | [
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220,
220,
220,
220,
220,
220,
357,
25,
36,
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11,
362,
... | 2.133038 | 451 |
"""
```
measurement(m::AnSchorfheide{T}, TTT::Matrix{T},
RRR::Matrix{T}, CCC::Vector{T}) where {T <: AbstractFloat}
```
Assign measurement equation
```
y_t = ZZ*s_t + DD + u_t
```
where
```
Var(ϵ_t) = QQ
Var(u_t) = EE
Cov(ϵ_t, u_t) = 0
```
"""
function measurement(m::AnSchorfheide{T}, TTT::Matrix{T}, # ... | [
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... | 1.939442 | 1,255 |
<reponame>devmotion/BlackBoxOptim.jl
mutable struct TspLibProblem
name::String
numcities::Int
weights::Matrix{Float64}
end
size(t::TspLibProblem) = t.numcities
function resetweights!(t::TspLibProblem, nc::Int)
t.numcities = nc
t.weights = zeros(Float64, nc, nc)
end
function cost(p::TspLibProblem,... | [
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66,
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198,
220,
220,
220,
195... | 2.016077 | 933 |
<reponame>cvdlab/larlib.jl
using Plasm, ViewerGL, LinearAlgebra
GL = ViewerGL
using LinearAlgebraicRepresentation
Lar = LinearAlgebraicRepresentation
#include("")
store = [];
scaling = 1.5;
V,(VV,EV,FV,CV) = Lar.cuboid([0.25,0.25,0.25],true,[-0.25,-0.25,-0.25]);
mybox = (V,CV,FV,EV);
for k=1:5
size = rand(... | [
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2348,
29230,
291,
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341,
2... | 1.86836 | 866 |
<reponame>QuantumControl-jl/QuantumControlBase.jl
using Test
using QuantumControlBase
using QuantumControlBase.Shapes: blackman
@testset "discretize/discretize_on_midpoints" begin
tlist = collect(range(0, 10, length=20))
t₀ = 0.0
T = 10.0
function control_func(t)
return blackman(t, t₀, T)
... | [
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25,
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198,
198,
31,
9288,
2617,
3... | 2.555963 | 545 |
<gh_stars>0
# This file is a part of Julia. License is MIT: https://julialang.org/license
struct Params
cache::Vector{InferenceResult}
world::UInt
global_cache::Bool
# optimization
inlining::Bool
ipo_constant_propagation::Bool
aggressive_constant_propagation::Bool
inline_cost_threshold... | [
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3712,
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90,
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... | 2.310782 | 1,419 |
<gh_stars>10-100
__precompile__()
module Materials
using LinearAlgebra
using ..adiff
# Material types
struct Hooke
E ::Float64
ν ::Float64
small ::Bool
Hooke(E,ν;small=false) = new(Float64(E),Float64(ν), small)
end
struct MooneyRivlin
C1 ::Float64
C2 ::Float64
K ::Float64
MooneyRivlin(C1... | [
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29,
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2,
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198,
7249,
9544,
2088,
198,
220,
412... | 1.71974 | 2,619 |
<filename>src/Functions/CommonStaticFunctions.jl
#Written By <NAME>
module CommonStaticFunctions
module Single
using Base.MathConstants
using ...Computation
import ....@Fun
export Sin, Cos, Tan, ASin, ACos, ATan, Tanh
Wrap(sig::Signature, f::Function) = MemoryWrapper(
... | [
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198,
2,
25354,
2750,
1279,
20608,
29,
198,
21412,
8070,
45442,
24629,
2733,
628,
220,
220,
220,
8265,
14206,
198,
220,
220,
220,
220,
220,
220,
220,
1262... | 1.797753 | 1,424 |
include("./BinaryIO.jl")
module ProgramTunnel
using Formatting
using ..BinaryIO
export recvText, sendText, recvBinary!, sendBinary!,
mkTunnel, defaultTunnelSet, getTunnelFilename!, reverseRole!
mutable struct Tunnel
fns :: AbstractArray{AbstractString}
next_ptr :: Integer
function Tunnel(na... | [
17256,
7,
1911,
14,
33,
3219,
9399,
13,
20362,
4943,
198,
198,
21412,
6118,
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3500,
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39344,
664,
85,
8206,
11,
3758,
8206,
11,
664,
85,
33,
3219,
28265,
... | 2.020352 | 1,818 |
<reponame>qiyang-ustc/FermionicOperatorStringAnalyzer.jl<gh_stars>1-10
@testset "FO Constructor" begin
fo = FO(:c,2,Up,false)
@test isa(fo,FO)
fo = FO(:c,-1,Dn,true)
@test isa(fo,FO)
end
@testset "PFO Constructor" begin
fo = FO(:c,2,Up,false)
pfo = PFO(fo,symbols(:n1))
@test isa(pfo,PFO)
... | [
27,
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261,
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29,
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648,
12,
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66,
14,
37,
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27,
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62,
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29,
16,
12,
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198,
31,
9288,
2617,
366,
6080,
28407,
273,
1,
2221,
220,
198,
220,
220,
2... | 1.8125 | 224 |
module GR_Spherical
using DifferentialEquations
using BoundaryValueDiffEq
using OrdinaryDiffEq
using Fun1d
using DataFrames
using CSV
using Plots
using Roots
using BenchmarkTools
using InteractiveUtils
using RecursiveArrayTools
using StaticArrays
using LinearAlgebra
using Profile
struct Param{T}
rtmin::T
rt... | [
21412,
10863,
62,
4561,
37910,
198,
198,
3500,
20615,
498,
23588,
602,
198,
3500,
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560,
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36,
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3500,
14230,
3219,
28813,
36,
80,
198,
3500,
11138,
16,
67,
198,
3500,
6060,
35439,
198,
3500,
44189,
198,
3500,
... | 1.683557 | 20,702 |
module test_loltools_spectatorv4
using Test
using LOLTools.SpectatorV4
include("helpers.jl")
struct SpectatorController <: ApplicationController
conn::Conn
end
function featured_games(c::SpectatorController)
render(JSON, (a=1,))
end
routes() do
get("/lol/spectator/v4/featured-games", SpectatorControlle... | [
21412,
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364,
13,
20362,
4943,
198,
198,
7249,
13058,
1352,
22130,
1279,
25,
15678,... | 2.80117 | 171 |
<reponame>Masicko/ImageToEIS
const i_YSZ = 0
const i_LSM = 1
const i_hole = 2
Base.@kwdef mutable struct parameters
R_YSZ::Float64 = 1/0.045 # S/cm
R_pol_YSZ::Float64 = 0
C_pol_YSZ::Float64 = 0.001
#
R_LSM::Float64 = 1/290 # S/cm
R_pol_LSM::Float64 = 40
C_pol_LSM::Float64 = 0.005
... | [
27,
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62,
13207,
796,
362,
628,
198,
14881,
13,
31,
46265,
4299,
4517,... | 1.610973 | 1,203 |
function μ(E0::Real, K1::Real, K2::Real, cϕ::Real, sϕ::Real, cθ::Real, sθ::Real)
n̂i = n̂(cϕ, sϕ, cθ, sθ);
return ((K1 - K2) * E0 * cθ * n̂i) + (K2 * [0.0; 0.0; E0]);
end
| [
8818,
18919,
7,
36,
15,
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15633,
11,
509,
16,
3712,
15633,
11,
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3712,
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11,
264,
139,
243,
3712,
15633,
11,
269,
138,
116,
3712,
15633,
11,
264,
138,
116,
3712,
15633,
8,
198,
220,
... | 1.5625 | 112 |
<reponame>oschulz/StatsFuns.jl
# functions related to beta distributions
# R implementations
# For pdf and logpdf we use the Julia implementation
using .RFunctions:
betacdf,
betaccdf,
betalogcdf,
betalogccdf,
betainvcdf,
betainvccdf,
betainvlogcdf,
betainvlogccdf
# Julia implementation... | [
27,
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261,
480,
29,
418,
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2,
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290,
2604,
12315,
356,
779,
262,
22300,
7822,
198,
3500,
764,
49,
24629,
... | 2.260317 | 315 |
###################################
# variables shared across scripts #
###################################
ALL_PROBLEM_NAMES = ["qap10", "qap15", "nug08-3rd", "nug20"]
# restart lengths to try for eahc problem
RESTART_LENGTHS_DICT = Dict(
"qap10" => 4 .^ collect(1:9),
"qap15" => 4 .^ collect(1:9),
"nug08-3rd" =... | [
29113,
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198,
2,
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1973,
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1303,
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62,
45,
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366,
80,
499,
1314,
1600,
366,
77,
1018,
2919,
12,
18,
4372,
1600,
366,
77,
... | 2.597561 | 164 |
module Core
include("token.jl")
include("pos_tag.jl")
include("sentence.jl")
include("sequence.jl")
include("cache.jl")
end | [
21412,
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198,
220,
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220,
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198,
220,
2291,
7203,
43167,
13,
20362,
4943,
198,
220,
2291,
7203,
... | 2.829787 | 47 |
"""
WSVarScoreTestBaseObs
A base per-observation object for the score test of within-subject variance
linear mixed model data instance without information on X1 or W1.
Contains base variables for testing
H0: β1 = 0 and τ1 = 0, H1: β1 ≠ 0 or τ1 ≠ 0,
for the full model of WiSER (with parameters β = [β1, β2],
τ ... | [
37811,
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220,
220,
220,
25290,
19852,
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14402,
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31310,
198,
198,
32,
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329,
262,
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286,
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198,
29127,
7668,
2746,
1366,
4554,
1231,
1321,
319,
1395,
16,
... | 1.814731 | 6,612 |
using Printf
function load_input(day::Integer)
day = @sprintf("%0.2d", day)
name = "day_$(day).txt"
path = joinpath("inputs", name)
return read(path, String)
end
| [
3500,
12578,
69,
198,
198,
8818,
3440,
62,
15414,
7,
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82,
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1110,
8,
198,
220,
220,
220,
1438,
796,
366,
820,
62,
3,
7,
820,
737,
141... | 2.386667 | 75 |
<reponame>jmmshn/LeetCode.jl<gh_stars>10-100
# ---
# title: 803. Bricks Falling When Hit
# id: problem803
# author: <NAME>
# date: 2020-10-31
# difficulty: Hard
# categories: Union Find
# link: <https://leetcode.com/problems/bricks-falling-when-hit/description/>
# hidden: true
# ---
#
# You are given an `m x n` binary... | [
27,
7856,
261,
480,
29,
73,
76,
907,
21116,
14,
3123,
316,
10669,
13,
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27,
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62,
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29,
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12,
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198,
2,
11420,
198,
2,
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25,
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3070,
13,
347,
23706,
42914,
1649,
7286,
198,
2,
4686,
25,
1917,
43564,
19... | 2.363259 | 1,203 |
################################################################################
#
# AlgAssAbsOrd / AlgAssAbsOrdElem
#
################################################################################
# Orders in algebras over the rationals
@attributes mutable struct AlgAssAbsOrd{S, T} <: Ring
algebra::S ... | [
29113,
29113,
14468,
198,
2,
198,
2,
220,
978,
70,
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35422,
1220,
978,
70,
8021,
24849,
35422,
36,
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198,
2,
198,
29113,
29113,
14468,
198,
198,
2,
30689,
287,
435,
469,
1671,
292,
625,
262,
9377,
82,
198,
31,
1078,
... | 2.175231 | 3,036 |
<reponame>mohamed82008/LazyArrays.jl<filename>src/linalg/mul.jl
const Mul{Style, Factors<:Tuple} = Applied{Style, typeof(*), Factors}
const MulArray{T, N, Args} = ApplyArray{T, N, typeof(*), Args}
const MulVector{T, Args} = MulArray{T, 1, Args}
const MulMatrix{T, Args} = MulArray{T, 2, Args}
Mul(A...) = applied... | [
27,
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261,
480,
29,
76,
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628,
198,
198,
9979,
17996,
90,
21466,
11,
41140,
27,
25,
51,
29291,
92,
7... | 2.219233 | 4,014 |
using Aerospace
using Base.Test
# write your own tests here
pitchi = 45.0*D2R;
yawi = 90.0*D2R;
rolli = 10.0*D2R;
Tr_bni = TR_BN(rolli, pitchi, yawi);
# Testing of the Algorithms
quat = QuatInit(rolli, pitchi, yawi);
Tr_bn = QuatToDCM(quat);
euler = ComputeEuler( Tr_bn );
# write your own tests here
@test (euler[1] ... | [
3500,
43226,
198,
3500,
7308,
13,
14402,
198,
198,
2,
3551,
534,
898,
5254,
994,
198,
79,
2007,
72,
796,
4153,
13,
15,
9,
35,
17,
49,
26,
198,
88,
23368,
796,
4101,
13,
15,
9,
35,
17,
49,
26,
198,
2487,
72,
796,
838,
13,
15,... | 2.049774 | 221 |
const DEFAULT_SAMPLE_SIZE = 1000
export sourceParticles
import MonteCarloMeasurements
using MonteCarloMeasurements: Particles, StaticParticles, AbstractParticles
foreach([<=, >=, <, >]) do cmp
MonteCarloMeasurements.register_primitive(cmp, eval)
end
export particles
particles(v::Vector{<:Real}) = Particles(v)
... | [
9979,
5550,
38865,
62,
49302,
16437,
62,
33489,
796,
8576,
198,
198,
39344,
2723,
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3500,
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9914,
5439,
47384,
902,
25,
2142,
2983,
11,
36125,
7841,
2983,
11,
27741,
78... | 2.414073 | 2,217 |
<reponame>marcom/PyCall.jl
#!/bin/bash
# -*- mode: julia -*-
#=
thisdir="$(dirname "${BASH_SOURCE[0]}")"
exec "$thisdir/julia.sh" --startup-file=no "$@" ${BASH_SOURCE[0]}
=#
pkgid = Base.PkgId(Base.UUID("438e738f-606a-5dbb-bf0a-cddfbfd45ab0"), "PyCall")
sysimage_path = unsafe_string(Base.JLOptions().image_file)
if has... | [
27,
7856,
261,
480,
29,
3876,
785,
14,
20519,
14134,
13,
20362,
198,
2,
48443,
8800,
14,
41757,
198,
2,
532,
9,
12,
4235,
25,
474,
43640,
532,
9,
12,
198,
2,
28,
198,
5661,
15908,
2625,
3,
7,
15908,
3672,
17971,
90,
33,
11211,
... | 2.242857 | 210 |
type AnnealingEpsilonGreedy <: BanditAlgorithm
counts::Vector{Int64}
values::Vector{Float64}
end
function AnnealingEpsilonGreedy(n_arms::Int64)
AnnealingEpsilonGreedy(zeros(Int64, n_arms), zeros(n_arms))
end
function initialize(algo::AnnealingEpsilonGreedy, n_arms::Int64)
algo.counts = zeros(Int64, n_arms)
... | [
4906,
15397,
4272,
36,
862,
33576,
43887,
4716,
1279,
25,
10243,
270,
2348,
42289,
198,
220,
9853,
3712,
38469,
90,
5317,
2414,
92,
198,
220,
3815,
3712,
38469,
90,
43879,
2414,
92,
198,
437,
198,
198,
8818,
15397,
4272,
36,
862,
3357... | 2.479893 | 373 |
<filename>src/newton.jl<gh_stars>0
"""
optimize_newton!(x, f, g, h)
Newton minimization of `f`, with first and second derivatives
`g` and `h`. Starts the iteration from `x0`.
"""
function optimize_newton(x, f, g, h;
grtol=1e-4,
gatol=1e-4,
prints=false)
while true
fx = f(x)
gx = g(x)
hx ... | [
27,
34345,
29,
10677,
14,
3605,
1122,
13,
20362,
27,
456,
62,
30783,
29,
15,
198,
37811,
198,
197,
40085,
1096,
62,
3605,
1122,
0,
7,
87,
11,
277,
11,
308,
11,
289,
8,
198,
198,
3791,
1122,
10356,
1634,
286,
4600,
69,
47671,
351... | 2.083333 | 252 |
<filename>docs/experiments/experiments/Policy Gradient/JuliaRL_PPO_Pendulum.jl
# ---
# title: JuliaRL\_PPO\_Pendulum
# cover: assets/JuliaRL_PPO_Pendulum.png
# description: PPO applied to Pendulum
# date: 2021-05-22
# author: "[<NAME>](https://github.com/findmyway)"
# ---
#+ tangle=true
using ReinforcementLearning
usi... | [
27,
34345,
29,
31628,
14,
23100,
6800,
14,
23100,
6800,
14,
36727,
17701,
1153,
14,
16980,
544,
7836,
62,
10246,
46,
62,
47,
437,
14452,
13,
20362,
198,
2,
11420,
198,
2,
3670,
25,
22300,
7836,
59,
62,
10246,
46,
59,
62,
47,
437,
... | 1.91471 | 1,618 |
using ..Helper: init
function simulate!(x, dt, sigma, rng)
sgm = sigma * sqrt(dt)
steps, = size(x)
for t = 2:steps
x[t] = (1 - dt) * x[t-1] + sgm * randn(rng)
end
end
function prepare(name)
res = init(name)
rng = MersenneTwister(res[:seed])
function run()
simulate!(res[:x... | [
3500,
11485,
47429,
25,
2315,
628,
198,
8818,
29308,
0,
7,
87,
11,
288,
83,
11,
264,
13495,
11,
374,
782,
8,
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220,
220,
220,
264,
39870,
796,
264,
13495,
1635,
19862,
17034,
7,
28664,
8,
198,
220,
220,
220,
4831,
11,
796,
25... | 1.973684 | 190 |
<filename>H/HDF5/build_tarballs.jl<gh_stars>0
using BinaryBuilder
# Collection of sources required to build HDF5
name = "HDF5"
version = v"1.12.0"
sources = [
FileSource("https://files.pythonhosted.org/packages/d5/f9/676c6a5c13806289da6177c538ce772e3e5b04ea10d76e6e72e9f0d042de/h5py-3.1.0-cp39-cp39-macosx_10_9_x86... | [
27,
34345,
29,
39,
14,
39,
8068,
20,
14,
11249,
62,
18870,
21591,
13,
20362,
27,
456,
62,
30783,
29,
15,
198,
3500,
45755,
32875,
198,
198,
2,
12251,
286,
4237,
2672,
284,
1382,
5572,
37,
20,
198,
3672,
796,
366,
39,
8068,
20,
1... | 2.144578 | 2,324 |
<gh_stars>0
# Functions for discretizing continuous values using various algorithms
using Discretizers
export get_bin_ids!, get_frequencies, get_frequencies_from_bin_ids
# Parameters:
# - values_x, arrays of floats (multiple arrays supported)
function get_root_n(values_x...)
return round(Int, sqrt(size(values_x[1]... | [
27,
456,
62,
30783,
29,
15,
198,
2,
40480,
329,
1221,
1186,
2890,
12948,
3815,
1262,
2972,
16113,
198,
198,
3500,
8444,
1186,
11341,
198,
198,
39344,
651,
62,
8800,
62,
2340,
28265,
651,
62,
69,
8897,
3976,
11,
651,
62,
69,
8897,
... | 2.35799 | 2,109 |
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