content stringlengths 6 1.03M | input_ids listlengths 4 535k | ratio_char_token float64 0.68 8.61 | token_count int64 4 535k |
|---|---|---|---|
<reponame>angusmoore/RSDSGE.jl
function createarglist(parameters,vars,transmatrix)
arg = Array{SymPy.Sym}(1)
arg = [transmatrix.completelygeneric] # Transition prob first
append!(arg,vars.contemps_sym) # Add in the steady state values
# Non-switching vars first
append!(arg,parameters.generic_sym[1,.... | [
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module MeasureToolbox # TODO make this an independent package
# Import the necessary packages.
import FastGaussQuadrature
import JuMP
import Distributions
const JuMPC = JuMP.Containers
using ..InfiniteOpt
# include jl files here
include("integrals.jl")
include("expectations.jl")
include("support_sums.jl")
end # end ... | [
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# ---
# title: 932. Beautiful Array
# id: problem932
# author: <NAME>
# date: 2020-10-31
# difficulty: Medium
# categories: Divide and Conquer
# link: <https://leetcode.com/problems/beautiful-array/description/>
# hidden: true
# ---
#
# For some fixed `N`, an array `A` is _beautiful_ if it is a permutation of the
# in... | [
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4... | 2.153061 | 392 |
<filename>stdlib/REPL/test/TerminalMenus/multiselect_menu.jl
# This file is a part of Julia. License is MIT: https://julialang.org/license
# This file tests the new Julia 1.6+ extension interface of TerminalMenus
# To trigger the new interface, at least one configuration keyword argument must be supplied.
# Check to ... | [
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1... | 2.480148 | 1,083 |
<reponame>prakharcode/AstroTime.jl
module Periods
import Base: *, /, get, isapprox, show
export TimeUnit, Second, Minute, Hour, Day, Year, Century,
J2000, J1950, MJD,
seconds, minutes, hours, days, years, centuries,
Period, *, /, get
include("constants.jl")
"""
All time units are subtypes of the abstrac... | [
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<filename>lang/Julia/monte-carlo-methods.jl
using Printf
function monteπ(n)
s = count(rand() ^ 2 + rand() ^ 2 < 1 for _ in 1:n)
return 4s / n
end
for n in 10 .^ (3:8)
p = monteπ(n)
println("$(lpad(n, 9)): π ≈ $(lpad(p, 10)), pct.err = ", @sprintf("%2.5f%%", abs(p - π) / π))
end
| [
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@testset "Global" begin
function kernel(X)
ptr = AMDGPU.get_global_pointer(Val(:myglobal), Float32)
Base.unsafe_store!(ptr, 3f0)
nothing
end
hk = AMDGPU.rocfunction(kernel, Tuple{Int32})
exe = hk.mod.exe
gbl = AMDGPU.get_global(exe.exe, :myglobal)
gbl_ptr = Base.unsafe_convert(Ptr{Float32}, gbl.ptr)
@test... | [
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1... | 2.26935 | 323 |
<filename>src/glover_mcfarlane.jl
@doc raw"""
K, γ, info = glover_mcfarlane(G::AbstractStateSpace{Continuous}, γ = 1.1)
Design a controller for `G` that maximizes the stability margin ϵ = 1/γ with normalized coprime factor uncertainty using the method of Glover and McFarlane
```
γ = 1/ϵ = ||[K;I] inv(I-G*K)*inv(M)... | [
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<gh_stars>1-10
# Add Max Trust Angle Constraints
using SparseArrays, LinearAlgebra
include("../constraints/constraints.jl")
function getAlpha(theta, deg = true)
if deg
return -tand(theta)
end
return -tan(theta)
end
"""
makeMaxThrustConstraint(NSteps::Int64, nDim::Int64, thetaMax::Float64)
... | [
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<reponame>eunjongkim/Touchstone.jl
module Touchstone
using ..Network
export TouchstoneData, read_touchstone
"""
Touchstone data in its raw form, imported from a touchstone file `*.sNp`
"""
struct TouchstoneData
nPort::Int
nPoint::Int
impedance::Float64
freq_unit::String
data_type::String
format_... | [
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824... | 2.238139 | 1,096 |
<gh_stars>0
# A module template for Koala supervised models
## INTRODUCTION
# This file briefly describes the low-level methods that must be
# implemented for each new Koala supervised learning algorithm. If
# this algorithm is called "SomeAlgorithm", then this implementation
# is conventionally contained in a module... | [
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... | 3.711228 | 2,289 |
<gh_stars>1-10
using Documenter
using QuranTree
makedocs(
sitename = "QuranTree.jl",
format = Documenter.HTML(
assets = ["assets/logo.ico"]
),
modules = [QuranTree],
authors = "<NAME>",
pages = [
"Home" => "index.md",
"Getting Started" => "man/getting_started.md",
... | [
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796,
1685... | 2.244828 | 580 |
# reimplements methods: hessfact, hessfact!
# and minor functions from LinAlg: full, A_mul_B! ..., reflectorApply!
import LinearAlgebra
import Base: A_mul_B!, Ac_mul_B!, A_mul_Bc!, copymutable, full
import LinearAlgebra: hessfact, hessfact!, Hessenberg, HessenbergQ
import LinearAlgebra: chkstride1, checksquare
impo... | [
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... | 2.136856 | 1,571 |
<reponame>jwscook/Kalman.jl<filename>example/trajectory_tracking/kalman.jl
using Kalman, GaussianDistributions, LinearAlgebra
using GaussianDistributions: ⊕ # independent sum of Gaussian r.v.
using Statistics
using GLMakie
using StaticArrays
function onestep(p, v)
v += @SVector randn(2)
w = 0.1 * @SVector rand... | [
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29... | 2.176136 | 880 |
<reponame>aviks/Optim.jl<gh_stars>1-10
function modindex(i::Integer, m::Integer)
x = mod(i, m)
if x == 0
return m
else
return x
end
end
function twoloop!(g_x::Vector,
rho::Vector,
s::Matrix,
y::Matrix,
m::Integer,
... | [
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198,... | 1.828002 | 2,657 |
<reponame>JuliaApproximation/BasisFunctions.jl
# Laguerre and Hermite fail due to linear algebra problems in BigFloat
supports_approximation(::Laguerre{BigFloat}) = false
supports_approximation(::Laguerre{Double64}) = false
supports_approximation(::Hermite{BigFloat}) = false
supports_approximation(::Hermite{Double64})... | [
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139... | 2.99297 | 569 |
A = zeros(UInt8, 10, 10, 10)
for i in 1:8
A[i, 5, i:i+2] = 0x01
end
A[9:10, 5, 8:10] = 0x01
blob_series = form_blobs(A)
@test length(blob_series) == 10
for blobs in blob_series
@test length(blobs) == 1
blob = blobs[1]
@test blob.x.p == 5
@test blob.ratio ≈ 3.0
@test blob.area == 3
@test blo... | [
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... | 1.865724 | 283 |
<filename>test/dependencies.jl
@testset "get_project_deps" begin
@testset "no jll" begin
apply([git_clone_patch, project_toml_patch, cd_patch]) do
options = CompatHelper.Options()
subdir = only(options.subdirs)
deps = CompatHelper.get_project_deps(
GitForg... | [
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... | 2.150396 | 1,895 |
<reponame>UnofficialJuliaMirrorSnapshots/TimeseriesPrediction.jl-f218859d-9706-56aa-9ebf-1fa4ed7b8020
using TimeseriesPrediction
using Test
using Statistics, LinearAlgebra
@testset "SymmetricEmbedding" begin
@testset "Ordering in β_groups" begin
γ = 0; τ = 1; r = 4; c = 0; bc = ConstantBoundary(10.);
... | [
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17... | 1.92672 | 2,238 |
<gh_stars>100-1000
module EventTrigger
using ..Ahorn, Maple
const placements = Ahorn.PlacementDict(
"Event" => Ahorn.EntityPlacement(
Maple.EventTrigger,
"rectangle"
)
)
function Ahorn.editingOptions(trigger::Maple.EventTrigger)
return Dict{String, Any}(
"event" => Maple.event_tri... | [
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7,
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220,
220,
220,
366,
9237,
1,
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7900,... | 2.453901 | 141 |
function load_synonym_table(db::SQLite.DB; logger::Union{Nothing,SimpleLogger} = nothing)::VLResult
# initialize -
synonym_dictionary = Dict{String,String}()
try
# load the synonym table -
sql_string = "SELECT * FROM LEXEME_SYNONYM_TABLE;"
# execute the call -
query_resu... | [
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198,
220,
220,
220,
... | 2.315927 | 383 |
"""
SumGreaterThan(x<:AbstractIntVar, v::Int)
Summing constraint, states that `x[1] + x[2] + ... + x[length(x)] >= lower`
"""
struct SumGreaterThan <: Constraint
x ::Array{<:AbstractIntVar}
lower ::Int
active ::StateObject{Bool}
numberOfFreeVars ::Sta... | [
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... | 2.358239 | 1,499 |
<reponame>Nosferican/MixedModels.jl
"""
UniformBlockDiagonal{T}
Homogeneous block diagonal matrices. `k` diagonal blocks each of size `m×m`
"""
struct UniformBlockDiagonal{T} <: AbstractMatrix{T}
data::Array{T,3}
facevec::Vector{SubArray{T,2,Array{T,3}}}
end
function UniformBlockDiagonal(dat::Array{T,3})... | [
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40039,
2603,
45977,
13,
220,
4600,
74,
63,
40039... | 2.061305 | 1,517 |
<gh_stars>0
@testset "Crimisini" begin
if visualtests
# Diagonal
img = [i + j > 10 ? 0.1 : 0.2 for i in 1:10, j in 1:10]
mask = [1.2cos(i) + i > j for i in 1:10, j in 1:10]
out = inpaint(img, mask, Criminisi(5,5))
@plottest plot_before_after(img,mask,out) joinpath(datadir,"Diagonal.png") !istrav... | [
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220,
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220,
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685,
72,
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474,
1875,
838,
5633,
657,
... | 2.149723 | 541 |
"""
Update the parameters of the model using the gardients (∇) and the learning rate (η).
"""
function update_model_weights(parameters, ∇, η)
L = trunc(Int, length(parameters) / 2)
# update the parameters (weights and biases) for all the layers
for l = 1:L
parameters[string("W_", l)] = min.(max.(pa... | [
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7,
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11,
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229,
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115,
8... | 2.392739 | 303 |
<gh_stars>10-100
using EmpiricalRisks
using Base.Test
## Auxiliary functions
function verify_multipred(pred::PredictionModel{1,0}, θ, X::DenseMatrix)
n = size(X, 2)
@test ninputs(pred, X) == n
rr = zeros(n)
for i = 1:n
rr[i] = predict(pred, θ, X[:,i])
end
@test_approx_eq predict(pred,... | [
27,
456,
62,
30783,
29,
940,
12,
3064,
198,
3500,
2295,
4063,
605,
49,
36730,
198,
3500,
7308,
13,
14402,
628,
198,
2235,
47105,
28129,
5499,
198,
198,
8818,
11767,
62,
16680,
541,
445,
7,
28764,
3712,
39156,
2867,
17633,
90,
16,
11... | 2.093588 | 1,154 |
<filename>src/worksheet.jl<gh_stars>0
function Worksheet(xf::XLSXFile, sheet_element::EzXML.Node)
@assert EzXML.nodename(sheet_element) == "sheet"
sheetId = parse(Int, sheet_element["sheetId"])
relationship_id = sheet_element["r:id"]
name = sheet_element["name"]
target = "xl/" * get_relationship_... | [
27,
34345,
29,
10677,
14,
5225,
25473,
13,
20362,
27,
456,
62,
30783,
29,
15,
198,
198,
8818,
10933,
25473,
7,
26152,
3712,
55,
6561,
55,
8979,
11,
9629,
62,
30854,
3712,
36,
89,
55,
5805,
13,
19667,
8,
198,
220,
220,
220,
2488,
... | 2.566667 | 3,270 |
<filename>src/JuliaTutorial.jl
module JuliaTutorial
using Match
export Menu
include("menu.jl")
include("LinearAlgebra.jl")
include("MatrixFactorizations.jl")
include("TextMining.jl")
include("RecSys.jl")
end
| [
27,
34345,
29,
10677,
14,
16980,
544,
51,
44917,
13,
20362,
198,
21412,
22300,
51,
44917,
198,
198,
3500,
13225,
198,
198,
39344,
21860,
628,
2291,
7203,
26272,
13,
20362,
4943,
198,
2291,
7203,
14993,
451,
2348,
29230,
13,
20362,
4943,... | 3.056338 | 71 |
<filename>src/joLinearFunctionConstructors/joNFFT.jl
# NFFT operators: joNFFT
## helper module
module joNFFT_etc
using NFFT
using FFTW: fftshift, ifftshift
using JOLI: jo_convert
function apply_nfft_centered(pln,n,v::Vector{vdt},rdt::DataType) where vdt<:Union{AbstractFloat,Complex}
iv=jo_conve... | [
27,
34345,
29,
10677,
14,
7639,
14993,
451,
22203,
42316,
669,
14,
7639,
45,
5777,
51,
13,
20362,
198,
2,
399,
5777,
51,
12879,
25,
2525,
45,
5777,
51,
198,
198,
2235,
31904,
8265,
198,
21412,
2525,
45,
5777,
51,
62,
14784,
198,
2... | 1.935768 | 1,588 |
<reponame>IanButterworth/ThreadPools.jl
import Core.Compiler
abstract type AbstractThreadPool end
_detect_type(fn, itr) = Core.Compiler.return_type(fn, Tuple{eltype(itr)})
"""
tforeach(fn, pool, itr)
Mimics `Base.foreach`, but launches the function evaluations onto the provided
pool to assign the tasks.
# Ex... | [
27,
7856,
261,
480,
29,
37776,
1537,
353,
9268,
14,
16818,
47,
10141,
13,
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198,
11748,
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13,
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5329,
198,
198,
397,
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2099,
27741,
16818,
27201,
886,
198,
198,
62,
15255,
478,
62,
4906,
7,
22184,
11,
340,
81,
8,
... | 2.186268 | 1,471 |
<reponame>JuliaTelecom/DigitalComm.jl
"""
---
Quadrature Amplitude Modulation (QAM) hard decoding function
Return the hard decoded constellation with voronoi baseds decision. The difference with bitDeMapping is that bitDeMapping returns the decoded bit sequence whereas hardConstellation returns the closest constellat... | [
27,
7856,
261,
480,
29,
16980,
544,
31709,
785,
14,
27640,
6935,
13,
20362,
198,
37811,
198,
6329,
220,
220,
198,
4507,
41909,
1300,
44074,
3984,
3401,
1741,
357,
48,
2390,
8,
1327,
39938,
2163,
198,
13615,
262,
1327,
875,
9043,
38712... | 2.417848 | 3,104 |
<filename>src/Potentials.jl
abstract type AbstractPotential end
struct ConstantPotential{T <: Number} <: AbstractPotential
_::T
end
struct CoeffPotential{P <: AbstractPotential, T <: Number} <: AbstractPotential
potential::P
coefficient::T
end
struct PotentialFunction{P <: AbstractPotential, F <: Function} ... | [
27,
34345,
29,
10677,
14,
25396,
14817,
13,
20362,
198,
397,
8709,
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25396,
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886,
198,
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1843,
90,
51,
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25,
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92,
1279,
25,
27741,
25396,
1843,
198,
220,
220,
220,
4808,
3712,
51,
198,
437,
... | 2.562564 | 983 |
<filename>src/evaluate.jl
using MLBase
using JSON
using SimplePlot
export
network_enrichment,
id2uniprot,
id2truth,
uniprot2truth,
id2celltype,
id2treatments,
id2target,
ishistone,
random_cor_matrix,
conditional_cov,
truth_matrix,
mask_matrix,
unique_ppi_pairs,
a... | [
27,
34345,
29,
10677,
14,
49786,
13,
20362,
198,
3500,
10373,
14881,
198,
3500,
19449,
198,
3500,
17427,
43328,
198,
198,
39344,
198,
220,
220,
220,
3127,
62,
268,
7527,
434,
11,
198,
220,
220,
220,
4686,
17,
403,
541,
10599,
11,
19... | 2.223735 | 6,463 |
import Base: convert
import SymEngine: convert, free_symbols
"""
is_linearcombination(L::Basic)
Return whether the expression `L` is a linear combination of its symbols.
### Input
- `L` -- expression
### Output
`true` if `L` is a linear combination or false otherwise.
### Examples
```jldoctest
julia> using S... | [
11748,
7308,
25,
10385,
198,
11748,
15845,
13798,
25,
10385,
11,
1479,
62,
1837,
2022,
10220,
198,
198,
37811,
198,
220,
220,
318,
62,
29127,
24011,
1883,
7,
43,
3712,
26416,
8,
198,
198,
13615,
1771,
262,
5408,
4600,
43,
63,
318,
2... | 2.53267 | 3,520 |
<filename>test/utils.jl<gh_stars>1-10
using RandomizedPropertyTest, NOVAS, BenchmarkTools, Random, DataFrames
rng = MersenneTwister(0)
# Include un-exported c wrappers
include("wrapper.jl")
# Add custom isapprox to test tuples
import Base.isapprox
Base.isapprox(x::Tuple, y::Tuple; kws...) = isapprox(collect(x), coll... | [
27,
34345,
29,
9288,
14,
26791,
13,
20362,
27,
456,
62,
30783,
29,
16,
12,
940,
198,
3500,
14534,
1143,
21746,
14402,
11,
8005,
53,
1921,
11,
25187,
4102,
33637,
11,
14534,
11,
6060,
35439,
198,
198,
81,
782,
796,
337,
364,
29727,
... | 2.217035 | 1,221 |
module ThermalTraj
using JuMP
using NLopt
using JSON
export printTest
export heading2XY
export initTrajModel!
export solveTraj!
function printTest()
println("Testing print from module ThermalTraj")
end
function heading2XY(h)
e = -(h-90.0)#*pi/180.0
if e <= -180.0
e += 360.0
end
return e*pi... | [
21412,
41590,
15721,
73,
198,
198,
3500,
12585,
7378,
198,
3500,
22879,
8738,
198,
3500,
19449,
198,
198,
39344,
3601,
14402,
198,
39344,
9087,
17,
34278,
198,
39344,
2315,
15721,
73,
17633,
0,
198,
39344,
8494,
15721,
73,
0,
198,
198,
... | 1.723515 | 2,105 |
import Base: length, first, last, getindex
abstract IndexRepr
immutable LineRepr <: IndexRepr
id::NTuple{2, Int}
end
interior_indices(::LineRepr) = ()
immutable CircleArcRepr <: IndexRepr
id::NTuple{3, Int}
end
interior_indices(::CircleArcRepr) = (2,)
immutable EllipseArcRepr <: IndexRepr
id::NTuple{3, ... | [
11748,
7308,
25,
4129,
11,
717,
11,
938,
11,
651,
9630,
198,
198,
397,
8709,
12901,
6207,
81,
198,
198,
8608,
18187,
6910,
6207,
81,
1279,
25,
12901,
6207,
81,
198,
220,
220,
220,
4686,
3712,
11251,
29291,
90,
17,
11,
2558,
92,
19... | 2.186616 | 6,575 |
# ---
# title: 1339. Maximum Product of Splitted Binary Tree
# id: problem1339
# author: <NAME>
# date: 2020-10-31
# difficulty: Medium
# categories: Dynamic Programming, Tree, Depth-first Search
# link: <https://leetcode.com/problems/maximum-product-of-splitted-binary-tree/description/>
# hidden: true
# ---
#
# Given... | [
2,
11420,
198,
2,
3670,
25,
1511,
2670,
13,
22246,
8721,
286,
13341,
2175,
45755,
12200,
198,
2,
4686,
25,
1917,
1485,
2670,
198,
2,
1772,
25,
1279,
20608,
29,
198,
2,
3128,
25,
12131,
12,
940,
12,
3132,
198,
2,
8722,
25,
13398,
... | 2.285926 | 675 |
using Optim
import Optim: optimize
# Fitting logistic regression
#
# TODO: add support for dual formulation
struct LogisticOptimizer
penalty::AbstractPenalty
dual::Bool
fit_intercept::Bool
algo::Optim.AbstractOptimizer
options::Optim.Options
end
function LogisticOptimizer(; penalty=L2Penalty(0.0)... | [
198,
3500,
30011,
198,
11748,
30011,
25,
27183,
198,
198,
2,
376,
2535,
2604,
2569,
20683,
198,
2,
198,
2,
16926,
46,
25,
751,
1104,
329,
10668,
31760,
198,
7249,
5972,
2569,
27871,
320,
7509,
198,
220,
220,
220,
7389,
3712,
23839,
... | 1.982158 | 2,354 |
<filename>practice.jl
using Statistics
using ProgressMeter
using MATLAB
include("atn_module.jl")
N = 10_000 # number of nodes
m = 5 # number of active links per active node
mu = 0.1 # probability I -> S
tmax = 3000 # maximum duration of the simulation
Ni = Int(0.01 * N) # init... | [
27,
34345,
29,
39541,
13,
20362,
198,
3500,
14370,
198,
3500,
18387,
44,
2357,
198,
3500,
36775,
48780,
198,
17256,
7203,
265,
77,
62,
21412,
13,
20362,
4943,
198,
45,
796,
838,
62,
830,
220,
220,
220,
220,
220,
220,
220,
220,
220,
... | 2.29918 | 488 |
"""
MetaWeights{InnerMetaGraph<:MetaGraph,U<:Real} <: AbstractMatrix{U}
Matrix-like wrapper for edge weights on a metagraph of type `InnerMetaGraph`.
"""
struct MetaWeights{InnerMetaGraph<:MetaGraph,U<:Real} <: AbstractMatrix{U}
g::InnerMetaGraph
end
Base.show(io::IO, mv::MetaWeights) = print(io, "MetaWeight... | [
37811,
198,
220,
220,
220,
30277,
1135,
2337,
90,
818,
1008,
48526,
37065,
27,
25,
48526,
37065,
11,
52,
27,
25,
15633,
92,
1279,
25,
27741,
46912,
90,
52,
92,
198,
198,
46912,
12,
2339,
29908,
329,
5743,
19590,
319,
257,
1138,
6111... | 2.414747 | 651 |
<filename>script/optimization_concepts/integer_programming.jl
#' ---
#' title: Integer Programming
#' ---
#' **Originally Contributed by**: <NAME>
#' While we already know how to set a variable as integer or binary in the `@variable` macro,
#' this tutorial covers other JuMP features for integer programming a... | [
27,
34345,
29,
12048,
14,
40085,
1634,
62,
43169,
82,
14,
41433,
62,
23065,
2229,
13,
20362,
198,
2,
6,
11420,
201,
198,
2,
6,
3670,
25,
34142,
30297,
201,
198,
2,
6,
11420,
201,
198,
201,
198,
2,
6,
12429,
22731,
2345,
6169,
41... | 2.56325 | 2,166 |
using DataFrames
using Libz
"""
parse_gtf_gene_features_to_df
Arguments:
filename: path to gtf file
gene_ids: An array of gene_ids - if an empty array, then everything is parsed.
feature_types: Set of feature types - e.g. Set({"gene"})
Returns:
"""
function parse_gtf_gene_features_to_df(filename, ... | [
3500,
6060,
35439,
198,
3500,
7980,
89,
198,
37811,
198,
220,
21136,
62,
13655,
69,
62,
70,
1734,
62,
40890,
62,
1462,
62,
7568,
628,
220,
20559,
2886,
25,
198,
220,
220,
220,
29472,
25,
3108,
284,
308,
27110,
2393,
198,
220,
220,
... | 1.996648 | 895 |
module ArrayInterfaceGPUArrays
using Adapt
using ArrayInterfaceCore
using GPUArraysCore
ArrayInterfaceCore.fast_scalar_indexing(::Type{<:GPUArraysCore.AbstractGPUArray}) = false
@inline ArrayInterfaceCore.allowed_getindex(x::GPUArraysCore.AbstractGPUArray, i...) = GPUArraysCore.@allowscalar(x[i...])
@inline ArrayInte... | [
21412,
15690,
39317,
33346,
3163,
20477,
198,
198,
3500,
30019,
198,
3500,
15690,
39317,
14055,
198,
3500,
11362,
3163,
20477,
14055,
198,
198,
19182,
39317,
14055,
13,
7217,
62,
1416,
282,
283,
62,
9630,
278,
7,
3712,
6030,
90,
27,
25,... | 2.932773 | 357 |
# MIT License
# Copyright (c) 2017: <NAME>, <NAME>, <NAME>, and contributors.
struct _2UKP
nSize::Int
P1::Vector{Int}
P2::Vector{Int}
W::Vector{Int}
C::Int
end
Base.show(io::IO, id::_2UKP) = print("Bi-Objective Knapsack Problem with $(id.nSize) variables.")
set2UKP(n::Int, p1::Vector{Int}, p2::Vec... | [
2,
17168,
13789,
198,
2,
15069,
357,
66,
8,
2177,
25,
1279,
20608,
22330,
1279,
20608,
22330,
1279,
20608,
22330,
290,
20420,
13,
198,
7249,
4808,
17,
15039,
47,
198,
220,
220,
220,
299,
10699,
3712,
5317,
198,
220,
220,
220,
350,
1... | 2.11973 | 593 |
using Bokeh
plotfile("display.html")
x = linspace(0, 2pi)
# plot returns a Plot object
myplot = plot(x, sin.(x))
# which can then be displayed with
showplot(myplot)
# if you wish to generate a plot but not display it, you can use genplot
genplot(myplot)
# you can also specify the filename directly to genplot:
genp... | [
3500,
347,
2088,
71,
198,
198,
29487,
7753,
7203,
13812,
13,
6494,
4943,
198,
198,
87,
796,
300,
1040,
10223,
7,
15,
11,
362,
14415,
8,
198,
198,
2,
7110,
5860,
257,
28114,
2134,
198,
1820,
29487,
796,
7110,
7,
87,
11,
7813,
12195... | 3.434932 | 292 |
<filename>src/FunctionalInterface/replace_macros.jl
## ------------------------------------------------------------------
function _macro_call_regex(name::String)
str = string("(?>",
"\\@", name, "(?>", "\\([^\\(]*\\)", ")?",
")")
return Regex(str)
end
## -----------------------------------------... | [
27,
34345,
29,
10677,
14,
22203,
282,
39317,
14,
33491,
62,
20285,
4951,
13,
20362,
198,
2235,
16529,
438,
198,
8818,
4808,
20285,
305,
62,
13345,
62,
260,
25636,
7,
3672,
3712,
10100,
8,
198,
220,
220,
220,
965,
796,
4731,
7203,
7,... | 2.472727 | 715 |
function test_complex_1()
Random.seed!(0)
f = x -> [exp(-30 / x), exp(-30 / x)]
xtrue = 100
y = f(xtrue)
T = 1
xDists = Uniform(10, 500)
noiseDist = Normal(0, 0.01)
signalModels = f
λ = 2.0^-1.5
kernel = GaussianKernel(λ * y)
ρ = 2.0^-20
xhat = perk(reshape(y, :, 1), T, ... | [
8818,
1332,
62,
41887,
62,
16,
3419,
628,
220,
220,
220,
14534,
13,
28826,
0,
7,
15,
8,
198,
220,
220,
220,
277,
796,
2124,
4613,
685,
11201,
32590,
1270,
1220,
2124,
828,
1033,
32590,
1270,
1220,
2124,
15437,
198,
220,
220,
220,
... | 1.953173 | 3,246 |
export ToeplitzOperator, HankelOperator
mutable struct ToeplitzOperator{T<:Number} <: Operator{T}
negative::Vector{T}
nonnegative::Vector{T}
end
ToeplitzOperator(V::Vector{T},W::Vector{Q}) where {T<:Number,Q<:Number} =
ToeplitzOperator{promote_type(T,Q)}(V,W)
ToeplitzOperator(V::AbstractVector,W::Abstra... | [
39344,
1675,
68,
489,
4224,
18843,
1352,
11,
9530,
7750,
18843,
1352,
628,
198,
76,
18187,
2878,
1675,
68,
489,
4224,
18843,
1352,
90,
51,
27,
25,
15057,
92,
1279,
25,
35946,
90,
51,
92,
198,
220,
220,
220,
4633,
3712,
38469,
90,
... | 2.205501 | 1,927 |
<reponame>joshday/AtomicTables.jl
#-----------------------------------------------------------------------------# CTable
"""
A Columnar data table. It is a simple wrapper around a NamedTuple of AbstractVectors (See
`Tables.ColumnTable`). By default, ranges (e.g. `1:10`) will be `collect`-ed.
## Examples
x =... | [
27,
7856,
261,
480,
29,
73,
3768,
820,
14,
2953,
10179,
51,
2977,
13,
20362,
198,
198,
2,
10097,
32501,
2,
327,
10962,
220,
198,
37811,
198,
32,
29201,
283,
1366,
3084,
13,
220,
632,
318,
257,
2829,
29908,
1088,
257,
34441,
51,
29... | 2.162666 | 3,166 |
<filename>server/server.jl
module Server
using DotEnv
DotEnv.config()
include("utils.jl")
include("controller/requests.jl")
include("types.jl")
include("controller/network.jl")
include("controller/commands.jl")
using .Types, .Requests, Sockets, Dates
function is_valid_command(command::String)
if (!startswith(co... | [
27,
34345,
29,
15388,
14,
15388,
13,
20362,
198,
21412,
9652,
198,
198,
3500,
22875,
4834,
85,
198,
35,
313,
4834,
85,
13,
11250,
3419,
198,
198,
17256,
7203,
26791,
13,
20362,
4943,
198,
17256,
7203,
36500,
14,
8897,
3558,
13,
20362,... | 2.314252 | 856 |
# WHERE clause.
mutable struct WhereClause <: AbstractSQLClause
over::Union{SQLClause, Nothing}
condition::SQLClause
WhereClause(;
over = nothing,
condition) =
new(over, condition)
end
WhereClause(condition; over = nothing) =
WhereClause(over = over, condition ... | [
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22... | 2.348884 | 493 |
export PeriodicInterval
immutable PeriodicInterval{T<:Number} <: PeriodicDomain{T}
a::T
b::T
PeriodicInterval()=new(-convert(T,π),convert(T,π))
PeriodicInterval(a,b)=new(a,b)
end
PeriodicInterval()=PeriodicInterval{Float64}()
PeriodicInterval(a::Int,b::Int) = PeriodicInterval(Float64(a),Float64(b))... | [
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<filename>examples/example-Eb.jl
println("Eb) Simulation of the four-step cascade after 1s-3p photo-excitation of Si^+: Configuration model.")
@warn("\n\n !!! This example does not work properly at present !!! \n\n")
#
using JLD
wr = load("zzz-Cascade-2019-04-26T08.jld"); data = wr["cascade data:"];
wa = Cascade.Sim... | [
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... | 2.932099 | 162 |
<reponame>anowacki/TauPy
"""
# TauPy
Use the Python package ObsPy to calculate seismic travel times and ray paths for
teleseismic phases.
## Phases defined only by event depth and distance
For this case, functions return vectors of `Phase`s, which only contain details of each
phase in terms of distance from the even... | [
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2116... | 2.793758 | 1,474 |
module ChartmetricScraper
using HTTP,
Dates
import JSON
export Token,
newtoken!,
albumrequest,
playlistrequest,
curatorrequest,
Request,
dorequest,
parseresponse,
getparameters,
setparameters!,
setparameter!,
writestate,
readstate!,
buildrequesturl,
getparse... | [
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220... | 2.553073 | 179 |
using Test
using JSON
using MianalyzerBackend
const GMeansController = MianalyzerBackend.GMeansController
include("../TestSupport.jl")
@testset "GMeanController" begin
@testset ".call" begin
request = Dict([("records", [[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0], [5.0, 5.0]])])
actual = JSON.parse(IOBu... | [
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1... | 2.213043 | 230 |
<filename>test/jacvec_operators.jl
using DiffEqBase, DiffEqOperators, ForwardDiff, LinearAlgebra, Test
const A = rand(300,300)
f(du,u) = mul!(du,A,u)
f(u) = A*u
x = rand(300)
v = rand(300)
du = similar(x)
cache1 = ForwardDiff.Dual{DiffEqOperators.JacVecTag}.(x, v)
cache2 = ForwardDiff.Dual{DiffEqOperators.JacVecTag}.(... | [
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8,... | 2.052206 | 1,360 |
begin
function printBitMasks(V)
E = Array{Int64}(2^6)
for i in 1:2^6
E[i] = -1
end
i = 1
for o in V
contains = false
for l in E
if l == o
contains = true
end
end
if !contains
E[i] = o
i += 1
end
end
F = Array{Int64}(i-1)
for j in 1:(i-1)
F[j] = E[j]
end
F=s... | [
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... | 1.732036 | 1,336 |
<gh_stars>0
ccall(:jl_exit_on_sigint, Void, (Cint,), 0)
push!(LOAD_PATH, "..")
using JukaiNLP: DepParser, Perceptron, Unlabeled, Labeled, FeedForward, UnlabeledFeedForward
using JukaiNLP: readconll, train!, decode, evaluate, toconll, initmodel!, MyAdaGrad
using JukaiNLP.DepParsing: Token, readconll_withprobs
using Me... | [
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2... | 2.46797 | 2,404 |
<gh_stars>10-100
using PastaQ
using ITensors
using Test
using LinearAlgebra
@testset "Gate generation: 1-qubit gates" begin
i = Index(2, tags = "Qubit")
g = gate("I", i)
@test plev(inds(g)[1]) == 1
@test plev(inds(g)[2]) == 0
ggdag = g * prime(dag(g), 1; plev=1)
@test ITensors.array(ggdag) ≈ Matrix{Int}... | [
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2... | 1.966522 | 11,052 |
<reponame>AStupidBear/FastRecurFlux.jl<gh_stars>0
import Base: broadcasted
LoopVectorization.vmap(f, x::ReverseDiff.TrackedArray) = ReverseDiff.track(vmap, f, x)
drelu(x, Δ) = ifelse(x > 0, Δ, zero(x))
ReverseDiff.@grad function broadcasted(::typeof(relu), x::ReverseDiff.TrackedArray)
relu.(x), Δ -> (nothing, dr... | [
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using Literate
Literate.markdown("foo.jl", "."; documenter=true)
Literate.notebook("foo.jl", "."; documenter=true)
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263,
28,
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8,
198
] | 2.738095 | 42 |
<reponame>RexWzh/LeetCode.jl
# ---
# title: 1025. Divisor Game
# id: problem1025
# author: Indigo
# date: 2022-04-14
# difficulty: Easy
# categories: Math, Dynamic Programming
# link: <https://leetcode.com/problems/divisor-game/description/>
# hidden: true
# ---
#
# Alice and Bob take turns playing a game, with Alice ... | [
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<filename>src/lib/SDL_ttf.jl
# Julia wrapper for header: /Users/bieler/Downloads/SDL2_ttf-2.0.14/libSDL2_ttf.h
# Automatically generated using Clang.jl wrap_c, version 0.0.0
include("SDL_ttf_h.jl")
function TTF_Linked_Version()
ccall((:TTF_Linked_Version, libsdl2_ttf), Ptr{SDL_version}, ())
end
function TTF_ByteS... | [
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10... | 2.214265 | 3,393 |
<filename>0012/lu!(F, X) example.jl
# ---
# jupyter:
# jupytext:
# formats: ipynb,jl:hydrogen
# text_representation:
# extension: .jl
# format_name: hydrogen
# format_version: '1.3'
# jupytext_version: 1.11.2
# kernelspec:
# display_name: Julia 1.6.2
# language: julia
# n... | [
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... | 2.021175 | 1,464 |
<gh_stars>1-10
using HDF5,JLD,KUnet
isdefined(:xtrn) || (@date @load "zn11oparse1.jld")
d0 = 6f0
c0 = 1f0
g0 = .1f0
nc = size(ytrn,1)
niters=1000
nbatch=100
ntest=10000
net=net1=net2=net3=nothing
KUnet.gpu(true)
for y in (:ytrn, :ydev, :ytst)
@eval $y=full($y)
end # This speeds up the accuracy fn
#xtrn1=xtrn[:,1:1... | [
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... | 1.854075 | 3,166 |
module SpecialArrays
using Adapt
using Base: @propagate_inbounds, @pure, @_inline_meta
using Base: require_one_based_indexing, tail, unsafe_length
using Compat
using DocStringExtensions
using Requires: @require
using UnsafeArrays
const NestedArray{V,M,N} = AbstractArray{<:AbstractArray{V,M},N}
include("tuple.jl"... | [
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7894... | 2.830189 | 265 |
"""
randommask(n, Bernoulli(p)[ symmetric=true])
Generate a random Bernoulli mask of n × n.
"""
function randommask(n::Int, distribution::Bernoulli{T}; symmetric=true) where T
n ≥ 2 || throw(DomainError(n, "n < 2"))
if symmetric
# Generates a random lower triangular matrix and then symmetrizes it
... | [
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198,
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43720,
2002,
... | 2.670913 | 1,413 |
using Makie, Dates, Observables, Printf, Images
mss = [Millisecond(25), Millisecond(125)]
t = Millisecond(Second(5))
fs = Int.(t./mss)
fs[end] += 1
Δt = vcat((fill(ms, f) for (ms, f) in zip(mss, fs))...)
Δt[1] = Millisecond(0)
ts = cumsum(Δt)
cd(tempdir())
dar = 2
w = length(ts)
h = dar*w
frame = Observable(1)
point =... | [
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95... | 2.236629 | 1,234 |
<filename>test/vector.jl
@testset "Vector: $TRef" for (TVec, T, TRef) in [
(ArbVector, Arb, Arb),
(AcbVector, Acb, Acb),
(ArbRefVector, Arb, ArbRef),
(AcbRefVector, Acb, AcbRef),
]
V = TVec(4, prec = 128)
@test size(V) == (4,)
@test precision(V) == 128
x = T(1.5)
V[3] = x
@test ... | [
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8... | 1.841445 | 1,356 |
# Code for video 3_3:
Pkg.add("RDatasets")
using RDatasets
data = dataset("datasets", "iris")
head(data)
# 6x5 DataFrames.DataFrame
# | Row | SepalLength | SepalWidth | PetalLength | PetalWidth | Species |
# |-----|-------------|------------|-------------|------------|----------|
# | 1 | 5.1 | 3.5 ... | [
2,
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292,
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1600,
366,
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4943,
201,
... | 1.604106 | 6,916 |
"""
A module which provides common astrodynamics
calculations.
# Extended help
**Exports**
$(EXPORTS)
**Imports**
$(IMPORTS)
"""
module Calculations
export Circular, Elliptical, Parabolic, Hyperbolic
export conic, keplerian, cartesian
export perifocal, semimajor_axis
export kepler, lambert, lambert_universal, lam... | [
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7... | 2.797422 | 543 |
<reponame>thabbott/D92PVInversion<gh_stars>0
using D91PVInversion
using IterativeSolvers
using LinearAlgebra
using SparseArrays
using Random
import PyPlot; const plt = PyPlot
# Define functional form of PV anomaly
function q′fun(x, y, z, A, L, H, p::Params)
πc = p.π0 - 0.5
Π = p.Π
return A*exp(-(x^2 + y^2)... | [
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1720... | 2.161182 | 1,793 |
<filename>examples/PlotPowerSpectrum.jl
using MatterPower
using Roots
using Plots, LaTeXStrings
# %% Specify a redshift
redshift = 0
# %% Define a function to return a linear matter power spectrum (in units of Mpc^3/h^3)
# as a function of the comoving wavenumber, kovh, in units of h/Mpc.
# Here is an example using E... | [
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796... | 2.06345 | 1,119 |
using statmech_tm_solver
using Documenter
DocMeta.setdocmeta!(statmech_tm_solver, :DocTestSetup, :(using statmech_tm_solver); recursive=true)
makedocs(;
modules=[statmech_tm_solver],
authors="<NAME> <<EMAIL>> and contributors",
repo="https://github.com/<EMAIL>/statmech_tm_solver.jl/blob/{commit}{path}#{li... | [
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1326,
354,... | 2.185897 | 312 |
<filename>src/validation.jl
##############################################################################
# Validation
##############################################################################
testloss(R::PointResults) = R.testloss
trainloss(R::PointResults) = R.trainloss
thetaopt(R::PointResults) = R.theta
lamb... | [
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27432... | 2.783333 | 900 |
# # Optimization of the default MARGO configuration
# ## Using `ClimateMARGO.jl`
using ClimateMARGO
using ClimateMARGO.Models
using ClimateMARGO.Optimization
# ## Loading preset configurations
# Load the pre-defined default MARGO parameters, which are described by the ClimateModelParameters struct
params = deepcopy(... | [
2,
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40569,
11230,
13,
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1424,
198,
3500,
13963,
40569,
11230,
... | 3.881757 | 296 |
"""
A structure that is assumed to hold a homogenous transform.
"""
struct basic_dh{T}
mat::SMatrix{4,4,T,16}
function basic_dh(t::SVector{3,T}) where {T}
mat = SMatrix{4,4,T,16}( one(T), zero(T), zero(T), zero(T),
zero(T), one(T), zero(T), zero(T),
... | [
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19,
11,
19,
11,
51,
11,
1433,
92,
198,
2... | 1.608326 | 2,234 |
struct CommissionReport
execId::String
commission::Float64
currency::String
realizedPNL::Union{Float64,Nothing}
yield::Union{Float64,Nothing}
yieldRedemptionDate::Union{Int,Nothing} # yyyymmdd format TODO: -> String or Date
end
struct ContractDescription
contract::Contract
derivativeSecTypes::Vect... | [
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90,
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2414,
... | 2.867203 | 994 |
<reponame>johnmyleswhite/IntertemporalChoiceHeuristics.jl
function update_weights!(
new_weights::Vector,
weights::Vector,
training_proportion::Real,
)
n = round(Int, training_proportion * sum(weights))
π = weights ./ sum(weights)
d = Distributions.Multinomial(n, π)
rand!(d, new_weights)
... | [
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220,
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220,
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11,
198,
220,
220,
220,
19590,
3712,
3846... | 2.585938 | 128 |
<reponame>LaudateCorpus1/AutoMLPipeline.jl<filename>examples/discourse_zevelev.jl
# from discourse discussion with zevelev
using Distributed
addprocs()
@everywhere using AutoMLPipeline, DataFrames
#Get models.
sk= AutoMLPipeline.SKLearners.learner_dict |> keys |> collect;
sk= sk |> x-> sort(x,lt=(x,y)->lowercase(x)<lo... | [
27,
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261,
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29,
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5114,
351,
41271,
303,
2768,
198,
3... | 2.314735 | 699 |
<reponame>gabehassler/RunMetrics.jl<gh_stars>0
using DataFrames, TimeZones, Statistics
const DESIGN_COLS = Dict("speed" => 2,
"climb" => 3)
struct PreDesign
hr::Vector{Float64}
time::Vector{Float64}
date::Float64
X::Matrix{Float64} # [speed climb]
function PreDesign(rs:... | [
27,
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261,
480,
29,
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14,
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9171,
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11,
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198,
198,
9979,
22196,
16284,
62,
25154,
50,
796,
360,
713,
72... | 1.806357 | 2,045 |
function Line(arg0::Line)
return Line((Line,), arg0)
end
function Line(arg0::Vector3D, arg1::Vector3D, arg2::jdouble)
return Line((Vector3D, Vector3D, jdouble), arg0, arg1, arg2)
end
function closest_point(obj::Line, arg0::Line)
return jcall(obj, "closestPoint", Vector3D, (Line,), arg0)
end
function cont... | [
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15,
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18,
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11,
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16,
3712,
38469,
18,
35,
11,
1822,
17,
3712,
... | 2.536151 | 899 |
import Base: reshape, vec, permutedims, permutedims!
function reshape(a::KnetArray{T}, dims::Dims) where T
if dims==size(a)
a
elseif prod(dims) != length(a)
throw(DimensionMismatch())
else
KnetArray{T,length(dims)}(a.ptr, dims)
end
end
reshape(a::KnetArray, dims::Union{Int,Colo... | [
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11,
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0,
198,
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7,
64,
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42,
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51,
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5391,
82,
3712,
35,
12078,
8,
810,
309,
198,
220,
220,
220,
611,
53... | 2.252336 | 535 |
const JNI_VERSION_1_1 = convert(Cint, 0x00010001)
const JNI_VERSION_1_2 = convert(Cint, 0x00010002)
const JNI_VERSION_1_4 = convert(Cint, 0x00010004)
const JNI_VERSION_1_6 = convert(Cint, 0x00010006)
const JNI_VERSION_1_8 = convert(Cint, 0x00010008)
const JNI_TRUE = convert(Cchar, 1)
const JNI_FALSE = convert(Cchar, 0... | [
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62,
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7,
34,
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18005,
8,
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7,
34,
600,
11,
657,
87,
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34215,
8,
198,
9979,
449,
22125,
... | 2.224819 | 5,391 |
abstract type AbstractSQLHandle end
#===================================================================================================
<SQLEnvironement>
===================================================================================================#
struct SQLEnvironement <: AbstractSQLHandle
ptr::Ptr{V... | [
198,
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2,
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220,
220,
220,
1279,
50,
48,
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972,
29,
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10052,
18604,
2,
198,
7249,
49747,
2538,
77,
2268,
972,
1279,
25,
27741,
17861,
... | 3.201655 | 3,263 |
<reponame>gszep/BifurcationFit.jl<filename>test/minimal/pitchfork.jl
######################################################## model
F(z::BorderedArray,θ::AbstractVector) = F(z.u,(θ=θ,p=z.p))
function F(u::AbstractVector,parameters::NamedTuple)
@unpack θ,p = parameters
f = first(u)*first(p)*first(θ)
F = similar(u,ty... | [
27,
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261,
480,
29,
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14,
33,
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333,
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198,
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7804,
2746,
198,
37,
7,
89,
3712,
33,
24071,
19182,
11,
138... | 2.453704 | 216 |
<filename>src/Types.jl<gh_stars>10-100
module Types
abstract type AbstractDelayed end
const SPACE_TYPE = Union{
Dict{Symbol, T} where T,
AbstractDelayed,
AbstractVector,
}
end # module
| [
27,
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29,
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14,
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27,
456,
62,
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29,
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397,
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198,
198,
9979,
37253,
62,
25216,
796,
4479,
90,
198,
220,
220,
220,
360,
713... | 2.666667 | 75 |
module PandaMPC
using LinearAlgebra, Plots, ECOS, Convex, DelimitedFiles
import LinearAlgebra, Plots, ECOS, Convex, DelimitedFiles
###############################################################################
# This block contains the ball's trajectory simulation functions
"""
Given an array with the initial posit... | [
21412,
41112,
44,
5662,
198,
198,
3500,
44800,
2348,
29230,
11,
1345,
1747,
11,
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11,
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87,
11,
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320,
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29230,
11,
1345,
1747,
11,
13182,
2640,
11,
1482,
303,
87,
11,
... | 1.918331 | 10,016 |
mutable struct DocumentTermMatrix{T}
dtm::SparseMatrixCSC{Int, Int}
terms::Vector{T}
column_indices::Dict{T, Int}
end
"""
columnindices(terms::Vector{String})
Creates a column index lookup dictionary from a vector of terms.
"""
function columnindices(terms::Vector{T}) where T
column_indices = Dic... | [
76,
18187,
2878,
16854,
40596,
46912,
90,
51,
92,
198,
220,
220,
220,
288,
17209,
3712,
50,
29572,
46912,
34,
6173,
90,
5317,
11,
2558,
92,
198,
220,
220,
220,
2846,
3712,
38469,
90,
51,
92,
198,
220,
220,
220,
5721,
62,
521,
1063... | 2.411801 | 3,203 |
Pointcloud = Array{T,N} where {T <: Real, N}
| [
12727,
17721,
796,
15690,
90,
51,
11,
45,
92,
810,
1391,
51,
1279,
25,
6416,
11,
399,
92,
198
] | 2.368421 | 19 |
using PermutationGroups
using Groups.KnuthBendix
@testset "Wajnryb presentation for Σ₄₁" begin
genus = 4
Fn = FreeGroup(2genus)
G = SpecialAutomorphismGroup(Fn)
T = Groups.mcg_twists(G)
# symplectic pairing in the free Group goes like this:
# f1 ↔ f5
# f2 ↔ f6
# f3 ↔ f7
# f4 ↔ f... | [
3500,
2448,
76,
7094,
38,
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198,
3500,
27441,
13,
25095,
1071,
33,
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198,
198,
31,
9288,
2617,
366,
54,
1228,
77,
563,
65,
10470,
329,
7377,
96,
158,
224,
226,
158,
224,
223,
1,
2221,
628,
220,
220,
220,
34306,
796,
604,... | 1.63172 | 3,367 |
#Wrappers for functions directly in the rospy namespace
export init_node, is_shutdown, spin,
get_param, has_param, set_param, delete_param,
logdebug, loginfo, logwarn, logerr, logfatal
"""
init_node(name; args...)
Initialize this node, registering it with the ROS master. All arguments are passed on ... | [
2,
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329,
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3264,
287,
262,
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2777,
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11,
318,
62,
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11,
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11,
198,
220,
220,
220,
220,
220,
220,
651,
62,
17143,
11,
468,
62,
17143,
11,
900,
62,
17143,
11,
... | 2.558287 | 1,261 |
<reponame>SabbahMohammed/Dev<filename>src/solvind second order diffEq.jl
using DifferentialEquations, LinearAlgebra
using Plots
plotly()
#=
m*x`` + b*x` + k*x + a*x^3 = -mg
y = x`
=#
function fun(du,u,p,t)
m,k,g,a,b = p
du[1] = u[2]
du[2] = -1/m*(b*u[2]+ k*u[1]+ a*u[1]^3+ m*g)
end
m, k, g, a = 1220.0, 35... | [
27,
7856,
261,
480,
29,
50,
6485,
993,
38443,
10573,
14,
13603,
27,
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29,
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1502,
814,
36,
80,
13,
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198,
3500,
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498,
23588,
602,
11,
44800,
2348,
29230,
198,
3500,
1345,
1747,
198,
29487,
... | 1.770833 | 288 |
function get_comp(c::Char)
if c=='A' return 'T'
elseif c=='T' return 'A'
elseif c=='C' return 'G'
elseif c=='G' return 'C'
else
println("$c is not A, T, C, or G.")
return 'N'
end
end
get_revcomp(s::String) = reverse(join([get_comp(c) for c in s])) | [
8818,
651,
62,
5589,
7,
66,
3712,
12441,
8,
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220,
220,
220,
611,
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855,
6,
32,
6,
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51,
6,
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220,
220,
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51,
6,
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705,
32,
6,
198,
220,
220,
220,
2073,
361,
269,
855,
6,
34... | 2.013986 | 143 |
<filename>src/data/water_value.jl
struct WaterValueCut{T <: AbstractFloat}
plant_indices::Dict{Plant, Int}
coeffs::Vector{T}
lb::T
indices::Vector{Int}
function WaterValueCut(plant_indices::Dict{Plant, Int}, coeffs::Vector{T}, lb::T, indices::Vector{Int}) where T <: AbstractFloat
new{T}(pla... | [
27,
34345,
29,
10677,
14,
7890,
14,
7050,
62,
8367,
13,
20362,
198,
7249,
5638,
11395,
26254,
90,
51,
1279,
25,
27741,
43879,
92,
198,
220,
220,
220,
4618,
62,
521,
1063,
3712,
35,
713,
90,
3646,
415,
11,
2558,
92,
198,
220,
220,
... | 2.774922 | 1,284 |
<gh_stars>100-1000
using Distributions, StatsBase, Random
Random.seed!(1)
dist1 = TriangularDist(0,10,5)
dist2 = DiscreteUniform(1,5)
theorMean1, theorMean2 = mean(dist1), mean(dist2)
N = 10^6
data1 = rand(dist1,N)
data2 = rand(dist2,N)
estMean1, estMean2 = mean(data1), mean(data2)
println("Symmetric Triangular Dist... | [
27,
456,
62,
30783,
29,
3064,
12,
12825,
198,
3500,
46567,
507,
11,
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11,
14534,
198,
29531,
13,
28826,
0,
7,
16,
8,
198,
198,
17080,
16,
796,
7563,
21413,
20344,
7,
15,
11,
940,
11,
20,
8,
198,
17080,
17,
796,
8444,... | 2.346154 | 208 |
<filename>src/clang/api/Lex/PreprocessorOptions.jl
# PreprocessorOptions
function PrintStats(x::PreprocessorOptions)
@check_ptrs x
return clang_PreprocessorOptions_PrintStats(x)
end
| [
27,
34345,
29,
10677,
14,
565,
648,
14,
15042,
14,
45117,
14,
6719,
41341,
29046,
13,
20362,
198,
2,
3771,
41341,
29046,
198,
8818,
12578,
29668,
7,
87,
3712,
6719,
41341,
29046,
8,
198,
220,
220,
220,
2488,
9122,
62,
457,
3808,
212... | 3.015873 | 63 |
<filename>src/ltsa.jl
# Local Tangent Space Alignment (LTSA)
# ---------------------------
# Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment,
# <NAME>; <NAME> (2004), SIAM Journal on Scientific Computing 26 (1): 313–338.
# doi:10.1137/s1064827502419154.
#### LTSA type
struct LT... | [
27,
34345,
29,
10677,
14,
75,
912,
64,
13,
20362,
198,
2,
10714,
18816,
298,
4687,
978,
16747,
357,
27734,
4090,
8,
198,
2,
220,
22369,
6329,
198,
2,
32641,
1869,
361,
10119,
290,
8504,
29127,
34024,
33396,
2884,
10714,
18816,
298,
... | 2.192893 | 788 |
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