content stringlengths 6 1.03M | input_ids listlengths 4 535k | ratio_char_token float64 0.68 8.61 | token_count int64 4 535k |
|---|---|---|---|
struct NullParameters end
struct NonlinearProblem{uType,isinplace,P,F,K} <: AbstractNonlinearProblem{uType,isinplace}
f::F
u0::uType
p::P
kwargs::K
@add_kwonly function NonlinearProblem{iip}(f,u0,p=NullParameters();kwargs...) where iip
new{typeof(u0),iip,typeof(p),typeof(f),typeof(kwargs)}(... | [
7249,
35886,
48944,
886,
198,
198,
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8504,
29127,
40781,
90,
84,
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11,
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5372,
11,
47,
11,
37,
11,
42,
92,
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25,
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15419,
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90,
84,
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11,
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5372,
92,
198,
220,
220,
220,
277,
3712,
37,
19... | 2.315436 | 1,043 |
<filename>src/sampling.jl
"""
```
sample_points_on_ellipse(A::Real, B::Real, H::Real, K::Real, τ::Real, N::Integer, α₁::Real, α₂::Real)
```
Samples N data points in the angle range [α₁, α₂] for an ellipse specified by semi-major (A) semi-minor (B) axes,
centroid (H,K) and orientation (τ). All angles are as... | [
27,
34345,
29,
10677,
14,
37687,
11347,
13,
20362,
198,
37811,
198,
15506,
63,
198,
220,
220,
220,
6291,
62,
13033,
62,
261,
62,
695,
541,
325,
7,
32,
3712,
15633,
11,
347,
3712,
15633,
11,
367,
3712,
15633,
11,
509,
3712,
15633,
... | 2.031863 | 408 |
# We have to be able to handle illegal and unexpected things
s = """
1=1
"""
msgs = lintstr(s)
@test msgs[1].code == :I171
@test contains(msgs[1].message, "LHS in assignment not understood by Lint")
s = """
local 5
"""
msgs = lintstr(s)
@test msgs[1].code == :E135
@test msgs[1].variable == "5"
@test contains(msgs[1].m... | [
2,
775,
423,
284,
307,
1498,
284,
5412,
5293,
290,
10059,
1243,
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37227,
198,
16,
28,
16,
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198,
907,
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796,
300,
600,
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7,
82,
8,
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31,
9288,
13845,
14542,
58,
16,
4083,
8189,
6624,
1058,
40,
2... | 2.457386 | 352 |
<gh_stars>10-100
#
#
# These methods are for testing purposes only.
# They are not optimized at all.
#
#
"""
Calculates `B_M B_M-1 ... B_1` and stabilizes the
matrix products by intermediate matrix decompositions.
Assumes that the input `Bs` are ordered as `[B_1, B_2, ..., B_M]`.
"""
function calc_Bchain_svd(Bs; ... | [
27,
456,
62,
30783,
29,
940,
12,
3064,
198,
2,
198,
2,
198,
2,
220,
220,
2312,
5050,
389,
329,
4856,
4959,
691,
13,
198,
2,
220,
220,
1119,
389,
407,
23392,
379,
477,
13,
198,
2,
198,
2,
628,
198,
37811,
198,
9771,
3129,
689,
... | 1.827105 | 1,793 |
X = Interval(0,1)
ll = LazyBox(Dict(1=>Interval(0,1)))
ll[3]
@test domaineq(ll[3],Interval(0,1))
@test ndims(ll) == 2
ll[5]
@test ndims(ll) == 3
ll[5]
@test ndims(ll) == 3
@test length(convert(Vector{Interval},ll)) == ndims(ll)
@test length(convert(Vector{Interval},ll,[1,2])) == 2
l1 = LazyBox(Float64)
l1[1]
l1[2] = ... | [
55,
796,
4225,
2100,
7,
15,
11,
16,
8,
198,
297,
796,
406,
12582,
14253,
7,
35,
713,
7,
16,
14804,
9492,
2100,
7,
15,
11,
16,
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198,
297,
58,
18,
60,
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31,
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2401,
5718,
80,
7,
297,
58,
18,
4357,
9492,
2100,
7,
... | 1.993994 | 333 |
<reponame>grenkoca/JuliaTutorials
# ------------------------------------------------------------------------------------------
# # Julia is fast
# (Originally from https://juliabox.com under tutorials/intro-to-julia/short-
# version/05.Julia_is_fast.ipynb)
#
# Very often, benchmarks are used to compare languages. Thes... | [
27,
7856,
261,
480,
29,
32762,
74,
11216,
14,
16980,
544,
51,
44917,
82,
198,
2,
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22369,
438,
198,
2,
1303,
22300,
318,
3049,
198,
2,
357,
22731,
422,
3740,
1378,
73,
32176,
397,
1140,
13,
785,
739,
27992,
14,
600,
305,
12,
... | 3.734609 | 2,404 |
using Test
using NeXLSpectrum
using NeXLCore
using Statistics
using Distributions
@testset "Spectrum" begin
les = LinearEnergyScale(-495.0, 5.0)
@testset "Energy Scale" begin
@test NeXLSpectrum.energy(200, les) == -495.0 + 5.0 * (200 - 1)
@test NeXLSpectrum.energy(1, les) == -495.0 + 5.0 * (1 ... | [
3500,
6208,
198,
3500,
3169,
55,
6561,
806,
6582,
198,
3500,
3169,
55,
5639,
382,
198,
3500,
14370,
198,
3500,
46567,
507,
198,
198,
31,
9288,
2617,
366,
49738,
6582,
1,
2221,
198,
220,
220,
220,
10287,
796,
44800,
28925,
29990,
32590... | 1.824168 | 4,146 |
<filename>src/Plots_scripts/PaperFigures.jl
include("filtering.jl");
gr(size=(600,600), tick_orientation = :out, grid = false,
linecolor = :black,
markerstrokecolor = :black,
thickness_scaling = 2,
markersize = 6)
################################ Adjust Streaks table ##################################
l... | [
27,
34345,
29,
10677,
14,
3646,
1747,
62,
46521,
14,
42950,
14989,
942,
13,
20362,
198,
17256,
7203,
10379,
20212,
13,
20362,
15341,
198,
2164,
7,
7857,
16193,
8054,
11,
8054,
828,
4378,
62,
13989,
341,
796,
1058,
448,
11,
10706,
796,... | 2.36445 | 9,277 |
<gh_stars>0
using VLKeggSDK
using DataFrames
using ProgressMeter
using BSON
# setup list of reactions -
rn_number_array = [
# upper glycolysis -
"rn:R00299" # 1
"rn:R00771" # 2
"rn:R00756" # 3
"rn:R00762" # 4
"rn:R01068" # 5
"rn:R01015" # 6
"rn:R01061" # 7
"rn:R01512" # 8
"rn:R... | [
27,
456,
62,
30783,
29,
15,
198,
3500,
569,
43,
8896,
1130,
10305,
42,
198,
3500,
6060,
35439,
198,
3500,
18387,
44,
2357,
198,
3500,
347,
11782,
198,
198,
2,
9058,
1351,
286,
12737,
532,
198,
35906,
62,
17618,
62,
18747,
796,
685,
... | 2.266886 | 1,821 |
<reponame>jpgmolina/DS-Julia2925
@testset "flatland" begin
import DSJulia.Flatland
@testset "rectangle" begin
rect = Flatland.Rectangle((1, 1), l=2, w=2)
square = Flatland.Square((2.0, 1), l=2)
@test Flatland.ncorners(rect) == Flatland.ncorners(square) == 4
@test Flatland.cen... | [
27,
7856,
261,
480,
29,
9479,
43132,
1437,
14,
5258,
12,
16980,
544,
1959,
1495,
628,
198,
31,
9288,
2617,
366,
38568,
1044,
1,
2221,
198,
220,
220,
220,
1330,
17400,
16980,
544,
13,
7414,
265,
1044,
628,
220,
220,
220,
2488,
9288,
... | 1.949695 | 1,312 |
# should be reimplement as macros by using Julia 1.5's @ccall macro
function igText(text)
ccall((:igText, libcimgui), Cvoid, (Cstring, Cstring), "%s", text)
end
function igTextColored(col, text)
ccall((:igTextColored, libcimgui), Cvoid, (ImVec4, Cstring), col, text)
end
function igTextDisabled(text)
ccall... | [
2,
815,
307,
21123,
26908,
355,
34749,
416,
1262,
22300,
352,
13,
20,
338,
2488,
535,
439,
15021,
198,
8818,
45329,
8206,
7,
5239,
8,
198,
220,
220,
220,
269,
13345,
19510,
25,
328,
8206,
11,
9195,
66,
320,
48317,
828,
327,
19382,
... | 2.475279 | 627 |
using SirenSeq
using Base.Test
module TestNames
testC = 0
nextName = "dummy"
testName(name::AbstractString) = ( global testC += 1 ; global nextName = name ; testC )
handler(r::Test.Success) = println("test $(testC): success")
handler(r::Test.Failure) = println("test $(testC): FAILURE!\n $(nextName)")
handler(r... | [
3500,
43904,
4653,
80,
198,
3500,
7308,
13,
14402,
628,
198,
21412,
6208,
36690,
198,
198,
9288,
34,
796,
657,
198,
19545,
5376,
796,
366,
67,
13513,
1,
198,
9288,
5376,
7,
3672,
3712,
23839,
10100,
8,
796,
357,
3298,
1332,
34,
1585... | 2.18356 | 1,618 |
<gh_stars>10-100
#
# error messages:
#
SOM_ERRORS = Dict(
:ERR_MPL =>
"""
Matplotlib is not correctly installed!
See the documentation at https://andreasdominik.github.io/SOM.jl/stable/
for details and potential solutions.
""",
:ERR_MATRIX =>
"""
Input data is not a numerical 2D-matrix!
Please pro... | [
27,
456,
62,
30783,
29,
940,
12,
3064,
198,
2,
198,
2,
4049,
6218,
25,
198,
2,
198,
50,
2662,
62,
24908,
50,
796,
360,
713,
7,
198,
25,
1137,
49,
62,
44,
6489,
5218,
198,
37811,
198,
220,
220,
220,
6550,
29487,
8019,
318,
407,... | 2.83908 | 261 |
<reponame>nlw0/Yggdrasil
# Note that this script can accept some limited command-line arguments, run
# `julia build_tarballs.jl --help` to see a usage message.
using BinaryBuilder, Pkg
name = "COSMA"
version = v"2.2.0"
# Collection of sources required to complete build
sources = [
ArchiveSource("https://github.co... | [
27,
7856,
261,
480,
29,
21283,
86,
15,
14,
56,
1130,
7109,
292,
346,
198,
2,
5740,
326,
428,
4226,
460,
2453,
617,
3614,
3141,
12,
1370,
7159,
11,
1057,
198,
2,
4600,
73,
43640,
1382,
62,
18870,
21591,
13,
20362,
1377,
16794,
63,
... | 2.2737 | 1,308 |
using Random
using StatsBase
using Distributions
using DataStructures
using LsqFit
###############################################################################
###############################################################################
### spread in mean-field population with offsprings generated by conditiona... | [
3500,
14534,
198,
3500,
20595,
14881,
198,
3500,
46567,
507,
198,
3500,
6060,
44909,
942,
198,
198,
3500,
406,
31166,
31805,
198,
198,
29113,
29113,
7804,
4242,
21017,
198,
29113,
29113,
7804,
4242,
21017,
198,
21017,
4104,
287,
1612,
12,... | 2.691772 | 6,745 |
# Author: <NAME>, <EMAIL>
# Date: 06/25/2014
module SISLES
export
CorrAEM,
LLAEM,
CorrAEMDBN,
StarDBN,
SideOnDBN,
PairwiseCorrAEMDBN,
SimplePilotResponse,
StochasticLinearPR,
LLDetPR,
SimpleADM,
LLADM,
AirSpace,
SimpleTCASSensor,
ACASXSensor... | [
2,
6434,
25,
1279,
20608,
22330,
1279,
27630,
4146,
29,
201,
198,
2,
7536,
25,
9130,
14,
1495,
14,
4967,
201,
198,
201,
198,
201,
198,
21412,
311,
1797,
28378,
201,
198,
201,
198,
39344,
201,
198,
220,
220,
220,
2744,
81,
32,
3620... | 2.356835 | 1,390 |
using DiscreteAdjoint, OrdinaryDiffEq, ForwardDiff
using Test
@testset "DiscreteAdjoint.jl" begin
function f(du, u, p, t)
du[1] = dx = p[1]*u[1] - p[2]*u[1]*u[2]
du[2] = dy = -p[3]*u[2] + p[4]*u[1]*u[2]
end
p = [1.5,1.0,3.0,1.0]; tspan = (0.0, 10.0); u0 = [1.0,1.0];
prob = ODEProb... | [
3500,
8444,
8374,
2782,
73,
1563,
11,
14230,
3219,
28813,
36,
80,
11,
19530,
28813,
198,
3500,
6208,
198,
198,
31,
9288,
2617,
366,
15642,
8374,
2782,
73,
1563,
13,
20362,
1,
2221,
198,
220,
220,
220,
220,
198,
220,
220,
220,
2163,
... | 1.774747 | 990 |
struct LJRefParam <: EoSParam
epsilon::PairParam{Float64}
sigma::PairParam{Float64}
Mw::SingleParam{Float64}
end
struct LJRefConsts <: EoSParam
n::Vector{Float64}
t::Vector{Float64}
d::Vector{Int}
c::Vector{Int}
beta::Vector{Float64}
gamma::Vector{Float64}
eta::Vector{Float64}
... | [
7249,
406,
41,
8134,
22973,
1279,
25,
412,
78,
4303,
41158,
198,
220,
220,
220,
304,
862,
33576,
3712,
47,
958,
22973,
90,
43879,
2414,
92,
198,
220,
220,
220,
264,
13495,
3712,
47,
958,
22973,
90,
43879,
2414,
92,
198,
220,
220,
... | 1.577433 | 4,378 |
<reponame>biona001/GeneticVariation.jl
module TestGeneticVariation
using Test
import BioCore.Testing:
get_bio_fmt_specimens,
random_seq,
random_interval
import BioCore.Exceptions.MissingFieldException
using BioSequences, GeneticVariation
import BufferedStreams: BufferedInputStream
import IntervalTrees: In... | [
27,
7856,
261,
480,
29,
65,
32792,
8298,
14,
13746,
5139,
23907,
341,
13,
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198,
21412,
6208,
13746,
5139,
23907,
341,
198,
198,
3500,
6208,
198,
198,
11748,
16024,
14055,
13,
44154,
25,
198,
220,
220,
220,
651,
62,
65,
952,
62... | 2.684385 | 301 |
module ExtensibleEffects
export effect, noeffect, NoEffect,
runhandlers, @runhandlers,
@insert_into_runhandlers,
@syntax_eff, @syntax_eff_noautorun, noautorun,
WriterHandler,
ContextManagerHandler, @runcontextmanager, @runcontextmanager_, ContextManagerCombinedHandler,
CallableHandler, @runcallable, # Call... | [
21412,
5683,
27339,
47738,
198,
39344,
1245,
11,
645,
10760,
11,
1400,
18610,
11,
198,
220,
1057,
4993,
8116,
11,
2488,
5143,
4993,
8116,
11,
198,
220,
2488,
28463,
62,
20424,
62,
5143,
4993,
8116,
11,
198,
220,
2488,
1837,
41641,
62,... | 3.141631 | 233 |
<filename>src/utils.jl
"Check if formula contains a predicate name."
has_pred(formula::Const, pred_names) = formula.name in pred_names
has_pred(formula::Var, pred_names) = false
has_pred(formula::Compound, pred_names) = formula.name in pred_names ||
any(has_pred(f, pred_names) for f in formula.args)
has_pred(formul... | [
27,
34345,
29,
10677,
14,
26791,
13,
20362,
198,
1,
9787,
611,
10451,
4909,
257,
44010,
1438,
526,
198,
10134,
62,
28764,
7,
687,
4712,
3712,
34184,
11,
2747,
62,
14933,
8,
796,
10451,
13,
3672,
287,
2747,
62,
14933,
198,
10134,
62,... | 2.513574 | 2,247 |
<gh_stars>0
@doc raw"""
PositiveNumbers <: Manifold{ℝ}
The hyperbolic manifold of positive numbers $H^1$ is a the hyperbolic manifold represented
by just positive numbers.
# Constructor
PositiveNumbers()
Generate the `ℝ`-valued hyperbolic model represented by positive positive numbers.
To use this with arra... | [
27,
456,
62,
30783,
29,
15,
198,
31,
15390,
8246,
37811,
198,
220,
220,
220,
33733,
49601,
1279,
25,
1869,
361,
727,
90,
158,
226,
251,
92,
198,
198,
464,
8718,
65,
4160,
48048,
286,
3967,
3146,
720,
39,
61,
16,
3,
318,
257,
262... | 2.531171 | 2,775 |
<gh_stars>0
include("base_extension_structs.jl")
include("single_qubit_gates.jl")
include("multi_qubit_gates.jl")
| [
27,
456,
62,
30783,
29,
15,
198,
17256,
7203,
8692,
62,
2302,
3004,
62,
7249,
82,
13,
20362,
4943,
198,
17256,
7203,
29762,
62,
421,
2545,
62,
70,
689,
13,
20362,
4943,
198,
17256,
7203,
41684,
62,
421,
2545,
62,
70,
689,
13,
2036... | 2.478261 | 46 |
#------------------------------
# functions to define and manipulate ODE-VAE model
#------------------------------
#------------------------------
# ODE systems
#------------------------------
# Linear
function linear_2d_system(du,u,p,t)
a11, a12, a21, a22 = p
z1,z2 = u
du[1] = dz1 = a11 * z1 + a12 * z2... | [
2,
1783,
26171,
198,
2,
5499,
284,
8160,
290,
18510,
440,
7206,
12,
11731,
36,
2746,
220,
198,
2,
1783,
26171,
198,
198,
2,
1783,
26171,
198,
2,
440,
7206,
3341,
198,
2,
1783,
26171,
198,
198,
2,
44800,
220,
198,
8818,
14174,
62,
... | 2.212213 | 1,654 |
<reponame>Gnimuc/Videre
using CSFML.LibCSFML
using ModernGL
using CSyntax
using CSyntax.CSwitch
# shader sources
const vert_source = """
#version 150 core
in vec2 position;
in vec3 color;
out vec3 Color;
void main()
{
Color = color;
gl_Position = vec4(position, 0.0, 1.0);
}"... | [
27,
7856,
261,
480,
29,
38,
77,
320,
1229,
14,
53,
485,
260,
198,
3500,
9429,
34708,
13,
25835,
7902,
34708,
198,
3500,
12495,
8763,
198,
3500,
9429,
33567,
897,
198,
3500,
9429,
33567,
897,
13,
7902,
42248,
198,
198,
2,
33030,
4237... | 2.361311 | 1,251 |
###
### Copying
###
###
### Copying methods for biological sequences.
###
### This file is a part of BioJulia.
### License is MIT: https://github.com/BioJulia/BioSequences.jl/blob/master/LICENSE.md
# TODO: Add generic emethods for other bioseqs like mers and refseqs
##########
"""
copy!(dst::LongSequence, src::Bio... | [
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125... | 2.300041 | 4,836 |
<reponame>IsaacRudich/PnB_SOP
struct ExtensionalArcFunction{T<:Real}
components ::Dict{Tuple{Int, Int}, T}
end
struct ExtensionalArcObjective<:ObjectiveFunction
f ::ExtensionalArcFunction
type ::ObjectiveType
end
"""
evaluateDecision(obj::ExtensionalArcObjective, i1::Int, i2::Int... | [
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... | 2.786885 | 549 |
"""
$(SIGNATURES)
Abstract type for admissions rules.
Not a ModelObject. This is combined with an `AbstractAdmProbFct` that potentially contains calibrated parameters.
"""
abstract type AbstractAdmissionsRule{I1, F1 <: Real} end
"""
$(SIGNATURES)
Abstract type for switches from which admissions rules are construct... | [
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65,
37,
310,
63,
326,
6196,
4909,
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10007,
13,
198,
... | 2.439252 | 1,391 |
using Base.Test
using NPZ, Compat
Debug = false
tmp = mktempdir()
if Debug
println("temporary directory: $tmp")
end
TestArrays = Any[
true,
false,
@compat(Int8(-42)),
@compat(Int16(-42)),
@compat(Int32(-42)),
@compat(Int64(-42)),
@compat(UInt8(42)),
@compat(UInt16(42)),
@compat(UInt32(42)),
@compat(UInt64... | [
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198,
198,
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197,
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7203,
11498,
5551,
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25,
720,
22065,
4943,
198,
437,
198,
1... | 1.994528 | 731 |
<filename>src/ParameterProfiles.jl<gh_stars>0
"""Functions for constructing parameter profiles"""
constantParameter(offset::Real=0.0) = x -> offset
export constantParameter
constant = constantParameter
export constant
function heaviside(x::Real, stepOpt::Real=1.0)
if x < 0
y = 0
elseif x > 0
y... | [
27,
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29,
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14,
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28,
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15,
8,
796,
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4613,
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... | 2.503618 | 2,073 |
<reponame>bueschgens/RadMod2D
using Pkg
Pkg.activate(".")
Pkg.instantiate()
using RadMod2D
using GLMakie
include("./models2D.jl")
include("./plot2D.jl")
#### calculation
elemsize = 0.01
n = 60
# m = model_circles_in_circle_rand_full(0.1, 1.8, elemsize)
# m = model_circles_in_circle_rand_half(0.1, 1.8, elemsize)
m =... | [
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10188... | 2.278723 | 940 |
mutable struct EM
probA::Float32
probB::Float32
probC::Float32
end
EM() = EM(0.5, 0.5, 0.5)
"""
E_step(self::EM, iter::Int32) -> Float32
返回一个浮点数μ^{i+1}
"""
function E_step(self::EM, data::Bool)
probFromB = self.probA * self.probB^data * (1-self.probB)^(1-data)
probSum = probFromB +
... | [
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7,
15,
13,
20,
... | 1.858642 | 1,620 |
<reponame>simonpea/tsodso_der
"""
EconomicDispatch(hours,
nodes::Nodes,
powerplants::PowerPlants,
renewables::Renewables,
)
Creates an economic dispatch model from power plants and renewables.
Solves using JuMP. Returns ED_mod, P_opt, P_R_opt, P_R_opt_dist, price.
"""
function EconomicDispatch... | [
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197,
197,
197,
197,
1176,
489,
1187,
... | 2.208737 | 8,676 |
{"score_count": 73538, "score": 7.62, "timestamp": 1558990020.0}
{"score_count": 73512, "score": 7.62, "timestamp": 1558546871.0}
{"score_count": 73027, "score": 7.62, "timestamp": 1554165772.0}
{"score_count": 73027, "score": 7.62, "timestamp": 1554103252.0}
{"score_count": 71978, "score": 7.63, "timestamp": 154466902... | [
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1... | 2.331293 | 1,307 |
<filename>src/rules/1 Algebraic functions/1.1 Binomial products/.jl
include("1.1.1 Linear/.jl")
include("1.1.2 Quadratic/.jl")
include("1.1.3 General/.jl")
include("1.1.4 Improper/.jl")
| [
27,
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81,
15... | 2.547945 | 73 |
#
println("Dd) Test of the PhotoRecombination module with ASF from an internally generated initial- and final-state multiplet.")
#
setDefaults("print summary: open", "zzz-PhotoRecombination.sum")
setDefaults("method: continuum, asymptotic Coulomb") ## setDefaults("method: continuum, Galerkin")
setDefaults("method: no... | [
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2,
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13185,
7203,
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10638,
25,
1280,... | 2.292929 | 495 |
<reponame>sdobber/BallroomSkatingSystem.jl
## Helper functions
"""
remove!(a, item)
Removes `item` from the collection `a`.
"""
function remove!(a, item)
deleteat!(a, findfirst(==(item), a))
end
"""
prepare_sums(results, depth)
Returns the DataFrame for the calculation of the majority of votes, as well ... | [
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262,
... | 2.474093 | 386 |
<reponame>quendera/EvolveImage
# Run all the code up until the variable definitions to load functions to memory
using TestImages #You don't need this line when using local files
using Images
##### Define the fitness function
function fitness(a,b)
score = sqrt(512*512-sum((a - b).^2))
return score
end
# ge... | [
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1303,
1639,
836,
470,
761,
428,
1627,
618,
1262,
1957,... | 2.311377 | 1,336 |
using LinearAlgebra
include("../ellpla.jl")
include("../expmap.jl")
x = [0.; 0.; 1.];
a = 1.;
b = 1.;
c = 1.05;
R = Matrix{Float64}(I, 3, 3)
point = [0.; 0.; 0.];
normal = [0.; 0.; 1.];
(conpnt, depth, A, B) = ellipsoid_plane_contact(x, a, b, c, R, point, normal)
println("--------------------------------------------... | [
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64,
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352,
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198,
... | 2.588771 | 659 |
<filename>src/rbd-fast_sensitivity.jl<gh_stars>10-100
struct RBDFAST <: GSAMethod
num_harmonics::Int
end
RBDFAST(;num_harmonics = 6) = RBDFAST(num_harmonics)
"""
Code based on the theory presented in:
<NAME>. (2008). Global sensitivity analysis: The primer. Chichester: Wiley, pp. 167-169.
and
<NAME>, <NA... | [
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220,
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220,
220,
220,
997,
62,
29155,
38530,
... | 2.356777 | 782 |
<gh_stars>1-10
@testset "airy" begin
@test_throws AmosException airyai(200im)
@test_throws AmosException airybi(200)
for T in [Float16, Float32, Float64,Complex{Float16}, Complex{Float32},Complex{Float64}]
@test airyai(T(1.8)) ≈ 0.0470362168668458052247
@test airyaiprime(T(1.8)) ≈ -0.068524... | [
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1... | 1.796972 | 6,738 |
<filename>Chapter06/StructOfArraysPattern/4_nested_struct.jl<gh_stars>100-1000
# What do we do if the struct has a nested structure?
using BenchmarkTools, CSV, Statistics
using StructArrays
struct Fare
fare_amount::Float64
extra::Float64
mta_tax::Float64
tip_amount::Float64
tolls_amount::Float64
... | [
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466,
356,
466,
611,
262,
2878,
468,
257,
28376,
4645,
30,
198,
198,... | 2 | 1,192 |
"""
restriction(submesh, supermesh)
Computes the restriction matrix relative to a submesh `submesh` of `supermesh`.
The restriction matrix has size `(m,n)`, where
m == numcells(submesh)
n == numcells(supermesh)
It has entries `1` at location `[i,j]` iff cell `i` of submesh equals cell `j` of supermesh.
... | [
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220,
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76,
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76,
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63,
286,
4600,
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16321,
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198,
198,
464,
17504,... | 2.981675 | 382 |
<filename>src/Tofu.jl
module Tofu
export ◻
import LinearAlgebra
import REPL
togetfield(ex) = ex
togetfield(ex::Expr) =
if ex.head == :. && ex.args[1] == :g
@assert length(ex.args) == 2
:($getfield(g, $(ex.args[2])))
else
Expr(ex.head, togetfield.(ex.args)...)
end
"""
@G ex
Con... | [
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7,
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8,
796,
409,
198,
83,
519,
3... | 1.998255 | 2,292 |
<gh_stars>1-10
"""
Construct model for HydroGen with FixedOutput Formulation
"""
function construct_device!(
psi_container::PSIContainer,
sys::PSY.System,
model::DeviceModel{H, FixedOutput},
::Type{S},
) where {H <: PSY.HydroGen, S <: PM.AbstractPowerModel}
devices = get_available_components(H, sys)... | [
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1... | 2.572931 | 6,561 |
<gh_stars>10-100
using DataDeps
register(DataDep(
"geographic-origin-music",
"https://archive.ics.uci.edu/ml/datasets/Geographical+Original+of+Music",
"http://archive.ics.uci.edu/ml/machine-learning-databases/00315/Geographical%20Original%20of%20Music.zip",
"37bd14730ef7e4786e094421982ad32536298a9d84d875cd35d2... | [
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220,
366,
5450,
1378,
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13,
873,
13,
42008,
13,
15532,
14,
4029... | 2.389513 | 267 |
# Tests using TaylorModel1 and RTaylorModel1
using TaylorModels
using LinearAlgebra: norm
using Test
const _num_tests = 1000
setformat(:full)
function check_containment(ftest, xx::TaylorModelN{N,T,S}, tma::TaylorModelN{N,T,S}) where {N,T,S}
xfp = diam.(tma.I) .* (rand(N) .- 0.5) .+ mid(tma.x0)
xbf = [big(x... | [
2,
30307,
1262,
8121,
17633,
16,
290,
11923,
7167,
17633,
16,
198,
198,
3500,
8121,
5841,
1424,
198,
3500,
44800,
2348,
29230,
25,
2593,
198,
3500,
6208,
198,
198,
9979,
4808,
22510,
62,
41989,
796,
8576,
198,
198,
2617,
18982,
7,
25,... | 1.761846 | 4,875 |
abstract type QuantumVariable{T} <: Convex.AbstractVariable{T} end
export ProbabilityVector, DensityMatrix, Choi
using Convex: ⪰
include("probability_vectors.jl")
include("choi.jl")
include("density_matrices.jl")
| [
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1... | 2.918919 | 74 |
<reponame>catawbasam/CapacityExpansion.jl<filename>examples/workflow_example_cep.jl<gh_stars>0
# This file exemplifies the workflow from data input to optimization result generation
using CapacityExpansion
using Clp
## LOAD DATA ##
state="GER_18" # or "GER_18" or "CA_1" or "TX_1"
years=[2015] #2016 works for GER_1 and ... | [
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62,
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29,
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2,
770,
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21433,
6945,
26... | 2.912946 | 896 |
<reponame>sortie/official-images<filename>test/tests/julia-downloads/container.jl<gh_stars>1000+
# https://github.com/docker-library/julia/pull/6
download("https://google.com")
# https://github.com/docker-library/julia/pull/9
if VERSION.major > 0 || (VERSION.major == 0 && VERSION.minor >= 7)
# https://github.com/dock... | [
27,
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480,
29,
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2,
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13,
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14,
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12,
... | 2.560694 | 173 |
using Test
using MPI
MPI.Init()
function allgatherv_array(A, counts::Vector{Cint})
comm = MPI.COMM_WORLD
B = MPI.Allgatherv(A, counts, comm)
end
comm = MPI.COMM_WORLD
size = MPI.Comm_size(comm)
rank = MPI.Comm_rank(comm)
# Defining this to make ones work for Char
Base.one(::Type{Char}) = '\01'
for typ in B... | [
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1032,
85,
62,
18747,
7,
32,
11,
9853,
3712,
38469,
90,
34,
600,
30072,
198,
220,
220,
220,
725,
796,
4904,
40,
13,
9858,
44,
62,
4535... | 2.147761 | 670 |
#=
Given a string of round, curly, and square open and closing brackets, return whether the brackets are balanced (well-formed).
For example, given the string "([])[]({})", you should return true.
Given the string "([)]" or "((()", you should return false.
=#
#=
For a string of brackets to be balanced, it needs to h... | [
2,
28,
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15056,
257,
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11,
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366,
26933,
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... | 2.390572 | 594 |
<reponame>tpght/Manifolds.jl
@doc doc"""
Euclidean{T<:Tuple} <: Manifold
Euclidean vector space $\mathbb R^n$.
# Constructor
Euclidean(n)
generates the $n$-dimensional vector space $\mathbb R^n$.
Euclidean(m, n)
generates the $mn$-dimensional vector space $\mathbb R^{m \times n}$, whose
elements are in... | [
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1... | 2.40245 | 1,143 |
<gh_stars>10-100
using StochasticArithmetic
using StochasticArithmetic.EFT
using Test, Jive
using Formatting
using LinearAlgebra
using Random
Random.seed!(42)
@onlyonce begin
const x1 = [-6.340663515282668472e-01, -1.032478304663339008e+00, -9.398547076618038787e+00, 8.775666080119043144e+00,
8.15... | [
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1... | 1.616483 | 12,255 |
function dden_complete(ygrid, W, eta, Z, mus, sig2; i, j)
K = size(W, 2)
L0 = size(eta[0], 3)
L1 = size(eta[1], 3)
L = Dict(0 => L0, 1 => L1)
dden = [begin
si = sqrt(sig2[i])
dd = 0.0
for k in 1:K
ddk = 0.0
z = Z[j, k]
for ell in 1... | [
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8,
198,
220,
406,
15,
796,
2546,
7,
17167,
58,
15,
4357,
513... | 1.595618 | 502 |
using OptiMode, BenchmarkTools
include("mpb_example.jl") # for now, just to load ε⁻¹
H,kz = solve_k(ω,ε⁻¹,Δx,Δy,Δz)
@benchmark solve_k($ω,$ε⁻¹,$Δx,$Δy,$Δz)
# BenchmarkTools.Trial:
# memory estimate: 250.60 MiB
# allocs estimate: 147758
# --------------
# minimum time: 1.178 s (0.97% GC)
# median time: ... | [
3500,
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198,
198,
39,
11,
74,
89,
796,
8494,
62,
74,
7,
49535,
11... | 1.786 | 1,000 |
struct Level{Tprice, Tvolume}
price::Tprice
volume::Tvolume
end
import Base: isless
isless(l1::Level, l2::Level) = isless(l1.price, l2.price)
function isunique(v_lev::Array{Level{Tprice,Tvolume},1}) where {Tprice,Tvolume}
length(unique([lv.price for lv in v_lev])) == length(v_lev)
end
| [
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318,
1203,
198,
271,
1203,
7,
75,
16,
3712,
4971,
11,
300,
... | 2.459016 | 122 |
using MPI
using ClimateMachine
using Logging
using ClimateMachine.Mesh.Topologies
using ClimateMachine.Mesh.Grids
using ClimateMachine.DGMethods
using ClimateMachine.BalanceLaws: update_auxiliary_state!
using ClimateMachine.DGMethods.NumericalFluxes
using ClimateMachine.DGMethods.FVReconstructions: FVConstant, FVLinear... | [
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38,
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198,
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13963,
37573,... | 1.85023 | 7,398 |
module Distance
include("dist.jl")
export
# Funtion
GetDistance,
# Types of distances
Euclidean,
CityBlock,
TotalVariation,
Chebyshev,
Jaccard,
BrayCurtis,
CosineDist,
SpanNormDist
end
| [
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220,
220,
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198,
220,
... | 2.205607 | 107 |
<reponame>pkofod/SimpleSolve.jl
function nlsolve(f, j, x, iterations = 10^5, r_norm = norm, r_abstol = sqrt(eps(eltype(x))))
xnext = x
r = f(x)
for i = 1:iterations
if r_norm(r) <= r_abstol
return r, xnext, :success
end
x = copy(xnext)
s = - j(x)\r
xnext =... | [
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1986... | 1.801587 | 252 |
<filename>src/collect_simple.jl
using RPOMDPModels, RPOMDPs, RPOMDPToolbox
using RobustValueIteration
using SimpleProbabilitySets
using DataFrames, ProgressMeter, CSV, BenchmarkTools
const RPBVI = RobustValueIteration
TOL = 1e-6
# intialize problems
ip = SimpleIPOMDP(0.8, 0.7, 0.66, 0.85)
rip = SimpleRIPOMDP(0.8, 0.7,... | [
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... | 2.186139 | 2,756 |
using LiquidsStructure
using Test
const AHS = AttractiveHardSpheres
const k∞ = inv(eps()) # large wavevector
@testset "Hard disks, Rosenfeld FMT" begin
η = 0.1
f = StructureFactor(HardDisks(η), RosenfeldFMT)
@test f(0.0) ≈ 0.6604201250395436
@test f(π/2) ≈ 0.7269796280941714
@test f(1π ) ≈ 0.905... | [
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32505... | 1.720344 | 11,979 |
<gh_stars>1-10
#fi = "test_22.jl"
read_all_lines(fi) = readlines(fi)
function read_all_lines(fis::Vector)
lins = String[]
for fi in fis
push!(lins, readlines(fi)...)
end
lins
end
function line_no(fi, no)
read_all_lines(fi)[no]
end
function words(fi)
wd = String[]
for line in read_al... | [
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7,
69,
271,
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38469,
... | 1.892486 | 1,637 |
# ------------------------------------------------------------------
# Licensed under the MIT License. See LICENSE in the project root.
# ------------------------------------------------------------------
"""
supportfun(geometry, direction)
Support function of `geometry` for given `direction`.
## References
* <... | [
2,
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198,
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286,
... | 3.21875 | 224 |
<filename>src/dataParsing.jl<gh_stars>0
using Statistics
using LaTeXStrings
using Measures
using Plots
using Plots.PlotMeasures
gr()
dropmean(A; dims=:) = dropdims(mean(A; dims=dims); dims=dims)
dropstd(A; dims=:) = dropdims(std(A; dims=dims); dims=dims)
# @userplot Ucurve
# @recipe function f(u::Ucurve)
# x,y... | [
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1345,
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198,
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1345,
1747,
13,
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5308,
13846,
... | 2.03076 | 1,658 |
using Cement_Hydration
using Test
@testset "Cement_Hydration.jl" begin
# Write your tests here.
end
| [
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220,
220,
220,
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534,
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994,
13,
198,
437,
198
] | 2.837838 | 37 |
<filename>analysis/behavior/src/comparisons.jl<gh_stars>0
module comparisons
"""
Code to facilitate the comparison of models with data.
"""
using Interpolations
import DataFrames: DataFrame
import MyterialColors: salmon, blue, green, purple, teal, indigo_dark
import Statistics: mean, std, median
import jcontrol:... | [
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198,
198,
... | 2.398787 | 2,144 |
<gh_stars>0
### Compute the gradients using a gradient function and matrices Js ###
base_kernel(k::Kernel) = eval(nameof(typeof(k)))
function compute_hyperparameter_gradient(k::Kernel,gradient_function::Function,J::Vector{<:AbstractMatrix})
return map(gradient_function,J)
end
function compute_hyperparameter_grad... | [
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7,
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... | 2.404987 | 1,484 |
# Defines the core Node abstraction
# Defines the core Node and operations on the graph
using Base
importall Base
typealias Float Float64
typealias TensorValue Union{Real, Array}
###############
# Basic types #
###############
abstract OpType
type Node
op::OpType
inputs::Vector{Node}
outputs::Vector{Nod... | [
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... | 2.335417 | 2,719 |
# TODO: rewrite in a better and more julian way.
#
# This is just kind of thrown together without any real planning but it seems to
# work. An obviouse design improvement would be to drop the string identifiers
# and use types or something instead. Conssider this as a functional outline for
# a future product.
s = raw"... | [
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561... | 2.116645 | 4,698 |
include("hrb_utils.jl")
type HBMeta
# Metadata attached to a Harwell-Boeing matrix.
title :: AbstractString
key :: AbstractString
totcrd :: Int
ptrcrd :: Int
indcrd :: Int
valcrd :: Int
rhscrd :: Int
mxtype :: AbstractString
nrow :: Int
ncol :: Int
nnzero :: Int
neltvl :: Int
hermitia... | [
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220,
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220,
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790... | 2.086741 | 2,617 |
mutable struct A
t::Array{Float64, 1}
s::Float64
end
mutable struct B
a::A
s::Float64
end
@testset "update" begin
# setfield_nested
b = B(A([1, 2, 3], 4), 5)
setfield_nested!(b, (:a, :t), [-1.0, -2.0, -3.0])
setfield_nested!(b, (:a, :s), -4.0)
setfield_nested!(b, (:s,), -5.0)
... | [
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198,
220,
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220,
257,
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32,
198,
220,
220,
220,... | 1.719081 | 566 |
import Term: cleantext, chars, textlen, split_lines
function same_widths(text::String)::Bool
widths = textlen.(split_lines(text))
return length(unique(widths)) == 1
end
function check_widths(text, width)
for line in split_lines(text)
@test textlen(line) <= width
end
end
"""
Extensively test... | [
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... | 2.513841 | 578 |
#=
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 conditions and the follo... | [
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11,
389,
10431,
2810,
326,
262,
1708,
34... | 1.950188 | 2,931 |
bshow(i) = bitstring(i)[end-7:end]
function large_enough_unsigned(bit_cnt)
unsigned_types = [UInt8, UInt16, UInt32, UInt64, UInt128]
atype = nothing
for xtype in unsigned_types
if sizeof(xtype) * 8 >= bit_cnt
atype = xtype
break
end
end
atype
end
"""
i... | [
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... | 1.65858 | 5,647 |
<reponame>cropbox/Cropbox.jl
using MacroTools: MacroTools, isexpr, isline, @capture, @q
using Setfield: @set
struct VarInfo{S<:Union{Symbol,Nothing}}
system::Symbol
name::Symbol
alias::Union{Symbol,Nothing}
args::Vector
kwargs::Vector
body#::Union{Expr,Symbol,Nothing}
state::S
type::Uni... | [
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3500,
5345,
3245,
25,
2488,
2617,
198,
198,
7249,... | 2.21275 | 14,745 |
<gh_stars>0
function anglelimits()
end
function voltagelimits()
end
| [
27,
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62,
30783,
29,
15,
198,
8818,
9848,
49196,
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417,
320,
896,
3419,
198,
198,
437,
198
] | 2.62963 | 27 |
<reponame>ethansaxenian/RosettaDecode
module NumericError
import Base: convert, promote_rule, +, -, *, /, ^
export Measure
type Measure <: Number
x::Float64
σ::Float64
Measure(x::Real) = new(Float64(x), 0)
Measure(x::Real, σ::Real) = new(Float64(x), σ)
end
Base.show(io::IO, x::Measure) = print(io, string(x.... | [
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using GaussianProcesses
using GaussianProcesses: KernelData
using BenchmarkLite
# Define Benchmark test
type KernelTest <: Proc
k::Kernel
d::Int
op::Function
KernelTest(k::Kernel, d::Int, op::Function) = new(k, d, op)
end
AbstractString(proc::KernelTest) = "$(typeof(proc.k)), d=$(proc.d)"
Base.length... | [
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... | 2.370748 | 294 |
#=
"ESSE: Environmental and State Dependent Diversification" submodule package
=#
module ESSE
using Random: randexp
using DelimitedFiles: readdlm, writedlm
using ProgressMeter: Progress, next!
using DifferentialEquations: ODEProblem, init, reinit!, solve!, Tsit5, DiffEqBase
using LinearAlgebra: BLAS.gemv!, rank, ... | [
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... | 2.829201 | 363 |
<filename>src/abstract_vlae.jl
abstract type AbstractVLAE end
function elbo(m::AbstractVLAE, x::AbstractArray{T,4}) where T
# encoder pass - KL divergence
μzs_σzs = _encoded_mu_vars(m, x)
zs = map(y->rptrick(y...), μzs_σzs)
kldl = sum(map(y->Flux.mean(kld(y...)), μzs_σzs))
# decoder pass -... | [
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19,
30072,
... | 1.998793 | 2,485 |
<reponame>masashitshit/Interpolations.jl
module Interpolations
export linerinterp
function linerinterp(grid,vals)
function funfun(x)
if x < grid[1]
return (vals[2]-vals[1])/(grid[2]-grid[1])*(x-grid[1])+vals[1]
end
if grid[end] <= x
... | [
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7,
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8,
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220,
220,
220,
220,
... | 1.645455 | 440 |
<filename>src/entities/wallbooster.jl
module WallBooster
using ..Ahorn, Maple
const placements = Ahorn.PlacementDict(
"Wall Booster (Right)" => Ahorn.EntityPlacement(
Maple.WallBooster,
"rectangle",
Dict{String, Any}(
"left" => true
)
),
"Wall Booster (Left)" =>... | [
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7,
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220,
... | 2.243716 | 915 |
struct GpuIndirectSolver <: LinearSolver end
if haskey(ENV, "JULIA_SCS_LIBRARY_PATH")
@isdefined(libscsgpuindir) && push!(available_solvers, GpuIndirectSolver)
else
import SCS_GPU_jll
const gpuindirect = SCS_GPU_jll.libscsgpuindir
push!(available_solvers, GpuIndirectSolver)
end
scsint_t(::Type{GpuIndi... | [
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198,
220,
220,
220,
2488,
271,
23211,
7... | 1.962707 | 724 |
<filename>src/SolveEig.jl
module SolveEig
using LinearAlgebra
using ..Interp:getD
using ..Interp:getCpts
using ..Interp:getCpts1
using ..Interp:baryW1
using ..Interp:barymat
using ..Interp:forward
using ..Interp:rev
using ..EdgeDetect:find
using ..Op:setup
#=
function many_ham(V, N; d2 = -0.5, d1 = 0.0, a = -1.0, b =... | [
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198,
3500,
11... | 1.575665 | 4,249 |
<gh_stars>1-10
### A Pluto.jl notebook ###
# v0.12.21
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
q... | [
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18274,
4487,
198,
198,
2,
770,
32217,
20922,
3544,
2488,
21653,... | 1.754919 | 11,943 |
<reponame>johnnychen94/NiLang.jl
using CUDA, GPUArrays
using NiLang, NiLang.AD
"""
A reversible swap kernel for GPU for SWAP gate in quantum computing.
See the irreversible version for comparison
http://tutorials.yaoquantum.org/dev/generated/developer-guide/2.cuda-acceleration/
"""
@i @inline function swap_kernel(sta... | [
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198,
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16075,
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3... | 2.306748 | 1,141 |
<gh_stars>10-100
# f(x)=hcat(x...)
Fhcat(x...)=(hcat(x...),nothing)
Fhcat_inplace(value,auxvalue,x...)=copy!(value,hcat(x...))
function Dhcat(derivativeIDX,f_c,faux_c,grad_c,grad_n,x...)
startind=1
for i=1:length(x)
endind=startind+length(x[i])-1
if pointer(x[i])==pointer(x[derivativeIDX]) # us... | [
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198,
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62,
259,
5372,
7,
8367,
11,
14644,
836... | 1.924638 | 690 |
<gh_stars>0
"""
# `module Data`
Provides methods to read files containing simulation data. Primarily this is
intended to load `.xyz` files and convert them to JuLIP-compatible data:
```
data = IPFitting.Data.load_data("mydata.xyz")
```
where `mydata.xyz` contains multiple configurations, will read in those
configurat... | [
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318,
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600,
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284,
3440,
4600,
13,
5431,
89,
63,
3696,
290,
... | 2.219439 | 1,996 |
# Module to communicate with GW instek PSP series power supplies
#=
note:
1) The serial port on the PSP-603 (and possibly others) is non standard.
Pin 4 needs +12V to power the line driver. The transmitter will swing from
Vpin4 - 4v to 0v. This worked with my RS232 adapter.
2) The equipment and probably ... | [
2,
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284,
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1176,
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16,
8,
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12,
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357,
392,
5457,
1854,
8,
318,
1729,
3210,
13,
220,
220,
198,
220,... | 2.564669 | 634 |
<reponame>JuliaAstrodynamics/Orekit.jl<filename>gen/HipparchusWrapper/OptimWrapper/NonlinearWrapper/VectorWrapper/VectorWrapper.jl
module VectorWrapper
using JavaCall
include("LeastsquaresWrapper/LeastsquaresWrapper.jl")
end
| [
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29,
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14,
38469,
36918,
2848,
14... | 2.814815 | 81 |
<reponame>Michiel-VL/PolyaViz<gh_stars>0
using Plots
function lineplot(st::SearchTrace, xdata::Symbol, ydata::Symbol; kwargs...)
return plot(st.df[:, xdata], st.df[:, ydata]; kwargs...)
end
function objective_plot(st::SearchTrace, xsym::Symbol=:it, ysym::Symbol=:v_s; kwargs...)
return lineplot(st, xsym, ysym;... | [
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13940,
23650,
11,
331,
7... | 2.392857 | 140 |
<reponame>LawrenceMMStewart/Bayesian_Optimization
include("Kernals.jl")
include("gaussian_process.jl")
function uniform(a,b,N)
rand(N)*(b-a)+a
end
using Distributions
n=50 #number of test points
N=10;#Number of training points
Xtest=linspace(-5,5,n); #Xtest are all of available points to check on the axis
X=unif... | [
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62,
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13,
20362,
4943,
628,
198,
8818,
8187,
7,
64,
... | 2.485507 | 690 |
export project_cardinality_parallel
function project_cardinality_parallel(x::Vector,
k ::Int64,
n_proc::Int64
)
"""
Project the vector x onto the set of vectors with cardinality (l0 'norm') less then or equal to k.
project m onto {m... | [
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
... | 1.915361 | 638 |
<filename>src/Gen2DAgentMotion.jl
module Gen2DAgentMotion
include("scene.jl")
include("planner.jl")
include("motion.jl")
include("distributions.jl")
end # module
| [
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7203,
11578,
1008,
13,
20362,
4943,
198,
17256,
7203,
38714,
13,
2036... | 2.877193 | 57 |
<reponame>ORNLJulia/Privacy.jl
module DPSGD
export DifferentialPrivacy, privacy_spent
export solve_niterations, solve_noise_multiplier
using
Flux,
LogarithmicNumbers,
Parameters,
Random,
Statistics,
Zygote,
LinearAlgebra
using SpecialFunctions: logerfc, gamma
Base.binomial(n, k) = gamma(... | [
27,
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261,
480,
29,
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43,
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544,
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39344,
8494,
62,
77,
2676,
602,
11,
8494,
62,
3919,
786,
62,
47945,
959... | 2.135958 | 4,597 |
<gh_stars>1-10
# Tests for General Direct Approach Cost DEA Models
@testset "CostGDADEAModel" begin
# Test using Book data
X = [2 2; 1 4; 4 1; 4 3; 5 5; 6 1; 2 5; 1.6 8];
Y = [1; 1; 1; 1; 1; 1; 1; 1];
W = [1 1; 1 1; 1 1; 1 1; 1 1; 1 1; 1 1; 1 1];
# Cost GDA
costgda = deacostgda(X, Y, W, :ERG)
... | [
27,
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62,
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29,
16,
12,
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198,
2,
30307,
329,
3611,
4128,
38066,
6446,
28647,
32329,
198,
31,
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2617,
366,
13729,
45113,
19266,
2390,
375,
417,
1,
2221,
628,
220,
220,
220,
1303,
6208,
1262,
4897,
1366,
198,
220,
220,... | 2.127986 | 1,172 |
module FinancialSymbology
using HTTP, StructArrays
const APIKEYNAME = "X-OPENFIGI-APIKEY"
include("Identifiers.jl")
using .Identifiers
include("apitypes.jl")
include("apiconstructors.jl")
include("prettyprinters.jl")
export Identifier, Sedol, Cusip, Isin, Figi, Ticker, Index
export OpenFigiAPI
export makeidentifi... | [
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11302,
13940,
2022,
1435,
198,
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3500,
14626,
11,
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3163,
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198,
198,
9979,
7824,
20373,
20608,
796,
366,
55,
12,
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1677,
16254,
40,
12,
17614,
20373,
1,
198,
198,
17256,
7203,
33234,
13350,
13,
20362,
4943,
198,... | 2.792202 | 2,180 |
export some
import Base: show
"""
some()
Creates a some operator, which filters out `nothing` items by the source Observable by emitting only
those that not equal to `nothing`.
# Producing
Stream of type `<: Subscribable{L}` where `L` refers to type of source stream `<: Subscribable{Union{L, Nothing}}`
# Exam... | [
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63,
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416,
262,
2723,
19243,
540,
416,
48143,
691,
198,
25591,
... | 2.653214 | 669 |
<reponame>JuliaTagBot/NestedMaps.jl
using NestedMaps, Test
@testset "fallback" begin
x = 1:10
@test nested_map(identity, x) == x
@test nested_map(sum, x) == sum(x)
end
@testset "tuple" begin
t = [(i, -i) for i in 1:10]
@test nested_map(identity, t) == (1:10, -(1:10))
@test nested_map(sum, t) =... | [
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220,
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198,
220,
22... | 1.969163 | 681 |
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