content stringlengths 6 1.03M | input_ids listlengths 4 535k | ratio_char_token float64 0.68 8.61 | token_count int64 4 535k |
|---|---|---|---|
<reponame>JamesKat94/HoJBot.jl
const QUOTE_CACHE = Cache{String,Float64}(Minute(1))
finnhub_token() = get(ENV, "FINNHUB_TOKEN", "")
function commander(c::Client, m::Message, ::Val{:ig})
@debug "ig_commander called"
command = extract_command("ig", m.content)
args = split(command)
@debug "parse result"... | [
27,
7856,
261,
480,
29,
14731,
25881,
5824,
14,
28900,
41,
20630,
13,
20362,
198,
9979,
19604,
23051,
62,
34,
2246,
13909,
796,
34088,
90,
10100,
11,
43879,
2414,
92,
7,
9452,
1133,
7,
16,
4008,
198,
198,
69,
3732,
40140,
62,
30001,... | 2.521862 | 8,142 |
<gh_stars>10-100
function p53(data::Dict)
# Setup basic dimensions of arrays
# Parse & check FEdict data
if :struc_el in keys(data)
struc_el = data[:struc_el]
else
println("No fin_el type specified.")
return
end
ndim = struc_el.ndim
nst = struc_el.nst
fin_el = struc_el.fin_el
... | [
27,
456,
62,
30783,
29,
940,
12,
3064,
198,
8818,
279,
4310,
7,
7890,
3712,
35,
713,
8,
198,
220,
220,
198,
220,
1303,
31122,
4096,
15225,
286,
26515,
198,
220,
220,
198,
220,
1303,
2547,
325,
1222,
2198,
376,
7407,
713,
1366,
198... | 2.008041 | 3,109 |
include("../src/read/read_datasets.jl")
include("../src/utils.jl")
include("../src/pq/PQ.jl");
include("../src/lsq_sparse/LSQ_SPGL1.jl");
include("../src/linscan/Linscan.jl");
function demo_lsq_sparse(
dataset_name="SIFT1M",
nread::Integer=Int(1e4))
# === Hyperparams ===
m = 7 # In LSQ we use m-1 codebo... | [
17256,
7203,
40720,
10677,
14,
961,
14,
961,
62,
19608,
292,
1039,
13,
20362,
4943,
198,
17256,
7203,
40720,
10677,
14,
26791,
13,
20362,
4943,
198,
17256,
7203,
40720,
10677,
14,
79,
80,
14,
47,
48,
13,
20362,
15341,
198,
17256,
7203... | 2.166935 | 1,240 |
"""
Lasso(; alpha = 0.1, tol = 1e-4, mi = 1e+8)
Lasso Regression structure. eEach parameters are as follows:
- `alpha` : leaarning rate.
- `tol` : Allowable error.
- `mi` : Maximum number of learning.
# Example
```jldoctest regression
julia> model = Lasso()
Lasso(Float64[], 0.1, 0.0001, 100000000)
julia> fit!(mod... | [
37811,
198,
220,
220,
220,
406,
28372,
7,
26,
17130,
796,
657,
13,
16,
11,
284,
75,
796,
352,
68,
12,
19,
11,
21504,
796,
352,
68,
10,
23,
8,
198,
43,
28372,
3310,
2234,
4645,
13,
304,
10871,
10007,
389,
355,
5679,
25,
198,
12... | 1.918665 | 959 |
<reponame>SamuelWiqvist/efficient_SDEMEM<gh_stars>0
# script to run inference for the OU SDEMEM model
using Pkg
using LinearAlgebra
using DataFrames
# TODO
# what to do w the adaptive tuning?
# updated resources for sbatch scripts
println("start run script")
# load functions
include(pwd()*"/src/SDEMEM OU process/ou... | [
27,
7856,
261,
480,
29,
16305,
2731,
54,
25011,
85,
396,
14,
16814,
62,
10305,
3620,
3620,
27,
456,
62,
30783,
29,
15,
198,
2,
4226,
284,
1057,
32278,
329,
262,
47070,
9834,
3620,
3620,
2746,
198,
3500,
350,
10025,
198,
3500,
44800,... | 1.377896 | 3,755 |
<filename>test/deabigdata.jl<gh_stars>1-10
# Tests for Big Data Radial DEA Models
@testset "BigData RadialDEAModel" begin
## Test Radial DEA Models with FLS Book data
X = [5 13; 16 12; 16 26; 17 15; 18 14; 23 6; 25 10; 27 22; 37 14; 42 25; 5 17]
Y = [12; 14; 25; 26; 8; 9; 27; 30; 31; 26; 12]
# Input o... | [
27,
34345,
29,
9288,
14,
2934,
397,
328,
7890,
13,
20362,
27,
456,
62,
30783,
29,
16,
12,
940,
198,
2,
30307,
329,
4403,
6060,
5325,
498,
28647,
32329,
198,
31,
9288,
2617,
366,
12804,
6601,
5325,
498,
7206,
2390,
375,
417,
1,
222... | 1.669217 | 7,972 |
using IterativeSolvers
using FactCheck
using Base.Test
# Type used in SOL test
type Wrapper
m::Int
n::Int
end
Base.size(op::Wrapper, dim::Integer) = (dim == 1) ? op.m :
(dim == 2) ? op.n : 1
Base.size(op::Wrapper) = (op.m, op.n)
Base.eltype(op::Wrapper) = Int
func... | [
3500,
40806,
876,
36949,
690,
198,
3500,
19020,
9787,
198,
3500,
7308,
13,
14402,
628,
198,
198,
2,
5994,
973,
287,
36817,
1332,
198,
4906,
27323,
2848,
198,
220,
220,
220,
285,
3712,
5317,
198,
220,
220,
220,
299,
3712,
5317,
198,
... | 1.811904 | 2,621 |
include("module.jl")
using Logging
Logging.global_logger(Logging.ConsoleLogger(Logging.Info))
F5_DETECTED_USELESS_NF = 0
F5_USELESS_NF = 0
F5_NF = 0
F5_BASIS_SIZE = 0
function zerof5()
global F5_DETECTED_USELESS_NF
global F5_USELESS_NF
global F5_NF
global F5_BASIS_SIZE
F5_DETECTED_... | [
198,
17256,
7203,
21412,
13,
20362,
4943,
198,
198,
3500,
5972,
2667,
198,
11187,
2667,
13,
20541,
62,
6404,
1362,
7,
11187,
2667,
13,
47581,
11187,
1362,
7,
11187,
2667,
13,
12360,
4008,
198,
198,
37,
20,
62,
35,
2767,
9782,
1961,
... | 2.017011 | 3,586 |
@testset "rand" begin
# Some arbitrary alignment
x = iterdates()
expected_times = _eval_fast(x).times
@testset "scalar" begin
n = rand(x)
# calling multiple times should give different nodes, since we use a different rng.
@test n != rand(x)
# If we specify an explicit r... | [
31,
9288,
2617,
366,
25192,
1,
2221,
198,
220,
220,
220,
1303,
2773,
14977,
19114,
198,
220,
220,
220,
2124,
796,
11629,
19581,
3419,
198,
220,
220,
220,
2938,
62,
22355,
796,
4808,
18206,
62,
7217,
7,
87,
737,
22355,
628,
220,
220,... | 2.051429 | 875 |
### Run Simulation ###
function initialize_sim_and_run(;m::MapData
, routes_path::OrderedDict{Symbol, OrderedDict{Int64,Int64}}
, routes_distances::OrderedDict{Symbol, OrderedDict{Int64,Float64}}
, routes_types_both_dir::Dict{Symbol,Symbol}
, orig_map_nodes_num::Int
, when_to_run_people::Float64 = s... | [
21017,
5660,
41798,
44386,
198,
8818,
41216,
62,
14323,
62,
392,
62,
5143,
7,
26,
76,
3712,
13912,
6601,
198,
197,
197,
197,
197,
11,
11926,
62,
6978,
3712,
35422,
1068,
35,
713,
90,
13940,
23650,
11,
14230,
1068,
35,
713,
90,
5317,... | 2.252166 | 4,616 |
<gh_stars>0
devices = Dict{Symbol, DeviceModel}(:Generators => DeviceModel(PSY.ThermalStandard, ThermalDispatch),
:Loads => DeviceModel(PSY.PowerLoad, StaticPowerLoad))
branches = Dict{Symbol, DeviceModel}(:L => DeviceModel(PSY.Line, StaticLineUnbounded))
services = Dict{Symbol, Ser... | [
27,
456,
62,
30783,
29,
15,
198,
42034,
796,
360,
713,
90,
13940,
23650,
11,
16232,
17633,
92,
7,
25,
8645,
2024,
5218,
16232,
17633,
7,
3705,
56,
13,
35048,
7617,
23615,
11,
41590,
49354,
828,
198,
220,
220,
220,
220,
220,
220,
2... | 2.700221 | 904 |
"""
ZeroKernel()
Create a kernel that always returning zero
```
κ(x,y) = 0.0
```
The output type depends of `x` and `y`
"""
struct ZeroKernel <: SimpleKernel end
kappa(κ::ZeroKernel, d::T) where {T<:Real} = zero(T)
metric(::ZeroKernel) = Delta()
Base.show(io::IO, ::ZeroKernel) = print(io, "Zero Kernel")
"... | [
37811,
198,
220,
220,
220,
12169,
42,
7948,
3419,
198,
198,
16447,
257,
9720,
326,
1464,
8024,
6632,
198,
15506,
63,
198,
220,
220,
220,
7377,
118,
7,
87,
11,
88,
8,
796,
657,
13,
15,
198,
15506,
63,
198,
464,
5072,
2099,
8338,
... | 2.294347 | 513 |
<gh_stars>0
module SpikeTrains
export SpikeTrain, draw_uncorrelated_spikes, draw_correlated_spikes, draw_coincident_spikes, length, iterate, convert, vcat, merge, make_exponentialShift, correlation_code, coincidence_code, plot_spike_trains
using Distributions, Plots#, PlotRecipes
struct SpikeTrain
times::Vector... | [
27,
456,
62,
30783,
29,
15,
198,
21412,
26309,
2898,
1299,
198,
198,
39344,
26309,
44077,
11,
3197,
62,
403,
10215,
5363,
62,
2777,
7938,
11,
3197,
62,
10215,
5363,
62,
2777,
7938,
11,
3197,
62,
1073,
1939,
738,
62,
2777,
7938,
11,
... | 2.480526 | 3,723 |
# DBL_EPSILON - jest to dokładność dla liczb zmiennoprzecinkowych
# dla Float64 jest to 2.2204460492503131e-16
DBL_EPSILON = 2.2204460492503131e-16
function DIFFSIGN(x, y)
if (x <=0 && y >= 0) || (x >= 0 && y <= 0)
return true
else
return false
end
end
function fun(x)
return 1 / (x - ... | [
198,
2,
360,
9148,
62,
36,
3705,
4146,
1340,
532,
474,
395,
284,
466,
74,
41615,
324,
3919,
129,
249,
38325,
288,
5031,
3476,
14969,
1976,
11632,
1697,
404,
81,
89,
721,
676,
322,
88,
354,
198,
2,
288,
5031,
48436,
2414,
474,
395,... | 1.624325 | 2,779 |
<reponame>ChevronETC/WaveFD
using BenchmarkTools, Random, Statistics, WaveFD
_nthreads = [2^i for i in 0:floor(Int,log2(Sys.CPU_THREADS))]
if Sys.CPU_THREADS ∉ _nthreads
push!(_nthreads, Sys.CPU_THREADS)
end
const SUITE = BenchmarkGroup()
z0,y0,x0,dz,dy,dx,nt,dt = 0.0,0.0,0.0,10.0,10.0,10.0,3000,0.001
n_2D = (z... | [
27,
7856,
261,
480,
29,
7376,
85,
1313,
2767,
34,
14,
39709,
26009,
198,
3500,
25187,
4102,
33637,
11,
14534,
11,
14370,
11,
17084,
26009,
198,
198,
62,
77,
16663,
82,
796,
685,
17,
61,
72,
329,
1312,
287,
657,
25,
28300,
7,
5317,... | 1.962761 | 9,345 |
<filename>MakieCore/src/attributes.jl
const Theme = Attributes
Base.broadcastable(x::AbstractScene) = Ref(x)
Base.broadcastable(x::AbstractPlot) = Ref(x)
Base.broadcastable(x::Attributes) = Ref(x)
# The rules that we use to convert values to a Observable in Attributes
value_convert(x::Observables.AbstractObservable)... | [
27,
34345,
29,
44,
461,
494,
14055,
14,
10677,
14,
1078,
7657,
13,
20362,
198,
198,
9979,
26729,
796,
49213,
198,
198,
14881,
13,
36654,
2701,
540,
7,
87,
3712,
23839,
36542,
8,
796,
6524,
7,
87,
8,
198,
14881,
13,
36654,
2701,
54... | 2.542382 | 3,728 |
<filename>test/runtests.jl
using NNLS
using Test
# import NonNegLeastSquares
using PyCall
using ECOS
using JuMP
using Random
using LinearAlgebra
import Libdl
const pyopt = pyimport_conda("scipy.optimize", "scipy")
macro wrappedallocs(expr)
argnames = [gensym() for a in expr.args]
quote
function g($(... | [
27,
34345,
29,
9288,
14,
81,
2797,
3558,
13,
20362,
198,
3500,
399,
45,
6561,
198,
3500,
6208,
198,
2,
1330,
8504,
32863,
3123,
459,
22266,
3565,
198,
3500,
9485,
14134,
198,
3500,
13182,
2640,
198,
3500,
12585,
7378,
198,
3500,
14534... | 2.330275 | 218 |
<filename>src/Resource/ResourceManagementClient/model_DeploymentExtendedFilter.jl
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
mutable struct DeploymentExtendedFilter <: SwaggerModel
provisioningState::Any # spec type:... | [
27,
34345,
29,
10677,
14,
26198,
14,
26198,
48032,
11792,
14,
19849,
62,
49322,
434,
11627,
1631,
22417,
13,
20362,
198,
2,
770,
2393,
373,
7560,
416,
262,
22300,
2451,
7928,
6127,
35986,
198,
2,
2141,
407,
13096,
428,
2393,
3264,
13,... | 3.340136 | 441 |
<reponame>ianshmean/LibSerialPort.jl
import Base: readline, readuntil
export readline, readuntil
#==
Timeout versions of tbe base functions
==#
"""
readline(s::IO, timeout::T; keep::Bool=false) where {T<:Real}
Like Base.readline, except times-out after `timeout` seconds.
"""
function readline(s::IO, timeout::T; ... | [
27,
7856,
261,
480,
29,
1547,
71,
32604,
14,
25835,
32634,
13924,
13,
20362,
198,
11748,
7308,
25,
1100,
1370,
11,
1100,
28446,
198,
39344,
1100,
1370,
11,
1100,
28446,
198,
198,
2,
855,
198,
48031,
6300,
286,
256,
1350,
2779,
5499,
... | 2.165493 | 852 |
<filename>src/constraints/chp_constraints.jl
# *********************************************************************************
# REopt, Copyright (c) 2019-2020, Alliance for Sustainable Energy, LLC.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permi... | [
27,
34345,
29,
10677,
14,
1102,
2536,
6003,
14,
354,
79,
62,
1102,
2536,
6003,
13,
20362,
198,
2,
41906,
17174,
8412,
9,
198,
2,
4526,
8738,
11,
15069,
357,
66,
8,
13130,
12,
42334,
11,
10302,
329,
45276,
6682,
11,
11419,
13,
198,... | 2.163868 | 5,895 |
module ENN
export TimedAutomata, TimePetriNets, Neurons
include(joinpath("TimedAutomata/","TimedAutomata.jl"))
include(joinpath("TimePetriNets/","TimePetriNets.jl"))
include(joinpath("Neurons/","Neurons.jl"))
end | [
21412,
412,
6144,
198,
198,
39344,
5045,
276,
38062,
1045,
11,
3862,
25803,
380,
45,
1039,
11,
3169,
333,
684,
198,
198,
17256,
7,
22179,
6978,
7203,
14967,
276,
38062,
1045,
14,
2430,
14967,
276,
38062,
1045,
13,
20362,
48774,
198,
1... | 2.654321 | 81 |
<gh_stars>0
using RecipesBase, Measures
export InvMcmcSampler, McmcOutput
struct InvMcmcSampler{T<:InvMcmcMove}
move::T
desired_samples::Int
burn_in::Int
lag::Int
K::Int
init::McmcInitialiser
pointers::InteractionSequence{Int}
function InvMcmcSampler(
move::T;
desired... | [
27,
456,
62,
30783,
29,
15,
198,
3500,
44229,
14881,
11,
45040,
198,
39344,
10001,
9742,
23209,
16305,
20053,
11,
1982,
23209,
26410,
198,
198,
7249,
10001,
9742,
23209,
16305,
20053,
90,
51,
27,
25,
19904,
9742,
23209,
21774,
92,
198,
... | 2.127139 | 2,454 |
using PFKernels
using PFKernels.PowerSystem
using PFKernels.ForwardDiff
using LinearAlgebra
AT = PFKernels.AT
function mynorm(x)
t1s{N} = ForwardDiff.Dual{Nothing,Float64, N} where N
n = length(x)
FT = t1s{n}
V = Vector
F = zeros(n)
adF = V{FT}(undef, n)
adx = V{FT}(undef, n)
# Forward... | [
3500,
350,
26236,
44930,
198,
3500,
350,
26236,
44930,
13,
13434,
11964,
198,
3500,
350,
26236,
44930,
13,
39746,
28813,
198,
3500,
44800,
2348,
29230,
198,
198,
1404,
796,
350,
26236,
44930,
13,
1404,
198,
198,
8818,
616,
27237,
7,
87,... | 2.085153 | 458 |
<reponame>Giarcr0b/MVO_Tool
# Copyright 2016, <NAME>, <NAME>, <NAME>, and contributors
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
##############################... | [
27,
7856,
261,
480,
29,
33704,
5605,
81,
15,
65,
14,
44,
29516,
62,
25391,
198,
2,
220,
15069,
1584,
11,
1279,
20608,
22330,
1279,
20608,
22330,
1279,
20608,
22330,
290,
20420,
198,
2,
220,
770,
8090,
6127,
5178,
318,
2426,
284,
262... | 2.23331 | 15,803 |
<gh_stars>10-100
############################################################
## joMatrix - extra functions
# elements(jo)
#elements(A::joAbstractDAparallelToggleOperator) = throw(joAbstractDAparallelToggleOperatorException("elements: pointless operation for joDAdistribute/joDAgather operations"))
# hasinverse(jo)
#... | [
27,
456,
62,
30783,
29,
940,
12,
3064,
198,
29113,
14468,
7804,
4242,
198,
2235,
2525,
46912,
532,
3131,
5499,
198,
198,
2,
4847,
7,
7639,
8,
198,
2,
68,
3639,
7,
32,
3712,
7639,
23839,
5631,
1845,
29363,
51,
20258,
18843,
1352,
8... | 3.036765 | 136 |
matmul_sizes(s::Integer) = (s,s,s)
matmul_sizes(m_k_n::Tuple{Vararg{<:Integer, 3}}) = m_k_n
"""
runbench(backend, TA, TB, TC; kwargs...)
"""
function runbench(backend::Symbol, ::Type{TA}, ::Type{TB}, ::Type{TC};
kwargs...) where {TA, TB, TC}
return runbench(Val(backend), TA, TB, TC; kwargs...... | [
6759,
76,
377,
62,
82,
4340,
7,
82,
3712,
46541,
8,
796,
357,
82,
11,
82,
11,
82,
8,
198,
6759,
76,
377,
62,
82,
4340,
7,
76,
62,
74,
62,
77,
3712,
51,
29291,
90,
19852,
853,
90,
27,
25,
46541,
11,
513,
11709,
8,
796,
285,... | 1.806538 | 1,499 |
# SPDX-License-Identifier: X11
# 2020-08-29
# Five Variables (100pt)
println(findfirst(x -> x == 0, parse.(Int, split(readline(), " "))))
| [
2,
30628,
55,
12,
34156,
12,
33234,
7483,
25,
1395,
1157,
198,
2,
12131,
12,
2919,
12,
1959,
198,
2,
10579,
15965,
2977,
357,
3064,
457,
8,
198,
198,
35235,
7,
19796,
11085,
7,
87,
4613,
2124,
6624,
657,
11,
21136,
12195,
5317,
11... | 2.622642 | 53 |
using Revise
using ReversibleSeismic
nx = 1000
ny = 1000
param = AcousticPropagatorParams(nx=nx, ny=ny,
Rcoef=0.2, dx=20.0, dy=20.0, dt=0.05, nstep=100)
c = 1000*ones(param.NX+2, param.NY+2)
src = (param.NX÷2, param.NY÷2)
srcv = Ricker(param, 100.0, 500.0)
tu = solve(param, src, srcv, c)
loss = sum(tu .^ 2)
@ass... | [
3500,
5416,
786,
198,
3500,
797,
37393,
4653,
1042,
291,
198,
198,
77,
87,
796,
8576,
198,
3281,
796,
8576,
198,
17143,
796,
4013,
21618,
24331,
363,
1352,
10044,
4105,
7,
77,
87,
28,
77,
87,
11,
299,
88,
28,
3281,
11,
198,
220,
... | 1.892966 | 327 |
<gh_stars>0
#=
JoiceTypeConstructorFunctions:
- Julia version: 1.6
- Author: connorforsythe
- Date: 2021-11-05
=#
export ConstructModelInfo
function checkModelInfo(Data::DataFrame, ChoiceColumn::Symbol, Parameters::Vector{Symbol}, ChoiceSetIDColumn::Symbol, RandomParameters::RandColsType=nothing, PanelIDColumn::Cols... | [
27,
456,
62,
30783,
29,
15,
198,
2,
28,
198,
41,
2942,
6030,
42316,
273,
24629,
2733,
25,
198,
12,
22300,
2196,
25,
352,
13,
21,
198,
12,
6434,
25,
369,
13099,
69,
669,
88,
1169,
198,
12,
7536,
25,
33448,
12,
1157,
12,
2713,
1... | 2.941434 | 1,827 |
function BigFraction(arg0::BigInteger)
return BigFraction((BigInteger,), arg0)
end
function BigFraction(arg0::BigInteger, arg1::BigInteger)
return BigFraction((BigInteger, BigInteger), arg0, arg1)
end
function BigFraction(arg0::jdouble)
return BigFraction((jdouble,), arg0)
end
function BigFraction(arg0::... | [
8818,
4403,
37,
7861,
7,
853,
15,
3712,
12804,
46541,
8,
198,
220,
220,
220,
1441,
4403,
37,
7861,
19510,
12804,
46541,
11,
828,
1822,
15,
8,
198,
437,
198,
198,
8818,
4403,
37,
7861,
7,
853,
15,
3712,
12804,
46541,
11,
1822,
16,
... | 2.605206 | 2,305 |
module QJuliaRegisters
# Plain baseline registers
const half2 = NTuple{2, VecElement{Float16}}
const half4 = NTuple{4, VecElement{Float16}}
#
const float2 = NTuple{2, VecElement{Float32}}
const float4 = NTuple{4, VecElement{Float32}}
const float8 = NTuple{8, VecElement{Float32}}
const float16 = NTuple{16, VecEle... | [
21412,
1195,
16980,
544,
8081,
6223,
198,
198,
2,
28847,
14805,
28441,
198,
9979,
2063,
17,
796,
24563,
29291,
90,
17,
11,
38692,
20180,
90,
43879,
1433,
11709,
198,
9979,
2063,
19,
796,
24563,
29291,
90,
19,
11,
38692,
20180,
90,
438... | 2.518229 | 1,536 |
<reponame>arnavgautam/Mimi.jl
module TestParameterTypes
using Mimi
using Test
import Mimi:
external_params, external_param, TimestepMatrix, TimestepVector,
ArrayModelParameter, ScalarModelParameter, FixedTimestep, build, import_params!
#
# Test that parameter type mismatches are caught
#
expr = :(
@defco... | [
27,
7856,
261,
480,
29,
1501,
615,
70,
2306,
321,
14,
44,
25236,
13,
20362,
198,
21412,
6208,
36301,
31431,
198,
198,
3500,
337,
25236,
198,
3500,
6208,
198,
198,
11748,
337,
25236,
25,
198,
220,
220,
220,
7097,
62,
37266,
11,
7097,... | 2.494631 | 4,284 |
# ThomasFermi.jl
# - Thomas Fermi Density Functional Theory
# Marder is: "Condensed Mattery Physics", <NAME>, 2000.
# Also very useful is <NAME>'s first DFT lecture, on the Thomas-Fermi method
# http://www.home.uni-osnabrueck.de/apostnik/Lectures/DFT-1.pdf
"""
ThomasFermi_T(n)
Thomas Fermi kinetic energy (T).
F... | [
2,
5658,
37,
7780,
72,
13,
20362,
198,
2,
220,
532,
5658,
376,
7780,
72,
360,
6377,
44224,
17003,
198,
198,
2,
337,
446,
263,
318,
25,
366,
25559,
15385,
337,
16296,
23123,
1600,
1279,
20608,
22330,
4751,
13,
220,
198,
198,
2,
441... | 2.234405 | 2,116 |
using LowRankIntegrators, SparseArrays
@testset "Burgers equation" begin
n = 1000 # spatial discretization
l = π # length of spatial domain
Δx = l/n # step size
x_range = Δx/2:Δx:l-Δx/2 # uniform grid
# boundary conditions
left(i) = i > 1 ? i - 1 : n
right(i) = i < n ? i + 1 : 1
# di... | [
3500,
7754,
27520,
34500,
18942,
11,
1338,
17208,
3163,
20477,
198,
198,
31,
9288,
2617,
366,
33,
3686,
364,
16022,
1,
2221,
198,
220,
220,
220,
299,
796,
8576,
1303,
21739,
1221,
1186,
1634,
628,
220,
220,
220,
300,
796,
18074,
222,
... | 1.975713 | 947 |
@defcomp climatedynamics begin
# Total radiative forcing
radforc = Parameter(index=[time])
# Average global temperature
temp = Variable(index=[time])
# lifetempconst
lifetempconst = Parameter()
# lifetemplin
lifetemplin = Parameter()
# lifetempqd
lifetempqd = Parameter()
... | [
171,
119,
123,
31,
4299,
5589,
5424,
515,
4989,
873,
2221,
198,
220,
220,
220,
1303,
7472,
19772,
876,
10833,
198,
220,
220,
220,
2511,
1640,
66,
796,
25139,
2357,
7,
9630,
41888,
2435,
12962,
628,
220,
220,
220,
1303,
13475,
3298,
... | 2.15103 | 437 |
<filename>src/entities/seekerbarrier.jl
module SeekerBarrier
using ..Ahorn, Maple
const placements = Ahorn.PlacementDict(
"Seeker Barrier" => Ahorn.EntityPlacement(
Maple.SeekerBarrier,
"rectangle"
),
)
Ahorn.minimumSize(entity::Maple.SeekerBarrier) = 8, 8
Ahorn.resizable(entity::Maple.Seeker... | [
27,
34345,
29,
10677,
14,
298,
871,
14,
325,
28233,
5657,
5277,
13,
20362,
198,
21412,
1001,
28233,
10374,
5277,
198,
198,
3500,
11485,
10910,
1211,
11,
21249,
198,
198,
9979,
21957,
3196,
796,
7900,
1211,
13,
3646,
5592,
35,
713,
7,
... | 2.429752 | 363 |
<reponame>richardreeve/Diversity.jl<gh_stars>10-100
using DataFrames
using LinearAlgebra
"""
UniqueTypes
A subtype of AbstractTypes where all individuals are completely
distinct. This type is the simplest AbstractTypes subtype, which
identifies all individuals as unique and completely distinct from each
other.
"... | [
27,
7856,
261,
480,
29,
7527,
446,
631,
303,
14,
35,
1608,
13,
20362,
27,
456,
62,
30783,
29,
940,
12,
3064,
198,
3500,
6060,
35439,
198,
3500,
44800,
2348,
29230,
198,
198,
37811,
198,
220,
220,
220,
30015,
31431,
198,
198,
32,
8... | 2.617894 | 2,023 |
<filename>docs/make.jl
using BRIKHEAD
using Documenter
makedocs(;
modules=[BRIKHEAD],
authors="<NAME> <<EMAIL>> and contributors",
repo="https://github.com/notZaki/BRIKHEAD.jl/blob/{commit}{path}#L{line}",
sitename="BRIKHEAD.jl",
format=Documenter.HTML(;
prettyurls=get(ENV, "CI", "false") =... | [
27,
34345,
29,
31628,
14,
15883,
13,
20362,
198,
3500,
347,
7112,
42,
37682,
198,
3500,
16854,
263,
198,
198,
76,
4335,
420,
82,
7,
26,
198,
220,
220,
220,
13103,
41888,
33,
7112,
42,
37682,
4357,
198,
220,
220,
220,
7035,
2625,
2... | 2.13253 | 249 |
<gh_stars>0
using IterativeSolvers
using Base.Test
@testset "SVD Lanczos" begin
srand(1234567)
#Thick restart methods
@testset "Thick restart with method=$method" for method in (:ritz, :harmonic)
for T in (Float32, Float64)
@testset "Diagonal Matrix{$T}" begin
n = 30
ns = 5
... | [
27,
456,
62,
30783,
29,
15,
198,
3500,
40806,
876,
36949,
690,
198,
3500,
7308,
13,
14402,
198,
198,
31,
9288,
2617,
366,
50,
8898,
21534,
37925,
1,
2221,
198,
198,
82,
25192,
7,
10163,
2231,
3134,
8,
198,
198,
2,
817,
624,
15765,... | 1.681579 | 1,520 |
<filename>src/problems/127.word-ladder.jl
# ---
# title: 127. Word Ladder
# id: problem127
# author: AquaIndigo
# date: 2020-11-07
# difficulty: Medium
# categories: Breadth-first Search
# link: <https://leetcode.com/problems/word-ladder/description/>
# hidden: true
# ---
#
# Given two words ( _beginWord_ and _endWord... | [
27,
34345,
29,
10677,
14,
1676,
22143,
14,
16799,
13,
4775,
12,
9435,
1082,
13,
20362,
198,
2,
11420,
198,
2,
3670,
25,
18112,
13,
9678,
12862,
1082,
198,
2,
4686,
25,
1917,
16799,
198,
2,
1772,
25,
24838,
5497,
14031,
198,
2,
312... | 2.013825 | 1,736 |
if basename(pwd()) == "aoc"
cd("2019/6")
end
struct Planet
name
parent
children
end
Planet(name) = Planet(name, nothing, Planet[])
function Base.getindex(planet::Planet, name::AbstractString)
if planet.name == name
planet
else
for child in planet.children
result = c... | [
361,
1615,
12453,
7,
79,
16993,
28955,
6624,
366,
64,
420,
1,
198,
220,
220,
220,
22927,
7203,
23344,
14,
21,
4943,
198,
437,
198,
198,
7249,
11397,
198,
220,
220,
220,
1438,
198,
220,
220,
220,
2560,
198,
220,
220,
220,
1751,
198... | 2.491779 | 669 |
# Core types and definitions
if VERSION < v"0.5.0-dev"
macro pure(ex)
esc(ex)
end
else
using Base: @pure
end
const Symbols = Tuple{Symbol,Vararg{Symbol}}
@doc """
Type-stable axis-specific indexing and identification with a
parametric type.
### Type parameters
```julia
immutable Axis{name,T}
``... | [
2,
7231,
3858,
290,
17336,
198,
198,
361,
44156,
2849,
1279,
410,
1,
15,
13,
20,
13,
15,
12,
7959,
1,
198,
220,
220,
220,
15021,
5899,
7,
1069,
8,
198,
220,
220,
220,
220,
220,
220,
220,
3671,
7,
1069,
8,
198,
220,
220,
220,
... | 2.475142 | 9,132 |
<filename>test/runtests.jl
# This file is a part of BAT.jl, licensed under the MIT License (MIT).
using Test
Test.@testset "Package BAT" begin
include("utils/test_utils.jl")
include("rngs/test_rngs.jl")
include("distributions/test_distributions.jl")
include("parameters/test_parameters.jl")
include... | [
27,
34345,
29,
9288,
14,
81,
2797,
3558,
13,
20362,
198,
2,
770,
2393,
318,
257,
636,
286,
37421,
13,
20362,
11,
11971,
739,
262,
17168,
13789,
357,
36393,
737,
198,
198,
3500,
6208,
198,
198,
14402,
13,
31,
9288,
2617,
366,
27813,
... | 2.598985 | 197 |
<reponame>jmmshn/LeetCode.jl<filename>src/problems/23.merge-k-sorted-lists.jl
# ---
# title: 23. Merge k Sorted Lists
# id: problem23
# author: Indigo
# date: 2021-04-14
# difficulty: Hard
# categories: Linked List, Divide and Conquer, Heap
# link: <https://leetcode.com/problems/merge-k-sorted-lists/description/>
# hid... | [
27,
7856,
261,
480,
29,
73,
76,
907,
21116,
14,
3123,
316,
10669,
13,
20362,
27,
34345,
29,
10677,
14,
1676,
22143,
14,
1954,
13,
647,
469,
12,
74,
12,
82,
9741,
12,
20713,
13,
20362,
198,
2,
11420,
198,
2,
3670,
25,
2242,
13,
... | 2.033105 | 876 |
using Plots
using FFTW
tan_taylor(x) = x+( x^3/3 )+( x^5*2/15.0 )+( x^7*17/315.0 )+( x^9*62/2835.0 )+( x^11*1382/155925.0 )+( x^13*21844/6081075.0 )+( x^15*929569/638512875.0 );
x = LinRange(-π/2+0.1, π/2-0.1, 1001);
tanx_taylor = tan_taylor.(x);
tanx_native = tan.(x);
delta = tanx_native-tanx_taylor
# Time domain... | [
3500,
1345,
1747,
198,
3500,
376,
9792,
54,
198,
198,
38006,
62,
83,
7167,
7,
87,
8,
796,
2124,
33747,
2124,
61,
18,
14,
18,
1267,
33747,
2124,
61,
20,
9,
17,
14,
1314,
13,
15,
1267,
33747,
2124,
61,
22,
9,
1558,
14,
27936,
13... | 1.965385 | 260 |
module Separators
using Genie, Stipple, StippleUI, StippleUI.API
import Genie.Renderer.Html: HTMLString, normal_element, template
export separator
function __init__()
Genie.Renderer.Html.register_normal_element("q__separator", context = Genie.Renderer.Html)
end
function separator(args...; wrap::Function = Stipple... | [
21412,
8621,
283,
2024,
198,
198,
3500,
49405,
11,
520,
18793,
11,
520,
18793,
10080,
11,
520,
18793,
10080,
13,
17614,
198,
11748,
49405,
13,
49,
437,
11882,
13,
39,
20369,
25,
11532,
10100,
11,
3487,
62,
30854,
11,
11055,
198,
198,
... | 2.824675 | 154 |
<gh_stars>0
module PostgreSQLLoader
using Octo.Repo: ExecuteResult
# https://github.com/invenia/LibPQ.jl
using LibPQ # v0.9.1
using .LibPQ.Tables
const current = Dict{Symbol, Any}(
:conn => nothing,
)
current_conn() = current[:conn]
# db_connect
function db_connect(; kwargs...)
if !isempty(kwargs)
... | [
27,
456,
62,
30783,
29,
15,
198,
21412,
2947,
16694,
50,
48,
3069,
1170,
263,
198,
198,
3500,
2556,
78,
13,
6207,
78,
25,
8393,
1133,
23004,
198,
198,
2,
3740,
1378,
12567,
13,
785,
14,
259,
574,
544,
14,
25835,
47,
48,
13,
2036... | 2.372404 | 674 |
<filename>src/clibrary.jl
struct Clibrary
handle::Ptr{Cvoid}
Clibrary(libName::Union{AbstractString, Nothing} = nothing, flags = Libdl.RTLD_LAZY | Libdl.RTLD_DEEPBIND | Libdl.RTLD_LOCAL) = new(Libdl.dlopen(libName === nothing ? _NullCString() : libName, flags))
end
Base.close(lib::Clibrary) = Libdl.dlclose(lib.h... | [
27,
34345,
29,
10677,
14,
565,
4115,
13,
20362,
628,
198,
7249,
1012,
4115,
198,
197,
28144,
3712,
46745,
90,
34,
19382,
92,
198,
197,
198,
197,
2601,
4115,
7,
8019,
5376,
3712,
38176,
90,
23839,
10100,
11,
10528,
92,
796,
2147,
11,... | 2.865 | 200 |
"""
LinearRegression()
Classic regression model. This struct has no parameter.
If you want to use polynomial model, use `Regression.make_design_matrix()`.
see also: [`make_design_matrix`](@ref)
# Example
```jldoctest regression
julia> x = [
16.862463771320925 68.10823385851712
15.382965696961577 65.431348... | [
37811,
198,
220,
220,
220,
44800,
8081,
2234,
3419,
198,
39914,
20683,
2746,
13,
770,
2878,
468,
645,
11507,
13,
198,
1532,
345,
765,
284,
779,
745,
6213,
49070,
2746,
11,
779,
4600,
8081,
2234,
13,
15883,
62,
26124,
62,
6759,
8609,
... | 2.277876 | 1,130 |
<reponame>MillironX/beefblup
# beefblup
# Julia package for performing single-variate BLUP to find beef cattle
# breeding values
# (C) 2021 <NAME>
# Licensed under BSD-3-Clause License
# cSpell:includeRegExp #.*
# cSpell:includeRegExp ("""|''')[^\1]*\1
module BeefBLUP
# Import the required packages
using CSV
using Da... | [
27,
7856,
261,
480,
29,
22603,
1934,
55,
14,
1350,
891,
2436,
929,
198,
2,
12023,
2436,
929,
198,
2,
22300,
5301,
329,
9489,
2060,
12,
25641,
378,
9878,
8577,
284,
1064,
12023,
17025,
198,
2,
18954,
3815,
198,
2,
357,
34,
8,
33448... | 2.434469 | 3,609 |
<gh_stars>1-10
"""
Execution state that encapsulates synchronization primitives and resources
bound to a command submission on the GPU.
Resources bound to the related execution can be either freed or released (dereferenced)
once the execution completes.
The execution is assumed to be completed when the execution
state... | [
27,
456,
62,
30783,
29,
16,
12,
940,
198,
37811,
198,
23002,
1009,
1181,
326,
32652,
15968,
42133,
2684,
20288,
290,
4133,
198,
7784,
284,
257,
3141,
14498,
319,
262,
11362,
13,
198,
198,
33236,
5421,
284,
262,
3519,
9706,
460,
307,
... | 2.738017 | 1,523 |
function setupCommBuffers!(domain::Domain, edgeNodes::Int)
function cache_align_real(n::Int64)
n = Int32(n)
(n + CACHE_COHERENCE_PAD_REAL%Int32 - one(Int32)) & ~(CACHE_COHERENCE_PAD_REAL%Int32 - one(Int32))
end
@unpack_Domain domain
# allocate a buffer large enough for nodal ghost data
... | [
8818,
9058,
6935,
36474,
364,
0,
7,
27830,
3712,
43961,
11,
5743,
45,
4147,
3712,
5317,
8,
198,
220,
220,
220,
2163,
12940,
62,
31494,
62,
5305,
7,
77,
3712,
5317,
2414,
8,
198,
220,
220,
220,
220,
220,
220,
220,
299,
796,
2558,
... | 2.11413 | 1,472 |
using ParallelAccelerator
#ParallelAccelerator.ParallelIR.set_debug_level(3)
@acc function find_chg(k,m,W,Wp)
W_tmp = [abs(W[i,j] - Wp[i,j]) for i in 1:m, j in 1:k]
s = [sum(W_tmp[:,j]) for j in 1:k]
chg = maximum(s)
end
function main(m::Int, k::Int)
W = Array{Float64}(m, k)
Wp = Array{Float64}(m, k... | [
3500,
42945,
12832,
7015,
1352,
198,
198,
2,
10044,
29363,
12832,
7015,
1352,
13,
10044,
29363,
4663,
13,
2617,
62,
24442,
62,
5715,
7,
18,
8,
198,
198,
31,
4134,
2163,
1064,
62,
354,
70,
7,
74,
11,
76,
11,
54,
11,
54,
79,
8,
... | 1.850427 | 234 |
using Iconv
using Test
@test iconv("笨熊", "UTF-8", "GBK") == togbk("笨熊") == g"笨熊"
@test iconv(g"笨熊", "GBK", "UTF-8") == toutf8(g"笨熊") == b"笨熊" | [
3500,
26544,
85,
198,
3500,
6208,
198,
198,
31,
9288,
7196,
85,
7203,
163,
105,
101,
163,
228,
232,
1600,
366,
48504,
12,
23,
1600,
366,
4579,
42,
4943,
6624,
284,
22296,
74,
7203,
163,
105,
101,
163,
228,
232,
4943,
6624,
308,
1,... | 1.42 | 100 |
<gh_stars>1-10
module GHWT_tf_1d
include("utils.jl")
using ..GraphSignal, ..GraphPartition, ..BasisSpecification, LinearAlgebra
include("common.jl")
export ghwt_tf_bestbasis, eghwt_bestbasis
"""
coeffdict = tf_init(dmatrix::Matrix{Float64},GP::GraphPart)
Store the expanding coeffcients from matrix ... | [
27,
456,
62,
30783,
29,
16,
12,
940,
198,
21412,
24739,
39386,
62,
27110,
62,
16,
67,
201,
198,
201,
198,
17256,
7203,
26791,
13,
20362,
4943,
201,
198,
201,
198,
3500,
11485,
37065,
11712,
282,
11,
11485,
37065,
7841,
653,
11,
1148... | 2.102084 | 5,613 |
using Test
using StaticArrays
using IntervalArithmetic
# using Revise
using HDGElasticity
function allapprox(v1,v2)
return all(v1 .≈ v2)
end
@test HDGElasticity.linear_map_slope(1.,2.,[1.,2.],[3.,4.]) ≈ [2.,2.]
map = HDGElasticity.LineMap(0.,1.,[0.,0.],[1.,1.])
@test map.xiL ≈ 0.0
@test map.xiR ≈ 1.0
@test allap... | [
3500,
6208,
198,
3500,
36125,
3163,
20477,
198,
3500,
4225,
2100,
3163,
29848,
198,
2,
1262,
5416,
786,
198,
3500,
5572,
8264,
75,
3477,
414,
198,
198,
8818,
477,
1324,
13907,
7,
85,
16,
11,
85,
17,
8,
198,
220,
220,
220,
1441,
47... | 1.91922 | 1,795 |
<filename>src/direct/sparseblocks.jl<gh_stars>0
struct BlockIndices
i1::UnitRange{Int}
i2::UnitRange{Int}
isdiag::Bool
end
# Remove isdiag field from comparison for dict
Base.isequal(b1::BlockIndices, b2::BlockIndices) = b1.i1 == b2.i1 && b1.i2 == b2.i2
Base.hash(b::BlockIndices, h::UInt) = hash((b.i1, b.... | [
27,
34345,
29,
10677,
14,
12942,
14,
82,
29572,
27372,
13,
20362,
27,
456,
62,
30783,
29,
15,
198,
198,
7249,
9726,
5497,
1063,
198,
220,
220,
220,
1312,
16,
3712,
26453,
17257,
90,
5317,
92,
198,
220,
220,
220,
1312,
17,
3712,
26... | 2.481544 | 4,172 |
using OffsetArrays
using StaticArrays
using DataStructures
export pgbf, cgbf, contract, addbf!, PGBF, CGBF, build_basis,eri_fetcher, Shell, Basis
export m2ao, shell_indices,nao
"""
PGBF(expn,xyz,I,J,K,norm)
Create a primitive Gaussian basis function
g(x,y,z) = norm * (x-x0)^I (y-y0)^J (z-z0)^K exp(-expn*r^2)... | [
3500,
3242,
2617,
3163,
20477,
198,
3500,
36125,
3163,
20477,
198,
3500,
6060,
44909,
942,
198,
39344,
23241,
19881,
11,
269,
70,
19881,
11,
2775,
11,
751,
19881,
28265,
350,
4579,
37,
11,
327,
4579,
37,
11,
1382,
62,
12093,
271,
11,
... | 1.779658 | 6,322 |
export Domain, SegmentDomain, tocanonical, fromcanonical, fromcanonicalD, ∂
export chebyshevpoints, fourierpoints, isambiguous, arclength
export components, component, ncomponents
# add indexing for all spaces, not just DirectSumSpace
# mimicking scalar vs vector
# prectype gives the precision, including for Vec... | [
198,
198,
39344,
20021,
11,
1001,
5154,
43961,
11,
284,
49883,
605,
11,
422,
49883,
605,
11,
422,
49883,
605,
35,
11,
18872,
224,
198,
39344,
1125,
48209,
258,
85,
13033,
11,
46287,
5277,
13033,
11,
318,
4131,
29709,
11,
610,
565,
3... | 2.579621 | 1,796 |
cluster = [1 1 1 1; 2 2 3 3; 4 4 5 5]
X = Array(reshape(1:24, 2, 3, 4))
@testset "pool" begin
@testset "GlobalPool" begin
glb_cltr = [1 1 1 1; 1 1 1 1; 1 1 1 1]
p = GlobalPool(:add, 3, 4)
@test p(X) == sumpool(glb_cltr, X)
end
@testset "LocalPool" begin
p = LocalPool(:add, ... | [
565,
5819,
796,
685,
16,
352,
352,
352,
26,
362,
362,
513,
513,
26,
604,
604,
642,
642,
60,
198,
55,
796,
15690,
7,
3447,
1758,
7,
16,
25,
1731,
11,
362,
11,
513,
11,
604,
4008,
198,
198,
31,
9288,
2617,
366,
7742,
1,
2221,
... | 1.737705 | 488 |
<reponame>lytemar/Julia-1.0-Programming-Cookbook
blockvalid(x, v) = count(isequal(v), x) ≤ 1
function backtrack!(x)
pos = findfirst(isequal(0), x)
isa(pos, Nothing) && return true
iloc = 3div(pos[1]-1, 3) .+ (1:3)
jloc = 3div(pos[2]-1, 3) .+ (1:3)
for k in 1:9
x[pos] = k
blockvalid(... | [
27,
7856,
261,
480,
29,
306,
11498,
283,
14,
16980,
544,
12,
16,
13,
15,
12,
15167,
2229,
12,
28937,
2070,
198,
9967,
12102,
7,
87,
11,
410,
8,
796,
954,
7,
786,
13255,
7,
85,
828,
2124,
8,
41305,
352,
198,
198,
8818,
736,
116... | 2.002475 | 404 |
abstract type RegressionType end
struct OLS
robust::Bool
function OLS(robust=true)
new(robust)
end
end
| [
397,
8709,
2099,
3310,
2234,
6030,
886,
198,
198,
7249,
440,
6561,
198,
220,
220,
220,
12373,
3712,
33,
970,
628,
220,
220,
220,
2163,
440,
6561,
7,
22609,
436,
28,
7942,
8,
198,
220,
220,
220,
220,
220,
220,
220,
649,
7,
22609,
... | 2.309091 | 55 |
geneCols = Dict{String,Symbol}(
"aliases" => :GeneSymbolAlias,
"symbol" => :OfficialGeneSymbol,
"region" => :GeneRegion,
"regionStart" => :GeneStart,
"regionEnd" => :GeneEnd,
"id" => :EnsemblGeneId,
"version" => :EnsemblGeneVersion,
"biotype" => :EnsemblGeneBiotype,
"name" => :GeneName
);
tr... | [
198,
70,
1734,
5216,
82,
796,
360,
713,
90,
10100,
11,
13940,
23650,
92,
7,
198,
220,
220,
366,
7344,
1386,
1,
5218,
1058,
39358,
13940,
23650,
40489,
11,
198,
220,
220,
366,
1837,
23650,
1,
5218,
1058,
28529,
39358,
13940,
23650,
1... | 2.120162 | 4,186 |
<gh_stars>10-100
using Documenter
using MERAKit
makedocs(sitename = "MERAKit.jl", modules = [MERAKit])
deploydocs(repo = "github.com/mhauru/MERAKit.jl.git")
| [
27,
456,
62,
30783,
29,
940,
12,
3064,
198,
3500,
16854,
263,
198,
3500,
34482,
10206,
270,
198,
198,
76,
4335,
420,
82,
7,
48937,
12453,
796,
366,
29296,
10206,
270,
13,
20362,
1600,
13103,
796,
685,
29296,
10206,
270,
12962,
198,
... | 2.323529 | 68 |
<reponame>JuliaPackageMirrors/CoordinateTransformations.jl<filename>src/CoordinateTransformations.jl
__precompile__()
module CoordinateTransformations
using StaticArrays
using Rotations
export RotMatrix, Quat, SpQuat, AngleAxis, RodriguesVec,
RotX, RotY, RotZ,
RotXY, RotYX, RotZX, RotXZ, RotYZ, RotZY,
... | [
27,
7856,
261,
480,
29,
16980,
544,
27813,
27453,
5965,
14,
7222,
45480,
41762,
602,
13,
20362,
27,
34345,
29,
10677,
14,
7222,
45480,
41762,
602,
13,
20362,
198,
834,
3866,
5589,
576,
834,
3419,
198,
198,
21412,
22819,
4559,
41762,
6... | 3.057971 | 414 |
<reponame>matbesancon/Manopt.jl
using Manifolds, Manopt, Test, ManifoldsBase
using Random
Random.seed!(42)
# Test the additional manifold functions
#
@testset "Additional Manifold functions" begin
@testset "mid point & reflect" begin
M = Sphere(2)
p = [1.0, 0.0, 0.0]
q = [0.0, 1.0, 0.0]
... | [
27,
7856,
261,
480,
29,
6759,
12636,
272,
1102,
14,
5124,
8738,
13,
20362,
198,
3500,
1869,
361,
10119,
11,
1869,
8738,
11,
6208,
11,
1869,
361,
10119,
14881,
198,
198,
3500,
14534,
198,
29531,
13,
28826,
0,
7,
3682,
8,
198,
2,
62... | 1.702415 | 2,070 |
using Documenter
push!(LOAD_PATH,"../src/")
using BasesAndSamples
makedocs(
sitename = "BasesAndSamples.jl Documentation",
format = Documenter.HTML(),
modules = [BasesAndSamples]
)
# Documenter can also automatically deploy documentation to gh-pages.
# See "Hosting Documentation" and deploydocs() in the D... | [
3500,
16854,
263,
198,
14689,
0,
7,
35613,
62,
34219,
553,
40720,
10677,
14,
4943,
198,
3500,
347,
1386,
1870,
50,
12629,
198,
198,
76,
4335,
420,
82,
7,
198,
220,
220,
220,
1650,
12453,
796,
366,
33,
1386,
1870,
50,
12629,
13,
20... | 2.954248 | 153 |
import GreyDecision.GreyNumbers: GreyNumber
import GreyDecision.Utility: makeminmax
import LinearAlgebra: det
@testset "Transpose" begin
x = [
GreyNumber(5.0) GreyNumber(3.0) GreyNumber(-2.0);
GreyNumber(-8.0) GreyNumber(6.0) GreyNumber(1.0);
GreyNumber(10.0) GreyNumber(7.0) GreyNumber(13.... | [
11748,
13980,
10707,
1166,
13,
49141,
49601,
25,
13980,
15057,
198,
11748,
13980,
10707,
1166,
13,
18274,
879,
25,
787,
1084,
9806,
198,
11748,
44800,
2348,
29230,
25,
1062,
628,
198,
31,
9288,
2617,
366,
8291,
3455,
1,
2221,
198,
220,
... | 1.861702 | 940 |
<filename>benchmark/_archive/parameter_tuning.jl<gh_stars>1-10
include("../src/experiments/0_setup.jl")
import DataFrames.DataFrame
using DataFrames, CSV
using FastGroupBy
using Base.Threads
K = 100
tries = vcat([Int(2^k-1) for k = 7:31], 3_000_000_000)
for N in tries
println(N)
if N < 2_000_000
by = ... | [
27,
34345,
29,
26968,
4102,
47835,
17474,
14,
17143,
2357,
62,
28286,
278,
13,
20362,
27,
456,
62,
30783,
29,
16,
12,
940,
198,
17256,
7203,
40720,
10677,
14,
23100,
6800,
14,
15,
62,
40406,
13,
20362,
4943,
198,
11748,
6060,
35439,
... | 2.086672 | 1,373 |
using FedDCD
include("./PrimalMethods.jl")
include("./DualMethods.jl")
# Softmax for mnist
fileTrain = "data/mnist.scale"
fileTest = "data/mnist.scale.t"
λ = 1e-2
participationRate = 0.3
localLr = 1e-2
numRounds = 100
# - FedAvg
μ = 0.0
RunFedAvgAndProx(
fileTrain,
fileTest,
λ,
μ,
participationRat... | [
3500,
10169,
35,
8610,
198,
17256,
7,
1911,
14,
23828,
282,
46202,
13,
20362,
4943,
198,
17256,
7,
1911,
14,
36248,
46202,
13,
20362,
4943,
198,
198,
2,
8297,
9806,
329,
285,
77,
396,
198,
7753,
44077,
796,
366,
7890,
14,
10295,
396... | 1.887915 | 4,791 |
#
# Recursive Fourier Propagation for BoostFractor
#
# V: 2019-08-06
#
# <NAME>
#
export dancer, dance_intro, cheerleader
"""
Propagates the fields through the system
* `amin`: Mimum (local) amplitude of a field, in order to be propagated
* `nmax`: Maximum number of beam iteration steps, directly... | [
2,
198,
2,
3311,
30753,
34296,
5277,
8772,
363,
341,
329,
19835,
37,
40450,
198,
2,
198,
2,
569,
25,
13130,
12,
2919,
12,
3312,
198,
2,
198,
2,
1279,
20608,
29,
198,
2,
198,
198,
39344,
38619,
11,
9280,
62,
600,
305,
11,
14042,
... | 2.322375 | 7,361 |
<filename>0011/SCI/src/SCI.jl<gh_stars>1-10
"""A scirntific module (calculate exp(x))"""
module SCI
abstract type AbstractProblem end
struct Problem{T} <: AbstractProblem x::T end
abstract type AbstractAlgorithm end
struct Builtin <: AbstractAlgorithm end
Base.@kwdef struct Taylor <: AbstractAlgorithm n::Int = 10 end... | [
27,
34345,
29,
405,
1157,
14,
6173,
40,
14,
10677,
14,
6173,
40,
13,
20362,
27,
456,
62,
30783,
29,
16,
12,
940,
198,
37811,
32,
629,
343,
429,
811,
8265,
357,
9948,
3129,
378,
1033,
7,
87,
4008,
37811,
198,
21412,
6374,
40,
198... | 2.632458 | 419 |
mutable struct Attribute
name::Symbol
value::String
Attribute(name::Symbol) = new(name, "")
end
const NULL_ATTRIBUTE = Attribute(:NULL)
mutable struct ChartType
concreteName::String
attributes::Vector{Attribute}
ancestors::Vector{ChartType}
ChartType(attributes::Vector{A}, ancestors::Vector{ChartType}=ChartType... | [
76,
18187,
2878,
3460,
4163,
198,
197,
3672,
3712,
13940,
23650,
198,
197,
8367,
3712,
10100,
198,
197,
33682,
7,
3672,
3712,
13940,
23650,
8,
796,
649,
7,
3672,
11,
366,
4943,
198,
437,
198,
9979,
15697,
62,
1404,
5446,
9865,
37780,
... | 2.539846 | 4,944 |
function _make_spectogram_gui()
popupmenu_spect = Menu()
popupmenu_spect_freq = MenuItem("Frequency Range")
popupmenu_spect_win = MenuItem("Window Size")
popupmenu_spect_overlap = MenuItem("Window Overlap")
push!(popupmenu_spect,popupmenu_spect_freq)
push!(popupmenu_spect,popupmenu_spect_win)
... | [
198,
8818,
4808,
15883,
62,
4443,
21857,
62,
48317,
3419,
628,
220,
220,
220,
46207,
26272,
62,
4443,
796,
21860,
3419,
198,
220,
220,
220,
46207,
26272,
62,
4443,
62,
19503,
80,
796,
21860,
7449,
7203,
37,
28707,
13667,
4943,
198,
22... | 1.949548 | 2,874 |
<reponame>CliMA/OceanModelComparison.jl<filename>unstable_bickley/periodic/Analysis.jl
module Analysis
using Oceananigans
using JLD2
using Plots
using Oceananigans.Grids
using Oceananigans.Fields: offset_data
import Oceananigans.Fields: Field
Field(loc::Tuple, grid::AbstractGrid, raw_data::Array) = Field(loc, CPU(),... | [
27,
7856,
261,
480,
29,
2601,
72,
5673,
14,
46607,
17633,
50249,
1653,
13,
20362,
27,
34345,
29,
403,
31284,
62,
65,
624,
1636,
14,
41007,
291,
14,
32750,
13,
20362,
198,
21412,
14691,
198,
198,
3500,
10692,
272,
34090,
198,
3500,
4... | 2.555556 | 756 |
using BenchmarkTools
BenchmarkTools.DEFAULT_PARAMETERS.samples = 100
function compute(file::String)::Int
chars = Dict(Char(Int('A') + i) => i + 1 for i ∈ 0:25)
return sum(i * chars[c] for (i, name) ∈ enumerate(sort(split(replace(file, "\"" => ""), ","))) for c ∈ name)
end
file = read("problems/0022/p022_names... | [
3500,
25187,
4102,
33637,
198,
44199,
4102,
33637,
13,
7206,
38865,
62,
27082,
2390,
2767,
4877,
13,
82,
12629,
796,
1802,
198,
198,
8818,
24061,
7,
7753,
3712,
10100,
2599,
25,
5317,
198,
220,
220,
220,
34534,
796,
360,
713,
7,
12441... | 2.533784 | 148 |
"""
data_harvest(all=true, paper=false)
Makes all the figures from the paper for the notebook.
This function calls PlotData to make the figures one at a time.
"""
function data_harvest(all=true, paper=false)
cvals = [.5, .99, 1]
itvals = [10, 10, 30]
level=5
for ic=1:3
figure(ic)
PlotData(cvals[ic]; ptitle=~pap... | [
37811,
198,
7890,
62,
9869,
4223,
7,
439,
28,
7942,
11,
3348,
28,
9562,
8,
198,
44,
1124,
477,
262,
5538,
422,
262,
3348,
329,
262,
20922,
13,
198,
1212,
2163,
3848,
28114,
6601,
284,
787,
262,
5538,
530,
379,
257,
640,
13,
198,
... | 2.188984 | 1,053 |
export ft_getstatus
function ft_getstatus(ft_handle::Culong)
amountinrxqueue = Ref{Cuint}()
amountintxqueue = Ref{Cuint}()
eventstatus = Ref{Cuint}()
ft_status = ccall((:FT_GetStatus, d2xx),
Cuint,
(Culong,Ref{Cuint},Ref{Cuint},Ref{Cuint}),
ft_hand... | [
39344,
10117,
62,
1136,
13376,
198,
198,
8818,
10117,
62,
1136,
13376,
7,
701,
62,
28144,
3712,
34,
377,
506,
8,
198,
220,
2033,
259,
40914,
36560,
796,
6524,
90,
34,
28611,
92,
3419,
198,
220,
2033,
600,
87,
36560,
796,
6524,
90,
... | 2.154167 | 240 |
<reponame>BioTurboNick/LocalizationMicroscopy.jl<filename>src/plotlocalizations.jl
"""
localizationsplot(localizations::Vector{Localizations}, color::Symbol)
localizationsplot(localizations::Vector{Vector{Localization}}, colors::Vector{Symbol})
Plot localizations by spatial coordinates. Assumes that the y-coor... | [
27,
7856,
261,
480,
29,
42787,
17483,
2127,
23609,
14,
14565,
1634,
13031,
1416,
11081,
13,
20362,
27,
34345,
29,
10677,
14,
29487,
12001,
4582,
13,
20362,
198,
37811,
198,
220,
220,
220,
1957,
4582,
29487,
7,
12001,
4582,
3712,
38469,
... | 2.977876 | 452 |
<reponame>JuliaGPU/VulkanAbstraction
include("prewrap/bitmasks.jl")
include("prewrap/types.jl")
include("prewrap/handles.jl")
include("prewrap/pointers.jl")
include("prewrap/conversions.jl")
include("prewrap/errors.jl")
include("prewrap/spirv.jl")
| [
27,
7856,
261,
480,
29,
16980,
544,
33346,
14,
53,
31263,
4826,
301,
7861,
198,
17256,
7203,
79,
1809,
2416,
14,
2545,
5356,
591,
13,
20362,
4943,
198,
17256,
7203,
79,
1809,
2416,
14,
19199,
13,
20362,
4943,
198,
17256,
7203,
79,
1... | 2.556701 | 97 |
<filename>src/streamers.jl
struct StreamerInfo
streamer
dependencies
end
struct Streamers
tkey::TKey
refs::Dict{Int32, Any}
elements::Vector{StreamerInfo}
end
Base.length(s::Streamers) = length(s.elements)
function Base.show(io::IO, s::Streamers)
for streamer_info in s.elements
printl... | [
27,
34345,
29,
10677,
14,
5532,
364,
13,
20362,
198,
7249,
13860,
263,
12360,
198,
220,
220,
220,
4269,
263,
198,
220,
220,
220,
20086,
198,
437,
198,
198,
7249,
13860,
364,
198,
220,
220,
220,
256,
2539,
3712,
51,
9218,
198,
220,
... | 2.331488 | 7,768 |
<reponame>adolgert/comorbid.jl<gh_stars>0
using comorbid
using Test
@testset "comorbid.jl" begin
@test isapprox(exact_burden_term([.1], [.8], [true]), [.08])
end
| [
27,
7856,
261,
480,
29,
324,
349,
70,
861,
14,
785,
273,
14065,
13,
20362,
27,
456,
62,
30783,
29,
15,
198,
3500,
401,
273,
14065,
198,
3500,
6208,
198,
198,
31,
9288,
2617,
366,
785,
273,
14065,
13,
20362,
1,
2221,
198,
220,
22... | 2.168831 | 77 |
module mcrosssection1
using FinEtools
using FinEtoolsFlexBeams.CrossSectionModule
using Test
function test()
cs = CrossSectionModule.CrossSectionCircle(s -> 5.9910, s -> [0.0, 0.0, 1.0])
par = cs.parameters(0.0)
for (c, r) in zip((par.A, par.J, par.I1, par.I2, par.I3), (112.75829799164978, 2023.564982469215... | [
21412,
285,
19692,
5458,
16,
198,
3500,
4463,
36,
31391,
198,
3500,
4463,
36,
31391,
37,
2588,
3856,
4105,
13,
21544,
16375,
26796,
198,
3500,
6208,
198,
8818,
1332,
3419,
198,
220,
220,
220,
50115,
796,
6372,
16375,
26796,
13,
21544,
... | 2.238095 | 861 |
<gh_stars>1-10
# ------------------------------------- #
# Helper methods used in generated code #
# ------------------------------------- #
# inlined function to extract a single variable. If `x` is a vector then
# extract a single element. If `x` is a Matrix then extract one column of the
# matrix
@inline _unpack_va... | [
27,
456,
62,
30783,
29,
16,
12,
940,
198,
2,
20368,
30934,
1303,
198,
2,
5053,
525,
5050,
973,
287,
7560,
2438,
1303,
198,
2,
20368,
30934,
1303,
198,
198,
2,
287,
10837,
2163,
284,
7925,
257,
2060,
7885,
13,
1002,
4600,
87,
63,
... | 2.269814 | 4,088 |
#=
# 5. Viscous flow about a moving body
In this notebook we will demonstrate the simulation of a moving body. It is straightforward
to set up a moving body. The main caveat is that the simulation is slower,
because the integrator must update the operators continuously throughout the simulation.
We will demonstrate th... | [
2,
28,
198,
2,
642,
13,
569,
2304,
516,
5202,
546,
257,
3867,
1767,
198,
818,
428,
20922,
356,
481,
10176,
262,
18640,
286,
257,
3867,
1767,
13,
632,
318,
15836,
198,
1462,
900,
510,
257,
3867,
1767,
13,
383,
1388,
36531,
318,
326... | 2.927759 | 1,495 |
<reponame>ettersi/TreeTensors.jl
module TreeTensors
importall Base
using Tensors
include("Trees.jl")
include("ModeTrees.jl")
# Tree tensor networks
export TreeTensor
typealias TensorDict{T} Dict{Tree,Tensor{T}}
immutable TreeTensor{T}
mtree::AbstractModeTree
tensors::TensorDict{T}
end
(::Type{TreeTensor{T}}... | [
27,
7856,
261,
480,
29,
316,
1010,
72,
14,
27660,
51,
641,
669,
13,
20362,
198,
21412,
12200,
51,
641,
669,
198,
198,
11748,
439,
7308,
198,
3500,
40280,
669,
198,
17256,
7203,
51,
6037,
13,
20362,
4943,
198,
17256,
7203,
19076,
51,... | 1.924831 | 7,410 |
<gh_stars>1-10
function simple_lp(bridged)
MOI.empty!(bridged)
@test MOI.is_empty(bridged)
# add 10 variables - only diagonal is relevant
X = MOI.add_variables(bridged, 2)
# add sdp constraints - only ensuring positivenesse of the diagonal
vov = MOI.VectorOfVariables(X)
c1 = MOI.add_cons... | [
27,
456,
62,
30783,
29,
16,
12,
940,
198,
8818,
2829,
62,
34431,
7,
10236,
2004,
8,
628,
220,
220,
220,
13070,
40,
13,
28920,
0,
7,
10236,
2004,
8,
198,
220,
220,
220,
2488,
9288,
13070,
40,
13,
271,
62,
28920,
7,
10236,
2004,
... | 2.02681 | 6,714 |
#=
# MNIST
We begin by importing the required packages.
We load MNIST via the MLDatasets.jl package.
=#
import Makie
import CairoMakie
import MLDatasets
import Flux
import RestrictedBoltzmannMachines as RBMs
import ConvolutionalRBMs as ConvRBMs
using Statistics: mean, var, std
using ValueHistories: MVHistory
using Ra... | [
2,
28,
198,
2,
29060,
8808,
198,
198,
1135,
2221,
416,
33332,
262,
2672,
10392,
13,
198,
1135,
3440,
29060,
8808,
2884,
262,
10373,
27354,
292,
1039,
13,
20362,
5301,
13,
198,
46249,
198,
198,
11748,
15841,
494,
198,
11748,
23732,
44,... | 2.389574 | 1,784 |
<reponame>JuliaML/LearnBase.jl
"""
Return the gradient of the learnable parameters w.r.t. some objective
"""
function grad end
function grad! end
"""
Proximal operator of a function (https://en.wikipedia.org/wiki/Proximal_operator)
"""
function prox end
function prox! end
"""
Anything that takes an input and performs... | [
27,
7856,
261,
480,
29,
16980,
544,
5805,
14,
20238,
14881,
13,
20362,
198,
37811,
198,
13615,
262,
31312,
286,
262,
2193,
540,
10007,
266,
13,
81,
13,
83,
13,
617,
9432,
198,
37811,
198,
8818,
3915,
886,
198,
8818,
3915,
0,
886,
... | 2.843212 | 1,569 |
# should be your environment and have for nothing to do whith the speed_test
cd("/Users/frank/.julia/dev/TypeDBClient_Speed")
using Pkg
Pkg.activate(".")
using TypeDBClient
using TypeDBClient: CoreSession, CoreClient
using UUIDs
g = TypeDBClient
client = g.CoreClient("127.0.0.1",1729)
Optional{T} = Union{Nothing,T}
... | [
2,
815,
307,
534,
2858,
290,
423,
329,
2147,
284,
466,
348,
342,
262,
2866,
62,
9288,
198,
10210,
7203,
14,
14490,
14,
8310,
962,
11757,
73,
43640,
14,
7959,
14,
6030,
11012,
11792,
62,
22785,
4943,
198,
3500,
350,
10025,
198,
47,
... | 2.68259 | 942 |
abstract type AbstractLearningUpdate end
function update!(model,
lu::T,
opt,
s_t::Array{Array{AF, 1}, 1},
a_t::Array{<:Integer, 1},
s_tp1::Array{Array{AF, 1}, 1},
r::Array{AF, 1},
terminal,
... | [
198,
397,
8709,
2099,
27741,
41730,
10260,
886,
628,
198,
8818,
4296,
0,
7,
19849,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
300,
84,
3712,
51,
11,
198,
220,
220,
220,
220,
220,
220... | 1.932168 | 2,860 |
<filename>src/features.jl<gh_stars>1-10
function asymmetry(df, id, x, y)
result = DataFrame(TrackID = Int[], len = Int[], asymmetry = Float64[])
@inbounds for n = minimum(df[!, id]):maximum(df[!, id])
m = extract(df, n, id, [x, y])
T = Matrix{Float64}(undef, 2, 2)
mean_x = @views mean(m[... | [
27,
34345,
29,
10677,
14,
40890,
13,
20362,
27,
456,
62,
30783,
29,
16,
12,
940,
198,
8818,
30372,
11973,
7,
7568,
11,
4686,
11,
2124,
11,
331,
8,
198,
220,
220,
220,
1255,
796,
6060,
19778,
7,
24802,
2389,
796,
2558,
58,
4357,
... | 1.692609 | 1,786 |
<filename>src/StringAndArray/CombinationsOf2Arrays.jl
"""
generate_array(
array_1::Array{Int64},
array_2::Array{Int64},
array_merged::Array{Int64},
i::Int64,
j::Int64,
k::Int64,
l::Int64,
size::Int64,
flag::Bool,
result::Array{Any,1}
... | [
27,
34345,
29,
10677,
14,
10100,
1870,
19182,
14,
20575,
7352,
5189,
17,
3163,
20477,
13,
20362,
198,
37811,
198,
220,
220,
220,
7716,
62,
18747,
7,
198,
220,
220,
220,
220,
220,
220,
220,
7177,
62,
16,
3712,
19182,
90,
5317,
2414,
... | 1.892399 | 2,026 |
include("eggify.jl")
using Metatheory.Library
using Metatheory.EGraphs.Schedulers
mult_t = commutative_monoid(:(*), 1)
plus_t = commutative_monoid(:(+), 0)
minus_t = @theory begin
a - a => 0
a + (-b) => a - b
end
mulplus_t = @theory begin
0 * a => 0
a * 0 => 0
a * (b + c) == ((a * b) + (a * c))
a + (b... | [
17256,
7203,
33856,
1958,
13,
20362,
4943,
198,
3500,
3395,
26221,
652,
13,
23377,
198,
3500,
3395,
26221,
652,
13,
7156,
1470,
82,
13,
50,
1740,
377,
364,
198,
198,
16680,
62,
83,
796,
725,
315,
876,
62,
2144,
1868,
7,
37498,
9,
... | 2.235653 | 819 |
<filename>docs/make.jl
using Documenter, StingerGraphs
makedocs()
deploydocs(
julia = "nightly",
repo = "github.com/stingergraph/StingerGraphs.jl.git"
)
| [
27,
34345,
29,
31628,
14,
15883,
13,
20362,
198,
3500,
16854,
263,
11,
520,
3889,
37065,
82,
198,
198,
76,
4335,
420,
82,
3419,
198,
198,
2934,
1420,
31628,
7,
198,
220,
220,
220,
474,
43640,
796,
366,
3847,
306,
1600,
198,
220,
2... | 2.362319 | 69 |
using RCall
function load_R_library(libname)
reval("library($libname)")
end
| [
3500,
13987,
439,
198,
198,
8818,
3440,
62,
49,
62,
32016,
7,
8019,
3672,
8,
198,
220,
220,
220,
302,
2100,
7203,
32016,
16763,
8019,
3672,
8,
4943,
198,
437,
198
] | 2.612903 | 31 |
struct DBFGS{T1,T2,T3} <: QuasiNewton{T1}
approx::T1
theta::T2
P::T3
end
DBFGS(approx) = DBFGS(approx, 0.2, nothing)
DBFGS(; inverse = true, theta = 0.2) = DBFGS(inverse ? Inverse() : Direct(), theta, nothing)
hasprecon(::DBFGS{<:Any,<:Any,<:Nothing}) = NoPrecon()
hasprecon(::DBFGS{<:Any,<:Any,<:Any}) = Has... | [
7249,
20137,
37,
14313,
90,
51,
16,
11,
51,
17,
11,
51,
18,
92,
1279,
25,
2264,
17053,
3791,
1122,
90,
51,
16,
92,
198,
220,
220,
220,
5561,
3712,
51,
16,
198,
220,
220,
220,
262,
8326,
3712,
51,
17,
198,
220,
220,
220,
350,
... | 1.850142 | 1,408 |
<reponame>rtwalker/StochasticPrograms.jl
"""
SynchronousExecution
Functor object for using synchronous execution in a progressive-hedging algorithm (assuming multiple Julia cores are available). Create by supplying a [`Synchronous`](@ref) object through `execution` in the `ProgressiveHedgingSolver` factory functio... | [
27,
7856,
261,
480,
29,
81,
4246,
20949,
14,
1273,
5374,
3477,
15167,
82,
13,
20362,
198,
37811,
198,
220,
220,
220,
16065,
11413,
516,
23002,
1009,
198,
198,
24629,
2715,
2134,
329,
1262,
18305,
516,
9706,
287,
257,
10393,
12,
704,
... | 1.986287 | 2,917 |
<reponame>hervasa2/SolidStateDetectors.jl
function simulate_waveforms( mcevents::TypedTables.Table, sim::Simulation{T},
output_dir::AbstractString,
output_base_name::AbstractString = "generated_waveforms";
chunk_n_physics_events::In... | [
27,
7856,
261,
480,
29,
372,
11017,
64,
17,
14,
46933,
9012,
47504,
669,
13,
20362,
198,
8818,
29308,
62,
19204,
23914,
7,
285,
344,
85,
658,
3712,
31467,
276,
51,
2977,
13,
10962,
11,
985,
3712,
8890,
1741,
90,
51,
5512,
198,
220... | 1.951299 | 924 |
<filename>src/CoolPkg.jl
module CoolPkg
export add2
"""
add2(a, b)
Adds two numbers together...
* `a`: A number
* `b`: Another number
"""
add2(a, b) = a + b
end
| [
27,
34345,
29,
10677,
14,
34530,
47,
10025,
13,
20362,
198,
21412,
15226,
47,
10025,
198,
198,
39344,
751,
17,
198,
198,
37811,
198,
220,
220,
220,
751,
17,
7,
64,
11,
275,
8,
220,
220,
220,
220,
198,
198,
46245,
734,
3146,
1978,
... | 2.175 | 80 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.