content stringlengths 6 1.03M | input_ids listlengths 4 535k | ratio_char_token float64 0.68 8.61 | token_count int64 4 535k |
|---|---|---|---|
<reponame>matzipan/AstroBase.jl<filename>test/ephemerides.jl
using Test
import AstroBase
using AstroTime: Epoch, TDBEpoch, SECONDS_PER_DAY, j2000, seconds, value, julian_twopart
using AstroBase.Ephemerides
using AstroBase.Bodies
using AstroBase.Constants: astronomical_unit
using ERFA
using LinearAlgebra: norm
using SP... | [
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... | 1.688678 | 5,467 |
<reponame>guo-yong-zhi/Luxor.jl<filename>test/animation-test.jl
#!/usr/bin/env julia
using Luxor
using Test
using Colors
using Random
Random.seed!(42)
demomovie = Movie(400, 400, "test", 0:359)
function backdrop(scene, framenumber)
background("black")
end
function frame(scene, framenumber)
sethue(Colors... | [
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2... | 2.407378 | 1,274 |
<filename>src/completely_factorized_vfhmm.jl
# Completely factorized variational inference
using NumericExtensions
const Ο΅ = 1.0e-64
# update variational paramter `ΞΈ` based on eq. (9a), (9b)
function _updateE!(fhmm::FHMM,
Y::AbstractMatrix,
ΞΈ::Array{Float64, 3}, # shape (K, M,... | [
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24... | 1.659184 | 3,653 |
# This file is a part of JuliaFEM.
# License is MIT: see https://github.com/JuliaFEM/Mortar3D.jl/blob/master/LICENSE
using Base.Test
using Mortar3D: project_from_surface_to_plane, project_from_plane_to_surface
@testset "project from surface to plane" begin
p = [1.0, 1.0, 3.0]
x0 = [0.0, 0.0, 0.0]
n0 = [0.... | [
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... | 1.896985 | 398 |
module KMeans
using StatsBase
function initialize(colors, fixed, n)
y = sample(1:size(colors)[2], n)
x = sample(1:size(colors)[3], n)
means = zeros(size(colors)[1], n)
for i in 1:n
means[:, i] = colors[:, y[i], x[i]]
end
return [fixed means]
end
function assign_clusters!(colors, me... | [
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2124... | 2.243976 | 664 |
<gh_stars>10-100
@testset "floydwarshall" begin
A = load_matrix_network("all_shortest_paths_example")
#A.nzval += abs(minimum(A)) + 1 # remove negative edges
nzvals = nonzeros(A)
val = abs(minimum(A)) + 1
for ii=1:length(nzvals)
nzvals[ii] += val
end
m = size(A,1)
D2 = zeros(Flo... | [
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... | 1.795389 | 347 |
# Use baremodule to shave off a few KB from the serialized `.ji` file
baremodule SPIRV_Cross_jll
using Base
using Base: UUID
import JLLWrappers
JLLWrappers.@generate_main_file_header("SPIRV_Cross")
JLLWrappers.@generate_main_file("SPIRV_Cross", UUID("b5475fc2-85c9-5de8-8430-71c9b732ec36"))
end # module SPIRV_Cross_jl... | [
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... | 2.458015 | 131 |
""" Tensor networks via the operad of undirected wiring diagrams.
"""
module TensorNetworks
export RelationDiagram, @tensor_network, parse_tensor_network,
contract_tensor_network, @contract_tensors_with, gen_tensor_notation
using MLStyle: @match
using ...CategoricalAlgebra.CSets
using ...WiringDiagrams.UndirectedWi... | [
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2... | 2.757364 | 3,429 |
# Collection of protected function for GP
"""Protected division"""
pdiv(x, y, undef=10e6) = ifelse(y==0 , x+undef , div(x,y))
"""Protected exponential"""
pexp(x, undef=10e15) = ifelse(x>=32, x+undef , exp(x))
"""Protected natural log"""
plog(x, undef=10e6) = ifelse(x==0 , -undef , log(abs(x)))
"""Protected sq... | [
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... | 2.022857 | 350 |
<reponame>JohannesNakayama/CellularAutomata.jl
using LightGraphs
using GraphPlot
using Plots
using GraphRecipes
g = LightGraphs.grid([20, 1], periodic=true)
n_start = copy(nv(g))
for v in 1:n_start
add_vertex!(g)
add_edge!(g, n_start + v, v)
end
n_new = copy(nv(g))
for v in (n_start + 1):n_new
add_edge!(g... | [
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4... | 2.205882 | 204 |
@testset "307.range-sum-query-mutable.jl" begin
@testset "307 case 1" begin
ST = SegmentTree([1, 3, 5])
@test sum_range(ST, 1, 3) == 9
update!(ST, 2, 2)
@test sum_range(ST, 1, 3) == 8
end
end
| [
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27... | 1.933333 | 120 |
abstract type AbstractMarker end
struct Marker{N} <: AbstractMarker end
const Marker0 = Marker{0} ; const Κβ = Marker0
const Marker1 = Marker{1} ; const Κβ = Marker1
const Marker2 = Marker{2} ; const Κβ = Marker2
const Marker3 = Marker{3} ; const Κβ = Marker3
string(::Type{Marker0}) = "Κβ" ; ... | [
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1... | 2.007916 | 379 |
import Adapt
using OffsetArrays
# Adapt an offset CuArray to work nicely with CUDA kernels.
Adapt.adapt_structure(to, x::OffsetArray) = OffsetArray(Adapt.adapt(to, parent(x)), x.offsets)
# Need to adapt SubArray indices as well.
# See: https://github.com/JuliaGPU/Adapt.jl/issues/16
#Adapt.adapt_structure(to, A::SubAr... | [
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2... | 2.796053 | 152 |
<reponame>jacobusmmsmit/queue-embedded-games-EURSS
using Random
using StatsBase
using Distributions
using StatsPlots
using DataFrames
using ShiftedArrays
using Measures
Random.seed!(123)
begin
arrival_rates = (0.1, 0.1)
job_size = 10
private_service_rate(job_size) = 1 / (1.5 * job_size)
public_service... | [
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35439,... | 2.154333 | 5,216 |
# BinDeps integration
using BinDeps
import BinDeps: PackageManager, can_use, package_available, available_version,
libdir, generate_steps, LibraryDependency, provider, provides, pkg_name
update_once = true
struct RPM <: PackageManager
package
end
can_use(::Type{RPM}) = iswindows()
function package_available... | [
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... | 2.549565 | 575 |
module SimpleDelimitedFiles
@doc raw"""
readdlm(source, [delim::Char = '\t'], [T::Type = Float64])
Read a delimited numerical matrix from the file named `source`. The elements
of the matrix should be of type `T`. The end of line delimiter is taken as `\n`.
For simple use cases, this is a more performant equivale... | [
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1... | 2.450847 | 590 |
using Documenter, UiPathOrchestratorJobSchedulingPlanCreate1
makedocs(;
modules=[UiPathOrchestratorJobSchedulingPlanCreate1],
format=Documenter.HTML(),
pages=[
"Home" => "index.md",
"Getting Started" => "getting_started.md",
"Function" => "function.md",
"Excel" => "excel.md"... | [
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16... | 2.323741 | 278 |
export getSensMat
"""
S = function getSensMat(...)
constructs sensitivity matrix.
WARNING: For large-scale problems this will be prohibively
expensive. Use with caution
Inputs:
sigma - model
pFor - forward problems
Examples:
S = getSensMat(sigma, pFor) # single pFor
Some methods of g... | [
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... | 1.93495 | 1,691 |
# `UnionFinder{T <: Integer}` is a graph containing a constant number of nodes
# which allows for union-find operations. All nodes are indexed by an integer
# of type `T` which is between 1 an the number of internal nodes.
mutable struct UnionFinder{T <: Integer}
sizes :: Vector{T}
parents :: Vector{T}
# `... | [
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51... | 2.489284 | 1,633 |
# Code snippets from https://github.com/JuliaLang/Compat.jl
function _compat(ex::Expr)
if VERSION < v"0.7.0-DEV.2562"
if ex.head == :call && ex.args[1] == :finalizer
ex.args[2], ex.args[3] = ex.args[3], ex.args[2]
end
end
return Expr(ex.head, map(_compat, ex.args)...)
end
_com... | [
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... | 2.2 | 240 |
<filename>src/SparseSymmetricTensors.jl
module SparseSymmetricTensors
#=------------------------------------------------------------------------------
Main File of the COOTensor class, Only type definitions and Constructor
functions should be placed here.
----------------------------------------------------------... | [
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... | 2.845924 | 4,355 |
<reponame>jinjianzhou/Perturbo.jl
using HDF5
ElecHam(fn::String) = ElecHam( (h5open(fn,"r") do fid
bdata = load_basic_data(fid)
#
num_wann = bdata[:num_wann]
w_center = bdata[:wannier_center_cryst]
lattice = bdata[:at]
rdim = bdata[:kc_dim]
#
hopping = load_electron_wannier(fid, num_wann)
w... | [
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494... | 2.046267 | 951 |
using LinearAlgebra
using Fermi.DIIS
# Functions specific to DF-CTF
#
############ PREPROCESSING ################
"""
Fermi.CoupledCluster.RCCSD{T}(Alg::CTF)
Compute a RCCSD wave function using the Compiled time factorization algorithm (CTF)
"""
function BCCD{T}(guess::BCCD{Tb},Alg::DFCTF) where { T <: AbstractFl... | [
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198,
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... | 1.496855 | 3,657 |
<reponame>shashi/Homework.jl<filename>src/encode.jl
encode(metadata, x) = x
encode(metadata, x::Dict) =
[(encode(metadata, k), encode(metadata, v)) for (k, v) in x] |> sort
encode(metadata, x::Union{AbstractArray, Tuple}) =
map(y -> encode(metadata, y), x)
function encode(metadata, x::Real)
precision = ... | [
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7... | 2.47479 | 238 |
<reponame>xinkai-zhou/MixedModelsBLB.jl<filename>test/test-blb.jl<gh_stars>1-10
module BLB
# Test that the BLB functions work properly
using MixedModelsBLB, Random, Distributions, DataFrames, StatsModels, Ipopt, Test, StableRNGs, WiSER
# Simulate a dataset
# Ξ²true = ones(3); ΟΒ²true = 1; Ξ£true = [1.0 0; 0 1.0]
rng =... | [
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6... | 2.025565 | 2,347 |
<filename>scripts/RotaLegacy.jl
using Pkg
Pkg.activate(Base.current_project())
cd("..")
# This environment is for development #############################################################
#begin
# using PyPlot
# using LaTeXStrings
# #matplotlib.use("TkAgg") #To use in Pluto.jl
# rc("text", usetex = true)
... | [
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291... | 1.721705 | 3,331 |
<gh_stars>10-100
# This file is auto-generated by AWSMetadata.jl
using AWS
using AWS.AWSServices: appconfig
using AWS.Compat
using AWS.UUIDs
"""
create_application(name)
create_application(name, params::Dict{String,<:Any})
An application in AppConfig is a logical unit of code that provides capabilities for yo... | [
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35... | 2.878504 | 17,696 |
using Markowitz
using LinearAlgebra
import Plots
assets = [ "Bonds - US Government"
"Bonds - US Corporate"
"Bonds - International"
"Bonds - High Yield"
"Bonds - Bank Loans"
"Bonds - Emerging USD Debt"
"Bonds - Emerging Local Debt"
"Alternativ... | [
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1294,
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1,
198... | 1.831289 | 3,343 |
<filename>examples/CHD_preventative_care.jl
using Logging
using JuMP, Gurobi
using DecisionProgramming
using CSV, DataFrames, PrettyTables
# Setting subproblem specific parameters
const chosen_risk_level = "12%"
# Reading tests' technical performance data (dummy data in this case)
data = CSV.read("CHD_preventative... | [
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... | 2.464156 | 3,027 |
import Pkg
Pkg.instantiate()
Pkg.add("Statistics")
Pkg.add("DataFrames")
Pkg.add("CSV")
Pkg.add("StatsBase")
using DataFrames, CSV, Statistics, StatsBase
data = CSV.read("../externals/Core.Math.Data/data/Pejcic_318.csv", copycols = true)
# describe(data[:ATT])
for col in eachcol(data);
if (eltype(col[2]) <: Re... | [
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2... | 2.407895 | 152 |
<gh_stars>1-10
module KRPC
using Sockets
using ProtoBuf
using LightXML
import MacroTools
include("proto/krpc.jl")
abstract type Request{S, P, R} end
"""
KRPC stream listener. See `add_stream` for usage information.
"""
struct Listener{T}
streams::Dict{UInt64, Pair{Request, Int}}
connection::T
current_value
chann... | [
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4943... | 2.756522 | 460 |
function velToConductMod(v,mid,a,b)
d = (b-a)./2.0;
dinv = 10;
tt = dinv.*(mid - v);
t = (d.*(tanh.(tt)+1) + a);
dt = -(dinv*d)*(sech.(tt)).^2;
dt = (2.0-v./mid).*dt + (-1./mid).*t;
t = t.*(2.0-v./mid);
return vec(t),spdiagm(vec(dt))
end
| [
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41... | 1.69863 | 146 |
<filename>test/MOI_wrapper_cached.jl
using MathOptInterface
const MOI = MathOptInterface
const MOIB = MOI.Bridges
const MOIT = MOI.Test
const MOIU = MOI.Utilities
MOIU.@model(ModelData,
(),
(MOI.EqualTo, MOI.GreaterThan, MOI.LessThan, MOI.Interval),
(MOI.SecondOrderCone,),
... | [
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796... | 2.135608 | 1,143 |
#"""
#"""
#function writevtk(
# trian::Grid, filebase; celldata=Dict(), nodaldata=Dict())
# write_vtk_file(trian,filebase,celldata=celldata,nodaldata=nodaldata)
#end
"""
writevtk(reffe::LagrangianRefFE,filebase)
"""
function writevtk(reffe::LagrangianRefFE,filebase)
p = get_polytope(reffe)
writevtk(p,fileb... | [
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2,
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2,
220,
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62,
85,
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62... | 2.037453 | 5,794 |
using MathOptInterface
const MOI = MathOptInterface
const VI = MOI.VariableIndex
const CI = MOI.ConstraintIndex
const SVF = MOI.SingleVariable
function var_bounds(o::SCIP.Optimizer, vi::VI)
return MOI.get(o, MOI.ConstraintSet(), CI{SVF,MOI.Interval{Float64}}(vi.value))
end
function chg_bounds(o::SCIP.Optimizer, ... | [
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13,... | 2.216329 | 11,954 |
<gh_stars>1-10
using ConvDiffMIPDECO
using jInv.Mesh
using jInv.ForwardShare
using jInv.InverseSolve
using jInv.LinearSolvers
using MUMPSjInv
using MAT
using LinearAlgebra
using SparseArrays
# filename= "2DmodelLShaped.mat"
filename= joinpath(dirname(pathof(ConvDiffMIPDECO)),"..","examples","Sources3D")
file = matread... | [
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13,... | 2.048919 | 1,758 |
<reponame>sadit/TextSearch.jl
# This file is a part of TextSearch.jl
#####
using CategoricalArrays
export EntropyWeighting
"""
EntropyWeighting(; smooth=0.0, lowerweight=0.0, weights=:balance)
Entropy weighting uses the empirical entropy of the vocabulary along classes to produce a notion of importance for each ... | [
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... | 2.240442 | 1,177 |
<reponame>odow/SetProg.jl
using SetProg
using Documenter, Literate
const EXAMPLES_DIR = joinpath(@__DIR__, "src", "examples")
const OUTPUT_DIR = joinpath(@__DIR__, "src/generated")
const EXAMPLES = readdir(EXAMPLES_DIR)
for example in EXAMPLES
example_filepath = joinpath(EXAMPLES_DIR, example)
Literate.mar... | [
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366... | 2.191837 | 490 |
# Parts of this code were taken / derived from Graphs.jl. See LICENSE for
# licensing details.
type FloydWarshallState{T}<:AbstractPathState
dists::Matrix{T}
parents::Matrix{Int}
end
doc"""Uses the [Floyd-Warshall algorithm](http://en.wikipedia.org/wiki/FloydβWarshall_algorithm)
to compute shortest paths bet... | [
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... | 2.055644 | 1,258 |
<reponame>lightbearer88/Batman.jl<filename>src/toolbelt/mcmcsampler.jl
using Random
mutable struct MCMCSample
pdf
end
function _transition_model(x)
randn()*0.1 + x
end
function _acceptance(v, vnew, x, y)
if (x<-1) || (x>1) || (y<-1) || (y>1)
return false
end
if vnew < v
return true
else
accep... | [
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198,
220,
... | 2.315789 | 209 |
export IntRangeDict, save
import Base: push!, in, show, foreach, collect, getindex, read, write
type IntRangeSpan{K<:Integer, V}
lv::K
rv::K
data::Vector{V}
end
immutable IntRangeDict{K<:Integer, V}
data::Vector{IntRangeSpan{K, V}}
IntRangeDict(data::Vector=[]) = new(data)
IntRangeDict(io::IO... | [
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92,
... | 1.84935 | 2,383 |
#****************************************************************************
# Molecular Dynamics Potentials (MDP)
# CESMIX-MIT Project
#
# Contributing authors: <NAME> (<EMAIL>, <EMAIL>)
#************************************************... | [
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2,
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220,
... | 2.305804 | 448 |
using PlanktonIndividuals, Serialization
grid = RectilinearGrid(size = (16, 16, 1), x = (0,32), y = (0,32), z = (0,-32))
model = PlanktonModel(CPU(), grid)
function tot_mass(nut, g)
mass = zeros(g.Nx, g.Ny, g.Nz)
for i in 1:g.Nx
for j in 1:g.Ny
for k in 1:g.Nz
mass[i,j,k]... | [
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15,
11,
2624,
828,
331,
796,
357,
15,
11,
2624,
828,
1976,
796,... | 1.887755 | 784 |
using Dash
app = dash()
app.layout = html_div(id="outer-div") do
html_div("A div", id="first-inner-div"),
html_br(),
html_div("Another div", id="second-inner-div")
end
run_server(app)
| [
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1... | 2.444444 | 81 |
<filename>src/Mesh/elementiterator.jl
"""
struct ElementIterator{E,M}
Return an iterator for iterating over all elements of type `E` on objects of
type `M`.
"""
struct ElementIterator{E,M} <: AbstractVector{E}
mesh::M
end
ElementIterator{E}(mesh::M) where {E,M <: AbstractMesh} = ElementIterator{E,M}(mesh)
E... | [
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... | 2.140878 | 1,732 |
expert = haskey(ENV, "QUEST_EXPERT") && ENV["QUEST_EXPERT"] == "1" ? true : false
# Execute commands to build QuEST
function _auxBuild(makePrecision::Int,precision::String,isWindows::Bool)::Nothing
mkdir("build"*precision)
cd("build"*precision)
isWindows ? wait(run(`cmake -DPRECISION=$makePrecision .. -G... | [
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... | 2.373684 | 380 |
"""
Force (re-)evaluation of the objective value at `x`.
Returns `f(x)` and stores the value in `obj.F`
"""
function value!!(obj::AbstractObjective, x)
obj.f_calls .+= 1
copyto!(obj.x_f, x)
obj.F = obj.f(x)
value(obj)
end
"""
Evaluates the objective value at `x`.
Returns `f(x)`, but does *not* store t... | [
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... | 2.143613 | 2,646 |
# ------------------ CMAX agent ------------------------
struct MountainCarCMAXAgent
mountaincar::MountainCar
cmax_planner::MountainCarCMAXPlanner
end
function run(agent::MountainCarCMAXAgent; max_steps = 1e5)
state = init(agent.mountaincar; cont = true)
num_steps = 0
while !checkGoal(agent.mounta... | [
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<gh_stars>0
const DEBUG = true
"""
@dbgassert(expr, [message]) -> true
If `DEBUG = true`, throws an error with an optionally specified `message` if the
given code expression evaluates to `false`, otherwise returns `true`.
"""
macro dbgassert(expr, msgs...)
if DEBUG
msg_str = isempty(msgs) ? string(exp... | [
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... | 2.697329 | 337 |
<filename>src/Wavg.jl
Wavg(a,w) = sum(w.*a)/sum(w) | [
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64,
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7,
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8
] | 1.666667 | 30 |
<gh_stars>1-10
"""
Arrow
`enum` type that can take three values: `In`, `Out`, or `Neither`, representing a directionality
associated with an index, i.e. the index leg is directed into or out of a given tensor
"""
@enum Arrow In=-1 Out=1 Neither=0
"""
-(dir::Arrow)
Reverse direction of a directed `Arrow`.
"""
f... | [
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... | 2.960938 | 128 |
<filename>julia-package/brainflow/src/c_interfaces.jl<gh_stars>100-1000
using Pkg
using Pkg.Artifacts
using SHA
using Tar
# we have an issue with unpack(), so wrote a custom download
# https://discourse.julialang.org/t/unable-to-automatically-install-artifact/51984/2
function download_brainflow_artifact()
url = b... | [
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29,
73,
43640,
12,
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14,
27825,
11125,
14,
10677,
14,
66,
62,
3849,
32186,
13,
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27,
456,
62,
30783,
29,
3064,
12,
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198,
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350,
10025,
198,
3500,
350,
10025,
13,
8001,
37199,
198,
3500,
25630,
198,
350... | 2.848525 | 746 |
quad_params = (m=0.5,
J=SMatrix{3,3}(Diagonal([0.0023, 0.0023, 0.004])),
Jinv=SMatrix{3,3}(Diagonal(1.0./[0.0023, 0.0023, 0.004])),
gravity=SVector(0,0,-9.81),
motor_dist=0.1750,
kf=1.0,
km=0.0245)
include("quaternions.jl")
function quadroto... | [
47003,
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357,
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20,
11,
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... | 1.875212 | 1,178 |
<reponame>akels/Verificatum.jl
using Test
using ShuffleProofs: marshal_s_Gq, unmarshal, decode
s_Gq = "ModPGroup(safe-prime modulus=2*order+1. order bit-length = 511)::00000000020100000020636f6d2e766572696669636174756d2e61726974686d2e4d6f645047726f757000000000040100000041009a91c3b704e382e0c772fa7cf0e5d6363edc53d156e84... | [
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584... | 2.012531 | 399 |
push!(ARGS, "../../input_files/dynamic/basin_refinement/8GPa/12n.dat")
include("../../Basin.jl")
pop!(ARGS)
| [
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1... | 2.22449 | 49 |
<reponame>UnofficialJuliaMirror/Transducers.jl-28d57a85-8fef-5791-bfe6-a80928e7c999<filename>examples/empty_result_handling.jl
# # Empty result handling
#
# Transducible processes such as [`foldl`](@ref) try to
# do the right thing even when `init` is not given, _if_ the given
# binary operation `step` is supported by
... | [
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10... | 2.049738 | 1,528 |
<reponame>JuliaBinaryWrappers/basiclu_jll.jl
# Autogenerated wrapper script for basiclu_jll for x86_64-w64-mingw32
export libbasiclu
using CompilerSupportLibraries_jll
JLLWrappers.@generate_wrapper_header("basiclu")
JLLWrappers.@declare_library_product(libbasiclu, "libbasiclu.dll")
function __init__()
JLLWrappers.... | [
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... | 2.391489 | 235 |
<reponame>RemoteSensingTools/vSmartMOM.jl
module SolarModel
using ..vSmartMOM # For locating default solar T
using DocStringExtensions # For simplifying docstring
using DelimitedFiles # For easily reading in solar spectrum
using Interpolations # For interpolating solar spectr... | [
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... | 2.347613 | 2,241 |
"""
InflationConstant <: InflationFunction
InflationConstant(c)
MetodologΓa de inflaciΓ³n constante con valor interanual `c`.
"""
struct InflationConstant <: InflationFunction
c::Float32
end
InflationConstant() = InflationConstant(1)
# MΓ©todo para obtener la variaciΓ³n interanual constante igual a c
fun... | [
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2... | 2.669841 | 315 |
<filename>src/units.jl
export SI
"Constants written in the same system of physical units."
struct UnitSystem
name::Symbol
"Boltzmann constant."
k::Float
"Atomic mass unit."
AMU::Float
UnitSystem(; name::Symbol, k::Number, AMU::Number) = new(name, k, AMU)
end
"Constants written in SI units"
co... | [
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... | 2.36612 | 183 |
<gh_stars>10-100
# ---
# title: 230. Kth Smallest Element in a BST
# id: problem230
# author: Indigo
# date: 2021-06-03
# difficulty: Medium
# categories: Binary Search, Tree
# link: <https://leetcode.com/problems/kth-smallest-element-in-a-bst/description/>
# hidden: true
# ---
#
# Given a binary search tree, write a ... | [
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12,
3070... | 2.100143 | 699 |
export AbstractGroup
"""
AbstractGroup
Abstract type for abstract groups. Currently the only subtype is `FiniteGroup`.
"""
abstract type AbstractGroup end
| [
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198... | 3.926829 | 41 |
<gh_stars>10-100
function generate_satisfy(domain::Domain, state::State,
domain_type::Symbol, state_type::Symbol)
satisfy_def = quote
function check(::$domain_type, state::$state_type, term::Const)
return getfield(state, term.name)
end
function check(dom... | [
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220,
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220,
220,
220,
220,
220,
220,
220,... | 2.055671 | 1,922 |
<reponame>sethaxen/PlutoStaticHTMLDemo.jl<gh_stars>0
using PlutoStaticHTMLDemo
using Test
@testset "PlutoStaticHTMLDemo.jl" begin
# Write your tests here.
end
| [
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45442... | 2.603175 | 63 |
<filename>src/cubehelix.jl
# following
# https://www.mrao.cam.ac.uk/~dag/CUBEHELIX/
#
# published in
# <NAME>., 2011, `A colour scheme for the display of astronomical intensity images', Bulletin of the Astronomical Society of India, 39, 289.
# (2011BASI...39..289G at ADS.)
import Colors: RGB
# parameters: N n... | [
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34,
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10426,
14,
198,
2,
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2,
31... | 2.129542 | 633 |
using JuMP
using ParameterJuMP
using Polyhedra
mutable struct Conitope{T, VT <: AbstractVector{T}, D<:Polyhedra.FullDim}
d::D
points::Vector{VT}
factory::Union{Nothing, JuMP.OptimizerFactory}
model::Union{Nothing, JuMP.Model}
z::Union{Nothing, Vector{ParameterJuMP.ParameterRef}}
t_0::Union{Noth... | [
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90,
51,
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360,
27,
25,
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430,
13,
13295,
29271,
... | 2.11383 | 940 |
using Nemo
# _Fast_ composed sums and composed products of polynomials,
# using the algorithm described in:
# "Fast computation of special resultants"
# by Bostan, Flajolet, Salvy, and Schost
# derivative of polynomial
derivative(c::Vector) = c[2:end] .* (1:length(c)-1)
function polyinv(coeffs::Vector, n... | [
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201,
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62,
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286,
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220,
201,
198,
2,
366,
22968,
29964,
286,
2041,
1255,
1187,
... | 2.091096 | 1,460 |
using Makie
cmp = :pu_or
# sheen = 32.0f0
sheen = 16.0f0
camera_translation = (0.0, 0.0, 0.0)
camera_rotation = (0.0, 0.2, 0.0)
# xlims = [-0.1, 1.1, -0.1, 1.1, -0.1, 1.1, -0.1, 1.1]
# ylims = [-0.1, 1.1, 1.1, -0.1, 1.1, 1.1, -0.1, -0.1]
# zlims = [ 0.6, 0.6, 0.6, 0.6, -0.6, -0.6, -0.6, -0.6]
lim = FRect3... | [
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15,
13,
15,
11,
657,
13,
15,
11,
657,
13,
... | 1.905355 | 1,606 |
using LayoutPointers, ArrayInterface, Aqua, Test
@testset "LayoutPointers.jl" begin
Aqua.test_all(LayoutPointers)
println("Grouped Strided Pointers")
@time @testset "Grouped Strided Pointers" begin
M, K, N = 4, 5, 6
A = Matrix{Float64}(undef, M, K);
B = Matrix{Float64}(undef, K, N);
C = Matrix... | [
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220,
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... | 2.167254 | 1,704 |
#=
Given an array of time intervals (start, end) for classroom lectures (possibly overlapping), find the minimum number of rooms required.
For example, given [(30, 75), (0, 50), (60, 150)], you should return 2.
=#
function number_of_rooms_required(lecture_intervals::Array{Tuple{Int64,Int64},1})
if length(lecture_... | [
2,
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11,
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11,
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828,
357,
15,
11,
... | 2.38642 | 810 |
"""
Some bit manipulation routines.
"""
module Bits
"""
hyperfloor(x)
Heighest power of 2 that is smaller than `x`.
# Example
```jldoctest
julia> bitstring(UInt8(123))
"01111011"
julia> bitstring(UInt8(GraphIdx.Bits.hyperfloor(123)))
"01000000"
```
"""
hyperfloor(x::Integer) =
Base._prevpow2(x)
end
| [
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318,
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198,
198,
2,
17934... | 2.40458 | 131 |
function get_jacobian(obj::DenseWeightedEvaluation)
return jcall(obj, "getJacobian", RealMatrix, ())
end
function get_point(obj::DenseWeightedEvaluation)
return jcall(obj, "getPoint", RealVector, ())
end
function get_residuals(obj::DenseWeightedEvaluation)
return jcall(obj, "getResiduals", RealVector, ())... | [
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4... | 2.739496 | 119 |
<filename>test/dependecy_parsing/arc_eager/gold_state.jl
@testset "GoldState" begin
tree = build_gold_tree()
config = build_configuration()
system = ArcEagerSystem()
state = GoldState(tree, config, system)
head_in_stack = DependencyParser.DependencyParsing.ArcEager.HEAD_IN_STACK
head_in_buffer = Dependency... | [
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220,
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220,
... | 2.743842 | 406 |
module IntegralVerification
using Statistics
using Random
export testIntegral, testExpressions
function doStatTest(testvalues, precision; verbose = true)
output = Statistics.mean(testvalues),Statistics.std(testvalues)
if verbose
println("mean = ",output[1])
println("std = ", output[2])
e... | [
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8,
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220,
... | 2.563667 | 589 |
module EMLmultiply
using DanaTypes
using DotPlusInheritance
using Reexport
@reexport using ...types.EMLtypes
import EMLtypes.length
include("multiply/Multiply.jl")
end | [
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5805... | 2.836066 | 61 |
using Parameters
import Base: length
using SparseArrays
abstract type AbstractPhase end
export AbstractPhase
abstract type IdealPhase <: AbstractPhase end
export IdealPhase
struct EmptyPhase <: AbstractPhase end
export EmptyPhase
include("Calculators/Ratevec.jl")
include("Calculators/Thermovec.jl")
include("Reacti... | [
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... | 2.355799 | 3,190 |
# Hyper parameters
Οs = [1.0, 0.0, 0.0]
Οp = 1e-7
# POβ mean and variance of observations fom WOA18
ΞΌDIPobs3D, ΟΒ²DIPobs3D = WorldOceanAtlasTools.fit_to_grid(grd, "POβ")
ΞΌDIPobs, ΟΒ²DIPobs = ΞΌDIPobs3D[iwet], ΟΒ²DIPobs3D[iwet]
ΞΌx = (ΞΌDIPobs, missing, missing)
ΟΒ²x = (ΟΒ²DIPobs, missing, missing)
Ξ΄convert(x) = @. exp(x)
cs = ... | [
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34... | 1.965517 | 522 |
<gh_stars>0
module BesselFunctions
using AndExport
# using GSL
# using SpecialFunctions
# @xport sphj(n, x) = GSL.sf_bessel_jl(n, x)
# @xport sphn(n, x) = GSL.sf_bessel_yl(n, x)
# @xport sphhβ(n, x) = sphj(n, x) + im*sphn(n, x)
# @xport sphhβ(n, x) = sphj(n, x) - im*sphn(n, x)
# # @xport sphj(n, x) = sqrt(Ο/2x) *... | [
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8,
... | 1.573301 | 1,221 |
export cgnr
mutable struct CGNR{vecT,T,Tsparse} <: AbstractLinearSolver
S
SHWS
reg::Regularization
cl::vecT
rl::vecT
zl::vecT
pl::vecT
vl::vecT
xl::vecT
Ξ±l::T
Ξ²l::T
ΞΆl::T
weights::vecT
enforceReal::Bool
enforcePositive::Bool
sparseTrafo::Tsparse
iterations::Int64
relTol::Float64
z... | [
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220,
... | 2.048239 | 3,918 |
"""
isinside(chart, point) -> Bool
Returns true is the given point is in the image of the given chart, false otherwise.
"""
function isinside(cell, point)
u = carttobary(cell, point)
T = eltype(u)
tol = eps(T) * 1e3
w = one(T)
for i in 1:dimension(cell)
0+tol < u[i] < 1-tol || return false
w -=... | [
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11... | 2.414079 | 483 |
module Simulacoes
#ExercΓcios 39 a 43 estΓ£o inclusos no package#
#data structure
include("./Data.jl")
export Data
include("./utotal.jl")
export utotal
include("./initial-point.jl")
export initial_point
include("./upair.jl")
export upair
include("./minimg.jl")
export minimg
... | [
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... | 2.324759 | 311 |
<gh_stars>1-10
using Test
using MultivariateMoments
@testset "VectorizedHermitianMatrix" begin
Q = MultivariateMoments.vectorized_hermitian_matrix(Int, (i, j) -> i == j ? i * 2 - 1 : 2 - im, 2, 1:2)
@test Q[1:2, 1:2] isa Matrix{Complex{Int}}
@test Q[1:2, 1:2] == [1 2 - im; 2 + im 3]
@test square_getind... | [
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220,
220,
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7854,
42524,
29252,
658,
13,
31364... | 1.900482 | 1,246 |
function runModels( root::String, modelsDict::Dict, dfd::DataFrame=NA)
res = OrderedDict()
q = Symbol[]
cnt=0
for (k,v) in OrderedDict(m=>modelsDict[m][:raneff] for m in [:ipen,:idolocc,:iocc])
for r in v push!(q,k); push!(q,r); cnt=cnt+1 end
end
x=reshape(q, (2,cnt))
ml = permutedims(x,... | [
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220,
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198,
... | 1.90402 | 26,193 |
<filename>src/generator/documentation.jl
function print_documentation(io::IO, node::ExprNode, indent, options, members::Bool=false; kwargs...)
print_documentation(io, node.cursor, indent, options, members; kwargs...)
end
const ESCAPE_PATTERN = r"""(\$|\\|"(?=""))"""
function print_documentation(io::IO, cursor::C... | [
27,
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29,
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28,
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26,
479,
86,
22... | 2.262215 | 5,526 |
#
# Using LabelledArrays source code and types to
# "pass through" all properties to an underlying
# LArray or SLArray. This allows us to write
# a new abstract type that *functions*
# like a LabelledArray type!
#
# All code in this file was copied, and modified
# from LabelledArrays.jl source code. Their license is... | [
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514,
284,
3551,
220,
198,
2,
257,
649,
12531,... | 2.922399 | 2,268 |
abstract type Sim end
struct POMDPSim <: Sim
simulator::Simulator
pomdp::POMDP
policy::Policy
updater::Updater
initial_belief::Any
initial_state::Any
metadata::Dict{Symbol}
end
problem(sim::POMDPSim) = sim.pomdp
struct MDPSim <: Sim
simulator::Simulator
mdp::MDP
policy::Policy... | [
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220,
220,
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2450,
3712,
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198... | 2.203583 | 3,851 |
<gh_stars>0
@assert begin
msh=FinVolMesh{2}()
newptind1=add_point!(msh, [0.0, 0.0])
newptind2=add_point!(msh, [1.0, 0.0])
newptind3=add_point!(msh, [0.0, 1.0])
@assert newptind1==1
@assert newptind2==2
@assert newptind3==3
newfaceind1=add_face!(msh, [newptind1, newptind2])
newface... | [
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220,
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521,
16,
28,
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62,
4122,
0,
7,
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71,
11,
685,
15,
13,
15,
11,
6... | 1.989529 | 382 |
import DataFrames
# using InvertedIndices
import Luxor
import Base
import CSV
import Printf
import Combinatorics
import Statistics
import StatsBase
import Random
import Distributions
import KernelDensity
import SparseArrays
import LinearAlgebra, LinearAlgebra.I
include(joinpath(WATERFALL_DIR,"src","Figure","Color"... | [
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14881,
198,
... | 2.898402 | 876 |
module AbstractLayers
export AbstractLayer, AL, forward, backward, parms, βparms, idims, odims
include("./tensor_dt.jl")
using .TensorDT: Tensor
## ======================================================================
## The Layer Abstraction
## =====================================================================... | [
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7,
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4943,
... | 3.698198 | 222 |
<gh_stars>0
module NumericalRelativity
# Tensor
#
# (array of functions, coordinates, (indices up or down)) -> tensor
# T[-1,-1] means lower-lower indices and T[1,1] means up-up
# You should be able to find the Rank of tensors
# Contracting is possible? At least with a function contract
using Symbolics
function part... | [
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273,
198,
2,
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12,
16,
... | 2.969163 | 227 |
using Test, PreallocationTools, ForwardDiff, LinearAlgebra
#test for downsizing cache
randmat = rand(5, 3)
sto = similar(randmat)
stod = dualcache(sto)
function claytonsample!(sto, Ο, Ξ±; randmat=randmat)
sto = get_tmp(sto, Ο)
sto .= randmat
Ο == 0 && return sto
n = size(sto, 1)
for i in 1:n
... | [
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7,
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8,
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301,
3... | 2.142012 | 676 |
<reponame>trthatcher/DiscriminantAnalysis.jl<filename>test/runtests.jl
using Test, LinearAlgebra, Statistics, DiscriminantAnalysis
const DA = DiscriminantAnalysis
function random_centroids(T::Type{<:AbstractFloat}, m::Int, p::Int)
M = zeros(T, p, m)
for k = 1:m
M[:, k] = T[rand() < 0.5 ? -2k : 2k for ... | [
27,
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11,
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3036,
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32750,
198,
198,
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... | 1.969251 | 748 |
<gh_stars>0
module Symbolic
using ModelingToolkit
import ModelingToolkit.Constant
struct Lag{T,N}
variable::T
lag::N
end
#you can't register a constructor so we have to introduce this intermediate.
lag(x,i) = Lag(x,i)
@register lag(x,i)
@variables t x y z
function apply(expr::Equation, data)
rhs = expr... | [
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220,
220,
220,
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3712,
51,
198,
220,
... | 2.574924 | 1,635 |
<filename>src/RKHS/RKHS.jl
# Methods for fitting and querying under the RKHS regularization framework.
# for univariate inputs.
function constructkernelmatrix( X,
ΞΈ)::Matrix{Float64} where T
#
K = Matrix{Float64}(undef,length(X),length(X))
constructkernelmatrix!(K,X,ΞΈ)
... | [
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2,
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13,
198,
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5678,
33885,
6759... | 1.720969 | 5,451 |
<filename>docs/src/examples/emulsion/fluid_species_large-freq.jl<gh_stars>0
# This example is used to generate plots in the paper, "Reflection from a multi-species material and its transmitted effective wavenumber." Proc. R. Soc. (2018): 20170864.
# Everything related to ploting has been commented so that this example... | [
27,
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29,
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14,
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14,
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2,
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318,
973,
284,
7716,
21528,
287,
262,
3348,
11,
366,
... | 2.475947 | 977 |
<filename>time/SPECTplan.jl
# SPECTplan.jl
using SPECTrecon: SPECTplan
using BenchmarkTools: @btime
using MATLAB
function call_SPECTplan_matlab(mpath, mumap, psfs, dy)
mat"""
addpath($mpath)
SPECTplan_matlab($mumap, $psfs, $dy);
"""
end
function SPECTplan_time()
T = Float32
nx = 64
ny =... | [
27,
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29,
2435,
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4102,
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25,
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65,
2435,
198,
3500,
36775,
48780,
198... | 2.216724 | 586 |
# Minimal example that demonstrates robot-robot and robot-target range limits.
using SubmodularMaximization
using POMDPs
using MCTS
using PyPlot
using Base.Iterators
using Printf
using Statistics
close()
steps = 100
horizon = SubmodularMaximization.default_horizon
show_observations = false
sparse = false
num_parti... | [
2,
1855,
4402,
1672,
326,
15687,
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12,
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290,
9379,
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934,
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320,
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198,
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350,
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6322,
82,
198,
3500,
337,
4177,
50,
198,
3500,
9485,
43328,
198,
3... | 2.152515 | 1,849 |
<filename>test/runtests.jl
include("corey.jl")
include("grid.jl")
include("theis.jl")
include("thiem.jl")
include("transport.jl")
| [
27,
34345,
29,
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14,
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3558,
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13,
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4943,
198,
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7203,
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271,
13,
20362,
4943,
198,
17256,
7203,
400,
26597,
13,
20362,
4943,
19... | 2.6 | 50 |
# Run tests locally
using Pkg
Pkg.activate(joinpath(@__DIR__, ".."))
# Pkg.test("Algames")
Pkg.activate(joinpath(@__DIR__, "../test"))
Pkg.activate(joinpath(@__DIR__, "../docs"))
# Pkg.add(Pkg.PackageSpec(;name="RobotDynamics", version="0.3.1"))
# Pkg.add(Pkg.PackageSpec(;name="TrajectoryOptimization", version="0.4.... | [
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4943,
628,
198,
47,
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13,
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7,
22179,
... | 2.373541 | 257 |
<filename>test/push_to_non_input.jl
using Reactive
function standard_push_test(non_input::Signal)
m = map(x->2x, non_input)
pval = number()
push!(non_input, pval)
step()
@fact value(non_input) --> pval
@fact value(m) --> 2pval
end
facts("Push to non-input nodes") do
a = Signal(number(); ... | [
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62,
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220,
220,
220,
285,
796,
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7,
87,
3784,... | 1.873079 | 1,497 |
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