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using OrdinaryDiffEq | |
using DiffEqFlux | |
using Flux | |
using Plots | |
#=function f(du, u, p, t) | |
du[1] = -p[1]*u[1] + p[2]*u[2]*u[3] | |
du[2] = p[1]*u[1] - p[2]*u[2]*u[3] - p[3]*u[2]*u[2] | |
du[3] = p[3]*u[2]*u[2] | |
end=# |
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using OrdinaryDiffEq | |
using Sundials | |
using Plots | |
using BenchmarkTools | |
const N = 40 # Number of heated units | |
const Cu = N == 1 ? [2e7] : (ones(N) .+ range(0,1.348,length=N))*1e7 # "Heat capacity of heated units"; | |
const Cd = 2e6*N # "Heat capacity of distribution circuit"; | |
const Gh = 200 # "Thermal conductance of heating elements"; |
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# Add this first: https://github.com/ranjanan/MonteCarloIntegration.jl | |
using Cubature | |
# using Cuba | |
using MonteCarloIntegration | |
# include("vegas.jl") | |
function koopman(g,prob,u0,p,args...;kwargs...) | |
g(solve(remake(prob,u0=u0,p=p),args...;kwargs...)) | |
end |
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import DecisionTree | |
using ScikitLearn | |
@sk_import tree: DecisionTreeClassifier | |
import PyCall: PyObject | |
using JuliaDB | |
function load_data() | |
isfile("balance.data") || | |
download("https://archive.ics.uci.edu/ml/machine-learning-databases/balance-scale/balance-scale.data", |
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Running tests: | |
From worker 3: testing Distributions.CategoricalDirectSampler | |
From worker 3: testing Distributions.AliasTable | |
From worker 3: testing Distributions.BinomialGeomSampler | |
From worker 3: testing Distributions.BinomialTPESampler | |
From worker 3: testing Distributions.BinomialPolySampler | |
From worker 2: [Discrete] | |
From worker 5: testing Distributions.Categorical(K=2, p=[0.5,0.5]) | |
From worker 2: ------------ | |
From worker 2: testing Bernoulli() |
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First, | |
git clone https://github.com/ranjanan/MT-Workloads.git | |
Then cd into ALS/ | |
On the julia REPL, | |
include("ALS.jl") | |
main() |