{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ContinuousDP: Stochastic Optimal Growth Model" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "using QuantEcon\n", "using BasisMatrices\n", "using ContinuousDPs\n", "using Random\n", "using PythonPlot\n", "const plt = PythonPlot.pyplot;" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "alpha = 0.4\n", "beta = 0.96\n", "mu = 0\n", "sigma = 0.1;" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "f(s, x) = log(x)\n", "g(s, x, e) = (s - x)^alpha * e;" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "shock_size = 250\n", "shocks = exp.(mu .+ sigma * randn(shock_size))\n", "weights = fill(1/shock_size, shock_size);" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 dimensional Basis on the hypercube formed by (1.0e-5,) × (4.0,).\n", "Basis families are Cheb\n" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_max = 4.\n", "n = 30\n", "s_min, s_max = 1e-5, grid_max\n", "basis = Basis(ChebParams(n, s_min, s_max))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "x_lb(s) = s_min\n", "x_ub(s) = s;" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "ab = alpha * beta\n", "c1 = log(1 - ab) / (1 - beta)\n", "c2 = (mu + alpha * log(ab)) / (1 - alpha)\n", "c3 = 1 / (1 - beta)\n", "c4 = 1 / (1 - ab)\n", "\n", "# True optimal policy\n", "c_star(y) = (1 - alpha * beta) * y\n", "\n", "# True value function\n", "v_star(y) = c1 + c2 * (c3 - c4) + c4 * log(y);" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "cdp = ContinuousDP(f, g, beta, shocks, weights, x_lb, x_ub, basis);" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MethodInstance for ContinuousDP(::typeof(f), ::typeof(g), ::Float64, ::Vector{Float64}, ::Vector{Float64}, ::typeof(x_lb), ::typeof(x_ub), ::Basis{1, Tuple{ChebParams{Float64}}})\n", " from ContinuousDP(\u001b[90mf\u001b[39m::\u001b[1mFunction\u001b[22m, \u001b[90mg\u001b[39m::\u001b[1mFunction\u001b[22m, \u001b[90mdiscount\u001b[39m::\u001b[1mFloat64\u001b[22m, \u001b[90mshocks\u001b[39m::\u001b[1mArray\u001b[22m\u001b[0m{Float64}, \u001b[90mweights\u001b[39m::\u001b[1mVector\u001b[22m\u001b[0m{Float64}, \u001b[90mx_lb\u001b[39m::\u001b[1mFunction\u001b[22m, \u001b[90mx_ub\u001b[39m::\u001b[1mFunction\u001b[22m, \u001b[90mbasis\u001b[39m::\u001b[1mBasis\u001b[22m)\u001b[90m @\u001b[39m \u001b[90mContinuousDPs\u001b[39m \u001b[90m~/Development/ContinuousDPs.jl/src/\u001b[39m\u001b[90m\u001b[4mcdp.jl:118\u001b[24m\u001b[39m\n", "Arguments\n", " #self#\u001b[36m::Type{ContinuousDP}\u001b[39m\n", " f\u001b[36m::Core.Const(Main.f)\u001b[39m\n", " g\u001b[36m::Core.Const(Main.g)\u001b[39m\n", " discount\u001b[36m::Float64\u001b[39m\n", " shocks\u001b[36m::Vector{Float64}\u001b[39m\n", " weights\u001b[36m::Vector{Float64}\u001b[39m\n", " x_lb\u001b[36m::Core.Const(Main.x_lb)\u001b[39m\n", " x_ub\u001b[36m::Core.Const(Main.x_ub)\u001b[39m\n", " basis\u001b[36m::Basis{1, Tuple{ChebParams{Float64}}}\u001b[39m\n", "Locals\n", " cdp\u001b[36m::ContinuousDP{1, Vector{Float64}, Vector{Float64}, typeof(f), typeof(g), typeof(x_lb), typeof(x_ub)}\u001b[39m\n", " interp\u001b[36m::ContinuousDPs.Interp{1, Vector{Float64}, Matrix{Float64}, LinearAlgebra.LU{Float64, Matrix{Float64}, Vector{Int64}}}\u001b[39m\n", "Body\u001b[36m::ContinuousDP{1, Vector{Float64}, Vector{Float64}, typeof(f), typeof(g), typeof(x_lb), typeof(x_ub)}\u001b[39m\n", " (interp = ContinuousDPs.Interp(basis))\n", "\u001b[90m│ \u001b[39m %2 = interp\u001b[36m::Core.PartialStruct(ContinuousDPs.Interp{1, Vector{Float64}, Matrix{Float64}, LinearAlgebra.LU{Float64, Matrix{Float64}, Vector{Int64}}}, Any[Basis{1, Tuple{ChebParams{Float64}}}, Vector{Float64}, Tuple{Vector{Float64}}, Int64, Tuple{Int64}, Tuple{Float64}, Tuple{Float64}, Matrix{Float64}, LinearAlgebra.LU{Float64, Matrix{Float64}, Vector{Int64}}])\u001b[39m\n", "\u001b[90m│ \u001b[39m (cdp = ContinuousDPs.ContinuousDP(f, g, discount, shocks, weights, x_lb, x_ub, %2))\n", "\u001b[90m│ \u001b[39m %4 = cdp\u001b[36m::ContinuousDP{1, Vector{Float64}, Vector{Float64}, typeof(f), typeof(g), typeof(x_lb), typeof(x_ub)}\u001b[39m\n", "\u001b[90m└──\u001b[39m return %4\n", "\n" ] } ], "source": [ "@code_warntype ContinuousDP(f, g, beta, shocks, weights, x_lb, x_ub, basis)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## First test" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "C_star = cdp.interp.Phi \\ v_star.(cdp.interp.S)\n", "Tv = Array{Float64}(undef, cdp.interp.length)\n", "C = copy(C_star)\n", "bellman_operator!(cdp, C, Tv);" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MethodInstance for bellman_operator!(::ContinuousDP{1, Vector{Float64}, Vector{Float64}, typeof(f), typeof(g), typeof(x_lb), typeof(x_ub)}, ::Vector{Float64}, ::Vector{Float64})\n", " from bellman_operator!(\u001b[90mcdp\u001b[39m::\u001b[1mContinuousDP\u001b[22m, \u001b[90mC\u001b[39m::\u001b[1mVector\u001b[22m\u001b[0m{Float64}, \u001b[90mTv\u001b[39m::\u001b[1mVector\u001b[22m\u001b[0m{Float64})\u001b[90m @\u001b[39m \u001b[90mContinuousDPs\u001b[39m \u001b[90m~/Development/ContinuousDPs.jl/src/\u001b[39m\u001b[90m\u001b[4mcdp.jl:379\u001b[24m\u001b[39m\n", "Arguments\n", " #self#\u001b[36m::Core.Const(QuantEcon.bellman_operator!)\u001b[39m\n", " cdp\u001b[36m::ContinuousDP{1, Vector{Float64}, Vector{Float64}, typeof(f), typeof(g), typeof(x_lb), typeof(x_ub)}\u001b[39m\n", " C\u001b[36m::Vector{Float64}\u001b[39m\n", " Tv@_4\u001b[36m::Vector{Float64}\u001b[39m\n", "Locals\n", " Tv@_5\u001b[36m::Vector{Float64}\u001b[39m\n", "Body\u001b[36m::Vector{Float64}\u001b[39m\n", "\u001b[90m1 ─\u001b[39m (Tv@_5 = Tv@_4)\n", "\u001b[90m│ \u001b[39m %2 = ContinuousDPs.s_wise_max!\u001b[36m::Core.Const(ContinuousDPs.s_wise_max!)\u001b[39m\n", "\u001b[90m│ \u001b[39m %3 = Base.getproperty(cdp, :interp)\u001b[91m\u001b[1m::ContinuousDPs.Interp{1, Vector{Float64}}\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %4 = Base.getproperty(%3, :S)\u001b[36m::Vector{Float64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %5 = Tv@_5\u001b[36m::Vector{Float64}\u001b[39m\n", "\u001b[90m│ \u001b[39m (Tv@_5 = (%2)(cdp, %4, C, %5))\n", "\u001b[90m│ \u001b[39m %7 = ContinuousDPs.ldiv!\u001b[36m::Core.Const(LinearAlgebra.ldiv!)\u001b[39m\n", "\u001b[90m│ \u001b[39m %8 = Base.getproperty(cdp, :interp)\u001b[91m\u001b[1m::ContinuousDPs.Interp{1, Vector{Float64}}\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %9 = Base.getproperty(%8, :Phi_lu)\u001b[91m\u001b[1m::LinearAlgebra.Factorization\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %10 = Tv@_5\u001b[36m::Vector{Float64}\u001b[39m\n", "\u001b[90m│ \u001b[39m (%7)(C, %9, %10)\n", "\u001b[90m│ \u001b[39m %12 = C\u001b[36m::Vector{Float64}\u001b[39m\n", "\u001b[90m└──\u001b[39m return %12\n", "\n" ] } ], "source": [ "@code_warntype bellman_operator!(cdp, C, Tv)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "grid_size = 200\n", "grid_y = collect(range(s_min, stop=s_max, length=grid_size))\n", "V_approx = funeval(C, cdp.interp.basis, grid_y);" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "sys:1: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n" ] }, { "data": { 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", "text/plain": [ "Figure()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(9, 5))\n", "ax.set_ylim(-35, -24)\n", "ax.plot(grid_y, V_approx, lw=2, alpha=0.6, label=L\"$Tv^*$\")\n", "ax.plot(grid_y, v_star.(grid_y), lw=2, alpha=0.6, label=L\"$v^*$\")\n", "ax.legend(loc=\"lower right\")\n", "plotshow()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.019175 seconds (692.98 k allocations: 47.283 MiB, 49.70% gc time)\n", " 0.011071 seconds (651.54 k allocations: 44.455 MiB, 20.66% gc time)\n", " 0.011436 seconds (672.26 k allocations: 45.869 MiB, 23.10% gc time)\n" ] } ], "source": [ "@time bellman_operator!(cdp, C, Tv)\n", "@time bellman_operator!(cdp, C, Tv)\n", "@time bellman_operator!(cdp, C, Tv);" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MethodInstance for ContinuousDPs._s_wise_max(::ContinuousDP{1, Vector{Float64}, Vector{Float64}, typeof(f), typeof(g), typeof(x_lb), typeof(x_ub)}, ::Float64, ::Vector{Float64})\n", " from _s_wise_max(\u001b[90mcdp\u001b[39m::\u001b[1mContinuousDP\u001b[22m, \u001b[90ms\u001b[39m, \u001b[90mC\u001b[39m)\u001b[90m @\u001b[39m \u001b[90mContinuousDPs\u001b[39m \u001b[90m~/Development/ContinuousDPs.jl/src/\u001b[39m\u001b[90m\u001b[4mcdp.jl:268\u001b[24m\u001b[39m\n", "Arguments\n", " #self#\u001b[36m::Core.Const(ContinuousDPs._s_wise_max)\u001b[39m\n", " cdp\u001b[36m::ContinuousDP{1, Vector{Float64}, Vector{Float64}, typeof(f), typeof(g), typeof(x_lb), typeof(x_ub)}\u001b[39m\n", " s\u001b[36m::Float64\u001b[39m\n", " C\u001b[36m::Vector{Float64}\u001b[39m\n", "Locals\n", " x\u001b[36m::Float64\u001b[39m\n", " v\u001b[36m::Float64\u001b[39m\n", " res\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n", " objective\u001b[36m::ContinuousDPs.var\"#objective#3\"{ContinuousDP{1, Vector{Float64}, Vector{Float64}, typeof(f), typeof(g), typeof(x_lb), typeof(x_ub)}, Float64, Vector{Float64}, Matrix{Float64}}\u001b[39m\n", " sp\u001b[36m::Matrix{Float64}\u001b[39m\n", "Body\u001b[36m::Tuple{Float64, Float64}\u001b[39m\n", "\u001b[90m1 ─\u001b[39m %1 = Core.apply_type(ContinuousDPs.Array, ContinuousDPs.Float64)\u001b[36m::Core.Const(Array{Float64})\u001b[39m\n", "\u001b[90m│ \u001b[39m %2 = ContinuousDPs.undef\u001b[36m::Core.Const(UndefInitializer())\u001b[39m\n", "\u001b[90m│ \u001b[39m %3 = ContinuousDPs.size\u001b[36m::Core.Const(size)\u001b[39m\n", "\u001b[90m│ \u001b[39m %4 = Base.getproperty(cdp, :shocks)\u001b[36m::Vector{Float64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %5 = (%3)(%4, 1)\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %6 = ContinuousDPs.length\u001b[36m::Core.Const(length)\u001b[39m\n", "\u001b[90m│ \u001b[39m %7 = (%6)(s)\u001b[36m::Core.Const(1)\u001b[39m\n", "\u001b[90m│ \u001b[39m (sp = (%1)(%2, %5, %7))\n", "\u001b[90m│ \u001b[39m %9 = ContinuousDPs.:(var\"#objective#3\")\u001b[36m::Core.Const(ContinuousDPs.var\"#objective#3\")\u001b[39m\n", "\u001b[90m│ \u001b[39m %10 = Core.typeof(cdp)\u001b[36m::Core.Const(ContinuousDP{1, Vector{Float64}, Vector{Float64}, typeof(f), typeof(g), typeof(x_lb), typeof(x_ub)})\u001b[39m\n", "\u001b[90m│ \u001b[39m %11 = Core.typeof(s)\u001b[36m::Core.Const(Float64)\u001b[39m\n", "\u001b[90m│ \u001b[39m %12 = Core.typeof(C)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", "\u001b[90m│ \u001b[39m %13 = sp\u001b[36m::Matrix{Float64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %14 = Core.typeof(%13)\u001b[36m::Core.Const(Matrix{Float64})\u001b[39m\n", "\u001b[90m│ \u001b[39m %15 = Core.apply_type(%9, %10, %11, %12, %14)\u001b[36m::Core.Const(ContinuousDPs.var\"#objective#3\"{ContinuousDP{1, Vector{Float64}, Vector{Float64}, typeof(f), typeof(g), typeof(x_lb), typeof(x_ub)}, Float64, Vector{Float64}, Matrix{Float64}})\u001b[39m\n", "\u001b[90m│ \u001b[39m %16 = sp\u001b[36m::Matrix{Float64}\u001b[39m\n", "\u001b[90m│ \u001b[39m (objective = %new(%15, cdp, s, C, %16))\n", "\u001b[90m│ \u001b[39m %18 = Optim.optimize\u001b[36m::Core.Const(Optim.optimize)\u001b[39m\n", "\u001b[90m│ \u001b[39m %19 = objective\u001b[36m::ContinuousDPs.var\"#objective#3\"{ContinuousDP{1, Vector{Float64}, Vector{Float64}, typeof(f), typeof(g), typeof(x_lb), typeof(x_ub)}, Float64, Vector{Float64}, Matrix{Float64}}\u001b[39m\n", "\u001b[90m│ \u001b[39m %20 = Base.getproperty(cdp, :x_lb)\u001b[36m::Core.Const(Main.x_lb)\u001b[39m\n", "\u001b[90m│ \u001b[39m %21 = (%20)(s)\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %22 = Base.getproperty(cdp, :x_ub)\u001b[36m::Core.Const(Main.x_ub)\u001b[39m\n", "\u001b[90m│ \u001b[39m %23 = (%22)(s)\u001b[36m::Float64\u001b[39m\n", "\u001b[90m│ \u001b[39m (res = (%18)(%19, %21, %23))\n", "\u001b[90m│ \u001b[39m %25 = ContinuousDPs.:-\u001b[36m::Core.Const(-)\u001b[39m\n", "\u001b[90m│ \u001b[39m %26 = res\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %27 = Base.getproperty(%26, :minimum)\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %28 = ContinuousDPs.Float64\u001b[36m::Core.Const(Float64)\u001b[39m\n", "\u001b[90m│ \u001b[39m %29 = Core.typeassert(%27, %28)\u001b[36m::Float64\u001b[39m\n", "\u001b[90m│ \u001b[39m (v = (%25)(%29))\n", "\u001b[90m│ \u001b[39m %31 = res\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %32 = Base.getproperty(%31, :minimizer)\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %33 = ContinuousDPs.Float64\u001b[36m::Core.Const(Float64)\u001b[39m\n", "\u001b[90m│ \u001b[39m (x = Core.typeassert(%32, %33))\n", "\u001b[90m│ \u001b[39m %35 = v\u001b[36m::Float64\u001b[39m\n", "\u001b[90m│ \u001b[39m %36 = x\u001b[36m::Float64\u001b[39m\n", "\u001b[90m│ \u001b[39m %37 = Core.tuple(%35, %36)\u001b[36m::Tuple{Float64, Float64}\u001b[39m\n", "\u001b[90m└──\u001b[39m return %37\n", "\n" ] } ], "source": [ "s = 2.\n", "@code_warntype ContinuousDPs._s_wise_max(cdp, s, C)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.000210 seconds (15.98 k allocations: 1.090 MiB)\n", " 0.002117 seconds (15.98 k allocations: 1.090 MiB, 88.90% gc time)\n", " 0.000218 seconds (15.98 k allocations: 1.090 MiB)\n" ] } ], "source": [ "@time ContinuousDPs._s_wise_max(cdp, s, C)\n", "@time ContinuousDPs._s_wise_max(cdp, s, C)\n", "@time ContinuousDPs._s_wise_max(cdp, s, C);" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "v_init_func(s) = 5 * log(s)\n", "w = v_init_func.(grid_y)\n", "n = 35\n", "\n", "fig, ax = subplots(figsize=(9, 6))\n", "ax.set_ylim(-50, 10)\n", "ax.set_xlim(minimum(grid_y), maximum(grid_y))\n", "lb = \"initial condition\"\n", "jet = ColorMap(\"jet\")\n", "ax.plot(grid_y, w, color=jet(0), lw=2, alpha=0.6, label=lb)\n", "\n", "S = cdp.interp.S\n", "V = v_init_func.(S)\n", "for i in 1:n\n", " C = cdp.interp.Phi \\ V\n", " bellman_operator!(cdp, C, V)\n", " w = funeval(C, cdp.interp.basis, grid_y)\n", " ax.plot(grid_y, w, color=jet(i / n), lw=2, alpha=0.6)\n", "end\n", "\n", "lb = \"true value function\"\n", "ax.plot(grid_y, v_star.(grid_y), \"k-\", lw=2, alpha=0.8, label=lb)\n", "ax.legend.(loc=\"lower right\")\n", "\n", "plotshow()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solve by policy iteration" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Compute iterate 6 with error 2.4868995751603507e-14\n", "Converged in 6 steps\n" ] } ], "source": [ "res = solve(cdp);" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MethodInstance for solve(::ContinuousDP{1, Vector{Float64}, Vector{Float64}, typeof(f), typeof(g), typeof(x_lb), typeof(x_ub)})\n", " from solve(\u001b[90mcdp\u001b[39m::\u001b[1mContinuousDP\u001b[22m\u001b[0m{N, TR, TS}; ...) where {N, TR, TS}\u001b[90m @\u001b[39m \u001b[90mContinuousDPs\u001b[39m \u001b[90m~/Development/ContinuousDPs.jl/src/\u001b[39m\u001b[90m\u001b[4mcdp.jl:581\u001b[24m\u001b[39m\n", "Static Parameters\n", " N = \u001b[36m1\u001b[39m\n", " TR = \u001b[36mVector{Float64}\u001b[39m\n", " TS = \u001b[36mVector{Float64}\u001b[39m\n", "Arguments\n", " #self#\u001b[36m::Core.Const(QuantEcon.solve)\u001b[39m\n", " cdp\u001b[36m::ContinuousDP{1, Vector{Float64}, Vector{Float64}, typeof(f), typeof(g), typeof(x_lb), typeof(x_ub)}\u001b[39m\n", "Body\u001b[36m::ContinuousDPs.CDPSolveResult{PFI, 1, Vector{Float64}, Vector{Float64}}\u001b[39m\n", "\u001b[90m1 ─\u001b[39m %1 = ContinuousDPs.PFI\u001b[36m::Core.Const(PFI)\u001b[39m\n", "\u001b[90m│ \u001b[39m %2 = (#self#)(cdp, %1)\u001b[36m::ContinuousDPs.CDPSolveResult{PFI, 1, Vector{Float64}, Vector{Float64}}\u001b[39m\n", "\u001b[90m└──\u001b[39m return %2\n", "\n" ] } ], "source": [ "@code_warntype solve(cdp)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Compute iterate 6 with error 2.4868995751603507e-14\n", "Converged in 6 steps\n", " 0.124732 seconds (7.42 M allocations: 498.285 MiB, 14.74% gc time)\n", "Compute iterate 6 with error 2.4868995751603507e-14\n", "Converged in 6 steps\n", " 0.126477 seconds (7.42 M allocations: 498.285 MiB, 15.02% gc time)\n", "Compute iterate 6 with error 2.4868995751603507e-14\n", "Converged in 6 steps\n", " 0.121432 seconds (7.42 M allocations: 498.285 MiB, 13.85% gc time)\n" ] } ], "source": [ "@time res = solve(cdp)\n", "@time res = solve(cdp)\n", "@time res = solve(cdp);" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "set_eval_nodes!(res, grid_y);" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MethodInstance for set_eval_nodes!(::ContinuousDPs.CDPSolveResult{PFI, 1, Vector{Float64}, Vector{Float64}}, ::Vector{Float64})\n", " from set_eval_nodes!(\u001b[90mres\u001b[39m::\u001b[1mContinuousDPs.CDPSolveResult\u001b[22m\u001b[0m{Algo, N}, \u001b[90ms_nodes_coord\u001b[39m::\u001b[1mVararg\u001b[22m\u001b[0m{AbstractVector, N}) where {Algo, N}\u001b[90m @\u001b[39m \u001b[90mContinuousDPs\u001b[39m \u001b[90m~/Development/ContinuousDPs.jl/src/\u001b[39m\u001b[90m\u001b[4mcdp.jl:225\u001b[24m\u001b[39m\n", "Static Parameters\n", " Algo = \u001b[36mPFI\u001b[39m\n", " N = \u001b[36m1\u001b[39m\n", "Arguments\n", " #self#\u001b[36m::Core.Const(ContinuousDPs.set_eval_nodes!)\u001b[39m\n", " res\u001b[36m::ContinuousDPs.CDPSolveResult{PFI, 1, Vector{Float64}, Vector{Float64}}\u001b[39m\n", " s_nodes_coord\u001b[36m::Tuple{Vector{Float64}}\u001b[39m\n", "Body\u001b[36m::ContinuousDPs.CDPSolveResult{PFI, 1, Vector{Float64}, Vector{Float64}}\u001b[39m\n", "\u001b[90m1 ─\u001b[39m %1 = ContinuousDPs.set_eval_nodes!(res, s_nodes_coord)\u001b[36m::ContinuousDPs.CDPSolveResult{PFI, 1, Vector{Float64}, Vector{Float64}}\u001b[39m\n", "\u001b[90m└──\u001b[39m return %1\n", "\n" ] } ], "source": [ "@code_warntype set_eval_nodes!(res, grid_y)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.070110 seconds (4.18 M allocations: 285.455 MiB, 18.87% gc time)\n", " 0.062794 seconds (4.18 M allocations: 285.455 MiB, 15.12% gc time)\n", " 0.064014 seconds (4.18 M allocations: 285.455 MiB, 14.91% gc time)\n" ] } ], "source": [ "@time set_eval_nodes!(res, grid_y)\n", "@time set_eval_nodes!(res, grid_y)\n", "@time set_eval_nodes!(res, grid_y);" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = subplots(figsize=(9, 5))\n", "ax.set_ylim(-35, -24)\n", "ax.plot(grid_y, res.V, lw=2, alpha=0.6, label=\"approximate value function\")\n", "ax.plot(grid_y, v_star.(grid_y), lw=2, alpha=0.6, label=\"true value function\")\n", "ax.legend(loc=\"lower right\")\n", "plotshow()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = subplots(figsize=(9, 5))\n", "ax.plot(grid_y, res.X, lw=2, alpha=0.6, label=\"approximate policy function\")\n", "ax.plot(grid_y, c_star.(grid_y), lw=2, alpha=0.6, label=\"true policy function\")\n", "ax.legend(loc=\"lower right\")\n", "plotshow()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = subplots(figsize=(9, 5))\n", "ax.plot(grid_y, res.resid, lw=2, alpha=0.6, label=\"residual\")\n", "plotshow()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "source": [ "## Simulate the controlled Markov process" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s_init = 0.1\n", "ts_length = 100\n", "y = simulate(res, s_init, ts_length)\n", "\n", "fig, ax = subplots(figsize=(9, 6))\n", "ax.plot(1:ts_length, y, lw=2, alpha=0.6, label=\"beta = $(cdp.discount)\" )\n", "ax.legend(loc=\"lower right\")\n", "plotshow()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(9, 6))\n", "\n", "for beta in (0.9, 0.94, 0.98)\n", " cdp.discount = beta\n", " res = solve(cdp, verbose=0)\n", " set_eval_nodes!(res, grid_y)\n", " y = simulate(res, s_init, ts_length)\n", " ax.plot(1:ts_length, y, lw=2, alpha=0.6, label=\"beta = $(cdp.discount)\" )\n", "end\n", "\n", "ax.legend(loc=\"lower right\")\n", "plotshow()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MethodInstance for simulate!(::MersenneTwister, ::Vector{Float64}, ::ContinuousDPs.CDPSolveResult{PFI, 1, Vector{Float64}, Vector{Float64}}, ::Float64)\n", "\u001b[90mrng\u001b[39m::\u001b[1mAbstractRNG\u001b[22m, \u001b[90ms_path\u001b[39m::\u001b[1mTS\u001b[22m, \u001b[90mres\u001b[39m::\u001b[1mContinuousDPs.CDPSolveResult\u001b[22m\u001b[0m{Algo, N, TR, TS}, \u001b[90ms_init\u001b[39m) where {Algo, N, TR, TS<:(VecOrMat)}\u001b[90m @\u001b[39m \u001b[90mContinuousDPs\u001b[39m \u001b[90m~/Development/ContinuousDPs.jl/src/\u001b[39m\u001b[90m\u001b[4mcdp.jl:706\u001b[24m\u001b[39m\n", "Static Parameters\n", " Algo = \u001b[36mPFI\u001b[39m\n", " N = \u001b[36m1\u001b[39m\n", " TR = \u001b[36mVector{Float64}\u001b[39m\n", " TS = \u001b[36mVector{Float64}\u001b[39m\n", "Arguments\n", " #self#\u001b[36m::Core.Const(QuantEcon.simulate!)\u001b[39m\n", " rng\u001b[36m::MersenneTwister\u001b[39m\n", " s_path\u001b[36m::Vector{Float64}\u001b[39m\n", " res\u001b[36m::ContinuousDPs.CDPSolveResult{PFI, 1, Vector{Float64}, Vector{Float64}}\u001b[39m\n", " s_init\u001b[36m::Float64\u001b[39m\n", "Locals\n", " @_6\u001b[33m\u001b[1m::Union{Nothing, Tuple{Int64, Int64}}\u001b[22m\u001b[39m\n", " #18\u001b[36m::ContinuousDPs.var\"#18#19\"\u001b[39m\n", " @_8\u001b[33m\u001b[1m::Union{Nothing, Tuple{Int64, Int64}}\u001b[22m\u001b[39m\n", " e_ind_tail\u001b[36m::Tuple{}\u001b[39m\n", " s_ind_front\u001b[36m::Tuple{}\u001b[39m\n", " X_interp\u001b[36m::Interpoland{Basis{1, Tuple{LinParams{Vector{Float64}}}}, Vector{Float64}, BasisMatrix{Tensor, SparseArrays.SparseMatrixCSC{Float64, Int64}}}\u001b[39m\n", " basis\u001b[36m::Basis{1, Tuple{LinParams{Vector{Float64}}}}\u001b[39m\n", " e_ind\u001b[36m::Vector{Int64}\u001b[39m\n", " r\u001b[36m::Vector{Float64}\u001b[39m\n", " cdf\u001b[36m::Vector{Float64}\u001b[39m\n", " ts_length\u001b[36m::Int64\u001b[39m\n", " t@_17\u001b[36m::Int64\u001b[39m\n", " e\u001b[36m::Float64\u001b[39m\n", " x\u001b[36m::Float64\u001b[39m\n", " s\u001b[36m::Float64\u001b[39m\n", " t@_21\u001b[36m::Int64\u001b[39m\n", "Body\u001b[36m::Vector{Float64}\u001b[39m\n", "\u001b[90m1 ─\u001b[39m Core.NewvarNode(:(@_6))\n", "\u001b[90m│ \u001b[39m Core.NewvarNode(:(#18))\n", "\u001b[90m│ \u001b[39m Core.NewvarNode(:(e_ind_tail))\n", "\u001b[90m│ \u001b[39m Core.NewvarNode(:(s_ind_front))\n", "\u001b[90m│ \u001b[39m Core.NewvarNode(:(X_interp))\n", "\u001b[90m│ \u001b[39m Core.NewvarNode(:(basis))\n", "\u001b[90m│ \u001b[39m %7 = ContinuousDPs.size\u001b[36m::Core.Const(size)\u001b[39m\n", "\u001b[90m│ \u001b[39m %8 = (%7)(s_path)\u001b[36m::Tuple{Int64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %9 = Base.lastindex(%8)\u001b[36m::Core.Const(1)\u001b[39m\n", "\u001b[90m│ \u001b[39m (ts_length = Base.getindex(%8, %9))\n", "\u001b[90m│ \u001b[39m %11 = ContinuousDPs.cumsum\u001b[36m::Core.Const(cumsum)\u001b[39m\n", "\u001b[90m│ \u001b[39m %12 = Base.getproperty(res, :cdp)\u001b[91m\u001b[1m::ContinuousDP{1, Vector{Float64}, Vector{Float64}}\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %13 = Base.getproperty(%12, :weights)\u001b[36m::Vector{Float64}\u001b[39m\n", "\u001b[90m│ \u001b[39m (cdf = (%11)(%13))\n", "\u001b[90m│ \u001b[39m %15 = ContinuousDPs.rand\u001b[36m::Core.Const(rand)\u001b[39m\n", "\u001b[90m│ \u001b[39m %16 = ContinuousDPs.:-\u001b[36m::Core.Const(-)\u001b[39m\n", "\u001b[90m│ \u001b[39m %17 = ts_length\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %18 = (%16)(%17, 1)\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m (r = (%15)(rng, %18))\n", "\u001b[90m│ \u001b[39m %20 = Core.apply_type(ContinuousDPs.Array, ContinuousDPs.Int)\u001b[36m::Core.Const(Array{Int64})\u001b[39m\n", "\u001b[90m│ \u001b[39m %21 = ContinuousDPs.undef\u001b[36m::Core.Const(UndefInitializer())\u001b[39m\n", "\u001b[90m│ \u001b[39m %22 = ContinuousDPs.:-\u001b[36m::Core.Const(-)\u001b[39m\n", "\u001b[90m│ \u001b[39m %23 = ts_length\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %24 = (%22)(%23, 1)\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m (e_ind = (%20)(%21, %24))\n", "\u001b[90m│ \u001b[39m %26 = ContinuousDPs.:(:)\u001b[36m::Core.Const(Colon())\u001b[39m\n", "\u001b[90m│ \u001b[39m %27 = ContinuousDPs.:-\u001b[36m::Core.Const(-)\u001b[39m\n", "\u001b[90m│ \u001b[39m %28 = ts_length\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %29 = (%27)(%28, 1)\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %30 = (%26)(1, %29)\u001b[36m::Core.PartialStruct(UnitRange{Int64}, Any[Core.Const(1), Int64])\u001b[39m\n", "\u001b[90m│ \u001b[39m (@_8 = Base.iterate(%30))\n", "\u001b[90m│ \u001b[39m %32 = @_8\u001b[33m\u001b[1m::Union{Nothing, Tuple{Int64, Int64}}\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %33 = (%32 === nothing)\u001b[36m::Bool\u001b[39m\n", "\u001b[90m│ \u001b[39m %34 = Base.not_int(%33)\u001b[36m::Bool\u001b[39m\n", "\u001b[90m└──\u001b[39m goto #4 if not %34\n", "\u001b[90m2 ┄\u001b[39m %36 = @_8\u001b[36m::Tuple{Int64, Int64}\u001b[39m\n", "\u001b[90m│ \u001b[39m (t@_17 = Core.getfield(%36, 1))\n", "\u001b[90m│ \u001b[39m %38 = Core.getfield(%36, 2)\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %39 = ContinuousDPs.:+\u001b[36m::Core.Const(+)\u001b[39m\n", "\u001b[90m│ \u001b[39m %40 = ContinuousDPs.searchsortedlast\u001b[36m::Core.Const(searchsortedlast)\u001b[39m\n", "\u001b[90m│ \u001b[39m %41 = cdf\u001b[36m::Vector{Float64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %42 = r\u001b[36m::Vector{Float64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %43 = t@_17\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %44 = Base.getindex(%42, %43)\u001b[36m::Float64\u001b[39m\n", "\u001b[90m│ \u001b[39m %45 = (%40)(%41, %44)\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %46 = (%39)(%45, 1)\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %47 = e_ind\u001b[36m::Vector{Int64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %48 = t@_17\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m Base.setindex!(%47, %46, %48)\n", "\u001b[90m│ \u001b[39m (@_8 = Base.iterate(%30, %38))\n", "\u001b[90m│ \u001b[39m %51 = @_8\u001b[33m\u001b[1m::Union{Nothing, Tuple{Int64, Int64}}\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %52 = (%51 === nothing)\u001b[36m::Bool\u001b[39m\n", "\u001b[90m│ \u001b[39m %53 = Base.not_int(%52)\u001b[36m::Bool\u001b[39m\n", "\u001b[90m└──\u001b[39m goto #4 if not %53\n", "\u001b[90m3 ─\u001b[39m goto #2\n", "\u001b[90m4 ┄\u001b[39m %56 = ContinuousDPs.Basis\u001b[36m::Core.Const(Basis)\u001b[39m\n", "\u001b[90m│ \u001b[39m %57 = ContinuousDPs.map\u001b[36m::Core.Const(map)\u001b[39m\n", "\u001b[90m│ \u001b[39m %58 = ContinuousDPs.LinParams\u001b[36m::Core.Const(LinParams)\u001b[39m\n", "\u001b[90m│ \u001b[39m %59 = Base.getproperty(res, :eval_nodes_coord)\u001b[36m::Tuple{Vector{Float64}}\u001b[39m\n", "\u001b[90m│ \u001b[39m %60 = ContinuousDPs.ntuple\u001b[36m::Core.Const(ntuple)\u001b[39m\n", "\u001b[90m│ \u001b[39m %61 = ContinuousDPs.:(var\"#18#19\")\u001b[36m::Core.Const(ContinuousDPs.var\"#18#19\")\u001b[39m\n", "\u001b[90m│ \u001b[39m (#18 = %new(%61))\n", "\u001b[90m│ \u001b[39m %63 = #18\u001b[36m::Core.Const(ContinuousDPs.var\"#18#19\"())\u001b[39m\n", "\u001b[90m│ \u001b[39m %64 = $(Expr(:static_parameter, 2))\u001b[36m::Core.Const(1)\u001b[39m\n", "\u001b[90m│ \u001b[39m %65 = (%60)(%63, %64)\u001b[36m::Core.Const((0,))\u001b[39m\n", "\u001b[90m│ \u001b[39m %66 = (%57)(%58, %59, %65)\u001b[36m::Tuple{LinParams{Vector{Float64}}}\u001b[39m\n", "\u001b[90m│ \u001b[39m (basis = (%56)(%66))\n", "\u001b[90m│ \u001b[39m %68 = ContinuousDPs.Interpoland\u001b[36m::Core.Const(Interpoland)\u001b[39m\n", "\u001b[90m│ \u001b[39m %69 = basis\u001b[36m::Basis{1, Tuple{LinParams{Vector{Float64}}}}\u001b[39m\n", "\u001b[90m│ \u001b[39m %70 = Base.getproperty(res, :X)\u001b[36m::Vector{Float64}\u001b[39m\n", "\u001b[90m│ \u001b[39m (X_interp = (%68)(%69, %70))\n", "\u001b[90m│ \u001b[39m %72 = Base.front\u001b[36m::Core.Const(Base.front)\u001b[39m\n", "\u001b[90m│ \u001b[39m %73 = ContinuousDPs.axes\u001b[36m::Core.Const(axes)\u001b[39m\n", "\u001b[90m│ \u001b[39m %74 = (%73)(s_path)\u001b[36m::Tuple{Base.OneTo{Int64}}\u001b[39m\n", "\u001b[90m│ \u001b[39m (s_ind_front = (%72)(%74))\n", "\u001b[90m│ \u001b[39m %76 = Base.tail\u001b[36m::Core.Const(Base.tail)\u001b[39m\n", "\u001b[90m│ \u001b[39m %77 = ContinuousDPs.axes\u001b[36m::Core.Const(axes)\u001b[39m\n", "\u001b[90m│ \u001b[39m %78 = Base.getproperty(res, :cdp)\u001b[91m\u001b[1m::ContinuousDP{1, Vector{Float64}, Vector{Float64}}\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %79 = Base.getproperty(%78, :shocks)\u001b[36m::Vector{Float64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %80 = (%77)(%79)\u001b[36m::Tuple{Base.OneTo{Int64}}\u001b[39m\n", "\u001b[90m│ \u001b[39m (e_ind_tail = (%76)(%80))\n", "\u001b[90m│ \u001b[39m %82 = s_ind_front\u001b[36m::Core.Const(())\u001b[39m\n", "\u001b[90m│ \u001b[39m %83 = Core.tuple(1)\u001b[36m::Core.Const((1,))\u001b[39m\n", "\u001b[90m│ \u001b[39m %84 = Core._apply_iterate(Base.iterate, Core.tuple, %82, %83)\u001b[36m::Core.Const((1,))\u001b[39m\n", "\u001b[90m│ \u001b[39m %85 = Core.tuple(s_path, s_init)\u001b[36m::Tuple{Vector{Float64}, Float64}\u001b[39m\n", "\u001b[90m│ \u001b[39m Core._apply_iterate(Base.iterate, Base.setindex!, %85, %84)\n", "\u001b[90m│ \u001b[39m %87 = ContinuousDPs.:(:)\u001b[36m::Core.Const(Colon())\u001b[39m\n", "\u001b[90m│ \u001b[39m %88 = ContinuousDPs.:-\u001b[36m::Core.Const(-)\u001b[39m\n", "\u001b[90m│ \u001b[39m %89 = ts_length\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %90 = (%88)(%89, 1)\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %91 = (%87)(1, %90)\u001b[36m::Core.PartialStruct(UnitRange{Int64}, Any[Core.Const(1), Int64])\u001b[39m\n", "\u001b[90m│ \u001b[39m (@_6 = Base.iterate(%91))\n", "\u001b[90m│ \u001b[39m %93 = @_6\u001b[33m\u001b[1m::Union{Nothing, Tuple{Int64, Int64}}\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %94 = (%93 === nothing)\u001b[36m::Bool\u001b[39m\n", "\u001b[90m│ \u001b[39m %95 = Base.not_int(%94)\u001b[36m::Bool\u001b[39m\n", "\u001b[90m└──\u001b[39m goto #7 if not %95\n", "\u001b[90m5 ┄\u001b[39m %97 = @_6\u001b[36m::Tuple{Int64, Int64}\u001b[39m\n", "\u001b[90m│ \u001b[39m (t@_21 = Core.getfield(%97, 1))\n", "\u001b[90m│ \u001b[39m %99 = Core.getfield(%97, 2)\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %100 = s_ind_front\u001b[36m::Core.Const(())\u001b[39m\n", "\u001b[90m│ \u001b[39m %101 = t@_21\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %102 = Core.tuple(%101)\u001b[36m::Tuple{Int64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %103 = Core._apply_iterate(Base.iterate, Core.tuple, %100, %102)\u001b[36m::Tuple{Int64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %104 = Core.tuple(s_path)\u001b[36m::Tuple{Vector{Float64}}\u001b[39m\n", "\u001b[90m│ \u001b[39m (s = Core._apply_iterate(Base.iterate, Base.getindex, %104, %103))\n", "\u001b[90m│ \u001b[39m %106 = X_interp\u001b[36m::Interpoland{Basis{1, Tuple{LinParams{Vector{Float64}}}}, Vector{Float64}, BasisMatrix{Tensor, SparseArrays.SparseMatrixCSC{Float64, Int64}}}\u001b[39m\n", "\u001b[90m│ \u001b[39m %107 = s\u001b[36m::Float64\u001b[39m\n", "\u001b[90m│ \u001b[39m (x = (%106)(%107))\n", "\u001b[90m│ \u001b[39m %109 = e_ind\u001b[36m::Vector{Int64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %110 = t@_21\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %111 = Base.getindex(%109, %110)\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %112 = Core.tuple(%111)\u001b[36m::Tuple{Int64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %113 = e_ind_tail\u001b[36m::Core.Const(())\u001b[39m\n", "\u001b[90m│ \u001b[39m %114 = Core._apply_iterate(Base.iterate, Core.tuple, %112, %113)\u001b[36m::Tuple{Int64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %115 = Base.getproperty(res, :cdp)\u001b[91m\u001b[1m::ContinuousDP{1, Vector{Float64}, Vector{Float64}}\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %116 = Base.getproperty(%115, :shocks)\u001b[36m::Vector{Float64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %117 = Core.tuple(%116)\u001b[36m::Tuple{Vector{Float64}}\u001b[39m\n", "\u001b[90m│ \u001b[39m (e = Core._apply_iterate(Base.iterate, Base.getindex, %117, %114))\n", "\u001b[90m│ \u001b[39m %119 = s_ind_front\u001b[36m::Core.Const(())\u001b[39m\n", "\u001b[90m│ \u001b[39m %120 = ContinuousDPs.:+\u001b[36m::Core.Const(+)\u001b[39m\n", "\u001b[90m│ \u001b[39m %121 = t@_21\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %122 = (%120)(%121, 1)\u001b[36m::Int64\u001b[39m\n", "\u001b[90m│ \u001b[39m %123 = Core.tuple(%122)\u001b[36m::Tuple{Int64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %124 = Core._apply_iterate(Base.iterate, Core.tuple, %119, %123)\u001b[36m::Tuple{Int64}\u001b[39m\n", "\u001b[90m│ \u001b[39m %125 = Base.getproperty(res, :cdp)\u001b[91m\u001b[1m::ContinuousDP{1, Vector{Float64}, Vector{Float64}}\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %126 = Base.getproperty(%125, :g)\u001b[91m\u001b[1m::Function\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %127 = s\u001b[36m::Float64\u001b[39m\n", "\u001b[90m│ \u001b[39m %128 = x\u001b[36m::Float64\u001b[39m\n", "\u001b[90m│ \u001b[39m %129 = e\u001b[36m::Float64\u001b[39m\n", "\u001b[90m│ \u001b[39m %130 = (%126)(%127, %128, %129)\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %131 = Core.tuple(s_path, %130)\u001b[91m\u001b[1m::Tuple{Vector{Float64}, Any}\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m Core._apply_iterate(Base.iterate, Base.setindex!, %131, %124)\n", "\u001b[90m│ \u001b[39m (@_6 = Base.iterate(%91, %99))\n", "\u001b[90m│ \u001b[39m %134 = @_6\u001b[33m\u001b[1m::Union{Nothing, Tuple{Int64, Int64}}\u001b[22m\u001b[39m\n", "\u001b[90m│ \u001b[39m %135 = (%134 === nothing)\u001b[36m::Bool\u001b[39m\n", "\u001b[90m│ \u001b[39m %136 = Base.not_int(%135)\u001b[36m::Bool\u001b[39m\n", "\u001b[90m└──\u001b[39m goto #7 if not %136\n", "\u001b[90m6 ─\u001b[39m goto #5\n", "\u001b[90m7 ┄\u001b[39m %139 = s_path\u001b[36m::Vector{Float64}\u001b[39m\n", "\u001b[90m└──\u001b[39m return %139\n", "\n" ] } ], "source": [ "@code_warntype simulate!(MersenneTwister(0), Array{Float64}(undef, ts_length), res, s_init)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.030516 seconds (53.20 k allocations: 2.874 MiB, 99.51% compilation time)\n", " 0.000096 seconds (4.96 k allocations: 243.797 KiB)\n", " 0.000101 seconds (4.96 k allocations: 243.797 KiB)\n" ] } ], "source": [ "@time simulate!(MersenneTwister(0), Array{Float64}(undef, ts_length), res, s_init)\n", "@time simulate!(MersenneTwister(0), Array{Float64}(undef, ts_length), res, s_init)\n", "@time simulate!(MersenneTwister(0), Array{Float64}(undef, ts_length), res, s_init);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 1.11.6", "language": "julia", "name": "julia-1.11" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.11.6" } }, "nbformat": 4, "nbformat_minor": 4 }