genkuroki/Mamba Test - MixtureModel Normal.ipynb

## Mamba Test - MixtureModel Normal.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Mamba Test - MixtureModel Normal\n\nMamba.jl\n\nhttps://github.com/brian-j-smith/Mamba.jl\n\nhttp://mambajl.readthedocs.io/en/latest/\n\nhttps://media.readthedocs.org/pdf/mambajl/latest/mambajl.pdf\n\nDistributions.jl\n\nhttps://github.com/JuliaStats/Distributions.jl\n\nhttp://distributionsjl.readthedocs.io/en/latest/\n\nhttps://media.readthedocs.org/pdf/distributionsjl/latest/distributionsjl.pdf\n\n色々よくわからないので、試行錯誤をしてみた記録"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# 乱数のseed値を指定\n\nsrand(5772)",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 1,
          "data": {
            "text/plain": "MersenneTwister(UInt32[0x0000168c], Base.dSFMT.DSFMT_state(Int32[469571889, 1072864864, -723378003, 1072961038, 1345538364, 1073277602, 427020977, 1073321420, 913614170, 1072794645  …  1196668179, 1073621217, -1743861739, 1072724622, 1536917313, -1897540292, 244847734, 640155144, 382, 0]), [1.2243, 1.9118, 1.47721, 1.07092, 1.77546, 1.48801, 1.6425, 1.66069, 1.36183, 1.91443  …  1.04945, 1.36123, 1.07923, 1.28892, 1.52009, 1.97459, 1.89657, 1.83432, 1.85971, 1.98063], 382)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "using Distributions\nimport PyPlot\nplt = PyPlot\nhist = plt.plt[:hist]\n\n## True model\nmutrue = 0.5\nmodeltrue = MixtureModel(Normal,[(0.0,1.0), (mutrue,1.0)])\n\nx=[linspace(-5.0,5.0,200);]\nplt.plot(x,pdf(modeltrue,x), label=\"true model\")\nplt.plot(x,pdf(Normal(0.0,1.0),x), label=\"pure Normal\", linestyle=\"dashed\")\nplt.legend()",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000002A1AD128>)",
            "image/png": 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"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "PyObject <matplotlib.legend.Legend object at 0x000000002B2D1400>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "## Data = Sample of size N\nN = 100\ndata = Dict{Symbol, Any}(\n    :x => rand(modeltrue, N)\n)\n\nhist(data[:x])\nplt.title(\"Sample generated by the true model\")\n;",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000002B274A20>)",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "using Mamba\n\n## Model Specification\nmodel = Model(\n    \n    x = Stochastic(1,\n        mu -> MixtureModel(Normal, [(0.0,1.0), (mu,1.0)]),\n        false\n    ),\n\n    mu = Stochastic(\n        () -> Normal(0.0, 1000.0),\n        true\n    )\n\n)\n\n## Initial Values\ninits = [\n    Dict{Symbol, Any}(\n        :x => data[:x],\n        :mu => randn()*1\n    )\n]\n\n## Sampling Scheme Assignment\nscheme1 = NUTS([:mu])\nsetsamplers!(model, [scheme1])\n\n## MCMC\nsim = mcmc(model, data, inits, 1000, burnin=250, thin=2, chains=1);\ndescribe(sim)\ndraw(plot(sim),width=10inch,height=4inch,nrow=1,ncol=2,ask=false)",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": "MCMC Simulation of 1000 Iterations x 1 Chain...\n\nChain 1:   1% [0:02:28 of 0:02:29 remaining]\nChain 1:  10% [0:00:15 of 0:00:17 remaining]\nChain 1:  20% [0:00:08 of 0:00:09 remaining]\nChain 1:  30% [0:00:05 of 0:00:07 remaining]\nChain 1:  40% [0:00:04 of 0:00:06 remaining]\nChain 1:  50% [0:00:03 of 0:00:05 remaining]\nChain 1:  60% [0:00:02 of 0:00:04 remaining]\nChain 1:  70% [0:00:01 of 0:00:04 remaining]\nChain 1:  80% [0:00:01 of 0:00:04 remaining]\nChain 1:  90% [0:00:00 of 0:00:03 remaining]\nChain 1: 100% [0:00:00 of 0:00:03 remaining]\n\nIterations = 252:1000\nThinning interval = 2\nChains = 1\nSamples per chain = 375\n\nEmpirical Posterior Estimates:\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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        {
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          "text": "      Mean       SD       Naive SE       MCSE    ESS\nmu 0.5917205 0.17436935 0.0090043946 0.004051068 375\n\nQuantiles:\n      2.5%       25.0%     50.0%     75.0%     97.5% \nmu 0.22759115 0.47853774 0.5858894 0.7020144 0.926922\n\n",
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    {
      "metadata": {
        "trusted": true,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "# MCMC の結果 sim の中身\n\ndump(sim)",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Mamba.ModelChains\n  value: Array{Float64}((375, 1, 1)) [0.706178; 0.531991; … ; 0.831797; 0.400186]\n  range: StepRange{Int64,Int64}\n    start: Int64 252\n    step: Int64 2\n    stop: Int64 1000\n  names: Array{AbstractString}((1,))\n    1: String \"mu\"\n  chains: Array{Int64}((1,)) [1]\n  model: Mamba.Model\n    nodes: Dict{Symbol,Any}\n      slots: Array{UInt8}((16,)) UInt8[0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01]\n      keys: Array{Symbol}((16,))\n        1: #undef\n        2: #undef\n        3: #undef\n        4: #undef\n        5: #undef\n        ...\n        12: #undef\n        13: #undef\n        14: #undef\n        15: #undef\n        16: Symbol x\n      vals: Array{Any}((16,))\n        1: #undef\n        2: #undef\n        3: #undef\n        4: #undef\n        5: #undef\n        ...\n        12: #undef\n        13: #undef\n        14: #undef\n        15: #undef\n        16: Mamba.ArrayStochastic{1}\n          value: Array{Float64}((100,)) [-1.51328, -0.613694, 0.137476, -1.73092, -0.239969, 0.282397, -0.260701, 0.81245, 0.72352, 0.0545754  …  -0.327766, -0.517881, 0.191877, -1.70242, 1.30951, 2.20758, 0.301249, 0.747148, -0.945356, 0.526697]\n          symbol: Symbol x\n          monitor: Array{Int64}((0,)) Int64[]\n          eval: Mamba.#330 (function of type Mamba.##330#331)\n          sources: Array{Symbol}((1,))\n            1: Symbol mu\n          targets: Array{Symbol}((0,))\n          distr: Distributions.MixtureModel{Distributions.Univariate,Distributions.Continuous,Distributions.Normal}\n            components: Array{Distributions.Normal}((2,))\n              1: Distributions.Normal{Float64}\n                μ: Float64 0.0\n                σ: Float64 1.0\n              2: Distributions.Normal{Float64}\n                μ: Float64 0.40018597276513607\n                σ: Float64 1.0\n            prior: Distributions.Categorical{Float64}\n              K: Int64 2\n              p: Array{Float64}((2,)) [0.5, 0.5]\n      ndel: Int64 0\n      count: Int64 2\n      age: UInt64 2\n      idxfloor: Int64 6\n      maxprobe: Int64 0\n    samplers: Array{Mamba.Sampler}((1,))\n      1: Mamba.Sampler{Mamba.NUTSTune}\n        params: Array{Symbol}((1,))\n          1: Symbol mu\n        eval: Mamba.#334 (function of type Mamba.##334#335)\n        tune: Mamba.NUTSTune\n          logfgrad: Nullable{Function}\n            hasvalue: Bool false\n            value: #undef\n          adapt: Bool false\n          alpha: Float64 2.0\n          epsilon: Float64 0.32724604531217416\n          epsilonbar: Float64 0.32724604531217416\n          gamma: Float64 0.05\n          Hbar: Float64 0.008374937877879761\n          kappa: Float64 0.75\n          m: Int64 250\n          mu: Float64 0.9162907318741551\n          nalpha: Int64 2\n          t0: Float64 10.0\n          target: Float64 0.6\n        targets: Array{Symbol}((1,))\n          1: Symbol x\n    states: Array{Mamba.ModelState}((1,))\n      1: Mamba.ModelState\n        value: Array{Float64}((1,)) [0.400186]\n        tune: Array{Any}((1,))\n          1: Mamba.NUTSTune\n            logfgrad: Nullable{Function}\n              hasvalue: Bool false\n              value: #undef\n            adapt: Bool false\n            alpha: Float64 2.0\n            epsilon: Float64 0.32724604531217416\n            epsilonbar: Float64 0.32724604531217416\n            gamma: Float64 0.05\n            Hbar: Float64 0.008374937877879761\n            kappa: Float64 0.75\n            m: Int64 250\n            mu: Float64 0.9162907318741551\n            nalpha: Int64 2\n            t0: Float64 10.0\n            target: Float64 0.6\n    iter: Int64 1000\n    burnin: Int64 250\n    hasinputs: Bool true\n    hasinits: Bool true\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "@show typeof(sim.value)\nsim.value[:,1,1]",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": "typeof(sim.value) = Array{Float64,3}\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "375-element Array{Float64,1}:\n 0.706178\n 0.531991\n 0.377347\n 0.227978\n 0.679853\n 0.679853\n 0.524304\n 0.360681\n 0.670987\n 0.670987\n 0.580806\n 0.640861\n 0.813662\n ⋮       \n 0.786836\n 0.786836\n 0.405552\n 0.832326\n 0.227383\n 0.481467\n 0.551174\n 0.560493\n 0.424928\n 0.831797\n 0.831797\n 0.400186"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# sim.value の中に MCMC の結果が入っているようだ。\n\nmuchain = sim.value[:,1,1]\nn = length(muchain)\nk = [1:n;]\nplt.figure(figsize=(10,4))\nplt.plot(k, muchain)\nplt.title(\"\\$\\\\mu\\$ chain\")\n;",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000002C181F60>)",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "hist(muchain,50)\nplt.title(\"histogram of \\$\\\\mu\\$ chain (posterior pdf)\")\n;",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000002B27ED68>)",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# predict() 函数が何をやっているかを調べてみよう。\n\nppd = predict(sim,:x)",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "Object of type \"Mamba.ModelChains\"\n\nIterations = 252:1000\nThinning interval = 2\nChains = 1\nSamples per chain = 375\n\n[2.92786 0.602065 … 0.716469 -1.17135; 1.59595 -0.544951 … -0.187482 0.373388; … ; 2.62703 0.307788 … 1.43172 -1.54867; 0.948715 -0.22593 … 1.56927 0.33882]"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "# N が大きな場合は以下を実行してはいけない。\n\n#draw(plot(ppd),width=10inch,height=4inch,nrow=1,ncol=2,ask=false)",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "typeof(ppd)\ndump(ppd)",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Mamba.ModelChains\n  value: Array{Float64}((375, 100, 1)) [2.92786 0.602065 … 0.716469 -1.17135; 1.59595 -0.544951 … -0.187482 0.373388; … ; 2.62703 0.307788 … 1.43172 -1.54867; 0.948715 -0.22593 … 1.56927 0.33882]\n  range: StepRange{Int64,Int64}\n    start: Int64 252\n    step: Int64 2\n    stop: Int64 1000\n  names: Array{AbstractString}((100,))\n    1: String \"x[1]\"\n    2: String \"x[2]\"\n    3: String \"x[3]\"\n    4: String \"x[4]\"\n    5: String \"x[5]\"\n    ...\n    96: String \"x[96]\"\n    97: String \"x[97]\"\n    98: String \"x[98]\"\n    99: String \"x[99]\"\n    100: String \"x[100]\"\n  chains: Array{Int64}((1,)) [1]\n  model: Mamba.Model\n    nodes: Dict{Symbol,Any}\n      slots: Array{UInt8}((16,)) UInt8[0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01]\n      keys: Array{Symbol}((16,))\n        1: #undef\n        2: #undef\n        3: #undef\n        4: #undef\n        5: #undef\n        ...\n        12: #undef\n        13: #undef\n        14: #undef\n        15: #undef\n        16: Symbol x\n      vals: Array{Any}((16,))\n        1: #undef\n        2: #undef\n        3: #undef\n        4: #undef\n        5: #undef\n        ...\n        12: #undef\n        13: #undef\n        14: #undef\n        15: #undef\n        16: Mamba.ArrayStochastic{1}\n          value: Array{Float64}((100,)) [-1.51328, -0.613694, 0.137476, -1.73092, -0.239969, 0.282397, -0.260701, 0.81245, 0.72352, 0.0545754  …  -0.327766, -0.517881, 0.191877, -1.70242, 1.30951, 2.20758, 0.301249, 0.747148, -0.945356, 0.526697]\n          symbol: Symbol x\n          monitor: Array{Int64}((0,)) Int64[]\n          eval: Mamba.#330 (function of type Mamba.##330#331)\n          sources: Array{Symbol}((1,))\n            1: Symbol mu\n          targets: Array{Symbol}((0,))\n          distr: Distributions.MixtureModel{Distributions.Univariate,Distributions.Continuous,Distributions.Normal}\n            components: Array{Distributions.Normal}((2,))\n              1: Distributions.Normal{Float64}\n                μ: Float64 0.0\n                σ: Float64 1.0\n              2: Distributions.Normal{Float64}\n                μ: Float64 0.40018597276513607\n                σ: Float64 1.0\n            prior: Distributions.Categorical{Float64}\n              K: Int64 2\n              p: Array{Float64}((2,)) [0.5, 0.5]\n      ndel: Int64 0\n      count: Int64 2\n      age: UInt64 2\n      idxfloor: Int64 6\n      maxprobe: Int64 0\n    samplers: Array{Mamba.Sampler}((1,))\n      1: Mamba.Sampler{Mamba.NUTSTune}\n        params: Array{Symbol}((1,))\n          1: Symbol mu\n        eval: Mamba.#334 (function of type Mamba.##334#335)\n        tune: Mamba.NUTSTune\n          logfgrad: Nullable{Function}\n            hasvalue: Bool false\n            value: #undef\n          adapt: Bool false\n          alpha: Float64 2.0\n          epsilon: Float64 0.32724604531217416\n          epsilonbar: Float64 0.32724604531217416\n          gamma: Float64 0.05\n          Hbar: Float64 0.008374937877879761\n          kappa: Float64 0.75\n          m: Int64 250\n          mu: Float64 0.9162907318741551\n          nalpha: Int64 2\n          t0: Float64 10.0\n          target: Float64 0.6\n        targets: Array{Symbol}((1,))\n          1: Symbol x\n    states: Array{Mamba.ModelState}((1,))\n      1: Mamba.ModelState\n        value: Array{Float64}((1,)) [0.400186]\n        tune: Array{Any}((1,))\n          1: Mamba.NUTSTune\n            logfgrad: Nullable{Function}\n              hasvalue: Bool false\n              value: #undef\n            adapt: Bool false\n            alpha: Float64 2.0\n            epsilon: Float64 0.32724604531217416\n            epsilonbar: Float64 0.32724604531217416\n            gamma: Float64 0.05\n            Hbar: Float64 0.008374937877879761\n            kappa: Float64 0.75\n            m: Int64 250\n            mu: Float64 0.9162907318741551\n            nalpha: Int64 2\n            t0: Float64 10.0\n            target: Float64 0.6\n    iter: Int64 1000\n    burnin: Int64 250\n    hasinputs: Bool true\n    hasinits: Bool true\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "ppd.value",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "375×100×1 Array{Float64,3}:\n[:, :, 1] =\n  2.92786     0.602065   1.67179   …   0.950844    0.716469    -1.17135  \n  1.59595    -0.544951   0.353214     -0.920899   -0.187482     0.373388 \n -0.173831   -2.13102   -0.980778      1.70801    -0.507231     0.549001 \n  0.177879   -0.945369  -0.961719     -0.90158    -0.43377      1.37838  \n -1.19352    -1.11051    1.94531       1.03939     0.608725     0.677452 \n  2.67577     1.13772   -0.15358   …   0.621678    0.894623     1.14859  \n  0.926061   -0.698342   0.517525     -2.44518     1.87728      2.30258  \n  0.533902   -1.08789   -0.401701     -0.689862   -0.754502     1.16917  \n  1.37003     1.27867    1.30506       1.15479    -0.894091     0.13579  \n  0.0841799  -0.362904   0.383548      0.798712    0.00437663  -0.590425 \n  1.46192     3.31859   -0.566286  …  -0.689974    1.06083     -0.720432 \n  1.74219     2.12362    0.57414      -1.16616    -1.20875      1.12679  \n  0.481825    1.8911     1.37234       0.256647    1.97494     -0.590897 \n  ⋮                                ⋱                                     \n  0.105754    1.21876   -1.31726       0.172691    1.22531      1.68229  \n  1.99328    -0.7239     1.18657       1.2618      1.05209     -1.76892  \n  0.294617    0.276882  -1.93447   …   0.406676   -0.109806     1.95557  \n  1.12597    -0.433035  -2.02706      -0.327656    0.561559     0.898768 \n -0.158147    0.352584   1.28189       2.2099     -1.74996     -1.1936   \n  1.4933      0.246715  -0.22817       1.92161    -0.199568     0.264518 \n  0.388968   -1.23767    1.39659      -0.0635424   1.95396      0.287643 \n -2.59794    -0.804458   2.16514   …   0.141164    0.775954    -0.64479  \n  0.627674    0.456528   0.975474     -0.658987   -0.795016    -1.5076   \n  0.678641   -1.20776    0.589931      0.604176    1.77858      0.0644663\n  2.62703     0.307788   1.51655       0.794351    1.43172     -1.54867  \n  0.948715   -0.22593    1.15949      -0.446981    1.56927      0.33882  "
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": true
      },
      "cell_type": "code",
      "source": "ppd.names",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "100-element Array{AbstractString,1}:\n \"x[1]\"  \n \"x[2]\"  \n \"x[3]\"  \n \"x[4]\"  \n \"x[5]\"  \n \"x[6]\"  \n \"x[7]\"  \n \"x[8]\"  \n \"x[9]\"  \n \"x[10]\" \n \"x[11]\" \n \"x[12]\" \n \"x[13]\" \n ⋮       \n \"x[89]\" \n \"x[90]\" \n \"x[91]\" \n \"x[92]\" \n \"x[93]\" \n \"x[94]\" \n \"x[95]\" \n \"x[96]\" \n \"x[97]\" \n \"x[98]\" \n \"x[99]\" \n \"x[100]\""
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "@show ppd.chains",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": "ppd.chains = [1]\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "1-element Array{Int64,1}:\n 1"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "@show ppd.model",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": "ppd.model = Object of type \"Mamba.Model\"\n-------------------------------------------------------------------------------\nmu:\nA monitored node of type \"Mamba.ScalarStochastic\"\n0.40018597276513607\n\nDistribution:\nDistributions.Normal{Float64}(μ=0.0, σ=1000.0)\nFunction:\nCodeInfo(:(begin \n        $(Expr(:inbounds, false))\n        # meta: location In[4] #2 12\n        # meta: location C:\\JuliaPkg\\v0.6\\Distributions\\src\\univariate\\continuous\\normal.jl Type 32\n        # meta: location C:\\JuliaPkg\\v0.6\\Distributions\\src\\univariate\\continuous\\normal.jl Type 28\n        # meta: location C:\\JuliaPkg\\v0.6\\Distributions\\src\\utils.jl # line 5:\n        unless (Base.not_int)((Base.lt_float)((Base.sitofp)(Float64, 0)::Float64, 1000.0)::Bool)::Bool goto 11 # line 6:\n        SSAValue(0) = $(Expr(:invoke, MethodInstance for string(::String, ::Vararg{String,N} where N), :(Distributions.string), \"Normal\", \": the condition \", \"σ > zero(σ)\", \" is not satisfied.\"))\n        (Distributions.throw)($(Expr(:new, :(Base.ArgumentError), SSAValue(0))))::Union{}\n        11: \n        # meta: pop location\n        # meta: pop location\n        # meta: pop location\n        # meta: pop location\n        $(Expr(:inbounds, :pop))\n        return $(Expr(:new, Distributions.Normal{Float64}, 0.0, 1000.0))\n    end))=>Distributions.Normal{Float64}\n\nSource Nodes:\nSymbol[]\n\nTarget Nodes:\nSymbol[:x]\n-------------------------------------------------------------------------------\nx:\nAn unmonitored node of type \"100-element Mamba.ArrayStochastic{1}\"\n[-1.51328, -0.613694, 0.137476, -1.73092, -0.239969, 0.282397, -0.260701, 0.81245, 0.72352, 0.0545754, 0.202631, -0.324068, 0.678407, 0.0817766, 0.575166, -0.372419, 0.0319536, 1.89184, -0.234456, 0.0882671, 2.26196, 1.49418, -0.468047, -0.327878, 1.44122, 1.07796, -0.760481, 0.668493, -1.74564, -0.440572, 0.220013, 0.0185139, -1.2583, 2.56771, 0.448092, 1.7885, 1.67369, 0.566049, 0.287655, 0.67096, 0.360607, -0.158057, 1.30154, -1.08341, 0.241543, 1.13131, 0.985, -0.064413, 0.103003, -1.31786, 0.64516, 0.958147, 1.35207, 1.17278, 0.67398, 0.0893836, 0.698768, 0.578606, 0.919198, 1.79386, -0.942503, 0.121906, 0.619655, -0.363604, 0.498975, 0.399274, -0.812801, 0.621883, 1.48499, -1.21985, -0.878734, 1.52993, 0.364829, 1.30287, 1.21261, 1.33562, 0.407708, 0.242239, 1.06226, 2.22473, 0.900952, 0.0703534, 1.97003, -0.829611, 0.955247, -0.223406, 1.01357, 0.791767, -0.232697, -1.38394, -0.327766, -0.517881, 0.191877, -1.70242, 1.30951, 2.20758, 0.301249, 0.747148, -0.945356, 0.526697]\n\nDistribution:\nMixtureModel{Distributions.Normal}(K = 2)\ncomponents[1] (prior = 0.5000): Distributions.Normal{Float64}(μ=0.0, σ=1.0)\ncomponents[2] (prior = 0.5000): Distributions.Normal{Float64}(μ=0.40018597276513607, σ=1.0)\n\nFunction:\nCodeInfo(:(begin \n        SSAValue(0) = $(Expr(:invoke, MethodInstance for getindex(::Mamba.Model, ::Symbol), :(Mamba.getindex), :(model), :(:mu)))\n        $(Expr(:inbounds, false))\n        # meta: location In[4] #1 7\n        SSAValue(1) = (Base.vect)((0.0, 1.0), (Core.tuple)(SSAValue(0), 1.0)::Tuple{Any,Float64})::Array{_,1} where _\n        # meta: location C:\\JuliaPkg\\v0.6\\Distributions\\src\\mixtures/mixturemodel.jl Type 57\n        #59 = $(Expr(:new, Distributions.##59#60{Distributions.Normal}))\n        SSAValue(3) = (Base.Generator)(#59, SSAValue(1))::Base.Generator{_,Distributions.##59#60{Distributions.Normal}} where _\n        components = (Base._collect)(Distributions.Normal, SSAValue(3), (Base.iteratorsize)((Base.typeof)(SSAValue(3))::Type{#s122} where #s122<:Base.Generator{_,Distributions.##59#60{Distributions.Normal}} where _))::Union{AbstractArray, AbstractSet, Associative}\n        # meta: pop location\n        # meta: pop location\n        $(Expr(:inbounds, :pop))\n        return (Distributions.MixtureModel)(components)::Distributions.MixtureModel{_,_,_} where _ where _ where _\n    end))=>Distributions.MixtureModel{_,_,_} where _ where _ where _\n\nSource Nodes:\nSymbol[:mu]\n\nTarget Nodes:\nSymbol[]\n\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": "Object of type \"Mamba.Model\"\n-------------------------------------------------------------------------------\nmu:\nA monitored node of type \"Mamba.ScalarStochastic\"\n0.40018597276513607\n-------------------------------------------------------------------------------\nx:\nAn unmonitored node of type \"100-element Mamba.ArrayStochastic{1}\"\n[-1.51328, -0.613694, 0.137476, -1.73092, -0.239969, 0.282397, -0.260701, 0.81245, 0.72352, 0.0545754  …  -0.327766, -0.517881, 0.191877, -1.70242, 1.30951, 2.20758, 0.301249, 0.747148, -0.945356, 0.526697]\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "hist(ppd.value[:,1,1],50);",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000002C181FD0>)",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "hist(ppd.value[:,2,1],50);",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000004E87DB38>)",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# predict() 函数が何をやっているのかよくわからないので、\n# 自前で(事後)予測分布を計算してみた。\n\nmuchain = sim.value[:,1,1]\nn = length(muchain)\nmixmodel(mu) = MixtureModel(Normal, [(0.0,1.0), (mu,1.0)])\npstar(x) = sum((mu->pdf(mixmodel(mu),x)).(muchain))/n\n\nx = [linspace(-3,3,300);]\nplt.figure()\nplt.title(\"pdf of the posterior predictive distribution\")\nplt.plot(x, pstar.(x), label=\"predictive pdf\")\nplt.plot(x, pdf(modeltrue,x), label=\"true pdf\", linestyle=\"dashed\")\nplt.legend()\n;",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000004EB76898>)",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "julia-0.6",
      "display_name": "Julia 0.6.0",
      "language": "julia"
    },
    "language_info": {
      "file_extension": ".jl",
      "name": "julia",
      "mimetype": "application/julia",
      "version": "0.6.0"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Julia/Baseball/Mamba Test - MixtureModel Normal.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}