day_4/4_henri/elfitutorialprobAI2022.ipynb

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    "colab": {
      "name": "elfitutorialprobAI2022.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# ELFI-tutorial and assignments\n",
        "## Probabilistic AI summerschool 2022\n",
        "\n",
        "In this tutorial we go over the basics of model building and inference in likelihood-free setting using `elfi` - engine for likelihood-free inference. \n",
        "\n",
        "Other than `elfi`, there are other LFI software packages for (mainly) R and Python that you may wish to check out. Among them are e.g.\n",
        "- `sbi`\n",
        "- `abcpy`\n",
        "- `pyabc`\n",
        "- `DIYABC`\n",
        "\n",
        "There's a [gitter-chatroom](https://gitter.im/elfi-dev/ELFI-workshops) named ELFI-workshops where you can copy-paste code and ask questions about the tutorial and the you'll be doing in the second part. \n",
        "\n",
        "First we need to install the package from PyPI"
      ],
      "metadata": {
        "id": "4PPE2Jy71z8E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q --upgrade elfi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SXBFFdLc1w6E",
        "outputId": "bc786dd9-35c1-4387-9e2d-66d7c4a7d5c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 166 kB 5.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 959 kB 40.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 296 kB 40.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 35.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 99 kB 7.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 596 kB 51.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 140 kB 51.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 837 kB 50.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 71 kB 7.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 189 kB 57.8 MB/s \n",
            "\u001b[?25h  Building wheel for GPy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for algopy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for paramz (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gym 0.17.3 requires cloudpickle<1.7.0,>=1.2.0, but you have cloudpickle 2.1.0 which is incompatible.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, load all the necessary packages for the tutorial and the assigments, and activate some `logging`.\n",
        "\n"
      ],
      "metadata": {
        "id": "7-uXhq5RL0I6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.stats as ss\n",
        "\n",
        "import elfi\n",
        "from elfi.methods.post_processing import adjust_posterior\n",
        "from elfi.model.extensions import ModelPrior\n",
        "from elfi.methods.bo.acquisition import MaxVar\n",
        "from elfi.methods.bo.acquisition import RandMaxVar\n",
        "from elfi.methods.bo.acquisition import ExpIntVar\n",
        "\n",
        "%matplotlib inline\n",
        "%precision 2\n",
        "\n",
        "import logging\n",
        "logging.basicConfig(level=logging.INFO)\n",
        "\n",
        "# Set an arbitrary global seed to keep the randomly generated quantities the same\n",
        "seed = 16062023\n",
        "np.random.seed(seed)\n",
        "\n"
      ],
      "metadata": {
        "id": "geLZnFZkZVGq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model building with ELFI: case MA(2) model\n",
        "\n",
        "We start the tutorial by using the 2nd order moving average model MA(2)  as an example. MA(2) is a common model used in univariate time series analysis. Assuming zero mean it can be written as\n",
        "\n",
        "$$\n",
        "x_t = w_t + \\theta_1 w_{t-1} + \\theta_2 w_{t-2},\n",
        "$$\n",
        "\n",
        "where $\\theta_1, \\theta_2 \\in \\mathbb{R}$ and $(w_k)_{k\\in \\mathbb{Z}} \\sim \\mathsf{Normal}(0,1)$ represents an independent and identically distributed sequence of white noise.\n",
        "\n",
        "In `elfi` the form of the simulators have few requirements and restrictions that we need to take into account. Let's look at the definition of `MA2`."
      ],
      "metadata": {
        "id": "xhdCBGe70avX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def MA2(t1, t2, n_obs=100, batch_size=1, random_state=None):\n",
        "    # Make inputs 2d arrays for numpy broadcasting with w\n",
        "    t1 = np.asanyarray(t1).reshape((-1, 1))\n",
        "    t2 = np.asanyarray(t2).reshape((-1, 1))\n",
        "    random_state = random_state or np.random\n",
        "\n",
        "    w = random_state.randn(batch_size, n_obs+2)  # i.i.d. sequence ~ N(0,1)\n",
        "    x = w[:, 2:] + t1 * w[:, 1:-1] + t2 * w[:, :-2]\n",
        "    return x"
      ],
      "metadata": {
        "id": "co2t8hnfZV_g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Above, `t1`, `t2`, and `n_obs` are the arguments specific to the MA2 process. The latter two, `batch_size` and `random_state` are ELFI specific keyword arguments. The `batch_size` argument tells how many simulations are needed. The `random_state` argument is for generating random quantities in your simulator. It is a `numpy.RandomState` object that has all the same methods as `numpy.random` module has. It is used for ensuring consistent results and handling random number generation in parallel settings.\n",
        "\n",
        "We can test the function by evaluating it with some parameter values. "
      ],
      "metadata": {
        "id": "8fRwk7Uk16ZX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "t1 = 0.1\n",
        "t2 = 0.3\n",
        "xobs = MA2(t1, t2)\n",
        "plt.plot(xobs.T);"
      ],
      "metadata": {
        "id": "Vr_EBUiEZjkC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "787e38d5-84a3-4b94-f4c4-68ee9a690a14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Simulator is stochastic and the output will vary unless you fix the state in `numpy.RandomState`. Here's a few more realisations:"
      ],
      "metadata": {
        "id": "-gpKCqod8Wi2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(MA2(t1, t2).T)\n",
        "plt.plot(MA2(t1, t2).T)\n",
        "plt.plot(MA2(t1, t2).T);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "-EL8_Tzh8USe",
        "outputId": "5c94d41c-ace1-40f8-9aef-a466814ac12f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Vectorization\n",
        "\n",
        "What is the purpose of the `batch_size` argument? In ELFI, operations are vectorized, meaning that instead of simulating a single MA2 sequence at a time, we simulate a *batch* of them. A vectorized function takes vectors as inputs, and computes the output for each element in the vector. Vectorization is a way to make operations efficient in Python. \n",
        "\n",
        "In the MA2 simulator above we rely on numpy to carry out the vectorized calculations. The arguments `t1` and `t2` are going to be **vectors** of length `batch_size` and the function returns a 2D array of shape `(batch_size, n_obs)` with each row corresponding to a single argument pair. Notice that for convenience, the funtion also works with scalars as they are first converted to vectors."
      ],
      "metadata": {
        "id": "-KATe4f55XNK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is a built-in tool (`elfi.tools.vectorize`) in ELFI to vectorize operations that are not vectorized. It is basically a `for` loop wrapper.\n"
      ],
      "metadata": {
        "id": "gy5-MsG45hZJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining the model\n",
        "\n",
        "ELFI includes a modeling syntax, where the generative model is specified as a directed acyclic graph ([DAG](https://en.wikipedia.org/wiki/Directed_acyclic_graph)). This provides an intuitive means to describe rather complex dependencies conveniently. Often the target of the generative model is a distance between the simulated and observed data. To start creating our model, we create an empty one, called... `model`"
      ],
      "metadata": {
        "id": "1_C0nHSc86fj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "lBD77S7a_nZM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = elfi.new_model()"
      ],
      "metadata": {
        "id": "5JqeenFC_n7g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "As discussed in the lectures, we need to define *prior* distributions for the unknown parameters $\\theta_1, \\theta_2$. In ELFI the priors can be any of the continuous and discrete distributions available in `scipy.stats` (we'll shortly have an example of custom priors). For simplicity, let's start by assuming that both parameters follow `Uniform(0, 2)`."
      ],
      "metadata": {
        "id": "yFuHup5l9FOS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# a node is defined by giving a distribution from scipy.stats together with any arguments (here 0 and 2)\n",
        "elfi.Prior(ss.uniform, 0, 2, name='t1', model = model)\n",
        "\n",
        "# some scipy.stats-distributions are accessible via keywords\n",
        "elfi.Prior('uniform', 0, 2, name='t2', model = model);"
      ],
      "metadata": {
        "id": "87-VqEFp5gqH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we define the *simulator* node for our model DAG with the `MA2` function, and give the priors to it as arguments. This means that the parameters for the simulations will be drawn from the priors. Because we have the observed data available for this node, we provide it here as well:"
      ],
      "metadata": {
        "id": "4815YVbn9iCC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "elfi.Simulator(MA2, model['t1'], model['t2'], observed=xobs, name='MA2');"
      ],
      "metadata": {
        "id": "ok56p1pd97Rn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In model building stage, it if often convenient to check whether the end result looks like it's supposed to."
      ],
      "metadata": {
        "id": "vKyyuBeUAOik"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "elfi.draw(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "jB1jarWFeaiS",
        "outputId": "09e3dba2-3943-4450-d3e2-56bcd8a15bc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.dot.Digraph at 0x7ff043af7b90>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: %3 Pages: 1 -->\n<svg width=\"98pt\" height=\"141pt\"\n viewBox=\"0.00 0.00 98.40 141.49\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 137.4913)\">\n<title>%3</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-137.4913 94.3968,-137.4913 94.3968,4 -4,4\"/>\n<!-- t1 -->\n<g id=\"node1\" class=\"node\">\n<title>t1</title>\n<ellipse fill=\"none\" stroke=\"#000000\" cx=\"18.1984\" cy=\"-115.293\" rx=\"18.399\" ry=\"18.399\"/>\n<text text-anchor=\"middle\" x=\"18.1984\" y=\"-111.593\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">t1</text>\n</g>\n<!-- MA2 -->\n<g id=\"node3\" class=\"node\">\n<title>MA2</title>\n<ellipse fill=\"#cccccc\" stroke=\"#000000\" cx=\"45.1984\" cy=\"-30.5473\" rx=\"30.5947\" ry=\"30.5947\"/>\n<text text-anchor=\"middle\" x=\"45.1984\" y=\"-26.8473\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">MA2</text>\n</g>\n<!-- t1&#45;&gt;MA2 -->\n<g id=\"edge1\" class=\"edge\">\n<title>t1&#45;&gt;MA2</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M23.7928,-97.7336C26.4109,-89.516 29.6416,-79.3759 32.7922,-69.4869\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"36.1897,-70.3526 35.8906,-59.762 29.52,-68.2276 36.1897,-70.3526\"/>\n</g>\n<!-- t2 -->\n<g id=\"node2\" class=\"node\">\n<title>t2</title>\n<ellipse fill=\"none\" stroke=\"#000000\" cx=\"72.1984\" cy=\"-115.293\" rx=\"18.399\" ry=\"18.399\"/>\n<text text-anchor=\"middle\" x=\"72.1984\" y=\"-111.593\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">t2</text>\n</g>\n<!-- t2&#45;&gt;MA2 -->\n<g id=\"edge2\" class=\"edge\">\n<title>t2&#45;&gt;MA2</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M66.604,-97.7336C63.9858,-89.516 60.7552,-79.3759 57.6045,-69.4869\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"60.8768,-68.2276 54.5062,-59.762 54.2071,-70.3526 60.8768,-68.2276\"/>\n</g>\n</g>\n</svg>\n"
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can test that the model is working by using `generate` method to draw random realisations of the graph."
      ],
      "metadata": {
        "id": "yY4ZKK6vBrTi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_output = model.generate(batch_size=1, outputs=['t1', 't2', 'MA2'])\n",
        "print(model_output)\n",
        "plt.plot(model_output['MA2'].T)\n",
        "plt.title('t1:' + str(model_output['t1'][0]) + ', t2:' + str(model_output['t2'][0]));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "pDuXwZKcBcMf",
        "outputId": "eba993b0-adbd-4062-8c4c-0e45b428d8f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'MA2': array([[ 2.42,  3.1 ,  2.76,  1.35, -0.43,  2.16,  3.34,  1.88,  1.68,\n",
            "         0.18,  2.3 ,  1.55,  2.  ,  0.61,  0.4 , -1.6 , -3.22, -1.32,\n",
            "         2.84,  1.92,  0.54, -0.13,  3.24,  2.02,  0.64, -2.08, -0.2 ,\n",
            "         2.12, -0.82, -2.05, -3.49,  1.57,  2.8 ,  0.49, -3.16, -1.71,\n",
            "        -0.62, -2.05, -3.17, -2.11, -0.45,  0.09,  2.73,  1.05,  0.89,\n",
            "        -0.59,  2.46,  1.85,  1.32, -0.39,  2.23,  0.34,  2.08, -1.2 ,\n",
            "         0.03, -1.63,  0.75,  0.18,  1.43,  2.78,  2.94,  3.5 , -0.58,\n",
            "        -1.13, -0.58,  4.6 ,  3.42,  2.31,  0.99,  1.18,  0.85, -0.19,\n",
            "         1.99, -1.46, -1.52, -1.69,  1.1 ,  2.53,  0.12,  0.38,  0.62,\n",
            "         1.89,  0.97,  0.05, -0.16, -2.09, -2.17, -0.38, -0.66,  1.56,\n",
            "         0.52, -0.25, -0.06, -0.46,  1.36,  1.93,  3.68,  2.01,  0.49,\n",
            "         1.07]]), 't2': array([1.45]), 't1': array([1.37])}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "But how does one compare the simulated sequences with the observed sequence? Looking at the plot of just a few observed sequences above, a direct pointwise comparison would probably not work very well: the three sequences look quite different although they were generated with the same parameter values. Indeed, the comparison of simulated sequences is often the most difficult (and ad hoc) part of ABC. Typically one chooses one or more summary statistics and then calculates the discrepancy between those.\n",
        "\n",
        "Here, we will apply the intuition arising from the definition of the MA(2) process, and use the autocovariances with lags 1 and 2 as the summary statistics. Note that since the rows of `x` correspond to independent simulations, we have to tell this numpy function to take row-wise means by the keyword argument `axis=1`:"
      ],
      "metadata": {
        "id": "zv6ExHrLAbxF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def autocov(x, lag=1):\n",
        "    C = np.mean(x[:,lag:] * x[:,:-lag], axis=1)\n",
        "    return C"
      ],
      "metadata": {
        "id": "-eZ2yW3iAx-G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "As is familiar by now, a `Summary` node is defined by giving the autocovariance function and the simulated data (which includes the observed as well):"
      ],
      "metadata": {
        "id": "d7aMrNjyA6D8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "elfi.Summary(autocov, model['MA2'], name='S1')\n",
        "elfi.Summary(autocov, model['MA2'], 2, name='S2');  # the optional keyword lag is given the value 2"
      ],
      "metadata": {
        "id": "DuBAtY6IBAXD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "elfi.draw(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "NKNTY_FLBLvI",
        "outputId": "99e8aa4f-aa40-4982-a5f7-4280b81375b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.dot.Digraph at 0x7ff043a4edd0>"
            ],
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: %3 Pages: 1 -->\n<svg width=\"110pt\" height=\"219pt\"\n viewBox=\"0.00 0.00 109.60 219.09\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 215.0877)\">\n<title>%3</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-215.0877 105.5963,-215.0877 105.5963,4 -4,4\"/>\n<!-- t1 -->\n<g id=\"node1\" class=\"node\">\n<title>t1</title>\n<ellipse fill=\"none\" stroke=\"#000000\" cx=\"23.7982\" cy=\"-192.8893\" rx=\"18.399\" ry=\"18.399\"/>\n<text text-anchor=\"middle\" x=\"23.7982\" y=\"-189.1893\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">t1</text>\n</g>\n<!-- MA2 -->\n<g id=\"node3\" class=\"node\">\n<title>MA2</title>\n<ellipse fill=\"#cccccc\" stroke=\"#000000\" cx=\"50.7982\" cy=\"-108.1436\" rx=\"30.5947\" ry=\"30.5947\"/>\n<text text-anchor=\"middle\" x=\"50.7982\" y=\"-104.4436\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">MA2</text>\n</g>\n<!-- t1&#45;&gt;MA2 -->\n<g id=\"edge1\" class=\"edge\">\n<title>t1&#45;&gt;MA2</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M29.3926,-175.3299C32.0107,-167.1123 35.2413,-156.9722 38.392,-147.0832\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"41.7895,-147.9489 41.4903,-137.3583 35.1198,-145.8239 41.7895,-147.9489\"/>\n</g>\n<!-- t2 -->\n<g id=\"node2\" class=\"node\">\n<title>t2</title>\n<ellipse fill=\"none\" stroke=\"#000000\" cx=\"77.7982\" cy=\"-192.8893\" rx=\"18.399\" ry=\"18.399\"/>\n<text text-anchor=\"middle\" x=\"77.7982\" y=\"-189.1893\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">t2</text>\n</g>\n<!-- t2&#45;&gt;MA2 -->\n<g id=\"edge2\" class=\"edge\">\n<title>t2&#45;&gt;MA2</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M72.2037,-175.3299C69.5856,-167.1123 66.355,-156.9722 63.2043,-147.0832\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"66.4765,-145.8239 60.106,-137.3583 59.8069,-147.9489 66.4765,-145.8239\"/>\n</g>\n<!-- S1 -->\n<g id=\"node4\" class=\"node\">\n<title>S1</title>\n<ellipse fill=\"#cccccc\" stroke=\"#000000\" cx=\"20.7982\" cy=\"-20.7982\" rx=\"20.5982\" ry=\"20.5982\"/>\n<text text-anchor=\"middle\" x=\"20.7982\" y=\"-17.0982\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">S1</text>\n</g>\n<!-- MA2&#45;&gt;S1 -->\n<g id=\"edge3\" class=\"edge\">\n<title>MA2&#45;&gt;S1</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M40.8086,-79.0588C37.6292,-69.8019 34.1104,-59.5571 30.9216,-50.2729\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"34.1813,-48.9884 27.6226,-40.6677 27.5609,-51.2623 34.1813,-48.9884\"/>\n</g>\n<!-- S2 -->\n<g id=\"node5\" class=\"node\">\n<title>S2</title>\n<ellipse fill=\"#cccccc\" stroke=\"#000000\" cx=\"80.7982\" cy=\"-20.7982\" rx=\"20.5982\" ry=\"20.5982\"/>\n<text text-anchor=\"middle\" x=\"80.7982\" y=\"-17.0982\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">S2</text>\n</g>\n<!-- MA2&#45;&gt;S2 -->\n<g id=\"edge4\" class=\"edge\">\n<title>MA2&#45;&gt;S2</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M60.7877,-79.0588C63.9671,-69.8019 67.4859,-59.5571 70.6747,-50.2729\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"74.0354,-51.2623 73.9737,-40.6677 67.415,-48.9884 74.0354,-51.2623\"/>\n</g>\n</g>\n</svg>\n"
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We choose the discrepancy as the common Euclidean L2-distance. ELFI can use many common distances directly from `scipy.spatial.distance` like this:"
      ],
      "metadata": {
        "id": "BgDE4P-o27Sd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "elfi.Distance('euclidean', model['S1'], model['S2'], name='d');"
      ],
      "metadata": {
        "id": "6Mfm83Oh25_b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "elfi.draw(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "8HyIjPrq4CjR",
        "outputId": "a176bb5e-decc-4a38-8743-657bb75f408b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "<graphviz.dot.Digraph at 0x7ff043a4e3d0>"
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          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modifying the model\n",
        "\n",
        "Although the above definition is perfectly valid, let's use the same priors as in [*Marin et al. (2012)*](http://link.springer.com/article/10.1007/s11222-011-9288-2) that guarantee that the problem will be identifiable (loosely speaking, the likelihood willl have just one mode). Marin et al. used priors for which $-2<\\theta_1<2$ with $\\theta_1+\\theta_2>-1$ and $\\theta_1-\\theta_2<1$ i.e. the parameters are sampled from a triangle (see below)."
      ],
      "metadata": {
        "id": "DZ-d27T-4ZGm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Custom priors\n",
        "\n",
        "In ELFI, custom distributions can be defined similar to distributions in `scipy.stats` (i.e. they need to have at least the `rvs` method implemented for the simplest algorithms). To be safe they can inherit `elfi.Distribution` which defines the methods needed. In this case we only need these for sampling, so implementing a static `rvs` method suffices. As was in the context of simulators, it is important to accept the keyword argument `random_state`, which is needed for ELFI's internal book-keeping of pseudo-random number generation. Also the `size` keyword is needed (which in the simple cases is the same as the `batch_size` in the simulator definition)."
      ],
      "metadata": {
        "id": "i6Y3msta4ogw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define prior for t1 as in Marin et al., 2012 with t1 in range [-b, b]\n",
        "class CustomPrior_t1(elfi.Distribution):\n",
        "    def rvs(b, size=1, random_state=None):\n",
        "        u = ss.uniform.rvs(loc=0, scale=1, size=size, random_state=random_state)\n",
        "        t1 = np.where(u<0.5, np.sqrt(2.*u)*b-b, -np.sqrt(2.*(1.-u))*b+b)\n",
        "        return t1\n",
        "\n",
        "# define prior for t2 conditionally on t1 as in Marin et al., 2012, in range [-a, a]\n",
        "class CustomPrior_t2(elfi.Distribution):\n",
        "    def rvs(t1, a, size=1, random_state=None):\n",
        "        locs = np.maximum(-a-t1, t1-a)\n",
        "        scales = a - locs\n",
        "        t2 = ss.uniform.rvs(loc=locs, scale=scales, size=size, random_state=random_state)\n",
        "        return t2"
      ],
      "metadata": {
        "id": "azrsTDTN4XE2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "These indeed define a uniform distribution on a triangle."
      ],
      "metadata": {
        "id": "Y4w_cjj_5CFz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "t1_1000 = CustomPrior_t1.rvs(2, 1000)\n",
        "t2_1000 = CustomPrior_t2.rvs(t1_1000, 1, 1000)\n",
        "plt.scatter(t1_1000, t2_1000, s=4, edgecolor='none');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "c_YrMPD05NLp",
        "outputId": "d2f9d4b2-30bf-41f8-8618-9c4103f2ef04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model['t1'].become(elfi.Prior(CustomPrior_t1, 2))\n",
        "model['t2'].become(elfi.Prior(CustomPrior_t2, model['t1'], 1))\n",
        "\n",
        "elfi.draw(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "m6FQLWbX5ymR",
        "outputId": "9042ef5d-c411-4d22-82cd-a2f934f8740e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ],
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          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Rejection ABC\n",
        "\n",
        "The simplest ABC algorithm samples parameters from their prior distributions, runs the simulator with these and compares them to the observations. The samples are either accepted or rejected depending on how large the distance is. The accepted samples represent samples from the approximate posterior distribution.\n",
        "\n",
        "In ELFI, ABC methods are initialized either with a node giving the distance, or with the `ElfiModel` object and the name of the distance node. Depending on the inference method, additional arguments may be accepted or required. \n",
        "\n",
        "A common optional keyword argument, accepted by all inference methods, `batch_size` defines how many simulations are performed in each passing through the graph. \n",
        "\n",
        "Another optional keyword is the seed. This ensures that the outcome will be always the same for the same data and model. If you leave it out, a random seed will be taken."
      ],
      "metadata": {
        "id": "2S1ZJckw6UN6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rej = elfi.Rejection(model['d'], batch_size=100, seed=seed)"
      ],
      "metadata": {
        "id": "Nrp-Al7h6PxB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "N = 1000\n",
        "rejABC_posterior = rej.sample(N, threshold=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zW0B0gdF7G9S",
        "outputId": "0dad4cfe-e831-4cba-f5ed-6af55743aa9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [==================================================] 100.0% Complete\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rejABC_posterior.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y2BX6lW2-nsA",
        "outputId": "9d4b9f9d-7c0e-448c-874c-69e5d1226113"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Method: Rejection\n",
            "Number of samples: 1000\n",
            "Number of simulations: 34200\n",
            "Threshold: 0.2\n",
            "Parameter                Mean               2.5%              97.5%\n",
            "t1:                     0.108             -0.169              0.386\n",
            "t2:                     0.224             -0.075              0.598\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `sample` method returns a `Sample` object, which contains several attributes and methods. Most notably the attribute `samples` contains an `OrderedDict` (i.e. an ordered Python dictionary) of the posterior numpy arrays for all the model parameters (`elfi.Prior`s in the model). For rejection sampling, other attributes include e.g. the `threshold`, which is the threshold value resulting in the requested quantile. "
      ],
      "metadata": {
        "id": "QJU-N1pg8nuB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rejABC_posterior.plot_pairs();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "B4OyJFB6825f",
        "outputId": "39d1186d-10b1-4c0f-b761-bbebdd365530"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sequential Monte Carlo ABC\n",
        "\n",
        "SMC-ABC sampler is initialised just like the Rejection sampler. Modelwise, a major difference is that the algorithm requires a prior pdf."
      ],
      "metadata": {
        "id": "zSByAbJUAFB6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# define prior for t1 as in Marin et al., 2012 with t1 in range [-b, b]\n",
        "class CustomPrior_t1(elfi.Distribution):\n",
        "    def rvs(b, size=1, random_state=None):\n",
        "        u = ss.uniform.rvs(loc=0, scale=1, size=size, random_state=random_state)\n",
        "        t1 = np.where(u<0.5, np.sqrt(2.*u)*b-b, -np.sqrt(2.*(1.-u))*b+b)\n",
        "        return t1\n",
        "    \n",
        "    def pdf(x, b):\n",
        "        p = 1./b - np.abs(x) / (b*b)\n",
        "        p = np.where(p < 0., 0., p)  # disallow values outside of [-b, b] (affects weights only)\n",
        "        return p\n",
        "\n",
        "    \n",
        "# define prior for t2 conditionally on t1 as in Marin et al., 2012, in range [-a, a]\n",
        "class CustomPrior_t2(elfi.Distribution):\n",
        "    def rvs(t1, a, size=1, random_state=None):\n",
        "        locs = np.maximum(-a-t1, t1-a)\n",
        "        scales = a - locs\n",
        "        t2 = ss.uniform.rvs(loc=locs, scale=scales, size=size, random_state=random_state)\n",
        "        return t2\n",
        "    \n",
        "    def pdf(x, t1, a):\n",
        "        locs = np.maximum(-a-t1, t1-a)\n",
        "        scales = a - locs\n",
        "        p = ss.uniform.pdf(x, loc=locs, scale=scales)\n",
        "        p = np.where(scales>0., p, 0.)  # disallow values outside of [-a, a] (affects weights only)\n",
        "        return p\n",
        "    \n",
        "    \n",
        "# Redefine the priors\n",
        "model['t1'].become(elfi.Prior(CustomPrior_t1, 2, model=model))\n",
        "model['t2'].become(elfi.Prior(CustomPrior_t2, model['t1'], 1))"
      ],
      "metadata": {
        "id": "Tgsub_qqBYee"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "smc = elfi.SMC(model['d'], batch_size=100, seed=seed)"
      ],
      "metadata": {
        "id": "4-2NWKzWZwzb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For SMC ABC, we have to define the number of output samples, the number of populations and a *schedule* i.e. a list of thresholds to use for each population. "
      ],
      "metadata": {
        "id": "c-6HcWpoAukw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "N = 1000\n",
        "thresholds = [0.7, 0.2, 0.05]\n",
        "result_smc = smc.sample(N, thresholds, bar=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K5Hdtm-qAtAI",
        "outputId": "c17efdb0-0c13-4298-99db-ff8d772c1d6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.inference.samplers:---------------- Starting round 0 ----------------\n",
            "INFO:elfi.methods.inference.samplers:---------------- Starting round 1 ----------------\n",
            "INFO:elfi.methods.inference.samplers:---------------- Starting round 2 ----------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_smc.summary(all=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fwSDXJoKZzAT",
        "outputId": "a076b2db-b492-495d-dab6-5932f08389d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Method: SMC\n",
            "Number of samples: 1000\n",
            "Number of simulations: 94500\n",
            "Threshold: 0.05\n",
            "Parameter                Mean               2.5%              97.5%\n",
            "t1:                     0.099             -0.107              0.325\n",
            "t2:                     0.244              0.012              0.526\n",
            "\n",
            "\n",
            "Population 0:\n",
            "Method: Rejection within SMC-ABC\n",
            "Number of samples: 1000\n",
            "Number of simulations: 2800\n",
            "Threshold: 0.683\n",
            "Parameter                Mean               2.5%              97.5%\n",
            "t1:                     0.074             -0.572              0.707\n",
            "t2:                     0.128             -0.493              0.888\n",
            "\n",
            "\n",
            "Population 1:\n",
            "Method: Rejection within SMC-ABC\n",
            "Number of samples: 1000\n",
            "Number of simulations: 16500\n",
            "Threshold: 0.2\n",
            "Parameter                Mean               2.5%              97.5%\n",
            "t1:                     0.094             -0.182              0.354\n",
            "t2:                     0.235             -0.072              0.629\n",
            "\n",
            "\n",
            "Population 2:\n",
            "Method: Rejection within SMC-ABC\n",
            "Number of samples: 1000\n",
            "Number of simulations: 75200\n",
            "Threshold: 0.05\n",
            "Parameter                Mean               2.5%              97.5%\n",
            "t1:                     0.099             -0.107              0.325\n",
            "t2:                     0.244              0.012              0.526\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_smc.plot_marginals(all=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "id": "sjDGqXACGVtJ",
        "outputId": "2a1e0ce4-aa30-43c4-ef19-38620c45ff27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the end `SMC` required 94500 samples to obtain 1000 samples below threshold = 0.05. Let's run rejection ABC until we have 1000 samples below threshold 0.05:"
      ],
      "metadata": {
        "id": "VrWdyevsZtC5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "N = 1000\n",
        "rejABC_posterior = rej.sample(N, threshold=0.05)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JRV6Q8M5aAu3",
        "outputId": "56136975-4438-4cab-df35-a748760c6853"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rejABC_posterior.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FMWuvsOdabLb",
        "outputId": "7e77d909-b747-4b5b-efa8-766fc5ec3209"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Method: Rejection\n",
            "Number of samples: 1000\n",
            "Number of simulations: 503600\n",
            "Threshold: 0.05\n",
            "Parameter                Mean               2.5%              97.5%\n",
            "t1:                     0.100             -0.131              0.355\n",
            "t2:                     0.243              0.002              0.517\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BOLFI \n",
        "\n",
        "In practice inference problems often have a complicated and computationally heavy simulator, and one simply cannot run it for millions of times. The Bayesian Optimization for Likelihood-Free Inference [BOLFI](http://jmlr.csail.mit.edu/papers/v17/15-017.html) framework is likely to prove useful in such situation: a statistical model (usually [Gaussian process](https://en.wikipedia.org/wiki/Gaussian_process), GP) is created for the discrepancy, and its minimum is inferred with [Bayesian optimization](https://en.wikipedia.org/wiki/Bayesian_optimization). This approach typically reduces the number of required simulator calls by several orders of magnitude.\n",
        "\n",
        "This tutorial demonstrates how to use BOLFI to do LFI in ELFI."
      ],
      "metadata": {
        "id": "37ayda3MHeUx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fitting the surrogate model\n",
        "\n",
        "Now we can immediately proceed with the inference. However, when dealing with a Gaussian process, it may be beneficial to take a logarithm of the discrepancies in order to reduce the effect that high discrepancies have on the GP. (Sometimes you may want to add a small constant to avoid very negative or even -Inf distances occurring especially if it is likely that there can be exact matches between simulated and observed data.) In ELFI such transformed node can be created easily:"
      ],
      "metadata": {
        "id": "ppj4KEn8HtzZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "elfi.Operation(np.log, model['d'], name='log_d');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DmaL_NnEHrvW",
        "outputId": "6c3e953a-a6b0-4085-e7d6-546a1e8c5327"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Operation(name='log_d')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
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      "cell_type": "code",
      "source": [
        "elfi.draw(model)"
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        "outputId": "6c464cd3-cc4e-401b-995d-6676b5ddaa00"
      },
      "execution_count": null,
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          },
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      "cell_type": "markdown",
      "source": [
        "As BOLFI is a more advanced inference method, its interface is also a bit more involved as compared to for example rejection sampling. But not much: Using the same graphical model as earlier, the inference could begin by defining a Gaussian process (GP) model, for which ELFI uses the [GPy](https://sheffieldml.github.io/GPy/) library. This could be given as an `elfi.GPyRegression` object via the keyword argument `target_model`. In this case, we are happy with the default that ELFI creates for us when we just give it each parameter some `bounds` as a dictionary.\n",
        "\n",
        "Other notable arguments include the `initial_evidence`, which gives the number of initialization points sampled straight from the priors before starting to optimize the acquisition of points, `update_interval` which defines how often the GP hyperparameters are optimized, and `acq_noise_var` which defines the diagonal covariance of noise added to the acquired points. Note that in general BOLFI does not benefit from a `batch_size` higher than one, since the acquisition surface is updated after each batch (especially so if the noise is 0!)."
      ],
      "metadata": {
        "id": "sPZnFF0hICih"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bolfi = elfi.BOLFI(model['log_d'], batch_size=1, initial_evidence=20, update_interval=10, \n",
        "                   bounds={'t1':(-2, 2), 't2':(-1, 1)}, acq_noise_var={'t1':0.1, 't2':0.1}, seed=seed)"
      ],
      "metadata": {
        "id": "rFTpcZeaIBVh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sometimes you may have some samples readily available. You could then initialize the GP model with a dictionary of previous results by giving `initial_evidence=result.outputs`.\n",
        "\n",
        "The BOLFI class can now try to `fit` the surrogate model (the GP) to the relationship between parameter values and the resulting discrepancies. We'll request only 100 evidence points (including the `initial_evidence` defined above)."
      ],
      "metadata": {
        "id": "-FizoEk2Iomy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "post = bolfi.fit(n_evidence=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XgTf3ww0Ip9j",
        "outputId": "f333e72e-a0c0-40f6-c19b-c53d206d63a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.inference.bolfi:BOLFI: Fitting the surrogate model...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [==================================================] 100.0% Complete\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.posteriors:Using optimized minimum value (-1.6062) of the GP discrepancy mean function as a threshold\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bolfi.target_model"
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2BLn1shQK9JL",
        "outputId": "a07ab912-1d90-4df8-e6de-771e8bf3ee28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "Name : GP regression\n",
              "Objective : 78.53641077165285\n",
              "Number of Parameters : 4\n",
              "Number of Optimization Parameters : 4\n",
              "Updates : True\n",
              "Parameters:\n",
              "  \u001b[1mGP_regression.         \u001b[0;0m  |                value  |  constraints  |     priors    \n",
              "  \u001b[1msum.rbf.variance       \u001b[0;0m  |   0.3630657802137458  |      +ve      |  Ga(0.0083, 1)\n",
              "  \u001b[1msum.rbf.lengthscale    \u001b[0;0m  |  0.48910102787502585  |      +ve      |   Ga(1.3, 1)  \n",
              "  \u001b[1msum.bias.variance      \u001b[0;0m  |  0.03436487581949289  |      +ve      |  Ga(0.0021, 1)\n",
              "  \u001b[1mGaussian_noise.variance\u001b[0;0m  |  0.12611712861991034  |      +ve      |               "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bolfi.plot_gp();"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "<Figure size 576x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gp = elfi.GPyRegression(model.parameter_names, bounds={'t1':(-2, 2), 't2': (-1, 1)})\n",
        "prior = ModelPrior(model)\n",
        "\n",
        "acq = MaxVar(model = gp, prior = prior)\n",
        "\n",
        "bolfi = elfi.BOLFI(model['log_d'],\n",
        "                   batch_size = 1,\n",
        "                   initial_evidence = 20,\n",
        "                   update_interval = 1,\n",
        "                   target_model = gp,\n",
        "                   acquisition_method = acq\n",
        "                   )\n",
        "\n",
        "bolfi.fit(100)\n",
        "bolfi.plot_gp();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "Sy-5LXEhN-hK",
        "outputId": "41d4173d-d91f-4b3c-fe5f-9c7cf8d67136"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.inference.bolfi:BOLFI: Fitting the surrogate model...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [==================================================] 100.0% Complete\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.posteriors:Using optimized minimum value (-1.7532) of the GP discrepancy mean function as a threshold\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gp = elfi.GPyRegression(model.parameter_names, bounds={'t1':(-2, 2), 't2': (-1, 1)})\n",
        "prior = ModelPrior(model)\n",
        "\n",
        "acq = RandMaxVar(model = gp,\n",
        "                 prior = prior,\n",
        "                 sampler='metropolis',\n",
        "                 sigma_proposals={'t1': 4 / 10, 't2': 2/10})\n",
        "\n",
        "bolfi = elfi.BOLFI(model['log_d'],\n",
        "                   batch_size = 1,\n",
        "                   initial_evidence = 20,\n",
        "                   update_interval = 1,\n",
        "                   target_model = gp,\n",
        "                   acquisition_method = acq\n",
        "                   )\n",
        "\n",
        "bolfi.fit(100)\n",
        "bolfi.plot_gp();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8QxKERu0W6ao",
        "outputId": "d8f5d3ab-335a-4fcd-bc1b-8632d6a78ea9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.inference.bolfi:BOLFI: Fitting the surrogate model...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ""
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.080\n",
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.380\n"
          ]
        },
        {
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            "Progress [==========----------------------------------------] 21.0% Complete\r"
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        },
        {
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          ]
        },
        {
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            "Progress [===========---------------------------------------] 22.0% Complete\r"
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        },
        {
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        },
        {
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        {
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        {
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        {
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        {
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [=========================================---------] 82.0% Complete\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.240\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ""
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.300\n",
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.160\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [==========================================--------] 85.0% Complete\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.280\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [===========================================-------] 86.0% Complete\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.200\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [===========================================-------] 87.0% Complete\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.240\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [============================================------] 88.0% Complete\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.340\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [============================================------] 89.0% Complete\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.280\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [=============================================-----] 90.0% Complete\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.000\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [=============================================-----] 91.0% Complete\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.340\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [==============================================----] 92.0% Complete\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.200\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [==============================================----] 93.0% Complete\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.260\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [===============================================---] 94.0% Complete\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.240\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [===============================================---] 95.0% Complete\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.300\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [================================================--] 96.0% Complete\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.280\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [================================================--] 97.0% Complete\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.280\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [=================================================-] 98.0% Complete\r"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.320\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [=================================================-] 99.0% Complete\rProgress [==================================================] 100.0% Complete\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.posteriors:Using optimized minimum value (-1.9308) of the GP discrepancy mean function as a threshold\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gp = elfi.GPyRegression(model.parameter_names, bounds={'t1':(-2, 2), 't2': (-1, 1)})\n",
        "prior = ModelPrior(model)\n",
        "\n",
        "acq = ExpIntVar(model = gp,\n",
        "                prior = prior,\n",
        "                sampler='metropolis',\n",
        "                sigma_proposals={'t1': 4 / 10, 't2': 2 / 10})\n",
        "\n",
        "bolfi = elfi.BOLFI(model['log_d'],\n",
        "                   batch_size = 1,\n",
        "                   initial_evidence = 20,\n",
        "                   update_interval = 1,\n",
        "                   target_model = gp,\n",
        "                   acquisition_method = acq\n",
        "                   )\n",
        "\n",
        "bolfi.fit(100)\n",
        "bolfi.plot_gp();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "G3Z0EKOXW7oy",
        "outputId": "301bf3da-eec6-4c34-d2f2-f7450cbc89fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.inference.bolfi:BOLFI: Fitting the surrogate model...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ""
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " /usr/local/lib/python3.7/dist-packages/scipy/stats/_distn_infrastructure.py:1740: RuntimeWarning:divide by zero encountered in true_divide\n",
            " /usr/local/lib/python3.7/dist-packages/GPy/kern/src/stationary.py:168: RuntimeWarning:overflow encountered in true_divide\n",
            " /usr/local/lib/python3.7/dist-packages/GPy/kern/src/rbf.py:52: RuntimeWarning:overflow encountered in square\n",
            " /usr/local/lib/python3.7/dist-packages/GPy/kern/src/rbf.py:76: RuntimeWarning:invalid value encountered in multiply\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress [==================================================] 100.0% Complete\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.posteriors:Using optimized minimum value (-1.8637) of the GP discrepancy mean function as a threshold\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 4 Axes>"
            ],
            "image/png": 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PgFStB0VE0YUzUKQLNSdOo2zZA2jqGUR73QmkpqRg2rIHUHPiFB708HiLxYL6hgYc+9ETyJ5chNk3LsHkmfO87qzzVCsFQNXCdVLNdwEkA3gEwDMAbgPA3QBEFFYMUKQLdQ02lNz7dcyMjR2+NjQ4iNrte0Y91ln7NOOLD+CT5m4Mxsag+p03UNL0GYZazo7aWeetVipJ9iHvhmVcAjQYKeUhAHDMQj0ipezUeEhEFIW4hEe64OzT5Mp9Nsm53PaN1Y/gXGMbUpLicf28a5GaGI/YrAJ8Wv26x5113rqQ7zt0GLV1F7H77T3YV21Bc1OT4iVACj8hhEkIcQzAhwCOCSGOCiGu13pcRBRdGKBIF8Y6DsU5i9RXWIb0RfcjZX45Du7ZjYH2i7ipzIy771mBwoICjzNHnmqlejs70NF9Bd39VzGheB6GUnNRc/wUPjl+hGfY6d+vADwkpZwqpZwKYA3sO/OIiMKGAYp0wWw2o2LNKiTUV6N2+wtIqK8eMZvkOouUlpaGcamZmDD/Dpw4uBeA767inma3juzdhckLb0XPqWr0NJ9HfFIKIIBDO19B42cXWFSub4NSyvedX0gpPwBwVcPxEFEUYg0U6YavPk2ufaJmTZuKmuOnkJQ1CZdam4Znq7x1FffUhby1/hTuXLsBg50tOHFwL2wHmzEuMQXdly4i48Z/GX4ci8p1aa8Q4ucAfgdAArgPwLtCiFIAkFLWaDk4IooODFCkS+675pLi44bPssvJzUUpgCPWagxevjBqtsqdpy7kN9+wAOMGr2DizHmYPHMeAODNXTsxSQ6yqFz/5js+f9/t+kLYA9Vt4R0OEUUjBijSHU+75i6c+A0a396Ga25fgYy8QsQMdGPywAW8/H83+RVu3Ge3nD/D/Wy8pV9fPeK+sbqhU/hJKZdqPQYiIgYo0p2t23YgvvgGfNzQjK6PP0VqSgom3vB3EGfetddIBXiWnfus1j23mlBzohr7d/4cnT29QF8njuzdhZjYmOFZKU87Af05e4/UI4SYCGADgMlSyi8LIWYDWCSl/KXGQyOiKMIARbpz9NhxdE7PR8rEQkzITUV/Txc+uViPtJY2bPuvVwJ6TtdZreziRByoOYTXfvxrzJqUjpiUTJiXrcDV2ERU79+PPTu3IaP4OAaGgIFzh7B+9TdGPQcbb2rqFdh33T3p+PoUgD8AYIAiorDhLjzSnY7OTojYGCSkpEMIx+fYGHR0Bt4v0bmLbyguBUdqP0Fi4XWY8qXv4Ni5i2hOnY6huBRMzJuEa+fMxVBmIer270RybzPmLC7Ha+9ah2eePPWT2rptRwhfPfkhW0r5RwBDACClvApgUNshEVG04QwUBS3Uy1ppyUloOnkA8UmpSM7OR0+LDd0nDyA3OSngsW3fuQvT7y5GX18fknOKkJCSjvikFPT3XUHGjAU4dfZT5OTmoqWtHUXXL0VLZwO+uHItAKDVdm749Tl3AjqxRkoT3UKILNgLxiGEKAPQru2QiCjaMEBRUNRY1lqwYD4ahrJgO/0BbAebkZ6Zg5K581EQ0xrw2CbPbkF3/1U0N7eiYOJ0AEBPiw3JKWkY7GlHb3c3AKCruxvxGIf0zJzh53GGJGc/KecuPcB3/ylSzb8A2AlguhBiH4AcAP+PtkMiomjDJTwKihrLWitXLMdQy1ksXPoVLF/zFBYu/QqGWs4OdyUPZGxzyuxNM2Ou9qH1YiO6m+px+ehbWLjky2g+9Dpi+joxNDiImL5ONB96HbNvXDL8PM6QNFa3dFKfECIWwBLHx2IA/wRgjpTyQ00HRkRRhzNQFBQ1lrU89W1SuuPOdWwXTh/DiYN70XvpArrPHkPTe79F/MKbcMOSu9DZehFXLpxE42en8CfrnzFtyiQkJcciITkVQ4ODw+0NnD8/FOOiwEkpB4UQ35BSvgDguNbjIaLoxQBFQVFrWctXV3IlYztZ/TY+Pv4hJsy/A9MX3YfLDSfR8v7vcW1OPD7d8zs0dg1g0Tcew/Q5C4bD0jcd7Q08haRQjIuCtk8I8RLsO++6nRfZgZyIwokBioLi6ZgUX8eqhHts3/jnf8H4L/w9knOmoL+nC5CA+asPAmfeRWdPL67KeNR9eAApSfGO3k/lqDlRjU0bN2g9fPJugePz0y7X2IGciMKKAYqCoudlLbPZjPzcLMj4cbh87hhSU1JQOmcW+i59hj0fnUX67JsxdcFS9LZ+hoN7duNGAHnTZnNXnc6xEzkR6QEDFAVNjWWtULVGmD9vDvoKJ45YYvzf136JPFM5kjPzMHClFym5hcD8O3Di4F4kJKdyV53OCSE2AHheSnnZ8XUGgH+VUlZoOzIiiibchUe642w/0FdYhpJ7H0NfYRkqN1fBYrEofi5PO+da60/hutIbMGvaVPQ0n0dfdweSsiah5UIdd9UZw5ed4QkApJRtAO7ScDxEFIU4A0W642w/0NfThXd+91N0XGpGfEIiNv7kZWz7L2WzUJ6WGG++YQHGDV5BVl4xSgGcOvspLn58FuNj+3Wz/Eg+xQohEqSUfQAghEgCkKDxmIgoyjBAUVgoWZKra7BhfEYHDr33JibMvwP52fnoaW7Agb/+EhaLRXHAcV9idM5wAeXIyivEtQPdaLxYg4oXnmN4MobfAnhbCPFrx9cPAqjScDxEFIUYoEh1SruVFxXk4929uzBhwZft9UkAxqVmDjfoDDbk6LnwncYmpfyBEOIogDscl56RUu7WckxEFH0YoEhVFosFqx97Av1Fi3C5oRmz4lKQk18MoBw/emkLcieOnpVauWI5tv/vQ0hf9HVIOYT+ni70NJ/HgtIbUPfeb0MyLqWF76E+748CJ4RIAfBXKeUbQohrAFwjhIiTUg5oPTYiih4sIifVOGee2gfjMXHBUgyl5qLm+Ck0NzXhamwi3j90xGOhuNlsxs03LEDf+eO4fO4YYrqaUDpnFsYNXtFkh1woi9pplGwhhNXlY7Uf97wHIFEIkQ/gDQArAbyi5iCJiNxxBopU4ywG/6zzdfS2fuZYjpuCU2c/Rc+lRmQVzhpuL5DlmJVyLtH968Or7ct+87036AzXrJDrmXqexkpBaZFSmhTeI6SUPUKI7wD4qZTyeSHEETUGR0TkDQMUqcZ5Ft3sG5fg4J7dwPw7kJQ1CRc/PouOE+9j6ddHTja4nqHnXqeUME4gKWYcKv9zM4oKdmBC4jj89+vvYCg+FVmTChEzlOWzrioUr8PbWCnshBBiEYC/B/Adx7VYDcdDRFGIAYpU4zwnb/LMebgRwImDe/Hp3jqMj+3HornTkJSWPuLx7mfoOeuUXIvQp+YV4mT129i6bRvyb7oXE6+9HpfOHce+t17FuMErWP1YLbaEeDedWuf9UcAeBfDvAF6TUh4XQkwDsEfjMRFRlGENFKnGtYll3rTZWLj0K5h3zXRseeE5rHvkoVENLr01sXRdQouJjYXt3Cmkl96Nwbhk9HZeRme/RNoN9yBu4kz0Fy0KeX2Sp2acbLipHSnlXinlMinlDxxfn5VSPqL1uIgounAGilQzVrsAf1sJuC+hdVxqRqppKXqa6tDW9Bnis/IxLiEJjUf+gqnXL0FeQU5I65PY9kAfhBAvSikfFUL8GfbDg0eQUi7TYFhEFKUYoEhVvtoF+NtKwH0JLT0zB71dFyEGB3ClpwtpE5PR21SHmNg4zJo2FRlZWSGvT1LjvD9SbKvj8w81HQURERigyABWrlg+3Dk8I68Q+cWzYN3zPyi59av4tLkT7adr0H/uEG68tRw5ublotZ0bVZ/k74499nvSLynlYcfnvUKIHMefm7UdFRFFKwYo0oy/YcXTEtpX165EzYnT6Gs+DltTK0qX3oNrTDcN1ye5tzvwpxO60o7pFH5CiP8A8DDs9ZtCCHEVwCYp5dOaDoyIoo6QclQpQcQymUzSarVqPQzCyLDi3udJaVgZK4itXbcefYVlI3bRtdrOIaG+Gps2blD8OAodIcRhf/tACSH+BcCXAayWUp5zXJsG4KcA3pBSvqDeSImIRuIMFGkilM0px6pP8rePE/s96d5KAF+UUrY4L0gpzwohvgXgrwAYoIgobNjGgDRR12BDRt7osFLXYAv5z3IWobvy1MfJ38eRZuJcw5OTow4qToPxEFEUY4AiTYQzrPjbx4n9nnSvP8DvERGFHGugSBOhrIFyfU5vtVDchadPCmugBgF0e/oWgEQpJWehiChsGKBIM6EMK4EEMoYl7SkJUEREesIictJMKJtTKi1KZ8sCIiIKBmugKCIoLUp3P18vK78YeSZ74CIiIhoLAxRFBKVF6eHcBUhERJGHAYoigtIddGxZQEREwWCAoohgNptRsWYVEuqrUbv9BSTUV/ssIGfLAiIiCgZ34VHU4i487XEXHhEZFXfhUdQK5S5AIiKKLgxQpAtKZoM4c0RERFpjDRRpztmTqa+wDCX3Poa+wjJUbq6CxWIJ6rFERERq4QwUacY5k7TrzXeQPHEaMnq6hnsyeWuCqbRhJhERkRo4A0WacJ1JSl90P1Lml+Pgnt24cPoYAO89mdi/iYiI9IAzUKQJ15mktIZmDKVmYsL8O3Di4F5MnjnPa08mZ/8m5wwUEHz/JtZUERGRUpyBIk24ziTNmjYVPc3nEZuUhvbWJp89mULdv4k1VUREFAjOQJEmXGeScnJzUQrgiLUag5cv+GyCaTabUQH7DFbtvu0oKsj32TDTlaeZJtZUERFRIBigSBMrVyxH5eYqAOXIyCtEzEA3Jg9cwMv/d9OYwSWQ/k2//vWvUbnplxiKT0XWpELEDGWhcnMVLl88j0Xmr494bEZeIWr3bVf6koiIKIowQJEmgplJ8pdzxunIkaOobWjGxJtWYOK116OnxYaPj76Fa+dch44ztSGvqSIiosjHAEWaUbMTuLO2Kc9Ujt4zLUguXYxekYDezstIyS0E5t+B86feR1pyEhqtu+GcCWtrrEejdTcq1qxSZVxERBQZWEROEcm1tqmzrQVp+TMQNz4XbU2fAQCSs/PR+lk9FiyYr+gQYiIiIoAzUBQh3AvEjx47PlzblJ6Zg8H+TgzEpqKvpwtSDuFyw0nE9HcNtyxgYCIiIiU4A0WG56kVga2pFZ8cPwIAmH3jEvScfB/xPRcRJwfQWPMWOg+9hoq132Fw0l62EMLq8rFa6wEREfmDM1BkeJ5aEcxbeg+O7v5vZGZlIm/abFzb9BmO7dmG/NwszJ83BysfrWR40ocWKaVJ60EQESnFAEWGV9dgQ4l55PEu15TdjtYP37HXNjl2+a3/2X8yNBERUUgwQJFhOeuejn98Eud+8TxKb/s7TJ45D4C9FcGCBfOxaeMGjUdJRESRiDVQZEiudU9LVj+NwZxZ+OCNHTj/8dGgj3chIiIaCwMUGZJr3dPEvElY/IWbkTF9Pvb/YRNbERARkeq4hEeG5F73lJObi9vLv4LarlNctiMiItVxBooMyXkYsauT1W+jvqEBy+5fhbXr1sNisWg0OiIiinScgSJDcj+M+GT127DueR1lyx7A9DkL0NZYj8rNVagAhpfy3JttOptoEhERKcUARYbkfhhxfUMDypY9gJnXXQ8Ajp5Q5di6bQfMZvOIs/FKzIUeAxYREZG/GKDIsFyPYFl2/ypMn7NgxPcz8gpRu287AM/NNl0DFhERkRKsgaKIMFZN1K6/vo3ezo4R38/IK0Rdgy2cwyQiogjBAEURYeWK5Wi07kar7RyGBgdRu++vsO55HTO++ABK7n0MGTcsw/43d+LC6WPD97Q11qOoIF/DURMRkVFxCY8iwlg1UQtMZdh/ZQA17/wv8qbNRltjPRqtu1GxZpW2AyciIkNigKKI4asmaqD9IkTLJ6g7fgh/qvwnlM4rQcUjD7H+iYiIAsIARYblqy2BsyYqK78YF04fw8E9u5E8azHmz70FJUUT0WjdrfHoiYjIyFgDRYbkehZeyb2Poa+wDJWbq4abZzprok5/eBhv/88f0ZM5C5cvtyMnKwNZ+cXIM9l34BEREQWCAYoMybUtQUxs7KhQZDabcc+tJvxt5y/Qfv4UklLTkTFxCj690IzmpibuwCMioqAwQJEh1TXYkJFXOOKaeyiqOXEatz/4OK413YyMjAmYkFeA5LKS+P4AACAASURBVJwpOHX2U+7AIyKioDBAkW5YLBasXbfer7PsPPV9cg9FzpA1+8YluHz0LXQ31SMuMQkt58+i0bobK1csV+21EBFRZGOAIl0Yq6bJnXvfp1bbuVGhyBmyJs+chxuXlmPw9Af49NUfIr7uACrWrOIOPCIiChh34ZEuKD1qxdn3aeNPXsbbx2oBIWC6bu6Ix7geOJw3bTYSklOHez8xPBERUTAYoEgX6hpsKDGPrmlynmXnTV9sCm7/xyeRkTf6gGD35ppFBfkMT0REFBIMUKQLrn2bnMYq9PZn1sq1uSYREVGosAaKdMGfmiZ3/uzEIyIiUgNnoEgXAlluKyrIxyfHj6CpZxBd3d1ITUlBbnIs2xMQEZHqGKBIN5Qut5XOnonvbfoNxi/8Mq7GJuLiyVp8WLsXa+/7ss/7fB0BQ0RE5A8u4ZFh1Zw4jWkl89C4709otfwPYrubMXHhbfj9W9Ve2x8obZdARETkCQMUGVZdgw09Pb2Y+dWHMXPZQ0ieYcZAcg56s67Fj17a4vGesY6AISIi8gcDFBlWUUE+Wj+rB+IS0XThPMT4PCROnoWU4oV4/9ARj7NKLDwnIqJQYIAiw1q5Yjli+rvw2cd/Q3zWZMTEJWDwSg9SYgaRVTjL46ySP0fAEBERjYVF5KRrzoLvo8eOo6OzE2nJSViwYP5w4XfF2u/gsQ0vQaRkIiW3AClXu9Bz8n3csOQu1H345qjnc+1O7my+6exOTkRE5C8GKNItZ8F3fPEN6Jyej5jYGDSdPICGoazhjuMPPvggdr29Fx831qC/bj+SM3Nw49JyJCSnYoKHWSV2JyciolBggCLdchZ8f9zQjJSJhUhISUd8Uipspz/AwqVfGe44vu6Rh1C5uQp5Jv9mldidnIiIgsUaKNItZ8F3V3c34pNTAQDJ2fnouNQ8ovDbbDajYs0qJNRXo3b7C0ior+asknFkCyGsLh+rtR4QEZE/OANFmvPW2NJZ8J2akoL+ni4kpKSjp8WG9MycUYXfnFUyrBYppUnrQRARKcUZKNKUr8aWzvPxcpNj0X2xHm11J9B25E3kF88a85w8IiIiNXEGijTl2tgSgOOzvbHlpo0bhgu+mz6x78LLTU5CQUwrVnKJjoiINMQARZqqa7ChxDy6sWXtvu0AuDRHRET6xABFmnLWOTlnoAD/G1vyUGAiItIKa6BIU846p1bbOQwNDqLVds6v+iYeCkxERFriDBRpKtDGlr5qpzgLRUREamOAIs0FUuc0Vu0UERGRmriER4bEQ4GJiEhLnIEiw3AtGk8YJ9B0ehuuuX2Fz+NbWGhORERqYIAiQ3AWjeeZylFidgSmN36D9kM70dg/4LF2ytM9zkOIGaKIiCgYDFBkCJ6KxkvufAAJ9dXYtHGD3/ew0JyIiEKBNVBkCM6DhV25HigcqnuIiIj8wQBFhhBI0TgLzYmISC0MUGQIrg03LzZ+hjd37cSuX/4QjZ9d8No8M9AmnURERGMRUkqtxxA2JpNJWq1WrYdBAbJYLPjRS1vw/qEjyCqchQVL7kJSWvrw7jtPdU3chadvQojDUkqT1uMgIlKKAYoMZe269egrLBtxdl6r7ZzPYnLSLwYoIjIq7sIjXXOfQTpy5CgWm78+4jHsQE5EROHGGijSLU8HBjd2DeBk9dsjHsfCcCIiCjcGKNIt1z5OMbGxyMovxvzyb+LYntdYGE5ERJpigCLd8tTHafqcBcjPzUJCfTVqt7+AhPpqrwXkREREamENFOmWs4+Ta8F4W2M95s+bw4JxIiLSFGegSLfYx4mIiPSKM1CkW2azGRWw10LV7tvu8cBgIiIiLTBAka6ZzWYGJiIi0h0u4REREREpxABFREREpBCX8MiQeMYdERFpiTNQZDieOpRXbq6CxWLRemhERBQlGKDIcDx1KM8zlWPrth1aD42IiKKEkFJqPYawEUJ0Ajip9ThUlg2gRetBqCpm3IyY+KTYmITkK85LEsBgZ2schq6e0XBkoRb5f5fANVLKNK0HQUSkVLTVQJ2UUpq0HoSahBDWSH+NgP11DvZ2RPTrjIa/SyGEVesxEBEFgkt4RERERAoxQBEREREpFG0BaovWAwiDaHiNQHS8Tr5GIiKdiqoiciIiIqJQiLYZKCIiIqKgMUARERERKcQARURERKQQAxQRERGRQgxQRERERAoxQBEREREpxABFREREpBADFBEREZFCDFBERERECjFAERERESnEAEVERESkEAMUERERkUIMUEREREQKMUARERERKcQARURERKQQAxQRERGRQgxQRERERAoxQBEREREpxABFREREpBADFBEREZFCDFBERERECjFAERERESnEAEVERESkEAMUERERkUIMUEREREQKMUARERERKcQARURERKQQAxQRERGRQgxQRERERAoxQBEREREpxABFREREpBADFBEREZFCDFBERERECjFAERERESnEAEVERESkEAMUERERkUIMUEREREQKaRqghBC/EkI0CSE+8vJ9IYT4iRDijBDiQyFEqcv3VgkhTjs+VoVv1ERERBTttJ6BegXAnT6+/2UAMx0fqwH8FACEEJkAvg/gRgBmAN8XQmSoOlIiIiIiB00DlJTyPQCXfDzkqwB+I+2qAUwQQkwCUA7gTSnlJSllG4A34TuIEREREYWM1jNQY8kH0ODy9XnHNW/XiYiIiFQ3TusBqE0IsRr25T+IcQnXj5swRdWfd7XvimrPPS4hMeTPmZAY8f8EgtZ35WpYf56a/4bUEui/zYGWT1qklDkhHo7qsrOz5dSpU7UeBlHUOHP2UyRm5EIIMXxNSokrbU2YMW0qAODw4cNh/e+J3t89bQAKXL6e4rhmA3Cr2/V3PT2BlHILgC0AEJ8zQ2bf+7wa4wQAtJ05hVjVnh3ImDFLleedMWuiKs8bSc6cuhi2n9V25lTYflaoBPpv87Of31sX4qGExdSpU2G1WrUeBlHUWLtuPfoKy5CVXzx8rdV2Dgn11di0cQMAQAgR1v+e6H0JbyeABxy78coAtEspPwOwG8CXhBAZjuLxLzmuRTQjvrESEREFa+WK5Wi07kar7RyGBgfRajuHRuturFyxXLMxaToDJYT4HewzSdlCiPOw76yLAwAp5c8A7AJwF4AzAHoAPOj43iUhxDMADjme6mkppa9idNUx3ES2GbMmhnUWymjazpxSbYaUiMhsNqMCwNZtO1C7bzuKCvJRsWYVzGazZmPSNEBJKb8xxvclgDVevvcrAL9SY1x6xjeqEJESqVe60JWYCrisqfsSkSFKSoy/egXt4xL9/j0QEWnBbDZrGpjc6b0GyhA4+2QwUuKhnS+h9JMa1EwvxcvLHo7O8CAlnju9C7e0ncV7GdPwxMy7ovP3QEQUAL3XQJEHoQ5sETerMobUK10o/aQGLWlZKP2kBqlXuvy+N5IK7sdfvYJb2s6iMSEVt7Sdxfirxtv9R0SkFQYoijpdiamomV6K7M5W1EwvtS/jRaH2cYl4L2Ma8vq68F7GNPsyHhER+YVLeEHSavmOtVBBEAIvL3tYcQ2UU8TUQgmBJ2bexRooIqIAMEBRdBICXUlpWo9Ce0KgPS4p6KdhmCeiQFgsFmzdtgN1DTYUFeRj5YrluioU94VLeAQg+uqgghVJtVBERFqwWCyo3FyFvsIylNz7GPoKy1C5uQoWi0XrofmFASoIWu++0/rn+01KpPZ2AlJqPRIiItKJrdt2IM9Ujqz8YsTExiIrvxh5pnJs3bZD66H5hQGK1OVoGfDiTx/BQztfiqgQxVkoIqLA1TXYkJFXOOJaRl4h6hpsGo1IGdZAkao8tQxg7RERUeCMXDfkqqggH22N9SPOt2trrEdRQb6Go/IfZ6AMLpTLeGrUQUV6ywDOQhFROBm9bsiVHs+3U4IzUOEWbUdnBNkyQIl5xZnDfz52TtOjEYOSMWOWcerbiCisXOuGADg+2+uGjDYLpcfz7ZRggApQQG9w0Xp0hootA1xDk6fr4QhSEdMXioh0r67BhhLz6Lqh2n3bNRpRcPR2vp0SXMILI7WOztD7Mp6W5hVneg1ZRERG46wbcmWkuqFIwhmoMHIeneGcgeLRGcFREozmFWcaellPz9hEkyh8Vq5YjsrNVQDKkZFXiLbGejRad6NizSqfxeWRUniuJwxQ4cSjM0ImkFklNUNUqJfxWAdFRJ54qxsCgMrNVcgzlaPEbA9WlZurUOG4z9v3GKICxwAVbiE6OkNNZ05djNjdZZyJIiKj81Q3tHbdeq/F5QAipvBcT1gDFSGiabYi2JomtWqiIjV0EpH++WpKafSGlXrFABWAaAorkYqF5UQUSXwVl7PwXB0MUORRpO3G80SNEBXKWSgjFGcbYYxE0cBXU0qjN6zUK9ZARZC2M6ci/g0t1KGHNVFEFAnGakpp5IaVeqVpgBJC3AngxwBiAfxCSvmc2/dfALDU8WUygFwp5QTH9wYBHHN8r15KuSw8o44ekVxMrqZQ7sjjbjwi8pevppRGblipV5oFKCFELIDNAL4I4DyAQ0KInVLKE87HSCkfc3n8WgALXZ6iV0q5IFzjJe2pVbfEWajARPpsJxGRL1rWQJkBnJFSnpVS9gP4PYCv+nj8NwD8Liwjo2HRUAsF6LuonEGFiEh/tAxQ+QAaXL4+77g2ihCiCEAxgHdcLicKIaxCiGohBCvhKGihDFFc+iQiimxG2YV3P4A/SSkHXa4VSSlNAL4J4EUhxHRPNwohVjuClnXoSns4xqqpSK2X8RpupERSdwcgZXgHFCgpkd7XbZzxesFZMSKKdloGKBuAApevpziueXI/3JbvpJQ2x+ezAN7FyPoo18dtkVKapJSmmMTxwY45Kul2GU9KfO2V5/HIUw/ia688H5JQouoslJR46kAVtv35+3jqQJWi8TKwEBHpi5YB6hCAmUKIYiFEPOwhaaf7g4QQ1wLIAHDA5VqGECLB8edsADcBOOF+L0W2pJ5OzPzIgo6MbMz8yIKkns6QPK9a9VDp/T246cJHaEqegJsufIT0/h5F9zNEERHph2YBSkp5FcDDAHYDqAXwRynlcSHE00II15YE9wP4vZQj/u96CQCrEOIogD0AnnPdvUehp+UslLdA05uchtNzzUhva8HpuWb0JqeFeWTKdMQnY9/kucjtuYx9k+eiIz5Z6yEFhEGOiEjjPlBSyl0Adrlde8rt6//wcN9+APNUHRzpnxB49duPI6mn0x6ehAjZU4eqtcGInlBC4OlFq5De32MPTwGMl32hiIj0wShF5KRAJL3BjrmcJgR6U9JDGp5UJQQ6ElKCGq+WM0CcfSIiHct2bhpzfKxW84cxQJHfdFtMrpJQ1UJFSksDhici0rkW56Yxx8cWNX8YAxTplp6bW2qNYYaISFsMUKSIJrNQGvZ60vMsVDhDFAMbEdFIDFCkbyr0eook4Qg2DE9ERKMxQJEuOWd+1Or1FMhY9ErNgMPwRETkGQMU6ZrfvZ4McKSLmsXkGTNmhTzsMDwREXmnaR8oUk/bmVOGfQMcMePjT68nxzLfzI8sOD3XjFe//XjI2xqEqi+U2kLVJ8qo/3aIiMKFM1Ckf2P0etLDMp+/wtHSIJjZKDVmsoiIIhFnoEgRtQNAIPVGzmU+5wyUWke6GGUWysk1CI01K8XQRETBslgs2LptB+oabCgqyMfKFcthNpu1HpZqGKAo9KRE6pUudCWmKlpKC7hYO9RHukipyvEwTiOOdwkTBiSi0Ii2kOAvi8WCys1VyDOVo8RciLbGelRurkIFELG/Hy7hUWhJiYd2voQXf/oIHtr5UviKukN1pAvbJhCRF86Q0FdYhpJ7H0NfYRkqN1fBYrFoPTTNbd22A3mmcmTlFyMmNhZZ+cXIM5Vj67YdWg9NNQxQFFKpV7pQ+kkNWtKyUPpJDVKvdPl1X6haBSwuDG75bqx6Kr23NCAi9URjSPBXXYMNGXmFI65l5BWirsGm0YjUxwBFIdWVmIqa6aXI7mxFzfRS+zKeihYXpo34cL3mN5cWCH63TQhSpJyPRxRNojEk+KuoIB9tjfUjrrU11qOoIF+jEamPNVDkN7/e9IXAy8seVlQDFeiszlghyfn9/fU+duV5aIEQ0noqIooYzpCQlV88fC3SQ4K/Vq5YjsrNVQDKkZFnr4FqtO5GxZpVWg9NNZyBotATAl1J/oUPtcKTv4/1uGQ3Rj2Vns/HIyL1rFyxHI3W3Wi1ncPQ4CBabefQaN2NlSuWaz00zZnNZlSsWYWE+mrUbn8BCfXVqFizKmILyAHOQJGWHEtnSmZ6Aq1xWlyY5nEmKlwtEIjI+MxmMypgr4Wq3bcdRQX5ER8SlDCbzVH1u2CAIr+EfLZESnx/7y8UdQ8PtkDcY4gKdQsEhbRoaUBEgYu2kEDeMUBFKymR3t+DjvhkTep8Uq90jVo6601JHzVGZ7BZXJTu+YlCwblkp4DRmmpSdGPvIqLQYw1UpJIS6X3dnvsYSYmnDlRh25+/j6cOVGnS66i4pND3bjeXfkwPv/piyMYY7CyWGlgLRWpi7yIidXAGKgChOrBVNVLiudO7cNtRG/ZNnounF60aMcuU3t+Dmy58hKbkCbjpwkf2maiElLANz1mE7WvpzFncHTM5DxOt+xHX1YGBtPEh+fne6qGIIpFr7yIAjs/23kWchSIKnKYzUEKIO4UQJ4UQZ4QQT3j4/reFEM1CiCOOj390+d4qIcRpx0fk7pMMwPirV3BL29kRAclVR3wy9k2ei9yey9g3ea59Gc8H1WZIfOx2601OQ/fiLyC5pQkXTYsxkKriEl6AQtlUk7NQpBb2LiJSh2YzUEKIWACbAXwRwHkAh4QQO6WUJ9we+gcp5cNu92YC+D4AEwAJ4LDj3rYwDF332scl4r2Mabitx+Y5IAmBpxet0qQGyu/QIQRqHv2efeYpNQRHtDhJibiuDiwuSMf+Bv+6pBMZGXsXEalDyxkoM4AzUsqzUsp+AL8H8FU/7y0H8KaU8pIjNL0J4E6Vxmk8QuCJmXdhxd3/76jlO9fHdCSk6LZR5OJC+7LeQNr4kIan0hefwR3/fB9KX3xGd+fccRaK1MDeRUTq0DJA5QNocPn6vOOau3uFEB8KIf4khChQeC+EEKuFEFYhhHXoSnsoxm0MOgxI/s4+hazQW0rEdbYPB6W4rg5MtO5HT3YuJlr3Y0mmvgIUkRqiscEhUTjovYj8zwB+J6XsE0L8E4AqALcpeQIp5RYAWwAgPmcG3zEVCtWsiBbhqfTFZzDRuh8XTYtR8+j3MJCajoumxcPXBlLTgTZ9LeOxLxSpgb2LiEJPywBlA1Dg8vUUx7VhUspWly9/AeB5l3tvdbv33ZCPkAzLfbbJuYvPva4q2B157AdFRBSdtFzCOwRgphCiWAgRD+B+ADtdHyCEmOTy5TIAtY4/7wbwJSFEhhAiA8CXHNfCJmPGrNA9mZQYP9Cru5qcUAn77BMwPNs0ahdfqOuqVMBaKCIi/dNsBkpKeVUI8TDswScWwK+klMeFEE8DsEopdwJ4RAixDMBVAJcAfNtx7yUhxDOwhzAAeFpKacxpAEfPplvazuK9jGl4YuZdun5zV0ug4akkOx21LR2jv6HWLj4iimpKu7qzC3zk0rQGSkq5C8Aut2tPufz53wH8u5d7fwXgV6oOMAycPZsaE1JxS9tZjL96Be1xSVoPK2RC2SvJqSQ73evXI8KUc7ZpDHpsrMlaKCL9cXZ1zzOVo8RciLbGelRurkIF4DEUKX08GQuPctGYs2dTXl8X3suYhvZxiVoPaVi4lpL8nX0qyU4fFZ48PcYvbjv09IhLeUT64trVPSY2Fln5xcgz2bu6h+LxZCx634UX+Rw9m8ZfvWIPTxG01KRo9snR4NLbctuoYCQlYjvaMZg+up7J67Key73uO/SCwUJyouhQ12BDiXl0V/fafdtD8vhw4JJi6DBABSFkZ+IJEVHLdoDCwnFPgcYlFHkKT/nPPom0A++jc9HNsD35rKIQ5WmH3uLC8bpbxgO4lEeRz0hv6GN1dXd/LQnjhK66wBt5SVGP/064hKeFCN915y/n0p2nQONUkpWG2PbLI35XsR3tSDvwPgZyJiLtwPuI7fDcINVT8IrrbMdAStrnO/SuX2R/bn/+LqREUndH2P/euJQXfSwWC9auW49l96/C2nXrYbFYtB6SKpxv6H2FZSi59zH0FZahcnOVbl+vr67unl5LU9cgat/4jW66wBt1SVHBv5NsZ+Nsx8dqNcfFABVujl13f6n5BZ47vUu3ISqcb9reWg6UZKUh/9knMfObdyP/2SeHf1eD6ePRuehmxDVfROeim+3LeF4MhyjXY1x+XIma71bgrZ/+HgBwx/+5f+yjXaTE1155Ho889SC+9srzuv17I+MzWqgIhtHe0H11dff0Wq65fQUmZ03QTRd4ox4sreDfSYuU0uTysUXNcXEJL8zCtesupH2qFPJn+W5E4biXlgOeZpoGx08AhIDtyWe91kB5MmqWq7sTEAITDx8YvpZ053fQm+K5CD2ppxMzP7KgIyMbMz+yIKmn0+tj1cClvOjh+mYBwPHZ/mah9ZJFqOmxRmgs3rq6e3stjf0D2LRxQ7iG55NRD5bW678TzkCFmZ533WnKrcFlSXa675kmIYbD1FhKstM9znK5X1t47WSvz9GbnIbTc81Ib2vB6blm9CaHrumnv7iUFx2MOksQCOcbuisjvKF7YoTXYtSDpfX6u+UMVJAUF5JH8K47IIDZJ3dSYm68xKCUAc00eeVllsvvZptC4NVvP26feUpOi7i/N9IPo84SBGLliuWo3FwFoBwZefai5kbrblSsWaX10BQzwmsxm82ogH2Ws3bfdhQV5I9YUtRjoTag398tA5QWInDXXUg46pSKjhwcsbtucPyEoJ/auStvVGNNP5ttOh8bzmU7T7iUF/n0+mahhrHe0I1Ei9cSSODxtgSp5x16ev13wgBFuhHX1YGiv1VjICMLafvf+7zmyclH7ydFjzE4hqjIptc3C7V4e0M3onC+lmADj3v4avzsAvJu/Jpua+/0+O+EASoEQtYPSicCrbUJdvluICUNMiYGaYer0TvjGgymucz2+NH7aazHjNlgE/o81sUThqjIpsc3C9KXYDYbeApfB978IZbOHvnfRz0UausZi8gjkJY78IIxNwEQUqLTVAYhJWI7P/8fs8/eT1Iitv0yYtsv+9UfKlKwqJwoegWz2cBbW4Aje0ccTRuxtXehwgBFIRF08Thc+ju1XRq1687TjrwpmUmYkpE43Csqb9NGdJZ9wa/+UERERhbMzjRP4eu60hvQWn/KcDv0tMQlvBCJtGW8cHM2vPS6685lR96kqXmY4vieaG9HhmUfejIykVb9Pk7/9040PvJvXmug/FnGMxIu5RFFp2A2G3ja6Tlu8ApuvmGBvelnFNTehQIDFOmLp113jsLwSVPzgKzkkd9Kt7cfSLceRM+MWRhMGw/ERNfEKkMUUfQJZrOB1/D18GoGJgUYoCKMUeufvHIpDMfSpeh69vkRM0uiowMYGkJ/2SLEt7YitrMjJG0PjIYhisJBr32ColWgmw0CCV/8ux+NASqEImEZL5DC5HnFmfaDdn00mfRV/zTq0F8XzuLx2MmTEPPeXoj2dsgJnwckOX48Bpbcirj39mJgya2YNDUP59uujHwSt9YGw8t4UnpsommUnXjuGKJITXruE0TKKQlf/Lv3LLrWOkgdoTho17GTzv3ewfTxwNKliLl4EQO3LIEcP7oRZtezz+Py//511OyU83nzK9dj5n13Ib9y/efP73q48IvPAENDiOu4jLiO0WMwEu7MI7UY7eBfCh3+3XvGABViEbeE5gdPB+0q4limm/nNu5H/7JMjA8xYAcnxGDl+PER7OyAlpmR+3uU9tv0yMl/7IxLO1yPztT/aQxpGHy5ser4Cd997K+6+dwlKX3iaIYrIjZHP6LNYLFi7bj2W3b8Ka9eth8Vi0XpIhmLkv3s1MUBFEK3Cm98H7UqJuM72UeHEZ48nwB6QJjgODpYS4rLbLJGUSH3ycUz4uy8h9cnH3QKY40NK+2fYQ9VAStrwQcLN112P3L9ZENt3BbH9/cizfOA7BEqJpO4OXYcshigKlnvoSBgndHmg61icy099hWUoufcx9BWWoXJzFUOUAno9zFdrmtZACSHuBPBjALEAfiGlfM7t+/8C4B8BXAXQDOAfpJR1ju8NAjjmeGi9lHJZ2AY+hkiohVLEn4N2HUtmE637cdG0GDWPfm/4cc4eT84O4q79m1xnk5xBKe69vRi4ZcnwjJRob0fce3sxNHEi4hx1UlMyJ+D8pV4Mpk/ApeX3IW3fu+i8aQnyNj2PtOoPEL/gRtR8twJx3Z0YSElD6QtPY+quVwEBNJq/4DMEfu2V5zHzIwtOzzXj1W8/rtsjY1gTRYHyVPPSdHobGt/4DUrufMBQZ/QF07HbyEJZ9B1N5zMqoVmAEkLEAtgM4IsAzgM4JITYKaU84fKwvwEwSSl7hBD/B8DzAO5zfK9XSrkgrIPWMc2XDn0ctLu4MA1xne0jlsziuuwH+47Z/8n1R3gISnLCBHsh+S1LhoPViDopIWCrsD83pMTMv1+GgZyJKDpyEMe6O+0HCUsJCIErORPRtMCMmu9WIOlUo8cw6Gm5UusDhn1hiKJAeAod19y+Au2HduqyT5CvsFDXYEOJefTykxpHlOhlp1qoi76j7XxGf2k5A2UGcEZKeRYAhBC/B/BVAMMBSkq5x+Xx1QC+FdYRBiHqZqHGMJCajoumxcMzUAOpbqHDQ/+nEbNPwOiglJ4Ocfky5Pjx6Hr2eXugGu8hgDmfW8oRM13OMcR1dWDi4QPonjgJOccOw/TDp3Cz9ZDHGSbncqVzBsrrTJWOMESRUt5CR2P/ADZt3KDRqDwbKyx4ahqpxvKTnnaqqTHrxvMZR9MylrFLewAAIABJREFUQOUDaHD5+jyAG308/jsA/uLydaIQwgr78t5zUkqP2wGEEKsBrAaA2NTsoAasV6GafQqohcHUDCR1d3hfunMSAjWPfs9j2wC/OQrKRXs7ZHo6Uiv+bcRynmt7A8AewM5f6h1x/4iZrlZ7nZNruGu+7nrkHLXigrcZJn+WK3WIIYqUCFfoCIWxwkK4lp/0tFQYzlm3aGaIInIhxLcAmABsdLlcJKU0AfgmgBeFENM93Sul3CKlNEkpTTGJ4T0bTfNlNbUpbV8ghH3JLJjQ4SgoFx0do5bz/L1/cPyEUcfE1Dz6Pbz1sz/Auu4ZXLzhJt8F8c7lSoOEJycWlpO/Vq5YjkbrbkOcizbWDjGz2YyKNavsS4/bX0BCfbUqy0962qnGom//CSGyAr1XyxkoG4ACl6+nOK6NIIS4A8CTAJZIKfuc16WUNsfns0KIdwEsBPCJmgMOhNpLeVqGtNQrXZrVA/msewqEM9wBqHn0e/jbnRdGzjCN0SjUKDgTRf4wUs2L62zZhdPHcOLgXrRcqMP42H5YLJbhpSe1x66nWTsWfStSLYQ4AuDXAP4ipf/bq7UMUIcAzBRCFMMenO6HfTZpmBBiIYCfA7hTStnkcj0DQI+Usk8IkQ3gJtgLzHUpUuuhuhJTtasHcl3O81T3FORzLyzJ/7wbuYF23vmDIYr8YZSaF2dYaPp0Gmo/OoqUaxZhwqTrMD0nJaw1SIGEFrWKzo0UgHVgFoA7APwDgJ8IIf4I4BUp5Zhv2poFKCnlVSHEwwB2w97G4FdSyuNCiKcBWKWUO2FfsksFsE3Y37Cc7QpKAPxcCDEE+zLkc26796KC5kuEWtcDOftD+TCqDsrF8JEuYzDazjt/MESRkbgGjYRxAjEx49DbPzAcOirWrMLqx55AbNEipCbGY9a0qcjJzUVrVmbYapB8hRZPQQkA1m3YhO7sazGYsRAX2rrx4YZN2Lh+bchCFAPT2BwzTm8CeFMIsRTAfwF4SAhxFMATUsoD3u7VtA+UlHIXgF1u155y+fMdXu7bD2CeuqMLrVDPQmkenpx8tC8wNEezzN7kNEPuvPOHsyaKQYr0zHV32/iMDux/cycS8mej7JbF6Bu8Yp9lWrMKhQUFKLlnBWJiY4fvDXfhtKfQ4m13Xk/zeTRnLkRm4XWIT05Ff08Xmq8M4EcvbcEffhO9wSfcrSAcNVDfArASwEUAawHsBLAAwDYAxd7uNUQReaQIVejRLDxJidTeztB24PZyBl5IKf0Zjqafw8XxAF799uP4ydO/9rh8d+zcpZCPN+S/Zx9YXE565rq77WPr+8gx343MWdfjzKcNI85k02vhtLdz5A5/9DEyZixAQko6hIhBQko6MmYsgPXDjzQdr5Y06hp/AEA6gOVSyq9IKV+VUl6VUloB/MzXjQxQYZYxY1ZQAUit8DTmm6iUeGjnS3jxp4/goZ0vhebN3dcZeAqeY/hoFy/HvCj9Gc5z8kac7ReunXdq/J79wBBFeuW6u63jUjOSs/MRn5yKru5uAJ/vdNPrzkFvu/MGh4Yw2DNy9/Bgz+ijrqKJRocWXyOlfEZKed79G1LKH/i6kQFKI4EEIa133JV+UoOWtCyUflKD1Ctd/t/s5Qy8uK4O32fgjfGcoq0NqesdZ+Ctfxyp69eNOg9PtI9xzp4Hzr5QY57tp4Kgfs9BYogiPXKdWUrPzEFPiw39PV1ITUkB8PksU7jaFSjlbWYsLzsTzYdeR3dTPeTQILqb6tF86HWUzivRaKTa06gVxF+FEMPFtEKIDCHEbn9u1LQGKtq5BiJv9VF6qXXqSkxFzfRSlH5Sg5rppehKTPXvRh9n4A2kpns9A8/bc41oornnbcRevIiBhaWIe/cdABJDk/NHHfOi6GcAw32hRrUyCIOAf88hwuJy0hvX3W3Xmm7+vAZq8eLhWSbnTjc9Fk57253379/9Z/zqT6/j8pG/4NKVXiQkJmFy8hDWPfKQ1kPWjEatIHKklJedX0gp24QQuf7cyAClE3oJSl4JgZeXPYzUK132N3U/Q0VSTydS9n+Ansl5I87Acz7nW//0b5i79nGPZ+Cdv9RrP87FsTSX8vwGxL2/FwPmMsQdPIChSZMRc/EiYi5cwMCttwGQiHv/vZF9ody7j/sbhrQqjg/w9xxKDFGkJ6672xobbCjNT0ZMzHm0vPdbQ2zP97U7b86cObo4O08vNOpfNSiEKJRS1gOAEKIIgF/rqAxQ5D8h0JWkbDnLuYNt4Smr5zPwxiIlUp98fORsk6UaA2WLEHewGn1fvx89/7wWQ4X2ad+YhgYMFRSM6jTufs6ergXwew41hijSk3DMLKm5+8vb+PU4Y6YljfpXPQngAyHEXgACwM1wHP82FgYoCsqxc5cwrzjT+wMcvaJaM+XoM/Acy3tFRw6ic9HNsD357KgZF9Hebj+yxXW2aelt6Kr8AUR7O1Ke34Dx3/o6Bm6+BYCwz1A5zsYzcrNLPWCIomihp4OAo124Q6WU8g0hRCmAMselR6WULf7cyyJyUp+XM/Ccu918FXg3DMZj4JYliGlqQt9938DlXW/Zw1FMDCDsgWlo4kTEvfsO4t59x/PZeL7aGHgpcCc7FpZTNNBo9xfpRwKASwA6AMwWQtziz02cgTIyKZHe34OO+OSgZlu0epN07nZzzkB5LPD2cWTLiPPwbr0NrjNQwzVQjjYGziLyEbNcPgrcw0JKTWud/MWZKNILtZbZ6hpsKDGP3v0VziacFBLZQgiry9dbpJRbfN0ghPgBgPsAHAcw5LgsAbw31g9jgDIqKfHUgSrcdOEj7Js8F08vWqXrN+H99Z1YXOhW1+PY7XasqwMzpk7xPn5vR7a4hytgVNAS7e1IdWtj4KyHiu1ox0TrfvRk544ucPdTwE00Hf2enLvtXl72sK7//hiiSGtqLrPp6SBgCkqLlNKk8J7lsPeC6lP6w7iEZ1Dp/T246cJHaEqegJsufIT0/h6thxQY1+U9L0tt3s6yc94vJ0yw3+/6Z4eGq3HoKjUjrqlx1CzXYPp4XDQtRnJLE5qvux4DKdHR7ylQXM4jLam5zKbXJpwUFmcBxAVyIwOUQXXEJ2Pf5LnI7bmMfZPn2pfxjMhZgzQ0FHxXcg/Pnb+hAqk1FnRdfyNs6ytH7c6r+W4Fmq+7HjlHrSj9cWXYaqGc/Z6yO1s16fcUKIYo0oqaTRb12oSTwqIHwBEhxM+FED9xfvhzI5fwjEoIPL1oVUhqoII15k48b1xqkJqvux5px/+Ggdy8UUttgEtPKAVcl+9SayyI7ewY1c4grrsTOf9/e3ceJ1V5JXz8d4BuaLpBlm5ANpFNEFCCbYuoiWtiMhlcEhMzE4O+5iWOilkc1CBZxjHG6ETzjppk+IwTiWMS4xqiJkRxF6FtCLKIsoiCzd4s3U1DI815/6hb7aWp5VbVrbp1q8738+lPd1XdW/Vcq+k6Puc851m+hJaq/mmn8dKSB/2e0mXpPBOEbKfZ8rmlQK432C0y85yvlNkMVJiJ0Ni1PDQfvgs3Nh1xO7oKr6WyH1XLl9B8ymmU7NjmvWN4EpvaSmk6/ayEzxktZO++c3t6faoyEe33FJL3z81mokyuFWuaLaANdouGqs6N9eXlXJuBKnJBfhBGg5f2VXAzZjO+K3E7hqc8C5WkC/nqnY3thewlzY1H9KnqGOyZo9lMlMkF9+xLWWkJuxc/ydZDGoou5H5w134BzvdI7VehX3suiMgo4KfAiUC36P2qOjzZuRZAmeDECF7ajkk8A5ROEHVE2k71k4DKdUxO0nYFyIIok02xVt5trZvP7BsKP3CKshYLWfcb4EfAvcA5wFV4zM5ZCs/4wuty/qNmduI02Uwk4aq8RMc4PaF8LVQ3ls4zvqitrWXGzFlMvXwaM2bOap95KvYGl9HaLzdrseCrMlVdAIiqfqiqPwb+wcuJFkCZvLJ6Z+MnNxJ0EE8URMV7rPPePfR4/WU+ruxHjzdfo6S5MeZxJnUjR/e3QMqkLV6dz7Jlb2dt5V1Y5KL2K1bwWkRaRaQTsFZErheRSwBPy6ItgCpiQX3geaov8jBbFCtQihtYqTLgvrsp2bGN7iuW0TT5zIwLxo+adVOlYn9TUc9sWRBl0vHwY09TevypvLtpB8+//CrvbtpB6fGn0tSyv+hnX2K1WLjk7GoefuxpXwIeK1Ln20B34AbgFOAKYJqXE60Gyvgm7XYGHaze2cj4ksP0iNNB3M1LOg8iXcd7LHqNlrHjKdm5g6033AQf+7j6LWSdxbPJ6qJMqt5esYqmEYMo7z+UXv0qONjSzPptG+kkwta6+cDn6D3AVQN1nafPt4LhbrHgd0f2Yi9SV9W3AJxZqBtU1fMKokBnoETkQhF5T0TWicgtMR7vKiKPOo8vFpFhrse+79z/noh8LpfjNplLNgu18qAkbUGQirYePVERetQtQrt0YWWrv8FNGDuLZ5PNRJlUNDY1IZ070bW8J/sbd7Nz80a2f7SB+i1bueTsamtw6fLwY0/TqXI4f3/pWZ5+4Db+/tKzdKocnnZdWDYblIaBiFSLyApgObBCRN4WkVO8nJtwBkpEegJVqrq+w/0nqerytEcceY7OwAPABcBHwFsiMk9V33EddjWwW1VHisjlwM+Ar4rIicDlwDhgIPCCiIxW1bZMxmQy59csFCLUz7qdkvpNfDxoSMazOZ2bGpHDh2k6ZTIluxso2dfk68q7aGfx6AxUWDqLZ5PNRBmvenQvY/t7b3L40CEaD7RBp0607a6nbGQNT71cV/RBk9uyZW+zvVNfek+8gEGVg2jZWc/qZc/TcLghreezfQD5H+BaVX0NQETOJLIy76RkJ8adgRKRrwDvAk+IyCoROdX18EMZDTeiBlinqu+r6kHgD8BFHY65CIg2tHocOE9ExLn/D6raqqobgHXO8xmP8mGGIOEslCplP7iJEdd8nUF3zM64rqit5zE0Tfk0JXt20TTl0wnrn9LqAeV0Fv/Ov/xnUafvOrLicuPFxIknM3b8yWx79fc0Ln2GQx8upd+YUxl68plFt+oumaaW/ZSfcDrl/YYinTpT3m8o5SecTlOLt3KGjjoWqa9dvoSXfnc/y5a9XSwF5W3R4AlAVV8HDnk5MdEM1CzgFFXdIiI1wMMi8n1VfQrw49NhELDJdfsj4LR4x6jqIRHZC/R17l/U4dyiCZfznR+zUNEu5bsq+9EnQQ3UUdx9njrse3dEU82GLDTKjHYWN0ex2SiTyBWXXcztD8ylW0VPxlz6r3x8YD8tOz5i9PBh9O7bN2s9j8K4RUrPHj1oajtM675GSrtH6sW07TA9e6T3t6empobZRFKDbz77IPXbG5hwziWcMPm8jOurQuIVEfkv4PeAAl8FXhaRSQCqujTeiYkCqM6qusV5gloROQd4RkSGOC8SCiIyHZgO0LmiMuDRmI4WbmxiytCj/+G7u5Q3nXW2txooZ+Vejzdfo+n0s6i/9SdHBVFtx/Q6slVCmrz2vTKfsCDKxBP9EJ/+3VvYtuwlKgcPZ9K40VT160dD/YaspJP8LsbOlZMnjGNLeTnbm7ezZ9sGKsrLGVFVzrHdx6X9nNEi9RkzZzFm6ORiKyg/2fn+ow73f4pIrHNuvBMTFZE3iciI6A0nmDqbSPos/XfqE/XAENftwc59MY8RkS7AMUCDx3Oj456jqtWqWt2pm3Wbhtyk7zIOMJwu5S/8+lFe+NbNnlJinRv3HrVyz+SXaErP0nqmo5qaGubceycjS/YwZkgVffv2zep+d2Ft0nnFZRdzcMNbjBlSxQVnf5oxQ6o4uOEtX/4bFWNBuaqek+ArbvAEiWeg/gXoJCI/U9WbnRdqEpHPE6mNytRbwCgROZ5I8HM58E8djplHpB/Dm8CXgRdVVUVkHvA7EbmHSBH5KKDgE7WFKt4slHuLldU7GxlbGaNuyZWya+t5DE2nn9U+A3XUrJUq6z74CJz6p47735ncGjm6P1uCHoTJK+500uo3nsjqfndBbZFSW1vLz++fQ93ylaDKpAljmXnDtZ6vMdX/Rh33Ejx8+BCtzl6CHVOWxVhQLiL9gTuAgar6eWeR2umq+mCyc+MGUKr6tvPkFwA3u+4/KCL7Mh20U9N0PTAf6Az8j6quEpHbgDpVnQc8SKT2ah2wi0iQhXPcH4F3iBR7XWcr8PLPig27mDCsN2UtTezv3iNhoBI3iHI5KoiKkbKLu3mwKmWzZ3J+3UK2nXI6AP2XvBnZxPg7P7BNhI3JE+6eR37qWO9UVlqSlWAhUV1VbW0tM++4jx0VIxhw8Rdpa9nL0ree5cZ/u4uf/+imlIIoL8e605SVx3dj0cKFtNa/w5QLptLao+dRKctoLVqR9d16iMiqu1ud22uAR4nEHwklWoX3L05vhBNEZLnrawPwduZjBlV9TlVHq+oIVf2Jc98PneAJVT2gqpep6khVrVHV913n/sQ57wRV/Ysf4ykGOU2dqHLW/7uNG354FZc+dJcvHbrd9UsxU3bRzYM7BGudG/fSv24hLZX9GFD7OgNqX6elsh/96xbali7GFLhY3bY3N+zhvQWP+bpFSrKu3g8/9jT7KsfQZ/QpdOvRm/L+w6iq+Uf2HCrNSurQnaZc98Em+ow+haqaf+TdutdipixjdT0vghYSlar6R+AwRCZ3AE8TMolSeL8D/gL8FHA3uWxSVaugNUlFm0s29h/AqJW1kZmo8sTtA5LNQsEnM1FtPXrSPKmGiiWLaZry6diF5k6Kb2UrlDpF6VtrzgQ+mYFKdUsXKyA3Jlxiddsee+E32L34yUiw4FO6MFlX7w831dPW+1OUdnf1iSvpxsY1y5m7YjHPPf8i1SeN58brp/sStLjTlM379tGrXwWlZeXUL94BxE5ZZmsGMI/tE5G+OIvjRGQy4KmANlEKb6/zJF/zY4Sm+LibS66dNCWSxvPJ6h17Of+/fkbF0lqaTzmN+lm3H50idKX4SieextJvz4400IxTA2XpO2MKU7x6p62HlPvuviPrrxMNUo4bMojNu/dxsKWZruU9adnbwMYVtRyiC8d+YQb9Bw9jxbplzLzjPu6eNSPjQMZd01RRXs7BlmYO7dtDzz5VQOHXN3n0PSL11iNE5A2gikjNdVK2mXARyfnKJ1dzySevvMlTsbbXIKakuZGS117m46r+VCytpXPT0Wm4zo17KXntZXb16htJ1UW7j4t8UqBuBeTGFLxoIOGWjeAh2etccdnFlO98l11rlnCgaTfb1iyj8d036Dn+bPoPG0O3Hr3pM/oU9lWO8SWl526SOXLYEHatWcKO2j8zpvqsrK5wDAtnR5TPOF9TgG8B47zutGIBlMkup7nkig92ez7FSxAV7RPVtmUzH048jZUHjwyEVu9sZOVBYVv1FLrv3J5Wqi4WS98Zkzu1tbXMmDmLqZdPy6grdsdu26kGD17Hkex1ampquHvWDCZ0+oitT9/JzlcfoYse5PhTL6BbWRkApd0raCsp96V1gLumaeerjzCh00dMGtSdvcufL5b6poScxWdfU9VDqrpKVVeq6sdezxf1obA3LEqrRmrll+4KehiB8G32SZWKA82Rvd7SmL1JpUN50noo1eStCLwcg/eZLwug/PXajWcuUdXqoMeRqurqaq2rqwt6GAXNvYKs44qwdD700+06nuo4UnmdGTNn8fLyDyif+HnK+0VSf637GjmwcTmn997na3qxGIhIyn9PROReoITIyrv2DgOJOpBHJdxM2JgjqHLtvPvbN8wNfM83V5+ojI4xxuSdZAXZqUq3ODrVcaTyOldcdjGvLryF9578BZ3Le9H1mH6UdK/guK4HuGL6jJTHatIy0fl+m+u+hB3IoyyAMp5FV9Xt7NGXSeuXRmaiUtz7Lek+eartfaO8rsozxhSeIBpdxpo9yvY4elUN4vCw02lqK+HQvl1o/XKunFHcqbVcUtVz0j3XAqgi4Ff6zr2qbumISZE0XhriBlGqXPrQXYxaWcva8TU8eeVNOQmiLH1nTP7JdVfseHvjZavhJkRmt04477IjnruhfgNL31nEVRk/u/FCRO4A7lLVPc7t3sCNqjo72blWRG68c62qy0b6rqyliVEra2nsXdneNwqy217AWhcYk58yLfxOVby98Q4fPpS1cRTj3nN56PPR4AlAVXcDX/ByogVQBc731gXOqrpMg6dYszn7u/dg7fgaeu7eydrxNb72jTLGhEuuu2LHC2ZaD6nv44iu6lu1+l0WzH+WHdu3tz/mZXbLr9WJBoDOItI1ekNEyoCuCY5vZyk8E5ijUnkiPHnlTTH3zstGKi+V2SdL3xmTe7nsip0oZejnONypwilfPZGFz89j4YGPmTxlCl3aDiTdey5eqtG9p51JySPAAhH5jXP7KmCulxNtBqqA5bxxph9EItu9xJjhynm6TZWyfY2+7OFnjMlvueoV5U4VDh5zMmdeeDGdd6zhlTk/9DS7FS/VmI299IqBqv4MuB0Y63z9u6p66ndkM1AmUElX5XWQ8UyU0xfqlV2SOA3pKmhfOOQkVgTdssEYk1U1NTXMJhKgeNkbz71ir6y0hM0Nexh74TeSzgp1XNU3cNQEBgw/kdVP3Oup79OHm+qpPL4bbyyqpXnfPirKyxk5bAhbrW4qLSJSDvxNVf8qIicAJ4hIiZeGmhZAFagwzT7lLIhSZdIv/p3yha/T11nlFy8oche0p9uywRgTLl5TdR3TaAvmP8vulrcZ3tLcPisUr1dUpqsLy0pLWLRwIX1Gn0KvfhUcbGlm0cKFTCgtSelaC1SliLg73M5R1TlJznkVOMtZffdXoA74KvDPyV7MUngmL6RaY5ROOq+kuZHyha8ftcovFndBeyYtG4wxhadjGq1pzy72HzzEMw/+nBf+9342r10RdzVdpqnCw4cP0Vr/Dof27QFVDu3bQ2v9Oxw+fMjvy0xJnhS271TVatdXsuAJIjuytACXAr9S1cuAcV5ezGagClBezz5luBWMWzSImjKkIul2LQs3NoEKfcfXtPeZSrjKzylo37B6oy9jNcbkXrrbtyTjTsNtXruCvfXrKT/5s3Tdv4/OAwaw+KX5jNm+JeasUqqpwo5aDylTLpjKu3WvUb94Bz37VDHlgqnsXf58xteVrpAXtouInE5kxulq577OXk60AMrkTpKtYFJN5UWfc8BtP2DUylr2TTmTpd/5wVHBTiR4inQ4f3LaTMr2Nx+1yi+maMsGY0zoZPND3Z2Ge2fxKwys+SKNbZ3pUnKQ7lWDOTh6MiteeoRZv74n5vmZrOo7bsggWnv05PyvX99+X0P9BnplscFosiDU7213cuw7wPeBp1R1lYgMB17ycqKl8ApMPs8+xdoKJlPuWqXyha/z93c3s3Bj0xFf0YLwG354FZfOvdtb8IS1LjAmzLK5Ws2dhtvbsJ2y3pV0+7iRXt06sWfDCspLuzCoX9+sBA+5bDAaDUJbh05m7Je+S+vQydz+wNyj0nNhbgiqqq+o6lRnNR6q+r6q3uDlXJuBMjnjZSuYVGehorVKidJysTqc7y/vmdG1GGPyWzb3sHOn4dr2bKb1o1VMrp5MVb9+QGRGqKt4KqPJ6LXTSQGmwuvMUq633fGDiPxCVb8jIn8msnnwEVR1arLnsACqgOTz7BPQvhVMshqohEGUa7NhRBI234zyEmTFGoMxJryy/aEeTcNdcVlklqbTx/s43NbG7q0bkzbD9Ou1s81rEHrFZRdz+wNzgc/Re8DQnPw38MHDzvf/SPcJAgmgRKQP8CgwDPgA+Iqz/4z7mInAr4CeQBvwE1V91HnsIeAzwF7n8CtVdVkuxm4ylEldUYzNhqNBVMIZJQ9BljGmsHT8UF+/ahlvz/8dAypKmDFzlm8F5anMCGWrqD1bvAahuZwV84uqLnG+vyIiVc7PO1J5jqBmoG4BFqjqnSJyi3P75g7HtADfUNW1IjIQWCIi812b/s1U1cdzOOa8lvezTymKNQuVUSouWZDV4bWNMeHm/lB/89kHqd/ewIRzLuGEyeelVFDuJejxMiMUxpVqqcws5XLbHb+IyI+B64nUg4uIHALuU9XbvJwfVBH5RXyy18xc4KjqN1Vdo6prnZ83A9uBqpyNMEQKKnhSpWJ/pPC7YyBz1GbDZRW+b7ViwZMxhaOmpob77r6DkyeM47yrbmLsGZ9NqaDcaxG1F2HcgiXXGzrnkoh8DzgDOFVV+6hqb+A04AwR+a6X5whqBqq/qm5xft4KJIwARKQGKAXWu+7+iYj8EFgA3KKqrVkZqcm+aG+oruVc++cHjmhzcMRMlDsVV1bBpXPvPjqdlwELnowpTOkWlPu5PD+bRe3ZFMaZJY+uAC5Q1Z3RO1T1fRH5OvA34N5kT5C1AEpEXgAGxHjoVvcNVVURiTuFICLHEin2mqaqh527v08k8CoF5hBJ/8WcchOR6cB0gM4VlSleRf4L/eyTqzfUiuPGM+GDlezs2Tf+9ilOKq5sX6OtrDPGeJJuQbmfQU8YV6oVuBJ38BSlqjtExNO+OFlL4anq+ao6PsbXn4BtTmAUDZC2x3oOEekJPAvcqqqLXM+9RSNagd8AccNjVZ0Tbeveqdsxfl6i8YG7N9SED1awcth4KpsajmhzEGtm6Kh0noeVdYnY7JMxhSvd3knRoMct3aAnl/2bjCcH03ysXVApvHnANOBO5/ufOh4gIqXAU8BvOxaLi8ixqrpFRIRI/dTK7A85/4R+9okOvaFGnsIv//E6Klr3HdXm4Kiich9X1lnwZExh67hKrGsXoaxTF26/5wGOG/J03NVwfi7PD+NKtQJ3sog0xrhfgG5enkDUxwJcr0SkL/BHYCjwIZE2BrtEpBq4RlW/6eQhfwOscp16paouE5EXiRSUC7DMOSdpW+vSqpFa+aW7/L6cwBRCAAWktD9eylu9JGHBU7Beu/HMJapaHfQ4UlVdXa11dXXJDzR5x72G2FQgAAAV6UlEQVQarmNQ5LX1ABCqdgTFQkRy+vckkBkoVW0Azotxfx3wTefn/wX+N87552Z1gCa3UugNldZ+eQmeyxhTXFItDO9YRB3GdgQmO2wvvJAqmNmnNPgR+FjwZExxynTftjC2IzDZYQFUCBVz8BSVSQBkwZMxxSvTwvAwb5xr/GUBlAmtVAOhFRt2WfBkTJHLdDWcnyvzTLhZABUyNvt0JC9BkQVOxpioTLtrWzsCExVUGwNjfGUBkjHGq0y6a1s7AhNlAVSI2OxTOK1bsy3m/fZ+GhNOBby9iUmBBVDGZEm8wKnj4xZIGZP/YvWDCjqIyscxFRMLoIzxUbKgKdE5FkgZk59S6f2Uq6DG+lEFz4rIQ8I+XPNfOsGTn+cbY7LDa++naFDTOnQyY7/0XVqHTub2B+ZSW1sb2JhM9lgAZYwP/Ap+LIgyJv947f2Uy6DG+lEFzwKoELDZp/y1bs0234MeC6KMyS9eez+lE9TU1tYyY+Yspl4+jRkzZ3merbJ+VMGzAMqYNGUz0LEgypj84bX3k9egJho0nXXBP/DP357NlvLRKaf8rB9V8CyAynM2+5SfchHgWBBlTH7w2nzTS1DjrpPaXzGIHqdewvsN+2loaEgp5ZdpQ1CTOVuFZ0yKLLAxpjAlWkHnpfeTlyab7jqppt07GTTlBA7u38ea9z+gql8/eg8Yyuo3nvA0XutHFSwLoPKYzT7ln1wHT+vWbLPfA2PSlEpLAb/aAiQLaj7cVM/YmkidVM8+VbTsrKd71WD2bNsAWB1TmFgKzxiPgpp5shkvY1KXakuBXK2gc9dJnXjaZ9jz9gvs2fgu5WVlVscUMhZA5anQzTqoUrG/CVSDHklWBB3EBP36xoRNqgGRH20BvKyoc9dJDRh+ImPGncTe1x9BVj5jdUwhYyk8kzlVrp13P5PWL2XpiEn8cur1IBL0qHxhgYsx4eROlUUlqi+Kzgz1HXR8+32ppNO8pgBj1UnN+vU9FjSFkAVQJmMVB5qZtH4pO3v0ZdL6pVQcaKa5rEfQw8pYvgVPVg9ljHepBkRXXHYxtz8wF/gcvQdEAqCtdfOZfd00T6/nnvECnO+RGa+OwZEVf2dNpYjUuW7PUdU52XoxS+HlobB9SDZ3q2DpiElUNjWwdMQkmrtVBD2kjOVb8BSVr+MyJt+k2icp07YA1hk8L+xU1WrXV9aCJ7AZKOMHEX459frIzFO3itCn7yxIMSb8vLQUiHVOujNDmaYATfgEEkCJSB/gUWAY8AHwFVXdHeO4NmCFc3Ojqk517j8e+APQF1gCXKGqB7M/8uwL2+xTO5Hg0naqvgVvYQieLJVnjDe5TJVdcdnF3Phvd7HnUCmtB/bTtVsZvboc5Oc/uiknr29yL6gU3i3AAlUdBSxwbseyX1UnOl9TXff/DLhXVUcCu4Grsztck7ecAvZf/OoGrp13f0arAMMQPBlj8lenkjK6DhlH+YmfoeuQcXQqKQt6SCaLgkrhXQSc7fw8F3gZuNnLiSIiwLnAP7nO/zHwKz8HGASbVUidXwXsYQuebBbKmPzy8GNPc8J5lx2RwmuonxCziNwUhqBmoPqr6hbn561AvE+CbiJSJyKLRCRa+dcX2KOqh5zbHwFxk8wiMt15jrrDB/b6MniTP/woYA9b8BQV1nEbU4isiLz4ZG0GSkReAAbEeOhW9w1VVRGJl3c5TlXrRWQ48KKIrABSioKcKvw5AKVVI/O2y6PNJqQpwwJ2C0KMMX7IRhF5KlvRmNzLWgClqufHe0xEtonIsaq6RUSOBbbHeY565/v7IvIy8CngCaCXiHRxZqEGAxbiF7M0C9gLIXiyVJ4x/ko3aMm0j1SscfixN5/JnqBSePOA6G/VNOBPHQ8Qkd4i0tX5uRI4A3hHVRV4CfhyovPDxD4Ac68QgidjjL9S3T/PLdM+Uh3lam8+k76gisjvBP4oIlcDHwJfARCRauAaVf0mMBb4LxE5TCTQu1NV33HOvxn4g4jcDvwdeDDXF2DCq9CCJ5uFMsYf8bqJ3/2fv2TAsclnpfxsm5DqVjQm9wIJoFS1ATgvxv11wDednxcCE+Kc/z5gc5gmZYUWPBlj/BMraNnf1MibK9/nC6ddmtNUmjXmzH+2lUvAbOYgdwo5eCrkazMmV6JBi9uyV54LJJWW6lY0JvcsgAqQBU+5YwGGMSaZWEFLw8Y1nDTp1COOy0V7Ar9rqoz/bC88U/CKJXiyWihj0uNeeVdWWsLuxU+y9ZBy3JBBnHXqRLq0HTji+Fyl0nK5FY1JnQVQAbEPOmOMyZ147QlitQvYWjef2TdMO+Jxv9oTmMJRVAFU125FdbkGZ/ZJlZ4HW2gs7Z7xZsP5zmahjDlaop5K8VbeRbdgqampaT9u9RtPcNyQQQlTadb8snhYRBEA+4DLjWjw9MM353LG5pW8MXA8t50+reCDKGPMkRIFSV7aBXhNpVnzy+JSdEXkFrwUh2jdU8+DLZyxeSXbu/fijM0r6XmwJeCRZV+x1HwZ41WifepirbxLt8bJ7+aXtbW1zJg5i6mXT2PGzFmeGnqa3Cm6AAosiComjaXdeWPgePq17OGNgeMjaTxjTFFJFCT52S7Azw2FM+mKbnKjaFN4I0f3D+T/1C14y74j3lcRbjt9WtHUQBljjpZon7pUa5wS8bP5ZbLaLBO8og2gILggymRPzPdThMau5bkfTICsmNyYTyQLkvxqF+DXhsK1tbU897cFdO61mmP69uPE0z7DwFETbCuXPFPUARTkNoiyDzRjjAlGLnoq+TGbFU3d9T51Kl0Hj6NtfxOLX5rPaUDX7hW2lUseKfoACnITRFnwlH02m2iMCVqmgVo0ddevpJylq9bQvWowvU46l6UvPsPxA3pb/6k8UpRF5LFYgGMKjQWUxoRPtBC9ql8/Jo0bTafm7bQ27aZl2/u2lUuesRmoHLDgLPssWDDGhF1tbS0bN21i3VOPUTl4OKOHD+OMyTU01G+ga8W5MYMna9wZHAugXKKBjp8fxhY8GWOM6ahj4DPpxFE89XIdwyb/A6tXvk1z5WCWrHiXEVWbOLjhrZipO2vcGSxL4cVgQU+42OxTfPbfxpj8E6vH0x1zfk+nyuGMPeOzTD73Qjp/tIw9S/7Muud/Gzd153fjTpMam4GKw4/CcgvEjDGmcKWbPovV46nk+FP56P33GHvGZxk4agIDR03gcFsbq5+4N+5zetmGxmSPzUAlkEkAZMFTbtgMizEmCJl0Co/VsbzvwGE0bEltSxk/t6ExqbMAKomRo/unHAxZ8GTyiQWZxvgvk/RZrMCnf0UJnQ42p7SljJ/b0JjUWQrPIy8F5hY4GWNMcYimzzavXcE7i1+hcdcOevSupKw5+b53sTqWH9zwFrNnXM3Sdxa1N+G85OxqHn7saW6/54GYKUI/t6ExqQskgBKRPsCjwDDgA+Arqrq7wzHnAPe67hoDXK6qT4vIQ8BngL3OY1eq6rIsDxuwICmf2MyKMSYoxw0ZxHuLFvDuquX0Ovl8BlUOYs+m99j61gfU1tYmDGISBT5XOcd4XWGXiw7rJragZqBuARao6p0icotz+2b3Aar6EjAR2gOudcDfXIfMVNXHczReY4wxpt0Vl13M1675Hsec+c90rxrMwZZmUDj5c//kacPfZIGPbSac/4KqgboImOv8PBdIlrD9MvAXVW3J6qiMMcYYD2pqahjUry/lpV3Ys2EFnZq3M2ncaEaMm8iHm5Kn8ZKJVWjee8BQX567gFWKSJ3ra3o2XyyoGaj+qrrF+XkrkCwvdjlwT4f7fiIiPwQWALeoaqvPYzR5zNJ3xpignTxhHK1D+7fPEgE01G9IaRVcvFYI0UJz93PbCrukdqpqda5eLGszUCLygoisjPF1kfs4VVVAEzzPscAEYL7r7u8TqYk6FehDh/Rfh/OnR6PRj/ftyeSSjDHGmHaZroJL1ArBVtjlv6zNQKnq+fEeE5FtInKsqm5xAqTtCZ7qK8BTqvqx67mjs1etIvIb4F8TjGMOMAegx5AxcQM1k8dUqTjQTHO3ChAJejTGGANkvgouUZ3TfXffYSvs8lxQKbx5wDTgTuf7nxIc+zUiM07tXMGXEKmfWpmtgZqAqXLtvPuZtH4pS0dM4pdTr2fd2kTxtoll3ZpttoLUmCzIZBVcsk7itsIuvwUVQN0J/FFErgY+JDLLhIhUA9eo6jed28OAIcArHc5/RESqAAGWAdfkZtgm1yoONDNp/VJ29ujLpPVLqTjQHPSQjDEGSH8rlyircwq3QFbhqWqDqp6nqqNU9XxV3eXcXxcNnpzbH6jqIFU93OH8c1V1gqqOV9Wvq6p9qhao5m4VLB0xicqmBpaOmBRJ4xljTMAy2colyuqcws06kZv8JsIvp17fXgNl6TtjTD7wo0+TdRIPNwugTP4TobmsR9CjMMaYdsnql7yyOqfwss2EjTHGmBTF2hDY6peKi81AGWOMMSmora1l65bNvPn8fzCg+nOcNOlUurQdYGvdfGZfN83T+ZkUn5v8YAGUCQ3rPm6MCVr7Jr+nXco5Jzay7JXn+OsbT3HWqROZff30pIGQ102CTf6zAMoYY4zxqGPx+OAxJ9NQv4GuGxd5CoBsk+DCYTVQxhhjjEeZbvJrmwQXDgugjDHGGI8yLR634vPCYQGUCQWrfzLG5IN4zS8nnTiKGTNnMfXyacyYOStuQ01rnlk4rAbKGGOM8ShW88tLzq7mqZfrPBWGW/PMwmEBlDHGGJOCjs0vZ8yclVJhuDXPLAwWQBlTBEaO7h/0EIwpWH50JbfeUOFjNVAm71n9kzEmn2VaGO7HxsQm9yyAMsYYYzKQaWG4uzdUp86d6TvoeAZUR1KAJn9ZAGWMMcZkoKamhtnXTaPrxkWsfuJeum5clFJhuPWGCiergTLGGGMylElheDQFGC1CB+sNFQY2A2XymtU/GWMKnfWGCiebgTLGGGMCZL2hwskCKGOMMSZg1hsqfCyFZ4wxxhiTokACKBG5TERWichhEalOcNyFIvKeiKwTkVtc9x8vIoud+x8VkdLcjNzkktU/+cOaaBpjjP+CmoFaCVwKvBrvABHpDDwAfB44EfiaiJzoPPwz4F5VHQnsBq7O7nCNMcaY9NXW1nrabNiERyABlKquVtX3khxWA6xT1fdV9SDwB+AiERHgXOBx57i5gC1VMMYYk5es03hhyucaqEHAJtftj5z7+gJ7VPVQh/uNMcaYvGOdxgtT1lbhicgLwIAYD92qqn/K1uvGGMd0YLpzs/W1G89cmavXDkglsDPoQeRAMVynL9e4xYeBZNEJQQ8gHUuWLGkWkWSz6IXA/p35oVOXkZ179P1YXHcp0NbUUHL/f/x0XVZf+xPF8F7m9O9J1gIoVT0/w6eoB4a4bg927msAeolIF2cWKnp/vHHMAeYAiEidqsYtWi8ExXCNUBzXWSzXGPQY0vReob83UDy/g4V+jVAc15nrvyf5nMJ7CxjlrLgrBS4H5qmqAi8BX3aOmwbkbEbLGGOMMSaoNgaXiMhHwOnAsyIy37l/oIg8B+DMLl0PzAdWA39U1VXOU9wMfE9E1hGpiXow19dgjDHGmOIVSCdyVX0KeCrG/ZuBL7huPwc8F+O494ms0kvVnDTOCZtiuEYojuu0a8xfYR13qorhOovhGqE4rjOn1yiRjJgxxhhjjPEqn2ugjDHGGGPyUtEFUCJyt4i8KyLLReQpEekV9Jj85nWrnDCKt71PIRGR/xGR7SJSsC03RGSIiLwkIu84v6vfDnpMiWS6/VRYiEgfEXleRNY633vHOa5NRJY5X/NyPc50JHtvRKSrszXYOmersGG5H2VmPFzjlSKyw/XefTOIcWYi2d9HifhP57/BchGZlK2xFF0ABTwPjFfVk4A1wPcDHk82JN0qJ4ySbO9TSB4CLgx6EFl2CLhRVU8EJgPX5fl7men2U2FxC7BAVUcBC5zbsexX1YnO19TcDS89Ht+bq4HdzhZh9xLZMiw0Uvj9e9T13v13Tgfpj4dI/Pfx88Ao52s68KtsDaToAihV/Zuri/kiIn2kCorHrXLCKOb2PgGPyXeq+iqwK+hxZJOqblHVpc7PTURW2ubtjgKZbD+V/dH56iIi22NBYW2T5eW9cV/748B5ztZhYVEIv39Jefj7eBHwW41YRKRv5LHZGEvRBVAd/B/gL0EPwngWb3sfE2JOquRTwOJgR5KxQvj97K+q0eb1W4H+cY7rJiJ1IrJIRMIQZHl5b9qPcf4ney+RNjlh4fX370tOautxERkS4/Gwy9m/w0DaGGSbl21kRORWImmER3I5Nr/ky1Y5xmRCRCqAJ4DvqGpjwGMpin9Tia7TfUNVVUTiLdM+TlXrRWQ48KKIrFDV9X6P1fjuz8DvVbVVRL5FZMbt3IDHFFoFGUAl20ZGRK4EvgicpyHt4+DDVjlhFG97HxNCIlJCJHh6RFWfDHo8Wdx+Kq8kuk4R2SYix6rqFiftsT3Oc9Q7398XkZeJzCDmcwDl5b2JHvORiHQBjiGydVhYJL1GVXVfz38Dd+VgXLmWs3+HRZfCE5ELgZuAqaraEvR4TEpibu8T8JhMGpzakgeB1ap6T9Dj8Ukh/H7OI7I9FsTZJktEeotIV+fnSuAM4J2cjTA9Xt4b97V/GXgxZP+DnfQaO9QCTSVSe1ho5gHfcFbjTQb2utLS/lLVovoC1hHJjy5zvn4d9JiycI2XEMn7tgLbgPlBj8nHa/sCkdWT64mkVgIfUxau8ffAFuBj5328OugxZeEazySyIf1y17/FLwQ9rgTjjflvChgIPOc6LtS/n0RqfhYAa4EXgD7O/dXAfzs/TwFWAG8730Px+xnrvQFuI/I/0wDdgMecz4haYHjQY87CNf4UWOW8dy8BY4IecxrXeNTfR+Aa4BrncSGyGnG98/tZna2xWCdyY4wxxpgUFV0KzxhjjDEmUxZAGWOMMcakyAIoY4wxxpgUWQBljDHGGJMiC6CMMcYYY1JkAZTJeyLSS0Sudd3+q4jsEZFnghyXMSZc3H9LRGSiiLwpIqucrU2+GvT4TLhYGwOT95y90p5R1fHO7fOA7sC3VPWLAQ7NGBMi7r8lIjKayI41a0VkILAEGKuqe4IcowkPm4EyYXAnMEJElonI3aq6AGgKelDGmNBp/1sC/F9VXQugqpuJbFtTFeTgTLgU5F54puDcAoxX1YlBD8QYE2ox/5aISA1QSn7v52fyjAVQxhhjipazP9zDwDRVPRz0eEx4WArPGGNMURKRnsCzRPaNWxT0eEy4WABlwqAJ6BH0IIwxodf+t0RESoGngN+q6uOBjsqEkq3CM6EgIr8DTgL+AkwGxgAVQAOR3eDnBzg8Y0xIuP6WlAODgVWuh69U1WWBDMyEjgVQxhhjjDEpshSeMcYYY0yKLIAyxhhjjEmRBVDGGGOMMSmyAMoYY4wxJkUWQBljjDHGpMgCKGOMMcaYFFkAZYwxxhiTIgugjDHGGGNS9P8BeZi3tbfchl4AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sampling from the posterior surrogate\n",
        "\n",
        "Finally, samples from the posterior can be acquired with an MCMC sampler. By default it runs 4 chains, and half of the requested samples are spent in adaptation/warmup. Note that depending on the smoothness of the GP approximation, the number of priors, their gradients etc., **the default sampler may be slow**."
      ],
      "metadata": {
        "id": "z9LXoOqeXgPV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "BOLFI_posterior = bolfi.sample(200, info_freq=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bb7WE8gNXbtS",
        "outputId": "42f63de7-1834-43d6-f620-d90f08b68d40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.posteriors:Using optimized minimum value (-1.8637) of the GP discrepancy mean function as a threshold\n",
            "INFO:elfi.methods.mcmc:NUTS: Performing 200 iterations with 100 adaptation steps.\n",
            "INFO:elfi.methods.mcmc:NUTS: Iterations performed: 100/200...\n",
            "INFO:elfi.methods.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n",
            "INFO:elfi.methods.mcmc:NUTS: Acceptance ratio: 0.431. After warmup 4 proposals were outside of the region allowed by priors and rejected, decreasing acceptance ratio.\n",
            "INFO:elfi.methods.mcmc:NUTS: Performing 200 iterations with 100 adaptation steps.\n",
            "INFO:elfi.methods.mcmc:NUTS: Iterations performed: 100/200...\n",
            "INFO:elfi.methods.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n",
            "INFO:elfi.methods.mcmc:NUTS: Acceptance ratio: 0.377. After warmup 3 proposals were outside of the region allowed by priors and rejected, decreasing acceptance ratio.\n",
            "INFO:elfi.methods.mcmc:NUTS: Performing 200 iterations with 100 adaptation steps.\n",
            "INFO:elfi.methods.mcmc:NUTS: Iterations performed: 100/200...\n",
            "INFO:elfi.methods.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n",
            "INFO:elfi.methods.mcmc:NUTS: Acceptance ratio: 0.420. After warmup 2 proposals were outside of the region allowed by priors and rejected, decreasing acceptance ratio.\n",
            "INFO:elfi.methods.mcmc:NUTS: Performing 200 iterations with 100 adaptation steps.\n",
            "INFO:elfi.methods.mcmc:NUTS: Iterations performed: 100/200...\n",
            "INFO:elfi.methods.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n",
            "INFO:elfi.methods.mcmc:NUTS: Acceptance ratio: 0.407. After warmup 2 proposals were outside of the region allowed by priors and rejected, decreasing acceptance ratio.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4 chains of 200 iterations acquired. Effective sample size and Rhat for each parameter:\n",
            "t1 468.46550943959176 1.00682048837027\n",
            "t2 495.36243962449896 0.9988746763107251\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BOLFI_posterior.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2lFLCEzPUK7y",
        "outputId": "b664a38d-4174-47e9-8dd5-b64bcc857bca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Method: BOLFI\n",
            "Number of samples: 400\n",
            "Number of simulations: 100\n",
            "Threshold: -1.86\n",
            "Parameter                Mean               2.5%              97.5%\n",
            "t1:                     0.040             -0.236              0.317\n",
            "t2:                     0.198             -0.084              0.500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BOLFI_posterior.plot_traces();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "hW7GoiXicQT6",
        "outputId": "1f64ebd1-2f22-47b8-99e1-d0198f49d806"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7ff0413ad590>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff041383150>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff03de4bc90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff0413fea10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ff041483bd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff04144c350>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff03dde64d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff03de1a650>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Instead of `nuts` sampler, we can also utilise simple `metropolis` mcmc-sampler which is often sufficient for small-scale problems. "
      ],
      "metadata": {
        "id": "vHghYg41cXDu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "BOLFI_posterior = bolfi.sample(2000, algorithm='metropolis', sigma_proposals={'t1': 4 / 10, 't2': 2 / 10})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6CtslytUcm50",
        "outputId": "b73ba379-5bbb-4da8-d2e7-283619589085"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:elfi.methods.posteriors:Using optimized minimum value (-1.8637) of the GP discrepancy mean function as a threshold\n",
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.269\n",
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.267\n",
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.267\n",
            "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.256\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4 chains of 2000 iterations acquired. Effective sample size and Rhat for each parameter:\n",
            "t1 1108.3714284055893 1.0044533953223462\n",
            "t2 660.2994743601151 1.0065897137304802\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BOLFI_posterior.summary()\n",
        "BOLFI_posterior.plot_traces();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 866
        },
        "id": "4YYdTUMPdMSt",
        "outputId": "9ab8e95c-7a53-4950-94ac-3fda790b1cab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Method: BOLFI\n",
            "Number of samples: 4000\n",
            "Number of simulations: 100\n",
            "Threshold: -1.86\n",
            "Parameter                Mean               2.5%              97.5%\n",
            "t1:                     0.063             -0.200              0.342\n",
            "t2:                     0.182             -0.038              0.445\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7ff0438e2290>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff0416aa7d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff04407a9d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff0416f0990>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7ff043b6fbd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff044060cd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff04170efd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7ff043fc2e10>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Assignment \n",
        "\n",
        "We model a first-come-first-serve single server queue model (M/G/1 : Memoryless/General/Single server) and infer it's three parameters $\\theta_1$, $\\theta_2$ and $\\theta_3$. \n",
        "\n",
        "In the model, the customer service times $U_n$ are uniformly distributed in the interval $[\\theta_1, \\theta_2]$ and arrival times $W_n$ between customers are exponentially distributed with rate $\\theta_3$. \n",
        "\n",
        "Start simulation with an empty queue and assume that the simulator output (observed data) are the customer departure times $y_1, \\ldots, y_{50}$, defined as\n",
        "$$y_n = U_n, \\quad \\text{if} \\quad \\sum_{i=1}^n W_i \\leq \\sum_{i=1}^{n-1}Y_i$$  \n",
        "$$ y_n = U_n + \\sum_{i=1}^n W_i - \\sum_{i=1}^{n-1}Y_i, \\quad \\text{otherwise} $$\n",
        "\n",
        "The priors can be set as \n",
        "$$\\theta_1 \\thicksim \\mathsf{Uniform}(0,10) \\\\ \\theta_2 - \\theta_1 \\thicksim \\mathsf{Uniform}(0, 10) \\\\ \\theta_3 \\thicksim \\mathsf{Uniform}(0, 0.5)$$\n",
        "\n",
        "Use e.g. $0-$, $0.25-$, $0.5-$, $0.75-$ and $1-$quantiles as summary statistics and euclidean distance. \n",
        "\n",
        "Use the following observed data: \n",
        "\n"
      ],
      "metadata": {
        "id": "IfM8LutpUNKn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xobs = np.array([[ 7.53,  9.99,  2.72,  2.28, 10.14,  8.95,  1.89,  6.16, 20.78,\n",
        "                   15.1 ,  3.05,  1.86,  1.09,  4.3 ,  3.08,  2.13,  4.78,  3.62,\n",
        "                   1.74,  4.48,  4.71,  1.05,  1.72,  3.36,  1.02,  1.86,  4.99,\n",
        "                   3.86,  3.25, 29.54, 28.94,  2.8 ,  2.67,  4.55,  1.07,  4.32,\n",
        "                   1.06,  1.52,  3.93,  3.86,  3.32,  2.07,  1.83,  6.26,  2.81,\n",
        "                   1.56,  2.44,  2.68,  4.83,  4.24]])"
      ],
      "metadata": {
        "id": "SCu7wS_Ke4lX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tasks\n",
        "# 1) Implement the model as ELFI-graph\n",
        "# 2) Infer the parameters using \n",
        "## - Rejection ABC\n",
        "## - SMC-ABC\n",
        "## - BOLFI using MaxVar -acquisition\n",
        "# 3) Plot the results"
      ],
      "metadata": {
        "id": "dbU5F72wfV0K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "4nJ7JQ-JeuOp"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}