{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "b8b5461b",
      "metadata": {
        "id": "b8b5461b"
      },
      "source": [
        "# Tutorial — Symbolic Regression Basics + PySR\n",
        "\n",
        "## Motivation\n",
        "\n",
        "In many physics problems, we do not only want accurate predictions. We also want expressions that can be inspected, plotted, and compared with known analytic structures.\n",
        "\n",
        "Suppose we have a dataset with input features $x$ and targets $y$. A neural network can learn a function $f(x)$ that predicts a target $y$, but the learned function is usually difficult to interpret.\n",
        "\n",
        "In symbolic regression, we ask for something more:\n",
        "\n",
        "> Can we find a simple analytic expression that describes the data?\n",
        "\n",
        "Instead of learning a black-box function, symbolic regression searches over mathematical expressions built from basic operations.\n",
        "\n",
        "For example, instead of only learning\n",
        "\n",
        "$$\n",
        "y \\approx f(x_0, x_1),\n",
        "$$\n",
        "\n",
        "we may hope to discover something like\n",
        "\n",
        "$$\n",
        "y \\approx \\sin(x_0) + x_1^2.\n",
        "$$\n",
        "\n",
        "In this notebook, we will build up symbolic regression step by step. We will start from a toy dataset where the true formula is known, use `SymPy` to understand symbolic expressions, compare with simple regression baselines, and then use `PySR` to search for compact analytic formulas.\n",
        "\n",
        "By the end, the aim is to understand:\n",
        "\n",
        "1. what symbolic regression is,\n",
        "2. how mathematical expressions are represented,\n",
        "3. how PySR searches for formulas,\n",
        "4. how to evaluate the discovered expression,\n",
        "5. how the same idea can be used for physics-motivated observables."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c8fe126a",
      "metadata": {
        "id": "c8fe126a"
      },
      "source": [
        "## 0. Setup\n",
        "\n",
        "We start with the standard Python tools for numerical work, plotting, and symbolic manipulation.\n",
        "\n",
        "`PySR` is the symbolic-regression package we will use later. In Google Colab, it can usually be installed with:\n",
        "\n",
        "```python\n",
        "!pip install pysr\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pysr #PySR uses Julia backend so the first import may take a little longer."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acsyUexSCo5U",
        "outputId": "807ea6df-f55a-4559-d94a-7f8c0f1a9210"
      },
      "id": "acsyUexSCo5U",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pysr\n",
            "  Downloading pysr-1.5.10-py3-none-any.whl.metadata (54 kB)\n",
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            "\u001b[?25hInstalling collected packages: tomli, semver, juliapkg, juliacall, pysr\n",
            "Successfully installed juliacall-0.9.26 juliapkg-0.1.23 pysr-1.5.10 semver-3.0.4 tomli-2.4.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9c9963aa",
      "metadata": {
        "id": "9c9963aa"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error, accuracy_score, roc_auc_score\n",
        "\n",
        "import sympy as sp # For symbolic calculations"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pysr\n",
        "from pysr import PySRRegressor"
      ],
      "metadata": {
        "id": "DT1TEDKsx9sp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5b961c0-a087-442c-bd59-fd62d11517ee"
      },
      "id": "DT1TEDKsx9sp",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[juliapkg] Found dependencies: /usr/local/lib/python3.12/dist-packages/juliacall/juliapkg.json\n",
            "[juliapkg] Found dependencies: /usr/local/lib/python3.12/dist-packages/pysr/juliapkg.json\n",
            "[juliapkg] Found dependencies: /usr/local/lib/python3.12/dist-packages/juliapkg/juliapkg.json\n",
            "[juliapkg] Locating Julia 1.10.3 - 1.11\n",
            "[juliapkg] WARNING: You have Julia 1.12.6 installed but 1.10.3 - 1.11 is required.\n",
            "[juliapkg]   It is recommended that you upgrade Julia or install JuliaUp.\n",
            "[juliapkg] Querying Julia versions from https://julialang-s3.julialang.org/bin/versions.json\n",
            "[juliapkg] WARNING: About to install Julia 1.11.9 to /root/.julia/environments/pyjuliapkg/pyjuliapkg/install.\n",
            "[juliapkg]   If you use juliapkg in more than one environment, you are likely to\n",
            "[juliapkg]   have Julia installed in multiple locations. It is recommended to\n",
            "[juliapkg]   install JuliaUp (https://github.com/JuliaLang/juliaup) or Julia\n",
            "[juliapkg]   (https://julialang.org/downloads) yourself.\n",
            "[juliapkg] Downloading Julia from https://julialang-s3.julialang.org/bin/linux/x64/1.11/julia-1.11.9-linux-x86_64.tar.gz\n",
            "             downloaded 0.1 MB of 273.2 MB\n",
            "             download complete\n",
            "[juliapkg] Verifying download\n",
            "[juliapkg] Installing Julia 1.11.9 to /root/.julia/environments/pyjuliapkg/pyjuliapkg/install\n",
            "[juliapkg] Using Julia 1.11.9 at /root/.julia/environments/pyjuliapkg/pyjuliapkg/install/bin/julia\n",
            "[juliapkg] Using Julia project at /root/.julia/environments/pyjuliapkg\n",
            "[juliapkg] Writing Project.toml:\n",
            "           | [deps]\n",
            "           | PythonCall = \"6099a3de-0909-46bc-b1f4-468b9a2dfc0d\"\n",
            "           | OpenSSL_jll = \"458c3c95-2e84-50aa-8efc-19380b2a3a95\"\n",
            "           | SymbolicRegression = \"8254be44-1295-4e6a-a16d-46603ac705cb\"\n",
            "           | Serialization = \"9e88b42a-f829-5b0c-bbe9-9e923198166b\"\n",
            "           | \n",
            "           | [compat]\n",
            "           | PythonCall = \"=0.9.26\"\n",
            "           | OpenSSL_jll = \"~3.0\"\n",
            "           | SymbolicRegression = \"~1.11\"\n",
            "           | Serialization = \"^1\"\n",
            "[juliapkg] Installing packages:\n",
            "           | import Pkg\n",
            "           | Pkg.Registry.update()\n",
            "           | Pkg.add([\n",
            "           |   Pkg.PackageSpec(name=\"PythonCall\", uuid=\"6099a3de-0909-46bc-b1f4-468b9a2dfc0d\"),\n",
            "           |   Pkg.PackageSpec(name=\"OpenSSL_jll\", uuid=\"458c3c95-2e84-50aa-8efc-19380b2a3a95\"),\n",
            "           |   Pkg.PackageSpec(name=\"SymbolicRegression\", uuid=\"8254be44-1295-4e6a-a16d-46603ac705cb\"),\n",
            "           |   Pkg.PackageSpec(name=\"Serialization\", uuid=\"9e88b42a-f829-5b0c-bbe9-9e923198166b\"),\n",
            "           | ])\n",
            "           | Pkg.resolve()\n",
            "           | Pkg.precompile()\n",
            "Detected IPython. Loading juliacall extension. See https://juliapy.github.io/PythonCall.jl/stable/compat/#IPython\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    import pysr\n",
        "    from pysr import PySRRegressor\n",
        "    PYSR_AVAILABLE = True\n",
        "    print(f\"PySR avaiable. Imported PySR version {pysr.__version__}\")\n",
        "except Exception as e:\n",
        "    PYSR_AVAILABLE = False\n",
        "    print(\"PySR could not be imported.\")\n",
        "    print(\"\\nError message:\")\n",
        "    print(e)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VdRzeM-ZzDq1",
        "outputId": "7c19c735-d33a-4da3-e821-24cc9dce8108"
      },
      "id": "VdRzeM-ZzDq1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PySR avaiable. Imported PySR version 1.5.10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f217497b",
      "metadata": {
        "id": "f217497b"
      },
      "source": [
        "## 1. What is symbolic regression?\n",
        "\n",
        "Symbolic regression has to choose both the structure of the formula and its numerical parameters.\n",
        "\n",
        "For example, these are different candidate models:\n",
        "\n",
        "$$\n",
        "f_1(x_0, x_1) = a x_0 + b x_1 + c\n",
        "$$\n",
        "\n",
        "$$\n",
        "f_2(x_0, x_1) = a \\sin(x_0) + b x_1^2 + c\n",
        "$$\n",
        "\n",
        "$$\n",
        "f_3(x_0, x_1) = a \\exp(x_0) + b \\log(|x_1| + \\epsilon)\n",
        "$$\n",
        "\n",
        "The algorithm has to search over both:\n",
        "\n",
        "1. the **form** of the expression, such as whether to use `sin`, `square`, `log`, `+`, or `*`;\n",
        "2. the **parameters** inside the expression, such as $a$, $b$, and $c$.\n",
        "\n",
        "This is harder than ordinary regression because the space of possible formulas grows very quickly. A symbolic-regression algorithm therefore needs a way to balance two goals:\n",
        "\n",
        "- fit the data well,\n",
        "- keep the expression simple enough to interpret."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3a710fc8",
      "metadata": {
        "id": "3a710fc8"
      },
      "source": [
        "## 2. A toy problem\n",
        "\n",
        "We first create a simple regression problem where the true formula is known:\n",
        "\n",
        "$$\n",
        "y = \\sin(x_0) + x_1^2 + \\epsilon,\n",
        "$$*italicized text*\n",
        "\n",
        "where $\\epsilon$ is a small random noise term.\n",
        "\n",
        "This is useful because we know what the symbolic-regression algorithm should ideally rediscover."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2e88d56d",
      "metadata": {
        "id": "2e88d56d"
      },
      "outputs": [],
      "source": [
        "def make_regression_data(n_samples=1000, noise=0.05, seed=42):\n",
        "    rng = np.random.default_rng(seed)\n",
        "    X = rng.uniform(-2.0, 2.0, size=(n_samples, 2))\n",
        "    y_clean = np.sin(X[:, 0]) + X[:, 1]**2\n",
        "    y = y_clean + noise * rng.normal(size=n_samples)\n",
        "    return X, y, y_clean\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X, y, y_clean = make_regression_data(n_samples=2000, noise=0.01)\n",
        "\n",
        "print(\"X shape:\", X.shape)\n",
        "print(\"y shape:\", y.shape)\n",
        "print(\"First 5 rows:\")\n",
        "pd.DataFrame({\"x0\": X[:5,0], \"x1\": X[:5,1], \"y\": y[:5], \"y_clean\": y_clean[:5]})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "m3NTVmu65p9K",
        "outputId": "36c0b9e5-8b4e-4bb2-d7a5-8da9a32a1dc6"
      },
      "id": "m3NTVmu65p9K",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X shape: (2000, 2)\n",
            "y shape: (2000,)\n",
            "First 5 rows:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         x0        x1         y   y_clean\n",
              "0  1.095824 -0.244486  0.938673  0.949079\n",
              "1  1.434392  0.789472  1.599011  1.613978\n",
              "2 -1.623291  1.902489  2.634369  2.620843\n",
              "3  1.044559  1.144257  2.198252  2.174028\n",
              "4 -1.487545 -0.198456 -0.961909 -0.957152"
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6f1d24ef-3457-4f4e-a85b-8ed2798522ee button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6f1d24ef-3457-4f4e-a85b-8ed2798522ee');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"x0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.512797852954017,\n        \"min\": -1.6232906084494019,\n        \"max\": 1.4343916796455298,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.4343916796455298,\n          -1.4875454692978165,\n          -1.6232906084494019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9149249161981111,\n        \"min\": -0.24448624099179073,\n        \"max\": 1.9024894065470237,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.7894721162374556,\n          -0.19845624841773146,\n          1.9024894065470237\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4072951409962884,\n        \"min\": -0.9619092570025791,\n        \"max\": 2.634369389553287,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.599010919378588,\n          -0.9619092570025791,\n          2.634369389553287\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y_clean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3984852632207379,\n        \"min\": -0.9571517658094529,\n        \"max\": 2.6208434504557214,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.6139775241458167,\n          -0.9571517658094529,\n          2.6208434504557214\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###1D projection: $y$ vs $x_0$"
      ],
      "metadata": {
        "id": "iTEGCdmd8Qgl"
      },
      "id": "iTEGCdmd8Qgl"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "22319476",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "22319476",
        "outputId": "1f9d721e-68d5-4813-8344-972bb5b17016"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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1wer7OOXMMjvEm566ia1dF+czSOSZjqdq6PRzMQIRTQQCgfAux+hZYVbdz+5dbp2emO0Ub3oTS3Ich0SOQW/Yh3XdAYCi2h7rpeV5O1VQEdFEIBAI71LUJi+rA5jl7rfWklQaxSnWru0DETAsh4VMqSOxXkrP22lpIsQQ0UQgEAjvUpQmL5blMJ7IWxLAzH9H6n6P37XZkkBpp1kmnFYeobUrXaw0hEm7Y73UAuOdLqCJaCIQCIR3IUqTFz+pXpjLIluq4KYN3ciXq7otEkKrwcZYEJcXsy33A2AqUNqKXWGAdpGj9rl2WUr0iDLhZ2ma6misl1JgvJPSRMhBRBOBQCC8C1GavPjV/nAsgPNzFUwl89i/c71ui4TQanBiPAlw9SzS4vuZCZQ2uytMq8jR+jkrLCVKIkGPKFP7rF3CUg255+2UNBFKENFEIBAI71KkJi/xat/nccHjovD4XZt1u8vEVgOW47C1P4zJVLFpsjRq+TCyK0x8DzmRo/VzanXWYynRIoj0iDKrXF1WW8+UnrcT0kQoQUQTgUAgvEuRm7ysWO3LWQ2UxJGZe6jtCpNz44lFzqWFLP72ldGmA2OlPiclhsxaStREjpIoE7efla4urcJSL3KH9johTYQcRDQRCATCuxy7VvtKbhir0LorTG7iF4sccBxOjKt/Tk4MGW07LSJHSpTdPNiFb7061mIFssrVJVUuKWFpdSoKJwomgIgmAoFAuGYxulq3arXfDquBll1hSoJEKHL2jsRweTEHmqIUP6ckhozWWavIEZejUKnJWqesEL9S5ZITlkZxepoBIUQ0EQgEwjWGVZOQVUKnHVYDpV1hSoJE/J1njo5q+pyW8uhFi8gRlgMAnnrhvKx1yirRqlVYSt1D6d5rJc2AECKaCAQC4RpjLU1CVsJPwkbcZvx3tH7ODvSIHP7vWqxTZsusVVgKURLuWlJRkJgmAoFAIBhCb04ep+e6sRotljWtgsQJgch67msmhspI4L3WeyoJd62pKMyU1S6IaCIQCASHYsTNthZy3ViNHsua1nZYK+2lV+RZ4bpVu6faLj+5VBRiEebEWCe6o3cnEAgEgiy8GGCqLI6PJXHw1LSm7z20ewj7NsfhddMdyXXDT47tupdwEo6HvE0T9LsFvWkN9PYpPffkhXsyzzR2Mm6KBxuuU/HfhuNBUFTrtawsq1UQSxOBQCA4EDNutk65mDphGdBiWXOSe6eTtNN1q+TCa9kByFSbLIUcx+Gj77nOkW5mIpoIBALBYfATg1k3W7snl04FoMtN0E5077QLKXHRTtetknCX2wHY5ffg4nwGz52cBkChL+zF2ZmM7WXVAxFNBAKB4BCadxUF8N7t6wA490gJIZ0MQJeboN+NuwjVhGK7jylRevbiHYAX5zKYTRcxGAvixHgSNw91Y9/muKP6PxFNBAKB4BAOnprG0SvLSBcZ/PjiIk6MJfH0B29FNOBpTDCddk/I4YQAdLuOEXEKWsquJhStdt3y1zFzvftu7Mexd5Yxnsgj5HPj5qFuMFUWS9kynnpwBwDnBOYT0UQgEAgOgJ/k00UGmWIVHheNC/NZPH9yGk/cc92acDU56bBVJ4g4q9D67PUIRbPtwJfp0kIW6TyDaMiLbesihvrlyxcXQdE0RnpCmEkXcXZ6BX0RnyOfFxFNBAKB4ABomsLGWAA/vrgIj6u+sbk37MNkqj7prQVXUzTg6XiOIyFOEnFC9LaP1mffTqHIl2kpW8Z0qoDBWADpQkW2bHIIhd5ITwgnxhMoMFXsHVnvmOclhIgmAoFAcAgP7xnGibEkLsxn0Rv2oTvowaZ4EEBrbhsnuZqcagVzQqJKIck8g+dPTmEyVdTcTnrdjO0Qio0yBT2Yz5QQ8XvAskAs6NHdL8VCrz/ix96RGJ64x1kLAh4imggEAsEhxENePP3BW/H8yemmE+Sd7mpyuhXMCe2UzDP49PfewIX5LHrCXkwmCgDU20nvs2+HUBSWyUVRyJYq6Aq4kSpUsG9zRPd9nWoRlIKIJgKBQHAQ8ZAXT9zTOuk5dWJpZ8C1UyxGRnj+5DQuLGThcdHIFKsAtLeT1mcvvJbd7cSX4dJCFn1hb1NMk16cZhFUgogmAoFA0Ek7Bnel3DZOmljaYQVzqvtPKyzLYTJVQG/Ih5ViPe4nkWOwMRYATVOoVlm43fIHdKg9+060j7hMVvTLtZCQlIgmAoFA0IgTJm8nTCTttoI53f2nBi8sJ5N1l9xyroztAxHs3hTHb/7Da43+9Kn3bcf1fWHF60jRyfax2rLlhHdMCSKaCAQCQSNrffI2i9yEptUKZsR6cK3kWxIKy42xIB7eM4RPf+8NnJlKI+z34PRUGl/6wQV847E7dF23E+1j5tpq33X6O0ZEE4FAIGjgWpm8jcDXUW1CE7cD/z2x2Dpw2yB6Iz5N93Z6ELxWxMKyWmUxkSwg7Pegb7UtJpIFVVedmHa2jxkrkJbvroV3jIgmAoFA0AA/OR0TTU7XMuJjXS4v5jRNaOIJssBUcXYmg5DPje++PoXD5+axf+eA6qTLX9upQfBG4NvK7aaxKR7E6ak0ACBbqmDXcFSXYOJpV/uYsQJp+e5aEMhENBEIBIIGknkGBaaKVJ5BKl/G3s09KDBVPPXCeUfGXliBcKI7MZ4COA6g6hOY1IQmZZE6NpZEKs/ghv4wZtNF5MpV1FgOx0YTAKQnXSmrhBOD4M3yqfdtx5d+cAETyQJ2DUfxqfdtN3SddmwSMGMF0vNdpwvkNS2ann76aXzmM5/B7/zO7+Av/uIvOl0cAoFwDXPw1DTOzmRwQ38YiVwZby9kAYpybOyFWYQTXf+q+4jlOGztj2AydXVCY1kO6WJF0SKVypexnCtjdqUEAFgfDaAn7JOdOOWsEteSYAKA6/vC+MZjd+h2yclhZ/s0LK2rYjeZZ7B3JKbpnnosSE7dJcqzZkXTa6+9hq9//eu45ZZbOl0UAoFwjSNeKXMchzNTaewajq6J4Fsj0DSFvogPL74xixoHuCjgF27d0MghJRRK8+kiKiyHdV1+SYvUvdv6EfS4kC4woCmgN+SVnTiXc2UcPjeHdLGKUqWmKK6M4MTJ2ArBZDfJPINCpYZUgcFitoxYwIPLizk8c3RUk5VVrwXJac+IZ02Kplwuh0cffRTf/OY38b/+1//qdHEIBMI1jnilnCpUsCkeRLJQASjKscG3puE4cPx/KaouhoAmF1ws6Gkc+3LLUBSAtEUqHvLiwK5BHDo9g4lkATsHuyUnzkOnZ5ApVVFkqnhnKYeFTAkfun3YsgNmrW5HJ4owq+HPPjw7vYIb+iM4NZlCsVLDQDSg2crqdAuSVtakaHryySfx/ve/Hz/zMz+jKprK5TLK5XLj50wmY3fxCATCNYh4pXzfjf14+eKibbEXWnes2QXLcljKMdi9MYb+iA+L2TKWcgxYti6chJa3nrAXy7kyFlaKSBYq2Lc5LjlB9oZ9ihMnb9Hbsb4Ly3kGc+kiIn4PDtw2aLo+WttR66Tu9HxCgHlBx9dxPJHHhbkMhuMh9Ed8YDkAHNdw2+qxBK5lwQSsQdH0ne98B6dOncJrr72m6fNf/OIX8bnPfc7mUhEIhGsdqZXy9X1hy1fOzTvWgri8mO3IFmy1OBTh36IBL/rDPng9LuzbfPUoDbkyKv2ev+5gNAC/m8ad1/VoTk8gh5ZAZDkRJNfWTs4nZJWgE9YxU6rirdkV+Nw0XBTAURQWs+W27XBzioVqTYmmqakp/M7v/A7+/d//HX6/X9N3PvOZz+CTn/xk4+dMJoPh4WG7ikggEK5xxAO31QN58461pOqONTtRikMR/u3uLb14aPcQogGP6bIJr3vndT2WWfDUApHFIqjAVBH0uiWFh9PzCUkJusfv2qyrbOI67ljfhalUER4XhV+4dQPA1S2Rdu9wc5pFb02JppMnT2JxcRG7d+9u/K5Wq+GVV17BX/7lX6JcLsPlcjV9x+fzweczt0ohEAiEdiA1Gdfjg8KYTBXbvgVbyrrG/9uuGBUrryuccPsiPtw82CU50Uu1+5HLy41s52JLkpPzCYnrUq7UcPjcPMYTeYz0hDSLDnEd80wN+3cONImvdohEp1n01pRoeuCBB3D27Nmm3z3++OO48cYb8alPfapFMBEIhHcvTln160FuMu50AK1UVm9+8rWrTFZcVzjhnp1ewb7NcTz14BZJa6Gw3RO5MgBO0ZLk1HxC4rqcn8uAAlCpcbpFh1QdhW1n5bOX6t9OtOitKdEUiUSwc+fOpt+FQiH09PS0/J5AILw7MWPOd4LQkpuMO1UuJwSkG6m73IQrh9gtWKjUcHZ6BYC0JUmvRaydfYuvy3gijy6/G8PxkCHR0Y4db0rvqxMtemtKNBEIBIIaRiZ3u+Mm9Ew6Ttma3emAdLPPRO+EK273ZJ7BQc+0qiVJrf56A8ytQFiXb706huNjSSxkSoZFh539UO19dZpFb82LppdffrnTRSAQCA7BqDnfLiuKmYm/0xavTgekW/FMjEy4fJ2sEq8HT03j2GgCPWGfaoC51Tj93D6195VlOccsInjWvGgiEAgEHiPmfJblMJ7I22JFcVoQq1asDkjX25ZWxbJYMeGa6QPL2TIOn5tHusCgVGXRG/IqBphLYbYfyrWBE0SI3PsqzDbvhB1zQohoIhAI1xR6Vta8JejCXAaZUhU71nchz9QssaI4IYjV6L2sCkg3ammzOpalU+Lg0JkZZEsVFCs1jC7lML/iQnfAo6lPWO0yVstH1Smk3lcnLzaIaCIQCNcUeqwL/OA8HA/hrdkVTKWK2L9zwBIXRieDWK2YGK0ISDcz+TnZraQFXjTftKEbidXDirsDbtyztQ/nZuonUyj1CbuEg9MEifh9dcJiQwkimggEwjWJ2gArHpx9bhoeF6U7CaASchO/3ROAFROjWdeW2cnPabEsehGK5g3RAHweF+4UWFImkgXsHZEWg3YJBycLEv7+TYsNjmscy9Pp8vE4/2hlAoFAsAF+cE7mmcbOopGekC2JGp96cEdDtDxzdBRPvXAezxwdRTLPWHYvHvHEGA95GxOjlu+KMeMSE7fvpnjQ0M4tLWXn0fNZu3lo9xD2bY7D66Ybgike8uKh3UPYGAtgMlXAwVPTLf3AqrYTY9d1rea+G/sBjsPpqTTAcfWfHQKxNBEIhHct7XIB8ZNSO1wjRtyCdsW5mG1fPeVyWqwOIG8tO3hqGifGU4r9QGvb6bUSOcntKVf2ly8uAhSF24ajSBUqePniIq7vC3eghK0Q0UQgEN61tNMF1E7XiJ6JkWU528SclvZV+puWcnU6+aYWxLvWtPQDtbYzKhKd4PZUKru4fSiKwngi7wgXIkBEE4FAILRlMG5nYLiWiZGfuMYTeVyYyxjOGq0FI5O+mrgQfn846seVpTxiQY9iHZww8ertB2YEpVo5OoVS2YXtU67UcH4ugy6/G996dcwR1kMimggEAqFNtNs1ojWhZ6ZUxVuzK/C56bbt8lOb9NXExcFT0zh6ZRnpIoN/P19CtcbC43ahv8uHaMCLu7f0rqlt9kpIib92WC3tEJlays63x+Fz86AADMdDjrEeEtFEIBAIbcIJrhGgdeLasb4LU6kiPC6qMYnbWUatk77S7sOJZGF1K38RuXIVNRboDdNI5Bj0h31NQsRprjut/UDpkGQ7rZZ2ikwtZY+HvHj8rs0YT+RRqXFY1+XHQqbkiJ1+RDQRCIRrhk4PqFrpdBnFE1eeqWH/zgE8ftfmtmRj1jrpy4kLmqawMRbAoTMzYCo1sCwHCvVTXn76+h74PC5EAx4AxqwyUn+zo29pzSMmJfbstFraLTK1lJ2mKYz0hNriztYDEU0EAqHjmJ2QOu1+6YRYM3tPqYnLbEC1njLpmfSlrvmB3UP4+/8aR7LGwecCKjUOlVo9r8+dmyPSeX+gPPlK9SMAqrFXdjx7NbFnNsje6H2tQKulzUk7/XiIaCIQCB3DKrHTKfdLJ8SaVfeUmriMTphGymTWVdkb9uGR24fxwptzqFRZ5MpVDEYDjXxIQrROvlL9CIBk3xLX+cBtg+iN+HTXQw6tYs/KnXVa7yv3zPQ+SzWx5xR3thAimggEQsdoTFJBj2Gx08ksx50Qa1bfU+z2MhIrY6ZMZp7Ro3eOIOh1YzyRx0hPCAd2DaI33CpctFplxP1obDkHiqIk+xZf55DPje++PoXD5+YbR/BICRQj/VFJ7JlN1WDkvnJizCohrxTD5RSIaCIQCB2BZTlcWshiKVvGfKYEF0Xh0kLW0Gq1E2e8CSfZ/lULg91irR0C0cjOrk6JVr2WCKXPyG11jwe9YGr1LON83wLQqPNsmg9E53BsNAGgWaCYERRS9TObqsHofQF5MaZVpKmVwWkB+1IQ0UQgEDoCTVNI5xlMpwqI+D3IliroCxtbVXYi9oGmKfRFfHjxjVnUOMBFAb9w6wZbhUI7BKIRIWKmTFaIK6sFo3Cre6rAwOui4HXTTbFfm+JBHBtLYnalBABYHw2gJ+xrOrLGqqSbWoSL8LOb4sGGgDPTR7SkOahWWVWRpkU4OvlcPCFENBEIhI7AshyiIS8GYwGwLNAVcCMa8hoaJDsW+8Bx4Pj/UhTA2X/uWbuPfrGrTJ0O3pdCaqu7z03D66bx2fffBLf76nGtfB1T+TJoCuhddVPdPNSNb706holkARtjAVxezGkSAlr6rhZhkcwzKFRqSBUYpAoV3Lu1V3MfUSqDnDh2u2lV0axFOHbKYqwXIpoIBEJHoGkK29ZFkC5UEAt6kCpUsG1dxNQg2c4BlmU5LOUY7N4YQ3/Eh8VsGUs5RrNwMyrwnBgca6RMTnXFyG11Fwom4GqdD+waxKHTM5hIFrBzsBsFptqo14nxVF1IU1TTtYRtpEc8ahEWB09N4+z0Cm7ojyCRZxD0ulXFqNqxJuKkkxPJAvaOXBXHarFXWi1ITtwtJ4aIJgKB0DGaB8lIRwZJo+LD6MrYKguLUwSTED0uOSe7YvRM3r1hX0MwAsBTL5xvqleNZbFtXRcmU9LX0isejQoUQN+RLA/tHpLspw/tHsLzJ6cwmSrg4Knpxu/lRLOe98SJCwIxRDQRCISO0clB0grxYmRl7FQLSzsRT6SJXBl3XtfjmInSSL/kP7cxFsSJ8eZg8hsHuvCJB7a07OwzIh71ChShu1BP4PjzJ6dwYjwlGfQt9XthG4jR+544pR9IQUQTgUDoOJ0YJK0QL3onV6dbWNrJQ7uHUGCqOHJ5GQCHQqWGZJ6RdAt1Cj3350X45cUswHEYXc6rnptmJo5Hq0ARugu1nvG3dySOyZSxoG8p7ErE2QmIaCIQCNccagOw1eJFjzXCrmDXtTLp8MRD3ka8TTzkxdnpFRz0TMu6hezAyjYTinAOgIemsH1jTPXcNKvjeIQCBWh1F2o944+vD6Av6FsJqxNxdgIimggEwjWD1gG4kzt1rJ4k19qkwyPvFprGiXF73ZdWt5lUXVKFChJ5pnG/dsfx8NcyesafXD+1uv+uNXc1EU0EAuGaQc8AfGDXIIDWwd9ui43Vk6RanZ1qgZJ2C8Uk3UJW10EuXscoLTFaeQb3bu1F0OvueBzPgduk+7laGeT6qZX9dy26q4loIhAIhnHS4KZ1ABZbGT5x/xb0RnxI5hk8c3S0bRYbq1xycnVOFysdOWhWD1rdQlaVM5ln8PzJaTx3choBrwsjPaHG/a04ALlQqeHIpUUAV6010YAHQPvj9lr6uUQguhaU8jaZZa3kZhJCRBOBQNCN01xC/ISnZQA2exSEk1Cqs1x9nPTs9LiFjCK8dn3nVxJBrxvTqQJOIIH+iN+SiToe8iLocSEW9KIn7MPZmQy+fXwCQY+rI229VvrzWsjNJISIJgKBoBunDMhiAXDfjf0A5F1uZo6C6CRK5ZCadJQsUE55dkLscP+I+8aBXYONNhnprVuYCkwVe0dilkzUfJv3hH2NNj9yabEhotrZ1mvB7cWXZS3kZhJCRBOBQNCFXQOyke/LCQD+WlIuN6NHQXQCLVYhuUlHqj4AHD+Z8pgtj1TfELZJX8SHvSPr8cQ9xkSMuM2k4poAqklEtautnez2kuvTTiibFohoIhAIurB6QDbqLtIi3uQyHfOftXNXkBXosQpp2cqu99k5UUxpQa5vfOL+LY1/G33GSv1V2OZ3ruZIOjuTaXyvncKlHf3ZyoXOWoGIJgKBoBsrB2Qzg6iSAJCbOKMBj+27gvj7m7W8mbEKydVHy7NzUtyTEeTEYW/EZ/oZK/VXcZsL29FK4aKl/Ha6vexc6DgdIpoIBIJurBqQjQyiwgG7L+zFzUPdWMqWWyYlNauKXbuCrDxbzgqLnpacPGLWijVAb6wXj9FnrLW/8v+2WrgY6Vt2iBGj/cPJbkOtENFEIBAMY3awMzKICgfsszMZ7Nscx1MP7pD8TidcblonFC0TqdnyK91DySXndGuAmVgvHiP1MTrp25F1/NhoAkD7xazZ/uFEN7geiGgiEAgttHOC1DOIyg3YcrR7Z46WCUWPtUCp/FqtcXqtXWvBGmAm1kutbdT6Sqcmfb5vhbwuzKSLmEsXkSpUcOC2QfRG9OdfMorZ/rHWdsuJIaKJQCA06EQsi55BtNMrfTW0lM+Ia0MpOafUMzLrXnOyNUCPpUPqd2bzV3Vq0uf71rOvTyFfrgIAXKUKDp2Zabu1yYr+oWcR4CSIaCIQ3oXIDVCdjGXROmA6eUIHlMtnhetLy7EpZu/hZGuAFmEqJ4CszF/ViXY5sGsQh8/NgeWADd1+9IR9bXWd2pFbaa1tOiCiiUB4F6E0QK2FWBagdULnT3N3CkoTilnXhpZnZKV7zUnPXYiacJYTQHJtw1/LbN+3+13pDfuwf+d6HBtLoifkRTLPYOeGLtufk525ldbKpgMeIpoIhHcRSgPUWohlEaJ2tlqnEaY+EP5sxlKm9Rk53Ronh9l0Cvw1lASQFfmrxLTTWuK0zQ1mhOJaWagJIaKJQHiXwLIcxhN5xQFqLU22Tl+hJvMMvn18onGA671be/HonSOmXRtanpGT3WtSGBUdUnVTE0Bm8lfJ0c6+6JTNDcvZMg6dmTElFNfaQg0goolAeFfAT0oX5jLIlKrYsb4LeabWMkCtlcnW7hWqFdc5eGoaL74xi9xq0O4Lb84h6HU3WfaM3Edv4PxawGrRoUUAGclfJUWnrCWd3txw6MyMJc9sLS3UACKaCIR3BfykNBwP4a3ZFUyliti/c0B2gHL6ZGvXCtUqNwtv1atxQDToBQWgVKlhPJEHy3KWuBad/oy0YkZ0yH3GjPjXm9NpLVpL9CIWNgd2DeIrL10xvINRiNZn5ZSFHBFNBMI1jnhS8rlpeFwUHr9rsyMGIaPwA/l4Im/ZCtUqiwdNUxjpCeHEWBLpAgMACPncGOkJgaYpx7sW24kR0aFV3LZLRK81a4lepISN0R2MctiRc8wOiGgiEK5xnLwSdsrqEbDezfLQ7iEUKrWmmKaHdg+tyeBXu5ETHZ1OjaH1PmvFrW0WPTFgVj0jpy0wiGgiEN4FOG0lbMXq0erB1GpxGQ958fH7t+DJ+25oXJ/HqSK2U0gddPvM0VFTqTHEuxb1YkTcvpueoZ4djBzHGU7h4LQFBhFNBIJO1uJq0mkrYbOChx9MY0GPocFU7nN2iMt23edagG8rM6kxlHYt6i0LEbfqKO1gPHplGRfnM0jkGGwfiCBdrOh6Dk58BkQ0EQgacZpv3QhOGPCtWD2mixXMp4u4MJ9FT9iLaMCL99zQo/p9tWfYLnHpNBHrJLT0DyXRqbZrUU+bE3FrnId2D+H4aAKJPIPesA/MatZ1vdbgh3YPgeM4TKaKjngGRDQRCBpxmm99rWJ09Sic7A6emkaF5dAb9mEhW0KhXMXlRR+eOTqqKGa1PkM9GbrNiB4imFrR0j/kRKfSrkUjeYWIuDVONODBQDRQF7/dASxkSroXR/wiZzJVxMaYMxaqRDQRCBpwom99LaNnBS+2Dh24bRATyQLWdflxy1AURy4tIZEvgwIUhZCVz/BasDo6GS39Q+6YGrldi2byCpF3XD9N4peiDLnWhIucE+NJUFTnF6pENBEIGnCib30to2cFL2UdajwLjkMiX0ZP2IuB7gAoipIVQlY+Q2J1tBel/qEmWKV2LerJK0SwDjPuTacuVIloIhA0QuIbpDFzqKkWl5zUwPmJB7Y0/r19IAKmxmEhU1IVQlY8Q6cO5tciRtINxENePHnfDS27FjfGAjgxngLQmUXPWugfVpfRbJJRJy5UiWgiEDRC4hua0euiMhJTIjdw9oZ9jWchzK6tJoSseIZ6BnPSV6xFTbBK9UmgLrQuL+YAjgPLcS39xM7ntBZcuVaXUdyeRtvWiQtVIprWIGQg7iyk7etodVHxA/Lhc/PIliq4aUO3LpeW0sBJ05QhIWT2GaoN5mthojRDp8YgNcEq1ScBNH4HisLW/kij30nFy/VGfACsq+NacOVaVUar+70TF6prSjR97Wtfw9e+9jWMj48DAHbs2IHPfvaz2L9/f2cL1iau9YGYsHbQk2Dw4KlpHBtNIF1gUKzUkMiVsSEa0OzS0jpwtmNQ5cugVqbnT07hxHjK0ROlFsT1a9cYpPSslbKHTyQLiAtyd40n8gDQ1E8nU1f7HS8WQj43vvv6FA6fm8e9W3sBisJStmy6jmrviRPEgJXuZrsEYqfbSMiaEk1DQ0N4+umnsWXLFnAch3/4h3/AgQMHcPr0aezYsaPTxbMN8Qu+1gdiwtpHS4LBg6emMZ7I48JcFsOxANZHAxhdymF2pQSfx4U7dcYndHLglBMLUkHKz5+cxnMnpxHwujDSEwKw9mKe5Opr9xikRZTJCVZh7q7esA/dQQ/uvqEXACT7qVAszKaLyJWrqLEcXnhjFhRFYdfGmOk6yr0nVhzYbBVWxQ5ZlQXc6awp0fTggw82/fyFL3wBX/va13Ds2LFrUjQJB5CNsSAuL2ZJ8CnBMaglGOQn12ypgvNzFexY34WFjBtdfjfutCA+oZ19X6tYOHhqGifGkwh63ZhOFXACCfRH/I4IYJVCz9luj9+12fYAeD2iTHzPg6emwdQ49IS9WM6V0Rf2NvUxcT/lxcKxsSRmV0oAgIFuP+ZX/92/6qYzW0ep98RpC2ArYofUsoBfK3PVmhJNQmq1Gp577jnk83n81E/9lOznyuUyyuVy4+dMJtOO4lmCOEcFOA6grq7k7RyI7ejga/GlcVqZnVQepQSDwsn1pg3dmErmEfa78cjtwziwaxC9YZ/h+7bbTa3HFcl/bqS3bmEqMFXsHYlZEsBq5bNXakO5+gL2npmn1M6AsqWR/+5Atx+3Dkcxv1KEz+NCNOABTVOyrlT+uaTyZdAU0Bf2YTFbz/m1mC1bUkfxe2L17ksr+oVVsUNSWcC/fXwCQY/LEVY1K1hzouns2bP4qZ/6KZRKJYTDYRw8eBA33XST7Oe/+MUv4nOf+1wbS2gNUi8Wy3HY2h+2NZ28HROSE+Ig9KK1zO0SMU6OZ1PLh5QvV7F/53o8ftfmNRlYq9V9If5cX8SHvSPr8cQ95sqm5dnr7YdGz3azczeT1H1vHurGt14dU+334u+mChXs2xxp2cElbideLBzYNYhDp+s7Ox+8ZX0jpsnKOgrTbFghPu3a8WZmTJPKAn7k0iJiQS96wj5HWNXMsuZE07Zt23DmzBmsrKzge9/7Hh577DEcOXJEVjh95jOfwSc/+cnGz5lMBsPDw+0qrmHkXiy7dxLYMSE5IQ5CL2plbreIcZo5Xw2pydWKPtupHElaxYIdokLp2Rvph2bOdrN7N5P4vgWmqrnfK7W9WjsJU1jw9VKqoxUWGbmyasXqHW+XFrJI5xlEQ15sWxcxNKaJs4An8gwACj1h3zUTVrLmRJPX68UNN9STlu3ZswevvfYa/s//+T/4+te/Lvl5n88Hn8+4K6CTyL1YdrrkrJ6Q2jHJWS0otJS5nSJmLSZTtGty7VTCO631sbreas/eSD/U0oZq9bCrvYX3BYCnXjivud8rldnImYNS97BqsWS2n9ix420pW8Z0qoDBWADpQgWAsTFNOG/duSp8z87Uw2KckqDSDGtONIlhWbYpZulaol05Kvjr2zEh2T3J2RUfoFTmdouYTgkFK2iHNcKoNcdolmIrP6flOnLPvlplDfdDrW3YqT7G39dIvxf/3cpt/1Yvloy2r+U73oIezGdKiPg9YFkgFvQYHtPE85ZQaDolQaUZ1pRo+sxnPoP9+/dj48aNyGaz+Od//me8/PLL+H//7/91umi2YtfAJbVqssO90O44CCviA+67sV+2zJ0QMU7MjNspzC4m7M5+bDXiZ3/fjf145ugoJpIFzKeLYFatMnr6oROTBkohrvuBXYO6r2HVtv92LpasFL5KCNvGRVHIliroCrglY8KMXBsw1tec3C8pjuO4ThdCKx/72Mfw0ksvYW5uDt3d3bjlllvwqU99Cj/7sz+r+RqZTAbd3d1YWVlBV1eXjaV1Ps8cHW2smoQxU4A9nbZaZeF205ZeE7BmEpRrC7l26FRgtpMHk7WCUr/XQyd28dE01VT++ZUSvC4KA9GA4zYIWImRI3iESD0rodVIaz+wqu8ojSvPn5zCZKqoK0bNzJigFtPUzjFH+JyGowH80u3DbevPWrXBmrI0PfPMM50uwjWD2qrJypfE7smlE/EBnVqpE8FkjrWQ/VgOue3qXjeNz77/ppYFybUksA+dmTHV1lZs+2dZzrR1R2ksTOYZfPp7b+DCfBY9YS8mEwVN9bQ63YDQrcZbNNslyA+emsaPLi1ifDmPlUIFz52cwtd+7XZc3xe29b56WFOiiWAd7XQxtWty6UR8gN2T0rU08TkBy2NBNE66Vj1HufILBZMdKQo6iZVC18i2f6n25PM/6UVpLHz+5DQuLGThcdHIFKum6mkEYduoldUO+Oc8vpxHKs+ApmlMJot4+vtv4Zsf2WvbffVCRJNNrIVBqR1xMnIDnl2uOqM4LWbIyXmZ2oGd/cPqWBBAftK14zmqlV9vigKjAgBozzhn1wJPaz+wSjyoJe+cTBXQG/JhpVjfuZbIMdgYC3RkHunEjl2apjAcDWClUAFN0/C4KHjdLkymio6aL4hospi1NNm1w8UkHvD4GIzPf/8tR7VPpwNjxfflD7ltd0K4Tov9d5Zy+NIPLjTen0+9b7vlpnn+WZsdiLVMunas1pX6qp4UBUffXsbx0YRiPJRTYvq0tLXevqvlnbfayqUk/jbFg5hczby+nCtj+0AEH+jQ4k3LDmI7xolfun0Yz52cwmSyCK/bBQr1dnGKYAKIaLKctZaEELDfxSQc8LwuChWWA1NlHdk+7RYMUpMPy3I4fG4e6QKDUpVFb8ir+TgJK8vRCTH7pR9cwJmpNMJ+D05PpfGlH1zANx67w9J7tCvXjt3HZUhdQ2myE5fn0nwWy7kyYgaSZrZ7nFNqa7PPU+lZWG3lUhJ/wr/1hrygaApfeelKU53auaiRKqvdJyXEQ1587ddux9Pff6sRDP+p9203XRcrIaLJQtZiEsJ2IFzZf/77b4GpsqR9VpGafAAgW6qgWKlhdCmHhYwbD96yXtNxElaWo91ils87FPZ70Cc4LNVq03y7cu2oCRitfV6vKFBKitsoD8dhOVdGT9iLge4AKIqStkgFPS1tZGacMyv8pb6n93mqlVP895a0B7fpT3vAoyT+hH/71qtjTXUqVGqy57fZNX5GA56mRKPinZt2nZRwfV8Y3/zIXke55IQQ0WQhdgdXm83M3Wlh4nbTazZJoxG0DM7iyWc8kQcA3LShG4lcGbMrJXT53eAA20SNU8Q+3z9OT6UB1IXjruGopQNnu+uqlGNJ66SiVxQoTczC8mwfiICpcVjIlFoE3aWFLJayZcxnSnBRFC4tZE0lwU3mGXz72DiOXF4GwOHebf14dN8m08Jfz/NUm9Dl/s6353KujH89NY2v/OiK6YWLWl+bSBYQC3oadZI6v41Pm2D1QkrYDn1hb+Mcvo2xIC4vZtt2UoJTd4MS0WQxdgQUm1HvTnG78Dgt4NoOtLa53OQD1AXShmgAPo8L+0ZimEwVbZvo27mTUo1PvW97I6Zp13DUctO8mbpakR1ZbaUudU+jIk/q78LyCJM7ii1S6TyD6VQBEb8H2VKlPnkK0BtjdPDUNF54cw75cn1X2ItvzCLocZkW/nqep9qErhZA/z/+5U3d6QCMkC5WMJ8uNu4V9LoxnykhXayiXGXRE/ZhIlnA8yencWLc+oWUsB1eeHMOFIBdG2M4MV63UIKqt227Tkpw2hxGRJPF2BFQbEa9O8HtIqTTAdftQE+bK00+E8n62U3CRHyAPaKGdzl0Wsxe3xfGNx67o8U0b2V/0SvcrRi0jeYGskvQ0jQl+y6yLIdoyIvBWAAsyyHopbGQLeN//ts5jPSEGvXXGmN0YNcgxhN5sCyHWNALDkC5yjZ+J763FhefkvtMTsAptb3a3/WkAzBSJyEHT02jwnLoDfuwnCuj4KnC53ahyFTxzlIOC5kSfnnPECZT1gsUYTv0R3x11xxFoX/VXc5yHLb2hzGZKrbtpASnzWFENNmElS45M/EDTnC7SN1vLQsmpYGypc05TrHN5SYf8e/sstCJJ7hPPLAFveHOHnDNslxDMNmxytQr3K0atI1OKnZbZ6XezeF4EG9MpVFlOWSKFXQHPajUuJb6a7XojPSEcHwsiVSBAQCEfW6M9IQa39fynNXcZ2o5spTaXi3+TEs6AL5844k8RnpCuO/Gfrx8cVFX3+XHj3VdftwyFMX8ShFnptK4eX0EiUIFc+kiIn4PPrBrqJHwU6o+RhG3A01ToAAsZsuaTkqwuq86ZQ4TQkSTwzGj3jvtdjE64VmxWrJrh9nzJ6cxmbpaHwAtddwUD+LoleXG7qTtAxGkixXFusvthOLROtFrWfkKaecqTq3sakddWF0+rS45KwdtI5NKR6yzHAdu9b9MjUXA49KcxFOqvT5x/xYUmGpTTBNfd5blFAPPedT6gprFS+k8SUA9gF6cDuDhPcNN3//2sXG88OYcWLYuLo9eXoLX49LVd8VjdqpQwaZ4EIUKi8FoAH43jTuv60FvxGebmBZe98Fb1jdimsRtIoXVfbXTc5gURDStAcy8HJ2MIdI74ekVWeIX007fd+OIg4UsekO+xgAKNAdoF5i66f7tpSyypSo2xoJgavVJwYrJXm6wkKo70CrohO3RrlWc1uci7i8cx9kayyVEeE3hv60etM1MKu2YKPidUks5Brs3xtAf8eGVK0tI5BgsrBSRLFQU6y/XXr0RHz7+wFY8+d4tjc/xx3SMJ/J4c3oFLMtJBp7z5dLbV+XGH71WX6B5HN0YC+LhPa3v0pHLy8iXq4gFvUgVGJyfy+CBG/t1912pzQO8xerO63oaf7dLTEtdV889rC6P0+JgiWhaA5h5Oax8sfS+OFYNcmLkJmE7rRLPn5zChfl6TANvoud3ugnreOTyMmJBD/wedyM2ZKDbr7o6t8PvDyjvuJOa4PaOxCyfnIVlOzaaaCkHIN1fJlNFbIwF6wGosGeV2bRTKOIDOA5LOaapX9kxaDvNPS1+p/rCXpydyQAAogEv+sM+eD0u7NscUa2/UnsJ6y3sF7PpIgpMFeu6Ao3Ac+Fn9YpXOwPo5b/PCf4f8LtpJPIMQFG6+q7Uva7vC8ve266+JG5/NfQsWvWMeU6LgyWiaQ2hNEAYGQi0YsSCY2SQG0/kNQ1yUgLh8bs222Y1qcc0FNET9jaCQJdzZWyKh0BRaJQhkSsD4NAT9qFcZfHOUhVz6WLDpK7mPjBqGRNPEBzHSQo6cXuw7NUDSC8tZAGOw+XFHJ45OmqZlY4vW8jrwky6iLl0EalCBQduG0Rv5GrslFx/eWj3ECjKvlWmsC+9+MYsOAC7N8aaRKbTBm07EL9TNw91Y9/mOCaSBdy9pVfXcSta2ksccBz2u8FyHEJeF7oCbkRDrYkc9YhXOwPo5X5/77Z+vPjGLMpVFmGfGw9sX4eekNdw33VKHKjWfElaFq1mxjynvHtENK1hzHRAPaZXoxYcrYMcX48LcxlkSlXsWN+FPFOTHOSkBMLEqqvMLt93I6ZhdZtxIsesxjSIdrpd14NCpYaz0yvoCfuwkCkh4vc0mdSFWB1gfPTKMi7OZxrlu3k4irPTKwCa20Oq3/Dbl2mKstRKx5ft2denGtvNXaUKDp2Zabm+VH+xU7DwyTT5ibvGAeC4xk4hXmTy9eCDgp0yeFtVFimrzFK2jKce3AHA2kSUwr8J31evi0ZP2IebNnQhVahg27pIy/f19oV2u3Ue3bcJQY+rEQgul8W7XUkbzfYPPUcaabXsOW0nnBGIaFrDGOmAwh0e/au7u8TuCCFmzNxaBzm+HsPxEN6aXcFUqoj9OwckBzk5gZAuVmwdJJtjGgJ4eM9wo62EdUzmGRz01AXJh24fbrGo8OhtV7U2fGj3EI6PJpDIM+gN+8CwHMBxDWuBsD3aHTt0YNcgDp+bA8sBG7r9jTwzwh2HNC2/BR6wdpUpFI3z6SKYWl0YuSiAo6jGTqGbB7saWdjlXHedwOrYvU4F2wrfqV+4dUOjfdVcgE516wjvB1wtJ//fdpyrCFjXP/QcaaSlDzlxJ5wRiGhaoxjtgN8+PoEX35hFjQNWCgy6Ah7cMRKXFV1WDKhqu72E9fC566dbP37XZtnvSQkEPtDarkFSbQDmf6d1oNbarloHwGjAg4FooN6O3QEsZEpYyjF46sGrgbdAZ2KHesM+7N+5HsfGkugJeZHMM9i5oaspuaKwbnYPoELRyLAcvC4KXjctmrjjKFRqqq67TmDHav3Arvbn6TIbcKyVdk7ISu9rO85VBKzpH0aONFJbtDpxJ5wRiGhaoxjpgCzL4cilReTKVUQDHhSYGlgJd4TSzhGrB1Qj9ZASCMKy2/kSarUCWeU+kIvfkrpfox0VAk87FTskVddOmOqlRKPXTeOz77+pMRnwloKnXjiv6roz2teMftfq1XpLnq77t0haRvWUT2859AYcOxm5Pm32XEWt7arUP4Dm9lW6ppEjjbQsGJ22E84IRDStYYx1wHpnpigKbppCla2fPZVS2Eps1MxtV+yBVoFgN2bN4FqS8gmD48uVGg6fm2+JmeDR2o7tjh2SqmunTPVyotHtpltEt/BzYted0f5mts/Ild8oVglXpx11AVhrtdJyLaU+bfRcRSM70sT94+ah7qbDvrUm3TR6pJFSO10LmyqIaLKBTvjQtdyPpincu7UXL7w5h1Klht6ID8P8+WYathJrrZPeAVRvPYS7vjq5YrEykFsu55QwOP78XAYUIJmZGdDeju2KHZJCGOfRKVO9VB4cqUN05WNujPc3K/qMsFw3D3ahUKnhqRfOG9oMYpVw1VsvO8dIqfFH684/LdeSa1+1Pm1EhBjZkSZM4rl3JI5ipdp0jeOjCYCiVJ+V3JFGVrBWBRNARJOldGq1pacDPnrnCIJed5O1wuiAIofRiUGtDFYOhkKMDOBWTThqOaf44PjJZAFdfjeG4yHV+2m9f6cHrk4JX7FolDtE1+qYG6v6jLBc33p1TNO7JtdPrBCuelxCRsZItfZR2qF19O1lHB9NYCAaMDQm6x3LlPq0XhFiZkdafUfsFCaSeVyYyzTGDY7jcGYqjV3DUc19UK6sVu7eNLL5pVMQ0WQha2E7pd3mUTvdLla3r9mcIVZMOFpyTvHB8ZviIZwYT2IhU2qrZaZdLrt2o9VNaFXMjR3WNbWyq/Vxo8JV3D4tLiHBzsNN8WBjF6med1it7HKLKGGb8EcZxQyMGUbGMi19WqvVRkt/EabNEJbx+ZNTODGeQjzkRaZUxVuzK/C56caxLMlCxXBog1XGgfqxVFOYTBWbruNEV68QIposQusL5hT1bFcZ7HK72CHGtAzgdgY1imOW+GsB0jmn7A7WFtOuwauT70O73YRWWte0lF2tj+sVrnJ9QlwvfudhyOfGd1+fwuFz8/j5HQO4vJjV/A6rlV3u74024Tgs58roCXsx0B0ARVG6xgwzfcOq/iPXX1rSZqxa9epZ/eOYTF0dK3es78JUqgiPi2o5lsVIH7Ri8do4lmo+i56wt5ED72N3X+d44wMRTRah9oI5XT2LMSNG7Dp2wsrJTU2EaXleZiwlV2OWssiWKrhpQzfy5WqjTp0I1hbjpDgVO2mnm9DqZ6hUdj0LDa1lkesT4hxF/M7D2XQRuXIVNZbD8bFEfRsKdXVMlHuH1cqu9Hdhm2wfiICpcYatswdua38qBiFy/aUpbUbtatoM8c5UAMgzNezfOdC061Z4LAv/zLRg1eL1+ZPTuLBQP5aKP2WB303o9FxORDRZiNIA5nT1zGOFuLNrcm/nKl3P8zJSx0bMUiyA83MVTCXz2L9zfaNOnQzWBvQNju1eEFjdrzrtJjSDWj9p50KDvydQt5QeG0tidqUEAFgfDaAn7APLcdjaH8Zkqqj4DquVXenvwjZJFhgcOj2je8xoScXwwBb0ho2nYuAx2sfE3gq1tBlSY6X4vnJ50tTKYbZPsSyHyVQBvSFf4xzPRI7BxligscvQybmciGiyELkBrFPbq41gpbizum7tWqXb/bzE1/d5XLIJPTvVP9QGR2Fb8H0mFvTYuiCwW5xpaWuzfcCuOtjlQhbfQ+uExt8nlS+DpoDe1ViVfZvjmt9htbJL/V3OUqw3/5ST4ifFyD0HYayUlrHSaB21WN+U7suXf3I1FGE5V149lmoYgPNzORHRZAN2r/jsoh1iwYrrWNVucgOL3c/Lyf1B2BZSg5d48D+waxCXFrJYzJYwv1ICTdcP/7VjQWDFRGZVEkijk54dFmezgcl60Dqh8fc9sGuwYenZOdjd+LyW3VJqZRf+XWw1KTBVnJ3JGGpnu+Mnj1nw3LU+B7ny8nWMBT1NdVTa2afF+qb1PRGWf2MsiIf3XP2c0nN3gqGBiKY2oRZ74IRgcS2TuZn4HafGcymtlO1a7ThtNSW3E0k8eEltz0/nGcymigj7PciWKugP+yzvv2YnsuVcuTF5t2PruR11EKM3h5AV6BVhvWGf6ufV6qF2H5qmWkRJKs/ghv6wrnbm/26XWzPkc2M2XcTsSgmpfBkHdg0acvmxLGdaWKSLFcyni41A7KDXjbDXhc9//y3ZvqTlHdD6nmjpR8LfO2kOIaKpTUh1EqmkZFoytdqJlt0aesu2VuK5hFixQpfKU2Pl9a1ES24b4aTPHycynsijO+jFYCyIGsehO+BBNNR6srtZjE5kfL89fG6ukST0+FgSHAc8cU97LQ9WL0o6+V7pfbZSbl0es+5dqeeTypeRyJUBqPcVqbHNikWNsK6b4kF89/Up5Mr1oGeaAg6dntFVTyMpGOTG6IOnplFhOfSGfVjOlVHwVOGLBcFUWclnIG5jjuNa3gEj74nWvu+kOYSIpjYjtwNCT6ZWO9GyW0NP2Zwaz6Vn27FeknkG3z42jiOXlwFwuHdbPx7dt0lyAHOCYFrOlXH43BzSxSpKlRpSBQbpQqUltw1NU+iL+BoHPrso4Bdu3YCgx4WVYgXxoAfJQgXb1kVsqZeRiYzvt+liFUWmivlMCaUKi+dOTgHg8PCe4bYEv6rVQe+iZK2lOJGrH8typt27Us/n3m39CHpcmvqKnOvM7M7YSwtZpPMMoiEvhqIB+D0u1FgO66MB9Ia8usdBoykYxPB9Z12XH1vWRXD8nWWMJwoIet3Ysi4CQDrAf1M8iKNXlnFxPoNEjsH2gQjSxUqjn5pd2Mj1fafNIUQ02YCWh9nSETgOp6fSuE1HplY70RLILuf/lnrZnBK/o+UFNVu2g6em8cKbc8ivripffGMWQY9Lc6bmdnPo9AwypbqoeGcph3y5ipHekHRuG44DB9T/S9V/bhYCkUagqNXotc4J+22pUsM7SzlcWcgB4DAUD+LEeAoURWkS/1a5U8V14K2Rehclau+Vk9wZgHz9aJpCKlc27d6VS9Eh11fEqQvkXGdG3k2+rkvZMqZTBQzGAkgXKlgX8YENe9Eb8SOZZ7BzsFvz9c2kYFCKsT0/s4LR5Rw4DriymEON47C+OyA5Rj+0ewjHRxNI5Bj0hn1gWA4HT0039VMzCxu5vu+0OYSIJgsxdU7RaqbWVKECSkOmVjsnW7XA6PmVErwuqsX/rTXxXSfjd+ReUKsmGZatJ6xkWQ6xoBccgHKVbfzO6knNbLAkP9juWN+F5TyDuXQRFEWhO+BtyW3DsvWz13ZvjKE/4sNitoylHNOIfeLjhr7yoyuOSIYp7Lc9YR/mMyUwVQaDsSD2jvQgU6poXphoFWxa214YuLwxFtSV9JFnraQ4kZvQl7Nl/OvpGYwmCvC4aPg9NLoDQUPuXaVNHUKk3ruNsQCeOzltynXWUtegB/OZEiJ+D1gWCHpdGE/k4aKAdLGKe7f26hoH1YSDXmHx0O4hcBzw9VfegZum0dflw2KmjPmVEg7cOii9I46rp3DwuWlE/G7Egq3WMjMLG6W+76Q5hIgmCzF7TpGWTK12riClgmX5s92EZfW6KFRYrsX/rSXxXaddcnIvqJWH7470hHB8LIlUgQEAhH1ujPSELHF38kj1A/66RvOuDEYD8Ltp3DLUjaDX3dIP1QbmQ6dnHDNR8wj77SO3DyNfruDcbBaZIoNkoaJ7xWo0mFmM8PmfGK9nr9aS9FGI8L0Sls0ud4aZHENS/ebQmRmcGE+iy+9BpliB10WjL+Iz5d5V+15T7N6Veuxed9CLPFOD10VjKB405DqTqquLopAtVdAVcOPCXAYcgN0bY0jkGQS9bt3jtpEUDHLEQ1589D2b8cNzc5jLlOBzuxDxuzHQ5cNH39Oa+gQQWKRXrbYLmRI+dPuw5GfNuFal+r5T5hCAiCbLMDJQSXUEYaZWKexYQUoFy/IDijAY+GN3X4dqlcXnv/8WmCrb4qpTq78VK3QzyL2gfFmtmmQe2j2EAlNtimkymqlZjkY/EATPAjDUN4SD7Z3X9Si6NjqV20ov/H3F79g7Szmcm7mA01PpphPhzaLnvZRqK61JH8XIiTUr3RlWLNTE/ebArkF85aUriIe8GOkNAQAKTBV7R2K2WRHE7X5xPoNEnsFd1/Ug5HWB4zgMRgO6XWdi+PJfWsiiL+xFd9CDseUChmMByw5qlvqeXmFB0xTuvbEfL74xi3KVRdjnxn03rpN1Z4ot0hG/x7QrnmXF7n3lvt9pwQQQ0WQZZvyuUj5nKeyamMTBsst5BtlSBYk80zIJyGVsNZrJtd2xF3KZcq2cZOIhLz7+wFY8+d4tAKSf78ZYACfGU4buxwfPLmXLmM+U4KIoXFzIwu+mNcedicurxbWh9lknxB3I9Se+HC9fXAQoCrcNR5EqVPDyxUVc3xc2dU+976VcW6lNeEo7z8TvqZXuDCsWalL9RtgGfREf9o6s17yjEdC/0BK2O8dxjdicdd0B3FTjMJXMN85nO7DLuBiQilv71qtjOD7WfNi2UdTqrKdNHt23CUFP3XU40hPSlKGdt0jfeV2ProShQuRSnDhBFKlBRJOFtKymNKpwPSsDqycmqWDZuXQRpUoNfRE/1nUHAFEwsNyAbEcQoNXITfp2+Mylngs/WFxezAEcB5bjdN+Ppimk8wymUwVEVoNn+8Je3Hhdj2rcmd7y6vmsE+IOlPqTWNzoPcBVDiPvpVI2azFKO8/kxJpV7gwjCzWtW8yN9hctCy25MvCxPJOp5nPp8uUq9u9cjwO3DeLQmRl85SXzcXlCC7uwrjcPdqFQqeGpF853PEhfTz+RskgbxUkxd3ohoslC+A6oNSDWiJXF6olJHCy7sBq8GA96wLDSB13KvWh2BQHaQad85sLBAhSFrf0RQ0kSoyEvBmMBsCzQFXAjGvI2HW8gF3dmJ3ra0I52VutPdlrD9L6XwrZSOwNMaeeZWn14S4eZGCGtbaZ3PDP6zilNuEpl4P9WP/fMg5DPjdfGknh7Mdtwods1mQvrylud7BYMetM2qNFJIe4kiGiyAa0BsUZeUDsmd+GA/6Hbh3HgtkHQNNUYfOQmAS2rSSWc4tIRl8kupAaLyZT0YKG2Wt+2LlLPpRT0ILWaG6k34lOMO2vXoKR0DzvdsVr6k13WMKPvJf+eabWO8WVXs/oC1rW11jZrync0mmiqh1obaEWqPYQ7U5XaUvi3F8/OgwKwa2MMiVwZQY8L0YCnLZO53fdwwhmNat932rivByKaLEarijartq3sYHIDfjssL05w6bQLLYOFkbOb9m2ONLWbk08Kt9ssr9afjIobu95Ls9YxpfpY1dZa2qyR78hDYyZdxFy6iFShggO3DRqOe5FCGA9YZqo4P59Fl9+Nb706hgO7BmXbkv93PFTPZs+y9d2K/REfsJrdGkBTzFNKtLvSirGwHYJhLbi+1vK4T0STxWh9KZyktoUDtBi7y+OkraTtQG2wsOrsJicOSu0wy2vtT1rv145VuxXWMSlLpdVtrfQ9/iyz4+NJgAMCPjdcdAWHzhjLdyQF/yzOzmYwvpxDIs/ARVHY0h9utJ9SWwr/RlH1HYtHryxjOVduZLe+78Z+HB9N4Ixgd6WZPqBnB6oVONn1ZSafk5MgoskGtL4UnZ7YnJQ1eC29OGZedKXBwsiAJ/d7Jw5K7VwoWHXNdqza68HJnGy6ASPPst2LsoOnplGusnDTFEoVFkEPjZs2dFs6YfPPYjFTqicBBuBx08iWqtgQDWAiWcAn7q/vWJ1IFlrSFwhTgYS8bmTLVSxkSujv8oGp1XO1AQAoCruGo0iu7q4E9KfyUBpb7Xw3hc9dylrWCZTawiljkx6IaLIBrS+F3a4CNeTOXDJCp/PxtAMrRaacVc/qic5pg1KnFwp6aMeq/WpwchEbY+qnCOjBSFsbqRvfTuujAbAARpdyoGkauXIVOzd0ad4UovQ5Ps3GYqaMqVQBHAAPTYPjOMyulODzuHDn5jh6Iz48tHsIz5+sB3wfPDXdaNN4yIuAp55UMh704NV3EuiP+HDv1n4sZEoYT+QB4OrzXk3lMbqYxUqphlKlhp6wT1Mf0CK2jbSzlu9IWcusxMrA/bUIEU02YsVgIUTPuWlaBqGJpPyZS1rplLWqE/dtl9UBkE4eacdus3aLKrWFwrvNMibODE5R1vUp/ngbLW1q5n0StlNvyIuFjBtdfjfu1CDUtN6XputpNmbS9dijXKmKeMiLsN/Tcq+Dp6ZxYrz5Pa0LqSk8d3IaQa8bI70h9IZ9WM6VMb9SbFhkADQ9b3AcsuVa41xGpSzYPFaLbb3Phs9FJrSWbRadSKAXLbs85b5npi2cNB7wENHUIYwMUlrOTesL1887W84xqoPQpngQ3319ytSZS51aRVh9X7VJvF2xAmJRkcwzeOboqKXisB2HFqshvr6TXMVC1mL8iZVji1aE7fTI7cOaF19a7ytMs8FUWfjdFfi9LnxozxA+sHuocS+5Nn3+5BROjKcQ8LownaoLr+6gB31hL3weV8tmirp7L47Li1ns8Ll1ZcE2K7bFz99MtvlyjcPhc3NNySv1vFfCvjSfLqLCcljX5dfcR4y2xXK2jENnZhw3HgBENNmGFeZb8fXkBlj+WiGvC4fPzqFSYzHSG8JkoqB43QO3DeLwuXnUWA7rowHdZy51KujQyvvKTTBSv29njAh/XTtEqd2HFltZpk7TrvgTwLpktVaOLVrLwreTlgz0Ru5L061pNvaOxFsyiEu16d6ROCZT9fuM9IRwAgkUmCref/N6PLynNRO18Hk/c3TUUBZsI2JbLku2nmcjrv9bsyvgAFRqnKH3iu9LsaAHF+az6A37cMtQVLUcQvS0xdUjveaRLVVw04ZuR40HABFNlqM1W62RgF+1c9NmUkUUmBpomkKmWFG9bm/Eh/07B3BsNIGesE/3mUud2gFo5X3lJhip35t1nRmJXbNalGoR30aEi11lcopp3q5yWGXJ4sed8UQeF+ayus45s+J9MiK49d5XKc2G/OfiTUkrAaA/4sfekZjikS18GYxmwTYitsXvH8cBT9xzneFs8+OJPCJ+j+Ez78TvZU/Yi+VcGQsrRV2HXetpi4OnpnFsNIF0gUGxUkMiV24E+TtlPCCiyWK0BgAaGaSkBgP+WsdGE5hbKcJFU/C4KAAUEjkGG2MBTS+Y0UG7XcGmVtxXqhxSk7Xc4cPiGBGtrjOjFhyrzfxK1+TrqFe4WGGd6pT4dgJWWbKE4062VMH5uQp8HpepscXo/fUIbrWdg0LEbcXnX1L7nJn6mX0+et5V/v3rCnhwaT6L505OAeDw3u3rdJVdWOa/+8kYTownJU920FJ24XsZDXjRH/bBK+HO1Ho9Jfg26An7UKqyGF3KNQX5O2U8IKLJQvSsmI28xHIvMP/dVKECDkDQ60K6UMH2gQge3jNs6JpKdRR+Ts/3rZhg+ftYMdnQNIWNsSBOjOs7fFiv60xP3IZcPxlP5A2Z+TfGgnh4T3M7K4lvvcLFKrfaWtpVZwdmXXLCceemDd1Nh8+aGVuM3B/QJ7i17BwUojUg2eg4JUU7reeX5rOYThUwuJrEk6Io3WW/esZl1vAZl0Dze3n3lt6Gy9CO9jCzoaCdENFkIXomHjMvsfjz/LX4wyblJks91xSjJni01MHMBKt2er1e1AYVtUlc60Sh5XN6xKTc6lrIwVPT+Mnby0gXKvjxxUWcGEvg6Q/eqponRq9wsdKt5sS8UmsF8bjDHz77+F2b29KWVghuPTsHzYwjTu5bdasb8N3XJzEYC2DvSA8ypYrsuCJXF7GrvX7GZdjybPB2vKtGNxS0EyKaLEbvxGNlp+PPHrOjM5u1KJidYK0OFFYbVNQmca0ThZbPaTkvK+Rz47uvT+HwuXns3zkgK6z4dk4XKlgpVuBx0bgwn8XzJ6fwxD3Xt5RNiF7hYodbzcmTmpORsx5qxawVuF2Cey3Ev5mBooAuvweZchVL2RLyTK3pndJyIHE9ri2D4XhIcMZl0bRVnkdvXzGyocDJz5OIJotxwkO3+r7Cgap/deeI3oGqaYLlOF2BhFYPlFLXkxtUrIgHU/qcUt34f8dDXsymi8iVq6ixnOJhqLzL8ccXF+Fx1Xcx9YS9ugZNPW7aa8Gt5uQBWitmxx2zi5J2Ce5rOf6NfwbDPSGcn13BVKrYWCCJP6O0wIoFPciUqnhrdgU+N215G2ntK2ZzfzkVIppswsxOIjs7jFF3YF/YixfenGt8/8Fb1uu+Dp+p9rTOTLVWD5RWXU/rRKH0ObWybIoHcWwsidmVEgBgfTSgmpX44T1DODGWwIX5LHrCXkQDXmyKBy3pV1IDYacXCUZxam4oM1i1iDC6KNHzeaOC+1oQ6mL4ZxDyuZHIlcFyADgOB3YNNvqk2gLr0kIWi9kS5ldK8Llo0Kubgoy0kdyzV+srwu85NY2IWdaUaPriF7+I559/HhcvXkQgEMBP//RP40tf+hK2bdvW6aKZxu4B3PT1KQqU6L964TPV3jYcRWo1U+31fWFN39UyUOoZ5K0cePVY2/SWpRHkny+DpoDe1RxSSiIvHvLi6Q/eiudPTmEyVWw8byuQGwjXgmAykzTQyvs6jU5Zb4xax5xgzdeKXsubWrJhpeeUzjOYTRUR9nuQLVWwaziKz/3iTkvdtHJ9RRycf2DX4DXrRl1TounIkSN48skncccdd6BareJ//I//gZ/7uZ/DW2+9hVAo1OniqaLUYewewM3m4FnKlrFrYwz9ER8Ws2UsZcu6XgDxCoWiKNmXSOp3SgOlEUHolIGXZTnFsjSC/HcN4tDpepD/zsFuTduOn7jnekvrZyYOpRNtLEwNYTZpoBHWkiVrLVpvnDz5Gnn2csmGl3PlxrvfF/bi5qFuLGXLLbni6hnTg6hxHLoDHkQF97MydlQpD5bwe9eqG1WXaJqamsLwsPIWdjv54Q9/2PTz3//936O/vx8nT57EPffc06FSqaPl2AqrBnA5EWLm+lasRIXXkDt9W8tAI3XPdu6msWpS1Tuo9oaNBflbOUjp7QdGJg4r2ld830KlhrPTKy39o53nyjndPdGJRcRaEpV6MfLs5ZINHzo907jW2ZkM9m2O46kHdzQ9J5q+mjE9HvQgWajUfy5WGocXa9mZqyV2VSpfltT88on7tzT+vVaEuBZ0iaYbb7wRv//7v49Pf/rTCAaDdpVJMysrKwCAeDwu+5lyuYxyudz4OZPJ2F4uMWovkBWiRGkAsuL6asHMWq6ldvq2kYGmXbtprB7gjU6onV6p6bFI6Kmjle0rvO+x0QRSBQY39Eda+oed1pV29Es7BE47+1enRGU74kaNPntxnzxw2yC+8qMrTdcaT+Q1fDeC+27sx6e/9wYuLGTRG/JhMlk/VksuFQVNU+iL+PDiG7OocYCLAn7h1g2yZeZ/Lze/iHdzi9OldNrKbxRdounf//3f8Xu/93t45pln8IUvfAEf+chHbCqWOizL4nd/93dx1113YefOnbKf++IXv4jPfe5zbSxZM1Iv0HgibzpHjhi1Acjs9aVWolacvs3HNBkdaNoVj2HlAL+Wt01rtUjoraNV7St131ShgkSeAdDcP4R1ATprldODXRaadva/Trh629VuZp691PvFX6tcqeH8XAZdfje+9epYo/z8Z8Xf/dtX3sGF+Sw8LhorxQqqNRaHz80rH97LceD4/1IUwF0VOkptz+eYmky1zi/ieKf7buzHyxcXbUlZ0A50iaaf/umfxvHjx/GP//iP+MM//EN89atfxV/8xV/g7rvvtqt8sjz55JM4d+4cfvKTnyh+7jOf+Qw++clPNn7OZDJtdTEKX6BylcVbsyuI+D1NnR6wPyOvWfM7/z2jgbQtZRTFNDUNNDpTEtgdj2G1yOlU4K2VqJWVryOfHkGpjlbv3hK37b1bexH0uiX7h50uIrv6pdUWmk64yexw9ar1l3a2m9lnL7WgPnxuHhSA4XgIx8eSKFRqCHpcLffnrTqTqSJ6wl5kivXA8slUAeu6/LKH97Ish6Ucg93C2NUcg+VsuZE0WXgfcczgZKqAjbFAy7MRt/vx0QRAUbLPQSkW0QnuW0OB4L/+67+OD37wg3j66aexf/9+/PzP/zy+/OUvY/PmzVaXT5Lf/u3fxosvvohXXnkFQ0PKndHn88Hn62xG0audfg4cgOFYQPFcOr3oGYD0Xl8uiBbQd1aZljIaTUlgdzyGHSLH6gnVaauxZJ5BoVJDqsAgVajg3q29snWUa1+jSLWtcJAXYqeLyI5+aYeVslNuMqtcvXYdkq6GUpmsfPbxkBeP37UZ44k8KjUO67r8WMiUcOTSImJBL3rCvpb78+/UZKLuklvOltHlc2PHhm7Z+su9h4fOzDTVUyzWCkwVZ2cyiIe8Tce+ABLtznE4PZXGbcPRlnKILVJysYidxtTuuZ/7uZ9DJpPBV7/6VXz/+9/Hxz/+cXz2s59FOKxtG7leOI7Dxz/+cRw8eBAvv/xy20SaWeQ6vZGXVu7z7VjVHn17GcdHExiIBrApHkRfxIez0/W4Mj2nb8uV0UxKAsDeeAyr29eqQdWpq7GDp6ZxdnoFN/RHkMgzCHrdiuUStu/Ng10oVGp46oXzhuok17ZWbJBQyl+jFvthBVYLeK1tYIcot8rVa+ch6UbKxN/PSmiawkhPqFH+uruZQk/YJ/vchO/UxlgAxSqLs9Mriof3tsRU7RrEV15qjqkSirVjY0mk8gxu6A9LlqOl3QsVbIoHkSpUQFFUUzm0xiJ2enGoSzT9zd/8DV577TW89tpruHDhAmiaxs6dO/Fbv/VbuPXWW/Gd73wHN910E55//nncfvvtlhf2ySefxD//8z/j0KFDiEQimJ+fBwB0d3cjEAhYfj8rEXd6vS+t2uTYjlXtpfkslnNlxFY79S1D3di3Oa5ZSCiVUU9KgnYhDGC0y5pl9lpO3KFlRIwI2/dbr45ZUietLkQt76Tc+9cJ0WpGwOuNv2lH/cw8Jz19TU+7GYmlvHmwC996dayRGgAUhaVs2bJ2E5b/zs3xhoWHv7+474rHrGSewUHPtGL9lWKqAGmxlsqXkciVZcshbndhTBNfjmqV1RyL2Gl0iaYvfOEL2LdvH379138dd955J/bs2dMkVn7zN38Tf/Inf4KPfOQjOHfunOWF/drXvgYAuO+++5p+/61vfaujQelaMTPYaZ0cbVvVchyWc2V0B9yYXSlhLl1EqlDB3/zqnoYfXc91Fe+Fzr4k/ERxaSGLdJ5BNOTFtnURx1hxeJwaUG72WbarTnp20InfP47j8MQ911u+QcBosLAaWuJvxhP5ljYwm9/Nqmcm95z09DUt7aZHJIrLVKjUGm31wptzoADs2hizbDEjKYJOKYsg4Op4q6ffyIkeKbF277b+hrtOqhxS972+L9zikptPF8GsWurUYhE7ie48TWp87GMfwx/90R8ZLpASHKd+wruTMWqtsGty1LsFdvtABO8s55FaygEAXKUKDp2Zscyy4ZQEe/xEsZgtYTZVxGAsiHShAqBzVhzhs5I1fTtoNWb0WRqtk1kBEQ14FF1y/PvX5ffg4nwGz52cBscBV5Zypt9Lo9YcPe4zq9J5SO38tao+SuVQGjv19jWr2km8+/KpF84jHqrnOGJZDqAow2d1aim/0flEbxm0iLV4yItqlYXbTWu+r9glV66y8LlpeN20aixiJ7E8I3h/fz9+9KMfWX3ZNY1abIUaVk+OegY14QuTLDD4rf/7OtJFChu6/apnoEmh1UXTSZfcRLKAWNCD+ZUSwn4PahyHeNCjq65W1UH4rKRM/k4RmmLMPEs9dTIyQesVEML37+J8BjPJAobiIbw2kapvyaauurKMvJdmrDlWBEErHY2jtt3d6voIkdu1JcaqccNM2hMATbtFabp+3NRitmz7YsaKsVfPfaRE1DNHRzW9g+LF30SygJDXhZl0EXPpIqJBL/7XQzejf7X99dSvXVgumiiKwr333mv1ZdckVq64pCYSoy+BkUGNpin0hn3Yv3M9jo0l0bMaz7FzQ5emMuhpC6e4lmgayJYq6A54VlMgRFTLZmcSTDmTf6eFphJGyqRnEtTbl41OjA/tHkK+XMH/PZYCBwp+D42Q1wWPm8bW/ohkfhotmLUiS9VfnLzQSEwQw9Tg9bpkt7sD0u3MshzGE3lT1jf+HTp8bh7ZUgU3bejWLG7NYGZx2tgtmq/vFn3gxn4Efe6W407sRqqd7YpL4++j5R2UK8OmeBDPvj6F/Op5e65SBS+8MdvxuEwl1tTZc2sNK+MdhBOJeGumnpfA7CBt1LLRidgPo/B1urSQRX/Y1xTTpIZdSTDVTP5OFExm0eKS09uXjU6M8ZAXIZ8HsZAX+XIVk8kCEnkGj9w+rLi5wUiQ894RbRO1uP7lSk02eaHWmKDJZAHL2RIe/KufYFM8iE+9b7vmnb/8xHhhLoNMqYod67uQZ2q6rSwHT03Xd0/lyyhVWSRyZWyIBtoSr2d0fPv28YlGJm2aAoI+Nz5+/5a2LWaUhJFcPJ4VaH0H5cbFA7sGcfjcHFgOhr0X7YaIJpuwKw5JyZyu9ftmXH1GTOFWtUW7diqJ66jHJWflMxc/q3aa/NuFmffBaF82MjHyz3bHhm4kcmXMrpTQ5XfjwK7BRll49PZToUgHx+HyYhbPHB1V/Z64/ufnMqAAyeSFWmOClrMlJPJ16+qpyRS+9IML+MZjd2ja+cuPS8PxEN6aXcFUqoj9Owc0Cw8+NujSQhZLuTIKlRry5RqmUkX4PC7c2Yb+bnR8O3JpEblyFdGgF+kCgyOXFvHkfTe07f2UmxPk4vEACg/vMT9+8n1QKYmt0rhoxnvRKYhosgm7gnStmJi1ThpKq2e9gYdWtIWSWJSrv9lJWfhfLZ+3Mwnmg7esb8Q0dTp+yaz4t0oAGxFARiZG4bPdEA00JvHecGviXL2LGr48f/vKOzgxngJNUZoXQ8Kdb11+N4bjIcW8QVL15e/PMDU8+Fc/QdjnRpXlUK7WExEurpRU21k8LvncNDwuSvacMyHivrC4Ut+AEfS6kGGrqNRquNNEagUjfVV/36YE/9/8L7tRmxOE8Xj8xpYT4/X+9cQ95txgWpLYqo2LTo3LlIOIJhuxozNYMTGrTRpSExoAU5McvyI32hZyA8NyroxDp1uDRZN5Bs+fnMJkqtjWpI/tSILZSdM13zcUz6/SgFVuTDNBwGYC1OUmcaOLGpatH3uh93tS+a345IXCvEFa3gGv14VN8SBefWcZHEeB5Vj4PTReeHNWNXbOzLjUlNRwLIlUsYLBWBA1jkMs5MMN68KaxBd/38bGiYgP4OpHg9g5BtA0hXu39uKFN+dQqtQQ9Lpw79betr2jWkQJx3F47uQ0BmNB3DzUjbPTK3ju5BQADg/vGTbcLlqT2CqN/07YAKQHIppsRK0zGO0kVk3McveWmtAAGJrkxALsE/dvQW9E/7E2cgPDodMzLeV6aPdQ/XTv+Sx6wl5MLOcVy2vly6r0zK2weon/3W6EsRsnVo9U+Pj9W3Rdw4y1VGnSthstg7tR8aD3e1I7cpXyBml9Z/9/+2/Ehb87gUypip5wANvXd6nGzvF/M+P2FPaFVL5czzgd8iJZqODGdeobMHiEY9eLb8yCA7BbIVeSVe/+o3eOAABevrxUtzGtZrtux0INAA7cpixK6jFMFE6MJ3F2egXTqQIGY4GWY0/0oOU91jP+rwXBBBDR1Bas3s1gpzKXy80CAPGgR/ckZ2VgdEuK/9sG8ZUfXWl5aZ97bQoXFrJwUcBUsoBpAHmmhgO3DTa9sHbGSJmJcbELK7ZkWxG7YURYOKUNAfXB3eiiRsv3lCyocnmD9LyzW/oj+Mhdm3FsNIGesA/JfN1Ko9Ui/bG7r1PN1yNEqi80J0vUtgEDAJZzZRw+N4d0sYoiUwVTY+GS2ThhdX+Kh7wIet3oCfkQD3lxdnoFBz3TkgfRWkmLKHlgi6TLGAAe3lNvx+dOTmEwFsDekR5kShXDcZfCZ8dxHFISh6w78cQCsxDR1AGs6kh2KHOpQezmwS6cnV7BhfksesM+dAc9uPsGdfOz1YHRUmJRWNb5lRI8NHB8NAGW5ZBjaihXagAFZIoM/vX0TJMPX+05WDXIdXrgsHaCsCZ2Q6+w6HQb6kFuUaPH1SYnUoQWVP4wVrlDv426y4xmSS8w1UYGZy39TMlCFQ/pT2p46PQMMqW6YBpdroKpsoiFvJIbJ4z2JyWvgdxYZ2a3sxp6k3HWxz8OJ8ZTyJQqpuMu+UPWz0gcsi51NIrTd8ZpgYimNmPXrjorkTLzMzUOPWEvlnNl9IW9qpOcOAgRMBcYLWwf4feFZfW6KFRYDgGvC8u5MphKDaAo9IV98HlcTT78aMDTlkFO7nnrWY2bxUqRLozdCPnchmM39FhL9bwz7XyPtCY91Cta5a75/MlpXFjIwuOikSnW89oojR1GLV5ano3UMzlyeRnxkFe1n8lZqKRcjlpp7G5c34XlPIO5dBG9YR8euLEfy6vjDp/bjm8TPWOw2jNUGuvsEvxG55KH9ww3zvY0G3fJH7K+aziK5Ooh67Ggt/lolNrVo1GMjP9OmhsBIprajl276qxEysw/0O3HrcNRzK/Ut/9GAx7J74oHF37lofSCah2sNsYCLUGLfFmrVRaf//5bYKostvRHAAAzqSJiQQ98bhfmV0otPvx2DHLi5z2/UoLXReHz33+rLW4mq0X6o3eOIOh1NwWCm0FLGWiawkbBjh+pd6ad7ju997KiP9UDxQvoDfmwUqwf6ZPIMdgYC8i2oVk3vpogbDrINVcGwGnqZ0oZyIX11VtmvjyD0QD8bhp3Xtcjm9uuL+LD2ekVANrGYC3PUC4BcdP7x3GWLJLNLEqtCu9oqduqEHv+5BROjKcQD3nBsBy8LqrpaBStOMklL4SIpg6wVrZYSpn5UyqZsZXyhRiJ7Tp4ahpHrywjXWTw44uLODGWxNMfvLXlc2433VTO/ogf92/rQ9DrkfXhaxrkYN4SKGUNY6qsJatOLdYOK0V6u3e68H3k8mI9hxHLcZLvjFZhomY90bvbS+0ZWtWf+Oc4may75JZzZWwfiODhPcOavmsHTbsJr+tBoVJTFSJq7aF3ohTvlrt5sAtLOQZ3XtfTlMBT/MxuHuzCvs3xljFYznqp5RnKvRub4kEcfXsZl+azjeeWLlYMCQC5RanUYctqmBWpUmPL3pE4JlPNbeVxUfjs+2/SbVl3qkueiKYOsNa2WOrJ66RngtASUzSRLCBdZJApVuFx0bgwn8XzJ6cl84vIxUZwHIfXJlp9+EqDnJWWQClrmFlBZuY0dqtSX7QDYR8BRWFrf1hyB5TeXTzC9tLTlnr7uJWiVfgcN8aCjeSEnRpHxO9PMs/goGdasZ+ptYfeiVL4+bPTK9i3OY6nHtzSIgjEz2wpx+CpB7c0yqR0fpreZyj+/UO7h3B8NIHlXBk9YS+YGoeDp6YNCQC5ODKjmLXmSI0tfBmF5xX+w3+Nt/XkCjshoqmDdPrha0WryNMzuGh5KepumQB+fHERHld9ldIb9mEyJf3ySA3izxwdxZWlnKKVQmqQ48tjpSVQbA0zM4EaPY19rfQ5QLqPTKaKkrEvau2q1F562tKICLKqP8n17067L/i6a+1ncu2hd6KU+7xU+cz0D6UyayEa8GAgGkAs5MVAd0D2GBqp+qmJP61xZHLo8QxIIfXM+bZRO6/QjtQd7YCIJoJmrNx5o/WleHjPME6MJZt27sltgRaXU4uVQgo7RYYVE6jRVZgTBhw98H1E6YgGHqV2VWov/t962lLvM7S6Pxm1yrQLozE1Riw6Wj+vlMdIy/tk5hkKY/Ko1fxNSvWSs/6I67ucLUFrHJkUwhQNpUoNPWEfLi1k8bevjGIypU+I0/TV46biIa/ieYVaN9o4NYyFiCaH0y7rgNz2aL07lPQMLlpeinjIi6c/eCuePznd9CJrqY8WK4USVgRqihG66ozunnPyKsxKtBzRwKPU79TaS29bGp1A9VgV1FCb6DtlVTT7fumdKNU+ryWPkbB/yOUbUiqzElpj8oRIiWE+I/pDu4dQYKo4cnkZAIewz4P5TKlxLz3jgDBFwztLOSxkStgUr4s7PUJcTuTJnVeoReyzLOdYCzkRTQ6lXTsHpAILX764iEsLWaTzDKIhL7atu5pgTmuZjJp25T73xD3Gzwrj69kOYaH23Dp55ppVWBFMrQWtRzQIMWIdMtqWVtTTaH+Q69925gRSwqp+rXeiVPu8VmucUr4hM+i1dovFcLnK4vC5uabdqvx7EA9567txaf2706RSNIR9bnQHvXDRlC7LlVwbG9lo49Qdc0KIaHIo7TK9i+9zfDQBUBQWs6XG4Y7pQqXxeTvKZJflpxPCQrjdVqqNnHDmmlGsCqbWgtWBoErtJfU3q9vV7KQuhVIQrpldhEawerwy4gITo6cPSeUbur4vbLj8cvcXW7vVXJRvza6AA1CpcQ1LmPiMQq+blt2dpmWDgjBFAwBdC02lNjay0cbIoezthogmB9KunQPi+3AchzNTadw21I35FSDs96DGcYgHPVePUrGwTFblKpGjncKifrzFNJ47OY2A14WRnhCAVpeJ1c+1nYOIVcHUWtBiKTQaX6L0NyXxZ/UuRyP9QSnOxuwuQqM07mvgmCW74O+txdrc0m6r+YbMll/p/krPgRfD44k8In4PhmOBJtEllbNMLJi0PGdxygj+Z44DJlPaFppa2ljchno3Asgdyt4piGhyIO1yLYnvkypUsCkeRKpYBU0D2VIF3QEPkqs+fqB1FWIEswO3VRmWreTgqWmcGE8i6HVjOlXACSTQH/E3PTc9z9VKq4cVk5fVwdRakBtc5Z6/FfWUEn+8BcdIfz14arpxjptYTOrpD+I6C89R1NO/7LBgp4sVzKeLuo9ZsgMjyXXtHG/l+rDScxCK4W+9OobjY0ksZEqNcj20ewgUpVwn4fX5jRTi5yyZMuJUPXZ0YyyguZ9btTFC7jlIHcreyQ0PRDR1AKuCpK0oh/g+wpim/rCvKaaJ5a5OkDcPdaPAVPHUC+cNTSRmXgKn7RoSCoqR3rqFqcBUsXck1vLc9ASu9oXrcRBL2XJbxKUSVgdTa0FucG3JVVOpNQ541VNPLVu6J5IFPH9yWndwLAAsZ8s4fG4e6QKDUpVFT9DTIiaVVt1SdQ753Pju61M4fG4e+3cO4MCuwabAZqO7CM08q4OnpjUfs2Sl9UnqWka30Ns13sq5frU8Bz7wW1wuNQs6f/2Q14WZdBFz6SJShUrLgeXC+wDNbcefnMAHoGuto/B6SsiVXVxfuUPZO2nFJKKpjeiZxOx0LUmVIxrwNO5zfV+4yeQvDi79xP1bcOiMMfUvtc1Vz0tgxcBv9WfFgqIv4sPekfWSCTj1BK6+8OYcKAC7NsYcIS7tCKYWohR/IfxMS66aS/XzrqSsOVJo3dJdz3Acq2c4NuB6OnRmBtlSBblyDW9OpQEK2DcSb8oGLbXaF+deEp6TOJsuIleuH0b77GuTOHxuDvt3rm/UQa5/6XFX6YF/HmrHLFkh4PlJWS7YXcvYoLar1a7JWCyG1I4FkiqX1HUA+bioZ1+fQr5cP6PQVarg0JkZzZnrpQLQ5Z6XnjQCav1A6jk4bacwEU02YGXQpx2dQ0s5hCZ/8ed5n7cR4SK1zfVDtw/rCgY3+hLpGbj1DvJ6RYNa4Gp/xFcfKCkK/aurw3aLSzF6g6m1oqetxc8/kWcAUOgJ+zTXU6n/S1lev/SDC7pdTyzLYTyRx00bunFqIoVEvgqaovH2Ug7fPj6Bj9+/paVeSmXbFA/i2FgSsyv17eUURSHP1MCCwjGJd1guhunebX1N9TNrUZFy8Usds2RGwCfzDL59bLxpmz3AYaA70HQtozFE4vqYQa3/601BoCRIlOr04G0bcPjcHFgO2NDtV12cittOHIAOtD4v4f35g3kHuv2Kz1cpnYK4PDxOy9dERJOFWB30Kcbu2BS1ifxqMGLd531iPNWotxbhIrXNNeL3NBLPacXoS6Rn4NY7yFuxUhUPXDRNgQKwmC3rXmHZGaehFkytF71t3RTAujmOAlPF2ZkMAPV6VqusYv8XP8dnjo5qdj3x8OPAhbkMMqUqsmUGLopGd9CDcpXFkUuLePK+G9QDkgVl4++ZypdBgUN19eT4Dd1+9IS8su9ww63ndeHZ16calimpfEVGUXsfzY59B09N44U355AvV1GtsVgpVhD2eVDjgF5R3ZViiISxZRwHSUuwUdQOFhfWRUsKArEgqbD1JJHC90Mt/i4e9CLs82Cg249knsHODV2aXJRSAehSz6tx/6AHF+az6Al7cetwFJzMocRGrVmd2CmsBBFNFqI0+LfLQqKGnAuCd8VJKX6pcteDESldwkV4LeE2V7Gf3Y5dcXoGbjODvNmXWjjoP3jL+kZMk5EVltNWaFIYaWu5AFalerasilfdHXLvIf8+aHE9ieHHgeF4COdnV1CusPC6acQCHmTLVQDS9WqxouXKuPO6niYxd2DXIA6dnsHhc/PIliroCfs07QqbSReRL1fBcpC0TJlBLm5HaK02OvbxFjuW5RALerGUK6PGAYVKDaNLOSxk3HhEYKmWKoswtixXriJXquK5k1MAODy8Z7gpNMEoB0+pHywuvQCVTrjL96HYqiDpDftwy1AUQP39kBP/wvg7psbB69Kew0ktAF1JAPWGfVjIlHDk0iISeUbyUGIj1iwhThBMABFNlqFl8G+HhURcJqVgu0sLdRPx2ZkV/OY/vNYU9C3s7EaCEeWQ2+YK2LsrTs/AbaeVxoggNLrC0hIwKieW24WWtlaLdYqH6sc2CH8nRvgOaZ1MtLqehIjHAZ+bRsBDo8DUUGU5hHxu3LtV3r330O7mjM+FSg3JPNN4D3rDvrp4um0Qh87Ut2Hv3NCluCvs2GgCc+kiAHXLlBmU3GBmEojymaWT+TKypQr8Hhf6wl7QNI0uvxsHdrVaqoX14mPLipUa3prNoMqy2LIugp+8k8CJsSQGogFTi1H+masdLK51XBH3Id7KubBSbOxkljrHshF/J5iDlHI4yaFksZOrS3fQg3y5gkSBQW/YB4aVPpRYrzXLiRDRZBFaXgi7LSQ8WoPt/vaVUZwYT2IiWWhJZCns7Erl1tvBla5l9644PQO31VYaM4LQ7CAi/j5fFqms70YtmGbQm1pAqi5Kn5F6h7ROJkZi1cRHcvzMTQMIelxNbgg54iFvU8bns9MrOOhpnXx6Iz5du8JShQpcKpYpMUYmMLl32IyLRSgkaboIn9uFW4aiyJWruHNzXNHNyD/7mzZ0YzlbxpvTafg9LtyxKY7XJ1JYzpURMzne0LT2g8W19CfxXBINeNEf9sHrcWHf5qunMwgFiDi5KSCfw0nLM9DyvIR1ec/1Pbi86AMNYJ3CocR6rFl6y9wuiGiyEK0DrF0WEh6tZ/tMpgqIBT2YXyk1JbKUE2VWr0rF5bFjO7QQPQO3+LP87hWjOClNAl8WqazvnSiT3HPR0mZaNzVIvUNaVt9GJnvxkRzvu3l9045UJdTeA6mdUlrKr8UyxWM0waeWd9ioxfTjD2zFk+/dgmSe0VwP/n6NkIBYfTIHVc9Bt5wroyfsxUB3oBFqoHe84T+v9WBxrf1JOJfcvaW3ZYezZFm41hQyZiz5gPLzkor/Oz6WBDQcSqxmzVLbKdlJiGiyECsC1qSC5/SsdrWKD+Fg0prIUtkFYaQeashNbHagp1xWvLTiXXGA/p1wVq3A+LLoEcvtQilmApDePq5VaJu1HOppE7kjOaQWClpjCM32Q62WKcB4gk873dr89fXUg+eh3UMoVGo4cmkRAa8LsYAHLIDtAxEwNU6TpUOMlADRc7C4EUuPeOEmF+QvJ7DsWrgZDT+RqmPrTkk3AEp1V147IaLJBowMEnIH5wpfSi0Bi3oGLmFskziRpRRaBiozQevCl+7mwS4UKjVDyTOtxIqBhqYp9EV8ePGNWdQ4wEUBv3DrBlNtacVBr3aIZavQ0o/19PV27cBpEXISFgy1Zyc1+Vg14Rm1dP3L61N4bUL+TEX+u+3afKDnGcZDXgQ9rkYur2Sewdb+CB7ec1UI6i2r3PPQe7C4HPw15OLEhLm7tAT5O82SL0RsXeZ3SgJAvlzDSG8Qtw5HbSmzEYhocghyB+caGSS1DlxSLiipzqhngjYzuEv5uzvpzrJ0oOE4cPx/Kap+wJMKdp31plcsdwot/diK/FhG0WMp0uN2lHov7Z7w5Mo/nynBQ1M4PppAwONqZLwX3l8tWa5Wt6RV1nmpv08kC025vCZTBUQDHtVM1kZ215p5JvwZlnxal4f3DCvn7tIY5K9ngWG2X5l5jsKdkhyAcrWI5WwZ8yvF1Y0YnU1sCRDR5AhaXkKOw+mpNG4bjhoaJPUqfrV4Az0np1s1uLdrklDCKncDy3JYyjHYvTGG/ogPi9kylnKMqtttPJGXbAP+30bbR9w/qlVW1+4arZh9Xlr6cSdyuBixFPHoeUeE72U7syILy++lKeSYGrKlKiYSBSxly9iyLoK7t1zdASg3PlgVyC+HnoSVepNeKl3brueRzDP49PfewLm5DCgO4Cjg+GgCsbBP0rWvN8hfbYFhZWobI7t/afrqTslUgQEAdAe8uL43BJ8oCL6TENHkAFpeQv7g3EIFlIagOqXrmkVp8jayytZabqekzjfqbhAHv+qpz9XkiFlkSxXctKEb+XLV8rPe7AqybE70F8TDe8xdV4/wbwdqh6Gq7TY18uzamXOLL3+1yuLz338Lb06n4XHTCPpcWClW4HFdDeJVEoFWBfLLoee7Skkvpa6hdm07nsfzJ6dwYT6LUqWGEsMi4KVxcSGH62oc5jOlFte+3iB/oWVNqr9Z4QJuOjsz4gO4+oJR6/giTrlx77Z+PLpvkyW5tKyCiCaHIH4JhTFNnUpMKM5svGN9F/JMTXGQF9dDKoeKFqwalOy0dkj9zoo8NY3kiLEAzs9VMJXMNwI8eaxoH7sCQw+emsZP3l5GulBZTfSXaEn0ZyXttDLxIkHLYaha3xEtz64TFjW3m27aSh8NeNEVc2N9NNBI8Km0gcPKQH4xer8rF1g9kWw9V1Ata7zc9cxwtT4epAsV0DSFSo1DT8iHdLGi6NpXC443mpbDiHVfOKa8+MYsOAC7dZydKdwpCTgnoaUQIpocgtRLqHWbsl60XlOY2fit2RVMpYrYv3NAcpDnr8nXYzlbxqEzM/jKS1cMWTHMDkpWmpqB5sMxlSw0ZvPUiAcvn8cFj6v1tHG56+nZkWeHC5S/brpQWbVK8In+pvDEPdcbvq4UVj9jLfAiQekwVLU2NNO3jcYGGX2uD+8ZxvHRBC4u5GS30kuJQKsD+cUY/a7w7+liBfPpYsu5glKJI5US4WpFTdRcmMsgV64h5HMhXaygJ+RFd9CNfLmK7evVXftmwiussO6LdwnXOAAcZ2jHsBPFEg8RTQ5D64tgBD2TTMvk7aYlJ2+5ax46M9OW3T5yWG1F0XIWlBYhomVQ1zN48b83kjzTDhcoTddPcBcm+usJe2WPizBDp3JfHdg1KHkYKr9Q0PMMrEItNkfLafVy14yFfbiRohCT2TAgJwLVLGpmd9uZtbYePDUte66gle43tXdTuDjNllawrsuPmwe7G+1dqNRwdnqlca19m+OarXF8HdqVlmNjLIgT4/V30UUBHEUZOjvTyRDRtAawarLRM8lonVSFZyTxn338rs2aTPN2vUBy4sVMwLPaWVAMU4PX67JEiBgZvIwICLviZB7eM4Tjo8u4uJBDT9iLaMArmejPDO3cUSamN+zD/p3rcWwsiZ5VYbJzQ5dlCwUjyD3/bx8bxwtvzoFl6+d7FZgqPv7AVt3XpCgKW/sjus4GkxNTarvttGLGYsf3H7lzBa10vym9m3KL08/94k4A9TZdzpYR9NTF+PV9IRy9sozvvj6FTfEgPvW+7bi+L9x0P3H79oW9mg60Fsay6R0n+XteXqwfzcVyHH7h1g2NmCannn1pBCKaHIzVuxn0TjIHbhtsfE5upXhpIYvFbAnzKyXQ9Op5dpAPVG6HS0Us+OZXSvC6KHz++28Zuqe47YRnQU2miljOlvDgX/0Em+JB/NZ9NwAwF9Old8A2KiDsiJNRsk5Y4fqzY0eZWsoNKaRi977y0pWOiDilRcKRy8vIl6uIBb1IFRgcubyMJ9+7RdWFJ3VNqWNBtCC30LJKXBpxT4v7j9y5gmafndhlBY5rSU+gNlbyVsJPPLAF/+Nf3sRbsysI+z04PZXGl35wAd947I6me4rb9+ahbuzbHFddHJkZm4X3BEVha3+44Y7X22favUtaL0Q0ORgrBxc9k4z45fnEA1skz3eiaQrpPIPZVBFhvwfZUgX9YR9oWj5FfrtcKk3bpl0UKiwHpsoavufGWAAnxlMAms+CWs6WkMjXE0Senkrjb15+G9947A7TMV2A9gFb/GwTuTLuvK5H1/etQso6oSWbtBJWHwIrvq6RM/ikBGendnwqv9t1F83V0OGr/1LKsxQPea9ek+MaB8WaxS4LobgOUoH5YqT6j9UTNk3XE9v+6+kZrBQrqNZY3DYcQ7pYafQxuX787eMTjYS4x0cTyJUrmEgWEPZ76jvT0GxBl3PHLWXLeOrBHY3yyCE3NqttgpEW2MUmYaiFTsQoGoGIJodix+CidZLRk5cpGvJiMBZEjePQHfAgGvKCZTnl3SptWI2Lt00zVVb3PZN5Bt8+PoEjlxZRqXH14xc4rnEWVNjjwoN/9RN0B1oHsXa7asRbdQuVGpJ5pq2Djpx1Qks2aSXk+qPaFmqt113KljGdKmAwFtB9Bp+eGB4jaK1bi+XrtkHQNIV7t/XjxTdmUa6yCPvcuHdbf8tGBj5mRty+/Bl6p6fSWN/tx3KeMZ2h365YusaRIj43vvv6FA6fm8f+nQM4sGtQ9kBf4RgltbnDsm3uHIdMsYICU4WbpjGVKuDgqauHMEuNldUqiyOXFrGyunMuW6rguZPTuL43jLfm6q62bKmCXcNRZMpVHPwvgTsu4muJgdIS/yR+dy8tZPG3r4y2HAsjFSPHJ9rkD6k28kyddD6nEkQ0ORQ7Bhct7hi9yfe2rYsgXaggHqwfxbFtXbOJ26rdMkbRsxNGzMFT03jxjVnkVndJFSs1vGdLX9OLvCkexOmpNICrgxhNU22Pt4mHvAh63YiHvIiHvDg7vYKDnum2DjotblFBNumg1y2ZTVoNrYO53glcuN18PlNCxO8BywIxE2fw6XF3qn1G76q7sWs1V8ah0zP4yo+uNA4LDnpcTZOcOM9UqsDghv5IS1/lz9C7bTiK01NpzK2UsEvH9nE5rBaXwj4ymy4iV66CqbJ49rXJxnlsSpYnmqaa2uTolWUcH01gIBrQ1b+k3Lwsy2ExW0Z3wFN3z1EUylW2kflaPD4KXXLzmTJy5SooigI4IFeq4ab1XfC6KEymitg1HMWn3re91R032KXJHSduA/E4CY7DifFmEZMvV/H9s3OoccCJsSQKlRoe3bcJy3kGF+YzKFdY7NjQhftu7Fe9p7jtOhWjqBcimhyMXeZjpe/rFTbNZVTP2Co3YGrNg2QEtdgsKVi2ntSzxgHRoBcUgFKlholk82D3qfdtx5d+cAETyUJjEOuEOLR70DFi8fDSFJgah4DXhelUAQDQF/Hpagutg7mR8wD567ooCtlSBV0Bt2xsi95ry6FVDBlddR86LW3hFE7o4n6SKlSQyDON8onzLPVHfHXXD0Upbh/X2kesjqXjn+WxsSRmV0oAAIqikGdqqLDc6mG28410KWo5ii7OZ5BYtdJqaXs1Ny+f5TpdrFsxwz43RnpCknUXPnevi0KlysLrccHvdWFdxIfXJlLYvj6CvZt78Eu3D6PL527JNbWUY/DUg/rzHAnf3b0jMVxezIGmKKzr8qNcZXH47BzGE3lUahzWRwPIlir1Q5DdNF66sACOA7xuGnMrpcYh1Vqp77wL4PhqCISTd9sR0eRg1MzHdrle1I6BEHZkvQOg+PPJPINnjo421QuAJXXVGpslBU3XU/qfGEsivZrSPyQx2F3fF8Y3HrujZcdJO+IlxNh1tIOeNBVSbtGt6yI4gQQKTBV7R9brtiwoDeYAJFftSvCf5a97aSGLvrC3LWfwaRFDRgWw1pQX4n5y79ZeBL1uxTxLNE2BAhrbx/eOxBrXNHN4tFXwzyyVL4MCh2qtHt9DoW4dYVlOMns7Xw6+rhzHYTlbRl/Ej3XdAckDl8Xwz3QxW8JsqojBWLDJzSuV5Vou1x3//Lr8HoR8brhdFGgKGOwOoFCpgS1VUKlxeHU0gdfGk4iGvDgzmUKuXMO6Ln8j15SRthWPzc8cHW08//OzK42kmuUqi3SBgYumwAES58VJW9Kk6svPAd8+PoGX3lrAcp5BT8iDn7lJOh+gEyCiaQ0gNh/b7e+VEkJqA6PRHTVS9QKgq65yL6fZNjtw2yAKlRqOXFoEQOHerb2yL7J4i267BK/wgM++iA83D3ZZusVXSxuqBhID6I/4sXckhifu0d9nxbFL/GBerrJ4a3YFEb8H33p1TLVdpcrZjjP4eLSKIaOWSq3fkxL08dVYRLkYrQdvWQ9QFKaSBYDjcHkxh2eOjra4+zoVi8K/bwd2DeLQ6RkcPjePTKmCSo0FAKyPBhr5tKTcaA/tHkK+XMErVxKgAGRKFUwm8siVq5KbKsSWu1jQg/mVEsJ+D2och7jAzRsPKWe5ltpJd3E+g4WVEq7rC6NcZeF10/B5aAzHQ1jX5cel+SyWc2X0hLxI5CvwuemWXFNGEbZJoVLDkYuLSOUZDHT5MdIbwuWFHApMDQPdfty3tQ9BrxsnxlON8+LCPjc2xaUtaUDre1hgqnjxzbmrYRBMDUGPy5FB4AARTWsC3lXUbn+v8Np2DIxSk8h4Ig8AmuqqJOTMuKvE1/2bX7sd8aDXUFtbIXjlyswf8HlhIYve0NWMxk89KL2lXOt1hX8XtiEn2i7NI1c/q3a3ye2aO3xuDhyA4VhAU7vKldOoqNX7/ukRQ0bbTsv35KzD4nJIfe5vXxnFifEkaIpatczUg/2tGpvMjmm9YV/TeWyHz80hU6qiN+RttDf/vPk4L/7IqleuLCNTquLm4SguzWfx2kQKA12+pk0VUn2Sf6Y0XY9r7A54Vncbysd3AtL9+6HdQ+A4Ds+dnMZgLIg7NseRWT3vb1M8hBPjScyvFBuCiQXQHfAg7HPjxoEw/F53I9eU2XaPh7wIelyIhbyochxmUkUMdPkx0O1HxOfC+27e0OhfvOCssiy6/R5cXsw2RLU4oL4ppm4siWS+XA+DCHhAURRKlZpu63E7IaLJ4cgd3io0j0thZYczGy8j9zm5SQSApolFSYyYiSvSK3KsCqwXo2bd4w/49LhorKzGS0wkC6r103s6/NEry/U4jxyD7QORpu3SSvWTm5y19hulI2kev2tzI75iXZcfC5lS0/ZrMUrl1Pu8zWyN1iqGjMT9KLW5FFJ/V3pWLMu1CKTJVKEpHYfedBc8Vm83589j4y1PE8kCdg5246HdQ01b+U+MJfHK5SX4PC6ki1UUmSqypSq6/B4s58q4vi/ctKlCqq8I3bz9YZ9mN69cv6vnN6JwYjyJTLHSGLvuu7EfJ8YSODOVRsBDI+hzo8jUGvF46WIV+wa6dMWGall49oR92NwbxgkkUGRq+NCeYXxgd31XIv/95XwF92zpxcnxJEaX81gpVTCdKrYE1EcDHomUCCWk82UUKyxcFNDX5ZeN+XKCkCKiyeGID28dXcpiXcTfZB4XDi525LowKkC0lEVpElGaWLSIESOrdT1WPX6nklL97BJv9QmsiJ6wF5liFQCH5VwZG2Pqmbf1ng5/fDSBRJ5Bb9gHZlVk6BGnRmJf1J4vH3PGx6nIJTCVcn0Iy8lfV4+oNWM51CuGtHzGindefA3hgeFyLle+DR/aPYRipWYq3YVVlmxxu/KWJ6E77cilReTKVUT8Hsymi5hIFHDjQAR9YS8mk1XMpYsoVWrojfgw0B0AtRrXJHeQr9h9rLaIkgvIF4r+h/cMgaKax66Dp6YbuxkXMmV4XRSGYgHZeDwt/ULPwrMvXN/EIXSxNx3Qe3YOC9kSeoI+ZIpVLGbKcLmoptMiPnb3dU1HrSTzDKIBD9J+DwpMCcUaB3Bo2X3npBxORDTZhBFFLLXKE75YPo8LVxazAEU1zONA8+BiV3yBEQGipSxyk4jaxKJlstY7QclZ9cTX5T/Hm/53rO9SbGuj4k1NNGyKB/H2Yg5LmTLSxQoGo368d7vyVl+9lq9owIOBaKD++e5Aw6JjRJxaeYxPMs+gUKkhVWCQKlQQ9rpQYdFIYFpgqo3gZqHrQ1xOvaLWql2KVq6W1dpVS9nE1zg+mgAoqvEzx3F44p7rZeOhzKS7sKJN9cVc1v+dLjBgqnXrxnSqgPXRAMI+NyJ+D+JBD5gah4VMqdEnmtKXCBJ+CoPsW+91tXx87KHYrQdIi36lXY8URcHrpvHZ99/USGwpvq84tQT/DPW0+4Fdg01B7MUq2xDEQhHZH/GB5QAPRYNbDRhPFxhEg15cmM+Cpim8OZ3G377yDi4v5hpHrewdiePyYhbpQgU0RQHgwNRY/PhC8+47J8TN8RDRZDFGFLHcd8QDeiJXBgX5eB87t53rFSB6yyL3O6X7aZ2stdZdbNWbSuaxf2frbi/+c7w5fznPYDAakK0f707SUxYtkzlvBaJpCjf0hxAL+hpbffW6RDWJU4oyLE6N9E2l53vw1DTOTq/ghv4IErkyUoUKbugPN6595PJyYxIXDrJGDpY10352o9SuWmO1Wq7BcTg9lcZtw1F0+T24OJfBcyenAVB4eM9QSxuaGXf4z+htU61xdWJour6h44U35zC/UoLbRSEa8KJYqWJ+pYRfu3MTfmXfpkYsorhP3LOtD8dHEzg1lcbIqkVOrYzi2MPJVRd6U3oO0akFvMARCjGpNuJd0VIuuYlkASGvCxOJPC4vZHFmKo18uYpf++nNkvOL3HFXc+kiwj43Brr9ODu9gm9jAkGPCxPJ+uHlDFsXSS4K6A56Gq7NgNeFlWIF2dXdi9liFcUKW++Dq0etPLxnCJ967gyuLObg89AIe93oDfuajuyxc14zAhFNFmNEESt9R/hi3Xldj+SJ12ovlpUdy8pJXwkt4tNI3IccUlY9j4vC43dtlrX+lSo1vLOUw1y6CL+bxp3X9RiqhxxqkzlvBYqFvBhYtQJdWsjim0fewVS6aMglKtWWVohTI/1BKSZKPIimCgwSuTIArP6XU7TSabmP2fZoB0rtqkdINF2jUMGmeBALmTKOXllGssCgJ+TFT95ZBkXVryF2v2p5tuzq5CqelDcJxIdam8od9qtnUn30zhEEvW784OwsJpNF1DgOfo8LIZ8bIZ+7kQRT2CeWs2U8c3QUL7w5i5lUEaHV/Eg/ODuHj9+/RbGMeaYqGXvIu/WE6Tm6/B5cnM/guwKRyr+/RsT9s69PYWGlhFKlniTz7/5zHOdnM3j6g7c2baoQX/PgqWn85O1lJHNlvL2URzTowdaBCDiu7t6MBjzojfjB1Dh4XXWrl/CA3uFoAIfemMVUsgCaplADkC4yiAnySU2minj+5DSqHBDxubGcL6NUqcHjptEXbt54I0wJYTTjuFUQ0WQhRhSx2nfEA3oyz+Cgp3UFxNPuAd0KS5AUel05ZtE68As/1xP2YWE1m/QtQ90oVGotx0zYGf8iLAtFUZhfKWE2XcB/vr2M3ogPk4mC5P2krqsk7oxYyqQw2h+UngFf9nu39TdWv2qLCyHCNpBywcq1h9UHHZtBLieYnrGI37U1mSo2go6/+P23sFKqIOR1we2isVKoyF5D6dkKjyPiU3cAwNmZTMt7oZb2Qe590iPI+ef34G0b8N/+6STSxSo2dPtbUhIAaFjr+BQGy9kSCgwLmqZQqdWPOnnyvhtkrV58xvWrsYdoij1kWa7J7Xd+dgVjS3n4vS48d3IKxWoNH79/SyPIX+09FLvWfvDmLBYAeNyu+t854K35LJ4/OY2PvmezZF9m2fpB7Jfns8iUKmCqNcyma/jOiUkEvS4UKzXEg16Uaxx6Q15EAp6GmxCoHwFD0xT+31sLCPvru/nSxQooqv5sqFWL9d6RGCZTBazr8qNUqaHA1IWdi6YaVm1+l2PY7wFTreHMVL5JZHeCNSeaXnnlFXz5y1/GyZMnMTc3h4MHD+IDH/hAp4sFwNhqWs9kDTQP2MLf87RrQLfTEtQOc6zWgV/tcx+6fbixvVk8mD9+12Zd9VASR0qTHX9djuMwnymD44BCpYZqN6vZJSo3Gcmt7I08Byv7plquoeVcGUHPjKxAU+u/WsSumTpY2ZfF7arX5cW3xWSqiI2x+oT04wsLmEoV0e33wOOm4aIoxY0GUhM6Xw7xcUQvvDmHLr8HN/SHGxnGtRyNozQuGBHk/RE/9u9cj2NjSfSE6ukEdm7oankvjo0mkC4wKFZqqLAATQGVKguXh+bzPSqWMVWopyEA0NiB+t7t/U1Jfe/d2geO4/D1V1KgaQq9IS+y5SpeemsBfheNK0s5xUOlhQJDmEaBoil4XBQYpgYKFPxeGrGgBz88P4eJ5NWjdYRpCmiaQirPIFFgwLIcatzVJKEAUGXrm06YGouFjBuP3D4Mt5tueafuGInhpYsVlKssQj43Hti+Dr0hb0tw+yuXl3BlIYsqx2Eg4sOO9V1YypbxT8fG8f0360e2rBQYdAc9uH1jDMlCRXfGcStZc6Ipn8/j1ltvxUc/+lE8/PDDnS5OC0ZeXr3f0SJY7F4BW2UJkpo87HQzarUgKMWDSE1SUoM5oJ6lW+1ealuC+bJUqyz+P988BgoAC6DEsJhKFfGB2wKa4krkJqOmM7neNnYmlxgrnqOcABO35Sfu3yJ55pi4/3IcGruC7BTtVopQHr5c4rprdXkJ2+LEeBInxupB4AGvC8urcZQ1msJNAxE8vKf1Gko77zbGAri0mGs5jqhSreHkRL3daZrChm5//QxLFZEq9z4ZFeRqruqJZH3LfanKYnQpBw9NgWXrAcuVGocayzUlVqVpqmV3mDDj+sZYAA/vGW60ufCA4Z/fMYANUT+WcwzcLhosB4wl8vjHYxNgWQ7Fag2b4qGmbON8ff/pv8bw4tl5sCyH44I0CsPxEFKFCphaPcAaHDCbLiIa9KJS45re6Y2xAD6wq94fgz43vC4a2UoVFACKArr8boACXDSFcoWt7zAMe3Fg12BLPzo+lsTNQ9145PbhlsN9heMmH5dZZQEPTaNYYXF+LoMP7RnCD8/PI1euojvgQZ6pggOH/i6/piztdrLmRNP+/fuxf//+ThdDFiMvr97v8J1TvJXTTsTmW7PJNqV2kwgnYLvcjFotCI3PKbSxllgyuXoIJ5r5dBEVtp5vSHwvubwwUhMvwCHgcwMcV88143PjAxraTa78fLn558xnIY45YAcLj7i/6T2mpCvgwaX5LJ47OQWAw8N7hhEPSW+tt2KAbhKhBg+G5ZHKrCzl8tIVHiAIAh/pCaFW4zCfKWG4O4Cbh7pbvisVO8XvvIsFPfX8TRwHF4Wm44hiIR9KK0XUAICrLxxuHAjIjif8v9XGBb3PSCluTiiAekNeLGTc6A17sS7ix0K2hFKFxea+cMt7eXkx29gdtm9zvHFYMO8d4MsfD3kxkyogV66ixnI4PpZYDUpnG8KS5TiAq1t2QNUPwR6JB1Ytc/WdaKk8g1OTKVRrHPoifsyni5hMFHDjQBf2XhfCHSNxXJhbQaFcBQcKS7kygh5X451eyJQwu1LEv54u4qs/fhtD0QDmMyWUqzW4KKDGAeCAco0DBQ6lCgu3i0KpwiIW9KI37JPsR0vZMp56cEfLcxGK+42xAKIhL+7b1odEjsFcpoQuvxu/eNsgfnh+of5dioKbplGtAYuZUsuuxXaz5kSTXsrlMsrlcuPnTCbTlvtqeaDiF1Xrdy4tZLGYLWF+pQSaBi7OZ2xT3XKryAtzmcZ2+zxT0x3kLbWbRDi52eFm1GpB4Nt4KVvGfKYEF0Xh0kLWUPyWXD2EwvfCfBa9YR9uGYo2lQmApDh9/uS05IG1927rbyTt6wp48OCtGzSftae6HX914O4Je5ty19jV74xcV+vzFdbr0nwW06kCBlcTNFIUZUk2cy3l03swrBhxZuVUnmnaQTiRlE/2ySMXBJ4qVEBRFMo1FrGgB1vXdeHsTAYHT003xMF4oh5fcnkx1zRZvjaRQtjnaoxPm+JBvP/m9XjlyhIACvds6cV0uoiBbj/6Iz4sZst4ezHXcmiwVMC4eCu+VYhj+/jDdwNeV0MAjcSD6A55sa0/jEsLObhoqimx6vMnp3BiPNXYHTYYrT+Hr/zoCvrC9d8tZcvYFA8i5HPj2OgC5lcPGF7fXbcwZUoVDEUDSOXLSKF+3NtCtowqy9Xd7uUqzs9nMRIP4sR4CkvZMqaSeRSZusCazxRRrbFwURRm0gW8NlZP9OlxubBnpBv9ER9eubKERI5pZBYHgLmVEopMFTUOuLKQRY0DfG4aHA2A5eDz0PC7KRQqHDwuCn6PG9yq2BW6g4++vdxYXPGJcMUZypstmykwVRZzK0XUOIAGcO+2fvR3+Ru7HEuVGvoiPgzHAvB6XJoOhreTa140ffGLX8TnPve5ThejibqVZQqTKfkdTnLQNIV0nsFsqoiA143lXAmJbFnT2VtGEK8ij40mQFEUhuMhvDW7gqlUsXF6uFbkMlnLueqsQk/8WDrPYDpVQMTvQbZUadnNIUZL0DaPePLsCXuxnCtjYaXYWEXxqzGxOOWDJ6WEwaP7NiHocTWZw7UiV36heNg+EGnJXWN1jJmZ3Yb88+UTXqodU8JxwHMnpzAYC2DvSA8ypauBznaIdmH/4zgOiVw9Yaj4YFj+s0os58o4fG4O6WIVpUoNPWEfUvlyYwehXLJPIXLWG35hNJ7Io8vvbpx3xn9G7Ara0O2HdzXQOFmowEUBc+n6OWzZUgX9YR8+8TNb8dv3Xz17TXgYrNyhwYC85VDKKmQFfAzTUq6M2VQRG6JB9Hf5kC/XQNMUXBTVsJ6Bohq7ufaOxBvvJb8D7vSxFGJBD3Zs6K6PmwB2bYzh+FgSTLUGCoDXTaHIsJhOFVFjOQzG6lYejuPgd7uQKVbr1iYAAQ+NeNiLLp8b3UEPXBSF+UwJXQEvXHQVxUoNxUoVQY8bW9aFUWU5FJgq9o2sR7FSt0ICQDTgRX/YB5/HhRsHIjg3mwFT48CBgtdFgeU4uAC4XDRomgO7agWnKQqldBGRoBc9IS/SxQrcdPMh5cdHE43FVY6p4dPfewPruv1NsVPiheCFuRVUaixWihVUWQ4nx5JYzpbx4b2bEPS6Mbacw+beMB7aPYQun9vW8yG1cM2Lps985jP45Cc/2fg5k8lgeHi4Y+VpWFnms+gJeyV3OKmZ06MhLwZjQcxnSnDTNNxuGsdscJeI3RgX5zKNCXnvdT3YtTEmuS1f7ZrNmayhOZO1FchZEMSugHobB8CyQFfAjajAFy/+vBAtdRCLN34QE66iGjmjJMQp/zfAmrgOpfLLxXlZZX3R6mLSei1hwkulA5bjIe9qDBOHE+MpZEoVSZFldZ9sEaHsVRF681A3vvXqmCbBeOj0DDKlep6wd5ZyWMiU8Au3bmjsIBTn/QGutqMW6w2f6+tbr47h+FiyUca9I3H88Pw88uUqYkFvo60fvKUPk6kC9o7EEfDQja383QFPy7sjbge5Q4PVLIfievBuMB49Gy5YlkMyz+DwuXmk8mXkyjVUaiwmknksZovIlWu4rjeEkd4QgPr2+dHFHE5ky+iP+PBb914PigKOjdUP251JFcEByDM1LOfqAdWgKPRHfOA4Dmem8tg1HEUk4MFrYymMJ3LY1BPEHZvi+PHlJXAsh+6gB6VqDUWmBreLRk/Yi+v7wvip1dQmx8eScFEUsqUK1nX5wNQ4FMpVBH1uDMeCjUXWE/c0b+i4e0tvQ8CkixV8+ntv4MR4Em4a4MCBpgCaBrp8buSYKjiOQ4GpwUVT8HtdqLFcI7j73q29jbYUp0D50YUFnJ9dQV/Yh+NjSSznyugN+5qSB6fyDBL5CopMXRx2+dw4NZXGB/7qJ+gJ+xANuNHfHUCBqeLbxycaljo7DARaueZFk8/ng8+nzUXRDp4/OY0LC3UrCy8a+IFASzI6mqawbV0EqTyDbKkCmgKGY0H0rO5KMDphypnxhW6MmXQRQa8bM+kiXhtLoi/ia8S+aIUXDLxY5HeTSAWY2oFUCgfhLha+zbetiyBdqCAW9CBVqGDbuohpS4gQ4aQhHMSkAst9brpJnFod18Gj1Hf0iDI9fZBf2feEfbIuJj2xfo2El3mmkaVaiYf3DDdcje1I0aEkQgtMVdPmCr5/7FjfheU8g7l0ERG/Bx++YyN6I76mvD9S7ajFegNAsq8d2DWIH56fAwDwEToeF4WPvufqzrlnjo7WkxgG6wfX8u+OXDvIiVQ1y7BUMPX+nQOyR8BIvbv8dSaS9SSO6SKDUpXFSpFBpcbB66aRL1fhc9NN4974cm51fPAiVajgKy9dxp5NMSRz5frBtt1+hH1uTCYLmFspgqLqu+4Ws/VErJviQSQLlbqQ6vKhL+IFRVHIlqtwUQDnouF106ApCtf1hVGpsYj4Pfip63qa+uilhWzTMSr3bOvDK5eWMJG8etaeUnvHQ148/cFb8U/HxvGji4tYzpXR7fegL+xDf7cfg90+/OjSMvLlKtZHA4j43VjMlHDjQKRh/ZF6XhTq5xKCA/weF1IFBi+8OYeb1nc1JQ+OB73wuWmk8iw4DihXGLAch0S+hqVsGTWOw7qIH6e8LrhpCrcNdXc8nvKaF01Oom5lKaA35Gu4pRI5BhtjAcXBTAzfUVOFClylCnpWD0404i55ZymHL/3gQmMg+dT7tiMW9DYGkr6wFzs3dOH7Z+cwGAvglsEo3pxJo8BUsXNDHwpMtSU3kRrCgZjfTdLuVYN44BW3efNkEWkaYK1I568kQNQmC6tdR3qFoNw99V5nOVvG4XPzSBfqE1VvyItUnmu4mPT0aaM73jqVc0ksQgHgqRfOy5ZfWD5h/xiMBhqJVXkrS9NxH2huR6mAXaV2kmofPnauXGUR9rlx77Z+BSuScvyJWpsrWYb5esymi41g6mOjCYkjYOo7I6XeXY7j8JO3E0jmS3hnKY+I34ORnhAyxQpoioXHRaOCet4hj4tGplTBz+8YwDtLOYT9HvRFfKCyZZyfzcDjorFlXQTZUgVMlcVAfwDJPIOI31PPTbUa0yR0gYpdohPJQiNJ5FSq2HQAsNiSJnfe3Zb+iC5LeDzkxSce2Irffu+Wps/x1wj7RxsLm2Sewf6d62W9C/zzGU/k0eVzo8JeFdflSg2xoAcD3QH4PC64aYCiKIT8HnBcCvOZMmosBxqAi6ZRrbEAgEypgjxTRaXGYilbRsDjQsAjfXRMO1hzoimXy+Htt99u/Dw2NoYzZ84gHo9j48aNHSyZOg0ry2rgMx8s9/CeYV2DPj+Q8fmBJpIF7NzQZWiV/PT338Ib0ysI+z04PZXGl35wAfuu62kMLmdnMti3OY5f3jNUD9qrseiP+LF3JAZq9fw7vQKiUxOVGKU2lyqjHVvR9QaWa/musH5aLTRWCEG91zl0ZgbZUgXFSg2jSzksZNx4UOBi0psA08xRHJ3sh/y9pcovZ30W9o87RdYHQL7/SAXs3rgujHSxolko87FzwlgTIVa+30oWqU3xII6NJTHLB1OvnpF4ZiqNXcPRpp2RHMfi8tLVWJpylcXhs3NIFhgsZstgqjXUWKDA1P/t97jQFfCjwFSRyNUQoOvJLHf0duH/e9/1ODmRxOmpNAAgW6rA56HRE/ZhXZcfOzZ0YypVRNjnauRx48WOlAtU7mcpQST3XKyIBZW7htwmESmEz+uvut5uEtfr+wONDQbCHbrHx5K4dTgG9+wKCkwVJaaGPFNDPcqqnm18pVABB4CiaOTKZVyYy3TsnV1zoun111/He9/73sbPfLzSY489hr//+7/vUKm002xlCTalydc76PdGfLKDk9qAlcwz+JfXp/DaRD2ZWjRY3+Ewnsijv8vfIgw+8cCWJjfGgV2D+MpLV0wJiE5OVPz91dpcj7vASsxMPHosPlYJQb3X4T9/04ZuJHJlzK7Utxp/eO/GxhZmvfXWuuOtnSema4mD45Eqv1x6EbX+ofR3PmB3IVMXG+fnsvj0995oHK2hpU5A3UqghJXvhdT41rC458ugKdQtlYUKNq66vi6uhhQMdPnx3KkZFJgqQl43btrQjbdmV8ABKFdZ5Ms1AFctInmmhqGoD0PdfkyvlEBHKHQHPPC5XYitxl196n3bGxb6XcNRXL8ugncWc43v7985IGmN0fOzEwQ9YGwsommqZWOK2LomfD8nkgU8cvsw7t3Wh8Pn5vHsa5PIFKsIel2rAfM0fB4aHpcLXjeFGicfUmI3a0403XfffY1TlNci8VBz5lxh7g6j25yFHVnrhHDw1DRem0gh5HNjKVvGeCIPF0Vh13AUm3tDLcKgN9wq0Jx0cKlR9La5Xfmj5DDSnnoTj1rxHPVeR/j5DdG6uf7O1X7G/51H62CtdXBvx4npwvewL+wFB2A5xyi+k+LyS6UXEae+UGsXqb/zAbuL2TIypXpc5YXVozX4BJ9KdTp8br4RyGvHBhQ5WJZDssDg0OmZph2iB3YN4tDpGVxayIIrV+FyURhdzGE+U0bY54bXTSFZqMLrdoEDMJWou+GGo35cWsxhYaXU2O5OUUA04MbtIzH4PC68Z0sfXptItcRnXd8Xxjceu6MxcQuft5o1Zq2itz5S76PUYeLiz3z8/gg+fMdG/OvpGUym6saFn7y9hPOzGYR9LmRLHK7vDXVsF92aE01rGfFAKszbwQ+kZs3aepP7PbB9HV66sIB8uYo9m6KNmCZAWhjIxy6YExBa6myHO09vmzvFtSiHEcuRVc/RjAC9U+NRJ1qyZ6u55Kx2sUrRCFL2unD47BwqNRYjvSFMyJwHKFV+mq6nvphJFhAJeBvb982Uk6/nxlgAP764CI+rPvGIT5aXqxN/pEiOqeGNqRRomkYqX8aBXYNNOcGsaE/hZo1vHxvHkcvLmE0VUK7VD7Y9PpZEvlzBJ35mGx7aPYRPfe8NXFwVlaUKC7+bxkqBQSJfhs/two4NQWzqCcHjqgv2E+Mp+NwueD0u1FgW7GoSx/5IYPVQ2EhD/MjFZ/ETt9PHhU6ixXUo/l1vxIcn7rnanu/d3t9k2fvU+7bbWmYliGhqI0JB88Kbc015O4CrA6nRl07rhCBc5cdDXuzY0I07NsXwG/de3/iMlgHAioFCi2WsHe4UI5YVJ2LEcmTVgG+1ABVnzz42msB6C45wsdNCyluOr2Z8LqLA1EBRwFSigGmqiHy50iIypFjOlrGQLYMDhWKlhoEuf2P7Pl8XrYjfofduX4cTY8lGYtXuoAeb4vJpP/ixhT9S5M2pNLJFDtGgBzRVT4Egd1ah8DmppQGQ2kWcL1fx4tk55EtVLOUYcABcFIVynsX/PTaJoNeDfLnujvO4KCzmGNRWvRG8V6LGcsiVq43n/dDuIVCrSWu7Ax6kCmUAFGIBD/q7/U3nu+np004dF9YqQguV0LLXSYhoahNCQdPPp9RfzdsBWLPabQRHKiT34+9hRaCx3s9JocUy1g53yrWEFW5eMxgVoMIBUfi+RPxuHL2cxUqxghv6w5IZ5PVgh4tVLBb6Ij68OZXG3EoRLppCtcaiyNYAClgpVhoiQ4j4/T90ZgalSg0+T71NyjUWw7GA5lxOQqTeoac/eGvLUUZyCMVmb9ADUIDfQ+O6vnDjEFaWbT6rkB+HlMSUOAN3NORFKs+gytZzPD37+hSWc2UUy1V43TSAembsbLmeP8hFU3j2tQmki1W4aAq5MotipYYaB3hdFGgKGOjyIx7yocBUsXckJimGhEJUbkcrofN0WjABRDS1DfEKl6YpUKjn7bBqtauU3E8poR1fnnajxTLWLnfKtYQ4bs7pSKW9uL4vjL5w3SKbyJWxUqggHHAjU6q2HOGity/Y4UppOax0sAt3XteD1Oqun1S+nlZhoMuPnRu6FZM08m5IqUB5DtC9gJB7h6IBT5MLRI0msTkSR7nKYn20vq1+52B3428hrwsz6SLm0kWkCpXGLl+pcvPttpQtY3I1KedKsQK3i0bA40KeqWc8r7JAuVYD6mtNcBwHn9uFiN+DQoUFx3HIFCvgQ0TdVD142EVRoGkKvREf9o2sb4nZkooNWwvvDKFzENHURg7cVj8NeiJZwIO3rG/K22HFalcpuZ/Wg1+1ujysmHC07l5biwHndoo6pWsruUecKjS/9IMLODOVbkp78Y3H7qjPjgCqNQ5uF436MuNqBnmp7fhaYp54rHTJiUXJUo7BUw9uaQiGw+fmkClWsGNDN/JMDTsHuxv3l7OkCgPlvW4a+0bimEoXdS8g1N4hre2glh2ev8+zr08hX64HmLtKlUZAr7jc1Spbb7egp350CDjkyixYjkWuzKJcraFW48AB8LiAGgv4PDSiQS/6w14UqywqNQ5ggev7w3h7MYdqjcP6bg+KVQ6B1aSwXQGPZMwcgWAEIpragHgi+8QDWxrxDFZNZEoWGf7f4r8JD5jUumq1Or5Ii6uk3TvW9CJ8hnbGX2m5ttXi2G74iZNPFAjUnzPD1DMC794Yw0yqgCuLeVRqNTDVGm5a34WH9ww1xzy9vYzjowkMmIx5MoKSKOHTgvA7vMSZmpXe24d2D6HAVPEfFxaRyJexnCuvHn5KtdxHCeH2fL3vkFTKBJqmEA14JK11B3YN4vC5ObAcsKHbj57VAPONsUBL+/BJOP9rNAEaQL5UBU0BVRYABzBVDtRqbVm2foBsLOjBR+8awQd2D+HQ6VUxWqKwobsevA0AO1ZTCkT8HuzfOaApfoxA0AoRTW1AKSbHyjgSpdWk+G/CAyb1rFqtji/S4iqR+0ynLSdSIkbcPnw2Yr1I1U2t7eUm4OdPTuPEePP39JwXaCf8xClMFLhrOAqv13U1hibix2K2/P9v78/j27rOe3302RvAxkyCoyhR1Dx4tgZHtpPGcmKfxs4kW5ma+vakPhnaHCdpTnraOrm3mX4nJ03a0/YmN/embRInPc1NE/fYcW3HblPHsh3HkmwNtmYPIiVSnAmAmLGn9fsDBASAAAiQoCjZ6/l8nIgkNrDW3htYX7zvu74vQY+X265cxu7tfYX0VX6ueaPGtiWqe5tLlLT7aps0Vusl6NOcpA0LIWAiniWeMVnf6c9FnuYQP/PdfQi5IvS8cW5X0A1CMJHQ6QxoKOQiaZXEaWfAze1XLWdvf5iOmZqldZ0+0qY9q2zgtYkEz7wywZFz02RNC2MmqpTH7VTQTYGtgNepYgsIuJ2FDgLlYrQ4el9uKimRNAspmhYZ2xazujovVk1OrQ/uWsZ5UL+Dcq36osVwxq70mAtpTliN8qLXvEAqdDovciMGUXermGpzq6e2q9ICvGNNW4k4zhoWjx0dLfG5ada5m6+oLTcKzG8nLr5nKy2ChbkKUeis3tPqnVXzdCGoJuzruVdrtQoZmEpi24I2n1YwY1zW6uGL77pizqLY+XzByYulYi+mfaenMG1B0O1iYCqBy6HyWxs6qz5n+XxShlWxbODef3mR48MxBJDScy0zHCrYItenzeNy4HEJVFVlZciLqihs6gnORNtyVPKPW+ovU5LXN1I0LSL5D8wTIzFiGZMrl7eQ1K0lcZGu9LdGQ/bVvhXX02i4mSzlbrr8NR2YSnJiJEZfu78gYvJpiP0DEU6NxhmKpOid+VlRlLrGWJhbmQN0vbVdc4njYyMxFMCwRNPOXbkwqNYwtRrVthPPFYUsnuvlPUF0SzAWyyxp3Vuj0UGo3SpkTUfOaDaS0oFcpGVNx9zGfvVusij/IvLY0VFiaR3DFuimzWQ8g27ZjMUyjCu5f+umzUQiy8o2X0VxWjwfqNxTT9etmbSsk8RMWk4AHqcDw7ZRUFjd4SNt2CjA5ctbShpn1zrvUjBJFhMpmhaR/AdmX7uf48PTDEbS3H5VT1Nqcmp9m5qrKDTPfHYR1VqUa6WN5vNBVum4heyma8Y30OK5xjImx4encc84Ap8Xngr3Hxikt83LjjUdxDJGXWPMO0BPxLOMxjI4ZnxkGrGJqCWOB6aStHicJUKvGRGZ8utf3jAV6hNm1YRAPWnbSoXJS02j92qlez1f1/TUy5OAYOfm7rrnVk1kV0sr540r04aF2+kAYCSWJZEx0U0bj8uJZeess0enM3hdDm5Y1zHn51BfyMvzZyIl48inXw8NRrFEzkbAOVMv5VYd9LS42dzTQl+bt2jDTO3mvxLJhUCKpkWi/APTPbOTY6G1JAupUyge23y/mVXyN6m2MMw3AlUrpTGf3XTNSueVz/XK5S0MRtK4HEphoW73azM1TIL9AxFiGaOmX1YxqppzgB6KpAh6XMQzBl0BrfC4RkRuNXF837P97OsPNy0iU35OhBCFhqnNEGb1zvVidGSe787P4vt1VZuPu25Ywz0zHegbObbT7+Lqla2zduiWbwARQnA2ki4YV56eSCAAv9tJi9uJX3OAyHWbV2d2NPrdOcG0a2tv1bHkrSRem0ziUMCrtZaMI5+WfW0ySZtXw+NS8WoObtrUxf/jhjUln2sX03WVvLGRommRaOZW+eIPjHKX5EZ2DDWzFqhWHU1+ntUiUAstNm80rdisdF75XGs15ty9va+kwXGxX1a5oWCxJUDIr9Hb5sW2ocXrLDhANyP9UG+0qtHnLD4nkZTB6pmGqRR1M290zPO9Vy+2hTUvKho53w8eHOLXr04STRk8eXKc/f1TdTfTfeDAzLFpnamEzuU9Qf7n7mvoDLoJJ3W+9/Rp7j8whFdzsKbDDzOCaVWbj/0DYTr9GmMxJy0eJ7dftbxQaP2/954hljWwLYHb5WDrqjYAvvXEK7Nq7/LXoNhKIpI2SGWMkvddeVq2lmfcxXZdJW9cpGhaRBa6QJUvHLu29JZ8qz85GmMqqdctBharFqjSPCtFoE6Nxfne069xNpKuuhDWk9KoJ6qQ/1vBC6ZJhfiV5lrpeSoZTIaTOvf+y4ucGIvT6XfPcrZWVYXNy4JEUwZtPlfNGo75shgRmfJzUqubeb1Uq+26VCh/737q5g10t3rmPC5//0dTBtNpA5dDnWmmO8jHblpf9bicGB/k/gNDJDImTqeKy6FyfDTOzw+d42M35Ywk9w+E8WlOzkZSTMSGUVWFy3uCfOTNa1CU893mi7fp79rayw+f7cepqoS8ThRV4YnjY1y2vIWOmc+SlGHhdaqF9/Z7tqyYZSVxNpKu2AYj/7MURpJLASmaFpGFLlCVRE7+W70QgqmETmfAzbLWXN6/lhhYTGftavMsj0AhxJy+UI1E6Cr9rlJbhmhSR7fEnM+3kLlWGkN5lOSBA4OcGI3jcqhMp3OeMuXXoFSALF4Nx2IKMdsWrJ/nfW+aNqqq1KztagaLne4pNOx1O/nZC4M8dnS0UM9YK2Kkqgqr2nwlzXQ7AhpnI+ma7+2cIIrgcamcmdLxulQUNddG5PFjI7x3RsS0+zXWdPoZj2eIpgw2dPvRLcGek+PVN5H4NJa1erGBNp/GZCLLVMrg0NkILqeDkNfJ0XPTqEqu0erZmabElawkLoY2GBLJQpCi6QIw35RcJZHzmVs2Fv59eU8Q3a5vx1Az04XVqLWTa8eadl4ej6MqypyibSERuvxiNR7PMBxJ09vmo9XnQnModXnb1Euj6cW737J2pm5EI5bOuSXnna2LuRhrcypRaXzVatjqmUt5K5VE1qxa27UQ5pP2qyeiWf67/Ht3OJomkTWxbFHoxTZXi5vd21eyv3+KE6NxOgIaIa9WsZlu6U7OOH1tXq5f28lEXGcykcXjcuB351rPPPzicIlFg6oobOwOsHNzN2OxzJwF6js3dfLwSyNkDCtXDI4gkbVQdYupRBZVgRUhX+HePhNO8ae3XcY3Hz85y0pCIrmUkaLpIqWayCn2JZlrx1D5h2C5GMm3dVko1T5sywXA9585XZdom69wyC9WbT4Xo9MZAh4XlhD0BN1oLkdd3jYLpZrYhdw37/y38KmEzvpOP2nT4ssPH6tY8H4xUkt0lIvFlG7i05x1CZTyVipOhzpnbdd8aCRFXWuu9WxW2NsfZng6A8DykJeAx8W/vniOXxwdQQF2bu7mrutXzzon7X5tppnuYEkqu9Zc4hmDYyMG21wONnYHCj5Hy0PeQkPd/BeugalkwaJhdDrXH26uL1B33bAGn+akfzLByZE444ksWTNnP2DpJl7NUXjsVEJnVZuXDcuCF01neomkWUjRdBFTK+JSa8dQtQ/0/OMnE1keOnSOb/3qlYaKbMup91t75dRTfb5QjVAsNFU1lxJo9boIpwyuXxu8IB/ctSJ6xUXBq9q8pE2bI0PTTa0xu1Bpp0rF/eVi8amXJwv3Xa35VWqlEs8YdPo1OgLuptV2NZqiriWw6tmskDIsTg5Pk8iaRJM6w9E0sbSBNnMfPvLiMD6Xo+I5ye3CXF9Xur076OaKFa0MhpO4HAo3beriurXtvDQYpSOQKwC/ckVLYecbwIZlQY6fm+bwYLTgrVWL8h2YP3thEMsWOFwqTtVFi9dFi8fFZCLL5T1Bdm/vKxwrBZPk9YQUTRcx9UZcyv821wf6Q4cqdxxvlHq+tTdawF0vcxkenhqL0x1wE/JrbF52Yf1dKhVGf/+Z0wVx+Zm3b6Tdr1U0/ZvvubkQLulziY5isTiVyAKirvlVaqVy5YpWblzf2dTarkZS1LXmmv/3XJsVfC4H67oCTGcMJuNZBIKA24nf7Sy4e+cdvyul+FRVqTo2VVXoCrp55MVhLAEOBd597QruuXkDeS8mn8vBqbE4CMHL4wnu/ZcXMWzBshYPT5wYQwBb+0JEUgZ7To6zvitQ1znMC8KnTo0DCm9a04bP7WQinks3795+8fQ2lEiajRRNlwDzSVHVanXSjFYohefxuSo+z1zpi/lSq81IpejbUtQGVUtLlovLZtaYXQiX9FnCKKlzQ9GYi8XiDes6Cu0zYO75VWqlsr4r0PTrV22nZ/lrzCWw5rp2+ffH8pCXLS1tjE6neW0iSSxjzHL3ridKXOlvKd3K9WoTAqEouVqlItF291vW8oNf97N/IIwCnBiN0xlwc3VvK7plE0+bHB+J4VTVWYX2tbb/t/s1Pv32jdxz84aSx1zsdXgSSTOQoul1xlwf9tX+Hk7phcaX9UQqommD0Wi68EHc6nPx1g2dhdcpNIlt8pbxWXUzhoXP5Zg17qUSTMXMJVLrTVfW4yS+WDsjyymPMqR0k/CM7UW5WAwndR501efSXa2VSrPHX81FvNI932gvx2Iq+Vft3NQJUNPdu5b4LW68vPf0FJGUzrZVbXQH3YzHs0wk9JJ5Fbf66Wn10hHI7Xwbj2VIZEzShkkyaxHPZOjwOQvX7Mf7zhSu785Nndx1w5qaafdqP0skr0ekaHodMtcHevHfr+5tIWVY/OH/fqHQH69ezyfdEoUP4q6Axq6tvQW/mJ88P0jGsGj1unA7HU3ZMl6xbubUOG2+XO1Lftz5thCLmaqql1oidq50ZSM1Y4u9MzJPPu0U8rroDHo4ci7GgweHSu6VhaRjL1T9i6pWN1/Nkx9/pULmeuZW6X3Y7tequntXE7+TiSw/PzjE/QeG8GlO1nT6gZwQm5qx8ggXFXNXa/UT8mp0B9y4nCorQl4sO1fInTVt9p+J8vEfPc+GZUF+dWKMRDa3C+7hl0bwac5LyiNLIllMpGi6SFmIwCj+QIfZH86V2mpE0yZp3WQyqdMb8tbl+dTT6uHavhBnJhMMRTP8v//jZcamM+iWIKNbTCSypA0Lh6LMe8t48RgqpYdAoSPgLllkir+RXwzGiPVEJSpRa1Gfa2fkYtVwTcazPHZ0lGhKJ2uJws6sWvdrrbqcpaKe6Fw9orXWHKoJq3ptQfLpz5/PeDB5NQdDkdzuy66gm+vWtHF6PMGhomLufFF920zavLjVz29t6GT39lzbpfz7/ui5aTKGRdDj5PBglBMjMTyak5BPQwEyhlW17koieSMiRdMFot4PnWYV9Nb7PPmFI2NYvDaRYCSaxuNU52zEWfzhfnw0jgIYpp3zlvFrtPpcpE0LBPS2eUu2jJefi0rnptr4S+pm1raT0k2OnIsVjtmxpo2zkQuTqqqX+URcqkYd4lkeOjw7jXqh/J0eOnyOeMYgbVicnkgwFnPyoev6qj6+fDz5nZtLHQWsVLhefM/nDSOb1X6n3utSnP40LMHTL08wFE0T9Di5pjcE5HrAdQY09r42RcqwuKInSDRt8o1fnCDk1zh8NkJCt3IbIXwaN23swONUORtJ8eDBIe7ctnKmlstmf/8UQY+T1R1+oimDeMbAqwmiM3VX/gp1VxLJGxkpmhaZRkVQIx/UtT6I63me4oWjI+BmLJYh6HFxw7qOOSMV+b8PTCVp8Tjpa/ezrNVLZ8DNZCJLh1/DoSisaPPQ3eLJtQcpqyEpbrcxl+dPfvwV62bKvKryx+bP/2KlqhqlkTFUS7k9dLj2zsfFnKdtC06NxdGcKnYGDMuizaeRzBqzvKbK7/v8tX7s6EhDaeDF5M5tK0npZqHGKDXzxWHPyfESw8ha4nsuMdTo+z+f/sw5b+scH4nhUhViMw7y3UEPXQGBoigkdIu0bjI1I3bG41kQMJnMogKRpI7HpXLgTJiMYdPu11jfndsh99G3ruPjOzewfyDC4cFoQTBduaKF7WvaS2qaLuTOU4nkYkeKpkWmURFUEl0QouIH9VwfxI0UBhdHbj54XR+7tvTSOeOVU4v87hyA+57tZ29/mLFYhlafi66ARmjGP6Z4y3/5udh3egoUpS7Pn2o7/CpFWC5UqqqZ1OOMvmtrL9964pVFiaLV8xyqqhBN6oxO58S1adnopsXR4fisa1jtWjeSBl5s2v0aPs1ZiNQdGZrmyGC0cE/mDSPdLscs8V2vGMqfh7aiDRHFDZ4ribAz4RQdfo1z0TSWEJimwOtyEM+YvOuqEK9MJFEVpSQ6nDEshG2TyNoAGLbA7YDptIktwDHTPNkxmSw55/dW2bVYvjNOIpHkkKJpESlf/EWRCILKu09Wt/t45tVJTo3GC0Zx0bRR8oFcT61LvYXB80nrFC8YXUE3qaxJJKkTSWYLLschr6tE4Ni2YGAqWXIuDg9G2doXKhEA+aLbSuOvtdupeOy15nSx1WbUWnwrzaPZBd+NREJsWxDya+eduj1O4llzlu1EeZPk4mudafXMmQa+UNeo0vuz+J4sNowsF9/1epSdGoszHs8wOp1BCMG/vjjMwFSS7qAbFIWJeHbWrs/8NZ5OGcQzJkGPE8OyubKzhY/v3FBiYTEWc+JxOVAVGIrqIMCyBIpCrt/ijIeTqoIQEE3p9IU8hfN7oXYtSiSvF6RoWgSKjelWt/t4+pVJTo7Gcq0zuvx858lXmEjoVbc57zs9lUtxBTR0S5TsTqq31iXv8DswlWy6+3bxgvHwi8MAbFvVxlQii8/lKJlPcQrtxEiskJpJ6har232EUwYoCqOxDJqq8NVHj5eMv1Lard4ak0aic0tFpUhEpTRqnmZH0Ro5p6qq5NKsKYM2n4tIyqB7ZucWM5GM69e2zxK9kZRRuNa10sAX+hpVsgUovieTWZPbr1peEhmC+iK5+X9HkzrDkTQBj4uxWAaf5uDy5S08/FKulcrWVW2zIlB3bltJMmtw4EwYTVVwqSrdLW7aZuoCb76sm32npzgxEmN1u4/1XQFOjsVJ6hYT8VzbFluAy6GgALYQMyMXrGzz8b7rVs06F9K1WyKpDymamkj5h/5Nm7tI6SavTSRIZAz62n0MRtIMT2fYVvRhWbxIhbwuekJe2vwaPa3eWc006611yff9aoR6vuHnFwy/28m5SIqx6Qwuh0rQk3utSumW/MLc1+7n+PA0g5E0t1/VU1LTpKkKuiXQTXtWqm4uv6N6BN+FMH9slPJIhKoypzVDPZHBhg1K5zin1VOfwZJrWCziKrmi5x9XLQ28FNeo1jjz86kWEa4U8Sv+DFjV5sOrOeht82HZNjFNJeBx0hXIiR8Uhe6gm6xh8YujIwxMJVnT4efObSvxu110BNwkZ7b+66ZdaCWz5+Q4KApb+kKEkzrPn4mwoTvAxmVBHnlxmOm0QavXiSXA63KgmzaGZXPF8ha++N4rL4ovCxLJpYoUTU0k/6Hv1xz89IVBfvhsPx7NgUtV8LudhLwu0oaNbYtceJ7Zi1TxB7JS9O29VrShUq1LvX2/oLFv+Pnx/eyFwYKXS9oweWEgQlfQXdUZOT82t1PF5VAK36rXdwUwTZuvPnoc3bSrLt4L8SK6kOaPjVAeiYhnDLoD7rrGtBBfp+LnqHVOqz1fuWjLX8PiaEW1x81lY3Ghr1Gj48xTLeJXHDncPxAGIegKumn3uciaNgKYSOioai4KNBhJ8+tXJ9EtG9vO/SwEnI2kuHJ5C5NJnZFomqDHxa4tvSVfWkamMwxPZ8jqJn63AyEEDlVhY3eQnZu7GJtOFxpVg4wmSSTNQIqmJlH8oX8umiaZNUlkTGzA7cx1AB+JZVEVQFEYj2erLvxzpWAq+TDNt+8XNP4Nf9eWXh47OoplC1a1+4hnDFK6yY41y+d0Rq4052o1TI2el2osRHCV08xF/HyNkA9LCFq9rhJrhkaZT6Sm1jmt9Xz1FkRXitJUopnXaD7UO848lcRWpcjh6nYfV/e2cjaS5t3XrgAhmEjovOea5aAo/PLYCPGMidOhcCacpNf2cjaSa+i8fzpDb8hbqP3KR+bKv7R4XA7cDhW3y8HlPUF0WzAWy1zQRtUSyRsFKZqaRP5Df+/pKUaiaQA6Alru26UQ+DUHLV4nOzd3Fz44qy389aRgKm3phsb7fs3nG35n0M3tV/Ww9/RUoYv6jjVtfOym+Rsv1vOYhXgRLbQWaDHqbYprhNp9LsIpo5CCaZRGruNcuw8beb5mptQu1K7HZjjTF0dA81SLHP6X3yoVmvnjTdNmz6lxNKeKADK6xWA4za5re9m9fSWKohTez8XnovhLy/KQl06/RtDr4ovvuoJY1pxlwSGRSJqHFE1NJP8BFUkZODIGG7oDvDqeIOhxcftVPeza2ktnIPdtsd4t3tWotlg12vdrvt/wKy1w1YRFPWKnEUE0nwVvoeaP9RRs10NtQRmc9yJXz3VspIlyPc/X7JTaYht0LlT41mP1URw59GsOxuJZvvSvRwu1Su1+raQ33Ph0Bs2hkjEtdMvG5VC5+bKumq7+lb60XNXbitOp0u68MCanEskbFSmamkj+g27Xlt7CTrZtq9oqFr0u9JvuXItVIwvQfL7hV3r+4q3Q8zVeXOwP+vlGcRot2C5nIYKyXua6jo1GhRptStuslFq9dWqNvk7x/PfOQ/jOdf7KI4cHB6MoFhiWqOphpbkcud16gM/loCOg8dSpCTZ0B2u6p8+3NY9EIlkYUjQtAp1B96J+22tksarn9ReycBenHC7GYuuFUmnreCMF23nqWXAXylz+VI1en3ruiwttJDrfaFFxAfVwNLeDNZLM1h39rff8VXTKr+FhdfWKFmJpg4DbiaIoBS+nRMbgmVcnq7qnX6i2ORKJpBQpmhaRxfrGDBef6/ViRB0aOTfNXjxKt4578bqd8y7Ynm+90XypJp7ne33Ka3GKme/ifSGL3aHyrk9VgYcOnSt4gNUSYvWev+LzkW+KOxbLzPKw2nt6CoCkbnHl8haODk+TNmwUBcZiGQamkrhUFadDqemeLgWTRHJhkaJpiVhofUUzv2k26gq92FGHRsaTf2yxx00zfGiKF+f9A5GSreONFmwvtN6oWcz3+tQztoWmKcupN1o2MJWs+z1QqYD6TDjFAwcG2T8QmVOIFZ+/HWtqnz9VVarW/aUMi/F4Zqa5tQu/5kS3cnOwLAvDAocDhBCkdLuuJtoSieTCIEXTEtHMDuoXYiz1LHbNEnKNnJsf7x3g4ZdGsG1RMPX89C2b5v3aUHlxtoVgU3eQs5H5FWzPt96omRG0+V6fZu6Qm+u55ipWz4vPrG5ybDROi8fJfc/21yUyKxVQX7milbOR+qKA7X6NO7et5IEDg5yN5MTW7u19VV835HXx0beuK/Gw+vavXuGRF4eZSmZJ6zYhj5PBSJqg24nmVJhMgGGbuBwKQY+LaEqn1VtfE22JRLL4SNF0AWmWu3Wzx9TsreXlO6waSUE1msp66uVJklmTNp9GJKXz1MuT3PO2jQs6j9UiQwsRMfXUG7UV9XA7NRbne0+/xtlIuumRp0bTaM26V+t5rrnus3wLkV+/NoUCbOwONCTkKonX/GvC3GnLBw8O8etXp4imdX51Yoz9/WH+4v3XllybvPA7NRYnmtQLTat3benlqVPjJLImlp2LJE2ldLqDbibiOq1ejXDKoMXjYkXISzRlsGNNO/9z9zV1NdGWSCSLjxRNF4BK356XysivUk3EYmwtrzRnoGm1I+cRRf9b+q/5kJ9TtcjQYtQbRdMGo9F0Ll0T0Ah5NTSHUlfKaLFpZq1arefKb62f6z7bc3IcAbR6XaQNi3jGZEXIOytNV+3eLBevti3qTlvm3weTiZwTt2EK9g2E+afnBvjMreejm3kRNh7PMBxJ09vmI5oySGZNRmNZIikDVVGwhMAwbUJejWVBDz0hL2+/rAtFVZmIZ1nV5mP39oujR6JEIskhRdMFoNK354tp11GlsRT7wzS6cNq2qDhnoK5oVb3nRlUVdm7u5pEXh8maNgF3zjx0Pgt6pfNzoXYnPXhwCMMWdAbcTCaydAXchPwaqqLU3ROuHi6GTQeVer19/5nThfPeFXRXNWXNi5aOgJuMaXN6IsFgJMVoLEOLx8V9z/aX9I6rFaHLeyUVPy7kddU8P6qqsKrNy0OH0xjmzLkU8PQrk3zq7RsLImxgKkmbz8XodIaAx4UlBO0+F0+/MoHmUHCqCqZl41QVett83LSpa9bry11xEsnFiRRNi0y1KE2+3uFCfTjWSnsUf/uOpg1+vO8MT50aBxR2burkrhvW1LVwFhdlnxiJ09fmZVmLByFyC0n+teqpHan33Nx1/Wp8LkdJIXgzz08tYTjf61YeETkTTrGsxcM1K0OFfmGr2ry5AnTq7wlXjcXadNDI7sFq/mHl3l5X97Zw/dr2ivdZsXjv9GuMxZyksyYuh0pfm5d9/WH2nZ4CRZlTmDdap5Uf7x3bVvKj3wwQtgwCbgcoCiAK750HDw5xYiRGLGPidqjEMwatXhdTKQNQuLavjalElnPRNG0+F//fu7bTPfN+KEYKJonk4kSKpkVmrijNYtoSFB9fT3pNVRUePDjEIy8OF7ZlP/zSCD7NyUffum5OIVO8EMUzBi+d0+mfSjKV0Lm8J8jVfaG6Wrs0wkKLz+tNC+VZiACpdmzJ/THTL+zObefbaDTSE64Szd50UO85mKuou9J9OZHQ+fJ7Npa8XjHFPkgf3L6SU2NxHKrKshYPY9NpDg1G2dIXmjNC1+j1HphKsrrdx+7tfXxwxyoefnEYW+TqkgJuJ1999Dij0TSGLVjZ5uP4SAxVVdjaFyrUNKV0kyPnYqwIeXG7HNywtr2iYJJIJBcvUjRdAJq11bvYiK8R6k2v5VMLloCQT0MBMoZVUi9SbwH3FStaeX5gCj1h0xlwo+dWmKpRhFrzrtSuonwcjQqm8tfoCmgcORcr/G3HmsqCbiECpNrW9kr3x0J7wuV/nkxkeezoCNGUQcbw0hFwL3jTQb3noB5Dz4XUSymKwqp23/nrljJY3e4jkjJQFKXq8zXyuj/eO8DPDw8TTemYtuDXr0zy5++9shDdHJvOoFuCrGFxdDiGLQSKomBYFr0hH//zfdfQ7tNQVaXknrvhIvBWk0gkjSNF0wVgoVu9/ZqDn74wyGNHR7j9quXz2klVj3BTVYU1HX7294eJpnQA/G4nazr8c467fCFKZAx6Wjxs6AqwrNXLWCwzZxShfN7li20zvYzKX+Pqla1cv7adk6NxEIKXx+N8/5nTJa8x351k4aTOAweGuP/AEF6XgzUd/pJjG+27V2vBLz9HU0mdM1MpkrrFRDxLZ9DN7+5YtaDUYj1eSY06aNf7haLadSuukcrXNNV6vnpeN787czKexRK5nw8NRXnyxDgfuylnJfDVR4+jGxbLWr0ATCZ0NIeCLWA4muahQ+eki7dE8jpCiqYLSKUPymofoMWLzrlomkTGwBbMq2cW1P+Bfee2laQMq6Smqd5vxMUL0Q3rOkgZVi4dV+NbfznF8+6e2WadX2yblWaqtKAPhlNs6g5weiJBImvi98xuzDvfyMiDB4d49rVJElmTM+EU4/Esm3qCvHVD56z0aJ5GC/eLX6vQX+30FCdHYjhUhaDHSTxjkDUtdm3pbficFY+x4JVk2hwfniY4U4RdzVOp1rlqREhUum7jsQxfee9VJedv7YworfV8c9Vp5dO2CIFpC1wOFRNwqSpnwjmRGMua53c9+jUs28btUHC7HLicKj7NWVFQSsEkkVy6SNG0RMxKvZU19c0vOk+/PMGJ4WlShoXH5SSgORaUXpnrmHa/xqffvpF7bt5Q8fGN9CoLJ3UedA01lJZUVYWuoJtHXhzGEuBQ4N3XrgDqrzuq5zXKF3REzhxzOm2QNiymEtmKW9kbjYzkF/roTMpIAcbjGfyays2XXVH1uHoL92tFd4QQZEwbzanS5tNwKNDT4p0VnWv0HObn/IsjIwgoFGEXj7H4cfWcq3pefy7BBnNbWpTPtzxCV+6ttGNdBwNTSVKGjUtVaPW5CpHXBw8OoVuCjoDGVCJnQhnwuEjrJoqioDmUuqK0Eonk0kGKpgtIJRM/v9vJz14Y5LGjo9x+Vc+siMK+01OYdu4bbtqwODYS40PX9S36B/FCCqCr7ZSqGyFybktCIBQFhGioLque1ypvifHyeBxVUcgYNqcnEwxG0jNb2UsdpxudU26buo8nT46T0k0UFFq8LjKm4MkT46zvCsw6ppHC/fLXKj5HkZTBlctbGI5lyBgWAY+LnZs6ZwmFRtOdkZTO3tcmeWU8TtDjIuh14XaVivlG0461qCRaHzs6W7BBbUuLWvN98OAQe09PMZnQGYqk6G3zMh7LolsWmjP33vO5nfz2lT0FS44z4RQ9rR6u7QsxOp1GACtDXp5+ZRIQ7NzcLeuWJJLXGVI0XQAqFXTnF8XhaHrGIVgUmnjmP+hDXhc9IS83b3YSTuoMT+cW8V1bK6dXFrNWolLk4+63rK1bONSLbQsmEjrbVrXRHXQzHs8ykdDnNCFsVACUL+j59ha6JdBNGyFAc+S61FcrYq6X3dtXsq9/it+8NoVHUwm6XbT7Nc5GKkfKqgnEesjfG+U1Pv2TCdZ2BhrahVftfvq/Hj7OCwNhDNsmOZ3h34+Ncc1MbVEl/6P51p1Vu6Z3v2UtA1NJDEuwrMXD6HS6xNKiPK1b/kWlfL6T8SyPHR0lmtJJZk18mhPbhsl4hlcnkzgUBYeq4nM56ZwRzsAsgZp3jf/U2+eu25NIJJcmUjRdACp9WK9q87FvIMzwdAaA5aHZO5uKF8/ibcrlO+gWq9lrtbYvWcPisaOjTW+SC7VrYWpFLuYrAAq/m4luORQI+Vz4NAeXL28tbGVfSEq03a/xjfdfy5/df5iTYwna/RqtPher231Vn69YIF7d20LKsPjyw8eqXt/ye+Azb99IZzDXX03M7OgqplY0q5bwMU2bY8PT2DNb7WNpg2hK502r20pakixmn7r8hoVnXpnk5GisYGmxYVmQJ06MlaR18+e31nwfOnyOaFpnIp4lZVggIKCpDEUzmJaNx+3CtG2iab0kXbtYrvESieTiRYqmRaaa4Fjb6UMRgoDmQFWg069VTDmVFFcXuXUXP6aRhaqehX+uti/HRmIogGGJRWnxMVctTKWU3HwEQPHx5dGtV8cTjE5nODUaZzKR5bKeING0Mac4rNW+4xsf2MIDB4Y4GyltLVOJYoF437P9c17faq7z9/7Li4X2LIPhdOHYvDjdW0Gc1rqfVFVBc6rEM2DZAoeq0hHQCn9f7D51+ea3+dT1VFIvWFocPzc9K62bp1b07kw4hdflwOFQ0GyFrGEzFE2TzFpYAqbTBqoCpmXTFdAWnn6WSCSXLJekaPrOd77DX/7lXzI6Osq1117Lt7/9bXbs2LHUw6pI+Yd1XnA4VBUUhbdf3o1fc3ImnOKq3tZZC2m5W3el1g/1LFSNeD7VavvSP5mgxZNLWy1Ws+H51A1Vi07VIygrHb9zUydHhqYZi+UigceGY9z7Ly/Oas6ap55oX7tf42M3Nb7IznV9qwmM//PCICfG4rgcKrG0WXJsNG2Q0k0iSZ1IMluov6lHgIZ8roJvUdDj5L1FEZ1aveUamXP5NRmNZdBUha8+erxw//aEckXty1q9jE6nOTwYZVtfiO4WT0lat1YRf77m7D9OjKEApi1wOlR0S+TMK2fGYwkIuZ0oqlpxrBKJ5I3BJSeafvrTn/K5z32O7373u1x//fX87d/+Le94xzs4deoU3d3dSz28ihS7GJcLjsmEzqfq8C6qJQDqKZDOGyvO5flUa9GEnKFgu08jktRxO9W6+tDNd1GZz46u4gWxEV+l8uN3be1lIvEKY/EM8YwFwInROA8cGOJjNy2sLce8xYMQM47hpee7kujbsaaNs5E0nX4302kDgKmEzqo2b+FeOnIuxobuAFOJLD6Xo2KtTiUBumlZC6YlCLgdvPPqFSVCf67ecvn7rZ77Im+NcCacQlOVQr1ZfmyFSJmiEJkxtgynDFAUppI6N5Sdp3IxbtuCcFInbZgkMyaJrIlDVRDYCBt8moOMaWHb4HGp/Nb6DibiWRlZkkjewFxyoumv//qv+fjHP87dd98NwHe/+10effRRfvCDH3Dvvfcu8egqUynVMhbLNOSAXEsAzFUgXTBW1Bx4XQ6SWbOq51O1qM1Dh88VRIFuCzRHLk0zVx+6M+HUBenWXi06Va+vUqXjV7V5efLkOC5HLrrQGXBXLN6er+llvdx8WTf7Tk9xaDDK6nYfN182+8tBpXvgwYNDnA2nAJhMZLm8J8ju7X1zjrceAep2qrgcyqzNAOXnsby3XErPFVpXs9qA2VG7T719A/+fX72KbtqFIu9TY3FWhjwlkbJ3Xr2cXxwZKXiMpXST8XiG7mBpq5LiiO2ZySTnomlsIXCq4HKqOBQF3bLRLRvNoWIogpBPI20KrqlRhyaRSF7/XFKiSdd1Dhw4wOc///nC71RV5dZbb+W5555bwpHVR63i0XqOnW+B9P6BMD7NyWA4iQA8LgcrWj10+LW6XJp3be3lW0+8UrLIak6VL77rCpzO2emK/Ov++tVJoimDJ0+Os79/qmpqC5q382+uCFKlmrBqx+/e3se+/jAnR+N0BtyF4u1K417V5mP/wPzagczFnpPjoChs6QsRSRnsOTnbqqDSPVA893LhWktMVmsm3YixZ6UNBABPvTxJmy/nZ/SzFwb5xZER3nl1acSz3KAToCug8fBLI4UxrWjxEE0ZJZGy9V0BfC4HbT4Nl0PhH587wz8+d4atfSH+7J2Xs74rUDBJ/fWrk0SSOsdGYjgUaPG6MCwbpwrdQTcup4ph2uhW7gvC5ctbuGZlSFoISCRvcC4p0TQ5OYllWSxbtqzk98uWLePkyZMVj8lms2Sz2cLPsVhsUcc4F5VSBPWykALpNZ05l+ShSAq/20lHwN2QS3OlBbOaYCo2dJxOG7gc6kxqa5CP3bS+5LGlESkvu7f3NTUiNVdNWLXXyo+rza9xWU+QNr9GX5u3sIutK6CBojAYThFN6ng1BwiBPdNfr1mLa7nwyDfxnXM3IJWvY6Vo0o41ufHOVZdVbmcw1xzLhf5INM3YdJpo2iBrWIzHM9g2nJlKMpXU+dPbLivM1685OBdNMxJNMx7P4tccjMeyOB3Q6tWIpA0uD3lnFYkPTCXpCLjZ3x8mnjFxOVUOno3wfz18nLdu7GRgKsmJkRjJrMloLINlCcyZ8+J2OnA5VD54XR93bFtJZ8BdKDyXKTmJRAKXmGiaD1//+tf5yle+stTDmMV8/GwWWiDdFXTzts1d+N0zhecrWup2aW7U3Tlv6JhPbXUENM5G0hV3/j3zyiTRtD4TkQrXjEjNl3qLwovHlX+soihs6g6gKErhdw+/NIIC+DRnwQyxO+hhU3egqTsJ6zX1nOs5KgmiO7et5IEDg5yNpHjw4FCh7U35+almZ1APxfeN26miuRykdZORaAYBuJ0qiazJw4fP8d9/e3Nhvj99YZBkNle8HklmMQV0+N2AoMXjwqHmfJTy5+Tq3hZ+9NwAJ0ZiTKcNxuMZFAQOFbKm4Pn+KSzLorfdTyxjcjacQgWcDjAsiGctVnf4ePfVy0uEff6LgRRMEokELjHR1NnZicPhYGxsrOT3Y2Nj9PT0VDzm85//PJ/73OcKP8diMfr6+hZ1nPWwED+bhRZI11uIW0yjgm339pXs758qbHcPebVZvkSFiFRaJ5Y2iyJSlYutF4Jp2nXtQquWVjozUxuUN0/MRwgt2ybocWHb0OZzVRSGC6WWYK33tSrdbwD7ByKFNFgkpbOhOzjr/CzkXs3fN/nmtn63k4m4zsh0BgT4NJWMkWv3kp/Lrq29PHZ0BMMSKAhiaYFtCyzbxqGqjMUybOwOEE0bRNMmOzd1woyg7Wv3ExuexqEqZA0Quk3WtBHkaviuNQXrO/2ci6QQ5KJWacPC41T54PY+dm+X6TeJRFKdS0o0aZrG9u3beeKJJ7jjjjsAsG2bJ554gk996lMVj3G73bjd9X0rbia1FrPFLhwupprYWewdbe1+jb94/7UzkYx0RV+iXESqvmLr+VIcJRmNptEtUfj9jjVtVaMw1Tx98r9TVaVgHRHPZGjxOomkDHasCV6Qa9iIoWml+63YQTv/u0jKYCqpz5pzM+5Vp1MtnNOV7V5CI07iGRPdFCiK4MrlLYWoTmfAze1XLZ+JNlmoioKl5HzBDMvCMC1emUiwLOih1evE63JwNpLG73YylchiWDYeh4rusMkYdsE2wLDhyFCU1Z1+3rSmg+FoChuFdgXedc3ypgt1iUTy+uOSEk0An/vc5/jIRz7Cddddx44dO/jbv/1bkslkYTfdUlPPYtaMlEujLEV6IedLtL7mArt7ex/7+8OcKCu2btZ4i6Mk+V1/thAgBC+PJ/j+M6dJ6SZHzsUq+lJViu6cCad4zzXLCzVNXQENr+YgrVu8PB7n+8+cnndErxbzNTStZexY7k2V39lW7GPUrHv1zm0rSRkWT50apzvoRlEUdNNmRauXz9y6qeSx+WiTLWBVu49E1iSlW2BbRFMWXiCcygm8s5E0q9p83H9gkFgm5z1l2jY+zUHWICduHQpCCGwEQbeT/+e7L+fpUxMlrvYSiUQyF5ecaPrQhz7ExMQEX/ziFxkdHWXLli08/vjjs4rDl4p6F7P57qK7FKm1wJ6PSNXnlN0IlSIsmlOlL+Tl+VQEVVHY2x8mktTZ0B1gWYsHIXLHVNtBVq2w+ntPv8b+gdxzVtpa38xWM/OJVM4lAiulbvMpyDu3rUQIOBtZ2L3a7tcKu9tMW+BQDNZ3Behu8XDobIRtq9sKj81Hm/b2h+mYccvPGiaHh6axhCCWMbFFLm23qs3He69dwWNHh5lM6AhyRd2WDS5Hzt8JIWZ2yWm88+rlbOwOsrE7KAu8JRJJQ1xyogngU5/6VNV03FJSLQ1S6YNZtmA4z3ydsueisuljO2cjpdcokswyEk2X9DHLt0yZa4daXlycjaRnba1vn2nu2uxWM/l55bfj1xP9qXa/Vfpd8SaFrqAbRK7NTF/IsyDxV/z+GItlCXhc2EC7z1XT+mJgKsmONe08/NIwQoDToZLVLZJZi2t6QwxFU+z+7m+IJHOpOa/mwDDJNUf2OPE6FRK6jcep8p4tvSWi743+3pNIJI1xSYqmi5XiRTpr2hwfnibocXHfs/1VFxv5oX2exTgX1UwfS9JSm7s5Mhgt6WP24MGhhgrzi8XZVCILiLrE83wIJ3VShkUkpRNJGezc1FmYZyXjzblq2Sp5e+WjpY+8OFxol/LkSZ3nByLz3t1YfJ5UFeIZg1ava8blvJ5aMIHHqZIyLASgIJhMZHjhTBjTzqXhLBsyhk2n340ArlrRwl+8/1pa3E5UVZHvN4lEsiCkaGoy+cXrsaMjCKCvzbsoTW0vNZYqojaX6WOhZUo8W+hjNhbLVIx81Jv+umFdR2H7frF4/sGvTzfFh+rBg0McGZpmQ3eQqaSO1+XAtkVJu5KbL+tmz8nxugvFi9ON+WhQd9CNJSCayvVwa8buxvx5OjUWpzvgJuTX2LwsWDHlVyze9g+ECfk0hqOZmWiTgkNRGImmyZg2LjUXVUobFl6Xg7dd1sWajsCiO9FLJJI3FlI0NZl2v8bdb1nLwFQSwxIsa/FUXYQvFhZzXI3s8lpMyk0f737L2pLfFyJFijIr3VVvM95icRZO6jzgHOLxYyOYtiBjmPzouTML9qEqFjUtHhfHhqf5u6cj/PA3A7hdDq5ZGWJff5h9p6dAUWqmB6vNqzhqpiq5JraKkjsX9exurPW3SuaulYRpyTy9Lk6NxplO5/od+t1OsqaN26GQtWycRq7lSTJroioKW/tCfHXX1Rfle00ikVzaSNG0CKiqwpoOf0M1J0vBYrtxw8L8qBaDakKhVqF0ozvVzr9GEiEEPs1BMms1JVJTnOI6ORqjfyKJqiokdQunajKVyLKi1cOhwShb+kIVmy7n78Nq8yo+F++5dgUH+sO8Npmcc3djIwK5XDCVH7trS29hnqdG4wUD0ZSea57c5tMYiqToafHgdjqIpHQ0h8qVK1r5s3deftG91yQSyesDKZoWgVo1J82i2jf1Rii4cacWx417Mf2o5vsc1YRCtULp+cyh+DViGXMm9ZfzCpuPD1WlAmkhBD87MIRHc9Dp14imDWIZg3PRNG6Xg9XtPiIpA2UmcnZ1bwv3Pdt/XpRs7a06r4pRszp2N9YrLiuJq/yxfreTn70wyGNHR9m5uYurVrTw6JERetu87FjTwUQ8w2AkzdouP10BrZDe27W1l5DHVbW1j0QikTQDKZoWgfKaE5/mbJoQyS84p8biRJN6SU1II69h24JTY3FeGYsTz5q4HApHR2JNdeNeDD+qatGMekRIPQKo/DlqeRzV8xpXrWjl+TMRdMumq0EfqmpzzftfgcL9BwaJZ00URcGvOQl5ndywtr2kpun6te2kDGuWoCkpXk/q3FB2bfL/rmd3YyPislxcCXF+9+FwNE0ia2LZgpcGo9ywroMPbF/J/oEIsYxBUre4/aoe7n7L2gV/aZBIJJJGkaKpySw0ujLX4/ILzkQ8W0hZRFMG0FjaS1UVwvEMU0kdp0MlrVt4XI6munFD8/2oyhfcRvyQ5iviiudwdW9LoWlvpdcrf42kbvGh61bidTkb9qGaK3Kze/tK0mbOLNKwBG1eF12tuXuutchnCuDLDx+bdU9+5u0bC2aToJDSTcJJveb5q0a957bS+yNvTrmvf4rh6Vw/ueUhLx0Bd26ct2wsNCouNt2ca0wSiUTSbKRoajK51iA+9g80tjDPVQ+SX/zOhFO0+1yMxjIlPc8aEWb5dEt/OI3mVLGFwKM5EMCqtua5cVdK9Sz0+coX3Eb9kOYj4orncN+z/XOmoCq9RsjrKvy9XvE8l/hu92t8+u0buefmDfzg1/3sHwhjmjY/fWGQx46OcPtVywv3UaV7sjPoLphNdgTcHDkXK7FaaPSaVZp3pecoF1dXr2wlbZhEUgZZ3cTjyqUcC+MMuKWnmUQiuSiQoqmJ5IXPy+NxEAJbiLoX5mpRhXIx1RXQOHIuhkNRiGeMQs+z+nxuzr/W/oEwLR4X0ZSB26niUBUu7wk2pWHpYhWY1+OHNJd4XKiIqyVk8v9f/Bp5o8hK6dSQ11V1DI1ExXLmmrlxnYumSWZNbJFrUJsyLHwuR8V7Mi/MOgLukvlMJrI8dOhcw3YFleZdyQKhK6Bx9cpWJuLZXOpwpo3Nhu4Afs2B26kS9Lq4qrdVGlFKJJKLCimamkix8EFR2NQdqCtlViuqUC6mrl7ZyvVr2zk1Fi8phK035VP8Wms6/QCkdJP3b+vlfdetaoq4KRSYp5tfYF7NDwlKhcVcomg+C3A1IVMuEPIiQ1UVHjw4xN7TU0wksgxH0vS2+RiPZ9l3eoqekLemKCme6441s8V3eTPirGkzEk0DsKLVQ4df46lT44VIUqV7stJ8Hjp0bs5o2lyR0QcO5IR5u19jb5kFwktD09ywroMvv+dKYHbqUHOqfPFdV8iibolEctEhRVOTqFarUU9Eo1axcflzTsSzhcVmPoWw5a/VFXSzY03zOrznz0M0rRNLm03ZZl9MpZ1dD7qGCimhmy/rLjF5bLYvVC2H8XKRMRnP8tjRUaIpnWTWwqs5sIQgmtSZSuq0+Vw1U4p5K4QHDgxyNpLiwYNDJfMpb0bsdqqEfBqOjEFHwM1UUgeUkkhS+T05y+hzSy/f+tUrc0bvikVRcZPjH+8dYM+pCc6GU3QG3MQzBhPxLNNpg+tWt3EummYkmiaSMti1pZfOoLvivS8Fk0QiuRiRoqlJLHSnWKXFuJ7nnE/EZDGbBedqurw8eXIclyO38M1nm309rwOzRdT3nzldUcA067WLXy9PsbDNN/y1bcFDh88RzxgkshbRlI5p25i2wLIFDkXhxFgCh6JwaixedXy5VGqk4nwqNSP+H3dczcMvDXMmnOLK5S2kjVzqCypH4moafTL7nsvVww3ysxcG8btdhWjlmXCKBw4M8fBLIySzJlnT5rWJBG6nA1WBrGnz9CsTtHg1VAUcGYOHDp+b5Qv1em9eLZFILm2kaGoiC/nwr1ZrsxgLymI3C969vY/9/WFOjMbnNERsFuUu0nkhcWoszveePs2ZcJI1Hf66I0+1zs2sOrOgmwMDkZKGv+GkzplwiitWtPLiYITpNGiOXNG9ZdkYQCJjEkvrdAUqNwaulbaFyuKmu9VTEp0qrx8qjsR1+jUUVWEinq1o9DkwlZx1z/147wAPvzTCVFLnXDSNadksD3nZsaaNM+Fcf702n4bmUBmYSqFbJk4l15Q3kjExTIsrVrQWdsY1e7OARCKRLCZSNDWRZnz4lx9Xz3PO9/UWa4EKeV38xfuvrWqI2OzFMf98+SjX/oEIkBMSumnx4mAUS8D+mcLoT799Y9XnqsfVeladWW8LmkMpafj70OFzrG73sbc/jKqqtPs11nUFWNHi5oWzUTxOhXDSRABj8SyT8SydQXfJ61SKNF69srVgUlkuiPLnuDg6deRcjOvXtvPl91xZiMT9+tVJoimD05MJ3A6Vt2zsqpomFOJ8RM22BU+9PEkya9IZ0BidzjIay7BrSy+7t59PU0ZSOpPxDPkjTQHxrElXwI2qKqxo9RBOGVy1omXBUVOJRCK5kEjRtAgsxof/QttWXIhv8ZPxLA8dPr/ravf20h1ilZylF1JvVDz/rkCu+H4wnCrsEtuxpo1HjoyQyJqEfBrRlM5Tp8a55+YNNd28956eoiPgrigkKkV/xuNZlrV6ZjX8/cwtOXEWSWRRFej0a0TSJus7/QyEU6gqeFQHGcMqpKrKKY80pnQzJ9h8rlmCqNr4BqaSJX+LpoyZJryQtCwm4xl623wlmw8qFfKHvC4MyyKeMUnpFgJY3uLmv/xWzmhy15ZeUrrJnlPjjE1ncKoKIDBt0C3B6nYvQY8LzeXg+rX1b16QSCSSiwUpmi5h6mlbcSEa5uZf47Gjo8QyBleuaJ01nnBS595/eZETY3E6/W7OhlMVx9sIxfN/+KURFGDrqraZXWJB/stvreXxY2MAnJdI1Yvnx6czhcLtjGnT6dcqOoavbvfN6isIzG74O9Pkdm2Xn+mUgcupcn1vKzdt7uLz/+clbDGzy60oVVUr0ghw7wMvMRHPRXjy9VDFFEensobFsZEYLR4n9z3bz53bVrKqzVeoN3M5FSwLRmJZPFrOSRxyImsqkWV4Oo1uCfYPRPin5wb4zK2bCPk0bDuJJQSGYTOZNPjOnldBCCYSOqvbffz/fu86dn/nWSYTWdwuJ8mMidvl4JbLl7F7e19NqwWJRCK5mJGi6RJlrnqXuZqyNpNcZGKCgckkacPCtGyu7WsrEQIPHBjkxGgcl0NlOm2UjHc+C2h+/m0+F91Bd27eikL3TIrrbCQnynZu6uThl0bIGDnH84DbwVcfPV4iIItF39mpJKpD5fREgrGYkw9d11cyvrn6ClbbVVe+3f/2q5aztz9Mx8zrl6eqysn/LZrUGYqkCHpcxDNGxXqo/HgeOzqKAvS1+wvXfvf2lezvn+L4aJwVrR7Shl1ovbJzcxc/+PVpjg/HeGU8gaKAU1UQwK9OjeN1qQxF0rR4nVg2ZBQLt0vl4ReHAdi2qq3wOu/d0sv9LwyimzZtfo33b1850/pFIpFILl2kaLrIqFdEVKx3aaApa7Pqo/LiZTptYiMQAsZiWY4NTxcER858MU1HQCOWNgCFyUS24D4+H+EUTRuMRtOcGI3TEdAQgFPJpcqKd3zddcMafJqTgakkY9MZDFugm3aJgMyn5KIpHVVV8blUVFWlxeNk19bektet1VewuPZsLguK+RT427ag1eeit82LbUOL10morO9evrD67resZWAqiWEJlrV4CilD07a5emUrU0kdRYF3XLWc267q4fGjo9z9g/3EsiY9QQ2BwLJz7VgUJXe9fv3qFGndYiqp41AUWrwuVoZ8jMQyIERBsOZbtHT4Nfonk6zt9MtUnEQieV0gRdNFQnkaLe9hU4tZ9S5zNGWt1Ww2//oDU43tMsu3jXny5DhBtwswUIAWj4tdW3oLj+kKaMQzJrG0gWELtvaFeNvl8/dUevDgEIYt6Ay4mUxkWd/pZ/va9llF0fn0lmnafPXR4+imXVLrY5p2wRU7Y9qcnkigqiprO3zcsK6DzsD5a9BIw9+57CKKx5X3JKpnx17/ZJKUbnFFT5CUYbN5WbDgV1VeL7am43yEaTSWQVMV/us/HSCWMblyeQuJrInP5eDpUxM8/OIw4ZSBEDCe0Al5NTKmRcDtRFUVvC4H05lcitHvdpLI5JrqdgbdjMczCEUpEaydQdn6RCKRvP6QomkJqNX53e928rMXBnns6Ci3X9VTU0iU17tUa8qa/3etZrM/3neGR14crnuXWTH5lM+J0Tir2n20elzctKmrVPQpCk5VIeR3oyDYvqadPSfH604dlkdTzoRTLGvxcM3KEGPTaTSXg3tu3gBULpp3OtXztT6mzfHhaYIeFz96boCuoJsjQ9N0+jXGYk5aPE5uWNcxKzrSqBdXrWhSpSL28VimqmDN3x997X6OD08zFM0U7o9K9WJCUGiJcyacQlMVdEsQTZukdZPJpE5vyMvAVBIhwBbg1xxE0wZGBjwuwbZVbazq8LGmw08ya3D/gXO4HCohr8aKVk8h3fnua1cUaprK5ykFk0QieT0hRdMFpFpRdnEEYziaJpHNfYvPFxvPVYNUy5Sw+Bt/tWazti146tR4Q7vMimn3azMWA4MzHeu9sywGJuJZrljRymQ8w0gsy1Mnx1nb5afN56qZOqx2zkrmWmfvvfO1PiMIoK/NW7AMuH5tO2fCKT50XR+7tvaWRJgqPUc9abVadhHF9U4/PzxMPG3Q4tPY3x8mqZt85pZNJecvf390B924nSouh8Ldb8ntWvve068V6sXCKZ3xeGamnYpg9/Y+WtxOvvLIMQwrt6vwtYkEI9E0HqfKDes6EAKeHwiTMm1ypWECj8vB9jVtfOptGwuRrKPnYiXeW2/d0FkYQ36cUiRJJJLXM1I0XUCqFWUXdmT1hxmezgCwPOStuauqErUW9LkKx/P7y4p3mdWLbQtCXhe7t/cVUkTFLT/y8/vZC4MksiaQ8/+ZPhshYwo6Ahohr8ZbN3bWFBf7+sMIIfjYTevnVRNUrdZnIqHz5ffkompznef5eHGVP65cBE2nDNKGRZtfMDKd5X8/dwafy8nu7efPX1fQXYgEOhR497UrKtSLmSQyBkndoivgZv9AhLRh4XU5OTESJ54x2NAdIOB2EvS4uKYvREo3GYykWRbUmExk8fhcrGn3kbVsHn1pBJ/LUWi4XMl7S/osSSSSNxJSNC0ytQqDiwVRftGPJHOePh0+V8X0T63FutKCXt7UVbdyImkqqXND0XMX7zLzu53s3DRbwJQTTur8eN8Znjo1DigE3A4URWFZi2dWum3Xll4eOzpaqIMZmEwSyxiEvBrjsSzdAfcs4VN8zlo8Lk6Oxrj/wBCgsHv7ynnXzBTX+jTa7ibPQgRCSZpPCAzbxqkqTKdNdNNGc6rsGwijKEVRRiFyZpFCIBQFhCh5rrNTKRAwFsvQ7tPYsbaD8Vian74whELOcNS0BWOxDB+8ro9dW3p56PC5gh/TVEKnw6+xIuRjOm0wOp2hd8YoVFEUPvrWdbT7NT52k6xTkkgkb1ykaFokKqWV6ikM3rm5i28+dpITo3FWt/u4+bLuqs9XrdapeEErb+oK8Op4HFBI6SbhpE67XyvZZZavq5mLBw8O8ciLwySyJkIIkrrFus4A16wMAaWisDPo5vareth7eoqJeJZo2sCvOfC4HATdDnpCXkJe16x55M/ZydEYw5E0vW0+9hcJinoX75IaoqCbq3tbKtbg5FlsYVAcKdu6MsTZSIrJhI7mVNnYHaDDrzEwlSxEAicSOttWtdEddBeiY5V24nUFNJKGzf6BKU5PJNBNQU+Lm6Ru0ep1cVlPkLvfsraksfJ0ysDlUElkLZxq7r7obfOyY00HsYxRtdhdIpFI3mhI0bRIVErF1ZNSeurUBCgKW/tChFMGe06Os74rMC+/pUrRrVfH44S8LjqDHo6ci/HgwaFCFKE8cjOXNcHAVBJLQMinoQC6mWYikWVsOj1TZ1TaHPbObSsRQnD/gSE6/BqKohBN64zFbLpbPETTxiwhWHxMb5uPN61tJ5aevZA3cj2ODE3POGlvnHX8hTADhdKoYDRt8ODBIX5xdIRExiTkc3HwbKTElHJ1u49nXpks6W+XP1/lz3Xvv7zIYDiF5lARCOJZk4DbxenJBLYQ/MH/foE2v8ZYNM3piVwhuMup0NvqZUWbl+vXtvH8mSixjDHvSJxEIpG8HpGiaRGolooLeV01U0qzjlOUnLfOzLb4RvyWYPZur6mkDih0Bj255xGiYhShHuGgqgprOvzs7w8TTekAtPo01nf6C20yipvD5p8nZ3Co8OvXJnllNE5Kt+jwaxgz7TvKhWAuJZQ7Zv9AmFjamJVanO/1qESj4nShESlVVc5HGTd18c3HT/LCQAQF2NgdKBHc+05PlfS3Kz9fqqoQ8roI+TU6/G7GbEHGNBAoRFM6TofKWCzDqbE47T4NzalizUSyLAtShlVUq6Q2tUm0RCKRvB6QoqnJ5BfRWqm4aoKp2nHF2+UrPV8tiqNbN8z0LjtwNsqp0TiTiWxJxCJPvcLhzm0rSRlWoaZp56ZO7rphTaFNxvefOV3xeXJb4QUj0TRdQQ871lWPHuV/3r19JWnz/GsVpxbnol6rgHp8mPIsRkTqqZcnEOQMJdNGrsfbipCXM+EULW4nPSHvrP525Q7wqqoQTeoMRlLYtk3GsFEVWNvpx7LhbDiFU1WJpQ3cLpXuFjcrQ15GYllC3pyZZzMaT0skEsnrESmamkT5IpqvRZrr23ql44SAM+FkyXHz2S0Gs4vDw0mdI0MvMpnI0hHQ0K3SiEUjwqHdr/Hpt2+s6I+UT99Vep5K0aNyIVNJlPhcDtp8Gh0Bd0lqsR7qOX+NiKtmt6fJn/dik83h6Qxul4MbioTz3tNThf52V69s5b5n+0vq0PKRJrdTJZa2aPE6cTsddAU8nBqL43IopHUbj6Zi2gKXQ6W3zVfoPVdstSAFk0QikZQiRVOTqLaIzvVtvfi4Z16Z5JmXJ0joFkLk6nzyLPTbf/6YkNdFT8hLm1+jpyxioapK1Ya09fRFy5MXPCdGYgX36aRuzXqe3dtXoiiVhUwlq4Hc1np3QynKPHOdv3ramxS7pp8YidHX7l9we5ry876vP0ynz8WYe8Zkc207u7b0Vux5l8qaPPziMLYt2NcfJqWbfPqWTawMeUjqJjP9gun0a7QHNC5TghwbieFxOhDApu5AwUX9BpmGk0gkkjmRoqkJNBKdqXXcydEY/VNJAlrusjzy4jA+l2NW3cpCKF6clZmIRXmEp1ZD2nood68ejKQL7tXFVBMy1fq2rZrZOZcfZzOsAipFtOoxpIxlTI4PT+N2qjXHUi2NV+n3N1/Wzb7TuWa6a9p9fGLnel4cjPKtX73CaDSNYYtCzzuvy8lTL0+SyBi0+TSiaYOnXp7knrdtRFFV3A6VpGVhWYKUYbF5WZA7t60sMiD1FXygZBpOIpFI6kOKpibQaHuNSscJIZiMZ3EpKm2+XAParGkXtp03c1GrFU2p1ZC2HsrTcuXu1ZWo9PtK5/PObecjUzvWtDUlMlLLcLR8XsVC7srlLQxG0rgcSs10abXnr/R7ABSFLX0hIimDv3/qNVAU2nyughN33s7h1FicoUiKqURO3DpnDDDz7utv2djFZDzLSCxTUqv0sZvWz7qfpGCSSCSS+pCiqUnMt+ao+Lgrlrfw2mSSyMxuNL/mYE2Hv+mLWiMRnkZSYOfTcjn36StWtJLMmnVHhObyUmr3azPRktmu4/OhkfmWC+OkbnH7VT1VxWC+QLvY+Tv/c6XdkANTSYDCY4UQvDg0zda+EMtavXQEco7dY9NpRmNZhqMpIikd0861RjGV3M654k0DvW1ePJpD1ipJJBJJk5CiqUnMt+ao3GPnx3sH+I8T40wls/jcTlKGVfcusUaZSxg0mgIrpOXavBwbMRgMJ7n9quVzCsj8OZvLSylfgL1/oDkF2I3Ot5Iwnivd59cc7D09VZjje65ZXnU3ZDJr8NDhEaLpnBjq9GuMxrKgKLR6NUJeF69OJDgXSRNO6li2QFXA5VDwu52FSFP5jklZqySRSCTNQYqmJjPfb/F5v55P37IJr8tZEAZHhqZ50FX/LrGFMt+IWXnUxu1yzJmWKxYYq9q8vDyeqOiltJAC7GbOtx5hXJ52000718VPUQr/Xy5sru5tYSqp8/ND5xiPZwGBV3My0/Gm4OAecDvwa04MS2DZAoGCEALLhg6/xmU9LSW+T7JWSSKRSJqLFE0XGbkGrPNPkS2UehbcWumrvQ1EqYoFxv6BSK6fmqLMOn6uAuzFnm855Y7p1foLCiE4PBhla1+IrqCHoUiKp05NMB7LlFgE3PdsPw+/eJZoSkcIgUNV8GsOnKpCPKMXbBZ+89oUHX4Nl1PB5VAQgGUraE6Fd18zO6InBZNEIpE0FymaLjJqiY8LGTloZCdY/m8p3SSS1Ikks+zc3F0zalOpnsgWgk3dQc5Gzkd9ahVgX72ylZRu8uWHjy3YYLKR85o/D6fG4kSTOiG/VtidVpx2i6QMlrd6OTQYxRIwndJp9bkwLFG4vne/ZW2u2F9A0OvCSumYliCWMWnxOFEUteDg3hHQZhrruomnTTyag3afi3dfs4LP3LppXvOWSCQSSf1I0XSRUUl8VGpHshg1TnNRy9DxwYNDHDkXY0N3gKlEFp/LUXOMleqJdqxp42M3zY76VCvAvu/Z/qYaTNZL/jyMxzOFJsLRlAHMTvdNJrIMT6cRto1u2bhUheFomuFomkgyy02buxibzjCZ0LFsG5dDRUHQ7nfxnmtXAHDkXAyAkFejO+Am5NdY0eqh1adxWU9Q1ixJJBLJBUKKpiaz0GhQJfGx5+T4koiDYmrtNMv/u9GUYn6xPzUWByF4eTzB9585PUsUVqo7WuhOv3rmW6s/YJvPxeh0hoDHhSUE7T7XrP6CAF9++BjbVrXRHXTz9CsTvDaepH8yiWmDe1rhSw8dRXM46GvzcjaSIuhx8sHtK/ndG9bQGXCXRPfeurGzkNK70JFHiUQikUjR1DSa0YuskhAo3opeLA5M08bpVJs7iSpjKneshtk1S/PZdZevJ/re06fZPxBGVZRZojDfdqV4h2H+PI9G0+gz1dK1XrOR+qy5rmPxeVBViGcMWr0uwimD69cGZ/UXLD4vrR4XumWhmwJFgYwhOHw2ynuuXcG1fSFGp9NoTpVPvX1T4fhaNVdSMEkkEsmFRYqmJjGfXmSVTAYriQ+g8LvR6QyaQ+Grjx5f1FRd8Y611e1+dm9fya6tvUDlnWYL2XVXqfB9Mp7locPnZomX4vOs2wLNoaA51YqvOVcNVqW/FZ7f56p6He/cthIxExnLp8vyNU22LbBtURC0xefltzZ08vxAGBC4HAqGKTAswVQii6IoRFJGVeEnBZJEIpEsPVI0NYFGU0XVFuziregDU8lZQuBMOIXmUDBsgW7ai5qq+/G+Mzx0+BzRlI5uCn7y/Fm2r25j87Ign3n7RjqD7pLHt/s17n7LWqDxBb6SUHzo8LlZIvTut6yddZ41p8oX33VFxajbAwcG2T8Qod2v8dxrk0BpDVal5z81FmcinmU0lsGhKJwai5dcx/y1OxtJs6k7yO7tuXRZNG3wD0+/xr8ePoduCa5c0cqfv+cK1ncFStJ1P/jNAHo8ixDnbSZuWNdZUvwukUgkkosTKZqaQK0IUSXKF+yUbuLTnDkn7IBW2HafJ5+iMU2brz56HN20F7WOx7YFe06OMRHLYglBxrA4M2US0ByFgudiodZoarI8zVbu/r1ray/feuKVip5N5TsLd6xpnyWYwkmdBw4Mcf+BIVQFTo3GiaR0To4l2Lmpi3VdgYoiFyCa1BmKpAh6XMQzBl0BreTcltokhFGU3Ln4h6df43vPnMawQQH29U/xjV+c4O8/8ibgvJC8Y8sK/uXAEFnDwu1ysHtbb8Xid4lEIpFcfEjR1CTKzQqT2cpb4YsLifML9lMvT9Lu12j3azz80ggKsHVVW0kEpFZdUTnFC3ClVim1XKy7AhpCwMBkinjWxKHATJCEwWgaRVFnRV/qTU0Wv06+Ae2yFk9F9+9K84ymjcLOwolYhpBP4+Xx+Kzi8bxruE9zcmx4GtsWaE6VaFLnm4+f5O8/8qaKz2/bgpBfo7fNi21Di9dJqKih7WQiy2NHR4imTdK6RWfQXagv+9cXhzFnBBOAbtoMVKg9+/hN6+kMuOmfTLC2M1C4b6RgkkgkkosfKZqaRD4aNB7P8P964AgnxuJ0+t2cnYlg5EVENG0wGk1zYjROR0Cj1eMCRKHnmG3nDB67g26yps1jR0c4ORpjOmUQ8musbPMWojJX97aQLPIpuvmybp48Mc7ZSC56gxBMJPTC3/acHK+YEiwWPT8/PEw8bZAyLGxxXjBBThCci6boDmoloqze1GT+dSo1oM1HevJUqpEq3ll44EyYtGHR0+opEWrFwqbTr+WCdgp0BDQcqsqZcApdt0pF7ozf01cfPU4kkSXk1VjW4iaSMti87Hxx91pfM3YAAB6DSURBVEOHzhFJGblGuNE0Ps3Bf37zGoCC87eY+c8WsKrNOysKJt26JRKJ5NJFiqYmkY+i/OLIMK9OJAl5XUync6ms4t1uDx4cwrAFnQE3k4ks3QE3163t5sjQNEKcX0jH41mOD08jgFfGEwxG0nicKqqicFlPkD+77TK++fjJgvh6dTzBT58/S9q06fS7iWUMHKrCtpmI1b7TU6Ao51OChoXXmRMR+dYk3UE306lcNEdzqBiWDYCqgENV6Q66cbscJdGXevu3lYurjoDGRDzD2HR6ZudZ6THF4iJPcfNbIcCCkka4ti146NA5YhmTVNZgIGMiZlRMUrcQwqQr4OZ/PHaCVW1e7tiaE473PdvPM69MEk3rjMezBDQH3S1udqw5X2OUH7/P5cDhULAF6JaNsHPX9coVrezrn8IwbSwB7X4Xn3/XFVXvFymYJBKJ5NJDiqYmkY+ihFMmGcNi1LDwu52kdJPOgMZXHz3OqjYvp8YSLGvxcM3KEGPTaVxOlduu6uHIYJQXzuRaiXhdDl4di+NxOVjb4eP0ZAqEIJY2cDlUfvPaFJ/8pwMkDQuP00EsbTI+nWE6Y7Ksxc10WieWMXJmiDMRp0ODUbb0hVjW4iFr2vx0/1kAOoNu4hmTeGYat0PBmDFY9GoOsqZNwO2g3e8GBa5ZGZoVfYH6ds4Vi6uMbhLPmCjAqxNJdm7qrHhMea1UV9DNkaHpwvMpwHgsUxBdkPN8cjtU4kAsreNyqgQ1J4msidelEvJrHDwT5l8Pp/nhc2d49zXL2fvaFEORNAJB0OMioVuEE1nOuhw8eHCoEJVb1ebjyZPjhLwaQghafS4mkwa2Lfjz91zBN35xgoFwilVtPj7/rstZ3xVo7k0mkUgkkiVFiqYmUBxFUSCXn1EglbVwuMGwcoLn/tNTJGeKvjd2B3h1PEHA4+Sbj53EsARBt5Nz0TRBr4u2medKGTaqkouUqIBl27hdCsPTGUI+18wIBNMZkxavk3xVjWnlhMV4PEs4ZbC63UckZZA1bZ59dZJo2iDocRJJGgQ9DhyqyonRXK2SbtkoCvjdDnrbvNx6RU8h1Xf92tkO1PWmnG6+rJt9p6d49rUpFOBNa9vRLYFPc1YsHC+vlbq6t4Xr17ZzJpzilsu6OTES59BgtJB+VFWFaFJnNJbJzU0YdHhcvP+6PkaiKQ6cjTI4lSKc0rFtQda0+T8vDCKAlGHlrhvgcTk4NZagI+AuSf3t3r6S/f1TheheyKuxut2Hqiqs7wrw9x950wXzz5JIJBLJhUeKpiZQ6Bd3egqBwONy4NVUlgU9hNMGr4zHCSd1dNOeSXUpjETTuBwqE4kML2Us3E4Vl0NBVRR006bdryGATd1BXh6Pk9JNhiJpPJpK0O3CsGwE0OJxMpXU6Q156Ax6SOkmUwmdLX2tbF/dVtiRtnVVG9/d8yrPvjpJPGPg01Qyug0Y2EJwWU+QM1NJ/G4Xqm5iC8GOVe38z/ddQ2cglwKbSxQV1zkV/5znF0dGGJhKkp2p/wkndVZ3+HO91yoUrJfXSk0kdL78no0A3PdsP5ozyZa+XPRrz8lxWr0uxuJZBIK0YRHyuhDA6HSa8bjOdEonnDJy2/0Bh0sha+XcvFu8GqOxDIqS6xvcEdToafWiKEoh9dfu1/iL91/LAwcGORtJF2rDipGCSSKRSF6/SNHUJPKL53A0zbhtk8wKhu0MumWTyJjMGFdjCXCqkMhaaE4bzaFiWYKEZeF1KlgCPE6DSMoo6cUWTRv82f2HOTmWoM3vwuVQmU7ppA2Ly3qCfPLmDRw6G+FMOMWqNi+7t/fR7tcKkY/vP3O6ILKypo2qKHhcgqxhs22ln5BPo38qRUdAQ0EjY1gsa/XQ7qvfODOc1PmnvQM8/fIEQsDNm7u464Y1hYLzp06Nk9QtvJqD6ZTBqdE4U0mdFo+L+57tL9kBV6tWqlxQKTN+Sp//Py/RP5kka1pYdi7c59UcHD0XJWOIXLROyV0Dm5ww82gqmsvBlctbcA5Di9dFu09DtwRjscysGq12v8bHblovC7klEonkDYgUTU0in6J6+uUJBqZSoCikdIuskVuobVHI/uBQVVTFwrJz0Q5VVVCEwONy4nGpuJxqwXH6+8+cZteWXjqDbr7xgS2FKMdoNE3A7aQ7mOtPduhspCRFFk7qhSa/q9p8vDwepyPgJmPaxDMmSd0EFHpDHj7/7ivZc3Kcl4aiRFM6AH63kzUd/sJzzeXDNBnP8oUHXmJvf5ismZvz2ZkWMJ++ZdPMo3IiI+R1ktYtsmau0LyvzVvRqqC4VmrHmjbu3LayIALLBZWwbU6OJXCoYJiC3DMLhBCMTWcwBXg1B+g50WoLQZtf471bevG5VCYSOh960yp2be1FVZTCfGvVaEkkEonkjYUUTU3ENG1GYxmWtXjoDGhMJrKcDadQFQXNIchaucepKgQ9DgwrVz/kVEFVVFZ3+Ohu8SCEQJlJ0/3shUEeOzrK7Vf1sGtLLx+7aX3B5DKeNhiezjASTRNJGQVxBbNNGBEC3RJEkzqxjAkINnQF6Gxxs+fkOHduW0nKsHjq1DiglBRnP3hwiL2np2bV+OQjYA8cGOKxYyO8Op4gY1pYlkABIpbJT58f5MM7VtMZdPOmNW38y4EkIxkTt0OhK+Tmmr42lrV4GItlZlkVtPs17ty2kgcODHFkeJqfvfAbLAHrO/384c0bgPOC6uXxBB0JnWhKR1XBtsEzk/LULYHLqRJw52rAdMPmhnWd/MX7rimcr/LIkbQFkEgkEkk5l5Ro+trXvsajjz7K4cOH0TSNaDS61EMqIR8BOTQYBSCeMVnZ5iOZze2oc6gCn+bAozkIeTVavE4SGRNVVQh5XXS3eNjUHeDIuWnOhFOMxTIksxZpw+Knz58tiKc7t61kdbuP///+s0zGs5i2YDpt8JPnz/Lpt29kPJYpeBVlDIuOgBuXQyGS1ImmDQIeB06Hg7aAxrKgm4GpnEXCp9++kXtmxEheLEzGszx2dJRoSidj2nT6NU6Nxfne06d5eTzO4bMR4hkTwxJYtsCyRCEVqQhBLGPy80NDfOym9fjcuYJvyxY4VIXuFi9TyVxkq5pVQd6o8tjwNBPxLAGPk0ODBt/d8yp//5E3FYTN9585zXgsS4vHRVq3cKoKAoFtg9vloMXrIuhxYgvB9lVBvvH+a2c14i1HCiaJRCKRFHNJiSZd1/nABz7AjTfeyPe///2lHk5F/uydl/ONX5zgTDjF1r4QfzhTa5R3gE5mTY4Ox2j3a4STOm/fvIz/8ltrC7U6qqrwiR89z7lImoxpY5g2iayJZTuwhc7e01MA7Nrayw+f7ce0BQGPEwWFJ46P4XWqPH5sjDOTSZxOB69NJBiLZfjA9pWcCafo8GsMT2d4eSzB0XNRXhrM1Tl9p+VV7rp+9ay020OHzxHPGKQNi9MTCcZiTta0+9g/EGY4muL0ZAqHCg5lJs2Yd3gkV/Du0xycjaTRdYuJeJatq9roDroZjKTpn0jgUBUiySw7N3fPSoMVu6dnDAvN6cCpqvg1R4n3FZxP5XW3uBmLpplK6kynDdoDbt68vgOf28lEPFtS7yWRSCQSSSNcUqLpK1/5CgA//OEPl3YgNai09Xzb6rbCjrIvP3ysZEfY2ch5J+y8cAr5NXpaPZyZTGKJXFG55lBZHvLSEci17gh5XCxr9WIDbT6NSEpnMqnnaqomEyR1Ey/Q6nHhcTlI6RYnRuLEMgYbuwNYtk3asHA7HBgCHnlxGJ/LUVJTZJo2Z8IprljRylQiy/B0Bq/LwVA0xXg8y3TKnCm4BkUR2EKh1etCt2xsW+B2ORDASDTN/3jsBKPRNPrM4/PGndtWtTGVyOJzOWYJmeJicI/LQTyTRXMqJLKCrd2hkp1q5bYHxaaY1VrKSCQSiUTSCJeUaLqUKN96XqunWvFCrqoKm5cFeXEwis/tRFGUwm6wzpno1PVrc01qd27q5OGXRsjMmFymDJPjIzFSuo0QChnDpsOvsCzo5uDZKBnDZCyWIZU1Wd7qIZBx4nc7EUDWtAtb/4ub6eaFzoqQF7fLgTJjlJnIWJi2QACqoqA6cm7jH9i2kkOD00RSWcJJA69LzdVumTa6JdAcCi6HQtDjoq/NO2frlXwEyas5ODE8Xahp+rN3Xl7xvOePl+k2iUQikTSb171oymazZLPZws+xWGwJR1Ofe/auLb08dnQUyxZsXBYkoDmYSOgEPE6u6m0tHHPXDWvwaU4GppKMTWd4bTLBSNqYETLg01RaPS5afS5eGU+Q0m06/G5002JZi4eMmSIys1suULRbrriIPC90NKfK9WvaOTUWI+B24lAVomkD3bTRnCrLW9y8d+tK7rp+NYEZwdUX8vDKRBJVUQriSHOqfPFdV/Cj5wbY1x+uuK2/mPIIkjSPlEgkEslSseSi6d577+Ub3/hGzcecOHGCyy67bF7P//Wvf72Q1rsYqMc9uzPo5varego71sJJnduv6uHut6yt2J8tv5vO73EhRJTRWAafluuHdtPGLoQQ7D0dxuXIiY2uoIc2v8bVvS08/coUIAo1RZVMJfNCJ+/39OLQNLpl0+p1YduCVR0+3nnV8oIVQfH8vv/M6VmRNadTrUs8FpOftxRMEolEIlkqllw0/fEf/zG///u/X/Mx69atq/n3Wnz+85/nc5/7XOHnWCxGX1/fvJ+vWcyVKqokKqodU+xbdO3KVpwjCi0eJzdt7Co8z/7+MCdG43QG3LT6XGxeFuSjb13Hp94+2727UgqxuOA6pZs89fIkebH14TetKmzdL59fNXFUb+sViUQikUguFhQhhJj7YRcXP/zhD/nsZz87L8uBWCxGa2sr09PTtLS0NH9wTaZeUVFuQLlra2+h/Un+7w8cGOJspLpBZbXnqvTYaq1SFjoPiUQikUguNPVqgyWPNDXC2bNnCYfDnD17FsuyOHz4MAAbNmwgEHh9dpSvV2jMFbnJtf+oL7JTTxSoUQEkBZNEIpFILnUuKdH0xS9+kR/96EeFn7du3QrAk08+yc0337xEo7q4mEucNCJepNCRSCQSieQ8l2R6biFcauk5iUQikUgki0u92kBuRZJIJBKJRCKpAymaJBKJRCKRSOpAiiaJRCKRSCSSOpCiSSKRSCQSiaQOpGiSSCQSiUQiqQMpmiQSiUQikUjqQIomiUQikUgkkjqQokkikUgkEomkDqRokkgkEolEIqkDKZokEolEIpFI6kCKJolEIpFIJJI6uKQa9jaDfKu9WCy2xCORSCQSiURyMZDXBHO1433DiaZ4PA5AX1/fEo9EIpFIJBLJxUQ8Hqe1tbXq3xUxl6x6nWHbNsPDwwSDQRRFaepzx2Ix+vr6GBwcrNkl+fWAnOvrEznX1ydyrq9P3khzhcWdrxCCeDzOihUrUNXqlUtvuEiTqqqsXLlyUV+jpaXlDXEDg5zr6xU519cncq6vT95Ic4XFm2+tCFMeWQgukUgkEolEUgdSNEkkEolEIpHUgRRNTcTtdvOlL30Jt9u91ENZdORcX5/Iub4+kXN9ffJGmitcHPN9wxWCSyQSiUQikcwHGWmSSCQSiUQiqQMpmiQSiUQikUjqQIomiUQikUgkkjqQokkikUgkEomkDqRoWgADAwN89KMfZe3atXi9XtavX8+XvvQldF2veVwmk+Gee+6ho6ODQCDA+973PsbGxi7QqOfP1772Nd785jfj8/kIhUJ1HfP7v//7KIpS8t9tt922uANtAvOZqxCCL37xiyxfvhyv18utt97KK6+8srgDbQLhcJi77rqLlpYWQqEQH/3oR0kkEjWPufnmm2dd1z/8wz+8QCOun+985zusWbMGj8fD9ddfz/79+2s+/v777+eyyy7D4/Fw9dVX84tf/OICjXThNDLXH/7wh7Oun8fjuYCjnT9PP/0073nPe1ixYgWKovDzn/98zmP27NnDtm3bcLvdbNiwgR/+8IeLPs5m0Ohc9+zZM+u6KorC6OjohRnwAvj617/Om970JoLBIN3d3dxxxx2cOnVqzuMu9HtWiqYFcPLkSWzb5u/+7u84duwYf/M3f8N3v/tdvvCFL9Q87r/9t//Gww8/zP33389TTz3F8PAwu3fvvkCjnj+6rvOBD3yAT37ykw0dd9tttzEyMlL47yc/+ckijbB5zGeu3/zmN/nWt77Fd7/7Xfbt24ff7+cd73gHmUxmEUe6cO666y6OHTvGL3/5Sx555BGefvppPvGJT8x53Mc//vGS6/rNb37zAoy2fn7605/yuc99ji996UscPHiQa6+9lne84x2Mj49XfPxvfvMbPvzhD/PRj36UQ4cOcccdd3DHHXdw9OjRCzzyxml0rpBzVS6+fmfOnLmAI54/yWSSa6+9lu985zt1Pb6/v593vetdvO1tb+Pw4cN89rOf5WMf+xj/9m//tsgjXTiNzjXPqVOnSq5td3f3Io2weTz11FPcc8897N27l1/+8pcYhsFv//Zvk0wmqx6zJO9ZIWkq3/zmN8XatWur/j0ajQqXyyXuv//+wu9OnDghAPHcc89diCEumPvuu0+0trbW9diPfOQjYteuXYs6nsWk3rnati16enrEX/7lXxZ+F41GhdvtFj/5yU8WcYQL4/jx4wIQzz//fOF3jz32mFAURZw7d67qcTt37hR/9Ed/dAFGOH927Ngh7rnnnsLPlmWJFStWiK9//esVH//BD35QvOtd7yr53fXXXy/+4A/+YFHH2QwanWsj7+GLGUA8+OCDNR/zp3/6p+LKK68s+d2HPvQh8Y53vGMRR9Z86pnrk08+KQARiUQuyJgWk/HxcQGIp556qupjluI9KyNNTWZ6epr29vaqfz9w4ACGYXDrrbcWfnfZZZexatUqnnvuuQsxxAvOnj176O7uZvPmzXzyk59kampqqYfUdPr7+xkdHS25rq2trVx//fUX9XV97rnnCIVCXHfddYXf3Xrrraiqyr59+2oe++Mf/5jOzk6uuuoqPv/5z5NKpRZ7uHWj6zoHDhwouR6qqnLrrbdWvR7PPfdcyeMB3vGOd1zU1w/mN1eARCLB6tWr6evrY9euXRw7duxCDPeCc6le14WwZcsWli9fzn/6T/+JZ599dqmHMy+mp6cBaq6nS3Ft33ANexeTV199lW9/+9v81V/9VdXHjI6OomnarDqZZcuWXRJ550a57bbb2L17N2vXruW1117jC1/4ArfffjvPPfccDodjqYfXNPLXbtmyZSW/v9iv6+jo6KzQvdPppL29vea4f/d3f5fVq1ezYsUKXnrpJf7sz/6MU6dO8cADDyz2kOticnISy7IqXo+TJ09WPGZ0dPSSu34wv7lu3ryZH/zgB1xzzTVMT0/zV3/1V7z5zW/m2LFji97Q/EJT7brGYjHS6TRer3eJRtZ8li9fzne/+12uu+46stks3/ve97j55pvZt28f27ZtW+rh1Y1t23z2s5/lLW95C1dddVXVxy3Fe1ZGmipw7733ViymK/6v/MPo3Llz3HbbbXzgAx/g4x//+BKNvHHmM9dG+J3f+R3e+973cvXVV3PHHXfwyCOP8Pzzz7Nnz57mTaJOFnuuFxOLPddPfOITvOMd7+Dqq6/mrrvu4h//8R958MEHee2115o4C8liceONN/Kf//N/ZsuWLezcuZMHHniArq4u/u7v/m6phyZZAJs3b+YP/uAP2L59O29+85v5wQ9+wJvf/Gb+5m/+ZqmH1hD33HMPR48e5Z//+Z+XeiizkJGmCvzxH/8xv//7v1/zMevWrSv8e3h4mLe97W28+c1v5u///u9rHtfT04Ou60Sj0ZJo09jYGD09PQsZ9rxodK4LZd26dXR2dvLqq69yyy23NO1562Ex55q/dmNjYyxfvrzw+7GxMbZs2TKv51wI9c61p6dnVrGwaZqEw+GG7sfrr78eyEVb169f3/B4m01nZycOh2PWrtRa77Oenp6GHn+xMJ+5luNyudi6dSuvvvrqYgxxSal2XVtaWl5XUaZq7Nixg1//+tdLPYy6+dSnPlXYkDJX1HMp3rNSNFWgq6uLrq6uuh577tw53va2t7F9+3buu+8+VLV28G779u24XC6eeOIJ3ve+9wG5nQ5nz57lxhtvXPDYG6WRuTaDoaEhpqamSoTFhWIx57p27Vp6enp44oknCiIpFouxb9++hncbNoN653rjjTcSjUY5cOAA27dvB+BXv/oVtm0XhFA9HD58GGBJrmslNE1j+/btPPHEE9xxxx1ALuT/xBNP8KlPfariMTfeeCNPPPEEn/3sZwu/++Uvf7kk78tGmM9cy7EsiyNHjvDOd75zEUe6NNx4442ztqFfCte1WRw+fPiieV/WQgjBpz/9aR588EH27NnD2rVr5zxmSd6zi1Zi/gZgaGhIbNiwQdxyyy1iaGhIjIyMFP4rfszmzZvFvn37Cr/7wz/8Q7Fq1Srxq1/9SrzwwgvixhtvFDfeeONSTKEhzpw5Iw4dOiS+8pWviEAgIA4dOiQOHTok4vF44TGbN28WDzzwgBBCiHg8Lv77f//v4rnnnhP9/f3iP/7jP8S2bdvExo0bRSaTWapp1EWjcxVCiL/4i78QoVBIPPTQQ+Kll14Su3btEmvXrhXpdHopplA3t912m9i6davYt2+f+PWvfy02btwoPvzhDxf+Xn4Pv/rqq+KrX/2qeOGFF0R/f7946KGHxLp168RNN920VFOoyD//8z8Lt9stfvjDH4rjx4+LT3ziEyIUConR0VEhhBC/93u/J+69997C45999lnhdDrFX/3VX4kTJ06IL33pS8LlcokjR44s1RTqptG5fuUrXxH/9m//Jl577TVx4MAB8Tu/8zvC4/GIY8eOLdUU6iYejxfej4D467/+a3Ho0CFx5swZIYQQ9957r/i93/u9wuNPnz4tfD6f+JM/+RNx4sQJ8Z3vfEc4HA7x+OOPL9UU6qbRuf7N3/yN+PnPfy5eeeUVceTIEfFHf/RHQlVV8R//8R9LNYW6+eQnPylaW1vFnj17StbSVCpVeMzF8J6VomkB3HfffQKo+F+e/v5+AYgnn3yy8Lt0Oi3+63/9r6KtrU34fD5x5513lgiti5WPfOQjFedaPDdA3HfffUIIIVKplPjt3/5t0dXVJVwul1i9erX4+Mc/Xvggv5hpdK5C5GwH/vzP/1wsW7ZMuN1uccstt4hTp05d+ME3yNTUlPjwhz8sAoGAaGlpEXfffXeJOCy/h8+ePStuuukm0d7eLtxut9iwYYP4kz/5EzE9Pb1EM6jOt7/9bbFq1SqhaZrYsWOH2Lt3b+FvO3fuFB/5yEdKHv+zn/1MbNq0SWiaJq688krx6KOPXuARz59G5vrZz3628Nhly5aJd77zneLgwYNLMOrGyW+rL/8vP7+PfOQjYufOnbOO2bJli9A0Taxbt67kfXsx0+hcv/GNb4j169cLj8cj2tvbxc033yx+9atfLc3gG6TaWlp8rS6G96wyM1iJRCKRSCQSSQ3k7jmJRCKRSCSSOpCiSSKRSCQSiaQOpGiSSCQSiUQiqQMpmiQSiUQikUjqQIomiUQikUgkkjqQokkikUgkEomkDqRokkgkEolEIqkDKZokEolEIpFI6kCKJolEIpFIJJI6kKJJIpG87vnJT36C1+tlZGSk8Lu7776ba665hunp6SUcmUQiuZSQbVQkEsnrHiEEW7Zs4aabbuLb3/42X/rSl/jBD37A3r176e3tXerhSSSSSwQZaZJIJK97FEXha1/7Gv/wD//A1772Nb797W/z+OOPFwTTI488wubNm9m4cSPf+973lni0EonkYkVGmiQSyRuGbdu2cezYMf793/+dnTt3AmCaJldccQVPPvkkra2tbN++nd/85jd0dHQs8WglEsnFhow0SSSSNwSPP/44J0+exLIsli1bVvj9/v37ufLKK+nt7SUQCHD77bfz7//+70s4UolEcrEiRZNEInndc/DgQT74wQ/y/e9/n1tuuYU///M/L/xteHi4pK6pt7eXc+fOLcUwJRLJRY5zqQcgkUgki8nAwADvete7+MIXvsCHP/xh1q1bx4033sjBgwfZtm3bUg9PIpFcQshIk0Qied0SDoe57bbb2LVrF/feey8A119/Pbfffjtf+MIXAFixYkVJZOncuXOsWLFiScYrkUgubmQhuEQieUNjmiaXX345e/bskYXgEomkJjI9J5FI3tA4nU7+1//6X7ztbW/Dtm3+9E//VAomiURSERlpkkgkEolEIqkDWdMkkUgkEolEUgdSNEkkEolEIpHUgRRNEolEIpFIJHUgRZNEIpFIJBJJHUjRJJFIJBKJRFIHUjRJJBKJRCKR1IEUTRKJRCKRSCR1IEWTRCKRSCQSSR1I0SSRSCQSiURSB1I0SSQSiUQikdSBFE0SiUQikUgkdSBFk0QikUgkEkkd/N8xr+6Qe5uipAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(6, 5))\n",
        "plt.scatter(X[:, 0], y, s=7, alpha=0.5)\n",
        "plt.xlabel(\"$x_0$\")\n",
        "plt.ylabel(\"$y$\")\n",
        "plt.title(\"$y$ vs $x_0$\")\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###1D projection: $y$ vs $x_1$"
      ],
      "metadata": {
        "id": "D6_f8-AX8Vxv"
      },
      "id": "D6_f8-AX8Vxv"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6, 5))\n",
        "plt.scatter(X[:, 1], y, s=7, alpha=0.5)\n",
        "plt.xlabel(\"$x_1$\")\n",
        "plt.ylabel(\"$y$\")\n",
        "plt.title(\"$y$ vs $x_1$\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "sBXbHP6NG7H8",
        "outputId": "4748d8b4-1e30-4a02-8e6e-ccfb87ecb058"
      },
      "id": "sBXbHP6NG7H8",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###3D Plot"
      ],
      "metadata": {
        "id": "9lkClKSXCw0o"
      },
      "id": "9lkClKSXCw0o"
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(7, 5))\n",
        "ax = fig.add_subplot(111, projection=\"3d\")\n",
        "\n",
        "sc = ax.scatter(X[:, 0], X[:, 1], y, c=y, s=10, alpha=0.7, cmap=\"plasma\")\n",
        "\n",
        "ax.set_xlabel(\"$x_0$\")\n",
        "ax.set_ylabel(\"$x_1$\")\n",
        "ax.set_zlabel(\"$y$\")\n",
        "ax.set_title(\"Toy regression data in 3D\")\n",
        "\n",
        "fig.colorbar(sc, ax=ax, label=\"$y$\", shrink=0.7)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "id": "w1ECawHO81gP",
        "outputId": "4681160d-f25d-4acd-fe4b-51ee9ab0a47e"
      },
      "id": "w1ECawHO81gP",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cfa0fd69",
      "metadata": {
        "id": "cfa0fd69"
      },
      "source": [
        "Each one-dimensional projection hides part of the structure. The target depends on both $x_0$ and $x_1$, so the symbolic-regression algorithm has to search over functions of both variables."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0f302607",
      "metadata": {
        "id": "0f302607"
      },
      "source": [
        "## 3. SymPy: formulas as symbolic objects\n",
        "\n",
        "Before using PySR, it is useful to see how Python can represent a mathematical formula.\n",
        "\n",
        "In `NumPy`, expressions are usually evaluated numerically. In `SymPy`, expressions are symbolic objects. This means we can manipulate them algebraically: simplify them, differentiate them, substitute values, and convert them into numerical functions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b25469eb",
      "metadata": {
        "id": "b25469eb"
      },
      "outputs": [],
      "source": [
        "x0, x1 = sp.symbols(\"x0 x1\")\n",
        "\n",
        "expr = sp.sin(x0) + x1**2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "expr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "id": "1J-fDSjJEMP4",
        "outputId": "e19f332c-ad64-4532-87d3-9d48b96a23b9"
      },
      "id": "1J-fDSjJEMP4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "x1**2 + sin(x0)"
            ],
            "text/latex": "$\\displaystyle x_{1}^{2} + \\sin{\\left(x_{0} \\right)}$"
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(expr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rvqQpzmtEO50",
        "outputId": "d38d7efb-13c1-4a13-9e37-ad1f4a86c7fc"
      },
      "id": "rvqQpzmtEO50",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x1**2 + sin(x0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8e7f57d0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "8e7f57d0",
        "outputId": "42487d06-ab23-4b4b-8739-8434c34f4be9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expression:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "x1**2 + sin(x0)"
            ],
            "text/latex": "$\\displaystyle x_{1}^{2} + \\sin{\\left(x_{0} \\right)}$"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Derivative with respect to x0:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "cos(x0)"
            ],
            "text/latex": "$\\displaystyle \\cos{\\left(x_{0} \\right)}$"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Derivative with respect to x1:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "2*x1"
            ],
            "text/latex": "$\\displaystyle 2 x_{1}$"
          },
          "metadata": {}
        }
      ],
      "source": [
        "print(\"Expression:\")\n",
        "display(expr)\n",
        "\n",
        "print(\"\\nDerivative with respect to x0:\")\n",
        "display(sp.diff(expr, x0))\n",
        "\n",
        "print(\"\\nDerivative with respect to x1:\")\n",
        "display(sp.diff(expr, x1))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "expr_at_point = expr.subs({x0: np.pi/2, x1: 2.0})\n",
        "\n",
        "print(\"Expression evaluated at x0 = pi/2, x1 = 2:\")\n",
        "display(expr_at_point)\n",
        "\n",
        "print(\"\\nNumerical value:\")\n",
        "print(float(expr_at_point))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "WFTeHkWTVGdC",
        "outputId": "a02fa8fe-3f28-4b8b-d6ae-14655f89b6b8"
      },
      "id": "WFTeHkWTVGdC",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expression evaluated at x0 = pi/2, x1 = 2:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "5.00000000000000"
            ],
            "text/latex": "$\\displaystyle 5.0$"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Numerical value:\n",
            "5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#print(\"Expanded expression:\")\n",
        "#display(sp.expand(expr))"
      ],
      "metadata": {
        "id": "4_UdNmHkEdb_"
      },
      "id": "4_UdNmHkEdb_",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To compare the symbolic expression with our numerical dataset, we convert the SymPy expression into a NumPy function."
      ],
      "metadata": {
        "id": "_sIAdg_DEk1p"
      },
      "id": "_sIAdg_DEk1p"
    },
    {
      "cell_type": "code",
      "source": [
        "f = sp.lambdify((x0, x1), expr, \"numpy\")\n",
        "y_sympy = f(X[:, 0], X[:, 1])\n",
        "\n",
        "print(\"MSE using the true symbolic expression:\", mean_squared_error(y_clean, y_sympy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zu9IxEzIVYGO",
        "outputId": "01cf0355-f554-43fb-a63d-8f783defaaa7"
      },
      "id": "zu9IxEzIVYGO",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE using the true symbolic expression: 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "08b85407",
      "metadata": {
        "id": "08b85407"
      },
      "source": [
        "### Expression complexity\n",
        "\n",
        "Symbolic regression usually has to balance accuracy and simplicity.\n",
        "\n",
        "A very complicated expression may fit the data well, but it may not be useful or interpretable. Therefore, symbolic-regression methods often penalize overly complex formulas.\n",
        "\n",
        "> #### Expressions as trees\n",
        "\n",
        "A mathematical formula can be represented as an **expression tree**. The internal nodes are operations, and the leaf nodes are variables or constants.\n",
        "\n",
        "For example, the tree below represents\n",
        "\n",
        "$$\n",
        "f(x) = x^3 + \\sin\\left(\\frac{\\pi}{3}\\right) - \\frac{x}{2}.\n",
        "$$\n",
        "\n",
        "Symbolic-regression algorithms often build and modify expressions by changing parts of this tree. For example, they may replace a `+` with a `*`, insert a `sin`, remove a branch, or change a constant.\n",
        "\n",
        "The complexity of an expression is roughly related to the size of this tree. A larger tree usually means a more complicated formula. One simple way to measure complexity is to count the number of nodes in the expression tree."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "Bm3dnK2yWdr3"
      },
      "id": "Bm3dnK2yWdr3"
    },
    {
      "cell_type": "code",
      "source": [
        "def expression_complexity(expr):\n",
        "    \"\"\"\n",
        "    Count the number of nodes in a SymPy expression tree.\n",
        "    \"\"\"\n",
        "    return sum(1 for _ in sp.preorder_traversal(expr))"
      ],
      "metadata": {
        "id": "SyikPfEdaWlL"
      },
      "id": "SyikPfEdaWlL",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = sp.symbols(\"x\")\n",
        "\n",
        "image_expr = x**3 + sp.sin(sp.pi / 3) - x / 2\n",
        "\n",
        "print(\"Expression from the tree image:\")\n",
        "display(image_expr)\n",
        "\n",
        "print(\"\\nComplexity :\", expression_complexity(image_expr))\n",
        "\n",
        "print(\"\\nInternal SymPy tree representation:\")\n",
        "print(sp.srepr(image_expr))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "id": "LZPRf5q7ahxu",
        "outputId": "c7fb7fbe-3dc2-4ef8-ec88-37d3494d267d"
      },
      "id": "LZPRf5q7ahxu",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expression from the tree image:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "x**3 - x/2 + sqrt(3)/2"
            ],
            "text/latex": "$\\displaystyle x^{3} - \\frac{x}{2} + \\frac{\\sqrt{3}}{2}$"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Complexity : 12\n",
            "\n",
            "Internal SymPy tree representation:\n",
            "Add(Pow(Symbol('x'), Integer(3)), Mul(Integer(-1), Rational(1, 2), Symbol('x')), Mul(Rational(1, 2), Pow(Integer(3), Rational(1, 2))))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "candidate_1 = sp.sin(x0) + x1**2\n",
        "candidate_2 = sp.sin(x0) + x1**2 + 0.001 * sp.cos(3*x0) * sp.exp(x1)\n",
        "candidate_3 = x0 + x1\n",
        "\n",
        "candidates = [candidate_1, candidate_2, candidate_3]\n",
        "\n",
        "for i, candidate in enumerate(candidates):\n",
        "    print(f\"candidate_{i+1}:\")\n",
        "    display(candidate)\n",
        "    print(\"complexity :\", expression_complexity(candidate))\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "o-sFWQ8kW1py",
        "outputId": "9dba71aa-20f1-4cab-98ed-c60b515d4e86"
      },
      "id": "o-sFWQ8kW1py",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "candidate_1:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "x1**2 + sin(x0)"
            ],
            "text/latex": "$\\displaystyle x_{1}^{2} + \\sin{\\left(x_{0} \\right)}$"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "complexity : 6\n",
            "\n",
            "candidate_2:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "x1**2 + 0.001*exp(x1)*cos(3*x0) + sin(x0)"
            ],
            "text/latex": "$\\displaystyle x_{1}^{2} + 0.001 e^{x_{1}} \\cos{\\left(3 x_{0} \\right)} + \\sin{\\left(x_{0} \\right)}$"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "complexity : 14\n",
            "\n",
            "candidate_3:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "x0 + x1"
            ],
            "text/latex": "$\\displaystyle x_{0} + x_{1}$"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "complexity : 3\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "235033d9",
      "metadata": {
        "id": "235033d9"
      },
      "source": [
        "The symbolic-regression tradeoff is roughly:\n",
        "\n",
        "$$\n",
        "\\text{good expression}\n",
        "=\n",
        "\\text{low prediction error}\n",
        "+\n",
        "\\text{low complexity}.\n",
        "$$\n",
        "\n",
        "This is why symbolic regression is not just about finding the most accurate formula. It is about finding a formula that is accurate enough and still simple enough to understand."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "acd4d653",
      "metadata": {
        "id": "acd4d653"
      },
      "source": [
        "## 4. Train/test split\n",
        "\n",
        "We split the data into a training set and a test set.\n",
        "\n",
        "The training set is used to search for expressions. The test set is kept separate and used at the end to check whether the discovered expression generalizes to unseen data.\n",
        "\n",
        "Symbolic regression can overfit, especially when very large expressions are allowed, so checking performance on a test set is important."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "50e1da72",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50e1da72",
        "outputId": "7efe443b-c946-45f7-81a6-14fb0e0c4f4f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train: (1500, 2) (1500,)\n",
            "Test : (500, 2) (500,)\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
        "\n",
        "print(\"Train:\", X_train.shape, y_train.shape)\n",
        "print(\"Test :\", X_test.shape, y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5f4504f3",
      "metadata": {
        "id": "5f4504f3"
      },
      "source": [
        "## 5. A simple baseline: polynomial regression\n",
        "\n",
        "Before using PySR, we try a simple baseline.\n",
        "\n",
        "Ordinary linear regression fits a model of the form\n",
        "\n",
        "$$\n",
        "\\hat y = b + \\sum_i c_i x_i,\n",
        "$$\n",
        "\n",
        "where the $x_i$ are the input features.\n",
        "\n",
        "Polynomial regression first creates new features such as $x_0^2$, $x_0x_1$, and $x_1^2$, and then fits a linear model using these polynomial features.\n",
        "\n",
        "This is useful because our true formula contains $x_1^2$. However, it does not contain $\\sin(x_0)$ unless we manually add that feature. This shows the limitation of choosing a fixed feature basis by hand."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we construct polynomial features up to degree 2."
      ],
      "metadata": {
        "id": "ZYBZp0cKchsp"
      },
      "id": "ZYBZp0cKchsp"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
        "\n",
        "X_train_poly = poly.fit_transform(X_train)\n",
        "X_test_poly = poly.transform(X_test)\n",
        "\n",
        "print(\"Original training shape:\", X_train.shape)\n",
        "print(\"Polynomial training shape:\", X_train_poly.shape)\n",
        "\n",
        "feature_names = poly.get_feature_names_out([\"x0\", \"x1\"])\n",
        "\n",
        "print(\"\\nPolynomial features:\")\n",
        "print(feature_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ie2aDcclgrJO",
        "outputId": "80b98fe9-0d28-48c7-ae02-77de4972ab21"
      },
      "id": "ie2aDcclgrJO",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original training shape: (1500, 2)\n",
            "Polynomial training shape: (1500, 5)\n",
            "\n",
            "Polynomial features:\n",
            "['x0' 'x1' 'x0^2' 'x0 x1' 'x1^2']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So even though we call this polynomial regression, the final model is still linear regression on the expanded feature matrix:\n",
        "\n",
        "$$ \\hat y = c_0 x_0 + c_1 x_1 + c_2 x_0^2 + c_3 x_0x_1 + c_4 x_1^2 + b $$\n",
        "\n",
        "The nonlinear part comes from manually constructing the features."
      ],
      "metadata": {
        "id": "oV6tKiAMhxK1"
      },
      "id": "oV6tKiAMhxK1"
    },
    {
      "cell_type": "markdown",
      "source": [
        "The expanded feature matrix now contains the polynomial terms as separate columns."
      ],
      "metadata": {
        "id": "UOhElA10cy8I"
      },
      "id": "UOhElA10cy8I"
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(X_train_poly[:5], columns=feature_names)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
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      "id": "83AXNzs_g2Y5",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         x0        x1      x0^2     x0 x1      x1^2\n",
              "0  1.306860  1.636204  1.707882  2.138289  2.677164\n",
              "1 -1.124574  1.264224  1.264666 -1.421713  1.598262\n",
              "2  1.942484  1.940187  3.773246  3.768783  3.764325\n",
              "3 -0.961031 -1.459115  0.923581  1.402254  2.129015\n",
              "4 -1.414255  0.991606  2.000118 -1.402385  0.983283"
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              "      <td>1.942484</td>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"x0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5537809195966927,\n        \"min\": -1.4142553017720818,\n        \"max\": 1.942484406048032,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -1.1245735648214712,\n          -1.4142553017720818,\n          1.942484406048032\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3534434782187834,\n        \"min\": -1.4591145519840558,\n        \"max\": 1.940186786883484,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.2642240670113245,\n          0.991606408592701,\n          1.940186786883484\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x0^2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.107619209787481,\n        \"min\": 0.923580566918418,\n        \"max\": 3.7732456677397757,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.2646657026952717,\n          2.0001180585904423,\n          3.7732456677397757\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x0 x1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.27523708938098,\n        \"min\": -1.4217129657720238,\n        \"max\": 3.768782578341604,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -1.4217129657720238,\n          -1.4023846206234005,\n          3.768782578341604\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"x1^2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0627440787282263,\n        \"min\": 0.9832832695621146,\n        \"max\": 3.7643247679972576,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.5982624916106538,\n          0.9832832695621146,\n          3.7643247679972576\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we fit a linear regression model on these polynomial features."
      ],
      "metadata": {
        "id": "bIAWZta5c4OX"
      },
      "id": "bIAWZta5c4OX"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "linreg = LinearRegression()\n",
        "\n",
        "linreg.fit(X_train_poly, y_train)\n",
        "\n",
        "y_pred_poly = linreg.predict(X_test_poly)\n",
        "\n",
        "mse_poly = mean_squared_error(y_test, y_pred_poly)\n",
        "\n",
        "print(f\"Polynomial baseline MSE: {mse_poly:.6f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "me2_I59Hg4Lf",
        "outputId": "a38ff3c6-0e3e-47c3-a713-a492435f412a"
      },
      "id": "me2_I59Hg4Lf",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polynomial baseline MSE: 0.027556\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The fitted coefficients show how much weight the model gives to each polynomial feature."
      ],
      "metadata": {
        "id": "icNoW_FCc-tf"
      },
      "id": "icNoW_FCc-tf"
    },
    {
      "cell_type": "code",
      "source": [
        "coef_table = pd.DataFrame({\"feature\": feature_names,\n",
        "                          \"coefficient\": linreg.coef_})\n",
        "\n",
        "coef_table"
      ],
      "metadata": {
        "colab": {
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          "height": 206
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      "id": "iGf1X5Z9g71f",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  feature  coefficient\n",
              "0      x0     0.651701\n",
              "1      x1     0.001348\n",
              "2    x0^2    -0.002042\n",
              "3   x0 x1     0.006779\n",
              "4    x1^2     0.997926"
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              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_30e3c674-8864-4900-b3ab-a3fed59485eb button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('coef_table');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "coef_table",
              "summary": "{\n  \"name\": \"coef_table\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"x1\",\n          \"x1^2\",\n          \"x0^2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coefficient\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.466997442715565,\n        \"min\": -0.0020424163160551734,\n        \"max\": 0.9979264643269036,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.001348040167721859,\n          0.9979264643269036,\n          -0.0020424163160551734\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#print(\"Intercept:\", linreg.intercept_)"
      ],
      "metadata": {
        "id": "bdS-FDurdOth"
      },
      "id": "bdS-FDurdOth",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "78e71a95",
      "metadata": {
        "id": "78e71a95"
      },
      "source": [
        "## 6. PySR: symbolic search over expressions\n",
        "\n",
        "Now we use `PySR` to search for an analytic expression directly from the data.\n",
        "\n",
        "Unlike polynomial regression, we do not manually choose a fixed feature basis. Instead, we give PySR a set of allowed mathematical operations, and it searches over expressions built from them.\n",
        "\n",
        "For example, with binary operators such as `+`, `-`, `*`, `/` and unary operators such as `sin`, `cos`, `exp`, and `log`, PySR can build expressions like\n",
        "\n",
        "$$\n",
        "\\sin(x_0) + x_1^2, \\quad x_0x_1 + \\cos(x_1), \\quad \\frac{x_0}{1 + \\log(x_1)}\n",
        "$$\n",
        "\n",
        "The search tries to balance two goals:\n",
        "\n",
        "$$\n",
        "\\text{low prediction error}\n",
        "\\quad + \\quad\n",
        "\\text{low expression complexity}.\n",
        "$$\n",
        "\n",
        "For real applications, number of iterations should be increased and the operator set should be chosen carefully."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We now define the PySR model.\n",
        "\n",
        "The most important settings are:\n",
        "\n",
        "- `niterations`      : how long the symbolic search runs,\n",
        "- `binary_operators` : two-input operations such as `+`, `-`, `*`, `/`,\n",
        "- `unary_operators`  : one-input operations such as `sin`, `cos`, `exp`, `log`,\n",
        "- `maxsize`          : maximum allowed expression size,\n",
        "- `parsimony`        : penalty for overly complicated expressions.\n",
        "- `model_selection`  : rule used to choose the final expression from the equation table.\n",
        "\n",
        "The exact best expression may change slightly depending on the random seed and search settings."
      ],
      "metadata": {
        "id": "fTcE-XTY5tmT"
      },
      "id": "fTcE-XTY5tmT"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4923aa8e",
      "metadata": {
        "id": "4923aa8e"
      },
      "outputs": [],
      "source": [
        "pysr_model = PySRRegressor(niterations=40,\n",
        "                          population_size=30,\n",
        "                          maxsize=18,\n",
        "                          binary_operators=[\"+\", \"-\", \"*\", \"/\"],\n",
        "                          unary_operators=[\"sin\", \"cos\", \"exp\", \"log\", \"sqrt\", \"abs\"],\n",
        "                          model_selection=\"best\",\n",
        "                          elementwise_loss=\"loss(prediction, target) = (prediction - target)^2\",\n",
        "                          parsimony=0.01,\n",
        "                          random_state=42,\n",
        "                          deterministic=True,\n",
        "                          parallelism=\"serial\",\n",
        "                          verbosity=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pysr_model.fit(X_train, y_train, variable_names=[\"x0\", \"x1\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CjXq62AuicHF",
        "outputId": "9ef8e52f-172d-4864-e8ce-4914a1af029f"
      },
      "id": "CjXq62AuicHF",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/pysr/sr.py:2811: UserWarning: Note: it looks like you are running in Jupyter. The progress bar will be turned off.\n",
            "  warnings.warn(\n",
            "Compiling Julia backend...\n",
            "INFO:pysr.sr:Compiling Julia backend...\n",
            "[ Info: Started!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Expressions evaluated per second: 2.390e+04\n",
            "Progress: 138 / 1240 total iterations (11.129%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           1.973e+00  0.000e+00  y = 1.3169\n",
            "2           1.091e+00  5.924e-01  y = abs(x1)\n",
            "3           5.966e-01  6.034e-01  y = x1 * x1\n",
            "5           1.894e-01  5.737e-01  y = x0 + (x1 * x1)\n",
            "6           9.965e-05  7.550e+00  y = (x1 * x1) + sin(x0)\n",
            "8           9.954e-05  5.487e-04  y = sin(x0 * 1.0008) + (x1 * x1)\n",
            "10          9.954e-05  2.712e-06  y = sin(x0 * 1.0008) + (x1 * (x1 * 0.99999))\n",
            "12          9.954e-05  6.288e-06  y = (sin(x0 * 1.0008) * 1) + ((x1 * 0.99999) * x1)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 1.860e+04\n",
            "Progress: 214 / 1240 total iterations (17.258%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           1.973e+00  0.000e+00  y = 1.3169\n",
            "2           1.091e+00  5.924e-01  y = abs(x1)\n",
            "3           5.966e-01  6.034e-01  y = x1 * x1\n",
            "5           1.894e-01  5.737e-01  y = x0 + (x1 * x1)\n",
            "6           9.965e-05  7.550e+00  y = (x1 * x1) + sin(x0)\n",
            "8           9.954e-05  5.487e-04  y = sin(x0 * 1.0008) + (x1 * x1)\n",
            "10          9.954e-05  2.712e-06  y = sin(x0 * 1.0008) + (x1 * (x1 * 0.99999))\n",
            "12          9.946e-05  4.181e-04  y = ((x1 * (x1 * 0.9998)) - -0.0004362) + sin(x0 * 1.0008)\n",
            "14          9.946e-05  3.994e-06  y = ((sin(x0 * 1.0008) * 1) + (x1 * (x1 * 0.99981))) - -0....\n",
            "                                      00045342\n",
            "16          9.945e-05  3.132e-05  y = (x1 * (x1 - 0.00026101)) + sin((x0 / 1.0004) / ((x0 / ...\n",
            "                                      1.0012) / x0))\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 1.830e+04\n",
            "Progress: 333 / 1240 total iterations (26.855%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           1.973e+00  0.000e+00  y = 1.3169\n",
            "2           1.091e+00  5.924e-01  y = abs(x1)\n",
            "3           5.966e-01  6.034e-01  y = x1 * x1\n",
            "5           1.894e-01  5.737e-01  y = x0 + (x1 * x1)\n",
            "6           9.965e-05  7.550e+00  y = (x1 * x1) + sin(x0)\n",
            "8           9.954e-05  5.487e-04  y = sin(x0 * 1.0008) + (x1 * x1)\n",
            "10          9.954e-05  2.712e-06  y = sin(x0 * 1.0008) + (x1 * (x1 * 0.99999))\n",
            "12          9.946e-05  4.181e-04  y = ((x1 * (x1 * 0.9998)) - -0.0004362) + sin(x0 * 1.0008)\n",
            "14          9.946e-05  3.994e-06  y = ((sin(x0 * 1.0008) * 1) + (x1 * (x1 * 0.99981))) - -0....\n",
            "                                      00045342\n",
            "16          9.945e-05  3.132e-05  y = (x1 * (x1 - 0.00026101)) + sin((x0 / 1.0004) / ((x0 / ...\n",
            "                                      1.0012) / x0))\n",
            "17          9.932e-05  1.322e-03  y = (x1 * x1) + sin(cos(abs(((x1 / x0) * -0.18618) * abs(0...\n",
            "                                      .04043))) * x0)\n",
            "18          9.931e-05  1.276e-04  y = sin((x0 / 1.0004) / ((x0 / 1.0012) / x0)) + abs((x1 * ...\n",
            "                                      abs(x1)) - 0.00026101)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 2.000e+04\n",
            "Progress: 467 / 1240 total iterations (37.661%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           1.973e+00  0.000e+00  y = 1.3169\n",
            "2           1.091e+00  5.924e-01  y = abs(x1)\n",
            "3           5.966e-01  6.034e-01  y = x1 * x1\n",
            "5           1.894e-01  5.737e-01  y = x0 + (x1 * x1)\n",
            "6           9.965e-05  7.550e+00  y = (x1 * x1) + sin(x0)\n",
            "8           9.954e-05  5.487e-04  y = sin(x0 * 1.0008) + (x1 * x1)\n",
            "10          9.931e-05  1.153e-03  y = sin(x0) + abs(0.00056491 - (x1 * abs(x1)))\n",
            "15          9.918e-05  2.694e-04  y = (x1 * x1) + sin(x0 * abs(abs(cos((0.072413 / x0) * -0....\n",
            "                                      17285))))\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 1.750e+04\n",
            "Progress: 536 / 1240 total iterations (43.226%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           1.973e+00  0.000e+00  y = 1.3169\n",
            "2           1.091e+00  5.924e-01  y = abs(x1)\n",
            "3           5.966e-01  6.034e-01  y = x1 * x1\n",
            "5           1.894e-01  5.737e-01  y = x0 + (x1 * x1)\n",
            "6           9.965e-05  7.550e+00  y = (x1 * x1) + sin(x0)\n",
            "8           9.954e-05  5.487e-04  y = sin(x0 * 1.0008) + (x1 * x1)\n",
            "10          9.931e-05  1.153e-03  y = sin(x0) + abs(0.00056491 - (x1 * abs(x1)))\n",
            "15          9.918e-05  2.694e-04  y = (x1 * x1) + sin(x0 * abs(abs(cos((0.072413 / x0) * -0....\n",
            "                                      17285))))\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 1.960e+04\n",
            "Progress: 673 / 1240 total iterations (54.274%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           1.973e+00  0.000e+00  y = 1.3169\n",
            "2           1.091e+00  5.924e-01  y = abs(x1)\n",
            "3           5.966e-01  6.034e-01  y = x1 * x1\n",
            "5           1.894e-01  5.737e-01  y = x0 + (x1 * x1)\n",
            "6           9.965e-05  7.550e+00  y = (x1 * x1) + sin(x0)\n",
            "8           9.954e-05  5.487e-04  y = sin(x0 * 1.0008) + (x1 * x1)\n",
            "10          9.931e-05  1.153e-03  y = sin(x0) + abs(0.00056491 - (x1 * abs(x1)))\n",
            "12          9.918e-05  6.794e-04  y = sin(x0 * abs(cos(-0.012768 / x0))) + (x1 * x1)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 1.970e+04\n",
            "Progress: 771 / 1240 total iterations (62.177%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           1.973e+00  0.000e+00  y = 1.3169\n",
            "2           1.091e+00  5.924e-01  y = abs(x1)\n",
            "3           5.966e-01  6.034e-01  y = x1 * x1\n",
            "5           1.894e-01  5.737e-01  y = x0 + (x1 * x1)\n",
            "6           9.965e-05  7.550e+00  y = (x1 * x1) + sin(x0)\n",
            "8           9.954e-05  5.487e-04  y = sin(x0 * 1.0008) + (x1 * x1)\n",
            "10          9.931e-05  1.153e-03  y = sin(x0) + abs(0.00056491 - (x1 * abs(x1)))\n",
            "12          9.918e-05  6.794e-04  y = sin(x0 * abs(cos(-0.012768 / x0))) + (x1 * x1)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 1.810e+04\n",
            "Progress: 870 / 1240 total iterations (70.161%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           1.973e+00  0.000e+00  y = 1.3169\n",
            "2           1.091e+00  5.924e-01  y = abs(x1)\n",
            "3           5.966e-01  6.034e-01  y = x1 * x1\n",
            "5           1.894e-01  5.737e-01  y = x0 + (x1 * x1)\n",
            "6           9.965e-05  7.550e+00  y = (x1 * x1) + sin(x0)\n",
            "8           9.954e-05  5.487e-04  y = sin(x0 * 1.0008) + (x1 * x1)\n",
            "10          9.931e-05  1.153e-03  y = sin(x0) + abs(0.00056491 - (x1 * abs(x1)))\n",
            "12          9.918e-05  6.794e-04  y = sin(x0 * abs(cos(-0.012768 / x0))) + (x1 * x1)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 1.960e+04\n",
            "Progress: 1000 / 1240 total iterations (80.645%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           1.973e+00  0.000e+00  y = 1.3169\n",
            "2           1.091e+00  5.924e-01  y = abs(x1)\n",
            "3           5.966e-01  6.034e-01  y = x1 * x1\n",
            "5           1.894e-01  5.737e-01  y = x0 + (x1 * x1)\n",
            "6           9.965e-05  7.550e+00  y = (x1 * x1) + sin(x0)\n",
            "8           9.954e-05  5.487e-04  y = sin(x0 * 1.0008) + (x1 * x1)\n",
            "10          9.931e-05  1.153e-03  y = sin(x0) + abs(0.00056491 - (x1 * abs(x1)))\n",
            "12          9.916e-05  7.861e-04  y = sin(x0 * cos(sin(-0.012768 / x0))) + (x1 * x1)\n",
            "13          9.910e-05  6.072e-04  y = (cos(sin(abs(-0.015688 / x0))) * sin(x0)) + (x1 * x1)\n",
            "14          9.909e-05  4.405e-05  y = sin(x0 * cos(abs(sin(sin(0.017237) / x0)))) + (x1 * x1...\n",
            "                                      )\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 1.880e+04\n",
            "Progress: 1082 / 1240 total iterations (87.258%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           1.973e+00  0.000e+00  y = 1.3169\n",
            "2           1.091e+00  5.924e-01  y = abs(x1)\n",
            "3           5.966e-01  6.034e-01  y = x1 * x1\n",
            "5           1.894e-01  5.737e-01  y = x0 + (x1 * x1)\n",
            "6           9.965e-05  7.550e+00  y = (x1 * x1) + sin(x0)\n",
            "8           9.954e-05  5.487e-04  y = sin(x0 * 1.0008) + (x1 * x1)\n",
            "10          9.931e-05  1.153e-03  y = sin(x0) + abs(0.00056491 - (x1 * abs(x1)))\n",
            "12          9.909e-05  1.092e-03  y = (cos(sin(0.015849 / x0)) * sin(x0)) + (x1 * x1)\n",
            "14          9.909e-05  1.994e-05  y = sin(x0 * cos(abs(sin(sin(0.017237) / x0)))) + (x1 * x1...\n",
            "                                      )\n",
            "15          9.909e-05  1.776e-05  y = (x1 * x1) + sin(cos(sin(abs((-0.70479 * -0.024334) / x...\n",
            "                                      0))) * x0)\n",
            "16          9.905e-05  4.076e-04  y = (sin(x0 * abs(cos(sin(sin(0.016463 / x0))))) + (x1 * x...\n",
            "                                      1)) + 0.00016872\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 1.740e+04\n",
            "Progress: 1167 / 1240 total iterations (94.113%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           1.973e+00  0.000e+00  y = 1.3169\n",
            "2           1.091e+00  5.924e-01  y = abs(x1)\n",
            "3           5.966e-01  6.034e-01  y = x1 * x1\n",
            "5           1.894e-01  5.737e-01  y = x0 + (x1 * x1)\n",
            "6           9.965e-05  7.550e+00  y = (x1 * x1) + sin(x0)\n",
            "8           9.954e-05  5.487e-04  y = sin(x0 * 1.0008) + (x1 * x1)\n",
            "10          9.931e-05  1.153e-03  y = sin(x0) + abs(0.00056491 - (x1 * abs(x1)))\n",
            "12          9.909e-05  1.092e-03  y = (cos(sin(0.015849 / x0)) * sin(x0)) + (x1 * x1)\n",
            "14          9.909e-05  1.994e-05  y = sin(x0 * cos(abs(sin(sin(0.017237) / x0)))) + (x1 * x1...\n",
            "                                      )\n",
            "15          9.905e-05  3.949e-04  y = (sin(x0 * abs(cos(sin(0.016463 / x0)))) + (x1 * x1)) +...\n",
            "                                       0.00016872\n",
            "16          9.905e-05  3.046e-05  y = (sin(x0 * abs(cos(sin(sin(0.016463 / x0))))) + (x1 * x...\n",
            "                                      1)) + 0.00016872\n",
            "17          9.897e-05  7.880e-04  y = (cos(sin(0.012168 / (x0 * cos(0.013293 / x0)))) * sin(...\n",
            "                                      x0)) + (x1 * x1)\n",
            "18          9.885e-05  1.262e-03  y = sin((0.5411 / cos(abs(cos(0.014437 / sin(sin(sin(x0)))...\n",
            "                                      )))) * x0) + (x1 * x1)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           1.973e+00  0.000e+00  y = 1.3169\n",
            "2           1.091e+00  5.924e-01  y = abs(x1)\n",
            "3           5.966e-01  6.034e-01  y = x1 * x1\n",
            "5           1.894e-01  5.737e-01  y = x0 + (x1 * x1)\n",
            "6           9.965e-05  7.550e+00  y = (x1 * x1) + sin(x0)\n",
            "8           9.954e-05  5.487e-04  y = sin(x0 * 1.0008) + (x1 * x1)\n",
            "10          9.931e-05  1.153e-03  y = sin(x0) + abs(0.00056491 - (x1 * abs(x1)))\n",
            "12          9.909e-05  1.092e-03  y = (cos(sin(0.015849 / x0)) * sin(x0)) + (x1 * x1)\n",
            "14          9.905e-05  2.174e-04  y = sin(x0 * cos(sin(0.016463 / x0))) + ((x1 * x1) + 0.000...\n",
            "                                      16872)\n",
            "16          9.905e-05  1.523e-05  y = (sin(x0 * abs(cos(sin(sin(0.016463 / x0))))) + (x1 * x...\n",
            "                                      1)) + 0.00016872\n",
            "17          9.885e-05  2.017e-03  y = sin((0.5411 / cos(abs(cos(0.014437 / sin(sin(x0)))))) ...\n",
            "                                      * x0) + (x1 * x1)\n",
            "18          9.885e-05  3.362e-05  y = sin((0.5411 / cos(abs(cos(0.014437 / sin(sin(sin(x0)))...\n",
            "                                      )))) * x0) + (x1 * x1)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[ Info: Final population:\n",
            "[ Info: Results saved to:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PySRRegressor.equations_ = [\n",
              "\t    pick     score                                           equation  \\\n",
              "\t0         0.000000                                          1.3168591   \n",
              "\t1         0.592447                                            abs(x1)   \n",
              "\t2         0.603390                                            x1 * x1   \n",
              "\t3         0.573676                                     x0 + (x1 * x1)   \n",
              "\t4   >>>>  7.550035                                (x1 * x1) + sin(x0)   \n",
              "\t5         0.000549                    sin(x0 * 1.0007796) + (x1 * x1)   \n",
              "\t6         0.001153       sin(x0) + abs(0.0005649085 - (x1 * abs(x1)))   \n",
              "\t7         0.001092  (cos(sin(0.015849385 / x0)) * sin(x0)) + (x1 *...   \n",
              "\t8         0.000217  sin(x0 * cos(sin(0.016463036 / x0))) + ((x1 * ...   \n",
              "\t9         0.000015  (sin(x0 * abs(cos(sin(sin(0.016463036 / x0))))...   \n",
              "\t10        0.002017  sin((0.54109997 / cos(abs(cos(0.014437185 / si...   \n",
              "\t11        0.000034  sin((0.54109997 / cos(abs(cos(0.014437185 / si...   \n",
              "\t\n",
              "\t        loss  complexity  \n",
              "\t0   1.972648           1  \n",
              "\t1   1.090820           2  \n",
              "\t2   0.596629           3  \n",
              "\t3   0.189415           5  \n",
              "\t4   0.000100           6  \n",
              "\t5   0.000100           8  \n",
              "\t6   0.000099          10  \n",
              "\t7   0.000099          12  \n",
              "\t8   0.000099          14  \n",
              "\t9   0.000099          16  \n",
              "\t10  0.000099          17  \n",
              "\t11  0.000099          18  \n",
              "]"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PySRRegressor.equations_ = [\n",
              "\t    pick     score                                           equation  \\\n",
              "\t0         0.000000                                          1.3168591   \n",
              "\t1         0.592447                                            abs(x1)   \n",
              "\t2         0.603390                                            x1 * x1   \n",
              "\t3         0.573676                                     x0 + (x1 * x1)   \n",
              "\t4   &gt;&gt;&gt;&gt;  7.550035                                (x1 * x1) + sin(x0)   \n",
              "\t5         0.000549                    sin(x0 * 1.0007796) + (x1 * x1)   \n",
              "\t6         0.001153       sin(x0) + abs(0.0005649085 - (x1 * abs(x1)))   \n",
              "\t7         0.001092  (cos(sin(0.015849385 / x0)) * sin(x0)) + (x1 *...   \n",
              "\t8         0.000217  sin(x0 * cos(sin(0.016463036 / x0))) + ((x1 * ...   \n",
              "\t9         0.000015  (sin(x0 * abs(cos(sin(sin(0.016463036 / x0))))...   \n",
              "\t10        0.002017  sin((0.54109997 / cos(abs(cos(0.014437185 / si...   \n",
              "\t11        0.000034  sin((0.54109997 / cos(abs(cos(0.014437185 / si...   \n",
              "\t\n",
              "\t        loss  complexity  \n",
              "\t0   1.972648           1  \n",
              "\t1   1.090820           2  \n",
              "\t2   0.596629           3  \n",
              "\t3   0.189415           5  \n",
              "\t4   0.000100           6  \n",
              "\t5   0.000100           8  \n",
              "\t6   0.000099          10  \n",
              "\t7   0.000099          12  \n",
              "\t8   0.000099          14  \n",
              "\t9   0.000099          16  \n",
              "\t10  0.000099          17  \n",
              "\t11  0.000099          18  \n",
              "]</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PySRRegressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>PySRRegressor.equations_ = [\n",
              "\t    pick     score                                           equation  \\\n",
              "\t0         0.000000                                          1.3168591   \n",
              "\t1         0.592447                                            abs(x1)   \n",
              "\t2         0.603390                                            x1 * x1   \n",
              "\t3         0.573676                                     x0 + (x1 * x1)   \n",
              "\t4   &gt;&gt;&gt;&gt;  7.550035                                (x1 * x1) + sin(x0)   \n",
              "\t5         0.000549                    sin(x0 * 1.0007796) + (x1 * x1)   \n",
              "\t6         0.001153       sin(x0) + abs(0.0005649085 - (x1 * abs(x1)))   \n",
              "\t7         0.001092  (cos(sin(0.015849385 / x0)) * sin(x0)) + (x1 *...   \n",
              "\t8         0.000217  sin(x0 * cos(sin(0.016463036 / x0))) + ((x1 * ...   \n",
              "\t9         0.000015  (sin(x0 * abs(cos(sin(sin(0.016463036 / x0))))...   \n",
              "\t10        0.002017  sin((0.54109997 / cos(abs(cos(0.014437185 / si...   \n",
              "\t11        0.000034  sin((0.54109997 / cos(abs(cos(0.014437185 / si...   \n",
              "\t\n",
              "\t        loss  complexity  \n",
              "\t0   1.972648           1  \n",
              "\t1   1.090820           2  \n",
              "\t2   0.596629           3  \n",
              "\t3   0.189415           5  \n",
              "\t4   0.000100           6  \n",
              "\t5   0.000100           8  \n",
              "\t6   0.000099          10  \n",
              "\t7   0.000099          12  \n",
              "\t8   0.000099          14  \n",
              "\t9   0.000099          16  \n",
              "\t10  0.000099          17  \n",
              "\t11  0.000099          18  \n",
              "]</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  - outputs/20260523_041332_HY0Y1D/hall_of_fame.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PySR returns a table of candidate equations.\n",
        "\n",
        "Usually, this table contains several expressions with different losses and complexities. Simpler expressions may have larger error, while more complicated expressions may fit the data better.\n",
        "\n",
        "The selected expression depends on the model-selection rule and the accuracy-complexity tradeoff.\n",
        "\n",
        "We inspect both the selected expression and the full equation table."
      ],
      "metadata": {
        "id": "o2RjscY18PrY"
      },
      "id": "o2RjscY18PrY"
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSelected expression:\")\n",
        "print(pysr_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uV5DB2j0iZyH",
        "outputId": "1a317cba-5333-40f1-b13d-58917fdcd667"
      },
      "id": "uV5DB2j0iZyH",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Selected expression:\n",
            "PySRRegressor.equations_ = [\n",
            "\t    pick     score                                           equation  \\\n",
            "\t0         0.000000                                          1.3168591   \n",
            "\t1         0.592447                                            abs(x1)   \n",
            "\t2         0.603390                                            x1 * x1   \n",
            "\t3         0.573676                                     x0 + (x1 * x1)   \n",
            "\t4   >>>>  7.550035                                (x1 * x1) + sin(x0)   \n",
            "\t5         0.000549                    sin(x0 * 1.0007796) + (x1 * x1)   \n",
            "\t6         0.001153       sin(x0) + abs(0.0005649085 - (x1 * abs(x1)))   \n",
            "\t7         0.001092  (cos(sin(0.015849385 / x0)) * sin(x0)) + (x1 *...   \n",
            "\t8         0.000217  sin(x0 * cos(sin(0.016463036 / x0))) + ((x1 * ...   \n",
            "\t9         0.000015  (sin(x0 * abs(cos(sin(sin(0.016463036 / x0))))...   \n",
            "\t10        0.002017  sin((0.54109997 / cos(abs(cos(0.014437185 / si...   \n",
            "\t11        0.000034  sin((0.54109997 / cos(abs(cos(0.014437185 / si...   \n",
            "\t\n",
            "\t        loss  complexity  \n",
            "\t0   1.972648           1  \n",
            "\t1   1.090820           2  \n",
            "\t2   0.596629           3  \n",
            "\t3   0.189415           5  \n",
            "\t4   0.000100           6  \n",
            "\t5   0.000100           8  \n",
            "\t6   0.000099          10  \n",
            "\t7   0.000099          12  \n",
            "\t8   0.000099          14  \n",
            "\t9   0.000099          16  \n",
            "\t10  0.000099          17  \n",
            "\t11  0.000099          18  \n",
            "]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Selected expression as SymPy:\")\n",
        "display(pysr_model.sympy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 56
        },
        "id": "_K6Qh2KMinrs",
        "outputId": "a4288342-b3fb-4461-944d-f1b1bb9b946b"
      },
      "id": "_K6Qh2KMinrs",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected expression as SymPy:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "x1*x1 + sin(x0)"
            ],
            "text/latex": "$\\displaystyle x_{1} x_{1} + \\sin{\\left(x_{0} \\right)}$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nFull Equation table:\")\n",
        "display(pysr_model.equations_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "VW3jpCrYieN3",
        "outputId": "1c08c97e-d733-4c9d-87ca-0168556a3897"
      },
      "id": "VW3jpCrYieN3",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Full Equation table:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    complexity      loss                                           equation  \\\n",
              "0            1  1.972648                                          1.3168591   \n",
              "1            2  1.090820                                            abs(x1)   \n",
              "2            3  0.596629                                            x1 * x1   \n",
              "3            5  0.189415                                     x0 + (x1 * x1)   \n",
              "4            6  0.000100                                (x1 * x1) + sin(x0)   \n",
              "5            8  0.000100                    sin(x0 * 1.0007796) + (x1 * x1)   \n",
              "6           10  0.000099       sin(x0) + abs(0.0005649085 - (x1 * abs(x1)))   \n",
              "7           12  0.000099  (cos(sin(0.015849385 / x0)) * sin(x0)) + (x1 *...   \n",
              "8           14  0.000099  sin(x0 * cos(sin(0.016463036 / x0))) + ((x1 * ...   \n",
              "9           16  0.000099  (sin(x0 * abs(cos(sin(sin(0.016463036 / x0))))...   \n",
              "10          17  0.000099  sin((0.54109997 / cos(abs(cos(0.014437185 / si...   \n",
              "11          18  0.000099  sin((0.54109997 / cos(abs(cos(0.014437185 / si...   \n",
              "\n",
              "       score                                       sympy_format  \\\n",
              "0   0.000000                                   1.31685910000000   \n",
              "1   0.592447                                            Abs(x1)   \n",
              "2   0.603390                                              x1*x1   \n",
              "3   0.573676                                         x0 + x1*x1   \n",
              "4   7.550035                                    x1*x1 + sin(x0)   \n",
              "5   0.000549                          x1*x1 + sin(x0*1.0007796)   \n",
              "6   0.001153           sin(x0) + Abs(x1*Abs(x1) - 0.0005649085)   \n",
              "7   0.001092           x1*x1 + sin(x0)*cos(sin(0.015849385/x0))   \n",
              "8   0.000217  x1*x1 + sin(x0*cos(sin(0.016463036/x0))) + 0.0...   \n",
              "9   0.000015  x1*x1 + sin(x0*Abs(cos(sin(sin(0.016463036/x0)...   \n",
              "10  0.002017  x1*x1 + sin(0.54109997*x0/cos(Abs(cos(0.014437...   \n",
              "11  0.000034  x1*x1 + sin(0.54109997*x0/cos(Abs(cos(0.014437...   \n",
              "\n",
              "                                        lambda_format  \n",
              "0                   PySRFunction(X=>1.31685910000000)  \n",
              "1                            PySRFunction(X=>Abs(x1))  \n",
              "2                              PySRFunction(X=>x1*x1)  \n",
              "3                         PySRFunction(X=>x0 + x1*x1)  \n",
              "4                    PySRFunction(X=>x1*x1 + sin(x0))  \n",
              "5          PySRFunction(X=>x1*x1 + sin(x0*1.0007796))  \n",
              "6   PySRFunction(X=>sin(x0) + Abs(x1*Abs(x1) - 0.0...  \n",
              "7   PySRFunction(X=>x1*x1 + sin(x0)*cos(sin(0.0158...  \n",
              "8   PySRFunction(X=>x1*x1 + sin(x0*cos(sin(0.01646...  \n",
              "9   PySRFunction(X=>x1*x1 + sin(x0*Abs(cos(sin(sin...  \n",
              "10  PySRFunction(X=>x1*x1 + sin(0.54109997*x0/cos(...  \n",
              "11  PySRFunction(X=>x1*x1 + sin(0.54109997*x0/cos(...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-171f2b40-1ecc-470f-a558-9e65c57e8c4a\" class=\"colab-df-container\">\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>complexity</th>\n",
              "      <th>loss</th>\n",
              "      <th>equation</th>\n",
              "      <th>score</th>\n",
              "      <th>sympy_format</th>\n",
              "      <th>lambda_format</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1.972648</td>\n",
              "      <td>1.3168591</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.31685910000000</td>\n",
              "      <td>PySRFunction(X=&gt;1.31685910000000)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1.090820</td>\n",
              "      <td>abs(x1)</td>\n",
              "      <td>0.592447</td>\n",
              "      <td>Abs(x1)</td>\n",
              "      <td>PySRFunction(X=&gt;Abs(x1))</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.596629</td>\n",
              "      <td>x1 * x1</td>\n",
              "      <td>0.603390</td>\n",
              "      <td>x1*x1</td>\n",
              "      <td>PySRFunction(X=&gt;x1*x1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>0.189415</td>\n",
              "      <td>x0 + (x1 * x1)</td>\n",
              "      <td>0.573676</td>\n",
              "      <td>x0 + x1*x1</td>\n",
              "      <td>PySRFunction(X=&gt;x0 + x1*x1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>(x1 * x1) + sin(x0)</td>\n",
              "      <td>7.550035</td>\n",
              "      <td>x1*x1 + sin(x0)</td>\n",
              "      <td>PySRFunction(X=&gt;x1*x1 + sin(x0))</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "      <td>0.000100</td>\n",
              "      <td>sin(x0 * 1.0007796) + (x1 * x1)</td>\n",
              "      <td>0.000549</td>\n",
              "      <td>x1*x1 + sin(x0*1.0007796)</td>\n",
              "      <td>PySRFunction(X=&gt;x1*x1 + sin(x0*1.0007796))</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>10</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>sin(x0) + abs(0.0005649085 - (x1 * abs(x1)))</td>\n",
              "      <td>0.001153</td>\n",
              "      <td>sin(x0) + Abs(x1*Abs(x1) - 0.0005649085)</td>\n",
              "      <td>PySRFunction(X=&gt;sin(x0) + Abs(x1*Abs(x1) - 0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>12</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>(cos(sin(0.015849385 / x0)) * sin(x0)) + (x1 *...</td>\n",
              "      <td>0.001092</td>\n",
              "      <td>x1*x1 + sin(x0)*cos(sin(0.015849385/x0))</td>\n",
              "      <td>PySRFunction(X=&gt;x1*x1 + sin(x0)*cos(sin(0.0158...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>14</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>sin(x0 * cos(sin(0.016463036 / x0))) + ((x1 * ...</td>\n",
              "      <td>0.000217</td>\n",
              "      <td>x1*x1 + sin(x0*cos(sin(0.016463036/x0))) + 0.0...</td>\n",
              "      <td>PySRFunction(X=&gt;x1*x1 + sin(x0*cos(sin(0.01646...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>16</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>(sin(x0 * abs(cos(sin(sin(0.016463036 / x0))))...</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>x1*x1 + sin(x0*Abs(cos(sin(sin(0.016463036/x0)...</td>\n",
              "      <td>PySRFunction(X=&gt;x1*x1 + sin(x0*Abs(cos(sin(sin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>17</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>sin((0.54109997 / cos(abs(cos(0.014437185 / si...</td>\n",
              "      <td>0.002017</td>\n",
              "      <td>x1*x1 + sin(0.54109997*x0/cos(Abs(cos(0.014437...</td>\n",
              "      <td>PySRFunction(X=&gt;x1*x1 + sin(0.54109997*x0/cos(...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>18</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>sin((0.54109997 / cos(abs(cos(0.014437185 / si...</td>\n",
              "      <td>0.000034</td>\n",
              "      <td>x1*x1 + sin(0.54109997*x0/cos(Abs(cos(0.014437...</td>\n",
              "      <td>PySRFunction(X=&gt;x1*x1 + sin(0.54109997*x0/cos(...</td>\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-171f2b40-1ecc-470f-a558-9e65c57e8c4a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-171f2b40-1ecc-470f-a558-9e65c57e8c4a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(pysr_model\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"complexity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 1,\n        \"max\": 18,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          17,\n          16,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6206792386432399,\n        \"min\": 9.884581e-05,\n        \"max\": 1.9726484,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          9.884914e-05,\n          9.9048695e-05,\n          1.9726484\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"equation\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"sin((0.54109997 / cos(abs(cos(0.014437185 / sin(sin(x0)))))) * x0) + (x1 * x1)\",\n          \"(sin(x0 * abs(cos(sin(sin(0.016463036 / x0))))) + (x1 * x1)) + 0.00016871947\",\n          \"1.3168591\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1490270809201455,\n        \"min\": 0.0,\n        \"max\": 7.550034783329842,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.0020167483655096174,\n          1.5244794295114738e-05,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sympy_format\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"x1*x1 + sin(0.54109997*x0/cos(Abs(cos(0.014437185/sin(sin(x0))))))\",\n          \"x1*x1 + sin(x0*Abs(cos(sin(sin(0.016463036/x0))))) + 0.00016871947\",\n          \"1.31685910000000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lambda_format\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"PySRFunction(X=>x1*x1 + sin(0.54109997*x0/cos(Abs(cos(0.014437185/sin(sin(x0)))))))\",\n          \"PySRFunction(X=>x1*x1 + sin(x0*Abs(cos(sin(sin(0.016463036/x0))))) + 0.00016871947)\",\n          \"PySRFunction(X=>1.31685910000000)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n\\nBest row from equation table:\")\n",
        "display(pysr_model.get_best())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "wYlnR-ntirOG",
        "outputId": "187a0811-72b0-4b1d-9064-72bf0c04de23"
      },
      "id": "wYlnR-ntirOG",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Best row from equation table:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "complexity                                      6\n",
              "loss                                       0.0001\n",
              "equation                      (x1 * x1) + sin(x0)\n",
              "score                                    7.550035\n",
              "sympy_format                      x1*x1 + sin(x0)\n",
              "lambda_format    PySRFunction(X=>x1*x1 + sin(x0))\n",
              "Name: 4, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>complexity</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loss</th>\n",
              "      <td>0.0001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>equation</th>\n",
              "      <td>(x1 * x1) + sin(x0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>score</th>\n",
              "      <td>7.550035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sympy_format</th>\n",
              "      <td>x1*x1 + sin(x0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lambda_format</th>\n",
              "      <td>PySRFunction(X=&gt;x1*x1 + sin(x0))</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we evaluate the selected expression on the test set and compare it with the polynomial baseline."
      ],
      "metadata": {
        "id": "R5Cr-UHdk2Hq"
      },
      "id": "R5Cr-UHdk2Hq"
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_pysr = pysr_model.predict(X_test)\n",
        "\n",
        "mse_pysr = mean_squared_error(y_test, y_pred_pysr)\n",
        "\n",
        "print(f\"Polynomial baseline MSE: {mse_poly:.6f}\")\n",
        "print(f\"PySR test MSE          : {mse_pysr:.6f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UCRsajVA8fRU",
        "outputId": "6d39dcb5-1127-4bc8-9ea4-70d63499490d"
      },
      "id": "UCRsajVA8fRU",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polynomial baseline MSE: 0.027556\n",
            "PySR test MSE          : 0.000102\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can plot the predictions made by PySR and the polynomial fit."
      ],
      "metadata": {
        "id": "jgu0O4M3lI8O"
      },
      "id": "jgu0O4M3lI8O"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6, 5))\n",
        "\n",
        "plt.scatter(y_test, y_pred_poly, s=7, c=\"r\", alpha=0.65, label=\"Polynomial regression\")\n",
        "plt.scatter(y_test, y_pred_pysr, s=7, c=\"g\", alpha=0.65, label=\"PySR\")\n",
        "\n",
        "lo = min(y_test.min(), y_pred_poly.min(), y_pred_pysr.min())\n",
        "hi = max(y_test.max(), y_pred_poly.max(), y_pred_pysr.max())\n",
        "\n",
        "plt.plot([lo, hi], [lo, hi], linestyle=\"--\", alpha=0.5, label=\"Perfect prediction\")\n",
        "\n",
        "plt.xlabel(\"True $y$\")\n",
        "plt.ylabel(\"Predicted $y$\")\n",
        "plt.title(\"Prediction vs truth\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "xRt-GkLNDBrE",
        "outputId": "02d77988-427c-4481-b1c5-e0dbe5f42576"
      },
      "id": "xRt-GkLNDBrE",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7. Two quick physics examples\n",
        "\n",
        "We now run PySR on two simple physics-inspired examples.\n",
        "\n",
        "The goal is to show that when the underlying relation is simple and the right operators are allowed, symbolic regression can recover compact formulas."
      ],
      "metadata": {
        "id": "F5su9RePCK73"
      },
      "id": "F5su9RePCK73"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.1 Hooke's law\n",
        "\n",
        "For a spring, Hooke's law is\n",
        "\n",
        "$$\n",
        "F = -kx.\n",
        "$$\n",
        "\n",
        "Here $x$ is the displacement, $k$ is the spring constant, and $F$ is the force.\n",
        "\n",
        "This is a linear relation, so ordinary linear regression would also recover it. We use it here as a simple check that PySR can find a compact expression."
      ],
      "metadata": {
        "id": "hrzzTgXmCTT_"
      },
      "id": "hrzzTgXmCTT_"
    },
    {
      "cell_type": "code",
      "source": [
        "k = 3.0\n",
        "\n",
        "x = np.linspace(-10, 10, 200)\n",
        "X_hooke = x.reshape(-1, 1)\n",
        "\n",
        "F = -k * x"
      ],
      "metadata": {
        "id": "dVcEEAMlCVLE"
      },
      "id": "dVcEEAMlCVLE",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hooke_model = PySRRegressor(niterations=80,\n",
        "                            binary_operators=[\"+\", \"-\", \"*\"],\n",
        "                            unary_operators=[],\n",
        "                            maxsize=10,\n",
        "                            model_selection=\"best\")"
      ],
      "metadata": {
        "id": "GKYUXzezCdGM"
      },
      "id": "GKYUXzezCdGM",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hooke_model.fit(X_hooke, F, variable_names=[\"x\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "riK6zUuTCnPp",
        "outputId": "40916dcc-2df3-4597-941f-4d63bc978010"
      },
      "id": "riK6zUuTCnPp",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/pysr/sr.py:2811: UserWarning: Note: it looks like you are running in Jupyter. The progress bar will be turned off.\n",
            "  warnings.warn(\n",
            "[ Info: Started!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Expressions evaluated per second: 1.600e+03\n",
            "Progress: 120 / 2480 total iterations (4.839%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.030e+02  0.000e+00  y = -0.00011234\n",
            "3           5.328e-13  1.699e+01  y = x * -3\n",
            "7           5.199e-13  6.111e-03  y = ((x * -2.7293) - 1.359e-07) * 1.0992\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 4.410e+04\n",
            "Progress: 547 / 2480 total iterations (22.056%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.030e+02  0.000e+00  y = -0.00011234\n",
            "3           5.328e-13  1.699e+01  y = x * -3\n",
            "5           5.147e-13  1.723e-02  y = (x * -2.7293) * 1.0992\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 4.770e+04\n",
            "Progress: 895 / 2480 total iterations (36.089%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.030e+02  0.000e+00  y = -0.00011234\n",
            "3           5.328e-13  1.699e+01  y = x * -3\n",
            "5           5.147e-13  1.723e-02  y = (x * -2.7293) * 1.0992\n",
            "7           4.468e-13  7.076e-02  y = (x * -1.2392) - (x * 1.7608)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 4.730e+04\n",
            "Progress: 1156 / 2480 total iterations (46.613%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.030e+02  0.000e+00  y = -0.00011234\n",
            "3           5.328e-13  1.699e+01  y = x * -3\n",
            "5           5.147e-13  1.723e-02  y = (x * -2.7293) * 1.0992\n",
            "7           4.468e-13  7.076e-02  y = (x * -1.2392) - (x * 1.7608)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 5.660e+04\n",
            "Progress: 1491 / 2480 total iterations (60.121%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.030e+02  0.000e+00  y = -0.00011234\n",
            "3           5.328e-13  1.699e+01  y = x * -3\n",
            "5           5.147e-13  1.723e-02  y = (x * -2.7293) * 1.0992\n",
            "7           4.468e-13  7.076e-02  y = (x * -1.2392) - (x * 1.7608)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 5.420e+04\n",
            "Progress: 1910 / 2480 total iterations (77.016%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.030e+02  0.000e+00  y = -0.00011234\n",
            "3           5.328e-13  1.699e+01  y = x * -3\n",
            "5           5.147e-13  1.723e-02  y = (x * -2.7293) * 1.0992\n",
            "7           4.333e-13  8.611e-02  y = (x * -1.2048) + (x * -1.7952)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 5.420e+04\n",
            "Progress: 2216 / 2480 total iterations (89.355%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.030e+02  0.000e+00  y = -0.00011234\n",
            "3           5.328e-13  1.699e+01  y = x * -3\n",
            "5           5.147e-13  1.723e-02  y = (x * -2.7293) * 1.0992\n",
            "7           4.333e-13  8.611e-02  y = (x * -1.2048) + (x * -1.7952)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.030e+02  0.000e+00  y = -0.00011234\n",
            "3           5.328e-13  1.699e+01  y = x * -3\n",
            "5           5.147e-13  1.723e-02  y = (x * -2.7293) * 1.0992\n",
            "7           4.333e-13  8.611e-02  y = (x * -1.2048) + (x * -1.7952)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[ Info: Final population:\n",
            "[ Info: Results saved to:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PySRRegressor.equations_ = [\n",
              "\t   pick      score                             equation          loss  \\\n",
              "\t0         0.000000                       -0.00011234348  3.030150e+02   \n",
              "\t1  >>>>  16.987239                             x * -3.0  5.327650e-13   \n",
              "\t2         0.017231         (x * -2.7292807) * 1.0991907  5.147172e-13   \n",
              "\t3         0.086106  (x * -1.2047834) + (x * -1.7952166)  4.332890e-13   \n",
              "\t\n",
              "\t   complexity  \n",
              "\t0           1  \n",
              "\t1           3  \n",
              "\t2           5  \n",
              "\t3           7  \n",
              "]"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PySRRegressor.equations_ = [\n",
              "\t   pick      score                             equation          loss  \\\n",
              "\t0         0.000000                       -0.00011234348  3.030150e+02   \n",
              "\t1  &gt;&gt;&gt;&gt;  16.987239                             x * -3.0  5.327650e-13   \n",
              "\t2         0.017231         (x * -2.7292807) * 1.0991907  5.147172e-13   \n",
              "\t3         0.086106  (x * -1.2047834) + (x * -1.7952166)  4.332890e-13   \n",
              "\t\n",
              "\t   complexity  \n",
              "\t0           1  \n",
              "\t1           3  \n",
              "\t2           5  \n",
              "\t3           7  \n",
              "]</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PySRRegressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>PySRRegressor.equations_ = [\n",
              "\t   pick      score                             equation          loss  \\\n",
              "\t0         0.000000                       -0.00011234348  3.030150e+02   \n",
              "\t1  &gt;&gt;&gt;&gt;  16.987239                             x * -3.0  5.327650e-13   \n",
              "\t2         0.017231         (x * -2.7292807) * 1.0991907  5.147172e-13   \n",
              "\t3         0.086106  (x * -1.2047834) + (x * -1.7952166)  4.332890e-13   \n",
              "\t\n",
              "\t   complexity  \n",
              "\t0           1  \n",
              "\t1           3  \n",
              "\t2           5  \n",
              "\t3           7  \n",
              "]</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  - outputs/20260523_042032_KGH07x/hall_of_fame.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Selected expression:\")\n",
        "print(hooke_model)\n",
        "\n",
        "print(\"\\nBest equation:\")\n",
        "print(hooke_model.sympy())\n",
        "#display(hooke_model.sympy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "wlJodMP4CxQW",
        "outputId": "d202caae-4679-4536-eba8-2c1f2f496fae"
      },
      "id": "wlJodMP4CxQW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected expression:\n",
            "PySRRegressor.equations_ = [\n",
            "\t   pick      score                             equation          loss  \\\n",
            "\t0         0.000000                       -0.00011234348  3.030150e+02   \n",
            "\t1  >>>>  16.987239                             x * -3.0  5.327650e-13   \n",
            "\t2         0.017231         (x * -2.7292807) * 1.0991907  5.147172e-13   \n",
            "\t3         0.086106  (x * -1.2047834) + (x * -1.7952166)  4.332890e-13   \n",
            "\t\n",
            "\t   complexity  \n",
            "\t0           1  \n",
            "\t1           3  \n",
            "\t2           5  \n",
            "\t3           7  \n",
            "]\n",
            "\n",
            "Best equation:\n",
            "x*(-3.0)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "x*(-3.0)"
            ],
            "text/latex": "$\\displaystyle x \\left(-3.0\\right)$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "F_pred = hooke_model.predict(X_hooke)\n",
        "\n",
        "mse_hooke = mean_squared_error(F, F_pred)\n",
        "\n",
        "print(f\"MSE          : {mse_hooke:.2e}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F4ImQCJKEZdU",
        "outputId": "57e002ff-ed23-4172-90dc-dae724e14d4b"
      },
      "id": "F4ImQCJKEZdU",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE          : 0.00e+00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "F_pred = hooke_model.predict(X_hooke)\n",
        "\n",
        "plt.figure(figsize=(6, 4))\n",
        "plt.scatter(x, F, s=4, c=\"r\", label=\"True force\")\n",
        "plt.plot(x, F_pred, label=\"PySR prediction\")\n",
        "\n",
        "plt.xlabel(\"Displacement $x$\")\n",
        "plt.ylabel(\"Force $F$\")\n",
        "plt.title(\"Hooke's law\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "mzwfYKBXdyrA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "06a8386b-cc64-4dc7-83b8-826deed8586a"
      },
      "id": "mzwfYKBXdyrA",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.2 Projectile motion\n",
        "\n",
        "For vertical motion under constant acceleration, the height is\n",
        "\n",
        "$$\n",
        "h(t) = v_0 t - \\frac{1}{2} g t^2.\n",
        "$$\n",
        "\n",
        "This is a quadratic function of time. A polynomial fit could also recover it, but PySR searches for the expression directly from the data."
      ],
      "metadata": {
        "id": "lmeuzxIuDL90"
      },
      "id": "lmeuzxIuDL90"
    },
    {
      "cell_type": "code",
      "source": [
        "g = 9.8\n",
        "v0 = 20.0\n",
        "\n",
        "t = np.linspace(0, 4, 200)\n",
        "X_proj = t.reshape(-1, 1)\n",
        "\n",
        "h = v0 * t - 0.5 * g * t**2"
      ],
      "metadata": {
        "id": "Qjbt87VPDQJ0"
      },
      "id": "Qjbt87VPDQJ0",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "proj_model = PySRRegressor(niterations=120,\n",
        "                            binary_operators=[\"+\", \"-\", \"*\"],\n",
        "                            unary_operators=[],\n",
        "                            maxsize=15,\n",
        "                            model_selection=\"best\")"
      ],
      "metadata": {
        "id": "_1L0IpfvDUKe"
      },
      "id": "_1L0IpfvDUKe",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "proj_model.fit(X_proj, h, variable_names=[\"t\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Svk_kEtMDc-s",
        "outputId": "01efcd72-77db-429b-fa3e-01c20ffdc62b"
      },
      "id": "Svk_kEtMDc-s",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/pysr/sr.py:2811: UserWarning: Note: it looks like you are running in Jupyter. The progress bar will be turned off.\n",
            "  warnings.warn(\n",
            "[ Info: Started!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Expressions evaluated per second: 1.130e+05\n",
            "Progress: 656 / 3720 total iterations (17.634%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.505e+01  0.000e+00  y = 13.801\n",
            "5           3.483e+01  1.541e-03  y = 13 - (t * -0.40027)\n",
            "7           8.963e-13  1.565e+01  y = (t * (t + -4.0816)) * -4.9\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 9.810e+04\n",
            "Progress: 1052 / 3720 total iterations (28.280%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.505e+01  0.000e+00  y = 13.801\n",
            "5           3.483e+01  1.541e-03  y = 13 - (t * -0.40027)\n",
            "7           8.963e-13  1.565e+01  y = (t * (t + -4.0816)) * -4.9\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 8.090e+04\n",
            "Progress: 1310 / 3720 total iterations (35.215%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.505e+01  0.000e+00  y = 13.801\n",
            "5           3.483e+01  1.541e-03  y = (t * 0.39992) - -13.001\n",
            "7           8.963e-13  1.565e+01  y = (t * (t + -4.0816)) * -4.9\n",
            "9           8.962e-13  8.405e-05  y = ((t * (t + -4.0816)) * -4.9) - 1.1183e-07\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 7.260e+04\n",
            "Progress: 1573 / 3720 total iterations (42.285%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.505e+01  0.000e+00  y = 13.801\n",
            "5           3.483e+01  1.541e-03  y = (t * 0.39992) - -13.001\n",
            "7           8.963e-13  1.565e+01  y = (t * (t + -4.0816)) * -4.9\n",
            "9           8.962e-13  8.405e-05  y = ((t * (t + -4.0816)) * -4.9) - 1.1183e-07\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 5.990e+04\n",
            "Progress: 1784 / 3720 total iterations (47.957%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.505e+01  0.000e+00  y = 13.801\n",
            "5           3.483e+01  1.541e-03  y = (t * 0.39992) - -13.001\n",
            "7           8.963e-13  1.565e+01  y = (t * (t + -4.0816)) * -4.9\n",
            "9           8.962e-13  8.405e-05  y = ((t * (t + -4.0816)) * -4.9) - 1.1183e-07\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 5.220e+04\n",
            "Progress: 2140 / 3720 total iterations (57.527%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.505e+01  0.000e+00  y = 13.801\n",
            "5           3.483e+01  1.541e-03  y = (t * 0.39992) - -13.001\n",
            "7           8.963e-13  1.565e+01  y = (t * (t + -4.0816)) * -4.9\n",
            "9           8.962e-13  8.405e-05  y = ((t * (t + -4.0816)) * -4.9) - 1.1183e-07\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 4.820e+04\n",
            "Progress: 2328 / 3720 total iterations (62.581%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.505e+01  0.000e+00  y = 13.801\n",
            "5           3.483e+01  1.541e-03  y = (t * 0.39992) - -13.001\n",
            "7           8.963e-13  1.565e+01  y = (t * (t + -4.0816)) * -4.9\n",
            "9           8.962e-13  8.405e-05  y = ((t * (t + -4.0816)) * -4.9) - 1.1183e-07\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 4.410e+04\n",
            "Progress: 2551 / 3720 total iterations (68.575%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.505e+01  0.000e+00  y = 13.801\n",
            "5           3.483e+01  1.541e-03  y = (t * 0.39992) - -13.001\n",
            "7           8.963e-13  1.565e+01  y = (t * (t + -4.0816)) * -4.9\n",
            "9           8.962e-13  8.405e-05  y = ((t * (t + -4.0816)) * -4.9) - 1.1183e-07\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 4.860e+04\n",
            "Progress: 2876 / 3720 total iterations (77.312%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.505e+01  0.000e+00  y = 13.801\n",
            "5           3.483e+01  1.541e-03  y = (t * 0.39992) - -13.001\n",
            "7           8.963e-13  1.565e+01  y = (t * (t + -4.0816)) * -4.9\n",
            "9           8.962e-13  8.405e-05  y = ((t * (t + -4.0816)) * -4.9) - 1.1183e-07\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 4.450e+04\n",
            "Progress: 3158 / 3720 total iterations (84.892%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.505e+01  0.000e+00  y = 13.801\n",
            "5           3.483e+01  1.541e-03  y = (t * 0.39992) - -13.001\n",
            "7           8.963e-13  1.565e+01  y = (t * (t + -4.0816)) * -4.9\n",
            "9           8.962e-13  8.405e-05  y = ((t * (t + -4.0816)) * -4.9) - 1.1183e-07\n",
            "11          8.904e-13  3.241e-03  y = ((t * (t + -4.0816)) * -4.9) - (1.1183e-07 * -1.341)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 4.850e+04\n",
            "Progress: 3423 / 3720 total iterations (92.016%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.505e+01  0.000e+00  y = 13.801\n",
            "5           3.483e+01  1.541e-03  y = (t * 0.39992) - -13.001\n",
            "7           8.963e-13  1.565e+01  y = (t * (t + -4.0816)) * -4.9\n",
            "9           8.962e-13  8.405e-05  y = ((t * (t + -4.0816)) * -4.9) - 1.1183e-07\n",
            "11          8.904e-13  3.241e-03  y = ((t * (t + -4.0816)) * -4.9) - (1.1183e-07 * -1.341)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           3.505e+01  0.000e+00  y = 13.801\n",
            "5           3.483e+01  1.541e-03  y = (t * 0.39992) - -13.001\n",
            "7           8.963e-13  1.565e+01  y = (t * (t + -4.0816)) * -4.9\n",
            "9           8.962e-13  8.405e-05  y = ((t * (t + -4.0816)) * -4.9) - 1.1183e-07\n",
            "11          8.904e-13  3.241e-03  y = ((t * (t + -4.0816)) * -4.9) - (1.1183e-07 * -1.341)\n",
            "13          8.878e-13  1.454e-03  y = ((t * (t + -4.0816)) * -4.9) - (1.1183e-07 * (-0.54897...\n",
            "                                       * t))\n",
            "15          8.674e-13  1.166e-02  y = ((t * (t + -4.0816)) * -4.9) - (1.1183e-07 * (-0.54897...\n",
            "                                       * (t + t)))\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[ Info: Final population:\n",
            "[ Info: Results saved to:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PySRRegressor.equations_ = [\n",
              "\t   pick      score                                           equation  \\\n",
              "\t0         0.000000                                          13.800853   \n",
              "\t1         0.001542                      (t * 0.39991626) - -13.001208   \n",
              "\t2  >>>>  15.645531                      (t * (t + -4.0816326)) * -4.9   \n",
              "\t3         0.000084     ((t * (t + -4.0816326)) * -4.9) - 1.1183147e-7   \n",
              "\t4         0.003241  ((t * (t + -4.0816326)) * -4.9) - (1.1183147e-...   \n",
              "\t5         0.001454  ((t * (t + -4.0816326)) * -4.9) - (1.1183147e-...   \n",
              "\t6         0.011659  ((t * (t + -4.0816326)) * -4.9) - (1.1183147e-...   \n",
              "\t\n",
              "\t           loss  complexity  \n",
              "\t0  3.505025e+01           1  \n",
              "\t1  3.483477e+01           5  \n",
              "\t2  8.963497e-13           7  \n",
              "\t3  8.961990e-13           9  \n",
              "\t4  8.904091e-13          11  \n",
              "\t5  8.878231e-13          13  \n",
              "\t6  8.673595e-13          15  \n",
              "]"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PySRRegressor.equations_ = [\n",
              "\t   pick      score                                           equation  \\\n",
              "\t0         0.000000                                          13.800853   \n",
              "\t1         0.001542                      (t * 0.39991626) - -13.001208   \n",
              "\t2  &gt;&gt;&gt;&gt;  15.645531                      (t * (t + -4.0816326)) * -4.9   \n",
              "\t3         0.000084     ((t * (t + -4.0816326)) * -4.9) - 1.1183147e-7   \n",
              "\t4         0.003241  ((t * (t + -4.0816326)) * -4.9) - (1.1183147e-...   \n",
              "\t5         0.001454  ((t * (t + -4.0816326)) * -4.9) - (1.1183147e-...   \n",
              "\t6         0.011659  ((t * (t + -4.0816326)) * -4.9) - (1.1183147e-...   \n",
              "\t\n",
              "\t           loss  complexity  \n",
              "\t0  3.505025e+01           1  \n",
              "\t1  3.483477e+01           5  \n",
              "\t2  8.963497e-13           7  \n",
              "\t3  8.961990e-13           9  \n",
              "\t4  8.904091e-13          11  \n",
              "\t5  8.878231e-13          13  \n",
              "\t6  8.673595e-13          15  \n",
              "]</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PySRRegressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>PySRRegressor.equations_ = [\n",
              "\t   pick      score                                           equation  \\\n",
              "\t0         0.000000                                          13.800853   \n",
              "\t1         0.001542                      (t * 0.39991626) - -13.001208   \n",
              "\t2  &gt;&gt;&gt;&gt;  15.645531                      (t * (t + -4.0816326)) * -4.9   \n",
              "\t3         0.000084     ((t * (t + -4.0816326)) * -4.9) - 1.1183147e-7   \n",
              "\t4         0.003241  ((t * (t + -4.0816326)) * -4.9) - (1.1183147e-...   \n",
              "\t5         0.001454  ((t * (t + -4.0816326)) * -4.9) - (1.1183147e-...   \n",
              "\t6         0.011659  ((t * (t + -4.0816326)) * -4.9) - (1.1183147e-...   \n",
              "\t\n",
              "\t           loss  complexity  \n",
              "\t0  3.505025e+01           1  \n",
              "\t1  3.483477e+01           5  \n",
              "\t2  8.963497e-13           7  \n",
              "\t3  8.961990e-13           9  \n",
              "\t4  8.904091e-13          11  \n",
              "\t5  8.878231e-13          13  \n",
              "\t6  8.673595e-13          15  \n",
              "]</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  - outputs/20260523_042407_gwPWPS/hall_of_fame.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Selected expression:\")\n",
        "print(proj_model)\n",
        "\n",
        "print(\"\\nBest equation:\")\n",
        "print(proj_model.sympy())\n",
        "#display(proj_model.sympy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jZENHSLwDy5m",
        "outputId": "31d933f4-d016-4405-a252-4080c7eee3d1"
      },
      "id": "jZENHSLwDy5m",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected expression:\n",
            "PySRRegressor.equations_ = [\n",
            "\t   pick      score                                           equation  \\\n",
            "\t0         0.000000                                          13.800853   \n",
            "\t1         0.001542                      (t * 0.39991626) - -13.001208   \n",
            "\t2  >>>>  15.645531                      (t * (t + -4.0816326)) * -4.9   \n",
            "\t3         0.000084     ((t * (t + -4.0816326)) * -4.9) - 1.1183147e-7   \n",
            "\t4         0.003241  ((t * (t + -4.0816326)) * -4.9) - (1.1183147e-...   \n",
            "\t5         0.001454  ((t * (t + -4.0816326)) * -4.9) - (1.1183147e-...   \n",
            "\t6         0.011659  ((t * (t + -4.0816326)) * -4.9) - (1.1183147e-...   \n",
            "\t\n",
            "\t           loss  complexity  \n",
            "\t0  3.505025e+01           1  \n",
            "\t1  3.483477e+01           5  \n",
            "\t2  8.963497e-13           7  \n",
            "\t3  8.961990e-13           9  \n",
            "\t4  8.904091e-13          11  \n",
            "\t5  8.878231e-13          13  \n",
            "\t6  8.673595e-13          15  \n",
            "]\n",
            "\n",
            "Best equation:\n",
            "t*(t - 4.0816326)*(-4.9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "h_pred = proj_model.predict(X_proj)\n",
        "\n",
        "mse_proj = mean_squared_error(h, h_pred)\n",
        "\n",
        "print(f\"MSE          : {mse_proj:.2e}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nwdS8vIgE1HY",
        "outputId": "4a394341-e295-4eb0-bfc1-d4dccf1541d3"
      },
      "id": "nwdS8vIgE1HY",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE          : 3.61e-13\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "h_pred = proj_model.predict(X_proj)\n",
        "\n",
        "plt.figure(figsize=(6, 4))\n",
        "plt.scatter(t, h, s=6, c=\"r\", label=\"True height\")\n",
        "plt.plot(t, h_pred, label=\"PySR prediction\")\n",
        "\n",
        "plt.xlabel(\"Time $t$\")\n",
        "plt.ylabel(\"Height $h(t)$\")\n",
        "plt.title(\"Projectile motion\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "BYQF-LfOzurc",
        "outputId": "eaefca90-f84c-4faf-c381-300fdf093c65"
      },
      "id": "BYQF-LfOzurc",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f592b546",
      "metadata": {
        "id": "f592b546"
      },
      "source": [
        "## 7. Classification-style symbolic regression\n",
        "\n",
        "Symbolic regression can also be used in classification-style problems.\n",
        "\n",
        "Instead of asking PySR to output a calibrated probability, we can train it to produce a **score**. For binary classification, we encode the labels as\n",
        "\n",
        "$$\n",
        "y \\in \\{-1, +1\\}.\n",
        "$$\n",
        "\n",
        "Then the sign of the symbolic expression gives the predicted class:\n",
        "\n",
        "$$\n",
        "\\hat{y} = \\mathrm{sign}(f(x)).\n",
        "$$\n",
        "\n",
        "As a simple example, we generate points in the $(x_0, x_1)$ plane and classify whether they are inside or outside a circle.\n",
        "\n",
        "The ideal decision boundary is\n",
        "\n",
        "$$\n",
        "x_0^2 + x_1^2 = R^2.\n",
        "$$\n",
        "\n",
        "Equivalently, a useful symbolic score is\n",
        "\n",
        "$$\n",
        "f(x_0, x_1) = R^2 - x_0^2 - x_1^2.\n",
        "$$\n",
        "\n",
        "The boundary is where $f(x_0, x_1)=0$.\n",
        "\n",
        "In this classification setup, however, PySR is trained only on the hard labels $-1$ and $+1$. Therefore, we should expect it to learn a useful separating score, but not necessarily the exact circle equation."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def make_circle_classification(n_samples=1500, radius=1.0, noise=0.10, seed=42):\n",
        "    rng = np.random.default_rng(seed)\n",
        "    X = rng.uniform(-2.0, 2.0, size=(n_samples, 2))\n",
        "    # Positive inside the circle, negative outside the circle\n",
        "    true_score = radius**2 - (X[:, 0]**2 + X[:, 1]**2)\n",
        "    noisy_score = true_score + noise * rng.normal(size=n_samples)\n",
        "    y01 = (noisy_score > 0).astype(int)\n",
        "    ypm = 2*y01 - 1\n",
        "\n",
        "    return X, y01, ypm, true_score\n",
        "\n",
        "\n",
        "Xc, y01, ypm, true_score = make_circle_classification(n_samples=2000, radius=1.0, noise=0.1, seed=42)"
      ],
      "metadata": {
        "id": "D_5xTjytFm37"
      },
      "id": "D_5xTjytFm37",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6, 6))\n",
        "plt.scatter(Xc[:, 0], Xc[:, 1], c=y01, s=8, alpha=0.7)\n",
        "\n",
        "theta = np.linspace(0, 2*np.pi, 300)\n",
        "plt.plot(1.0*np.cos(theta), 1.0*np.sin(theta), linestyle=\"--\", linewidth=2, label=\"True boundary\")\n",
        "\n",
        "plt.xlabel(\"$x_0$\")\n",
        "plt.ylabel(\"$x_1$\")\n",
        "plt.title(\"Circle classification toy data\")\n",
        "plt.axis(\"equal\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "gQRFqLxVI4-3",
        "outputId": "e9b0beae-b8f5-463e-aa47-320677fe7faf"
      },
      "id": "gQRFqLxVI4-3",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Xc_train, Xc_test, yc_train, yc_test = train_test_split(Xc, ypm, test_size=0.25, random_state=42, stratify=ypm)\n",
        "\n",
        "print(\"Train:\", Xc_train.shape, yc_train.shape)\n",
        "print(\"Test :\", Xc_test.shape, yc_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5XR6g8s1GDfy",
        "outputId": "5cc0c5ec-fa9f-4e10-d58f-a95548ae37cd"
      },
      "id": "5XR6g8s1GDfy",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train: (1500, 2) (1500,)\n",
            "Test : (500, 2) (500,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For this example, we keep the operator set very simple. The circle boundary only needs addition, subtraction, and multiplication:\n",
        "\n",
        "$$\n",
        "R^2 - x_0^2 - x_1^2.\n",
        "$$\n",
        "\n",
        "Since $x_0^2$ can be written as $x_0*x_0$, we do not need to add a separate square operator."
      ],
      "metadata": {
        "id": "-TzfV94sGTX6"
      },
      "id": "-TzfV94sGTX6"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "021e6f24",
      "metadata": {
        "id": "021e6f24"
      },
      "outputs": [],
      "source": [
        "cls_symbolic = PySRRegressor(niterations=50,\n",
        "                            population_size=30,\n",
        "                            maxsize=12,\n",
        "                            binary_operators=[\"+\", \"-\", \"*\"],\n",
        "                            unary_operators=[],\n",
        "                            #extra_sympy_mappings={\"square\": lambda x: x**2},\n",
        "                            model_selection=\"best\",\n",
        "                            elementwise_loss=\"loss(prediction, target) = (prediction - target)^2\",\n",
        "                            parsimony=0.01,\n",
        "                            random_state=42)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cls_symbolic.fit(Xc_train, yc_train, variable_names=[\"x0\", \"x1\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TzAEvI6AJbqe",
        "outputId": "963fbd99-319c-4450-edec-342cfd888288"
      },
      "id": "TzAEvI6AJbqe",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/pysr/sr.py:2811: UserWarning: Note: it looks like you are running in Jupyter. The progress bar will be turned off.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.12/dist-packages/pysr/sr.py:1873: UserWarning: Note: Setting `random_state` without also setting `deterministic=True` and `parallelism='serial'` will result in non-deterministic searches.\n",
            "  warnings.warn(\n",
            "[ Info: Started!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Expressions evaluated per second: 5.260e+04\n",
            "Progress: 323 / 1550 total iterations (20.839%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           6.527e-01  0.000e+00  y = -0.58933\n",
            "5           5.143e-01  5.958e-02  y = x1 * (x1 * -0.38797)\n",
            "7           5.039e-01  1.014e-02  y = ((x1 * x1) * -0.32402) + -0.15338\n",
            "9           3.941e-01  1.229e-01  y = ((x0 * x0) + (x1 * x1)) * -0.24728\n",
            "11          3.937e-01  5.720e-04  y = ((x0 * -0.23563) * x0) + ((x1 * x1) * -0.24728)\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 5.930e+04\n",
            "Progress: 722 / 1550 total iterations (46.581%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           6.527e-01  0.000e+00  y = -0.58933\n",
            "5           5.143e-01  5.958e-02  y = x1 * (x1 * -0.38797)\n",
            "7           5.039e-01  1.014e-02  y = ((x1 * x1) * -0.32402) + -0.15338\n",
            "9           3.941e-01  1.229e-01  y = ((x0 * x0) + (x1 * x1)) * -0.24728\n",
            "11          3.885e-01  7.226e-03  y = (((x0 * x0) + (x1 * x1)) * -0.24728) - -0.11295\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 5.890e+04\n",
            "Progress: 1051 / 1550 total iterations (67.806%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           6.527e-01  0.000e+00  y = -0.58933\n",
            "5           5.143e-01  5.958e-02  y = x1 * (x1 * -0.38797)\n",
            "7           5.039e-01  1.014e-02  y = ((x1 * x1) * -0.32402) + -0.15338\n",
            "9           3.941e-01  1.229e-01  y = ((x0 * x0) + (x1 * x1)) * -0.24728\n",
            "11          3.885e-01  7.226e-03  y = (((x0 * x0) + (x1 * x1)) * -0.24728) - -0.11295\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 5.430e+04\n",
            "Progress: 1289 / 1550 total iterations (83.161%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           6.527e-01  0.000e+00  y = -0.58933\n",
            "5           5.143e-01  5.958e-02  y = x1 * (x1 * -0.38797)\n",
            "7           5.039e-01  1.014e-02  y = ((x1 * x1) * -0.32402) + -0.15338\n",
            "9           3.941e-01  1.229e-01  y = ((x0 * x0) + (x1 * x1)) * -0.24728\n",
            "11          3.877e-01  8.219e-03  y = (((x0 * x0) + (x1 * x1)) + -0.3893) * -0.24728\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "\n",
            "Expressions evaluated per second: 4.720e+04\n",
            "Progress: 1427 / 1550 total iterations (92.065%)\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           6.527e-01  0.000e+00  y = -0.58933\n",
            "5           5.143e-01  5.958e-02  y = x1 * (x1 * -0.38797)\n",
            "7           5.039e-01  1.014e-02  y = ((x1 * x1) * -0.32402) + -0.15338\n",
            "9           3.941e-01  1.229e-01  y = ((x0 * x0) + (x1 * x1)) * -0.24728\n",
            "11          3.877e-01  8.219e-03  y = (((x0 * x0) + (x1 * x1)) + -0.3893) * -0.24728\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "════════════════════════════════════════════════════════════════════════════════════════════════════\n",
            "Press 'q' and then <enter> to stop execution early.\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n",
            "Complexity  Loss       Score      Equation\n",
            "1           6.527e-01  0.000e+00  y = -0.58933\n",
            "5           5.143e-01  5.958e-02  y = x1 * (x1 * -0.38797)\n",
            "7           5.039e-01  1.014e-02  y = ((x1 * x1) * -0.32402) + -0.15338\n",
            "9           3.941e-01  1.229e-01  y = ((x0 * x0) + (x1 * x1)) * -0.24728\n",
            "11          3.877e-01  8.219e-03  y = (((x0 * x0) + (x1 * x1)) + -0.3893) * -0.24728\n",
            "───────────────────────────────────────────────────────────────────────────────────────────────────\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[ Info: Final population:\n",
            "[ Info: Results saved to:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PySRRegressor.equations_ = [\n",
              "\t   pick     score                                           equation  \\\n",
              "\t0        0.000000                                          -0.589325   \n",
              "\t1        0.059584                            x1 * (x1 * -0.38797104)   \n",
              "\t2        0.010144             ((x1 * x1) * -0.32402292) + -0.1533785   \n",
              "\t3  >>>>  0.122878              ((x0 * x0) + (x1 * x1)) * -0.24727781   \n",
              "\t4        0.008219  (((x0 * x0) + (x1 * x1)) + -0.3892987) * -0.24...   \n",
              "\t\n",
              "\t       loss  complexity  \n",
              "\t0  0.652686           1  \n",
              "\t1  0.514277           5  \n",
              "\t2  0.503949           7  \n",
              "\t3  0.394145           9  \n",
              "\t4  0.387719          11  \n",
              "]"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PySRRegressor.equations_ = [\n",
              "\t   pick     score                                           equation  \\\n",
              "\t0        0.000000                                          -0.589325   \n",
              "\t1        0.059584                            x1 * (x1 * -0.38797104)   \n",
              "\t2        0.010144             ((x1 * x1) * -0.32402292) + -0.1533785   \n",
              "\t3  &gt;&gt;&gt;&gt;  0.122878              ((x0 * x0) + (x1 * x1)) * -0.24727781   \n",
              "\t4        0.008219  (((x0 * x0) + (x1 * x1)) + -0.3892987) * -0.24...   \n",
              "\t\n",
              "\t       loss  complexity  \n",
              "\t0  0.652686           1  \n",
              "\t1  0.514277           5  \n",
              "\t2  0.503949           7  \n",
              "\t3  0.394145           9  \n",
              "\t4  0.387719          11  \n",
              "]</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PySRRegressor</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>PySRRegressor.equations_ = [\n",
              "\t   pick     score                                           equation  \\\n",
              "\t0        0.000000                                          -0.589325   \n",
              "\t1        0.059584                            x1 * (x1 * -0.38797104)   \n",
              "\t2        0.010144             ((x1 * x1) * -0.32402292) + -0.1533785   \n",
              "\t3  &gt;&gt;&gt;&gt;  0.122878              ((x0 * x0) + (x1 * x1)) * -0.24727781   \n",
              "\t4        0.008219  (((x0 * x0) + (x1 * x1)) + -0.3892987) * -0.24...   \n",
              "\t\n",
              "\t       loss  complexity  \n",
              "\t0  0.652686           1  \n",
              "\t1  0.514277           5  \n",
              "\t2  0.503949           7  \n",
              "\t3  0.394145           9  \n",
              "\t4  0.387719          11  \n",
              "]</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  - outputs/20260523_044930_Py1AZQ/hall_of_fame.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nSelected expression:\")\n",
        "print(cls_symbolic)\n",
        "\n",
        "print(\"\\nSelected expression as SymPy:\")\n",
        "display(cls_symbolic.sympy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "jug_wEqLGdaU",
        "outputId": "bc38e7f8-8b8a-4bb2-81b5-abf692526beb"
      },
      "id": "jug_wEqLGdaU",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Selected expression:\n",
            "PySRRegressor.equations_ = [\n",
            "\t   pick     score                                           equation  \\\n",
            "\t0        0.000000                                          -0.589325   \n",
            "\t1        0.059584                            x1 * (x1 * -0.38797104)   \n",
            "\t2        0.010144             ((x1 * x1) * -0.32402292) + -0.1533785   \n",
            "\t3  >>>>  0.122878              ((x0 * x0) + (x1 * x1)) * -0.24727781   \n",
            "\t4        0.008219  (((x0 * x0) + (x1 * x1)) + -0.3892987) * -0.24...   \n",
            "\t\n",
            "\t       loss  complexity  \n",
            "\t0  0.652686           1  \n",
            "\t1  0.514277           5  \n",
            "\t2  0.503949           7  \n",
            "\t3  0.394145           9  \n",
            "\t4  0.387719          11  \n",
            "]\n",
            "\n",
            "Selected expression as SymPy:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(x0*x0 + x1*x1)*(-0.24727781)"
            ],
            "text/latex": "$\\displaystyle \\left(x_{0} x_{0} + x_{1} x_{1}\\right) \\left(-0.24727781\\right)$"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nBest row from equation table:\")\n",
        "display(cls_symbolic.get_best())\n",
        "\n",
        "#print(\"\\nFull equation table:\")\n",
        "#display(cls_symbolic.equations_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "tLsPRh5GJsUY",
        "outputId": "b1ac56fb-dd6e-4563-9ded-e7ef608effa0"
      },
      "id": "tLsPRh5GJsUY",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Best row from equation table:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "complexity                                                    9\n",
              "loss                                                   0.394145\n",
              "equation                  ((x0 * x0) + (x1 * x1)) * -0.24727781\n",
              "score                                                  0.122878\n",
              "sympy_format                      (x0*x0 + x1*x1)*(-0.24727781)\n",
              "lambda_format    PySRFunction(X=>(x0*x0 + x1*x1)*(-0.24727781))\n",
              "Name: 3, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>complexity</th>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loss</th>\n",
              "      <td>0.394145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>equation</th>\n",
              "      <td>((x0 * x0) + (x1 * x1)) * -0.24727781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>score</th>\n",
              "      <td>0.122878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sympy_format</th>\n",
              "      <td>(x0*x0 + x1*x1)*(-0.24727781)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lambda_format</th>\n",
              "      <td>PySRFunction(X=&gt;(x0*x0 + x1*x1)*(-0.24727781))</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f3411829",
      "metadata": {
        "id": "f3411829"
      },
      "outputs": [],
      "source": [
        "def evaluate_score_model(model, X, y_pm):\n",
        "    score = model.predict(X)\n",
        "\n",
        "    pred_pm = np.where(score >= 0.0, 1.0, -1.0)\n",
        "\n",
        "    mse = np.mean((score - y_pm)**2)\n",
        "    acc = accuracy_score(y_pm, pred_pm)\n",
        "\n",
        "    y_binary = (y_pm > 0).astype(int)\n",
        "    auc = roc_auc_score(y_binary, score)\n",
        "\n",
        "    return mse, acc, auc, score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse_cls, acc_cls, auc_cls, score_test = evaluate_score_model(cls_symbolic, Xc_test, yc_test)\n",
        "\n",
        "print(f\"Test MSE                       : {mse_cls:.4f}\")\n",
        "print(f\"Test accuracy                  : {acc_cls:.4f}\")\n",
        "print(f\"Test AUC using raw score       : {auc_cls:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "unC0E2jDHgHE",
        "outputId": "acfe70ae-a87c-433c-bc2a-794e97335436"
      },
      "id": "unC0E2jDHgHE",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test MSE                       : 0.3897\n",
            "Test accuracy                  : 0.7960\n",
            "Test AUC using raw score       : 0.9984\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here the MSE measures how close the symbolic score is to the target labels $-1$ and $+1$.\n",
        "\n",
        "The accuracy uses only the sign of the score:\n",
        "\n",
        "$$\n",
        "\\hat{y} =\n",
        "\\begin{cases}\n",
        "+1, & f(x) \\geq 0, \\\\\n",
        "-1, & f(x) < 0.\n",
        "\\end{cases}\n",
        "$$\n",
        "\n",
        "The AUC uses the raw score values. This is useful because the score contains ranking information, not just the final class label."
      ],
      "metadata": {
        "id": "fHfqU_ZTH1y1"
      },
      "id": "fHfqU_ZTH1y1"
    },
    {
      "cell_type": "code",
      "source": [
        "x0_grid = np.linspace(-2.2, 2.2, 250)\n",
        "x1_grid = np.linspace(-2.2, 2.2, 250)\n",
        "\n",
        "xx, yy = np.meshgrid(x0_grid, x1_grid)\n",
        "grid = np.c_[xx.ravel(), yy.ravel()]\n",
        "\n",
        "score_grid = cls_symbolic.predict(grid).reshape(xx.shape)\n",
        "\n",
        "plt.figure(figsize=(6, 6))\n",
        "\n",
        "plt.contourf(xx, yy, score_grid, levels=50, alpha=0.6)\n",
        "plt.contour(xx, yy, score_grid, levels=[0.0], linewidths=2, label=\"Learned boundary\")\n",
        "\n",
        "plt.scatter(\n",
        "    Xc_test[:, 0],\n",
        "    Xc_test[:, 1],\n",
        "    c=(yc_test > 0),\n",
        "    s=8,\n",
        "    edgecolors=\"k\",\n",
        "    linewidths=0.2\n",
        ")\n",
        "\n",
        "theta = np.linspace(0, 2*np.pi, 300)\n",
        "plt.plot(np.cos(theta), np.sin(theta), linestyle=\"--\", linewidth=2, label=\"True boundary\")\n",
        "\n",
        "plt.colorbar()\n",
        "plt.xlabel(\"$x_0$\")\n",
        "plt.ylabel(\"$x_1$\")\n",
        "plt.title(\"Symbolic score and decision boundary\")\n",
        "plt.axis(\"equal\")\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "EJMg0uyhKera",
        "outputId": "806ae07f-08bf-4724-d59c-e6a1a933313e"
      },
      "id": "EJMg0uyhKera",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipykernel_3578/2764148158.py:12: UserWarning: The following kwargs were not used by contour: 'label'\n",
            "  plt.contour(xx, yy, score_grid, levels=[0.0], linewidths=2, label=\"Learned boundary\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1781ceab",
      "metadata": {
        "id": "1781ceab"
      },
      "source": [
        "## 8. Connecting to a HEP Problem\n",
        "\n",
        "The toy examples above used synthetic data, but the workflow is the same for a real HEP dataset.\n",
        "\n",
        "In this example, the CSV is assumed to contain event-level features such as jet and Higgs kinematic variables, together with a label column called `sign_w`.\n",
        "\n",
        "The goal is to train a symbolic expression as a score:\n",
        "\n",
        "$$\n",
        "f(x_0, x_1, \\ldots, x_n),\n",
        "$$\n",
        "\n",
        "where the sign of the expression gives the predicted class:\n",
        "\n",
        "$$\n",
        "\\hat{y} = \\mathrm{sign}(f(x)).\n",
        "$$\n",
        "\n",
        "This mirrors the production workflow:\n",
        "\n",
        "1. load a CSV file,\n",
        "2. choose the feature columns,\n",
        "3. convert labels from `0, 1` to `-1, +1` if needed,\n",
        "4. optionally subsample the data,\n",
        "5. split into train and test sets,\n",
        "6. fit `PySRRegressor`,\n",
        "7. evaluate MSE, accuracy, and AUC."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Helper functions"
      ],
      "metadata": {
        "id": "sPzXiVfpULZx"
      },
      "id": "sPzXiVfpULZx"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a64e0394",
      "metadata": {
        "id": "a64e0394"
      },
      "outputs": [],
      "source": [
        "def prepare_symbolic_array(df, label_column=\"sign_w\", feature_columns=None):\n",
        "    if feature_columns is None:\n",
        "        feature_columns = [c for c in df.columns if c != label_column]\n",
        "\n",
        "    for col in feature_columns:\n",
        "        if col not in df.columns:\n",
        "            raise ValueError(f\"Feature column {col!r} not found.\")\n",
        "\n",
        "    if label_column not in df.columns:\n",
        "        raise ValueError(f\"Label column {label_column!r} not found.\")\n",
        "\n",
        "    X = df[feature_columns].values.astype(np.float64)\n",
        "    y = df[label_column].values.astype(np.float64)\n",
        "\n",
        "    unique = sorted(np.unique(y))\n",
        "    print(\"Original label values:\", unique)\n",
        "\n",
        "    if set(unique) == {-1.0, 1.0}:\n",
        "        pass\n",
        "    elif set(unique) == {0.0, 1.0}:\n",
        "        y = 2.0*y - 1.0\n",
        "    else:\n",
        "        raise ValueError(f\"Expected labels to be {{-1,+1}} or {{0,1}}, got {unique}\")\n",
        "\n",
        "    return X, y, feature_columns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def subsample_data(X, y, max_events=None, seed=42):\n",
        "    if max_events is None or max_events >= len(X):\n",
        "        return X, y\n",
        "\n",
        "    rng = np.random.default_rng(seed)\n",
        "    idx = rng.choice(len(X), size=max_events, replace=False)\n",
        "\n",
        "    return X[idx], y[idx]"
      ],
      "metadata": {
        "id": "L37js3RAUab1"
      },
      "id": "L37js3RAUab1",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_model(model, X, y):\n",
        "    y_pred = model.predict(X)\n",
        "    mse = np.mean((y_pred - y)**2)\n",
        "\n",
        "    y_pred_sign = np.where(y_pred >= 0.0, 1.0, -1.0)\n",
        "    acc = accuracy_score(y, y_pred_sign)\n",
        "\n",
        "    y_binary = (y > 0).astype(int)\n",
        "    auc = roc_auc_score(y_binary, y_pred)\n",
        "\n",
        "    return mse, acc, auc, y_pred"
      ],
      "metadata": {
        "id": "AdFg7X_KUgd4"
      },
      "id": "AdFg7X_KUgd4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load a CSV file\n",
        "\n",
        "In Colab, upload the CSV file to the session first. Then set `filename` to the uploaded file path.\n",
        "\n",
        "For example, if the file appears in the Colab file browser as\n",
        "\n",
        "```text\n",
        "/content/CHWtill_hjj_big_data.csv"
      ],
      "metadata": {
        "id": "LwuBy2DFUry5"
      },
      "id": "LwuBy2DFUry5"
    },
    {
      "cell_type": "code",
      "source": [
        "filename = \"/content/CHWtill_hjj_big_data.csv\"\n",
        "\n",
        "df_hep = pd.read_csv(filename)\n",
        "\n",
        "print(\"Dataframe shape:\", df_hep.shape)\n",
        "print(\"\\nColumns:\")\n",
        "print(df_hep.columns.tolist())\n",
        "\n",
        "df_hep.head()"
      ],
      "metadata": {
        "id": "4cPsLjA-U8j6"
      },
      "id": "4cPsLjA-U8j6",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Choose features\n",
        "\n",
        "For this tutorial, we use a small set of interpretable event-level features.\n",
        "\n",
        "The exact feature list should match the columns in your CSV file. Keeping the number of features small is useful for a live symbolic-regression tutorial, because the search space grows quickly as we add more variables."
      ],
      "metadata": {
        "id": "X-ZY85DiVB1O"
      },
      "id": "X-ZY85DiVB1O"
    },
    {
      "cell_type": "code",
      "source": [
        "feature_cols = [\"jet0Pt\", \"jet0Eta\", \"jet0Phi\",\n",
        "                \"jet1Pt\", \"jet1Eta\", \"jet1Phi\",\n",
        "                \"hPt\", \"hEta\", \"hPhi\",\n",
        "                \"deltaphi\"]\n",
        "\n",
        "label_column = \"sign_w\"\n",
        "\n",
        "X_hep, y_hep, feature_cols = prepare_symbolic_array(df_hep, label_column=label_column, feature_columns=feature_cols)\n",
        "\n",
        "print(\"\\nX shape:\", X_hep.shape)\n",
        "print(\"\\ny shape:\", y_hep.shape)\n",
        "print(\"\\nFeatures used:\")\n",
        "for i, col in enumerate(feature_cols):\n",
        "    print(f\"{i:02d}: {col}\")"
      ],
      "metadata": {
        "id": "qzHBJkp2VD-C"
      },
      "id": "qzHBJkp2VD-C",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Quick checks\n",
        "\n",
        "Before running PySR, it is useful to inspect the labels and a few basic feature distributions."
      ],
      "metadata": {
        "id": "O9drJ6NUVKq3"
      },
      "id": "O9drJ6NUVKq3"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(5, 4))\n",
        "plt.bar(label_counts.index.astype(str), label_counts.values)\n",
        "plt.xlabel(\"Label\")\n",
        "plt.ylabel(\"Number of events\")\n",
        "plt.title(\"Class balance\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "sXqcAhfFVM3s"
      },
      "id": "sXqcAhfFVM3s",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_hep[feature_cols].hist(figsize=(12, 8), bins=40)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "uWKWgQjEVNW3"
      },
      "id": "uWKWgQjEVNW3",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Subsample and split\n",
        "\n",
        "For a tutorial run, we use only a subset of the full CSV. This keeps the PySR search fast enough to run live.\n",
        "\n",
        "For serious runs, increase `max_events`, `niterations`, and possibly run the script outside the notebook."
      ],
      "metadata": {
        "id": "Mh8gY6oHVPRd"
      },
      "id": "Mh8gY6oHVPRd"
    },
    {
      "cell_type": "code",
      "source": [
        "X_hep_s, y_hep_s = subsample_data(X_hep, y_hep, max_events=5000, seed=42)\n",
        "\n",
        "Xh_train, Xh_test, yh_train, yh_test = train_test_split(X_hep_s, y_hep_s, test_size=0.25, random_state=42, stratify=y_hep_s)\n",
        "\n",
        "print(\"Train:\", Xh_train.shape, yh_train.shape)\n",
        "print(\"Test :\", Xh_test.shape, yh_test.shape)"
      ],
      "metadata": {
        "id": "_FH9Ch0CVSBi"
      },
      "id": "_FH9Ch0CVSBi",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fit PySR on the HEP data\n",
        "\n",
        "Here we use a small PySR configuration suitable for a tutorial.\n",
        "\n",
        "The symbolic expression should be interpreted as a score, not as a calibrated probability. The sign of the score gives the predicted class."
      ],
      "metadata": {
        "id": "XrIQ-2QbVTbe"
      },
      "id": "XrIQ-2QbVTbe"
    },
    {
      "cell_type": "code",
      "source": [
        "if PYSR_AVAILABLE:\n",
        "    hep_symbolic = PySRRegressor(\n",
        "        niterations=30,\n",
        "        population_size=30,\n",
        "        maxsize=18,\n",
        "        binary_operators=[\"+\", \"-\", \"*\", \"/\"],\n",
        "        unary_operators=[\"sin\", \"cos\", \"log\", \"sqrt\", \"abs\"],\n",
        "        model_selection=\"best\",\n",
        "        elementwise_loss=\"loss(prediction, target) = (prediction - target)^2\",\n",
        "        parsimony=0.01,\n",
        "        random_state=42,\n",
        "        deterministic=True,\n",
        "        parallelism=\"serial\",\n",
        "        verbosity=1,\n",
        "    )\n",
        "\n",
        "    hep_symbolic.fit(\n",
        "        Xh_train,\n",
        "        yh_train,\n",
        "        variable_names=feature_cols,\n",
        "    )\n",
        "\n",
        "    print(\"\\nSelected expression:\")\n",
        "    print(hep_symbolic)\n",
        "\n",
        "    print(\"\\nSelected expression as SymPy:\")\n",
        "    display(hep_symbolic.sympy())\n",
        "\n",
        "    print(\"\\nBest row from equation table:\")\n",
        "    display(hep_symbolic.get_best())\n",
        "\n",
        "    print(\"\\nFull equation table:\")\n",
        "    display(hep_symbolic.equations_)\n",
        "\n",
        "else:\n",
        "    print(\"Skipping HEP PySR example because PySR is not installed.\")"
      ],
      "metadata": {
        "id": "eV9BmExvVUpM"
      },
      "id": "eV9BmExvVUpM",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluate the symbolic score\n",
        "\n",
        "We evaluate the expression in three ways:\n",
        "\n",
        "- **MSE**: how close the raw score is to the target labels $-1$ and $+1$,\n",
        "- **accuracy**: classification accuracy after taking the sign of the score,\n",
        "- **AUC**: ranking quality using the raw score."
      ],
      "metadata": {
        "id": "x6pdH9IvVWdK"
      },
      "id": "x6pdH9IvVWdK"
    },
    {
      "cell_type": "code",
      "source": [
        "if PYSR_AVAILABLE:\n",
        "    hep_mse, hep_acc, hep_auc, hep_score_test = evaluate_score_model(\n",
        "        hep_symbolic,\n",
        "        Xh_test,\n",
        "        yh_test,\n",
        "    )\n",
        "\n",
        "    print(f\"HEP test MSE                    : {hep_mse:.6f}\")\n",
        "    print(f\"HEP test accuracy from sign(score): {hep_acc:.6f}\")\n",
        "    print(f\"HEP test AUC using raw score     : {hep_auc:.6f}\")"
      ],
      "metadata": {
        "id": "THb4d0j0VXie"
      },
      "id": "THb4d0j0VXie",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Score distributions\n",
        "\n",
        "A useful diagnostic is to plot the learned symbolic score separately for the two classes.\n",
        "\n",
        "If the symbolic expression is useful, the two classes should have different score distributions."
      ],
      "metadata": {
        "id": "RnLTZjWwVY0g"
      },
      "id": "RnLTZjWwVY0g"
    },
    {
      "cell_type": "code",
      "source": [
        "if PYSR_AVAILABLE:\n",
        "    plt.figure(figsize=(6, 5))\n",
        "\n",
        "    plt.hist(\n",
        "        hep_score_test[yh_test < 0],\n",
        "        bins=50,\n",
        "        alpha=0.6,\n",
        "        density=True,\n",
        "        label=\"class -1\",\n",
        "    )\n",
        "\n",
        "    plt.hist(\n",
        "        hep_score_test[yh_test > 0],\n",
        "        bins=50,\n",
        "        alpha=0.6,\n",
        "        density=True,\n",
        "        label=\"class +1\",\n",
        "    )\n",
        "\n",
        "    plt.axvline(0.0, linestyle=\"--\", label=\"decision boundary\")\n",
        "\n",
        "    plt.xlabel(\"Symbolic score\")\n",
        "    plt.ylabel(\"Density\")\n",
        "    plt.title(\"Symbolic score distributions\")\n",
        "    plt.legend()\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "xrLYUXVOVZu7"
      },
      "id": "xrLYUXVOVZu7",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "141fa42d",
      "metadata": {
        "id": "141fa42d"
      },
      "source": [
        "## 9. Summary\n",
        "\n",
        "In this tutorial, we introduced symbolic regression as a way to search for compact analytic expressions directly from data.\n",
        "\n",
        "The main points are:\n",
        "\n",
        "1. Symbolic regression is not just fitting numerical coefficients. It searches over formula structures.\n",
        "2. The operator set defines the space of possible expressions. Allowing `sin`, `log`, `/`, or `sqrt` changes what the algorithm can discover.\n",
        "3. There is always a tradeoff between accuracy and simplicity.\n",
        "\n",
        "For physics applications, the most important lesson is that the discovered formula is not automatically meaningful just because it performs well. It should be checked against physical intuition, symmetries, dimensional structure, variable definitions, and stability under changes in the data or training settings.\n",
        "\n",
        "A useful symbolic expression is one that is not only accurate, but also simple enough to interpret."
      ]
    }
  ],
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