firstann.ipynb

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        "<a href=\"https://colab.research.google.com/github/mariajmolina/UMDAOSC650/blob/main/firstann.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "\n",
        "# Let's build our first ANN!\n",
        "\n",
        "Steps involved:\n",
        "\n",
        "- Select and preprocess data (select variables/features, normalize and/or standardize, split train and test data)\n",
        "- Select model architecture and hyperparameters\n",
        "- Start training your model\n",
        "- Track the training loss and validationeat."
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      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import xarray as xr\n",
        "import keras\n",
        "import sklearn"
      ],
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        "id": "YKdUbZ_gKr2v"
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      "execution_count": 1,
      "outputs": []
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      "source": [
        "First create some toy data to play with."
      ],
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        "id": "idvV3obyKxxd"
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      "source": [
        "np.random.seed(123)\n",
        "\n",
        "# set the time range from 1999-2019\n",
        "times = pd.date_range(\"1999-01-01\", \"2019-12-31\", name=\"time\")\n",
        "\n",
        "# create an \"annual cycle\"\n",
        "annual_cycle = np.sin(2 * np.pi * (times.dayofyear.values / 365.25 - 0.28))\n",
        "\n",
        "# create some base \"temperatures\"\n",
        "base = 10 + 15 * annual_cycle.reshape(-1, 1)\n",
        "tmin_values = base + 3 * np.random.randn(annual_cycle.size, 1)\n",
        "tmax_values = base + 10 + 3 * np.random.randn(annual_cycle.size, 1)\n",
        "\n",
        "# let's put these dummy temperatures into an xarray dataset\n",
        "ds = xr.Dataset(\n",
        "    {\n",
        "        \"tmin\": ((\"time\", \"location\"), tmin_values),\n",
        "        \"tmax\": ((\"time\", \"location\"), tmax_values),\n",
        "    },\n",
        "    {\"time\": times, \"location\": [\"IA\"]},\n",
        ")"
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        "id": "oFPZO73LKtkY"
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      "execution_count": 2,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "# what does this look like\n",
        "ds"
      ],
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          "height": 300
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        "id": "3CpDM1ZgK0XF",
        "outputId": "5982233b-7649-4250-c704-c431f8362869"
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      "execution_count": 3,
      "outputs": [
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              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 184kB\n",
              "Dimensions:   (time: 7670, location: 1)\n",
              "Coordinates:\n",
              "  * time      (time) datetime64[ns] 61kB 1999-01-01 1999-01-02 ... 2019-12-31\n",
              "  * location  (location) &lt;U2 8B &#x27;IA&#x27;\n",
              "Data variables:\n",
              "    tmin      (time, location) float64 61kB -8.037 -1.83 ... -4.857 -1.792\n",
              "    tmax      (time, location) float64 61kB 9.607 7.746 -1.154 ... 10.84 7.317</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-58e544a1-965d-47dc-999c-25e6d1f987c7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-58e544a1-965d-47dc-999c-25e6d1f987c7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 7670</li><li><span class='xr-has-index'>location</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-66a1b282-bb4b-4233-934c-724e073f0d3a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-66a1b282-bb4b-4233-934c-724e073f0d3a' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1999-01-01 ... 2019-12-31</div><input id='attrs-3e4da28d-2237-402b-b95c-0fba4f9d77d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e4da28d-2237-402b-b95c-0fba4f9d77d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f56944d-e49f-42a9-a68a-0acd4463bcc2' class='xr-var-data-in' type='checkbox'><label for='data-7f56944d-e49f-42a9-a68a-0acd4463bcc2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1999-01-01T00:00:00.000000000&#x27;, &#x27;1999-01-02T00:00:00.000000000&#x27;,\n",
              "       &#x27;1999-01-03T00:00:00.000000000&#x27;, ..., &#x27;2019-12-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2019-12-30T00:00:00.000000000&#x27;, &#x27;2019-12-31T00:00:00.000000000&#x27;],\n",
              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>location</span></div><div class='xr-var-dims'>(location)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;IA&#x27;</div><input id='attrs-d9f7690d-334f-4528-ad93-180c02fdb91a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9f7690d-334f-4528-ad93-180c02fdb91a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ddde34a-3f47-480e-aad4-89f3c97eb477' class='xr-var-data-in' type='checkbox'><label for='data-7ddde34a-3f47-480e-aad4-89f3c97eb477' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;IA&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-35433b8b-aa05-47f0-a23c-4d6d627c9ef1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35433b8b-aa05-47f0-a23c-4d6d627c9ef1' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tmin</span></div><div class='xr-var-dims'>(time, location)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-8.037 -1.83 ... -4.857 -1.792</div><input id='attrs-151b9b05-3496-422f-9740-932254245772' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-151b9b05-3496-422f-9740-932254245772' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-943da108-c536-4948-b282-4f99dfe48162' class='xr-var-data-in' type='checkbox'><label for='data-943da108-c536-4948-b282-4f99dfe48162' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-8.03736932],\n",
              "       [-1.83023614],\n",
              "       [-4.0107458 ],\n",
              "       ...,\n",
              "       [-5.87696127],\n",
              "       [-4.85677245],\n",
              "       [-1.79212274]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tmax</span></div><div class='xr-var-dims'>(time, location)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.607 7.746 -1.154 ... 10.84 7.317</div><input id='attrs-c3b3f632-2c2b-43a2-9e83-aa9304f30b73' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c3b3f632-2c2b-43a2-9e83-aa9304f30b73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c132b04-deb6-48be-9b12-d6575083b093' class='xr-var-data-in' type='checkbox'><label for='data-4c132b04-deb6-48be-9b12-d6575083b093' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 9.60748649],\n",
              "       [ 7.74561557],\n",
              "       [-1.15380322],\n",
              "       ...,\n",
              "       [ 4.47352059],\n",
              "       [10.84085422],\n",
              "       [ 7.31691539]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f0783106-36b5-4d08-87f1-b00f21873782' class='xr-section-summary-in' type='checkbox'  ><label for='section-f0783106-36b5-4d08-87f1-b00f21873782' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8c792b04-2fbe-46e9-9a3e-43a838bd1365' class='xr-index-data-in' type='checkbox'/><label for='index-8c792b04-2fbe-46e9-9a3e-43a838bd1365' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1999-01-01&#x27;, &#x27;1999-01-02&#x27;, &#x27;1999-01-03&#x27;, &#x27;1999-01-04&#x27;,\n",
              "               &#x27;1999-01-05&#x27;, &#x27;1999-01-06&#x27;, &#x27;1999-01-07&#x27;, &#x27;1999-01-08&#x27;,\n",
              "               &#x27;1999-01-09&#x27;, &#x27;1999-01-10&#x27;,\n",
              "               ...\n",
              "               &#x27;2019-12-22&#x27;, &#x27;2019-12-23&#x27;, &#x27;2019-12-24&#x27;, &#x27;2019-12-25&#x27;,\n",
              "               &#x27;2019-12-26&#x27;, &#x27;2019-12-27&#x27;, &#x27;2019-12-28&#x27;, &#x27;2019-12-29&#x27;,\n",
              "               &#x27;2019-12-30&#x27;, &#x27;2019-12-31&#x27;],\n",
              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=7670, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>location</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-92cd9f3a-844a-4be1-9033-4c7a986bc1e4' class='xr-index-data-in' type='checkbox'/><label for='index-92cd9f3a-844a-4be1-9033-4c7a986bc1e4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;IA&#x27;], dtype=&#x27;object&#x27;, name=&#x27;location&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-050d8fa7-b5f6-4618-a12e-b7b81100673b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-050d8fa7-b5f6-4618-a12e-b7b81100673b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# some time series plots\n",
        "ds.tmin.plot()\n",
        "ds.tmax.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "HFp3AtfDK2I9",
        "outputId": "1a496b69-a5ad-46f3-986d-2adf98311b9b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lets now preprocess our dataset in preparation for training\n",
        "\n",
        "- Lets structure the supervised learning problem (what is the input/feature(s)/label?)\n",
        "- Since this is time series data and there is autocorrelation, we will split data into train and test set using years rather than randomly splitting data\n",
        "- Standardize and normalize the training set"
      ],
      "metadata": {
        "id": "2MEededRK5nj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# split by years\n",
        "train = ds.sel(time=slice('1999-01-01T00:00:00.000000000','2014-12-31T00:00:00.000000000'))\n",
        "test = ds.sel(time=slice('2015-01-01T00:00:00.000000000','2019-12-31T00:00:00.000000000'))"
      ],
      "metadata": {
        "id": "ieTJWr4kK4EY"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For random train test splitting, check out\n",
        "\n",
        "`from sklearn.model_selection import train_test_split`\n",
        "\n",
        "Now lets set up the input and label."
      ],
      "metadata": {
        "id": "bxsIyzRpLE8S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_train = train.tmin\n",
        "label_train = train.tmax"
      ],
      "metadata": {
        "id": "xCN4aBu2LRF3"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "And finally, let's go ahead and min-max scale our training data. We can levarage the sklearn function that will save our min and max values from the training set for us."
      ],
      "metadata": {
        "id": "JZoQUTcoLShU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "-CxyFXfCLCTW"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create our scaler object\n",
        "scaler_input = MinMaxScaler()\n",
        "scaler_label = MinMaxScaler()"
      ],
      "metadata": {
        "id": "PJqJZ3enLOGV"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler_input.fit(input_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "ECZz_3PILZ4c",
        "outputId": "f407dc30-2622-4d8a-c2ed-bf5924a5cf91"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ],
            "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>MinMaxScaler()</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>MinMaxScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.MinMaxScaler.html\">?<span>Documentation for MinMaxScaler</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>MinMaxScaler()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler_label.fit(label_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "sAoRgsMxLmrw",
        "outputId": "83158b11-0f6c-4345-a2a3-79b535f92de9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ],
            "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>MinMaxScaler()</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>MinMaxScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.MinMaxScaler.html\">?<span>Documentation for MinMaxScaler</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>MinMaxScaler()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(scaler_input.data_max_)\n",
        "print(scaler_input.data_min_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zIa6uIz2Lbpd",
        "outputId": "38b4b19c-fcdf-42b2-d3cd-cf59d3fd9c3a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[33.52620933]\n",
            "[-13.18084936]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(scaler_label.data_max_)\n",
        "print(scaler_label.data_min_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w4nz_9ViLdv6",
        "outputId": "64f99ce6-15c7-43b2-83a9-2e8656d2c9b3"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[43.97570471]\n",
            "[-4.77030373]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_train_scaled = scaler_input.transform(input_train)\n",
        "label_train_scaled = scaler_label.transform(label_train)"
      ],
      "metadata": {
        "id": "nm4E9kCqLfDS"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's confirm that this worked!"
      ],
      "metadata": {
        "id": "qV1CYbG_Lqnz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(0,input_train_scaled.shape[0]), input_train_scaled, c='k')\n",
        "plt.show()\n",
        "plt.plot(range(0,input_train_scaled.shape[0]), input_train, c='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "1D_Eo46bLkDM",
        "outputId": "7707afdc-93d3-41fe-eb67-79bd65ccbe18"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(0,label_train_scaled.shape[0]), label_train_scaled, c='k')\n",
        "plt.show()\n",
        "plt.plot(range(0,label_train_scaled.shape[0]), label_train, c='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "by_BoWLjLtGD",
        "outputId": "309fe670-f9a6-4af4-9d73-1c71ec15af97"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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nxAo19eLBgwfMdbKGdDl1dlzGw4cPiepDyIKOigZErNCIOqZPnw5NmzblbYYNpBvjpk2bwrVr14jKNBs01o04gofjR1qnM3lmf8bU1FTImzcvMXlmwwofJ+ioaMAKL1wUWDt9v/32m9M0Vlh05gwsw+aH9Dv8999/icoTDRF3UrFcNG2FOo+OCiGaNWtm2iBlGzZsgLp160L79u15m0INNZW1Z8+e8OTJEwbWvMQKDQjLKJtWyC9nqHlG0vnQvXt3pvrUQFInD/udfYix/CiyQr3x4G2AVdi4cSMcPnyYy64LozRv3py3CdRRW1l79eoFS5YsoWzNS27cuAFPnjyBV199lYk+gJeOhaMFm1r55JNPiMlyhogxhXhsB2c9vWf2ju7FixdO01h5Wt/s7w8AR1Q04eyF37lzh5El5kfUhoHWOSNKfPXVV0z19erVi6g8lvEg5s6dy0yXWo4fP85c5zfffMNUn5qOnjQkO9fvvvuOmCy1iDS6jo4KguhEVEeFNawbtIULFzLVxxqW8SkAANzd3Znq40Hp0qV5m2AINevTSLN48WLmOpVgXSdogI6KBrBzNS+8viqwzLClQYMGTPWRnEYDwPKSDsn6inlqftBR0YAVhtBEQU3jQXIqTdR35wpRRlnCepoiWzayTaio5ZQ16KggmUFHBRGW/PnzQ9euXXmbYQhnDe7evXsZWYLQwOyOxenTp3mbgCBOQUcFEZrFixdDYmKiYTk8OpQRI0bAlClTHKYxe0fn6pj9a33gwIG8TZAF6wWSGXRUEC6wbuB5NHxqjlc3e0cnIps3b2amy+wd6v79+3mbQB01dQzrodigo6IBszdKIqGlYbByI4JlijwRERG8TTANoq6RwnqBZEaXozJ9+nQoWrQoeHt7Q2hoKBw6dMhh+ilTpkDp0qXBx8cHgoOD4dNPP4Vnz57pMhhB9IANH3n27NnD2wTEIFb+CEjHys948uRJqFq1Km8zqKPZUVm+fDlERkbCqFGj4OjRo1C5cmWIiIhQjMi6ZMkSGDJkCIwaNQrOnj0LP//8MyxfvhyGDRtm2HjEvLjC1I/V6dOnD28TEIOQ3rVEihkzZvA2wRS0bdsWjh07xtsM6mgupZMnT4ZevXpBjx49oFy5cjBr1izInj07/PLLL7Lp9+3bB3Xq1IFOnTpB0aJF4c0334SOHTs6HYUREezszIuo787KX3uuwNOnT+HHH3/kbYZu1DoqrOvP559/TkwW6zp28OBBZrru3bvHTBdPNDkqycnJcOTIEQgPD/9PQLZsEB4errgoq3bt2nDkyJEMx+Ty5cuwYcMGaNasmQGz+YCdCsDdu3eZ68R8R0Tm448/5m2CbkQdUTEr9+7dg1q1ajHTJ+oHGGk0HUp49+5dSE1NhYCAAJvrAQEBiqdBdurUCe7evQt169YFSZIgJSUFPvzwQ4dTP8+fP4fnz59n/D8hIUGLmdRwlULhiJEjRxIZlkXn4yWYDwhPtIyomLWssrT7xo0bzHS5EtTd6R07dsDYsWNhxowZcPToUfj9999h/fr1MGbMGMV7oqKiwM/PL+MvODiYtpnEsLoz8+jRIyJycI0KgvBH1Kkf1pBqj6yeT7zQNKLi7+8P7u7uEB8fb3M9Pj4eAgMDZe/58ssvoWvXrvDBBx8AAEDFihXhyZMn0Lt3bxg+fLhsRRk6dChERkZm/D8hIcE0zoqZvzxEhUR+YgOCIFnhMZVrZVi3M67SrmkaUfH09IRq1apBdHR0xrW0tDSIjo6GsLAw2XuePn2axRlJP3FUKZO9vLzA19fX5o83kiTB0aNHVaWzMqSeD505fty6dYuIHHyH5BF1caSZ2zWWoTDMnE8io2lEBQAgMjISunfvDtWrV4eaNWvClClT4MmTJ9CjRw8AAOjWrRsEBQVBVFQUAAC0bNkSJk+eDCEhIRAaGgoXL16EL7/8Elq2bGmqI9KXLVumKp3VC6pZn09Uu3l09pnXfyFikXkkWSRErT9qePLkCW8TqGHm96IFzY5K+/bt4c6dOzBy5EiIi4uDKlWqwKZNmzIW2MbGxtqMoIwYMQLc3NxgxIgRcOPGDciXLx+0bNlSVXhxkVi8eLGqdFYvODwiWZLIU1HfS2JiIhw7dgxCQkKY6cSREMSVYFn3WbczajeaXLx4EUqUKEHZGnpodlQAAPr37w/9+/eX/W3Hjh22Cjw8YNSoUTBq1Cg9qoRBbQEUNSQ1KUg9H3aW/9G4cWO4f/8+M30k8j41NZXIYZEIP/bt26c6raiOvhpYtsmi5tOdO3dM7ajgJnqVbNy4UVU6UQsqKVJTU3mboAuR38uDBw8My0hKSlKdloSjUrp0aYiNjTUsB+HDkSNHoE6dOqrTi1x/HLFw4UIi9UstouaT2Y+7QEeFMKIWVFLwGFGx8tQPKT766CPVaUk4KpcuXTIsA+GHltEUAIBz585RskQZLc63Et27d1eVzurbkzds2MDbBEOgo0IYUQsqKUiNqODUD1nmz5+vOi3mPaIVHscEXL9+nblOo4ja/pu9zqOjQhhRCyopeKzBSU5OZq7Typi90UIQxLVAR4UwVndUeKxRyZs3L5w8edKQjAsXLhCyxvzwcFT+/PNP5joRZbS2U1Zv186cOYNTzAKDjgphrF5QeU39jB492pA+jB3yHzwclZYtWzLXyZJFixZhGTMxbdq0gUWLFhmWk5KSQsAa8pi9X0JHhTBXrlzhbQJVrL792owcP35cU3qc+iFPt27dYOTIkcz0vXjxgpkuV4HEOhxR44OZeWsyADoqxGnevDlvE6hC6ovhp59+0pQeO1dlqlSpwtsEBADWrl1r6P5Tp06pTluuXDlDunigNZS9GUcB1IaxYM3cuXPh4sWLvM3QDToqhDHjSnUtkGo8li9frik9S0fFjA2kFng9H06NOGbLli2q05qx02EdIFDrNDXrekFi+7UW9u7dy1QfSdBRQTTBa+qHpaNy5MgRZrpciTFjxvA2QWjkTpK3ElrrsNE6r3XUlnXb9s8//zDVZ2asXTMQ4pBYTLtz507N97B0VERdEEcKXiMqWkYM7NEyLWJWRB41JFFmtD6fUZ3R0dGG7teK/fExomHmkWJ0VCzAxIkTmRVCEl8dc+bMIWAJPaz+ZcsLI2W0YsWKqtPieiZED0bbUD0fYIg6sEW2AF988QX8/vvvTHTRHB7t27ev4m8sOx8SjorWfGK5tsnMX1ZqMOvzWd3BYj31g7FirAM6KipgebKtXlgtrnOFNSokHJWVK1dqSl+8eHHDOlmh16nCjsAx6KjYMnbsWEqWyBMTEwOHDh1iqpMlZq5/6KiowNGXviiwauRIrFGRqzD58uVzWJFYNuIkThrVuiCXZVwMox1AeHi4rvvM3FCyQGsZf/ToESVL6KD1/Ws5v4oUoaGhuu+1uqPJE3RUVHDgwAHeJggDrREVSZKEcVQGDBhgWIbInfL06dMN3c/jJF0eXL58mak++zLesGFDh+nNtt1b5DqBiA06KiowQzRWVh05L0fFbODXlfn57bffNKU3+s7t7/fz83OYnuVhnXgODnmwjVAPOioqMIOjworTp08b3oZ39+7dLNckSYLOnTsr3rNkyRLd+uLj43XfqxfRv3b37dvH2wTLYbQj1lpmZsyYoVsXD6cBHRVEL+ioqIDHicEi06hRI0P337p1K8u1tLQ0qF+/vsP79Ea25LFAburUqcx1amHo0KFU5Hp7eyv+hh2VY7SeEyNXj0TG6u8fR0jogY6KCrSOqDx9+tSQPj0V2kyVxN3dPcu19GcuUKCA4n16F5yaKW/MDp47pB+5kUZH0UvNVq5jY2OZ6jPaDovM0aNHuSw25gU6KirQ6qgsWLCAkiXWQC7KKM2vLbM16GZGlLzu27cv3Lt3T9e9kiRBhw4dqI06qcXNzQ1Kly7t8Hcz0adPH9nrX3/9NRV9LNfwALB9H9WqVYMePXpouoe1o0gSdFRUoLUTZV1BAMzTaN2/f182RL3Vh4VdhVdeeYW3CQAAMGvWLPj444913fvPP/9oPjQTwFgdZLk9XQ9nzpwxLOPmzZuy1814ErQcSu9w2LBhjC2RZ/To0bxN0A06KirQOqJi1GmwHwLOmzevIXkice3aNdnr6Y6KI4cFnRnxadGiheJvpN/f4sWLHf5++vRpXXJ5OA0zZ87UfM+NGzd069P6LkiEaJCb8gUAqFGjhmHZIqCUp1Z5Pp6go6IC1rt+ihQpovkes4yoKNmJTog1eOuttxR/I/mOO3bsCDly5HCYRq8+HmXxxIkTWa4pdezp/PXXX7TMoYJcOzpjxgzTtF3OsMpziAg6Kipg7agkJSXZ/D9//vxM9dNEqTLTzGNHDci8efOo6XU1ihYtCiVKlGCmz5lDYXbn19PTEwCAaZ7SRKmOW72Dr1evnuz1q1evsjXExKCjogLWUz/2zJ07Fxo2bAiVKlUiKpcHPEZUlHSOHDlSdzh4JCsFCxYEAICAgADZ30m+YzWyzDSi4kgny5g3jqaT/v77b0Oy5dpRmnktiqOqNHXfq1cvxpaYF3RUVMA74FvRokVh+/btDuf/aXyVBAcHE5fpzFFp3Lix4r00Gp5ChQoRl+nqONpSS5ImTZpA6dKloVOnTrK/6623ojkq+fLlg5w5czKxI93ZlKNmzZpw9OhR3bKVntEqIypay43cdnTarF+/Hp49e8Zcr1HQUVEB7xEV2nKVCAoKYqbrvffeAwDj59DI4Szf/vjjD+I69UC6g5wwYQKXlf65cuWirsPNzQ28vb3h7Nmz8Ouvv8qmYe1wPHjwgKi8zPaLMv27e/du3ffKtaM02zQzdMisQ1m0aNECBg4cyFQnCdBRUYHWyLTr1q0jql9NZWbtxJw8eVLXfUp29u7dGwCcn2+iB2cdVqtWrYjrVOKNN95Q/G3VqlVEdXXp0gWaNm1KVKYjnJXBx48fM9Wpd2THWXn54YcfZK/HxcXp0qdG56JFi3TLJokR54/1GhUzHCab/oHGkp9++om5TqOgo6ICrY7K5s2bdeu6f/++4m8iDZHWrl2bqLxs2ZwXRb2NpFkC8O3cuZOoPF9fX6HKjFkWDzorZ46CsNHitddeY65TDhqOilWIiYnhbYJlQUdFMOTm29M7G0edjlIwJVro/TpWegaaHapS8K727dtT0ykC2bNnV/xNlIWGpPjoo4+IyRJljYrV3pHV16iQHklH/gMdFZ2EhoY6/F3vQYaOYiM4qtATJkzQpY81RhwVkg13bGwsl4iYjp6TRsekdHbSxYsXievi2eFMmTKFm24SsHZK5PRNmjSJqk6zbE+mUTcQY6CjohNnp+Nu27aNkSX0CAwMJC6Tx4iKHEpbaGnD+jkLFSoku1iYxzEPNLcnkwzdL+KICgubfHx8AACgWLFiimnMMvWzZcsW3feWLFmSoCUICdBR0YmzRZ9y59kYhXUnx3IHjmhfVTyglQdyi4V5OCokadKkCTXZVpty0cr69euhWbNmsr999tlnsHfvXl1yWY6ozJgxg7hMhB/oqOhEzeJP0rRr1464zISEBNnrDRo0cBhTgTRWclT0OqksO0i9U5OOcPYOST5f27ZticmyR5QRFRr1PZ3ExETF38qWLQvr169X/L1u3bq6dLq6A4joBx0VneTKlYtqYylHhQoViO+cYB0d8cmTJ7LXWa9RoekYOcpTKzlkPKGZj47KGc2RHHtoOioPHz6kJlsJpVE8Gu8S65m1QEfFCbdv35a97unp6TDuBa2vB9LRYn///Xei8pwxa9Ys2euZp9KUgluxDCVuhPnz5+u6j2Xj+vTpU2a60jGLo6lk58KFC2HZsmVU6racTJrPyDr4Go8TqVlipqlUMzpx6Kg4QS6wWenSpZlE35SDdCFzNk0xaNAgh0HKtCIXLbJRo0Y2UXD//fdf2XtJjmDxGoZ2c3NT3G1k9qFxZ2XTLA2k0nvo2rUrlYCEjnSyvI/m+3EUtdcs5cIRZnJUzAg6KjoYPHgwN92sKnW6nvHjxxsKYKckNzP9+/e3+b+3tzcxfUrwdArWrl3LTTdNaJVNue2iVujcSEDyPCOaeeru7q7rPr31lHX5+PPPP5nqU4J0dGtRQEdFBzRPbhUFlucVNW/enIouR3h6ejLXmc5rr70muz2adeNKs4zKHaKnV5/cdlGaeSUXzDBzR0vjgEBnU3G+vr6y180yneZo8wHrcj9y5Ej4/vvvicoUZVq6RIkSvE2gAjoqOqhevbrTNEeOHGFgCT1odWJyFdrLy4uKLkfw+iJP1ztgwIAsv7F2bs0c0pzm+xsyZEiWa5mj/IaFhUG/fv2I6lyzZo3D3318fGTXk5ll6od1fXOk76uvviJ+MN+0adMc/r5z507o0KEDUZ16MeNoJDoqOqhUqZLTNKNGjWJgCT1odZrnz5+nIlcLpBck6+HDDz/kbQJxRyVzA8hj+z4pjh07luVa5mdzc3ODH3/8kaVJAADw1ltvZblmdkfFzc2NebRmHtSvXx+6d+/O2wzTYt7WhBOdO3fmbQITatasydsEauzYsYO3CUJ81dAcUaF97okI+UcTtVOTejty1qNpjuy0+rtMxypOFw/QUdFIWFiYzf9JDwGLQP78+WH06NG8zaBG8eLFuelWc8AkK2h2VvXq1aMmG0CsqQSe+swyojJv3jzVdmj53RVxNs1kRdBR0Yh9ZXY0BGzWSjZ06FCHJ++SJPO2ZBrIbYdWw9mzZwlb8pL08iNC2Vi5ciVReSw7cxEcPVLcv38/yzWrOSpff/214m+O9B48eJCGOabmk08+MXS/GesOOioUEaEzEp0bN25Qla93df/u3bsJWyIeffr04W2CaZBbH0KKn3/+Wfe9etsYuRD6SjuLSKAUCddZp1mnTh1d+szYGSPKoKOiES2LBEk5KqwdHitVcr1HttPKc5Hy1syONOt8jIyMZKpPLXqn75YuXZrlWps2bQxaow8MoU+OgIAA6NChA9SvX18xwrcZQUdFI1oqgNaOYOHChVrNMYSSfSwCrrEiPj5e131m7sTVQvpgQitP/Xh4eKhKd+bMGSL6aE/9yKE3KBsiDgEBAbB06VLYuXOnpd4nOioCwXr72vHjx2Wvd+vWjakdNHF0Cqwj9H6pXrhwQdd9pAkJCXGaxszOmNqOfP/+/Uz16TnnyYjTtWzZMt33igKv0Q/7jRG0ee2116jKr1GjBlX5PEFHxQlHjx61+T/NERXWKJ1P4ePjw9gS8dBzdsfp06ehVKlSDtOwapR//fVXKnIdnd5t/2xdu3alYoMWateuTUSOqNOvvXv3FiI2EU3kpqmcoSb/9uzZA7GxsXpM0oWztoE0jmLXmA10VJwwaNAgm/+L7KjwOLrdquhxVEieiWSEwYMHQ9myZW2uLVq0SPfCxMwUK1ZMdVqlLakkkKuHIkxZkqrzGzZsUJ32+vXrRHTaQ3sEIB1nbWqnTp2o6M2WLZsQwR9pYUaHRAl0VCjC2lHJnTs33Llzx5AMK037GGHw4MGav1RFaRiaNWuW5VqXLl1gxIgRNtecnZxtFNaRTmnqe/XVV6nJlqNBgwaq09JqZ6Kjo5k8N4339tdff6lOO2fOHOL6RUCU9ogE6KhoROQRFQCAXbt2MdepFhGmwlq1aqU6LY0oxFZqPOyxP2iR9bPSDNtfuHBhVen0lHGj+aRVp9oD9IoUKQJjxozRYxJXkpKS4NGjR6rT8zyglCRWblvQUdEIj8LAsoOn+Xxt27alJlstUVFRqtMePnyYuH4WAd94NViTJ0/magctfUpTZqtWrcpyTc97Ze3Aa5kCZGEb6femNcij2h1dojNy5Eib/+MaFRdG7iUrnWtCopJXqlQJChQooDq9lkLIevjc2QmxLOA1qpMeo+LTTz+lrkvpHdJ89idPnmQZUWEN6wZYBMcbgO57FWEUlDYsD9AsUaJElmskYvTs2rULmjdvbnNNy/Sh6KCjopHy5ctnudaiRQvZtCQq+ebNm6k1wKwdFS0UKVKEtwlEWbVqFdy+fRvq16+vmObkyZMMLSILqyMXHEGr7Gqpx2aY+hFFNoDz05NJI3dqOct4IwcOHMhyTW/07Mz4+/tnufbjjz/CN998A7///rth+bxBR0UjoaGhqtOSqOQ0w1ovXryYmmyjKDl/IqPU4E6aNAmyZcsG+fLlc3g/qfVFcl9tCBt4jEC4wqgHKaZOnZrlGktHJW/evMx0+fn5wfDhw5nt3qKJLkdl+vTpULRoUfD29obQ0FA4dOiQw/QPHz6Efv36QYECBcDLywtKlSqlafudWTHagAwfPlxzTBMtXydylVaUEZUhQ4bwNoEYrKdEAgMDZa9jh+YcVz0ETyn4I+0yI0mSTZtDMwhb+/btZRfOspz64YH9OxSljdeC5je0fPlyiIyMhFGjRsHRo0ehcuXKEBERAbdv35ZNn5ycDG+88QZcvXoVVq5cCefOnYM5c+ZQPzXXCrAOEATAvhArrb8pVKgQFX1aG1498VTsKVOmjGEZJEBHxTlyAcBy5cql+v4pU6Zo1rlx40bN92SG1Fo4WrL//vtv1WkHDBhgWJ8cy5Ytg7lz58r+ZrTNe/r0qaH7EedodlQmT54MvXr1gh49ekC5cuVg1qxZkD17dvjll19k0//yyy9w//59WLNmDdSpUweKFi0KDRo0gMqVKxs2njZGz+0wWsmxY+HPvXv3DN2/YMECqFatWpbrSms6qlatSm1LKK3yJMoUIi0ne8aMGZrSO4rea8/du3chOjpao0W2aHmvDx48oCZbiZo1ayr+Zr9GhcQOHLly0L59e8iRI4dqGVqO0Pjjjz9Up+WBy42oJCcnw5EjRyA8PPw/AdmyQXh4uOKZGmvXroWwsDDo168fBAQEQIUKFWDs2LEOD0R7/vw5JCQk2PyxZufOnbILZ7VgRkdDqRDTCvwkSh7JORMkUNoZ4uXlJXv92LFjWbYZkoJWXtOINyMSWhd2a9ltYdQRBtD2XuUWk5KSrZfMbQ6JTpSEzY0aNVKdVvSpI/v8eP78OSdL9KMph+/evQupqalZ5twDAgIgLi5O9p7Lly/DypUrITU1FTZs2ABffvklTJo0Cb755htFPVFRUeDn55fxxyPM8W+//aYpfdGiRbNc49EJ0/KWtXyNmBGjw+8A5vxSQcjD8vwYAG3tzM6dO6nJ1oO9w86jDsnp1LKwPSkpiaQ5xBHlY9AI1F3BtLQ0yJ8/P8yePRuqVasG7du3h+HDh8OsWbMU7xk6dCg8evQo4+/atWu0zTTMtGnTslwzYwFhHYNDq1yj5xkp6cuXLx8Vh1gk58WM5VELtWrVMixDpPelFjNvT+7YsaPN/0mMTrAu53KzA2+99RZTG6yOplLh7+8P7u7uEB8fb3M9Pj5ecadBgQIFoFSpUjZbwMqWLQtxcXGKCxW9vLzA19fX5o81IjTqPGIy0HJUSH1l2kc/JQmtvFNi1KhRhvQBAFy4cEF1Wvt3aLUD2Vhu/UzHDGvtRGXmzJng5eVlUy5Fmfoxqm/x4sXQp08fTWcO0UKEvswomhwVT09PqFatms3ir7S0NIiOjlbcVlanTh24ePGizeKk8+fPQ4ECBUx3xsLq1asVf5MrDGYsIFoaCi3z6yTm4gHI7MJhiaP8JOEoGNklce3aNaonHANo2zEjh+jz6bR2p2nBaEA6RyH1abZh6Wt/MusgEdNEBEcle/bsMGvWLHjzzTeZ2mJVNI+zRUZGwpw5c2DBggVw9uxZ6Nu3Lzx58gR69OgBAC9P3x06dGhG+r59+8L9+/dhwIABcP78eVi/fj2MHTsW+vXrR+4pGOFogRVLR2XTpk1U5DoiJCQkyzV/f3/FRdT2KOUF60Ylf/781GTzmDZwtCjdHrm87tmzJ9y6dYukSTYYXdvUq1cv1WnNOG1DAqN16IMPPqAm2xFNmjQBANtDAR1FblaLCI6KSIhunxo07wVr37493LlzB0aOHAlxcXFQpUoV2LRpU8YC29jYWJt5xuDgYPjrr7/g008/hUqVKkFQUBAMGDAABg8eTO4pBIVWAXEUg4bW9MWCBQtkY55MmTLFUJAmrXlk9PkcBV+Tkx0fH6/prCWRUcrrxMREas+4Zs0aqF69uu77Fy1apDotOir60jrKNxaHZ3p4eMCpU6cgJSUFcubMaVgu645ZhFE1R7ikowIA0L9/f+jfv7/sbzt27MhyLSwsTPaMAytBY0SFxyiEUqOltAbJ6nzxxRewZcsW3fc76gRodKyso+BmHj2Vg9a2bzlo5KfZpqdJw6qTMxoKIjOij9L26tUL5syZQ8kaayL2BnATYQWvFUB7Y682Pan8Yf3VrBRxWQ4eZSAlJUV1WiX7jOTpJ598oin95cuXdetigX1eTJgwgZMl6rFK25NOhw4dDN0ven6w3hEken6oAR0VQoiymJZ1R8765FezPR9ttm3bpjqtUl4b2RKqdQSH5uJYGu/KWdRdEToBM29PlmPixImG7mdp84MHD2DNmjXM9JHCbJsSjMcrRgDAuKOiJey2lSCxeI4Uch2d1pDjamSq+U0NcuUrT548muUYsUPrvSRCpCudrO1oa73eZ6R9jovaxeiOoLlGRUsoeVLoKcNGMFL+w8PD4ejRo8z06UHunTdu3Bh2797N1A4j4IiKAjNnzjQsQ0sDIsJ+ewCAggULMtMVEBCgea5WSyUn8dXAOsqoFuTOGFmwYIFiehpTP1ohsf2U5dlCr7zyClX5SmekaUGEUR2SsB6lNYJWJ4UHcvmxZ88eDpboBx0VQrCc+ilcuDAVuQAvt59rwUijMmPGDKpfT446bRqw7jBu3LiR5VrJkiUV04vgqJDAz89P9jqNYIUkRoAc8eLFC8MyzDj1M2jQICpyAbTbXKVKFTqGKKB0UjUtrODIoqMiCHKNrFIBU2qoleRowcfHx9D9SmgdcgYA2W2tWp7v7t27qtPSwkprag4dOmRYhpa4L1qh4ag4y0+jnYDW3ZDr1683pE9r/iud8m2EPn36wPjx4xV/Jz2i8v333ztMzzpCM41Ra7mz5qwEOiqEEGUxrZWoUKFClmtaGrHr16+TNMcpIoxM6ImJodbu48eP67IpMzzWPBhBhHeamWbNmsFrr71mc01LOyO35ip37tyK6Y0G5pSzjUSsFC06WY9g8MDRB6YV+iF0VAgh1wCbrYDQbkDsod0JsA6/rtVZpRGcj6ajQuJ9iT6iIoJj4mj6To53330XTpw4oVtf8+bNFX8z2ibwOK/s33//NXQ/Ih7oqFDks88+Y67TSCXXMyRJs2GX+9LTok9rsC4azyKSs1qiRAnZ6yyDT5EYlVHCjFM/cjg7dkDOpvDwcN36SJxYrMTjx4+zXKPtDL733ntU5ZsNkdogvaCjQgi5wrBs2TLiMmnC+uvH2b1G58dp79iwR4TpP0d5qhQl9ptvvjEsWy2dOnUydL+j7bwk7Nu6dStxmVpp27at5nvu3LmjSxft8O/vvPMOETmHDx9WnVZukTlibtBRMSmibGc2gp5OQMs9Igzjsw7BLsIz06RWrVqa71HrLMbHxxMJS2AUluegyR15QpLNmzdrvkeuDLdq1Ur1/fbv2wwjChMmTKC2fssMz+8MdFQIYfRcHq0dDI3jw2l2cnp2/ei5x9n9jqDx/I62t9LQR/Mdiu4EGZ36iYuL06yTdCcQEBDAdCQwX758TtMUL16cqE49eZaUlERVPm8GDRqk6QBOLZgxP+xBR4UQSvO8rA9jtNJ2WNKHOn7xxReG5InCr7/+CuHh4bLbr1k6Kj179qSmSw8hISHEZbKuT45CD9BAROdTS6gGOexHJlhPAevln3/+oSLXCo4KhtAnhNLQ5L1795jaIWLDo4SzKKVGK9i0adNs/u/r62tInih06dIFAACio6Oz/EYi8qtaihQpouu+58+fg5eXl9N0Ws9Q6dWrF8TGxsK4ceNsrqstR0ZH8Egwb948pvrM1F6oxf6gzrp163KyBCEFjqjIoGcI2NvbW/a6mhX1t2/fht69e2vWKcfkyZN130tzMa2c7DfeeIOaPjNA41lo7uCwt1evI6nmvtjYWM2nzHp4eMCAAQN06ROFMmXKOE1DstyokWX2OkezTpCEVj6bqfwrYY43yBilMPKvvvqqZllqCp/SlISeAma/a4EkRk81tcfZkCzrL1zSso0Gy9IDyxEVmty6dUvXfWZvlNWUQZLPqEZfu3btiOlD2KP1hHMRQUdFhvj4eNnres79UBPg6vbt25rl0sDZ88nFhbly5YpTuZIk6TqFWK5BXrVqlap75VbQ0/4ytLe3e/fuVPXJwdKRM8uXqpnOwhFxjRlrR4XHCI6e3WRKVKxYkZgsEpQpUwbq1KmT5XpMTAx7Y3RijpbGxLRs2ZK3Capp0KCB5nvUOCDvvfceNGnSRI9JWVATMCwlJYVLY7Ft2zab/5ulI9dLhw4deJuQBdKxbJx1mqGhobpl69GnNo2o+tTAY1SwVKlSxGTt2rVL130087lXr15ZrtFYfE4La7ekFkfu0D4jOIuIqZeFCxfquk9vB3P27Fk4c+ZMluvOGgKjDcW6dets/q81FLro2C9S1BuQT8QRjqSkJF27woYNG6ZLnxIijqjwwD4QHe0pPZL5wHrn1tGjR52mMfuUKDoqKqEZZpxG6G9H3L17F3766acs14cMGaJZloidjl4CAwOJynO2y0hNDAuRuH//vs3/Re3k7FFTjsaPHy+7vsvZM3p7exM9YRgdlZfYj1QlJCRwskQ7rPNUzQJss4OOikp4rDegRfPmzeHDDz/Mcj1PnjwcrCGP3sP39I786KVJkyYwcOBApjobN26s+14znHysd+rn3LlzstdFnIohiajTk6I6UCLiCnklZilFqHLo0CFisuSmWEhBekTF2RqcYsWK6T4zRQ9ubm7w/fffE5OXP39+p2kaNmyoW779wnC9DaSa90pTthaqVKnCVKcrjKhYZWcaafS+C3RUXBS5Fy9iYSBpU65cuYjJIoXeL3ilfKldu7bTe/39/XXpFAG5lf32GOlUSY2osJ7S07PjLB21BzaSguYIzs6dO4nJsl+vJIdSefn888+d3qu3jHz33Xe67hOhfafpqOAaFReBR0FmOSyrZjeNWTB7pdQL7ee2X4shyqiHM9l6I+gCsI9mTLPTkRtN0/sOr1696jTNzZs3Za/T/BhgeaCjKIjgZNEGHRWVqCkMwcHBRGV37NhRlzw9DVnhwoV16QIASExM1H2vI1zV4aBJs2bNdN+rZhqEN0Z2iomAWaZ+aK9XMks+iICZbVcLOioEkdurrpclS5Y43U2gVECTk5OJ2aEGX19fGD58OHG5ejsdV6i4cqh57mrVqhGTL+IaFb2IMqIoYlwTOdS8Q5rxa0hj5o8iEcoDbdBRUYmRwhAVFaX5HrNVnLFjx2pKv337dqdpaJ4lY0VoP7d9QDu9WGlLO2mdZul0aDsqejh8+DBTfUaQCzCHa1SUQUdFJUYaED1BoYwULDMUSiO7T1ijZifQ8+fPGVjCF/uFpSJ2qqKU/b59+/I2gSpqpn5Yvws9h5ymw7os6z0HLCkpKcs1EeshadBRIYgojaRVIDn107p1a912rFixwmmaKVOm6JY/e/Zs3fdmxsfHh4gcOR4+fEhM1j///KPrPlEXSgYFBWW5NmvWLLh79y4Ha9igd0RlzZo1FKx5CckyKip//vlnlmtmmS40AjoqMph9ftQqDhPJqZ/cuXMbNcchK1eu1H1v2bJlidigd2umGiZPnkxMlt7zcdScTVKkSBHmi36VjrJwdiDpjz/+mOWanoNPeaC3bjZv3pywJWQQYfGuGhv0LmI2e5+AjooA6I2maXYv2Rmvv/46MVm0K+qNGzd030vqPdqfj0ISuaktEctftmzZ4MiRI0x1Tpo0Sfa6ozJ38OBB+Pjjj22uRUZGqgpJIEK+Hzt2zGkas3eOiDigoyIAWKHladOmDTFZtNf8iBqKnBSso4nKdcZq3yHrd1GwYEHN91y/fj3LNbkpJFGZMWOG0zRy70sEJ8vMuGpfYe3W1SSQPpreKoWZZKOmJU/sTz1eunSp03us3gDLdf5mnyIVDauVIau/L4Qd6KgIgJkcldGjR1OTTRMteWI/erB3717S5thghg7KbOezdOjQgbcJmjFDOdCCCCMqBQoUUJVOhLxXsw7MVUM2oKNCEFEKEc1C+d5771GTLUetWrWIyKFdUXk3dFrOaqpfv75m+XKOipFn7tixI5w6dUr3/VoRLb6K2TsONRw4cED3vaTqk5liqzx+/Njh7xcuXIBOnToxskYs0FERAL2N1qBBgwhb4pycOXMy1bdlyxYicrTksdxuDGfwdlS0HJ6nZys16edbtmyZw0Mi1Rx65wj790075LtWRBhtMIIaJ6Rr165Zrql9RlKOnJ71Q6LywQcf8DaBG+ioCIDeqZ+2bduqlkeKPHnyUJMtR44cOYjI0dJRNW7cOMu1mJgYh/fExsZqNSkDEh2Ulvfi7GgGOUaOHJnlmlG7HZ0RJbfVWku5tk/rKCDfmTNnVMslhZkcFaVzwJzVCYQszkZcrAw6KgJAOjCUKwwrs+bevXu8TXCI1d756tWrs1wz4qjMnDlTMe3atWvVG0YIERyVCxcuqEqXL18++Pnnn7Nc17MlX1RnTA9qFtkroae+OovLQ1qfSKCjIoPeylSxYkVd9504cSLLNVEX05oVzJP/IJUXInc69s9469YtxbRDhw6lbY4qRF1oCmDsMEs9iBCAzRFJSUnM14uwXNMlGuioEOStt94iJkttZ3Ly5End95oVtV+CmRFtjYIzrHZwHwmMjKgY+RrVi9Z8VttZkurEtciR+wgT2VHVSqNGjTSlN3pCvZ68c+WPV3RUCOKo8D158kSTLLUFq0KFCprkkqBu3brMdWZm7ty5mu8xW0Vlba+eIwBEzlN7x1Q0W41M/Xh7exOxQUtnmS1bNqhTp47NNdHy1AgdO3bkbYJTrJTfWkFHhRFGFltqhXaB5h2FlfXXCG1IfJkaGW0AAHjnnXeo6iSB0WcUCSOOyqJFi0ibQxyzHRDo5uYG+fLl422Gw4XdopdpmqCjIii0hvm+/PJL3XLT4T3k60w/6QB6anSSRi7EOinM2uBpsdt+RIXH1B+tfK5QoYKhGCXpaC3T9ukd3e9oR5eo2H+A8XiGmjVrMtdpBtBRkcFs6xnscdRAaom3YSWMdhqsO3c9QdnUQupZRHZ4rDz1Qwqa+nh/zJBg3bp1ir/t2rXLkOzAwEDZ61qXCKjllVdeoSKXFeioyHDt2jWu+rNnz64YI0UpfWaUGmWjQbTS4T31M27cOPjjjz8UfzdyoB0P5Oz9999/OVgiNkZGVER7/6wdlYSEBKb6jMqW25HEOrq0ow/WVq1aGdL19ttvG7pfK2Y8UiIz6KjIYF8hSHzdaqm4Dx48gNy5c6tOP3v2bFXpVq1apVqm6Dg6WfncuXNZrplt6kcrRs4y0itbtM4/M/adzMKFCzlZIs+jR4+yXKNZxsaOHUtNNg3y5s2b5RqpDy216BlZVxuigvXHHqkF2LxAR0UFvXr1YqrP09NTU3r7Qq/Ugcg1jnoQvdN+9913s1wTuVNlTZkyZaBFixaq04uSd0ZGVHisN3DU0Y0ZMybLNaP16sGDB4q/yQUsFHlERQ7aW8xJHLvAuq9wFdBRkcFIw6x1m9uCBQt061LC6gfwOeLo0aOy10XpbOXgsTbB0fy7PaKMqGjR9+qrr1K0JCt9+vTJcq1w4cLw1VdfyaaPj4/Pcs1oOXB0jAKNqSZH99Mo06zXDurRx7ouN2jQgKk+XqCjogKaw2YkTiO2b4S0diB6I+qKSMuWLWWvG+1UHYVgl6N///6G9PGA1kI+Hug5eNEI1atXl70+evToLNdSUlKEcJy1dqr2NkdGRjo8Q4k0JHY6OULLGhVR4HH8Aw/QUZHBvkK2bt1a9b1ap21ooLUR1NqoizyiIrdoEMC4o6I1IBrrIWASHZ+S0yzKiIoWlA7So4WWxYozZsyQvW62qZh//vkHoqKiqOiTu1/usFCamMFR8fX15W0CE9BRkcG+AdaytcsVtv+K7Kgo2WaGRoc3Ss4YTYfk6dOnqtOK7BhpOeX777//lr0u+vZkufR79+4lIlsE7KfOsM0QB3RU7Dhx4gRcvnxZ9/2FChUiaI06kpKSbP7vymtUzIjo+alUnvLnz29Y9tmzZw3LMBuivG+tdnh4eGS5tnXrVtm0L1680GUTT1asWGHzf6X1bo4Q2ZmWwyy7wdBRsaNdu3ZU5MbFxVGRCwDw7Nkzm/+fP38eYmJiqOkTGaXG12wNiFb8/PyoyZbLuzlz5qju6Lp06ULNDpGQW48ih1K+iT6iUq5cOdVptcSBEgX755szZ45mGaKXUXuGDx8Od+/e5W2GU9BRsUNpjYNR1DZierCvHA0bNoSQkBBqBVCUL0It0BjGZbmQ0BlathtrxeiRBAsXLoSlS5eqlm1W5IKUaUH0eqVle7DS9JZaRM8LKyFSO6YEOiqMoFkYlBp7+wi7pDoFMzYipDvEvXv3gre3NwwfPpyoXL3QDCAll3dadoq5ublpWsOhxQ6RUPsOeIyokMg7EaZzbt26xdsE03L9+nXo3bs3bzN0gY6KHbQaQ5qNLOsGvEyZMkz1aYHU1M9rr73m8PfIyEgAeDnHaz/1phWjHZRcgDuSyOVdrVq1iMiWe/YPPvhAtR1m4eLFi9CvXz+4evWqMFM/WmEdGVaOUaNGqU67f/9+ipaYj6CgIBg8eHCW66KXOwCdjsr06dOhaNGi4O3tDaGhoXDo0CFV9y1btgzc3Nwchj/nDcvGkNY0Uzq0nqVfv35U5JKAlKOipfK+/vrrmmSTpkaNGlTlkyhHWt7Lzz//bFgfDxyVmQYNGsCMGTOgadOmuu5Xi9K7IiG7VKlShmUYJTk5WXVaUs40LTZt2sRcp6PoxSKj2VFZvnw5REZGwqhRo+Do0aNQuXJliIiIgNu3bzu87+rVq/D5559DvXr1dBtrZuQaCrnzLMwA65M4e/bsaVgGTUeF95ebXFRUkpBwVEg4c6KPqDgqMzdv3gSAl7FHzDqikj6KyAKlvKD9cac1srg9WspoREQEfPnll4b0aUWEUTE9aHZUJk+eDL169YIePXpAuXLlYNasWZA9e3b45ZdfFO9JTU2Fzp07w1dffQXFixc3ZDBtWDaGcoUmKChIsxzWDbijUN00mDt3ruq0pEZUtB7cRwO1gfhoBxkkUb58fHwIWGINaDoqNIPzifAOr1+/TlV+t27dqMrnDe3zkmihyVFJTk6GI0eOQHh4+H8CsmWD8PBwh1+VX3/9NeTPnx/ef/99VXqeP38OCQkJNn9mR21DUbt2bcqWZEVrA5kzZ05KlshDswGnpVOLPiVdn376qaH7SXHnzh2q8tUi+ogKS/vMNhqbO3duTZFlzRpmoGzZsprSi3xelkhoclTu3r0LqampEBAQYHM9ICBAMU7Inj174Oeff9a0Jz0qKgr8/Pwy/oKDg7WYKSQ8FtOq1UnTtokTJ1KTrSU2DWtHRQtGw73TttXZwmJWGC2nNGMZaYHEiEqJEiVkr9Nco2KEO3fuwJYtW1SnV1pQTRuj+fTGG28YtuHTTz+FZs2aaVqPoxYvL68s13iXDTVQ3fWTmJgIXbt2hTlz5oC/v7/q+4YOHQqPHj3K+LPfZksTs3qcovLFF19Qky23iJTHYlqj5MuXT4ipJqtjNM4JKcy6PdkI7u7ump4vX758sn0G7XpJ80RpOeTey5QpU2Djxo2wbNkyQ7bIUbVq1SzXbty4QVwPaTQ5Kv7+/uDu7p7liPL4+HgIDAzMkv7SpUtw9epVaNmyJXh4eICHhwcsXLgQ1q5dCx4eHnDp0iVZPV5eXuDr62vzxwreFVoPar+iaD+b0vukhdx8NSlHRS4mxpgxY2D79u2a5KhFrgFRixm+iMyIlkisJOAxxSkyPJx31nXJ0ftKPwcrNTUV/vnnHyL65J5v4MCBRGTTRJOj4unpCdWqVYPo6OiMa2lpaRAdHQ1hYWFZ0pcpUwZOnjwJMTExGX+tWrWCRo0aQUxMjCWmdEijp6FRukfpwDBaiLKWQQ4SIyojR47kvhVZDnRU9HP+/HnZ659//jls27aNqS3oqNgi9yw0AxsCOH8Hx48fVzx4Vuv6FLV07dqVmGy55zPDGlDNbz0yMhLmzJkDCxYsgLNnz0Lfvn3hyZMn0KNHDwB4uWp66NChAADg7e0NFSpUsPnLlSsX5MyZEypUqEB9t4Ie7t+/T0Uuj/Uin3zyCTFZZkfkqR8tyB3iJ6qtatDyXmh0wq1atZK9PmHChCxr8Zyh1j6aUz8014SpZcSIEUTkyIWyoL3zyNk7qFKliuKW4ubNm9MwSfH4CVdCs6PSvn17mDhxIowcORKqVKkCMTExsGnTpoxKHRsbi2GOGSPiV9Tp06eF2uonuqOi1j656QgzOypaoFHOWU9XAtB9X8OGDcty7cWLF7Bx40ZqOjOHWbh16xZ8++23ROR27949yzUtO4f0YOTdkBwN1yvPqmQ9t1sF/fv3h/79+8v+tmPHDof3zp8/X49Kl4F04bx16xazRYSZba9VqxY8fvyYid7MKDU0Wg8lFNVRkcPMjgpv20nWN9YLMdUycuTIjIBzNJg0aVJGaHajx0lkRm6ah8SZUVrYs2cP1K1bV1VadCzogWf9MOLvv//mco4Q7ailSvBwUgCUG3utw7LOOg3eHawZkTt6gffUD0lEmPqRQ0toCD2cO3eOqvzMqI0tpBf7d5C+Jm3u3Lnw9ddfE9cnQpnm1VZrQdeICqKPP//8E1q2bMlU57///us0jSt0uoMGDdKU3tmiPREaGLPBMoieyCjlg9yGBBLcu3ePilzaiPC+00+M7tWrl9O0Zp36uXLlChM9RsARFYacOnXKaRrShZ0lothhT548eTQv3BbFeRM1T/Wg5llYPu/q1auFCSnu7++v6/gMhCxmihS7bt06gpaIDToqDKG1XZjWanMzwsrBcKaH1MGNrLeYp3P16lWIjY1lrpflF2bbtm2JylOLXNnRE53Yw8M1B8SfP39u8//U1FRYu3YtEdlyZezChQu676VxDwBAhw4doEWLFrruNSPoqDBk/fr1VOSWK1dOVaG10te5FvQswDNyymiXLl2IxT3gEZvm6dOnUKxYMShSpAjR01blyt/u3budpklHlMiyRhFltE50lMqC/bq72bNnQ+vWranZ8eDBA1XpWE79uFoZQkdFMPQGNCpSpAhhS8zJ7du3s1xbs2aNZjnpc9NKOGpEFi1aRCSUNgCf004z56H916sR5EYNtm7dmvFvZw09rxEQtajtqOwjewOw73gqV67MVJ8elPJzwYIFNv//448/qOukda+rfjxqBR0VwZgwYYKu+2ge727UBpocOnTIaZqQkBDNckuWLKnHHN0o5R3JEQ09vP3228RkOZoOS0lJgZCQEHjnnXcU02h14hs2bJjlmtZt6jRYtWoVbxMsFSZCru6sXr2amPwjR44Qk2WPo/KIIyr/gY6KYBQrVoy3CdRRisGjh9DQUDh+/DgxeenMnDnT4e+sGorp06dnNFg8Dg/766+/mOjZt28fHD9+nGgHI3dWzOHDh4nJV8umTZucptFTnoYMGaLHHAAgPwL74MEDaNCgAcydO5eoXL3InT2nBjnn4KOPPjJqjiZ9mX/7/fffqenOzOLFi5no0Qs6KhYncwPIatTlyZMnir8dO3YMpk2bRlTfvn37YMeOHRlBp0igNXw6Lfbs2QN//vknALw8FJEFzsoDqRDpWnSS4s6dO7BkyRKHZZQ0tKLCjh49Wve9uXPnJmcIAHzzzTewa9cuGDt2LDGZrKdheOh0dM/69euJjmg6omvXrnDixAkmuvSAjgoFaDTkzhBp6sdRdEo/Pz8quhs1agTfffcdFdlysMzXM2fOMNfpCBoOE40RKjmZbdu2hc6dO0Pv3r2J62ONmtOFk5KSqNvh5ubG/GC71NRUSElJgR49esDmzZuJyWXtqISHhyv+tm/fPt226EHkdUvoqGTi2rVrROSMGTNG13ZDIzgbQnS01oF0J8G6Q+UxX6tmbQwptm7dCrVr185wWGgjikNEg+TkZAAAWLJkiWIavdMGjRo1kr3+999/65JHAhZTdzwibq9fvx5WrFhBfK1N+fLldd+rJx8chZbANSr/gY5KJrZv305MlpqvHVa89dZbkC9fPsWvnqpVqzK2yNywXpC5detW2L9/P+zZs4e6LlqnhyshYoO7bds2XfeVKFFC9vr+/fuNmGMIqzqdSUlJDsuq3ucuVKiQXpN0waP8t2/fnrlOo6CjkgmSOyxEOtTujz/+gIcPH8ouVHz48CGx6Rg1jYOIHZNWaGwZFqVDIems2+Po3bOa+lEDqRg4WtBrq0gfRKwRrS0hXYdpfRDpDYHBE/NZTBG5gqZ3nlC0SgQg/3y01oywRMS81oqXlxdvEzKg5TTVqVMny7UbN27AhQsXLPEOeeBoOzcAOweYxvtzNvUhWplp06YNbxMsCzoq/+f+/fvwwQcfZLmu96Aw1l6rUqXO7JXTbrQ+/PBDp4vqWK/dMQu//PILbxMAgK7TV7NmTejcubPNtePHj0OpUqXg7t27xPUprRmxEs7qtCgjdaxh/dyrV6+GN954g6hMWmtUzFgm0FH5P9988w1ReXoKEo350dOnTxOXqcSZM2cgMjLSYRoaDpxoX1Z6KFeuHG8TMqDZkNWtW1f2+qVLl4jr+uyzz4jLpIXeMqzHUVF6B6IRGhrq8HeR6n21atWIy+SxSFlU0FH5P6S31ylVojFjxsCKFStktw2WKlVKtz5RCt+WLVt4m4AYwM3NjWpZ0iq7Vq1aAKDPkdN6YrbVWLVqFRw8eDDLdRp11JHTULRoUV0yCxYsqPgbzwXKrEBH5T9c8/hNGUi/PKWKO3LkSADIerCWUdTYT/IZc+fOLXtYV2xsLEyePJmYHhExY0UXHaX6MmLECHBzc9M9BWt15MrigwcPIDY2Ftq1ayd7D+mRiHnz5jn8ncb6qylTpsCsWbMUf8c6qowZ8wZHVCjhbIpj0aJFRPU5OkclHZIF9Pjx4zB79mzZ36KioojpUcO9e/eY6nOEo/gciPYy6O7uDs2aNSMeSdUqFC9ePMu1Nm3awIULFxTvYb1+TqQpGjPh6EBQV8tTdFQowbogpY/UaCF9WF0PwcHB0KtXL933k2TYsGGKv+XNm5ehJS9j1iAIK+SiYO/atYvaQswOHTpovoeWY+RqnTUpcETFxLCa+qFlg55omviV6hilKJUvXrxgbAkdevTokeVaamoql4ZMqb6YsVHVg95ON0eOHLLXR40aRVyX3nvLlCmjW58jHNli9XKDu34QIojo7YtwxL0VcBS2W8T3rsTPP/+c5do777zjcMjZKFobSTM2qiJw9uxZxd9YT/04O4lcNHLmzMnbBKqYsU6ho0IJZx2WGQuLq6E0chIXF0dFn5p1RiRRKqOVKlWiplOp3JvJwaMBy+c3oktPULP8+fPr1ucIR89RunRp3XKtHu3XjH0POir/h/TL0/PVUqxYMaI2IMZQclRozf+LfMx6OhEREVTkinD6N0+CgoJ4m6AKZ5FwWeKorhlxjswYYl4LZqxT1n4jBvH399d9r54Oi9aXRzpmLKDp8IjPUq9ePdnrtPKxTJkywjeSrVu3NnS/1hEVM5dZtbRu3RqmTZvG2wxVuMLIl9VHVGh9bNBE7FaRM0YixTrrcHg0wGZu9MPDw3XdZ6RhVdp+7cprfWh1VK7sqKxZswYKFCjA2wwhadKkCXOdon8sABirh6Ls1tSC+G+EEax3/cjpy5UrF1Eb7Ll8+TJV+SJi5L0qBaqiNfWDZMUVHBVEmd69eyv+RquusXZUWJ/zZcYRI5d2VIYPHw7t2rWDQ4cOCbE9+eOPPyZqgz2PHz+mKt9VOHDgAG8TTItSPTt37hxjSxAz4MhpYD26Rwu5MAGILS7tqGzbtg1WrVoFN2/eJC67Zs2aDn+Xmz7Inj07cTtcHRqNzs6dOxV/M/o1ZvURBKXnU9rCarb8WL9+PW8TLIXZ3j9CB5d2VNI7FaXKYGQxLesw8ogYGB1WpXGCNkmMOn5FihTRlN5sHVWzZs14m+AyuPI0q6s9u0s7KukvOy0tTbZBnDt3rm7ZOXPmhICAAMXfzdYAm5UhQ4bwNkETjg5aswJadw15eOC5qa6Mow7ZSlMmZghNwBOXbgWcjaho/fqzx5EzIoKjYnWv/Pjx41CxYkXeZmgiX758vE2gitYyZ8aFfwg5RGgnWcA62KPZwBEV4LPdlEYF/PHHH4nLNDMVK1Y0nTPmKg2zWlg7Kqx3YCDWwOiUrdnaKda4tKOSeUSFdQdBwznq168fcZlmxoyVX3RHhXWe0t6yb0+LFi2Y6kMcY4Y6XKRIEeZ2miFfSIKOCrx0Gvbs2UNcvuidDmsOHjzI2wThceVgcnLUqFGDqT5X6wBEx1XaUCx3jnFpR+XkyZMA8HIB49WrV7na8tlnn3HVzwJnW7YR12mY1YBfqoirgOXOMS7tqNy5cwcAAHbt2kVFvpZOp2DBglRscESOHDmY60Qcw9pRefXVVzWlt3qDavXnM1v4dB7vY968eZrS48cFfVzaUXF1Jk2axNsETSxfvpy3CdRh3eh98803TPVpgUcnZXVHxQzn2GSGx/t48803mevU+pxWL6f2mKvUIsRo3ry58MHF7Hn33Xd5m0Ad1mtUrP41OHDgQE3prR63xWwdXEREBJQrVw66dOnC2xSqmO29sAYdFYpo6QSwoCIAuJg2MyTqRKtWrTSlN2M8Cy1rv8zWznh5ecGpU6dg0aJFvE2hitU/GIyCjoog4KJBMfn111+Z6hO9wbJ6uTGjo6LFuTXb1A+A+GWORJnBDxTHmK/UmgiRR1REr/xKDBs2jKm+ChUqqE5LIo6N6I6K1TFjJFyRHZVBgwYx1ccDEmvnUlNTNaU3a/utF3RUFKhUqRJvE6hi1oIu8jZuEsHCkpOTCViiHpEdo5IlS/I2gTrR0dGGZYj6QeTn5wfjx49npo8X1apVMywDHRXHoKOiAOsvdxxRERPWHbnWL97JkydTsoQ/WreJmpHXX3/dsAwtIypY78XkxYsXTPVNmTKFqT6joKNCEVG/dHjoIwVrx0GLPhK2NW7cGN5++21VaR8+fAiffvqpYZ1aYFluSMQWqlWrFgFLxKZBgwaq0/r4+FC0BNHL8+fPmerz8/Njqs8o6KhQRORhdURM3N3dYeXKlarSmq2x4YGPjw8UKFCAtxlUGTt2rOq0H3/8MUVLXI/ChQsTkcM6VITZPlTRUXFRWBdUUmt+8uTJQ0SOWtDZtMVsDRwAwJgxY3ibQBUt0YV5RMBGnKPVUTFjPTQCOioU0bK11epTPz179iQix83NDebPn09EFmnM6NTwsJl18K7333+fqT4EQciCjgpFmjVrpjqt1R0VV/sCQJQZMWIEbxMQBpB2gtVGpi5SpAhRvQh/0FFRQOthbUaxuuPgCo6KGUdUtELiPbpCWUDIExQU5DTNjh074NixYwyssSasF/WqBR0VBZo2bcrbBEQArO58aH0+Eov+rO6ovPPOO7xNoMqFCxegfv36zPWq2YbdoEEDyJ07NwNrXsKrLNPSu337dipyjYKOigKsI1RafYTDrJ2T1R0VLYwbNw7Cw8MNyzFrWVCLl5eXqnTt27enbIktpPK9RIkSXD7kRAwzz6t9KF++PBW5orZ31j4q1AUJCgqCGzdu8DYjC1bvnADEreSkGDx4MG8TTIHaKKOenp6ULbGFdfh80p2piI4Kaw4dOgRbt26Fvn37UpEvahuGjoogkAi/DgCwYMECVV+9ruA4kEDUimtmrF721HaoVh/VJH3Ao4h1kXWe1qhRA2rUqEFNvoh5DIBTP8JAKj4I6+BW7733HlN9iPlBR+UlJEc42rRp4zRNSkoKMX1qOjTSIyA4okKOxo0by15HRwVxCKnGW60cUvpYB2BjDesQ+q4AOiovIemouELZM+PJ1qJSqFAh2d1RojqDumrK9OnToWjRouDt7Q2hoaFw6NAhxbRz5syBevXqQe7cuSF37twQHh7uMD1iDNaOilpIDgOzbJRZz+uzRtQObuDAgbxN0I3atSdWd9hIl63hw4cTlefqmClKseZWePny5RAZGQmjRo2Co0ePQuXKlSEiIgJu374tm37Hjh3QsWNH2L59O+zfvx+Cg4PhzTffFHLBpyvBupFkHY2UFFWqVIG6devyNiMLPLaHkkJN2VMzlUGajz76iIiccePGEZFjdkg7KlY/s4k1cvVQ1A8XzY7K5MmToVevXtCjRw8oV64czJo1C7Jnzw6//PKLbPpff/0VPvroI6hSpQqUKVMG5s6dC2lpaRAdHW3YeCtBynFQOwJASp/agp09e3Yi+ljj7u4Ou3fvVpWWZSUPCAhgpos0Hh7WXsOv9qA6HFFBeCJX/iwx9ZOcnAxHjhyx2VWSLVs2CA8Ph/3796uS8fTpU3jx4oXDtQ3Pnz+HhIQEmz+WdO3alZgsb29vYrLUYPXGD3mJmTsBNRFGXYFSpUoRkyXi+g0zl1FXQK6voLmjyAiaHJW7d+9Campqlq+5gIAAiIuLUyVj8ODBULBgQYdbaKOiosDPzy/jLzg4WIuZhqlQoQIxWXv27FGVjvViWrVBqZyBjREfaAV8YgE60y8ZMGAAMVlq2qxvv/2WmD7E/MiNvrOO7aMWpisFx40bB8uWLYPVq1c7HGkYOnQoPHr0KOPv2rVrDK0kS7Vq1XibIIufnx9vExADkJo+QUfzP1gv4CXZKaiZ8i1evDgxfWrAsiU2cn2wqB8Rmlo7f39/cHd3h/j4eJvr8fHxEBgY6PDeiRMnwrhx42Dr1q1QqVIlh2m9vLyIffHrgUcFI7W7hPWuH2yM/gPzwrwkJSUxnaadMmUKUXlqyh7rr2VXqA+iduxq8PHxyXJN1OfR1Dt6enpCtWrVbBbCpi+MDQsLU7zvu+++gzFjxsCmTZugevXq+q21MKQaEdYFTU1j5O/vz8AS/rhCw8wK1uWY9VoyktM+amnWrBlTfa5QH6z2jKKGY9A8fhwZGQndu3eH6tWrQ82aNWHKlCnw5MkT6NGjBwAAdOvWDYKCgiAqKgoAAMaPHw8jR46EJUuWQNGiRTPWsuTIkQNy5MhB8FHIwbrw/fvvv8RkFS1alJgsNVSuXNlpGrW7ZhD1kCqjVmtoEWVwRAVxhiVGVABenvg5ceJEGDlyJFSpUgViYmJg06ZNGQtsY2Nj4datWxnpZ86cCcnJydCuXTsoUKBAxt/EiRPJPYXJUbudUQ2sPeLu3bs7TVOkSBGiOrEBRBBbnNWJK1euEG0bateu7TSNK9RTUTt2vVhmRAUAoH///tC/f3/Z33bs2GHz/6tXr+pRwRWsYOpxd3cHDw8Ph+eIWK0yK8Gy3LhKniJkID3S2qBBA6LyEDEQNcaRmO4TZ1zBUWEJdqrkIVVGX331VSJyEL6QHJVVi6N1iQCu0Y4OHTqUmCxn+ckC0idekwIdFReFpPPgrEFCR0Vc3n//fcWTVK3EvHnzeJtAFTVTsKRp2bKlw99ZRzlt27YtMVljxoyBXLlyOUxz7tw56N27NzGdamNu0QRHVBDLwtpREdXxMeMXpI+PD2zdupW3GVkg/Y7fe+89ovJEQ8QOhvUZWc2bNycma8SIEXDnzh2HaUhGFgYQY32ICDbIIaZViKUg3em88847ROUhCGny5s3L2wTupO/8NCsiHkvgqqCjIgPLL+NGjRox08UL0o4Krqsgz9ixY3mbYCl4BqwUBdbhJ1xl5NYVQUeFAe3atVP8zQodhDPHjsZw4ogRI4jLdGU+/vhj3ibYYMZptMy4eidn5tO9RYV0mAczgY6KDKQbSUeNFssGLTQ0lIteGrpKlChBXKZRWHaupHWJNjctwg4II7i6o9K3b1/eJliO48eP8zaBG2K1ThZFlEZr6dKlGf92NMqjFbN//ZIiT548vE3QjWiOiqjbJNUiSp3nBY+vf6vnuSsfJCveUnEBsOqIioeHBzx9+hRu3rwJr732GjO9rkLDhg15m6AbqzfyrHHl/GzZsiV07dqVuV5XznOrI9ZnlEURqQL5+Pigk0KBHj16CPWetcJ6RCUoKIipPtaYuSwYJSoqisuOmXr16jHTlflgXoQ+6KjI0KVLF2a6zL5WBHmJ2ae/WJeNcuXKMdXniNmzZxOX6Qp1TekZeUwjXrp0CYoXL85EV8mSJeH1119nogt5CToqMhQrVoyoPFdotFwZd3d3GDZsGG8zDCHaGhWWVK9enbhMV6jzSs4567IUGBjIzEkBeOmoIGxx3daJIa7QaClRp04d3iZQZezYsZCUlGT6xssVyqhS9FYao2E88pPlSLAjaD673NoXf39/avrkmDt3LlN9CDoqWRg9ejRxma7QCSixe/du3iZQxc3NzfQ7VABco4w+fPiQmS4e+SmKs0xzRCVnzpzUZKulQIECvE1wOdBRsYPkaZhqsPoaFVfoAGng7KBAGqMALGPT8FjTwzKiMY9yL8o6KZqOily+YhtjfdBRyUSJEiWofB2LUpFEsQNxzh9//MFcZ4MGDZjrFAFROnijtGrVircJAGDt9U7OTlRG6GDdEqWRs2fPwj///EOlM3cks2jRosT16bHDapQuXZq3CYZ49dVXoWLFioq/sy6nZtbFAx6ddUhIiBCh62k+u5xTydLR3LdvHzNdyH+go/J/XnnlFWp7/+0b5TJlygAAQIcOHZguBGPdOUyZMoWpvnT69esHmzZt4qKbJB07dlT8jUbjbOYjAIxglcW0AMA0RpLSM1rZCS1btixvE1wSdFQ4sHfvXli3bh0sWrSIqV7WDchHH33EVF86P/74I/GRql69eslet3KjnBkrP6eVHBURHEDWo0lWLpssCQwM5G2CIuio/B+aFdy+IuXJkwdatGihuF2SlR2Ien766Sf46aefeJtBFSwf5EBHhR0iPLMV2Lp1K28TFEFHhQGZG61x48YJYYfZ9Smt31i4cCEVfW5ubuDr6yt7nRaFChVS/K1mzZrU9MpB8zkXLlwIERER1OTzoE2bNlz0itBps16jgpChfPnyvE1QBB0VxnTq1Imbbis5KlWrVs1y7enTp1wOQ6OFo7LSpEkThpaQp1KlShn/7tq1K9c1RTQ6v1GjRsGvv/4KdevWJS5bLaGhodC1a1dYsGABU72sR1Rc+VRhVwEdFcYEBwdz0231oX0fHx+q8uXyj2ZsDnd3d6hdu3aW6zly5DD9u/zqq69g6NChcPjwYS76+/XrR1W+l5cXdOrUifkunMxOV6tWrWDhwoXQrVs3pjawLpvz5s1jomfFihVM9Mjh6kHm0FFhgCidipVGVHjQsGHDLNd69uzJ3hAOkH6XOXLkgLFjx0K1atWIylXL22+/nfFvK00nZH6WL774gosNLEdUypQpwyQi79tvvw3t2rWjrgeRBx2V/2Olxoo10dHRkD9//izXaTsqrM/4CAgIsJlyCQ8Ppz6KI0q5tJrTGRQUxNsEKmQuL7yOdrDiGhVR6qGrgo4KA1g38t27d2eq7/XXX4e4uLgs12k/98WLF2HgwIFUddiTJ0+ejH+npaVR1yfXQM6aNYu63nSaNWsG2bNnh/Xr11PXRSuOUWZ27NgBK1asYBpvhCUTJ04EAIBBgwZxs8HKkWkRPmCJYkB6PJE333yTib558+bBjh07slyn6Tjw+OL28/ODkJAQ5npZkiNHjox/t2rVCh48eACdO3dmpr9z586QmJjIpOxu27YNChUqBGvXrqWmo0GDBtCuXTub8mqlr+UGDRrAkydPYPz48dxsoOmotG7dmppsR7Bq3zZv3gzlypXjpl9U0FH5PzQbq2rVqsGdO3dg48aN1HRkxs3NTejgPWamVq1aGf9m0cFl3qK8Zs0aqmeNKDWGrL6Q69evD9euXYOWLVtS12Xlhj979uxc9dPM24iICJut7KzeIytn9o033oDTp08z0WUm0FFhhL+/P9MhUSs3xDzp27dvxr9ZNF6ZddB+p1YaWdCCqz43LWifnly9enVq8kWFVt0vVaoUANh+gIkIOiouhBWdF9bPlDmasCt0cFZ9Rh5TP+lnfFkdlg61Fds0lmzduhW+/PJLWL16NW9THMI2hjuCECZv3ry8TaAKnmhsbjLnqavkL+3nzLyI3VXylBbBwcHw9ddf8zbDKTiiYlF4VGDWETABAJo2bQqffPIJs6BPmaG5XgRBREepjaF9hplVR/kQZdBR+T+uUPhpOy8lSpSgKl8ONzc3mDp1Krz33nvMdC5fvhxq164N06ZNo66rffv2AABQpEgR6rpcFZp1v0qVKtRki8jx48epbzN3hbbaHrlgk64ETv24ELwCQFmNd999F959910muiIiIiAmJgaKFy/ORB9Cls8//xwAXo78denShbM1ZElfiJmZzGc40YJF/CKRGDt2bEaIC1cFHRWLknn05IcffgBfX1/q2xZd8UuHNm5ublC5cmXmet3d3SE1NZXroXqsoFluvby8YPjw4QBgvfUUbdq0ge+//x62bdsG69atY6bX1RbTDh06lLcJ3EFHxQXo2bMn1cPzEGuQudG/f/8+PHjwAKecEEXc3Nxg4MCBkJyczM1RYUX9+vWZ60T+w6UdlVq1asGBAwd4m0EdVl8drvB1Y2UydwC+vr7g6+vL0RoEkYfl1M+lS5dg165dlpu2Mxsu7agUK1Ysw1Gx2rQFD6fBanmIuAZYbs0Fy6mf4sWL4/owAcBdPwgV7ty5w9sERCM4IkYXzF8yuNoaFQQdFYQS/v7+vE1AEFWwGlHBTpUMmad+cA2Va+DSjoqVGw6rng6L0KNFixYAAC63NgXrh7nw9PTM+PesWbM4WoKwwqUdFQRB/qNFixawa9cuuHTpEm9TLImVP4xYMnToUKhYsSJMnDgRChYsyNschAEuvZg285cjflUhro6bmxvUq1ePtxmIyWDtgPn7+8OJEyeY6uRFeHg4bxOEwKVHVEaPHs3bBGrg1A+CICxo1KgRbxMsS/ny5XmbIAQu7ahY+eRdHGZGEHWgI2+M6tWrw6FDhyAuLo63KYhFcempH1c8gh1BEFtw149xatSowdsExMK4tKPi7u4O7du3h3v37kHp0qV5m0MNVg0xfpkiCIIYp3z58nD69Gno1KkTb1OEwKUdFQCAZcuW8TaBClb+ekMQEvj4+EBSUhKEhITwNgVBbDhy5AjExcVhnJj/4/KOilXJnz9/xr99fHw4WoIgYnLv3j1ISkqCXLly8TYFQWzw8vJCJyUT6KhYFB8fH7h58ya4u7uDhwe+ZgSxx8fHh6kTj6OcCKIP7MEsTIECBXibgCAIgiCGcOntyQhZcDEtgiiDIyoIog90VBAEQRAEERZ0VBBi4BcjgiAIQhp0VBBi4NQPgiAIQhp0VBAEQRiAI44Iog9djsr06dOhaNGi4O3tDaGhoXDo0CGH6VesWAFlypQBb29vqFixImzYsEGXsQiCIAiCuBaaHZXly5dDZGQkjBo1Co4ePQqVK1eGiIgIuH37tmz6ffv2QceOHeH999+HY8eOQZs2baBNmzZw6tQpw8YjCIKYBRxRQRB9aHZUJk+eDL169YIePXpAuXLlYNasWZA9e3b45ZdfZNNPnToVmjRpAl988QWULVsWxowZA1WrVoUff/zRsPEIgiBmITQ0lLcJCGJKNDkqycnJcOTIEQgPD/9PQLZsEB4eDvv375e9Z//+/TbpAQAiIiIU0wMAPH/+HBISEmz+EARBzMz48eNh9OjRcPr0ad6mIIip0OSo3L17F1JTUyEgIMDmekBAAMTFxcneExcXpyk9AEBUVBT4+fll/AUHB2sxE+FEUFAQbxMQRFhy5swJo0aNgnLlyvE2BUFMhZC7foYOHQqPHj3K+Lt27RpvkxAVFC9eHFatWgU7d+7kbQqCIAhiETSd9ePv7w/u7u4QHx9vcz0+Ph4CAwNl7wkMDNSUHuDlyZFeXl5aTEMEoW3btrxNQBAEQSyEphEVT09PqFatGkRHR2dcS0tLg+joaAgLC5O9JywszCY9AMCWLVsU0yMIgiAIgqSj+fTkyMhI6N69O1SvXh1q1qwJU6ZMgSdPnkCPHj0AAKBbt24QFBQEUVFRAAAwYMAAaNCgAUyaNAmaN28Oy5Ytg8OHD8Ps2bPJPgmCIAiCIJZDs6PSvn17uHPnDowcORLi4uKgSpUqsGnTpowFs7GxsZAt238DNbVr14YlS5bAiBEjYNiwYVCyZElYs2YNVKhQgdxTIAiCIAhiSdwkExzQkpCQAH5+fvDo0SPw9fXlbQ6CIAiCICog0X8LuesHQRAEQRAEAB0VBEEQBEEEBh0VBEEQBEGEBR0VBEEQBEGEBR0VBEEQBEGEBR0VBEEQBEGEBR0VBEEQBEGEBR0VBEEQBEGEBR0VBEEQBEGERXMIfR6kB89NSEjgbAmCIAiCIGpJ77eNBME3haOSmJgIAADBwcGcLUEQBEEQRCuJiYng5+en615TnPWTlpYGN2/ehJw5c4KbmxsxuQkJCRAcHAzXrl3DM4RUgnmmD8w37WCeaQfzTDuYZ9rRkmeSJEFiYiIULFjQ5sBiLZhiRCVbtmxQqFAhavJ9fX2xgGoE80wfmG/awTzTDuaZdjDPtKM2z/SOpKSDi2kRBEEQBBEWdFQQBEEQBBEWl3ZUvLy8YNSoUeDl5cXbFNOAeaYPzDftYJ5pB/NMO5hn2mGdZ6ZYTIsgCIIgiGvi0iMqCIIgCIKIDToqCIIgCIIICzoqCIIgCIIICzoqCIIgCIIIi0s7KtOnT4eiRYuCt7c3hIaGwqFDh3ibxIRdu3ZBy5YtoWDBguDm5gZr1qyx+V2SJBg5ciQUKFAAfHx8IDw8HC5cuGCT5v79+9C5c2fw9fWFXLlywfvvvw+PHz+2SXPixAmoV68eeHt7Q3BwMHz33Xe0H40aUVFRUKNGDciZMyfkz58f2rRpA+fOnbNJ8+zZM+jXrx/kzZsXcuTIAW+//TbEx8fbpImNjYXmzZtD9uzZIX/+/PDFF19ASkqKTZodO3ZA1apVwcvLC0qUKAHz58+n/XhUmDlzJlSqVCkjKFRYWBhs3Lgx43fML+eMGzcO3NzcYODAgRnXMN+yMnr0aHBzc7P5K1OmTMbvmGfy3LhxA7p06QJ58+YFHx8fqFixIhw+fDjjd2H6AslFWbZsmeTp6Sn98ssv0unTp6VevXpJuXLlkuLj43mbRp0NGzZIw4cPl37//XcJAKTVq1fb/D5u3DjJz89PWrNmjXT8+HGpVatWUrFixaSkpKSMNE2aNJEqV64sHThwQNq9e7dUokQJqWPHjhm/P3r0SAoICJA6d+4snTp1Slq6dKnk4+Mj/fTTT6wekygRERHSvHnzpFOnTkkxMTFSs2bNpMKFC0uPHz/OSPPhhx9KwcHBUnR0tHT48GGpVq1aUu3atTN+T0lJkSpUqCCFh4dLx44dkzZs2CD5+/tLQ4cOzUhz+fJlKXv27FJkZKR05swZadq0aZK7u7u0adMmps9LgrVr10rr16+Xzp8/L507d04aNmyY9Morr0inTp2SJAnzyxmHDh2SihYtKlWqVEkaMGBAxnXMt6yMGjVKKl++vHTr1q2Mvzt37mT8jnmWlfv370tFihSR3nvvPengwYPS5cuXpb/++ku6ePFiRhpR+gKXdVRq1qwp9evXL+P/qampUsGCBaWoqCiOVrHH3lFJS0uTAgMDpQkTJmRce/jwoeTl5SUtXbpUkiRJOnPmjAQA0t9//52RZuPGjZKbm5t048YNSZIkacaMGVLu3Lml58+fZ6QZPHiwVLp0acpPxIbbt29LACDt3LlTkqSXefTKK69IK1asyEhz9uxZCQCk/fv3S5L00kHMli2bFBcXl5Fm5syZkq+vb0Y+DRo0SCpfvryNrvbt20sRERG0H4kJuXPnlubOnYv55YTExESpZMmS0pYtW6QGDRpkOCqYb/KMGjVKqly5suxvmGfyDB48WKpbt67i7yL1BS459ZOcnAxHjhyB8PDwjGvZsmWD8PBw2L9/P0fL+HPlyhWIi4uzyRs/Pz8IDQ3NyJv9+/dDrly5oHr16hlpwsPDIVu2bHDw4MGMNPXr1wdPT8+MNBEREXDu3Dl48OABo6ehx6NHjwAAIE+ePAAAcOTIEXjx4oVNvpUpUwYKFy5sk28VK1aEgICAjDQRERGQkJAAp0+fzkiTWUZ6GrOXy9TUVFi2bBk8efIEwsLCML+c0K9fP2jevHmWZ8N8U+bChQtQsGBBKF68OHTu3BliY2MBAPNMibVr10L16tXhnXfegfz580NISAjMmTMn43eR+gKXdFTu3r0LqampNoUSACAgIADi4uI4WSUG6c/vKG/i4uIgf/78Nr97eHhAnjx5bNLIycisw6ykpaXBwIEDoU6dOlChQgUAePlMnp6ekCtXLpu09vnmLE+U0iQkJEBSUhKNx6HKyZMnIUeOHODl5QUffvghrF69GsqVK4f55YBly5bB0aNHISoqKstvmG/yhIaGwvz582HTpk0wc+ZMuHLlCtSrVw8SExMxzxS4fPkyzJw5E0qWLAl//fUX9O3bFz755BNYsGABAIjVF5ji9GQEEYl+/frBqVOnYM+ePbxNEZ7SpUtDTEwMPHr0CFauXAndu3eHnTt38jZLWK5duwYDBgyALVu2gLe3N29zTEPTpk0z/l2pUiUIDQ2FIkWKwG+//QY+Pj4cLROXtLQ0qF69OowdOxYAAEJCQuDUqVMwa9Ys6N69O2frbHHJERV/f39wd3fPsuo7Pj4eAgMDOVklBunP7yhvAgMD4fbt2za/p6SkwP37923SyMnIrMOM9O/fH/7880/Yvn07FCpUKON6YGAgJCcnw8OHD23S2+ebszxRSuPr62vKBtfT0xNKlCgB1apVg6ioKKhcuTJMnToV80uBI0eOwO3bt6Fq1arg4eEBHh4esHPnTvjhhx/Aw8MDAgICMN9UkCtXLihVqhRcvHgRy5oCBQoUgHLlytlcK1u2bMaUmUh9gUs6Kp6enlCtWjWIjo7OuJaWlgbR0dEQFhbG0TL+FCtWDAIDA23yJiEhAQ4ePJiRN2FhYfDw4UM4cuRIRppt27ZBWloahIaGZqTZtWsXvHjxIiPNli1boHTp0pA7d25GT0MOSZKgf//+sHr1ati2bRsUK1bM5vdq1arBK6+8YpNv586dg9jYWJt8O3nypE3F3rJlC/j6+mY0GGFhYTYy0tNYpVympaXB8+fPMb8UaNy4MZw8eRJiYmIy/qpXrw6dO3fO+Dfmm3MeP34Mly5dggIFCmBZU6BOnTpZQiycP38eihQpAgCC9QWql91ajGXLlkleXl7S/PnzpTNnzki9e/eWcuXKZbPq26okJiZKx44dk44dOyYBgDR58mTp2LFj0r///itJ0sstably5ZL++OMP6cSJE1Lr1q1lt6SFhIRIBw8elPbs2SOVLFnSZkvaw4cPpYCAAKlr167SqVOnpGXLlknZs2c37fbkvn37Sn5+ftKOHTtstkA+ffo0I82HH34oFS5cWNq2bZt0+PBhKSwsTAoLC8v4PX0L5JtvvinFxMRImzZtkvLlyye7BfKLL76Qzp49K02fPt20WyCHDBki7dy5U7py5Yp04sQJaciQIZKbm5u0efNmSZIwv9SSedePJGG+yfHZZ59JO3bskK5cuSLt3btXCg8Pl/z9/aXbt29LkoR5JsehQ4ckDw8P6dtvv5UuXLgg/frrr1L27NmlxYsXZ6QRpS9wWUdFkiRp2rRpUuHChSVPT0+pZs2a0oEDB3ibxITt27dLAJDlr3v37pIkvdyW9uWXX0oBAQGSl5eX1LhxY+ncuXM2Mu7duyd17NhRypEjh+Tr6yv16NFDSkxMtElz/PhxqW7dupKXl5cUFBQkjRs3jtUjEkcuvwBAmjdvXkaapKQk6aOPPpJy584tZc+eXXrrrbekW7du2ci5evWq1LRpU8nHx0fy9/eXPvvsM+nFixc2abZv3y5VqVJF8vT0lIoXL26jw0z07NlTKlKkiOTp6Snly5dPaty4cYaTIkmYX2qxd1Qw37LSvn17qUCBApKnp6cUFBQktW/f3iYeCOaZPOvWrZMqVKggeXl5SWXKlJFmz55t87sofYGbJEmS6rEiBEEQBEEQhrjkGhUEQRAEQcwBOioIgiAIgggLOioIgiAIgggLOioIgiAIgggLOioIgiAIgggLOioIgiAIgggLOioIgiAIgggLOioIgiAIgggLOioIgiAIgggLOioIgiAIgggLOioIgiAIgggLOioIgiAIggjL/wA7vl5hGPWDOQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that our toy data in ready, including our input variable and the label, lets build our ANN (artificial neural network)"
      ],
      "metadata": {
        "id": "LEjtXzvFLxDz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ANN Time! (regression type)\n",
        "\n",
        "Documentation: https://keras.io/api/models/sequential/"
      ],
      "metadata": {
        "id": "nAQbr71lL5C-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential() # instantiate model object"
      ],
      "metadata": {
        "id": "aieabf8nLu_K"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating our input layer (not required, but I recommend this because it lets the model get built continuously as you are adding layers rather than waiting till the end to build)."
      ],
      "metadata": {
        "id": "hoiTAQetL-Dj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(keras.Input(shape=(1,), name=\"input_layer\"))"
      ],
      "metadata": {
        "id": "oH4cy1w-L8UO"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Docs: https://keras.io/api/layers/core_layers/input/\n",
        "\n",
        "Now lets add our hidden layers"
      ],
      "metadata": {
        "id": "FtSQKEyDL_5B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(\n",
        "    keras.layers.Dense(units=8, activation=keras.activations.relu))\n",
        "model.add(\n",
        "    keras.layers.Dense(units=8, activation=keras.activations.relu))"
      ],
      "metadata": {
        "id": "Jtr3AYjiL_as"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Docs: https://keras.io/api/layers/core_layers/dense/\n",
        "\n",
        "Since this is a regression task, let's let the last layer be a single output value (per sample) with linear activation (aka None)."
      ],
      "metadata": {
        "id": "6zBuLMY8MHE6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(keras.layers.Dense(units=1))"
      ],
      "metadata": {
        "id": "wKJg-o27MCWn"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now compiling our model. We will use gradient descent (with momentum) optimizer called SGD and MSE as a loss function.\n",
        "\n",
        "For more options and docs, see:\n",
        "\n",
        "- https://keras.io/api/optimizers/\n",
        "- https://keras.io/api/losses/"
      ],
      "metadata": {
        "id": "w7G-rSIIMKYY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=keras.optimizers.SGD(learning_rate=0.01),\n",
        "    loss='mse'\n",
        ")"
      ],
      "metadata": {
        "id": "iPRfqUtzMJxO"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "What does our model \"look\" like?"
      ],
      "metadata": {
        "id": "1-LOi7FlMQQt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "3dqoJYHFMO2x",
        "outputId": "c733fb6d-1f3b-4019-f936-d9d1411f1222"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)                   │              \u001b[38;5;34m16\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)                   │              \u001b[38;5;34m72\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m9\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">72</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m97\u001b[0m (388.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">97</span> (388.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m97\u001b[0m (388.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">97</span> (388.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Call model.fit to train! YAY :)\n",
        "\n",
        "Documentation: https://keras.io/api/models/model_training_apis/"
      ],
      "metadata": {
        "id": "Ho04K5j3MTaS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    x=input_train_scaled,\n",
        "    y=label_train_scaled,\n",
        "    batch_size=32,\n",
        "    epochs=20,\n",
        "    verbose='auto',\n",
        "    validation_split=0.1,\n",
        "    validation_data=None,\n",
        "    shuffle=True,\n",
        "    class_weight=None,\n",
        "    sample_weight=None,\n",
        "    initial_epoch=0,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQ7-u0WGMRi3",
        "outputId": "a7d9de99-2c32-49b4-ff6e-22641ff57311"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0651 - val_loss: 0.0134\n",
            "Epoch 2/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0118 - val_loss: 0.0100\n",
            "Epoch 3/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0094 - val_loss: 0.0085\n",
            "Epoch 4/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.0079 - val_loss: 0.0077\n",
            "Epoch 5/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0074 - val_loss: 0.0075\n",
            "Epoch 6/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0075 - val_loss: 0.0073\n",
            "Epoch 7/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0073 - val_loss: 0.0072\n",
            "Epoch 8/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 8ms/step - loss: 0.0071 - val_loss: 0.0072\n",
            "Epoch 9/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.0074 - val_loss: 0.0072\n",
            "Epoch 10/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0076 - val_loss: 0.0071\n",
            "Epoch 11/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0070 - val_loss: 0.0071\n",
            "Epoch 12/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0076 - val_loss: 0.0071\n",
            "Epoch 13/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0073 - val_loss: 0.0071\n",
            "Epoch 14/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0073 - val_loss: 0.0071\n",
            "Epoch 15/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0072 - val_loss: 0.0071\n",
            "Epoch 16/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0072 - val_loss: 0.0071\n",
            "Epoch 17/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0073 - val_loss: 0.0071\n",
            "Epoch 18/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0072 - val_loss: 0.0071\n",
            "Epoch 19/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0072 - val_loss: 0.0071\n",
            "Epoch 20/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0070 - val_loss: 0.0071\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lets plot our training and validation loss curves."
      ],
      "metadata": {
        "id": "Xan0ofFxMY0q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(0,20), history.history['loss'], c='k', label='training')\n",
        "plt.plot(range(0,20), history.history['val_loss'], c='r', label='validation')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "OgMaUG6IMWYx",
        "outputId": "71917f12-82c7-44bf-c231-c793b0c855ab"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Now lets do inference\n",
        "\n",
        "How does our model perform on the testing set?\n",
        "\n",
        "Rescale our test data using our earlier scaler objects"
      ],
      "metadata": {
        "id": "u12cJ7PgMdG2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_test = test.tmin\n",
        "label_test = test.tmax\n",
        "\n",
        "input_test_scaled = scaler_input.transform(input_test)\n",
        "label_test_scaled = scaler_label.transform(label_test)"
      ],
      "metadata": {
        "id": "C0EjYzrgMblA"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Quick double check!"
      ],
      "metadata": {
        "id": "pvoHDqgNMis8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(0,input_test_scaled.shape[0]), input_test_scaled, c='k')\n",
        "plt.show()\n",
        "plt.plot(range(0,input_test_scaled.shape[0]), input_test, c='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "MYzX9PgiMhP_",
        "outputId": "0b3be392-95e1-417b-bd4c-f953ec40d3ac"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(0,label_test_scaled.shape[0]), label_test_scaled, c='k')\n",
        "plt.show()\n",
        "plt.plot(range(0,label_test_scaled.shape[0]), label_test, c='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "2H-vQDa0Mj6A",
        "outputId": "61faed1f-d42a-4cd0-cd1e-037f453ecb1d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to predict, use predict call\n",
        "predictions = model.predict(\n",
        "    x=input_test_scaled,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1tgJveqeMmlt",
        "outputId": "a08aed8e-4d81-4105-8753-ae80a19e9e83"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m58/58\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(label_test_scaled, predictions)\n",
        "plt.xlabel('Labels')\n",
        "plt.ylabel('ML predictions')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "tF67OoVZMrD6",
        "outputId": "a14a776b-665f-4a48-ed66-40384aa90bcc"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MSE\n",
        "# np.mean(square(y_true - y_pred))\n",
        "np.mean(np.square(label_test_scaled - predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2A1GFWYvMsqY",
        "outputId": "59cf2cf0-1266-49bb-dca6-3e4b08003527"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.006868718024703924"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Now, let's train a new ANN for a classification task Part 1\n",
        "We will first do an example with sigmoid as the final output layer activation and integer encoding for the labels."
      ],
      "metadata": {
        "id": "cOMjKb-EdbkL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# split training and testing data by years\n",
        "train = ds.sel(time=slice('1999-01-01T00:00:00.000000000','2014-12-31T00:00:00.000000000'))\n",
        "test = ds.sel(time=slice('2015-01-01T00:00:00.000000000','2019-12-31T00:00:00.000000000'))"
      ],
      "metadata": {
        "id": "JGAuXJt1MuYP"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# minimum temperature will be the input feature and maximum temperature will be the ground truth\n",
        "input_train = train.tmin\n",
        "label_train = train.tmax"
      ],
      "metadata": {
        "id": "O7eve-0A-hMq"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create our scaler object for the input feature\n",
        "# transform as before\n",
        "scaler_input = MinMaxScaler()\n",
        "scaler_input.fit(input_train)\n",
        "input_train_scaled = scaler_input.transform(input_train)"
      ],
      "metadata": {
        "id": "30UjCV5cduNR"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# for our labels, we will need to convert the continuous data into binary classes\n",
        "# we set values greater than or equal to 20 degrees as the positive class (1)\n",
        "# anything less will be the negative class (0)\n",
        "label_train_scaled = xr.where(label_train >= 20, 1, 0)"
      ],
      "metadata": {
        "id": "qPXCOTYZduKu"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check for class imbalance (looks fine!)\n",
        "label_train_scaled.isel(location=0).plot.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "TeOfKOszduIE",
        "outputId": "224120cc-8254-4472-af70-2e509a1e82cc"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([2909.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "        2935.]),\n",
              " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# now, let's create a new ANN\n",
        "# most things will be consistent to the earlier example\n",
        "model = keras.Sequential() # instantiate model object\n",
        "model.add(keras.Input(shape=(1,), name=\"input_layer\"))\n",
        "model.add(\n",
        "    keras.layers.Dense(units=8, activation=keras.activations.relu))\n",
        "model.add(\n",
        "    keras.layers.Dense(units=8, activation=keras.activations.relu))"
      ],
      "metadata": {
        "id": "lrt-dxG_duE8"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# our first change is the output layer activation function (now sigmoid instead of linear)\n",
        "model.add(keras.layers.Dense(units=1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "fgpDqMSkdt-7"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# and our loss function now is binary cross entropy\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.SGD(learning_rate=0.01),\n",
        "    loss=keras.losses.BinaryCrossentropy()\n",
        ")"
      ],
      "metadata": {
        "id": "l1J5JORNdt8G"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check out the new model\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "R29k4cIPfMLm",
        "outputId": "0be9028d-08c3-4dc2-deeb-a81867409a7b"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)                   │              \u001b[38;5;34m16\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)                   │              \u001b[38;5;34m72\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │               \u001b[38;5;34m9\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">72</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m97\u001b[0m (388.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">97</span> (388.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m97\u001b[0m (388.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">97</span> (388.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    x=input_train_scaled,\n",
        "    y=label_train_scaled,\n",
        "    batch_size=32,\n",
        "    epochs=20,\n",
        "    verbose='auto',\n",
        "    validation_split=0.1,\n",
        "    validation_data=None,\n",
        "    shuffle=True,\n",
        "    class_weight=None,\n",
        "    sample_weight=None,\n",
        "    initial_epoch=0,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cWliH2NfQ1D",
        "outputId": "de5213b0-44de-449b-8899-377b1af1a159"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.6794 - val_loss: 0.6526\n",
            "Epoch 2/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.6506 - val_loss: 0.6185\n",
            "Epoch 3/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6172 - val_loss: 0.5795\n",
            "Epoch 4/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5767 - val_loss: 0.5342\n",
            "Epoch 5/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5377 - val_loss: 0.4836\n",
            "Epoch 6/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4866 - val_loss: 0.4261\n",
            "Epoch 7/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4210 - val_loss: 0.3665\n",
            "Epoch 8/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3691 - val_loss: 0.3215\n",
            "Epoch 9/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3282 - val_loss: 0.2866\n",
            "Epoch 10/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3025 - val_loss: 0.2590\n",
            "Epoch 11/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2744 - val_loss: 0.2378\n",
            "Epoch 12/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.2556 - val_loss: 0.2214\n",
            "Epoch 13/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2317 - val_loss: 0.2087\n",
            "Epoch 14/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2289 - val_loss: 0.1985\n",
            "Epoch 15/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2147 - val_loss: 0.1923\n",
            "Epoch 16/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2026 - val_loss: 0.1855\n",
            "Epoch 17/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2051 - val_loss: 0.1813\n",
            "Epoch 18/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1990 - val_loss: 0.1773\n",
            "Epoch 19/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.1856 - val_loss: 0.1755\n",
            "Epoch 20/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.1931 - val_loss: 0.1720\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(0,20), history.history['loss'], c='k', label='training')\n",
        "plt.plot(range(0,20), history.history['val_loss'], c='r', label='validation')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "ad6d-2f7fT8V",
        "outputId": "3d1b2b69-bafe-4e8e-d7cc-0fa73aa24a70"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ANN Classification Part 2\n",
        "We now do an example with softmax as the final output layer activation and one-hot encoding for the labels."
      ],
      "metadata": {
        "id": "NoPF9wFb-51V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# steps here as earlier:\n",
        "\n",
        "# split training and testing data by years\n",
        "train = ds.sel(time=slice('1999-01-01T00:00:00.000000000','2014-12-31T00:00:00.000000000'))\n",
        "test = ds.sel(time=slice('2015-01-01T00:00:00.000000000','2019-12-31T00:00:00.000000000'))\n",
        "\n",
        "# minimum temperature will be the input feature and maximum temperature will be the ground truth\n",
        "input_train = train.tmin\n",
        "label_train = train.tmax\n",
        "\n",
        "# create our scaler object for the input feature\n",
        "# transform as before\n",
        "scaler_input = MinMaxScaler()\n",
        "scaler_input.fit(input_train)\n",
        "input_train_scaled = scaler_input.transform(input_train)\n",
        "\n",
        "# for our labels, we will need to convert the continuous data into binary classes\n",
        "# we set values greater than or equal to 20 degrees as the positive class (1)\n",
        "# anything less will be the negative class (0)\n",
        "label_train = xr.where(label_train >= 20, 1, 0)"
      ],
      "metadata": {
        "id": "Sv2q_UN6faJN"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "O593nwnI_kRI",
        "outputId": "11079842-0242-440a-87a0-8623954be363"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.DataArray 'tmax' (time: 5844, location: 1)> Size: 47kB\n",
              "array([[0],\n",
              "       [0],\n",
              "       [0],\n",
              "       ...,\n",
              "       [0],\n",
              "       [0],\n",
              "       [0]])\n",
              "Coordinates:\n",
              "  * time      (time) datetime64[ns] 47kB 1999-01-01 1999-01-02 ... 2014-12-31\n",
              "  * location  (location) <U2 8B 'IA'"
            ],
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              "  margin-bottom: 0;\n",
              "}\n",
              "\n",
              ".xr-obj-type,\n",
              ".xr-array-name {\n",
              "  margin-left: 2px;\n",
              "  margin-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-obj-type {\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-sections {\n",
              "  padding-left: 0 !important;\n",
              "  display: grid;\n",
              "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
              "}\n",
              "\n",
              ".xr-section-item {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-section-item input {\n",
              "  display: inline-block;\n",
              "  opacity: 0;\n",
              "  height: 0;\n",
              "}\n",
              "\n",
              ".xr-section-item input + label {\n",
              "  color: var(--xr-disabled-color);\n",
              "}\n",
              "\n",
              ".xr-section-item input:enabled + label {\n",
              "  cursor: pointer;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-section-item input:focus + label {\n",
              "  border: 2px solid var(--xr-font-color0);\n",
              "}\n",
              "\n",
              ".xr-section-item input:enabled + label:hover {\n",
              "  color: var(--xr-font-color0);\n",
              "}\n",
              "\n",
              ".xr-section-summary {\n",
              "  grid-column: 1;\n",
              "  color: var(--xr-font-color2);\n",
              "  font-weight: 500;\n",
              "}\n",
              "\n",
              ".xr-section-summary > span {\n",
              "  display: inline-block;\n",
              "  padding-left: 0.5em;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:disabled + label {\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-section-summary-in + label:before {\n",
              "  display: inline-block;\n",
              "  content: \"►\";\n",
              "  font-size: 11px;\n",
              "  width: 15px;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:disabled + label:before {\n",
              "  color: var(--xr-disabled-color);\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked + label:before {\n",
              "  content: \"▼\";\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked + label > span {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-section-summary,\n",
              ".xr-section-inline-details {\n",
              "  padding-top: 4px;\n",
              "  padding-bottom: 4px;\n",
              "}\n",
              "\n",
              ".xr-section-inline-details {\n",
              "  grid-column: 2 / -1;\n",
              "}\n",
              "\n",
              ".xr-section-details {\n",
              "  display: none;\n",
              "  grid-column: 1 / -1;\n",
              "  margin-bottom: 5px;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked ~ .xr-section-details {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-array-wrap {\n",
              "  grid-column: 1 / -1;\n",
              "  display: grid;\n",
              "  grid-template-columns: 20px auto;\n",
              "}\n",
              "\n",
              ".xr-array-wrap > label {\n",
              "  grid-column: 1;\n",
              "  vertical-align: top;\n",
              "}\n",
              "\n",
              ".xr-preview {\n",
              "  color: var(--xr-font-color3);\n",
              "}\n",
              "\n",
              ".xr-array-preview,\n",
              ".xr-array-data {\n",
              "  padding: 0 5px !important;\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-array-data,\n",
              ".xr-array-in:checked ~ .xr-array-preview {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-array-in:checked ~ .xr-array-data,\n",
              ".xr-array-preview {\n",
              "  display: inline-block;\n",
              "}\n",
              "\n",
              ".xr-dim-list {\n",
              "  display: inline-block !important;\n",
              "  list-style: none;\n",
              "  padding: 0 !important;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list li {\n",
              "  display: inline-block;\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list:before {\n",
              "  content: \"(\";\n",
              "}\n",
              "\n",
              ".xr-dim-list:after {\n",
              "  content: \")\";\n",
              "}\n",
              "\n",
              ".xr-dim-list li:not(:last-child):after {\n",
              "  content: \",\";\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-has-index {\n",
              "  font-weight: bold;\n",
              "}\n",
              "\n",
              ".xr-var-list,\n",
              ".xr-var-item {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-var-item > div,\n",
              ".xr-var-item label,\n",
              ".xr-var-item > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-even);\n",
              "  margin-bottom: 0;\n",
              "}\n",
              "\n",
              ".xr-var-item > .xr-var-name:hover span {\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-var-list > li:nth-child(odd) > div,\n",
              ".xr-var-list > li:nth-child(odd) > label,\n",
              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-odd);\n",
              "}\n",
              "\n",
              ".xr-var-name {\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-var-dims {\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-var-dtype {\n",
              "  grid-column: 3;\n",
              "  text-align: right;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-preview {\n",
              "  grid-column: 4;\n",
              "}\n",
              "\n",
              ".xr-index-preview {\n",
              "  grid-column: 2 / 5;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-name,\n",
              ".xr-var-dims,\n",
              ".xr-var-dtype,\n",
              ".xr-preview,\n",
              ".xr-attrs dt {\n",
              "  white-space: nowrap;\n",
              "  overflow: hidden;\n",
              "  text-overflow: ellipsis;\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-var-name:hover,\n",
              ".xr-var-dims:hover,\n",
              ".xr-var-dtype:hover,\n",
              ".xr-attrs dt:hover {\n",
              "  overflow: visible;\n",
              "  width: auto;\n",
              "  z-index: 1;\n",
              "}\n",
              "\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  display: none;\n",
              "  background-color: var(--xr-background-color) !important;\n",
              "  padding-bottom: 5px !important;\n",
              "}\n",
              "\n",
              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
              ".xr-var-data-in:checked ~ .xr-var-data,\n",
              ".xr-index-data-in:checked ~ .xr-index-data {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              ".xr-var-data > table {\n",
              "  float: right;\n",
              "}\n",
              "\n",
              ".xr-var-name span,\n",
              ".xr-var-data,\n",
              ".xr-index-name div,\n",
              ".xr-index-data,\n",
              ".xr-attrs {\n",
              "  padding-left: 25px !important;\n",
              "}\n",
              "\n",
              ".xr-attrs,\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  grid-column: 1 / -1;\n",
              "}\n",
              "\n",
              "dl.xr-attrs {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  display: grid;\n",
              "  grid-template-columns: 125px auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt,\n",
              ".xr-attrs dd {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  float: left;\n",
              "  padding-right: 10px;\n",
              "  width: auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt {\n",
              "  font-weight: normal;\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-attrs dt:hover span {\n",
              "  display: inline-block;\n",
              "  background: var(--xr-background-color);\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-attrs dd {\n",
              "  grid-column: 2;\n",
              "  white-space: pre-wrap;\n",
              "  word-break: break-all;\n",
              "}\n",
              "\n",
              ".xr-icon-database,\n",
              ".xr-icon-file-text2,\n",
              ".xr-no-icon {\n",
              "  display: inline-block;\n",
              "  vertical-align: middle;\n",
              "  width: 1em;\n",
              "  height: 1.5em !important;\n",
              "  stroke-width: 0;\n",
              "  stroke: currentColor;\n",
              "  fill: currentColor;\n",
              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tmax&#x27; (time: 5844, location: 1)&gt; Size: 47kB\n",
              "array([[0],\n",
              "       [0],\n",
              "       [0],\n",
              "       ...,\n",
              "       [0],\n",
              "       [0],\n",
              "       [0]])\n",
              "Coordinates:\n",
              "  * time      (time) datetime64[ns] 47kB 1999-01-01 1999-01-02 ... 2014-12-31\n",
              "  * location  (location) &lt;U2 8B &#x27;IA&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tmax'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5844</li><li><span class='xr-has-index'>location</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9ed4b49f-2261-4d62-9923-e55c2b1e0ea8' class='xr-array-in' type='checkbox' checked><label for='section-9ed4b49f-2261-4d62-9923-e55c2b1e0ea8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</span></div><div class='xr-array-data'><pre>array([[0],\n",
              "       [0],\n",
              "       [0],\n",
              "       ...,\n",
              "       [0],\n",
              "       [0],\n",
              "       [0]])</pre></div></div></li><li class='xr-section-item'><input id='section-f9b5f4f9-3172-4a6a-b7df-ea9d27701365' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f9b5f4f9-3172-4a6a-b7df-ea9d27701365' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1999-01-01 ... 2014-12-31</div><input id='attrs-74aa75ad-9b9f-4644-ae19-408f57409b46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74aa75ad-9b9f-4644-ae19-408f57409b46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3648da3e-90cc-4e69-bb10-1bf23b48b07f' class='xr-var-data-in' type='checkbox'><label for='data-3648da3e-90cc-4e69-bb10-1bf23b48b07f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1999-01-01T00:00:00.000000000&#x27;, &#x27;1999-01-02T00:00:00.000000000&#x27;,\n",
              "       &#x27;1999-01-03T00:00:00.000000000&#x27;, ..., &#x27;2014-12-29T00:00:00.000000000&#x27;,\n",
              "       &#x27;2014-12-30T00:00:00.000000000&#x27;, &#x27;2014-12-31T00:00:00.000000000&#x27;],\n",
              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>location</span></div><div class='xr-var-dims'>(location)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;IA&#x27;</div><input id='attrs-e07ed0eb-c27f-4204-8be4-af9123169b07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e07ed0eb-c27f-4204-8be4-af9123169b07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08dc5a6f-38a2-4616-8492-cf43b52f5e70' class='xr-var-data-in' type='checkbox'><label for='data-08dc5a6f-38a2-4616-8492-cf43b52f5e70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;IA&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-305dafef-d687-429b-9e73-bdb6d69f156c' class='xr-section-summary-in' type='checkbox'  ><label for='section-305dafef-d687-429b-9e73-bdb6d69f156c' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-265125cf-7113-4e6e-9b20-dec64dfa19b6' class='xr-index-data-in' type='checkbox'/><label for='index-265125cf-7113-4e6e-9b20-dec64dfa19b6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1999-01-01&#x27;, &#x27;1999-01-02&#x27;, &#x27;1999-01-03&#x27;, &#x27;1999-01-04&#x27;,\n",
              "               &#x27;1999-01-05&#x27;, &#x27;1999-01-06&#x27;, &#x27;1999-01-07&#x27;, &#x27;1999-01-08&#x27;,\n",
              "               &#x27;1999-01-09&#x27;, &#x27;1999-01-10&#x27;,\n",
              "               ...\n",
              "               &#x27;2014-12-22&#x27;, &#x27;2014-12-23&#x27;, &#x27;2014-12-24&#x27;, &#x27;2014-12-25&#x27;,\n",
              "               &#x27;2014-12-26&#x27;, &#x27;2014-12-27&#x27;, &#x27;2014-12-28&#x27;, &#x27;2014-12-29&#x27;,\n",
              "               &#x27;2014-12-30&#x27;, &#x27;2014-12-31&#x27;],\n",
              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=5844, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>location</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-9f787e72-9a72-410a-ac7c-723d4a25b7d7' class='xr-index-data-in' type='checkbox'/><label for='index-9f787e72-9a72-410a-ac7c-723d4a25b7d7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;IA&#x27;], dtype=&#x27;object&#x27;, name=&#x27;location&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0d92ebea-603c-41b4-a1df-d5f61beb0421' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0d92ebea-603c-41b4-a1df-d5f61beb0421' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now, let's one hot encode our binary classes\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "onehot = OneHotEncoder()"
      ],
      "metadata": {
        "id": "ulJxUE2s_hAh"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "onehot.fit(label_train)\n",
        "label_train_scaled = onehot.transform(label_train).toarray()"
      ],
      "metadata": {
        "id": "95e-nfC__XOJ"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_train_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bSS79Hs7_ypp",
        "outputId": "264de69b-a331-40db-bb54-503a472fe799"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0.],\n",
              "       [1., 0.],\n",
              "       [1., 0.],\n",
              "       ...,\n",
              "       [1., 0.],\n",
              "       [1., 0.],\n",
              "       [1., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# now, let's create a new ANN\n",
        "# most things will be consistent to the earlier example\n",
        "model = keras.Sequential() # instantiate model object\n",
        "model.add(keras.Input(shape=(1,), name=\"input_layer\"))\n",
        "model.add(\n",
        "    keras.layers.Dense(units=8, activation=keras.activations.relu))\n",
        "model.add(\n",
        "    keras.layers.Dense(units=8, activation=keras.activations.relu))"
      ],
      "metadata": {
        "id": "6f2F3ud8Aba2"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# our first change is the output layer activation function (now softmax with 2 classes/nodes)\n",
        "model.add(keras.layers.Dense(units=2, activation='softmax'))"
      ],
      "metadata": {
        "id": "Jm_dOOngA61S"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# and our loss function now is categorical cross entropy\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.SGD(learning_rate=0.01),\n",
        "    loss=keras.losses.CategoricalCrossentropy()\n",
        ")"
      ],
      "metadata": {
        "id": "m_OgTwriBBh3"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check out the new model\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "iwHhR0fHBkqJ",
        "outputId": "d2340614-49e8-4c3b-b8a2-06030cff62ea"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)                   │              \u001b[38;5;34m16\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)                   │              \u001b[38;5;34m72\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)                   │              \u001b[38;5;34m18\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">72</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m106\u001b[0m (424.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">106</span> (424.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m106\u001b[0m (424.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">106</span> (424.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    x=input_train_scaled,\n",
        "    y=label_train_scaled,\n",
        "    batch_size=32,\n",
        "    epochs=20,\n",
        "    verbose='auto',\n",
        "    validation_split=0.1,\n",
        "    validation_data=None,\n",
        "    shuffle=True,\n",
        "    class_weight=None,\n",
        "    sample_weight=None,\n",
        "    initial_epoch=0,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GNQ7nkqUCKaf",
        "outputId": "84580755-a6d9-4118-98f2-f684e8326c6e"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.6815 - val_loss: 0.6371\n",
            "Epoch 2/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.6263 - val_loss: 0.5744\n",
            "Epoch 3/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.5666 - val_loss: 0.4964\n",
            "Epoch 4/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4845 - val_loss: 0.4106\n",
            "Epoch 5/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.4014 - val_loss: 0.3366\n",
            "Epoch 6/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.3339 - val_loss: 0.2777\n",
            "Epoch 7/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.2749 - val_loss: 0.2391\n",
            "Epoch 8/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.2513 - val_loss: 0.2145\n",
            "Epoch 9/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.2255 - val_loss: 0.1995\n",
            "Epoch 10/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2120 - val_loss: 0.1873\n",
            "Epoch 11/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.1982 - val_loss: 0.1807\n",
            "Epoch 12/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.1993 - val_loss: 0.1775\n",
            "Epoch 13/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.1872 - val_loss: 0.1742\n",
            "Epoch 14/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.1785 - val_loss: 0.1705\n",
            "Epoch 15/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.1761 - val_loss: 0.1694\n",
            "Epoch 16/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.1886 - val_loss: 0.1682\n",
            "Epoch 17/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - loss: 0.1780 - val_loss: 0.1664\n",
            "Epoch 18/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 0.1847 - val_loss: 0.1645\n",
            "Epoch 19/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.1755 - val_loss: 0.1717\n",
            "Epoch 20/20\n",
            "\u001b[1m165/165\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.1863 - val_loss: 0.1658\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(0,20), history.history['loss'], c='k', label='training')\n",
        "plt.plot(range(0,20), history.history['val_loss'], c='r', label='validation')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "cBkMYOjqCNMy",
        "outputId": "a01c24e1-617f-45e6-899c-7c9484c0e2fa"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FZqyICcYCcO1"
      },
      "execution_count": 51,
      "outputs": []
    }
  ]
}