diff --git a/docs/tutorials/formula_interface/formula_interface.ipynb b/docs/tutorials/formula_interface/formula_interface.ipynb
index b131bd98..22ff93d3 100644
--- a/docs/tutorials/formula_interface/formula_interface.ipynb
+++ b/docs/tutorials/formula_interface/formula_interface.ipynb
@@ -18,7 +18,7 @@
     "\n",
     "This tutorial reimplements and extends the combined frequency-severity model from Chapter 4 of the [GLM tutorial](tutorials/glm_french_motor_tutorial/glm_french_motor.html). If you would like to know more about the setting, the data, or GLM modeling in general, please check that out first.\n",
     "\n",
-    "**Sneak Peak**\n",
+    "**Sneak Peek**\n",
     "\n",
     "Formulas can provide a concise and convenient way to specify many of the usual pre-processing steps, such as converting to categorical types, creating interactions, applying transformations, or even spline interpolation. As an example, consider the following formula:\n",
     "\n",
@@ -52,13 +52,19 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:11.302339Z",
+     "iopub.status.busy": "2026-03-16T16:10:11.302010Z",
+     "iopub.status.idle": "2026-03-16T16:10:12.926504Z",
+     "shell.execute_reply": "2026-03-16T16:10:12.926009Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import pytest\n",
     "import scipy.optimize as optimize\n",
     "import scipy.stats\n",
     "from dask_ml.preprocessing import Categorizer\n",
@@ -83,7 +89,14 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:12.928178Z",
+     "iopub.status.busy": "2026-03-16T16:10:12.927994Z",
+     "iopub.status.idle": "2026-03-16T16:10:15.244947Z",
+     "shell.execute_reply": "2026-03-16T16:10:15.244591Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -365,7 +378,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. Reproducing the Model From the GLM Turorial<a class=\"anchor\"></a>\n",
+    "## 2. Reproducing the Model From the GLM Tutorial<a class=\"anchor\"></a>\n",
     "\n",
     "Now, let us start by fitting a very simple model. As usual, let's divide our samples into a training and a test set so that we get valid out-of-sample goodness-of-fit measures. Perhaps less usually, we do not create separate `y` and `X` data frames for our label and features – the formula will take care of that for us.\n",
     "\n",
@@ -379,7 +392,14 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:15.259806Z",
+     "iopub.status.busy": "2026-03-16T16:10:15.259694Z",
+     "iopub.status.idle": "2026-03-16T16:10:15.471707Z",
+     "shell.execute_reply": "2026-03-16T16:10:15.470808Z"
+    }
+   },
    "outputs": [],
    "source": [
     "ss = ShuffleSplit(n_splits=1, test_size=0.1, random_state=42)\n",
@@ -398,13 +418,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This example demonstrates the basic idea behind formulas: the outcome variable and the predictors are separated by a tilde (`~`), and different prefictors are separated by plus signs (`+`). Thus, formulas provide a concise way of specifying a model without the need to create dataframes by hand."
+    "This example demonstrates the basic idea behind formulas: the outcome variable and the predictors are separated by a tilde (`~`), and different predictors are separated by plus signs (`+`). Thus, formulas provide a concise way of specifying a model without the need to create dataframes by hand."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:15.473697Z",
+     "iopub.status.busy": "2026-03-16T16:10:15.473584Z",
+     "iopub.status.idle": "2026-03-16T16:10:22.769294Z",
+     "shell.execute_reply": "2026-03-16T16:10:22.768802Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -539,22 +566,29 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:22.770437Z",
+     "iopub.status.busy": "2026-03-16T16:10:22.770347Z",
+     "iopub.status.idle": "2026-03-16T16:10:22.819876Z",
+     "shell.execute_reply": "2026-03-16T16:10:22.819390Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "ClaimNb             int64\n",
        "Exposure          float64\n",
-       "Area               object\n",
+       "Area                  str\n",
        "VehPower            int64\n",
        "VehAge              int64\n",
        "DrivAge             int64\n",
        "BonusMalus          int64\n",
-       "VehBrand           object\n",
-       "VehGas             object\n",
+       "VehBrand              str\n",
+       "VehGas                str\n",
        "Density             int64\n",
-       "Region             object\n",
+       "Region                str\n",
        "ClaimAmount       float64\n",
        "ClaimAmountCut    float64\n",
        "PurePremium       float64\n",
@@ -577,13 +611,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Even though some of the variables are integers in this dataset, they are handled as categoricals thanks to the `C()` function. Strings, such as `VehBrand` or `VehGas` would have been handled as categorical by default anyway, but using the `C()` function never hurts: if applied to something that is already a caetgorical variable, it does not have any effect outside of the feature name."
+    "Even though some of the variables are integers in this dataset, they are handled as categoricals thanks to the `C()` function. Strings, such as `VehBrand` or `VehGas` would have been handled as categorical by default anyway, but using the `C()` function never hurts: if applied to something that is already a categorical variable, it does not have any effect outside of the feature name."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:22.821622Z",
+     "iopub.status.busy": "2026-03-16T16:10:22.821528Z",
+     "iopub.status.idle": "2026-03-16T16:10:30.355393Z",
+     "shell.execute_reply": "2026-03-16T16:10:30.355061Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -719,13 +760,20 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:30.356740Z",
+     "iopub.status.busy": "2026-03-16T16:10:30.356657Z",
+     "iopub.status.idle": "2026-03-16T16:10:30.401890Z",
+     "shell.execute_reply": "2026-03-16T16:10:30.401512Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "array([303.77443311, 548.47789523, 244.34438579, ..., 109.81572865,\n",
-       "        67.98332028, 297.21717383])"
+       "        67.98332028, 297.21717383], shape=(67802,))"
       ]
      },
      "execution_count": 7,
@@ -743,7 +791,7 @@
    "source": [
     "## 4. Interactions and Structural Full-Rankness<a class=\"anchor\"></a>\n",
     "\n",
-    "One of the biggest strengths of Wilkinson-formuals lie in their ability of concisely specifying interactions between terms. `glum` implements this as well, and in a very efficient way: the interactions of categorical features are encoded as a new categorical feature, making it possible to interact high-cardinality categoricals with each other. If this is not possible, because, for example, a categorical is interacted with a numeric variable, sparse representations are used when appropriate. In general, just as with `glum`'s categorical handling in general, you can be assured that `glum` you don't have to worry too much about the actual implementation, and can expect that `glum` will do the most efficient thing behind the scenes.\n",
+    "One of the biggest strengths of Wilkinson-formulas lie in their ability of concisely specifying interactions between terms. `glum` implements this as well, and in a very efficient way: the interactions of categorical features are encoded as a new categorical feature, making it possible to interact high-cardinality categoricals with each other. If this is not possible, because, for example, a categorical is interacted with a numeric variable, sparse representations are used when appropriate. In general, just as with `glum`'s categorical handling in general, you can be assured that you don't have to worry too much about the actual implementation, and can expect that `glum` will do the most efficient thing behind the scenes.\n",
     "\n",
     "Let's see how that looks like on the insurance example! Suppose that we expect `VehPower` to have a different effect depending on `DrivAge` (e.g. performance cars might not be great for new drivers, but may be less problematic for more experienced ones). We can include the interaction of these variables as follows."
    ]
@@ -751,7 +799,14 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:30.403259Z",
+     "iopub.status.busy": "2026-03-16T16:10:30.403170Z",
+     "iopub.status.idle": "2026-03-16T16:10:38.273339Z",
+     "shell.execute_reply": "2026-03-16T16:10:38.272842Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -891,7 +946,14 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:38.274479Z",
+     "iopub.status.busy": "2026-03-16T16:10:38.274413Z",
+     "iopub.status.idle": "2026-03-16T16:10:38.276592Z",
+     "shell.execute_reply": "2026-03-16T16:10:38.276208Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -976,7 +1038,7 @@
     " 2. Include the logarithm of a certain variable in the model.\n",
     " 3. Include a basis spline interpolation of a variable to capture non-linearities in its effect.\n",
     "\n",
-    "1\\. works because because formulas can contain [Python operations](https://matthewwardrop.github.io/formulaic/guides/grammar/). 2. and 3. work because formulas are evaluated within a context that is aware of a number of [transforms](https://matthewwardrop.github.io/formulaic/guides/transforms/). To be precise, 2. is a regular transform and 3. is a stateful transform.\n",
+    "1\\. works because formulas can contain [Python operations](https://matthewwardrop.github.io/formulaic/guides/grammar/). 2. and 3. work because formulas are evaluated within a context that is aware of a number of [transforms](https://matthewwardrop.github.io/formulaic/guides/transforms/). To be precise, 2. is a regular transform and 3. is a stateful transform.\n",
     "\n",
     "Let's try it out!"
    ]
@@ -984,7 +1046,14 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:38.277724Z",
+     "iopub.status.busy": "2026-03-16T16:10:38.277661Z",
+     "iopub.status.idle": "2026-03-16T16:10:47.196866Z",
+     "shell.execute_reply": "2026-03-16T16:10:47.196455Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1118,7 +1187,14 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:47.197980Z",
+     "iopub.status.busy": "2026-03-16T16:10:47.197903Z",
+     "iopub.status.idle": "2026-03-16T16:10:47.783163Z",
+     "shell.execute_reply": "2026-03-16T16:10:47.782765Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1201,13 +1277,20 @@
    "source": [
     "### Variable Names\n",
     "\n",
-    "`glum`'s formula interface provides a lot of control over how the resulting features are named. By default, it follows `formulaic`'s standards, but it can be customized by setting the `interaction_separator` and `categorical_format` paremeters."
+    "`glum`'s formula interface provides a lot of control over how the resulting features are named. By default, it follows `formulaic`'s standards, but it can be customized by setting the `interaction_separator` and `categorical_format` parameters."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:47.784411Z",
+     "iopub.status.busy": "2026-03-16T16:10:47.784343Z",
+     "iopub.status.idle": "2026-03-16T16:10:50.289682Z",
+     "shell.execute_reply": "2026-03-16T16:10:50.289219Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1345,12 +1428,27 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:50.290873Z",
+     "iopub.status.busy": "2026-03-16T16:10:50.290798Z",
+     "iopub.status.idle": "2026-03-16T16:10:50.305162Z",
+     "shell.execute_reply": "2026-03-16T16:10:50.304698Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Caught expected ValueError: The formula sets the intercept to False, contradicting fit_intercept=True. You should use fit_intercept to specify the intercept.\n"
+     ]
+    }
+   ],
    "source": [
     "formula_noint = \"PurePremium ~ DrivAge * VehPower - 1\"\n",
     "\n",
-    "with pytest.raises(ValueError, match=\"The formula sets the intercept to False\"):\n",
+    "try:\n",
     "    t_glm8 = GeneralizedLinearRegressor(\n",
     "        family=TweedieDist,\n",
     "        alpha_search=True,\n",
@@ -1359,7 +1457,11 @@
     "        formula=formula_noint,\n",
     "        interaction_separator=\"__x__\",\n",
     "        categorical_format=\"{name}__{category}\",\n",
-    "    ).fit(df_train, sample_weight=df[\"Exposure\"].values[train])"
+    "    ).fit(df_train, sample_weight=df[\"Exposure\"].values[train])\n",
+    "    raise AssertionError(\"Expected ValueError was not raised\")\n",
+    "except ValueError as e:\n",
+    "    assert \"The formula sets the intercept to False\" in str(e)\n",
+    "    print(f\"Caught expected ValueError: {e}\")"
    ]
   },
   {
@@ -1374,7 +1476,14 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:50.306335Z",
+     "iopub.status.busy": "2026-03-16T16:10:50.306261Z",
+     "iopub.status.idle": "2026-03-16T16:10:52.806289Z",
+     "shell.execute_reply": "2026-03-16T16:10:52.805883Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1515,8 +1624,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:52.807428Z",
+     "iopub.status.busy": "2026-03-16T16:10:52.807354Z",
+     "iopub.status.idle": "2026-03-16T16:10:54.748684Z",
+     "shell.execute_reply": "2026-03-16T16:10:54.748153Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1628,6 +1744,7 @@
     "\n",
     "t_glm9 = GeneralizedLinearRegressor(\n",
     "    family=TweedieDist,\n",
+    "    \n",
     "    alpha_search=True,\n",
     "    l1_ratio=1,\n",
     "    fit_intercept=False,\n",
@@ -1661,9 +1778,8 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  },
-  "orig_nbformat": 4
+   "version": "3.14.3"
+  }
  },
  "nbformat": 4,
  "nbformat_minor": 2
diff --git a/docs/tutorials/glm_french_motor_tutorial/glm_french_motor.ipynb b/docs/tutorials/glm_french_motor_tutorial/glm_french_motor.ipynb
index 9164b2c3..28c36114 100644
--- a/docs/tutorials/glm_french_motor_tutorial/glm_french_motor.ipynb
+++ b/docs/tutorials/glm_french_motor_tutorial/glm_french_motor.ipynb
@@ -24,7 +24,7 @@
     "1. Modeling the total claim amount per exposure directly\n",
     "2. Modeling number of claims and claim amount separately with a frequency and a severity model\n",
     "\n",
-    "In this tutorial, we demonstrate both approaches. We start with the second option as it shows how to use two different families/distributions (Poisson and Gamma) within a GLM on a single dataset. We then show the first approach using a single poison-gamma Tweedie regressor (i.e. a Tweedie with power $p \\in (1,2)$)\n",
+    "In this tutorial, we demonstrate both approaches. We start with the second option as it shows how to use two different families/distributions (Poisson and Gamma) within a GLM on a single dataset. We then show the first approach using a single Poisson-Gamma Tweedie regressor (i.e. a Tweedie with power $p \\in (1,2)$)\n",
     "\n",
     "\n",
     "\n",
@@ -37,8 +37,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:11.302340Z",
+     "iopub.status.busy": "2026-03-16T16:10:11.302036Z",
+     "iopub.status.idle": "2026-03-16T16:10:12.926468Z",
+     "shell.execute_reply": "2026-03-16T16:10:12.926006Z"
+    }
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -50,6 +57,7 @@
     "from sklearn.metrics import mean_absolute_error\n",
     "from sklearn.model_selection import ShuffleSplit\n",
     "from glum import GeneralizedLinearRegressor\n",
+    "\n",
     "from glum import TweedieDistribution\n",
     "\n",
     "from load_transform import load_transform"
@@ -80,7 +88,14 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:12.928178Z",
+     "iopub.status.busy": "2026-03-16T16:10:12.927989Z",
+     "iopub.status.idle": "2026-03-16T16:10:15.121111Z",
+     "shell.execute_reply": "2026-03-16T16:10:15.120577Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -388,18 +403,23 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:15.137701Z",
+     "iopub.status.busy": "2026-03-16T16:10:15.137590Z",
+     "iopub.status.idle": "2026-03-16T16:10:15.205183Z",
+     "shell.execute_reply": "2026-03-16T16:10:15.204673Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -436,7 +456,14 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:15.206331Z",
+     "iopub.status.busy": "2026-03-16T16:10:15.206260Z",
+     "iopub.status.idle": "2026-03-16T16:10:15.402395Z",
+     "shell.execute_reply": "2026-03-16T16:10:15.401951Z"
+    }
+   },
    "outputs": [],
    "source": [
     "z = df['ClaimNb'].values\n",
@@ -475,7 +502,14 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:15.403943Z",
+     "iopub.status.busy": "2026-03-16T16:10:15.403861Z",
+     "iopub.status.idle": "2026-03-16T16:10:20.377729Z",
+     "shell.execute_reply": "2026-03-16T16:10:20.377293Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -499,24 +533,24 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>intercept</th>\n",
-       "      <th>VehBrand__B1</th>\n",
-       "      <th>VehBrand__B10</th>\n",
-       "      <th>VehBrand__B11</th>\n",
-       "      <th>VehBrand__B12</th>\n",
-       "      <th>VehBrand__B13</th>\n",
-       "      <th>VehBrand__B14</th>\n",
-       "      <th>VehBrand__B2</th>\n",
-       "      <th>VehBrand__B3</th>\n",
-       "      <th>VehBrand__B4</th>\n",
+       "      <th>VehBrand[B1]</th>\n",
+       "      <th>VehBrand[B10]</th>\n",
+       "      <th>VehBrand[B11]</th>\n",
+       "      <th>VehBrand[B12]</th>\n",
+       "      <th>VehBrand[B13]</th>\n",
+       "      <th>VehBrand[B14]</th>\n",
+       "      <th>VehBrand[B2]</th>\n",
+       "      <th>VehBrand[B3]</th>\n",
+       "      <th>VehBrand[B4]</th>\n",
        "      <th>...</th>\n",
-       "      <th>VehAge__1</th>\n",
-       "      <th>VehAge__2</th>\n",
-       "      <th>VehPower__4</th>\n",
-       "      <th>VehPower__5</th>\n",
-       "      <th>VehPower__6</th>\n",
-       "      <th>VehPower__7</th>\n",
-       "      <th>VehPower__8</th>\n",
-       "      <th>VehPower__9</th>\n",
+       "      <th>VehAge[1]</th>\n",
+       "      <th>VehAge[2]</th>\n",
+       "      <th>VehPower[4]</th>\n",
+       "      <th>VehPower[5]</th>\n",
+       "      <th>VehPower[6]</th>\n",
+       "      <th>VehPower[7]</th>\n",
+       "      <th>VehPower[8]</th>\n",
+       "      <th>VehPower[9]</th>\n",
        "      <th>BonusMalus</th>\n",
        "      <th>Density</th>\n",
        "    </tr>\n",
@@ -552,19 +586,19 @@
        "</div>"
       ],
       "text/plain": [
-       "             intercept  VehBrand__B1  VehBrand__B10  VehBrand__B11  \\\n",
+       "             intercept  VehBrand[B1]  VehBrand[B10]  VehBrand[B11]  \\\n",
        "coefficient  -4.269268     -0.003721      -0.010846       0.138466   \n",
        "\n",
-       "             VehBrand__B12  VehBrand__B13  VehBrand__B14  VehBrand__B2  \\\n",
+       "             VehBrand[B12]  VehBrand[B13]  VehBrand[B14]  VehBrand[B2]  \\\n",
        "coefficient      -0.259298            0.0      -0.110712     -0.003604   \n",
        "\n",
-       "             VehBrand__B3  VehBrand__B4  ...  VehAge__1  VehAge__2  \\\n",
+       "             VehBrand[B3]  VehBrand[B4]  ...  VehAge[1]  VehAge[2]  \\\n",
        "coefficient      0.044075           0.0  ...   0.045494  -0.139428   \n",
        "\n",
-       "             VehPower__4  VehPower__5  VehPower__6  VehPower__7  VehPower__8  \\\n",
+       "             VehPower[4]  VehPower[5]  VehPower[6]  VehPower[7]  VehPower[8]  \\\n",
        "coefficient    -0.070054    -0.028142          0.0          0.0     0.016531   \n",
        "\n",
-       "             VehPower__9  BonusMalus   Density  \n",
+       "             VehPower[9]  BonusMalus   Density  \n",
        "coefficient     0.164711    0.026764  0.000004  \n",
        "\n",
        "[1 rows x 60 columns]"
@@ -601,14 +635,21 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:20.378862Z",
+     "iopub.status.busy": "2026-03-16T16:10:20.378777Z",
+     "iopub.status.idle": "2026-03-16T16:10:20.420110Z",
+     "shell.execute_reply": "2026-03-16T16:10:20.419707Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "training loss f_glm1: 0.45704947333555146\n",
-      "test loss f_glm1: 0.45793061314157685\n"
+      "training loss f_glm1: 0.45704947333555124\n",
+      "test loss f_glm1: 0.45793061314157707\n"
      ]
     }
    ],
@@ -634,12 +675,19 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:20.421299Z",
+     "iopub.status.busy": "2026-03-16T16:10:20.421216Z",
+     "iopub.status.idle": "2026-03-16T16:10:20.453403Z",
+     "shell.execute_reply": "2026-03-16T16:10:20.453007Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(23785, 23785.198509368805)"
+       "(np.int64(23785), np.float64(23785.198509368805))"
       ]
      },
      "execution_count": 7,
@@ -668,24 +716,29 @@
     "- $y = \\frac{z}{w}$: severity\n",
     "\n",
     "### 3.1 Why Gamma distributions\n",
-    "The severity $y$ is a positive, real number, $y \\in (0, \\infty)$. Theoretically, especially for liability claims, one could have arbitrary large numbers&mdash;very unlikely but possible. A very simple distribution for this range is an Exponential distribution, or its generalization, a Gamma distribution $y \\sim Gamma$. In the insurance industry, it is well known that the severity might be skewed by a few very large losses. It's common to model these tail losses separately so here we cut out claims larger than 100,000 to focus on modeling small and moderate claims."
+    "The severity $y$ is a positive, real number, $y \\in (0, \\infty)$. Theoretically, especially for liability claims, one could have arbitrarily large numbers&mdash;very unlikely but possible. A very simple distribution for this range is an Exponential distribution, or its generalization, a Gamma distribution $y \\sim Gamma$. In the insurance industry, it is well known that the severity might be skewed by a few very large losses. It's common to model these tail losses separately so here we cut out claims larger than 100,000 to focus on modeling small and moderate claims."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:20.455599Z",
+     "iopub.status.busy": "2026-03-16T16:10:20.455381Z",
+     "iopub.status.idle": "2026-03-16T16:10:20.670912Z",
+     "shell.execute_reply": "2026-03-16T16:10:20.670483Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -711,30 +764,33 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:20.672301Z",
+     "iopub.status.busy": "2026-03-16T16:10:20.672091Z",
+     "iopub.status.idle": "2026-03-16T16:10:20.818832Z",
+     "shell.execute_reply": "2026-03-16T16:10:20.818379Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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CiqBv7969mgyM8xzrRcBgGeBgGWyTM7k9towbMBwvy0DM3s0EAnU88PuOgADHEnl+OG7NmjULtzxKYRCoIb8PSbzogsAenHMoUUHeFkr2DM4kyBufwV5DDXwGy9/UkJAQzVHCA+cpgrV+/frp9/zcuXN6Axjogjq4iQx+sHCHN3PmTL0rRvIeipVRypA7d26988CPHe7icIHG3Q2KJhGZx1RgA2hFgCJd3PVYfmFsodganwVfWHSc5YjxQ2t7J4YAJarwhXcE+xb7FUmk+EHDBdryc3hiexytFz+ekyZNivI6ESwjUENAhotNdI6Do/2I442LKgJp4zMgsEArOaMFlatQmoGLFYqxV69eHa7FFPYLAhi0DImpfRldCADtlYgYRf847wA/+LgYINHTmeoh/C6g1AfnKy5S2Oe4IBvQSgbVjkhEdrZKyFmRlUYgeRqBBhKLcSFGNY6zEJgiAdwIAnAzgoR4XOSNUinAPsV6UQWGG0BUXVlWCUWFEQSjtARQyoB9jOAM56YB5zjOdaMFpCuMFnoIoCyDI3xGR/C58R3F8cVNLgJGe8ENIKEZLZMi2ufR/S3DZ8D2W0JVK6qrLYMbSzgXUYqIUlUEaLimIXE+0DG4cQA/dPjxwl248SOIiwnuthHMoPgRJxqy3XEyo6gWFyv80C1ZskRiGu6kIoMvJb6gaHWEImzUyyIIw2fEBQ0lTrh44a4Dd//4kcRdLpbB63DH4SkoxkVWP1rL4GKDOyGDp7YHzadxV4o8AhQR4y4ULRiiUq1jwI8HqjKwL9FUHfsYFyM030VQg6o2Z4+DPQj88KOPVltYH8473MnhooQ7NGfOA3tQDYiAC3kTyDnARRktYwAXaQQ+CFYcbZcn9mV04TxCFSEuvrg4oUoAF1FUf6AkAvse8D1Gb7L9+/fXqmYE2jjfUL2EKgPkeFi2uMO68D1HdQrugtGCyxLWjTt9BOiofsKFBRdtlELgnMX/2DdRgVwhHCecY6jeQSCCaQa0OEKVBPJkUOpsVOE5AxdyHEfc7WNfoZQauRr2Whti3xmBub2uHCKCqjOjGgZBGEoVkKeCm0WjSh3bgeOH6kRsAwJGXNTx/cd5idIzV+H7hipO/NbgeCOgRXCKY2gJJeC4OUGLKQRtOJeNFqkR5dLhphaPiODcQRUXfhvwfcL2ILgyqhIjg8+NY4ybEATiyCnC74Ft9V39+vW1FA/7FPOwHFrBoWQSN+ZBwdsZzb4Cu2LevHnm52j9g2no2MzygVYVRuuJS5cumXLnzm3q1auXaefOndp6BxnrVatWtWrd4cnWUhGx17ICmfkjRozQVhtoYYCs/nz58mmrgmPHjpmX27Rpk3aqlTBhQlPq1Km1w0B8Rrwv3t+AzH3sF1uOWiY48vTpU1PatGnttrxyx/Y4aqWDFi7GvkCngTiWS5cu1fWidYcB+xH705l13rp1y9S1a1dTlixZtIUOWjHVrVvXdPjwYZePgyNoSVSmTBl9LT4vzrmNGzdaLeNKaynLVjcdO3Y05cqVS1usoYMytAjr3r27VauN6OxLR62l0PLEmc7lnP1caFnz9ttv63cU+xfHAsekZcuWpoMHD9rdp1WqVDG99NJL+tmxja+//rp2yGYLrXWwDfY60TTg9wDHHa128N7YJ3huud3G9+TatWtOfYdOnz5tqlGjhilJkiQ6z16LNbSWxO/U+fPnTc4w9vPQoUNNX375pbYGixcvnnbo+Pfffzt8HTrey58/v8lZ9lpL4dzF+YXParsf0eKpd+/e+hmx/9KnT2/68MMP9ftlCfOxXyNrQWS07EKLObwvjgfe96effrJqlbR582ZT48aNdb04D9DiDOtZuHBhpOesLXutpXDuVa9eXY9h8uTJTW+88Ya22MNy2J6IWkvhuoIWZDly5NDvWalSpUyrVq0K91m//fZb/ZypUqXSY4nzvm3btnr+BItY+MfbAZYvQHEhqp3QtwLgzgh3x+g0y7JI1rgzQ1Ib+hJB6xjLJC3cfaPUAX1DRKfPECIiV6GaCCVR6DMFib6eglIUNMZA0jJKEYh8DaulHEDRJ6qZ0GzPUesjNLm1DXyM57a91hIReQqqupB3gSoiVKdFNCBtdKvrUcWB5FTk3jgaNJLI24K6KTgSgFEHbySzoSks/kazVjSpRskNmngi0Q7z0LQV9dBGTg3qZDEN9beoR0ayGZINUa9p5CsQEXka8tVwE4aSZCT6Otv821VoxYh8GPx2oml9MLS6If8U1NVSaFps9KViCYmASDRDc24088a4Kcg0R/NEJBIiwc5I4kNyHxK6kLGOLzrmIwCybD1BREREMSeogxsiIiIKPEFdLUVERESBh8ENERERBZSgay2FVkzoph5d3Lujy3MiIiLyPGTRoOdsdKxrOXCwPUEX3CCwsez9loiIiPwHepWObIDioAtujEEJsXOcGTmaiIiIvA9DcaBwIqLBhYM2uDGqohDYMLghIiLyL86klHg1oRgDz2F0VkRhGFgOQx+gl83I+qbBB7N9YERsIiIiIq8GNxgpuVOnTjpCLEZFff78uY6qiiHtI4Mg6NKlS+ZH0Ix0SkRERL5bLbVs2TKr5xgXBSU4O3bskEqVKkX4WiyXLFkyD28hERER+Rufyrm5c+eO/p8iRYpIl8XYTY8fP5YCBQrIp59+ancYBXjy5Ik+LBOSnIFBMzH8AhGRO8SNGzfcQLtEFODBDdqvd+/eXSpUqCCFChVyuBxGop04caKULFlSg5ZffvlFqlatqrk49kp7kNeDsaBc2Y7Lly/L7du3o/xZiIjsQWlzunTp2McWUbCMLYXcG4xsu2HDhkjbr9uqX7++/lgsXLjQqZIbNCVDKZG91lLI30Fgg2ovDITJjv6IKLrwM/vw4UO5evWqBji4SSMi1+D6nTRpUofXb58ruenSpYsGJuvWrXM5sIGyZcvKr7/+ande/Pjx9eFsVZQR2GAEcCIidwkNDdX/EeDgN4ZVVEQB2loKdzOdO3eWuXPnyqpVqyR79uxRWs+uXbvccidk5NigxIaIyN2M3xbm8xF5Voi3q6J+//13WbBggfZ1g1wXQLGTcZfTt29fuXDhgkyfPl2fjxo1SrJlyyYFCxaUp0+faonNnDlz9OEurIoiIk/gbwtREAQ348eP1/9fffXVcE3C33vvPXMOzNmzZ83zEND07NlTAx4EQAhykKtTp06dGN56IiIisvQizCRbT92Uq/ceS5okCeTl7CkkTuxYwZtQ7AsJSWhafurUKa0eS5AggQQStCZDc/lbt24FRP9AgfZ5KDgE8m8M0bL9l+TLRQfl0p3H5p2RPmkCGVC/gNQqlD5GE4q9mnMT6NHr5hM3ZMHuC/o/nhMREQVqYPPhrzutAhu4fOexTsf8mOQTraUCjaejV3+BKsR48eJ5ezOIiMiDcPOOa569W3hMQ6UU5lcvkC7GqqhYchNA0Sv68/noo4+0mSmKvNEh4rZt26yW2bhxoxQtWlTnlylTRvbt22eed+bMGe0zKHny5JIoUSLNZ1qyZIl5/sGDBzW3KXHixJI2bVpp2bKlXL9+3TwfuVNo/YbOGFOlSiXVq1eX5s2bS7Nmzay2AS1FMB+5VYCa0WHDhkmOHDk0jwrbN3v2bKvXYDvy5Mmj81Eddfr0abfvPyIich1ybGyvebYBDuZjuZjC4CYGo1fAfE9VUX3yySfaamzatGmyc+dOyZUrl9SsWVNu3vzfCdWrVy8ZMWKEBj0Igho0aGBulorWawiQ0N8Qgp6hQ4dqIGMkdleuXFmKFSsm27dv13HBrly5Im+++abVNuC9Q0JCNIj68ccf5Z133tE+jO7fv29e5u+//9bBUZs2barPMXwGAh0kmB84cEC6desmLVq00IFV4dy5c9KkSRMNrHbv3i3vv/++9OnTxyP7kIiIXIPkYXcu5w6slvJS9Foup3s7CUSwgODg559/ltq1a+u0SZMm6WjrkydPltKlS+u0AQMGaImKEYig08R58+ZpkIJWaQg4ChcurPNRkmLAukuUKCGDBg0yT5syZYr29nz06FEtVQEEVCiFMeTMmVNLgfAeKOkBNP9HCRESwrDd3333nfZzVK5cOfP7oqdqBEcIqPDemDZy5EhtSps3b15z8EVERN6VJkkCty7nDiy5CZDo9cSJE1oC88orr1gN1Pfyyy/LoUOHzNOMAMIYoBSBgjEfVVpff/21rgNB0N69e83LYqT21atXa0mO8ciXL5/5vQ2lSpWy2i5swxtvvCG//fabPkcwg36NUKJjVHWhBQkCLst1o18jY73YPvRCbdlHiOXnICIi73k5ewrNK3WUTYPpmI/lYgpLbgIkejVa9Nt2EobpkXUcZsxHdQ+qsdBv0PLly3XQ0W+//VaHxwgLC9PSFnulJZa9Q6OUxhYCGZTAoNt5lCQh38coXcJ6Ae+ZMWNGq9cZw2YEWW8FRER+JU7sWNpgBnmluJpY/mIbVx/Mj8n+blhyEyDRK6qD0DIJ1TkGlOQgPyZ//vzmaVu2bDH/jT5iUKVklMAAqpk6dOigQ2L06NFDq7YAVVLIh0Hv0Hgvy4e9gMZS+fLldb2zZs3SEhyU5BitqAoUKKBBDKrEbNeL1xjLWG637ecgIiLvqlUovYxvUULSJbW+ecdzTI/plsIsuQmQ6BUBxocffqgJw6huypIli+a+YCTitm3byp49e3S5gQMH6qCgaO3Uv39/bbXUqFEjnffxxx9riQryZxD4IA/GCIyQbIxAB62f8B543fHjx2XmzJk6PaJBAFEy9Pbbb8uECRM0mEL1lgHDbqDHaSQRoxQHLbzQUdOmTZu0eqpVq1YabKEECa2w2rdvr1VkyC0iIiLfUatQem3u7Qs9FLPkJoCi1yFDhmhCMBJ3UdKC4AMtk9C023KZrl27SsmSJbUFFFoyGaUoGBUdQQwCmlq1amk+zrhx43RehgwZtAUUlkHVVaFChXQ96C0yduzITyNUTSG/BlVPlnlB8NVXX8nnn3+u1WB4b6x/0aJF5oFUEaihFRimoZk4giTLxGYiIvINcWLH0gYzDYtl1P+9EdgAh1/wUNfovjK+BhH5Dg6/QBQzwy+wWsrD0SsRERHFLFZLERERUUBhcENEREQBhcENERERBRQGN0RERBRQGNwQERFRQGFwQ0RERAGFwQ0REREFFAY3REREFFAY3AQIjJz9wQcf6LhSGMtp9+7d8uqrr+p4UURERMGEwU2AWLZsmQ4m+ddff+mYURj7CSN7Y9wmA0b0HjVqlMvrXrNmjQZMGKMK3cdb2rp1q87Dw1vee+89OX36tNPzsF+qV68uqVOn1i68y5Urp2NwRQQDj2LQUIxUHhoaqmNgjR49Otx+atiwoaRPn14HMi1WrJiOgm4Jo7ZjbC0MXor1YET2kSNHhtu+UqVKSbJkyczr+eWXX6yWWbdundSvX1/H/MK+nz9/vt3tPnTokDRo0EC7LMcgpWXLltUR2OHmzZvSpUsXHUMsYcKEOobXRx99pF2b21q8eLGUKVNGtxmDpjZp0sRqvnEOWD4wBpgBx8DeMjhv7cE4ZiEhIfrZPbFvrly5oucGlsFnx1hqx44ds1pm4sSJeoOAcwTruX37drj14Dtl+5n69Olj9zMRUcxhcBMgTpw4oRfV8uXLS7p06fTCgFIcXNDcBeuaN2+e1bQpU6boRTGm4cI8duxYLbGy3Ae//vprhPOMix+CmyVLlugI41WqVNGL4a5duxy+H5ZDMIR1HDhwQEdU79u3r/zwww/mZTCSeZEiRXSQz71790qbNm3k3Xff1QE/Dbggd+7cWbcBgcenn36qD1xIDThuWP/mzZt1Pa1bt9aHZQD24MEDHUTU8v1t4TNjlHUEUAi8EKB99tln5nHTLl68qI8RI0bIvn37NDhGsIFR5C3h82AwVmwD1oHAA6O825o6daoG1sYDI7rbWrlypdUyr732WrhlEFxhv1WtWjXcPHfsG5wXjRo1kpMnT8qCBQv0uGfNmlWqVaumrzU8fPhQg55+/fpJRAYOHGj1mXA8icjLTEHmzp07uOLp/7YePXpkOnjwoP7vT1q1aqWfyXhkzZpVp1euXNnUtWtX89+Wy7hy6FevXq3Lf/rpp6Zq1aqZpz98+NCUNGlS02effRZufRs3bjRVrFjRlCBBAlOmTJlMXbp0Md2/f988/5dffjGVLFnSlDhxYlPatGlNzZs3N125ciXce65cuVKXCw0NNZUrV850+PBhnf/gwQNT3759TTVq1DBVrVrV1Lt3b32/LVu2RDjPkQIFCpi+/PJLkys6duxoqlKlSoTL1KlTx9S6desIl2ncuLGpRYsWES5TvHhx3f/2YD/Nmzcv3PS33nor0vXa+uOPP0zx4sUzPXv2TJ/j/4wZM5p++umnCF/naBsMp06d0mV27doV6TZgu/FZBwwYYCpatGiky7u6b44cOaLT9+/fb572/PlzU4oUKUyTJk0Ktw7jXLx161a4efiujRw50uQsf/2NIfL167ctltw4CXd0jh62VTURLfvo0SOnlnUFqkdw95gpUya9c9y2bVu4ZVCcj/mWd5kGFKXjrj0yuHtfv369uVoDd/Qoli9RooTVcigFqFmzplZd4O561qxZWh2DEgvD06dPtcoMJQGoNsBo7KgmsIW79G+//Va2b9+upVEoDQFUJQwaNEi6du2qpRJbtmyRf/75R6tOIppnT1hYmNy7d09LBVyBEobIXhPZMig1QIlP5cqV7c7H9RnbfuTIEalUqZLT24bPhKqkPHny6LFIkyaNfn5H1VeW24tqGOxr2Llzp1y4cEFix44txYsX19LB2rVra+mVLRxfVFmVLl1aq6SwDbZQRYZtQdXc7Nmz7Zb+oMRpwIABkX7GqO6bJ0+e6P9GCRbEiRNH4sWLp+epq4YOHarVjKgi++abb/TcJiIvMwWZqJbc2JZ6WD5wd24pYcKEDpdFCYqlVKlS2V3OVbh7NEpsDJYlNxHdZebNm9c0d+5ch+u2vHNt1KiRuYQDpRajR4/WO2PLbW7ZsqXpgw8+sFrH+vXrTbFjx3Z4x7p161Zdx71798KV3BgWL16s07AOPFBiZFk6U6lSJdO///4b4Tx7hg0bpnftliVHkdm0aZMpbty4puXLlztc5s8//9RSEMsSAgNKQzAP+2TgwIHh5t++fduUKFEiU0hIiCl+/PimyZMnO3wfe6UTly5d0uk4F7/77jstMRk8eLApVqxYpjVr1thdz/Xr101ZsmQx9e/f3zxtxowZuh5Mnz17tmn79u1aypYyZUrTjRs3zMt99dVXuk/wPiNGjND3xTTDtWvXdDtwDLZt26bHB58dJXiGo0ePmtKkSaMlK+Co5Ca6++bp06f6XXjjjTdMN2/eND158kT3DZbFOeNKyQ0+E/bnnj17tNQH3+e2bds63B6W3BDFTMkNgxsLwRrcRMbyx33hwoWm7Nmzm06cOKFVTrgg2gY3qOLBhRsXIONh7BPsX9i5c6epQYMGetFE1ZQx/8CBA1bvefXqVfN68RpMO3PmjF5Yf/jhB1NYWJhWy6Ha4/jx43qxjGierd9//13fe8WKFU7vDwQrqVOntrp429tn+NzTpk2zO//kyZOmvXv3miZOnKiBFbbD0osXL0zHjh0zBwuo/sM6nb2AX7hwQacjELFUv359U7NmzcKtAz8WZcqUMdWqVUsv/obffvtN1/Pjjz+apz1+/FjP2wkTJjj8/Njml156yRSRzp07mwoXLmyuFipVqpRp/Pjx5vmOgpvo7htAkIZ1Y36cOHFMNWvWNNWuXVsfrgQ3thAAYll8L+xhcEMUM8HNf2XPFKn79+87nIcibUtXr151uCyK9y05auXjq+rUqSPt27fXpFMk4aI43haqI7AMWt7YQvIxqt1q1KihDyToIlEXVV2oPrEt0o8bN675b6NFFtaPqp5OnTpZLZszZ059QETzDKguw+f4888/NZnUGQcPHtQk2Hbt2jlMHF27dq3um++++04TY+3Jnj27/l+4cGFtufPFF19oayzL8yRXrlz6N6o7kHw8ePBgbb3jDFQPoWqpQIECVtPRysu26gVVckicTZw4sSaMW+5zVEOB5Xrix48vOXLkMFdP2oNWWXfv3tXPljZtWofL/PTTT+ZtQNUjqumM6kscZ8Qn+BzLly83Jx9Hd99AyZIltbsEVMPhnMM5iGo7tMSKDnwmOH78uN3vBhHFDAY3TkIrF28vG13IKXjx4kW01oFADrk3w4YNk6VLl9pdBjk4yMkwLkC2kJNz/fp1GTJkiDatBlzYoiqifCFH82bMmKH5O/i/bt26Tr0PPhMusGgFhNwKe5DjU69ePc3DQL9DzsAF3MgDic4ytscauS/IR7F09OhRbRlkQACCoBIBy8KFC63yUIwgAPOwHrS8gmfPnmlQbrkeWwhSsC402Y5oGSN4Qp4PzgtL48aNk1WrVmlujhEMumPfWEITeUAzcJyDll0nRIXR4s74XETkHQxuggiSf9EEuVmzZnrBwt09oKkw7nwbN27s1HpwAejVq5fDO9PevXvrHSxKT1DCgQAOd9crVqyQ77//XktvcPHF3x06dJD9+/dH+6LiCgQ0KFFBIja28/LlyzodfbgYFzuUYKCp9+HDh82BDZqMo7Spe/fu5tcg2MNdvxHYIFBCInPTpk3Ny+CzGknFaKKOz499DihFQVNs9DdjwLFACQJKmlCqgCbr06dPl/Hjx1uVJKJ0wICEbJRE4H2Mpvk4Rm+99ZYm22Lb0cwbzdKxnUZpCT4PmjyjBA2BDh6Az4TPhqADxwgJvghEEdAMHz5cl3njjTf0f6wTnxX9BWEfrl69WhPBEdzhPINp06ZpiRCSklHygteMGTNGg0DANPTNZAmJxwiQLKe7a9+gtA6fEc8RVOGYoXk49ocBnwkPY11YDt0h4DVYF5qjI1kd+xbnDRL5u3XrpknT3ugegYgsmIJMIDYFdzbnZvPmzaYiRYpoEqblocffU6dOdbjuyHIObHNujATh6tWraz4Nck/wvt988415PnJMsmXLptuCJt7I5bFsKmzvPTEP05BDEx32msXjgfwcA/aH5WdC/oe911juc9sm+fbyrMaMGWMqWLCg5vkgJwXNmMeNG6d5JAYk9ObKlUtzmpInT677Z+bMmVafwdg/EX0GQLKtsS7kmMyfPz/SddjuY+Tg9OjRQ5N9kyRJot0BWCZJL1261FSsWDFz7lShQoVMo0aNMjcnh59//tmUP39+nY91oHm/vRwoS/Zybty1b5AIjy4KkBSOvC80JUdise3721uP8V3ZsWOH5ikh5wfbg8R8vAZdETjiz78xRP6UcxML/0gQwZ0p7rKMJq+W0KQbd3koArctniciii7+xhB55vpti/3cEBERUUBhcENEREQBhcENERERBRQGN0RERBRQGNzYEWQ51kQUQ/jbQhQzGNxYMHpmRb8fRETuZvy2WPYCTUTux078LKDTMvSoagyfgNGljS7/iYiiU2KDwAa/LfiNsR2yhYjci8GNjXTp0kU6PhQRUVQgsDF+Y4jIcxjc2EBJDcaFQdfvGEOHiMgdUBXFEhuimMHgxgH8CPGHiIiIyP8woZiIiIgCCoMbIiIiCigMboiIiCigMLghIiKigMLghoiIiAIKgxsiIiIKKAxuiIiIKKAwuCEiIqKAwuCGiIiIAgqDGyIiIgooDG6IiIgooDC4ISIiooDC4IaIiIgCCoMbIiIiCigMboiIiCigMLghIiKigOLV4Gbw4MFSunRpSZIkiaRJk0YaNWokR44cifR1a9eulZIlS0qCBAkkR44cMmHChBjZXiIiIvJ9Xg1uEKR06tRJtmzZIitWrJDnz59LjRo15MGDBw5fc+rUKalTp45UrFhRdu3aJf369ZOPPvpI5syZE6PbTkRERL4plslkMomPuHbtmpbgIOipVKmS3WV69+4tCxculEOHDpmndejQQfbs2SObN2+O9D3u3r0rSZMmlTt37shLL73k1u0nIiIiz3Dl+u1TOTfYYEiRIoXDZRDAoHTHUs2aNWX79u3y7NmzcMs/efJEd4jlg4iIiAKXzwQ3KEDq3r27VKhQQQoVKuRwucuXL0vatGmtpuE5qrSuX79uN68HkZ7xyJw5s0e2n4iIiHyDzwQ3nTt3lr1798qMGTMiXTZWrFhWz42aNdvp0LdvXy0RMh7nzp1z41YTERGRrwkRH9ClSxfNo1m3bp1kypQpwmXTpUunpTeWrl69KiEhIZIyZcpwy8ePH18fREREFByiFNy8ePFC5s2bp0m9KC3Jly+fNuNGgOEKlLggsMG61qxZI9mzZ4/0NeXKlZNFixZZTVu+fLmUKlVK4saN6/JnISIioiAPbvbv3y8NGzbU0pO8efPqtKNHj0rq1Km19KVw4cJOrwvNwH///XdZsGCB9nVjlMggNyY0NNRcrXThwgWZPn26uWXUDz/8oPk57dq10wTjyZMnO1WdRURERIHP5abgZcuW1eba06ZNk+TJk+u0W7duyXvvvafVQ840xza/uZ0cGZg6daquD/D/6dOntWTHgKbi3bp1kwMHDkiGDBm0eTiCHmewKTgREZH/ceX67XJwgxIVNLsuWLBguBId9Db86NEj8WUMboiIiDwDLZdv374tqVKl8q9+blAVdeXKlXDTUWqTK1cuV1dHREREAeDp06fy5ptvyquvvmq3a5aY5FRwY9kB3qBBg3S4g9mzZ8v58+f1gb8//vhjGTp0qOe3mIiIiHzOwIEDtYHQsWPHtGsXb3KqWip27NhW+TG2/cpYPkdLKl/GaikiIiL3u3fvnrac7tOnj1SvXt2r12+nWkutXr3aXdtGREREAeLJkyfmvuTQ6nnlypUOGwvFJKeCm8qVK3t+S4iIiMhv3Lx5U8d2bNKkiXbbAr4Q2LjUz83Zs2edWi5LlizR2R4iIiLycVevXtWqJ+TWoLsW9DvniRZSHg9usmXLZjciQ76NMR3/oxkYERERBaaLFy9K1apV5fDhwzokEqqifCmwcSm42bVrl93pCG5mzpwpY8aMkcSJE7tz24iIiMiHnDlzRgObEydOSObMmeWff/6R3Llzi69xOrgpWrRouGmI1pAVjeEXPvnkE+nZs6e7t4+IiIh8wPHjx+W1116Tc+fO6ViQq1at0lodX+RyJ36wY8cOrWurV6+eDseAD/zFF1+w5IaIiChArV27VgMbdOa7fv16nw1sXB44E0FM//79Zc6cOdoL4cGDByVHjhye2zoiIiLyCW3bttV+7+rUqSNp06YVX+Z0yU3Hjh11PCl0noOxpTCaNwMbIiKiwLVr1y5t8m1o3bq1zwc2Lg2ciWgtQYIEki9fvgiX27lzp/gy9lBMREQUuQ0bNmgpDa77yLGNrFdgT3N7D8UwYMAAd2wbERER+biVK1dKw4YN5eHDh5pPiwIOf+J0yU2gYMkNERGRY4sXL5amTZvq0Aq1atWSuXPnSmhoqPjT9du/QjEiIiLymDlz5kjjxo01sMEgmPPnz/eJwMajraXgxo0b8vnnn+tgmuh+OSwszGq+ZeIRERER+YfZs2fLW2+9pdf1Zs2ayfTp0yVu3Ljij1wOblq0aKE9E6JJGDKmfWWQLCIiIoo6dNabJk0arYr66aefJE6cOH67O0Oikj2Nh70ei4mIiMg/5c6dW7Zt2yYZMmTwuwRiWy5vPZqEPXr0yDNbQ0RERDFmxIgRsnTpUvPzTJky+X1gAy5/gnHjxmkvxeiGGfk3yF62fBAREZFvM5lMOmxSr169pEmTJnLy5EkJJC5XSyVLlkybYWHwLNsdhfybFy9euHP7iIiIyI1MJpP07t1bhg8frs/RSCjQRhxwObh55513JF68eDr8AhOKiYiI/EdYWJh89NFHMnbsWH0+atQo6dq1qwQal4Ob/fv361gTGBWUiIiI/MOLFy/kgw8+kClTpmhNy4QJE/R5IHI556ZUqVI65DkRERH5j8mTJ2tgg4ThadOmBWxgE6WSmy5dumgRFpKQChcuHK6DnyJFirhz+4iIiMgN2rRpI+vXr5cGDRrIG2+8EdD71OWxpew1EUPxlr8kFHNsKSIiChaPHz/WQgh/7pDPo6OCG06dOhWdbSMiIqIYcP/+fS2lyZIli7k6Kli4HNxkzZrVM1tCREREbnHnzh2pW7eubNy4URInTix9+/YNqoZATgU3CxculNq1a2vRFv6OCKJEIiIi8o6bN29KzZo1Zfv27do33bJly4IqsHE65wZFWZcvX9YBtSIq1mLODRERkfdcvXpVqlevLnv37pVUqVLJihUrpFixYgFxSNyec4NOf+z9TURERL7h4sWLUrVqVTl8+LCkS5dOVq5cKQULFpRg5HLODREREfmegwcPyokTJyRz5szyzz//6CjfwSpKwc2DBw904MyzZ8/K06dPreahW2ciIiKKWdWqVZP58+dLgQIFJFu2bEG9+13u5wZDL9SpU0cePnyoQU6KFCnk+vXrkjBhQs3J8fWRRdnPDRERBVJpDRr7BEMpzV0Xcm5cbvTerVs3qV+/vmZjh4aGypYtW+TMmTNSsmRJGTFiRHS2m4iIiJy0e/duqVy5subZ4DpM0QhusDN79OihvR3i8eTJE63fGzZsmPTr18/V1REREZGLtm7dKlWqVNGak7Rp00qSJEm4D6MT3KD4C02+ATsUeTeAoiLjbyIiIvIMjA+F/Jrbt29L+fLltVUUUkQoGgnFxYsX146B8uTJo1Hj559/rpHjL7/8ogNpEhERkWcgkGnYsKHmveIajI510QMxRbPkZtCgQZI+fXr9+6uvvpKUKVPKhx9+qB0HTZw40dXVERERkRPWrFkj9erV08CmVq1asnjxYgY27mot5e/YWoqIiPwRGvKgtCZnzpwyY8YMiR8/vgSTu54cFZyIiIhiHvJqVq9ercnDyH+laAY3yLMxkogjs3PnTqeWIyIioohNmjRJWyV37txZnzNx2I3BTaNGjZxcHREREbnDmDFjpGvXruZChldeeYU71p3BzYABA5xdHxEREUXT0KFDpU+fPvo3+pZDk2/yQGupW7duyffff68JPbaQ3ONoHhERETkHbXxQoGAENuhuZfjw4U6nhpCLwc0PP/wg69ats5uhjOxldCqEAIeIiIiiFtj07t1bBg4cqM8HDx4sX375JQMbTwY3c+bMkQ4dOjic3759e5k9e3ZUtoGIiCjooYM+lNLA6NGjzaU35Dqnm4KfOHEiwlFHMQ/LEBERkeuqV6+u1VCZMmWSdu3acRfGRHCDQTIvXrwoWbJksTsf82LHdrnDYyIioqD1/Plzefz4sbmnYVRDUfQ5HY2gGdr8+fMdzp83b54uQ0RERJF7+vSpvPnmm1KnTh0dUoG8UHKDDoSaNWumxWUYSwolOfDixQsZN26cjBw5Un7//Xc3bhoREVFgevTokTRt2lSWLl0q8eLF0w5wK1So4O3NCs6xpfr376/Z2+j6OUeOHJrBjTyb+/fvS69evWTIkCHi6zi2FBEReROumQ0aNNChFEJDQ2XBggWab0Puu367PHDm1q1b5bfffpPjx49rs7U8efLI22+/LS+//LL4AwY3RETkLbgw161bVzZu3Kh5NhjZu1KlSjwg3h44E0GMvwQyREREvjSqd82aNWX79u2SLFkyWbZsmZQpU8bbmxWQOCo4ERFRDLh8+bKcOnVKUqVKJStWrJBixYpxv3sIgxsiIqIYUKBAAVm+fLnEjx9fChYsyH3uQQxuiIiIPOT06dNy/vx5c0uoEiVKcF/HAK/2uoexqurXry8ZMmTQllcR9aMDa9as0eVsH4cPH46xbSYiInLGsWPHNFm4Vq1a8u+//3Kn+Wpwg6zuo0ePmg8ankfHgwcPpGjRojoopyuOHDkily5dMj8iGhaCiIgoph08eFADm3Pnzmn/cHiQj1ZLoYSlW7duGtR07dpVBg0aFK03r127tj5clSZNGs00JyIi8jW7d+/WfmuuX78uhQsX1uThtGnTenuzgopLJTcYXqF06dLSsmVLbQ7urUxvbEf69OmlatWq2glSRJ48eaJt4y0fREREnoDqpypVqmhgU7JkSb1GMbDx4ZIbHCzkt9y6dUv27Nmjgc3atWt12qpVqyQmIKCZOHGinjAIWn755RcNcJCL46gTJPSozIHIiIjI0/bt2yfVqlXTHojLly8vS5Ys0U7nKOa53EPxW2+9pQfvn3/+kZkzZ7pvQ2LF0sE3GzVq5NLrkJCM1y5cuNDufARBeBhQcpM5c2anejgkIiJyZSDMJk2a6CCYuCYZI31TzPdQ7FK11KxZsyRFihTSrl07SZkypT73trJly2pysyPoTwA7wfJBRETkbhgAc/bs2ZqXysDGjxKK0T6/Ro0a+vc333wjV69eFW/btWuXVlcRERHFtDlz5siGDRvku+++01qEBAkS8CD4W3Bj2eQarZWi22IJ9ZIYgNOAbqmRZY7SoSxZskjfvn3lwoULMn36dJ0/atQoyZYtm/bsiOK/X3/9VU8sPIiIiGISrkGtWrWSsLAwbWyDQaTJN3i1h2IMHoZEZUP37t31f5wsP//8s/Zhc/bsWfN8BDQ9e/bUgAfDxCPIQfFfnTp1vLL9REQUnCZNmiTt27cXpK22bt1a81HJjxOKgykhiYiIyNaYMWO0rzfo2LGjfP/99xI7tlc7/A8Kdz2VUExERBTMhg4dag5sevTooT3sM7DxPS4HN8iLISIiCjaHDh2S/v3769+fffaZDB8+XJOIKQBybnLlyqUd5rVt21Zef/11ZoYTEVFQyJ8/v0ybNk3Hi+rTp4+3N4fcWXKD3okx/AGK49KlS6cJVVu3bnV1NURERD4PLaEwlILhnXfeYWATiMFNoUKFtD0/WixNnTpVLl++LBUqVNCWS5h+7do1z2wpERFRDHrx4oV2WovOYi9evMh970einFAcEhIijRs3lj/++EMTrE6cOKHNtDGs+7vvvqvNuImIiPzRs2fPdJDoKVOmaK4payiCJLhBHzVoAofegVFig8AGAQ4G0USpTsOGDd27pURERDEA4xGi35oZM2bojTyGGnJ13EPys4RiBDKojjpy5Ih2nofeg/G/0RQue/bs8uOPP0q+fPk8sb1EREQe8+jRI2natKksXbpUxyZED/h169blHg/04Gb8+PHSpk0b7ZERCcX2YOiEyZMnu2P7iIiIYgSGBGrQoIGsXr1ae8HHyN7VqlXj3g+G4GbFihUavNh2WoSOjtE8DvMwMiqGUCAiIvIXDx8+1MRhjOi9ZMkSqVixorc3iWIquMmZM6cmC6dJk8Zq+s2bN7VKCtnlRERE/gbXtZUrV+o1DgNhUhAlFDsaigrFeRzqnYiI/MnVq1dl7ty55udo8cvAJohKbowRu9HV9Oeffy4JEyY0z0Npzb///ivFihXzzFYSERG5GVr2Iqfm6NGj2iIKve5TkAU3u3btMpfc7Nu3T/NqDPi7aNGi2hyciIjI1505c0Zee+01OXnypGTOnFmvYRSEwQ2yxwGtpEaPHh3pcONERES+6Pjx4xrYoBFMjhw55J9//pFs2bJ5e7PImwnF6OOGiIjIHx08eFCropA0jP7YkECcMWNGb28WeSO4adKkifz8889aWoO/I2KZmEVEROQr0My7cuXKOhBmkSJFtGsT25a/FETBTdKkSTWR2PibiIjI32C4oBYtWsiGDRvk77//lhQpUnh7k8hDYpkcte22A4uePXtWUqdObdVayp/cvXtXA7Q7d+4wb4iIKMjgOobO+hIlSuTtTSEPXr9ju3pS5M6dW5vPERER+Trk1GCsKAyGCaiFYGAT+FwKbjDkAoKbGzdueG6LiIiI3OCvv/6SevXqaS4oBn2m4OFyD8XDhg2TXr16yf79+z2zRURERNE0e/Zsady4sZbY4H+jI1oKDi7l3EDy5Mm1vvL58+faeR9GTrUdY8qXMeeGiCiw/frrrzp4c1hYmDRv3lymTZsmcePG9fZmUQxev13u52bUqFHR2TYiIiKPmTRpkrRv315zRNu0aSMTJ06UOHHicI8HGZdLbvwdS26IiAIT+q/JlSuX3tl36tRJxowZo7miFBg81lrKcOLECfn000+1uA8jqsKyZcvkwIEDUdtiIiKiaEqVKpUsXrxY+vfvL99//z0DmyDmcnCzdu1aKVy4sI4Cjgz0+/fv6/S9e/fKgAEDPLGNREREdqHy4fz58+bnr7zyinz99dfmjmcpOLkc3PTp00dPHHRbbTkyeJUqVWTz5s3u3j4iIiKHgc0nn3yiN9y7d+/mXqKoBzf79u3TZnW20Gsx+78hIqKY8OLFC+nSpYuMGDFCbt++rbUJRFEObpIlS6ajqdratWsXR1YlIiKPwzBAVatWlbFjx2r1E1pEoYUUUZSDm7ffflt69+4tly9f1pMK/Qhs3LhRevbsKe+++66rqyMiInLazJkzdURv5H9iGIXff/9d2rVrxz1I0WsK/uzZM3nvvff0BMNLQ0JCtHgQQc/PP//s8/0JsCk4EZF/WrBggTRq1Ej/LlOmjHbWh6bfFBzuutAUPMr93KA5OKqiUHJTvHhxHXPKHzC4ISLyT7iRrl69ulSsWFG7I2Gvw8HlrieDGxQFVq5cWfwVgxsiIv+AmoJx48bJBx98YB7qBwGOr9cQkB924oeoOUuWLNoknINnEhGRJxw/flwqVKggH3/8sV5vDAxsyBkuBzcXL17UfgXWr1+vSV14YKRwy06UiIiIogKVCZMnT5ZixYrJ1q1btYVu+fLluTMp5saWOnXqlGaqz5gxQw4fPiyVKlWSVatWiS9jtRQRkW9CX2mogkLv94AUiF9++UUyZ87s7U2jYEkoNqD+c+nSpfLZZ5/pEAx47ssY3BAR+R50wtekSROtHUArXPSEjy5GWA1FMTZwJqBvm44dO0r69Om1GXjBggXlr7/+iurqiIgoiGXIkEEePnwoefPmlS1btmh/agxsKKpCXH1Bv379tBoK0XW1atVk1KhR2u9AwoQJo7wRREQUfK5cuSJp06bVv1H19Pfff0uhQoV4PaFoc7nkZs2aNVpUeOHCBR1aHqU2DGyIiMhZyIb4/vvvJVu2bLJkyRLz9JdffpnXE/JOyc2mTZvc885ERBR0MHRP69atZdmyZfr8jz/+kDp16nh7syjARCnnBtnrr7zyitaRnjlzRqehegpdYxMREdmzaNEiKVy4sAY2CRIk0NKbqVOncmeR94Ob8ePHS/fu3TXSxjDzRuso9EWAAIeIiMjSgwcPpEOHDtKgQQO5fv269o+2fft26dy5sw7ATOT14AaR9qRJk6R///5WmeylSpWSffv2uXv7iIjIz6H/sx9//FH/7tGjh3bOhxa2RD6Tc4OO+zBQpq348eNrdE5ERGSpfv362rQbLWzxIPK5kpvs2bPL7t27w01HR34FChRw13YREZGfOnfunLz++uuaPGwYMmQIAxvy3ZKbXr16SadOneTx48fanA/Fi+j3ZvDgwfLTTz95ZiuJiMgvzJo1S/NrkJMJs2fP9vYmURByObhBE77nz5/r4JnoTRL93GTMmFFGjx4tzZo188xWEhGRz3eNjwRhtKaFMmXKaGkNkTdEa2wpZL2HhYVJmjRpxF9wbCkiIvfCcDwtWrSQ06dPS+zYsbXBCcYbjBs3Lnc1+dfYUpAqVSo5dOiQ5tvcunUrOqsiIiI/hP7NKlWqpIENehxet26dDBw4kIEN+Ue11PDhw+X+/fvy5Zdf6nMU+NSuXVuWL1+uz1F6888//7B5HxFREKlatarkyJFDypUrp12F4M6ayNucLrlB0rBlaygkiSFCX79+vVZPoZ8bI/AhIqLAhBtbjAeFlARInDixNiyZPn06Axvyv+AG/dugV0kDTu6mTZvqMAwpUqSQTz/9VDZv3uyp7SQiIi+7ceOGvPHGG1K3bl354YcfzNOTJ0/u1e0iinJw8+zZM+2oz4BApnz58ubnGGcKJThERBR4Vq5cqTe4c+bMkZCQEPPQO0R+HdzkypVLq6Hg7NmzcvToUalcubJ5/vnz5yVlypSe2UoiIvKKJ0+eSM+ePaV69epy8eJFyZs3r2zZskW6devGI0L+n1D84Ycfah8GyLHBiY3kMcscHIwdYm9YBiIi8k9oDdu8eXPZs2ePPkfnfCNGjJBEiRJ5e9OI3BPctG/fXosi//rrL232N2DAAKv5iOjbtGnj7OqIiMjHYbzAAwcOaLcfU6ZM0TGiiAK+E7/oQjUXmpjv2LFDLl26JPPmzZNGjRpF+Jq1a9dK9+7d9QuHPB/0lIy7CWexEz8iIseePn0q8eLFMz//888/pWLFipIuXTruNgqOTvzccVdQtGhRq6z7yFps1alTR79ou3btkn79+slHH32kCW5ERBQ9KJnPmTOnuRoK0DqKgQ0F/NhS7oROAPFw1oQJEyRLliwyatQofZ4/f37Zvn271gGjWToREbkO4wT26NFDf2MBAyHPnDmTu5L8lldLblyF5uc1atSwmlazZk0NcNBUnYiIXIO0gBIlSpgDG1T7T5s2jbuRAj+42bt3r7k3Sm+6fPmypE2b1moanmOUckd97KAZI+rpLB9ERMEO/dQMHTpUypYtK0eOHJH06dPrcDrffvutVZ9mRAEb3KCJtxE8YAwR9FLpLbFixbJ6buRD2043oHgVCUjGI3PmzDGynUREvgxD6vTp00dvDps0aSL79u3TvmyIgia4SZYsmSbzAkZ+9VYpDpLaUHpj6erVq9pE3VEHgn379tXMauNx7ty5GNpaIiLfhf5r6tWrJ5MnT9axAtkJKwVdQjGSddEbMYotUUKCQTLjxIljd9mTJ0+Kp6DjwEWLFllNQzEqtidu3Lh2X4PiVRaxElGwQ5X8kCFDdBzAhAkT6m/4woULHZZ6EwV8cDNx4kQttjx+/Lg2vW7Xrp0kSZIk2m9+//59XacBpUO7d+/WgTjRKgqlLhcuXNDRZgH92aDZOBLesA1IMMZdB4pXiXzZizCTbD11U67eeyxpkiSQl7OnkDixeVGhmLFp0yZp0aKF/sbeu3dPvv/+e53OwIYk2JuC16pVy5xZ37VrV7cEN2jlVKVKFfNzBC3QqlUr+fnnn7VjP4xjZciePbuORo4xTcaOHaud+I0ZM4bNwMmnLdt/Sb5cdFAu3XlsnpY+aQIZUL+A1CqU3qvbRoENrUi/+uor+eabbzSdIFu2bNKsWTNvbxaRb/dQjMEyEflnzJhR/AV7KKaYDmw+/HWn2H7JjDKb8S1KMMAhj0CpOEpr/v33X33esmVLLbFBwwoif+TRHooR/Q8cOFDfIGvWrFp9hIRj3B34QnNxIl+qikKJjb27B2Ma5mM5IndatmyZFCtWTAMb/Faj6h7V+wxsKFi43ENx//79Nc8FiWmvvPKKNsXeuHGjfPHFF/L48WMt/iQi0Rwby6ooWwhpMB/Llctpv7UfUVQUKlRIx4dCYwsENbgJJQomLgc36Lnyp59+kgYNGpinYXwoVE117NiRwQ3R/0PysDuXI4oIOuLLmzev/p0pUya96cyTJ4/Dlq1EgczlaqmbN29Kvnz5wk3HNMwjov+gVZQ7lyNy1At7z549daw9y64y8JyBDQUrl4MbR6N4YxrmEdF/0NwbraIcNfjGdMzHckRRceDAASlTpowOmYAUAXSPQURRqJYaNmyY1K1bV1auXKmd6qG1FPpQQM+/aKZNRP9BPzZo7o3WUghkLNOGjYAH89nfDbkKgQy6w+jVq5fmOqZKlUpzIS3TBYiCmcslN+ip+OjRo9K4cWO5ffu2VkWhgz/U91asWNEzW0nkp9CPDZp7p0tqXfWE52wGTlGBIWhwg9mlSxcNbGrWrKnjQjGwIXJTPzf+iP3ckDewh2JylwULFkijRo10WBmUpHfu3Flix3b5PpUooK/fDG6IiPzMoEGDtKQGTb6JgsVdT3biR0REMWfnzp1SqVIlHY7G0K9fPwY2RBFgcENE5INevHih1U5ly5aV9evXS+/evb29SUSB21qKiDyDeTlkQOvTd999V9asWaPP0Whj5MiR3EFEngxunj9/rl+6EydOyNtvv60jhF+8eFHrwBInThyVVRIFNY4cToZZs2ZJhw4dtDVqokSJZMyYMdK6dWvtdoOIPBTcnDlzRmrVqiVnz57VnjGrV6+uwQ2KT9EsccKECa6ukiioORo5/PKdxzqdTcaDB4a3ee+99/Tvl19+WX799VfJnTu3tzeLKPBzbrp27aqDsd26dUtCQ0PN09HvzT///OPu7SMKaBw5nCy9/vrrOmzCp59+Khs2bGBgQxRTJTf4wmFANow4aylr1qxy4cKFqG4HUVDiyOHBDVX8KJ1Bfg36qkE1FFpHJUjA8caIYrTkJiwsTLP4bZ0/f16rp4jIeRw5PHghZ7FChQqaTzN69GjzdAY2RF4IbpBjM2rUKPNzJLndv39fBgwYIHXq1HHDJhEFD0+MHI6qrs0nbsiC3Rf0fzwn34FO4adOnSrFihWTf//9Vzsly5Ahg7c3iyi4q6XQHLFKlSpSoEABTSBGa6ljx47pwG0zZszwzFYSBfjI4UgetheCxPr/caicHTmcra58G8bi++CDD2TOnDn6HJ3zTZ8+Xav1ich9ojT8wqNHj2TmzJmyY8cOraYqUaKEvPPOO1YJxr6KY0uRr7aWEgcjhzvbWspRqytX10OegXzFZs2aaW5iSEiIfPXVVzqqd5w4cbjLiZzAsaXctHOIYkp0S1xQ9VRh6Cqr19srAdrQ+zWJE5v9pXgDEoXR23D27Nnlt99+01anROSZ67fL1VKDBw+WtGnTSps2baymT5kyRa5du8YuwomiAAFM9QLptPUUkoyRY4OqKGcDEba68t0fY+NHGCXcixYt0iRitIoiIh9KKP7xxx8lX7584aYXLFiQHfgRRQMCmXI5U0rDYhn1f1dKWNjqyregtn/s2LGSJUsW2bVrl3l6zZo1GdgQ+WJwc/nyZUmfPnwxeerUqa1GrSUi/251RVFz5coVqVevnnTu3FmLzydPnsxdSeTrwU3mzJm1Ez9bmMbmjETebXXlqKwH09O70OqKouavv/6SwoULy5IlSyR+/Pjafw3GhiKimOVyzs37778vH3/8sTx79kxee+01nYZhFz755BPp0aOHJ7aRiCKBKiwkH6O1VCwHra4wn8nEnvHw4UPp2bOnjB8/Xp8jwPn999+lUKFCPHeJ/CG4QRCDvho6duwoT58+Nfeo2bt3b+nbt68ntpGInExKRnNv21ZX6VxodUVRgyEUjMCmW7duMmjQIPY0TORv/dwAeiU+dOiQ9m2DUWtRBOsP2BScAh2ahUe11RVFDfr7wvhQrVq10l7cicj92M+Nm3YOEZE9586dky+//FLzaRImTMidROTv/dw8ePBAhgwZonk2V69e1TsWSydPnnR9i4mI/MQff/wh7du3l9u3b2tgw4RhogBJKF67dq20bNlSm4Rj4EwiomC4a+zSpYuOBQWlS5fW50QUAMHN0qVLZfHixfLKK694ZouIiHzMpk2bpEWLFnLq1CmJHTu2Np4YMGCAxI0b19ubRkTuCG6SJ08uKVKwrwwiCg4YBwrJwqiCx+jdaBmFIRSIKIA68cNItp9//rn260BEFOiqVKkiyZIl05KbPXv2MLAhCsSm4MWLF5cTJ07o2CnZsmULVyyLkW99GVtLEVFE8Nu2efNmKV++vHnahQsXJGPGjNxxRIHaWqpRo0bR2TYiIp+FDkrREmr27Nkyb9488+8dAxsi/+JycIMkOiKiQLNq1SrNrUEpTUhIiJw/f97bm0REMZVzQ0QUSJ48eSK9evWSatWqaWCTJ08erZbCqN5EFCQlNy9evJCRI0dqR1Znz541jy9lWaxLROQPMITM22+/Lbt379bnH3zwgXz33XeSKFEib28aEcVkyQ26HMeX/80339Sknu7du0uTJk2074cvvvgiOttCRBSjjh07poFNqlSpZP78+fLjjz8ysCEKxtZSOXPm1O7G69atK0mSJNEfBmPali1b5PfffxdfxtZSRMEN/dXgZswwbtw4vUFLly6dV7eLiNx3/Xa55Oby5ctSuHBh/Ttx4sT6JlCvXj3tuZiIyFfhN6pQoUKaW2Po2LEjAxuiAONycJMpUya5dOmS/p0rVy5Zvny5/r1t2zaJHz+++7eQiCia0Olop06d9CYMeTaDBg3iPiUKYC4HN40bN9YRwaFr167y2WefSe7cubUJZZs2bTyxjUREUbZr1y4pWbKkVj9Bt27d5Ntvv+UeJQpgLufc2EKeDQaVQylOgwYNxNcx54bseRFmkq2nbsrVe48lTZIE8nL2FBInNke89/fcmhEjRsinn34qz549k/Tp08u0adOkevXq3t40IvLw9TvawY2/YXBDtpbtvyRfLjool+48Nk9LnzSBDKhfQGoVSs8d5qcmTZqkTbuNEueJEydqqygi8k9uD24WLlwotWvX1nGk8HdEfL30hsEN2QY2H/66U2y/BEaZzfgWJXwmwGHpUuTwcxYrVixznk2pUqW0u4q2bduapxORf3J7cINmk2gllSZNGqsmlOFWFiuWdvLnyxjckGWwUGHoKqsSG0u4FKZLmkA29H7N61VULF2K2KNHj7QKCg0c1qxZI3HixNHp+D0y/iYi/+b2puCou0ZgY/zt6OHrgQ2RJeTYOApsAFE/5mM5Xyhdst3Wy3ce63TMD1b4zZk6dao2avj8889lw4YNVqXLDGyIgpNLraWQlFelShU5evSo57aIKIYgedidy3mqdAn5QPaKV41pmI/lggkKnJcsWSLFihXTVprotyZLliwyY8YM80jeRBS8XBpbCjk3+/fvZ901BQS0inLnct4uXSqXM6UEg9u3b2uPwqtXr9bnyZIl0xZR6McmQQLvHSsi8uN+btCfzeTJkz2zNUQxCM290SrKUTYNpmM+lvMWfyhdimmoc3/8+LF2GtqzZ085ceKE9OjRg4ENEUV9VHCMAv7TTz/JihUrtCWC7ei5GFSTyB8gSRjNvZG3gkDGsmLHCHgw35vJxP5QuuRp169f12Thvn37amCDhgto5o3hX7JmzertzSOiQAhuUC1VokQJ/ds294ZNLcnfoJk3mnvb9nOTzqKfG282wTZKl5A8bIqgRZc3S5c8BU25R48eLUOGDNFWEkgO/uabb3RewYIFI3wtm80TBTeXgxujnpsoUCCAqV4gnd0AxttNsP2hdMkTLaDQkzBaPxkDXBYtWlQbMzjD28eMiLyPPRQT+UEHf8FywV66dKl88sknWkIMaAGF0pq33347wj62fPGYEZH3+rlxueTGGAH8zz//lLNnz2oOjqW5c+dGZZVEPsWZJtj95+2X1/KllXghLuflu7V0KZDMmjVLA5uotICK7JhhT2E+9mOg7Tcisubyr/LMmTPllVdekYMHD8q8efO07xv8vWrVKo2oiAJBZE2w4caDp1J28MoY60QPF2Q0925YLKP+HwgX6JMnT8qZM2fMz7/66ivp1atXlFpA+UunjETkg8HNoEGDZOTIkfLXX39JvHjxNOHv0KFD8uabb2oRsqvGjRsn2bNn1x+xkiVLyvr16x0ui27VkbRs+zh8+LDL70vkjqbVNx88C/pegqPi6tWr8vHHH0u+fPk0iDFkzpxZhg0bJilSuJ4gzWbzRBTl4AZ3VHXr1tW/0c/EgwcPNMDo1q2bjrrrahE0fuD69+8vu3btkooVK+oAnajuisiRI0fk0qVL5ge6XidyJ1ebVgdjL8FRce3aNc2pwQ0NboxQ8nvv3j158uRJtNfNZvNEFOXgBndU+DGCjBkzmhP/0Gsomm66An3iYLTe999/X/Lnzy+jRo3SO7fx48dH+DqMc5UuXTrzg+PHUEx38GeJ1R3O9VXTp08fDWqGDx9uHrH777//1gdulIKhU0Yi8tHgBqUr6MAPUBXVtWtXadeunTRv3lyqVq3q9HqQiLxjxw6pUaOG1XQ837RpU4SvLV68uKRPn17fj03TyZNNsF0RTL0Eu2r69OkydOhQLelF9fOiRYtk69at4b7/7jpmtgFOoDabJ6JoBje7d+/W/3/44Qdp1qyZ/o0eQ9H9+ZUrV3SsF1eGZcCdHPqzSJs2rdV0PL98+bLd1yCgQdXXnDlztFVW3rx5NcBZt26dw/dBcTeaj1k+iFzp4C9FongS7L0Eu+rGjRuyb98+8/P27dtLzZo1dcRutLasV6+eRzr9NI4ZOja0hOdsBk4UPJzu5wZ9TKDEBFVI6HMiui2jLl68qNVaKKUpV66ceTr6tPjll1+cThKuX7++/kjiR9OeL774Qr788stw051pJ08ET5+HaasoJA9LBL0Eb+j9WtCXCiCoQXXzmDFjtAoKN0XO9E/jbuyhmCi4+7lx+ldn48aNOuwC6s1RgtKiRYtoVQmlSpVKc2VsS2nQisK2NCciZcuWlWPHjjmcj9Il7Ajjce7cuShvMwUn9GMzqHFhDWJY3WHfzZs3tV8aBDRoUXn//n39fqNU1xsCsdk8ETnP6eAGpSsYrA7BCBJ+z58/L9WqVZOcOXNqaQueuwLNyFH3buTvGPC8fPnyTq8HrawQbDmCREVEeJYPIlexusNxUPPZZ59JtmzZ9HcAjQ0wVAL6wNq5c2eE300iIp8cfgHNwqdOnarJgmiSXb16dVmyZIlLTcFbtmwpEyZM0OAJ+TQIoA4cOKCj/aLUBWPLYP2A1lT4EcWgeUhI/vXXX3VQPeTgIOfH3cVaRLZY3WEN/V2hahiKFCmi1cANGzaM0aooHhOi4HDX08MvGFBqg2oqNN/u16+fNul0xVtvvaV19AMHDtTgqFChQhocIbABTLPs8wYBDRKYEfCEhoZqkLN48WKpU6dOdD4GkcvVHcEKXT7s3btXKlWqpM/R59W7776rAU2jRo1iPL8mWMbcIqIYKrlZu3atTJkyRUtNULeOZuHoswY5ML6MJTf+KSbvzlkSYD+oQckpHghgTp8+7fWSTw6SSRRc7nqq5AbJuD///LM+Tp06pbkx33//vQY2iRIliu52E3n97pwlAdZQsoruHzDkCn5QACWsyLErUMC1foDciYNkElFEnC5DRj4NWkJgLKjXX39dx5PasGGDtG7dmoENefzu3HZAxMt3HluN6YSL3eYTN2TB7gv6f1SGQnD2vYIBqoQ/+ugjHS8OeTQIbFAN/Mcff8iePXu8GtgAB8kkIreU3CDHBVVQ6HyLwx2QL92dh4WZ5KvFh6JVssOSAGsYHmHs2LESFham/Vshtw43Nd7os8YeDpJJRG4Jbhx1kkfk7bvzjr/vCjfPKG1xtldaV0oCAi2hGGl3K1eu1J6D0TDAaCzw9ddfS+nSpbUXcE/0JhwdHCSTiDzWWorIk6IzVpNlyU71AukiTT4OxpKA58+fy59//inDhg3TnoQRwCB/LleuXDofXTH4KmOQTASxpgh6jeYgmUTByTfKmIk8MFaTK6N1B1NJAAavxPAIuXPn1qFUENgkTJhQc2z8pWEAB8kkooiw5Ib89u7cWc6UtgRLScDmzZs1bw49C0Pq1Kk1qPnwww8lZcqUftlrtG1LOhwn9nNDFNwY3JDP350jdwbBhWXQYfs8uqUtkb0XYL4/jlGEzi8x3AkULlxYc2xQ9dSjRw9p1aqVNhbwVwhwUO0YU30gEZF/YLUU+e2YTuPeLqGlLY4uY5ie3oXSlkAbPwoJwsihwdAmRl+diRMn1i4cDh8+LB06dPDrwMbAQTKJyK1jS/kj9lDsnxz1Gmz0TSMOSluiEpRE1EOxr/de/OLFC1m0aJH2JIxexC0DnVKlSnl124iIYur6zeCG/F5EvQq7s8rCl3svxvAIkydP1t6EMTQChISEaMIwxmNDdRQRkT9jcOOmnUP+w16JyoqDl90WjPj6OEbLli2T2rVr699IDP7ggw+kY8eOkilTJq9tExGROzG4cdPOIf/lzmAEgVOFoascdvJntKTa0Pu1GKmiQq/BS5YskevXr8t7771nnvbGG29InTp1tLQmEHJpiIhiZOBMIn/g7qEUfKX34lu3bumgtRgW4cSJE5IiRQp56623NJDBsAgYHoWIiBjcUABydzDi7d6Ld+zYoQPWzpgxQx49eqTTkiVLJm3atJEnT56wlIaIyAZLbijguDsY8WbvxSNGjJBevXqZnxcpUkRzaVq0aOE3vQkTEcU09nNDAceVYARVWJtP3JAFuy/o/3juqPdid/WnE5Fjx47J0aNHzc/r16+vHfAhjwb902CohPbt2zOwISKKAEtuKOAYwUhEVVOYf+vB03CJwvZaU3m69+LHjx/LvHnzZOLEibJmzRrNo5k5c6bOy5s3r1y5ckWroYiIyDksuaGAgyCjQdGIW0IVyviSdPp9Z7gACGNLIYhBaytP91586NAh6d69u2TMmFFLZhDYYGRuBDto/WRgYENE5BqW3JDPQdXQlpM3tJoI5STlcqSSsv+f+OtMh3x4/cI91sGJrX8OXXW5NZU7xzFq3bq1tnwyoD+atm3bapJwlixZXF4fERH9D4Mb8ikoMekzd5/cfvjMPO2H1SckYbw4Ei8kttV0Rx3yRdZaCuyk1jjVmsoYx8gVGOEELZ7y589vzpUpVKiQxIkTR+rWrasd7tWqVUufExFR9LFainwqsOnw606rAMbw8OmLcNMdVSG5q0l2dNdz6dIlGT58uAYypUuXtuqH5v3335czZ87IggULNMBhYENE5D4suSGfgKqkLxYedOk1jqqQ3NUkOyrrQb7MwoULtcrp77//NufOJEiQQC5cuGBeDr1s4kFERO7H4IZ8AqqALt91vaTEXhWS0VoKJTv2ap8QAsWK5bhqyhhOwdWm3egaPHv27HLz5k3ztPLly+sQCW+++SaDGSKiGMJqKfIJUQlsHFUhGU23wTbV13jermL2/4IcB/OdadqNkpg//vjD/BxjnRQtWlQyZ84s/fv3lyNHjsjGjRulXbt2DGyIiGIQS24oxkbpjihYuHn/iVurkIym27ajgqezSEIuniV5hPPtefDggcyfP19++eUXWbFihTbdrly5sqRNm1bnY4iE1KlT61hP5L7zg4jIFQxuyO2Q4GsbNDhq2WRIkShelN4roiqkyJpuO9u0+9mzZ7Jy5Ur57bfftLO9hw8fmudVrFhRrl69ag5ujP/JvecHEZErYpnQTjWIuDJkOkXtwoUWTLYnlREuOOrwDn3aNJ+0xaX3imyd7jJlyhTtg8aQI0cOHdvp3XfflZw5c3rsfQNRVM8PIqK7Lly/WXZObq1qwB25o87xAPMjGr8pIkgCdlfvwBH1R4OBKn/99Vfz9EaNGmkne507d5bNmzfL8ePH5csvv2RgE4PnB5G98ymyceEoeLFaitwmss7zIusczxi/ydFP1NjmxSV5ovhuzdNAQLNnzx75888/NTkYgQuULVtWS2cgRYoUcvbsWc2vIe+cH0SWWLVJkWFwQ27jbKd3jpZzlATsqXwMlL6ghMYIaCA0NFRH4m7WrJnVsgxsvH9+EEVUtWl06smqTQIGN+Q2znZ6F9Fy7hy/ybaEZu/evdpU27Bz504NbNDBHoY/wGjc9erVk8SJE0frvchz5wcFt8iqNh2NC0fBh8ENuY0znec50zleVMZvsufFixeyYcMGbeE0d+5cOXfunJw4cUITgqFHjx7yzjvvSJ06dRjQ+NH5QcGLVZvkLAY35DaWeTO4UJmi2DledDx69EibbSOgWbRokVy/ft08DyUy+/btMwc3lSpV8th2kG+eH+TfWLVJzmJrKXIrI28Gd+C2/di0fiWbJA2N5/ZWDZa9GSxdulQaNGggU6dO1cAGycCtWrXSYAf90TRs2NCt703uOT/c3fKNAhOrNslZ7OeGPNoD7cqDl2Xe7gty88EzlxKEbXuwLZk1uew4c8v8PNmz67L4r0U6SGXVqlVlwIAB5h6ES5QoITVr1pTGjRtrJ3shISyg9DXsoZiiet5UGLoq0qrNDb1fYwlgkPdzw+CGfK7DNnvNPGOFPZOHZw/IoxPb9PH81kXzPCQJ796922Ofg4h873dFHFRtsgQwcLkS3PCWlnymVQNe88OqYzJy5THr5U0muTCxgzy/c+V/E2OHSIIshaV186bSu/1//dEQUeBzZtw4IgY35BOtGnA39vncPXL28C55dHKnPL1yQtK8OVD7l8Ejfsb8EvbsiYTmKCWhuUpLaLbiEid+QtmVJIFkypyFR5EoiHiqywgKHAxuyGutGlAis2v/QZn+01qZ9udCeXx2n5ie/e91z66fkXips+nfKap3kFjxE0qsWP/LgWePtkTBy11dRlBgYnBDXmvVcGfTTOk07DerabETJpPQ7MUlQfYSEvJS6v9NT+C4Yz32aEtERJYY3JBHO2y7eO22PD5/QB6f3SuPz+yRZJVaafAC8TPkFYkTIgkyFdBgJjRbCYmbJptV6Ywz2KMtERFZYnBDbvXkyRMdOXv16tVy/6+/5ezuHSJhz83zH5/eZQ5uEmQpIpk/mimx40Wtu332aEtERPYwuKFo9wh8+/ZtSZ/+vxYKGKupSpUqVsvEeSm1JMhSVBJkKyqhWYuZp8eKE6KPqGCPtkRE5AiDG3IJ+hfYtGmTrFu3TtavXy/btm2TJk2ayIwZM3R+gQIFtN8Z/I8g50Xa/DJ44+1oj6qNRhCWHRuz2ScRETnC4IacatXUrVs3WbNmjY7NFBYWZjX/2LH/9UuDIMayQ73NJ25IrE1bnNrLtuMNGbpVyyMfvprT3ENxqsTxdcHrD57o+tkElIiILDG4IbOnT59qYIKcmUuXLsmQIUPMAcvGjRtlz549+jxnzpw66CSGNsD/xkCU9tx68DRcqYu9vJnP6uaXrxYfsuobJ1loXB2PqvNruczNPtEfTs8/91gt58xwDkREFDw4/EIQO3PmjAYyW7dulS1btsjOnTs1IRgwHhOqoBImTKjP58yZK4cv3pYM+YpJvhxZnSotcTT8gqVYFt2l/9dD8XGZuvGU3H4UfiwqiMpwDkRE5P84/AKFgxGxkR9Tu3ZtiR37v6bWffr0kZkzZ1otlzJlSilbtqyUL19enj17Zg5SRhxNKpfuxBe5cEnkn0uRlpZENPyCAbHRD82Lm9ex4uBlGbXyaLjXYJA8BDVJE8Z1aTgHij4OcElE/ojVUgEayKAUZseOHfr/9u3b5ezZszrv8OHDkjdvXv27QoUKcvLkSSldurQGNGXKlJFcuXJZJf86Kn0xAg5HpSWRDb8AqKpKnii+U2NRwe2H/yvNiWw4B4o+ewOYsgqQiPwBgxs/T/Q9deqUpEmTRhIn/q8H32HDhknv3r3DLYuAJV++fHL9+nVzcNOpUyd9uHPwS1d7DTaWcyYYcmV9FD1RDWqJiHwBgxs/ce/ePTl48KDs3btXE3vxwN+og1y0aJHUq1dPl8uTJ48GMvi/RIkSUrJkSX3gb3tDxEdU7eDq4JdR6TXYWM5dQQl7K46+6AS1RES+gMGNj3n48KEcOnRI0qVLJxkzZtRp8+fPl8aNG9tdPl68eHLx4kXz81q1amkicJIkSaJU7ZDupfjS/OUski1VIjl25Z5T22wvMDGGX8CdvsmJ3oWjG5Swt2L3iU5QS0TkCxjceMnjx4+1zxjkwCCYOXDggD6QA4Pqpm+//Va6d++uyxpNrRHwFC5cWIoUKSLFihXTB6qY4saNa15vggQJ9BHlaoe7T2Tkyv/1W+OMo5fvyeT1JyVFoniSLmmoufQHCcd4D9v+a+z1LuxMMJQsYVy59fCZU+ujqHO1SpGIyNcwuPGg58+fy+nTp7WTuyNHjmjPvcbQBAhkXn75ZbuvS5UqlbmlEqC3X+TKoCWTOzjTkskVY9ecsHr+v/5pcmtuRrjSoaQJpFnpLPLkeZhVJ3yRBUODmxTW/+2tj/3cuI+rVYpERL6G/dy40bVr1+Srr77S8ZUQ0CCwQYBj6Nixo4wdO1b/vn//vuTOnVuTfPEoWLCg+YEEYXexl1OD580nOddrcHSgpGVIk8Kam2Fsw+nrD2TG1rNaQmSvBY4zLXTYPNmzsH8rDF0VaZXiht6vsaSMiHyynxsGN25069YtSZHivxwSQ2hoqPboi+qjunXrSuvWra3mu+NC7Wgdf+2+IH3n75N7j19YBQq1C6WTKRtPS0yw7KTPUVWYbSd8DF68zzhW4qAUja2liCimMbhx086Jis8//1wyZcqkpTI5cuaS808SyPUHT+0GLs72I4KL/aZj12XOrvPy8OkLKZ0thbQqn03ihcSWhTvPS+95++TRszCraqEUieLKyesPxRfgM63tVUUqD1/tMFGVpQG+h/3cEJEvYXDjpp3jCqO04fKdR3LzwVM5f+uRLNhzQW4++F/uTLLQEKmaP60kih8iD588l9k7L4Rbj+2dMS4w3f/Yo0GNrVSJ48r1+447totIROM9eYIxdlRkZrQryxY4PoSlaETkK/xq+IVx48bJ8OHDdaBG5JuMGjVKB2R0ZO3atdqKCAm5GTJkkE8++UQ6dOggvnaHa8/tR89ljp2AxlE/ImFhJun4+y6Hy0Y1sIGYDGzgzE3nSpHYAse3GAOWEhH5k/8GGfKSWbNmyccffyz9+/eXXbt2aVCDsY+MoQJsoTfeOnXq6HJYvl+/fvLRRx/JnDlzxNu5Ce7oXde2H5F+8/ZKoMia4r8BOCPDFjhERBRdXk0oxlhG6Dl3/Pjx5mn58+eXRo0ayeDBg8Mtj2EFFi5cqP3CGFBqg956Mbp1TFdLGa1K3BnYBBojl8bIuWELHCIiigpXrt9eK7l5+vSpDuxYo0YNq+l4vmnTJruvQQBju3zNmjV1YEjLfmEsPXnyRHeI5cNd3DUekjeExvX8obfsXA/Jz/jfcrq95dgJHxERRZfXght0SvfixQtJmzat1XQ8v3z5st3XYLq95dGXDNZnD0qAEOkZj8yZM7vtM3gqPwQX+5SJ4oknDW1cWFsxeRJKbCybDON/PMf0iJYjIiKKDq8nFGOQR0uoJbOdFtny9qYb+vbtax7GAFBy464AxxP5Ican+KphIfly0X65cu+p29+jeoE00qBEJokXL47dfmfc0Xnf2OYlpGzOlOFKYhDAWHbqF9W+fYiIiHwuuMEQA3HixAlXSnP16tVwpTMGjK1kb/mQkBCHQxPEjx9fH55gjIfkzqopy6EEYscW6fD/Ham5A+KHthWyS/+6BaxKUpxp6eUMIzxBr8Sv5E7lcDm2wCEiooAMbjCadcmSJWXFihVWI17jecOGDe2+ply5crJo0SKracuXL5dSpUpZDR4ZUyzHQ4pO6Ue9IijNSBuuFAPBx4QWJeSjmbvl6fP/ddLnLARer+RMKQnjh2hrpZbl/uv4z1FJirmPntuPZMHui/q35boaFE0vC/dcchgIcYwnIiKSYG8thabgLVu2lAkTJmjgMnHiRJk0aZL2YZM1a1atUrpw4YJMnz7d3BS8UKFC0r59e2nXrp0mGKO11IwZM6Rp06Ze68Qvon5uUFvmaA8jhmlXMbv0rfNfSUpErbJGrzgqEzeclMcWPREj4OhbM59sO3tTTt94qAFM9fxp5fbjZ9Gu7nHUeZvl9FSJ42u79esPnrB6iYiIPMqveihGJ37Dhg3TTvwQuIwcOVIqVaqk89577z0dfHLNmjVWnfh169bN3Ikfmoe70olfTPVQnCJxfEn3UgIpmTW57DhzS4OBFAnjyeHL9+TcrYcOS1KceQ/mqhARUbC560/BTaCNLUVERERB2s8NERERkScwuCEiIqKAwuCGiIiIAgqDGyIiIgooDG6IiIgooDC4ISIiooDC4IaIiIgCCoMbIiIiCigMboiIiCigeG3gTG8xOmRGT4dERETkH4zrtjMDKwRdcHPv3j39P3PmzN7eFCIiIorCdRzDMEQk6MaWCgsLk4sXL0qSJEkkFobsdnNUiaDp3LlzHLfKB/H4+DYeH9/FY+PbguX4mEwmDWwwaHbs2BFn1QRdyQ12SKZMmTz6Hji5AvkE83c8Pr6Nx8d38dj4tmA4PkkjKbExMKGYiIiIAgqDGyIiIgooDG7cKH78+DJgwAD9n3wPj49v4/HxXTw2vo3HJ7ygSygmIiKiwMaSGyIiIgooDG6IiIgooDC4ISIiooDC4IaIiIgCCoMbG+vWrZP69etrD4jowXj+/PlW85F//cUXX+j80NBQefXVV+XAgQNWyzx58kS6dOkiqVKlkkSJEkmDBg3k/PnzVsvcunVLWrZsqR0S4YG/b9++7anjHBAGDx4spUuX1t6l06RJI40aNZIjR45YLcPj4z3jx4+XIkWKmDsSK1eunCxdupTHxke/S/h9+/jjj83T+N3xHlxTcDwsH+nSpeOxiQ60lqL/WbJkial///6mOXPmoBWZad68eVa7Z8iQIaYkSZLo/H379pneeustU/r06U137941L9OhQwdTxowZTStWrDDt3LnTVKVKFVPRokVNz58/Ny9Tq1YtU6FChUybNm3SB/6uV68eD0UEatasaZo6dapp//79pt27d5vq1q1rypIli+n+/fs8Pj5g4cKFpsWLF5uOHDmij379+pnixo2rxwv43fENW7duNWXLls1UpEgRU9euXc3TeXy8Z8CAAaaCBQuaLl26ZH5cvXqVxyYaGNxEtHNsgpuwsDBTunTp9EfA8PjxY1PSpElNEyZM0Oe3b9/WH/SZM2eal7lw4YIpduzYpmXLlunzgwcP6rq3bNliXmbz5s067fDhw9E5nkEFX37ss7Vr1+pzHh/fkzx5ctNPP/3EY+Mj7t27Z8qdO7feeFWuXNkc3PC74/3gBjfA9vDYRA2rpVxw6tQpuXz5stSoUcOq86TKlSvLpk2b9PmOHTvk2bNnVsugCqtQoULmZTZv3qxVUWXKlDEvU7ZsWZ1mLEORu3Pnjv6fIkUKHh8f8+LFC5k5c6Y8ePBAq6f43fENnTp1krp160q1atWspvP4eN+xY8f0WpE9e3Zp1qyZnDx5Uqfz2ERN0A2cGR0IbCBt2rRW0/H8zJkz5mXixYsnyZMnD7eM8Xr8j5wRW5hmLEMRQ8Fa9+7dpUKFCho48vj4hn379mkw8/jxY0mcOLHMmzdPChQoYA7a+d3xHgSbO3fulG3btoWbx98278KN7vTp0yVPnjxy5coV+frrr6V8+fKaz8ljEzUMbqIAyV62F1rbabZsl7G3vDProf907txZ9u7dKxs2bODx8SF58+aV3bt3a3L8nDlzpFWrVrJ27VrzfH53vOPcuXPStWtXWb58uSRIkMDhcjw+3lG7dm3z34ULF9YbhJw5c8q0adO0VJ/HxnWslnKBkb1uW7py9epV8x0plnn69Km2hopoGUTntq5duxbuzpbCQ0u0hQsXyurVqyVTpkw8Pj4EpZa5cuWSUqVKaYucokWLyujRo/nd8TJUl+M3qGTJkhISEqIPBJ1jxozRv43fHf62+Qa0skWQg6oqXneihsGNC1AXihNtxYoV5mkIZPAjgSJEwI9H3LhxrZa5dOmS7N+/37wMonLki2zdutW8zL///qvTjGVI7JZsocRm7ty5smrVKj0ePD6+f8zQNQK/O95VtWpVrTJEqZrxQAD6zjvv6N85cuTgb5sPwXfm0KFDkj59en53oiqKicgB3Zpg165d+sDu+e677/TvM2fO6Hy0lELrqLlz52pT8ObNm9ttCp4pUybTypUrtSn4a6+9ZrcpOJpiopUUHoULF2ZT8Eh8+OGHuu/XrFlj1WTy4cOH5mV4fLynb9++pnXr1plOnTpl2rt3rzYFRyvB5cuX89j4IMvWUsDvjvf06NFDf9dOnjyprWjRLQi6HDl9+jSPTRQxuLGxevVqDWpsH61atTI3y0OzPTQJjx8/vqlSpUoa5Fh69OiRqXPnzqYUKVKYQkND9UQ9e/as1TI3btwwvfPOO3oC44G/b926FdXjGBTsHRc80PeNgcfHe9q0aWPKmjWrKV68eKbUqVObqlatag5seGx8P7jhd8d7jP7S0I1IhgwZTE2aNDEdOHCAxyYaYuGfKBf7EBEREfkY5twQERFRQGFwQ0RERAGFwQ0REREFFAY3REREFFAY3BAREVFAYXBDREREAYXBDREREQUUBjdE5Dcw9lH16tV17J1kyZKJP8qWLZuMGjXK25tBFNAY3BAFuPfee09He+7QoUO4eR07dtR5WMYfjBw5Usdqw3hIR48etbvMgwcPpHfv3jpeEkbATp06tbz66qvy119/iS/Ytm2bfPDBB+bn2P/z58/36jYRBZoQb28AEXle5syZZebMmRochIaG6rTHjx/LjBkzJEuWLH5zCE6cOKGD0+bOndvhMgjiMCjtDz/8IAUKFJAbN27Ipk2b9H9vwiC7GDUdwRYReRZLboiCQIkSJTSIwYjqBvyNoKd48eJWy2JElmHDhmnJBwKhokWLyuzZs83zX7x4IW3bttXRijE/b968Mnr0aKt1oCSoUaNGMmLECB3ZOGXKlNKpUyd59uxZhNs5fvx4yZkzpwYBWO8vv/xiVZ0zZ84cmT59eoSlTYsWLZJ+/fpJnTp19DUIhrp06SKtWrWyCjQ++eQTyZgxo1ZxlSlTRtasWaPz7ty5o59r2bJlVuvF/sKy9+/f1+cXLlyQt956S5InT66fr2HDhnL69Olw+2Dw4MGSIUMGyZMnT7hqKfwNjRs31s+E51hH7NixZfv27Vbv//3330vWrFn1+BBRxBjcEAWJ1q1by9SpU83Pp0yZIm3atAm33KeffqrLIdA4cOCAdOvWTVq0aCFr167V+WFhYZIpUyb5448/5ODBg/L5559rMIHnllavXq0lLfh/2rRp8vPPP+vDkXnz5knXrl2lR48esn//fmnfvr1uM15vVOfUqlVL3nzzTa2asg2oDOnSpZMlS5bIvXv3ItwXGzdu1NKsvXv3yhtvvKHrPnbsmCRNmlTq1q0rv/32m9Vrfv/9dw1gEidOLA8fPpQqVaro3+vWrZMNGzbo31gHAifDP//8I4cOHZIVK1bYrRbDZwLsb3wmPEeAU61aNatjZSxjVDESUSSiM+omEfk+jGjfsGFD07Vr13Qk+1OnTplOnz5tSpAggU7DPGPU+/v37+v0TZs2Wa2jbdu2pubNmzt8j44dO5qaNm1q9Z4YIfz58+fmaW+88YaOfuxI+fLlTe3atbOahtfUqVPH/NxyWx1Zu3atKVOmTDrCcqlSpUwff/yxacOGDeb5x48fN8WKFct04cIFq9dhFPO+ffvq33PnzjUlTpzY9ODBA31+584d3S+LFy/W55MnTzblzZtXR9I2PHnyxBQaGmr6+++/zfsgbdq0Ot0S9svIkSPNz/EzPG/ePKtlZs2aZUqePLnp8ePH+nz37t26zTh2RBQ5ltwQBYlUqVJpiQRKUVAKgL8xzRJKYpCLgxZJKIkwHqgKQimMYcKECVKqVCnNH8H8SZMmydmzZ63WVbBgQYkTJ475Oaqnrl696nD7UMLxyiuvWE3Dc0x3RaVKleTkyZNaatK0aVMtfapYsaJ89dVXOn/nzp1atYNqIsvPiJIp4zNi34SEhMjChQv1OarDkiRJIjVq1NDnO3bskOPHj+s04/UpUqTQfWe5nwoXLqxVbK5CdRbeH6VZRikbSoqMaiwiihgTiomCCKqhOnfurH+PHTs23HxUOcHixYs1H8VS/Pjx9X9UP6Gq6ttvv5Vy5crpBX748OHy77//Wi0fN25cq+eoTjHW74htlQuCkKhUw+C9EdDg0adPH/n6669l4MCB2ooK24CgCwGKZfAFCFIAAcnrr7+uVVHNmjXT/5Ffg4ADsA7k8thWXYFlwjBydKIC79+yZUsNQps0aaLvz+bjRM5jcEMURCxzQmrWrBluPloXIYhBKUzlypXtrmP9+vVSvnx5bUZusCytiKr8+fNr7sq7775rnoZWTpgeXfhcz58/15IVJFAjKRqlSAh+HHnnnXe0pAYlP8j7MUp+jATtWbNmSZo0aeSll16K1rYhEMP22Hr//felUKFCMm7cOE3ERpBDRM5hcEMURFBSYVTz2JZaAEphevbsqSUzKJ2oUKGC3L17V4MMlGqgxVGuXLm0murvv//WFlNo0YREWPwdHb169dJkYQQOVatW1VZPaKG0cuVKl9aDPm2aN2+u1WZoxYSqNiQ8o1oHgQgeCFwQRKH0CcHO9evXZdWqVVqNhFZWgOAubdq0uiyqg8qWLWt+D0xDaRUSjFEihARrBITYXnwOPHcW1o0qNFTBIbBE6ytAUIf3RGkTStyMJvxEFDnm3BAFGeMC7whKKNACCk2YcYFFCQ8CDSN4QT8yKEVANQ2aUKP/GMtSnKhCnglaQCFoQL7Ojz/+qNUyCFZcge1FXhFKXbD9aAaOaZatubBeBDdomYUm5w0aNNBqNTSNN6A6DEHSnj17NJixlDBhQm0lheb12Bd4HwQgjx49crkkBwEWWlPZa5aPJvcoabPXqo2IHIuFrOII5hMRkZd888032lx93759PAZELmDJDRGRj0FHgajqQ8d9H330kbc3h8jvMLghIvIxaNGGfCfk/bBKish1rJYiIiKigMKSGyIiIgooDG6IiIgooDC4ISIiooDC4IaIiIgCCoMbIiIiCigMboiIiCigMLghIiKigMLghoiIiAIKgxsiIiKSQPJ/XZlO53hKQsEAAAAASUVORK5CYII=",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -750,8 +806,8 @@
     "    x_cnb = x['ClaimNb']\n",
     "    n = x_sev.shape[0]\n",
     "    names = {\n",
-    "        'Sev_mean': np.average(x_sev, sample_weight=x_cnb),\n",
-    "        'Sev_var': 1/(n-1) * np.sum((x_cnb/np.sum(x_cnb)) * (x_sev-np.average(x_sev, sample_weight=x_cnb))**2),\n",
+    "        'Sev_mean': np.average(x_sev, weights=x_cnb),\n",
+    "        'Sev_var': 1/(n-1) * np.sum((x_cnb/np.sum(x_cnb)) * (x_sev-np.average(x_sev, weights=x_cnb))**2),\n",
     "        'ClaimNb_sum': x_cnb.sum()\n",
     "    }\n",
     "    return pd.Series(names, index=['Sev_mean', 'Sev_var', 'ClaimNb_sum'])\n",
@@ -782,7 +838,7 @@
     "    plt.xlabel('Mean of Severity ')\n",
     "    plt.ylabel('Variance of Severity * ClaimNb')\n",
     "    plt.legend()\n",
-    "    plt.title('Man-Variance of Claim Severity by {}'.format(col))\n",
+    "    plt.title('Mean-Variance of Claim Severity by {}'.format(col))\n",
     "    plt.show()"
    ]
   },
@@ -814,7 +870,14 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:20.820092Z",
+     "iopub.status.busy": "2026-03-16T16:10:20.819990Z",
+     "iopub.status.idle": "2026-03-16T16:10:20.836528Z",
+     "shell.execute_reply": "2026-03-16T16:10:20.836074Z"
+    }
+   },
    "outputs": [],
    "source": [
     "idx = df['ClaimAmountCut'].values > 0\n",
@@ -852,6 +915,12 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:20.837627Z",
+     "iopub.status.busy": "2026-03-16T16:10:20.837545Z",
+     "iopub.status.idle": "2026-03-16T16:10:22.077403Z",
+     "shell.execute_reply": "2026-03-16T16:10:22.076917Z"
+    },
     "scrolled": true
    },
    "outputs": [
@@ -877,24 +946,24 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>intercept</th>\n",
-       "      <th>VehBrand__B1</th>\n",
-       "      <th>VehBrand__B10</th>\n",
-       "      <th>VehBrand__B11</th>\n",
-       "      <th>VehBrand__B12</th>\n",
-       "      <th>VehBrand__B13</th>\n",
-       "      <th>VehBrand__B14</th>\n",
-       "      <th>VehBrand__B2</th>\n",
-       "      <th>VehBrand__B3</th>\n",
-       "      <th>VehBrand__B4</th>\n",
+       "      <th>VehBrand[B1]</th>\n",
+       "      <th>VehBrand[B10]</th>\n",
+       "      <th>VehBrand[B11]</th>\n",
+       "      <th>VehBrand[B12]</th>\n",
+       "      <th>VehBrand[B13]</th>\n",
+       "      <th>VehBrand[B14]</th>\n",
+       "      <th>VehBrand[B2]</th>\n",
+       "      <th>VehBrand[B3]</th>\n",
+       "      <th>VehBrand[B4]</th>\n",
        "      <th>...</th>\n",
-       "      <th>VehAge__1</th>\n",
-       "      <th>VehAge__2</th>\n",
-       "      <th>VehPower__4</th>\n",
-       "      <th>VehPower__5</th>\n",
-       "      <th>VehPower__6</th>\n",
-       "      <th>VehPower__7</th>\n",
-       "      <th>VehPower__8</th>\n",
-       "      <th>VehPower__9</th>\n",
+       "      <th>VehAge[1]</th>\n",
+       "      <th>VehAge[2]</th>\n",
+       "      <th>VehPower[4]</th>\n",
+       "      <th>VehPower[5]</th>\n",
+       "      <th>VehPower[6]</th>\n",
+       "      <th>VehPower[7]</th>\n",
+       "      <th>VehPower[8]</th>\n",
+       "      <th>VehPower[9]</th>\n",
        "      <th>BonusMalus</th>\n",
        "      <th>Density</th>\n",
        "    </tr>\n",
@@ -930,19 +999,19 @@
        "</div>"
       ],
       "text/plain": [
-       "             intercept  VehBrand__B1  VehBrand__B10  VehBrand__B11  \\\n",
+       "             intercept  VehBrand[B1]  VehBrand[B10]  VehBrand[B11]  \\\n",
        "coefficient     7.3389     -0.034591       0.040528        0.13116   \n",
        "\n",
-       "             VehBrand__B12  VehBrand__B13  VehBrand__B14  VehBrand__B2  \\\n",
+       "             VehBrand[B12]  VehBrand[B13]  VehBrand[B14]  VehBrand[B2]  \\\n",
        "coefficient       0.035838       0.100753      -0.073995     -0.033196   \n",
        "\n",
-       "             VehBrand__B3  VehBrand__B4  ...  VehAge__1  VehAge__2  \\\n",
+       "             VehBrand[B3]  VehBrand[B4]  ...  VehAge[1]  VehAge[2]  \\\n",
        "coefficient           0.0      0.049078  ...        0.0  -0.024827   \n",
        "\n",
-       "             VehPower__4  VehPower__5  VehPower__6  VehPower__7  VehPower__8  \\\n",
+       "             VehPower[4]  VehPower[5]  VehPower[6]  VehPower[7]  VehPower[8]  \\\n",
        "coefficient    -0.009537    -0.089972     0.071376     0.009361    -0.042491   \n",
        "\n",
-       "             VehPower__9  BonusMalus   Density  \n",
+       "             VehPower[9]  BonusMalus   Density  \n",
        "coefficient     0.051636    0.002365 -0.000001  \n",
        "\n",
        "[1 rows x 60 columns]"
@@ -972,20 +1041,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:22.078568Z",
+     "iopub.status.busy": "2026-03-16T16:10:22.078478Z",
+     "iopub.status.idle": "2026-03-16T16:10:22.111590Z",
+     "shell.execute_reply": "2026-03-16T16:10:22.111098Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "training loss (deviance) s_glm1:     1.29010461534461\n",
-      "training mean absolute error s_glm1: 1566.1785138646032\n",
+      "training mean absolute error s_glm1: 1566.1785138646026\n",
       "\n",
       "testing loss s_glm1 (deviance):      1.2975718597070154\n",
-      "testing mean absolute error s_glm1:  1504.4458958597086\n",
+      "testing mean absolute error s_glm1:  1504.445895859708\n",
       "\n",
-      "testing loss Mean (deviance):        1.3115309309577132\n",
+      "testing loss Mean (deviance):        1.3115309309577148\n",
       "testing mean absolute error Mean:    1689.205530922944\n"
      ]
     }
@@ -1012,11 +1088,11 @@
     "\n",
     "print('\\ntesting loss Mean (deviance):        {}'.format(\n",
     "    GammaDist.deviance(\n",
-    "        y_test_g, np.average(z_train_g, sample_weight=w_train_g)*np.ones_like(z_test_g), sample_weight=w_test_g\n",
+    "        y_test_g, np.average(z_train_g, weights=w_train_g)*np.ones_like(z_test_g), sample_weight=w_test_g\n",
     "    )/np.sum(w_test_g)\n",
     "))\n",
     "print('testing mean absolute error Mean:    {}'.format(\n",
-    "    mean_absolute_error(y_test_g, np.average(z_train_g, sample_weight=w_train_g)*np.ones_like(z_test_g))\n",
+    "    mean_absolute_error(y_test_g, np.average(z_train_g, weights=w_train_g)*np.ones_like(z_test_g))\n",
     "))"
    ]
   },
@@ -1039,14 +1115,21 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:22.112853Z",
+     "iopub.status.busy": "2026-03-16T16:10:22.112728Z",
+     "iopub.status.idle": "2026-03-16T16:10:22.194578Z",
+     "shell.execute_reply": "2026-03-16T16:10:22.194264Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total claim amount on train set, observed = 44594644.68, predicted = 44549152.42247057\n",
-      "Total claim amount on test set, observed = 4707551.37, predicted = 4946960.354743531\n"
+      "Total claim amount on train set, observed = 44594644.68, predicted = 44549152.422470555\n",
+      "Total claim amount on test set, observed = 4707551.370000001, predicted = 4946960.354743528\n"
      ]
     }
    ],
@@ -1076,7 +1159,14 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:22.195908Z",
+     "iopub.status.busy": "2026-03-16T16:10:22.195831Z",
+     "iopub.status.idle": "2026-03-16T16:10:22.388781Z",
+     "shell.execute_reply": "2026-03-16T16:10:22.388215Z"
+    }
+   },
    "outputs": [],
    "source": [
     "weight = df['Exposure'].values\n",
@@ -1101,7 +1191,14 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:22.390127Z",
+     "iopub.status.busy": "2026-03-16T16:10:22.390034Z",
+     "iopub.status.idle": "2026-03-16T16:10:29.998385Z",
+     "shell.execute_reply": "2026-03-16T16:10:29.997938Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1125,24 +1222,24 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>intercept</th>\n",
-       "      <th>VehBrand__B1</th>\n",
-       "      <th>VehBrand__B10</th>\n",
-       "      <th>VehBrand__B11</th>\n",
-       "      <th>VehBrand__B12</th>\n",
-       "      <th>VehBrand__B13</th>\n",
-       "      <th>VehBrand__B14</th>\n",
-       "      <th>VehBrand__B2</th>\n",
-       "      <th>VehBrand__B3</th>\n",
-       "      <th>VehBrand__B4</th>\n",
+       "      <th>VehBrand[B1]</th>\n",
+       "      <th>VehBrand[B10]</th>\n",
+       "      <th>VehBrand[B11]</th>\n",
+       "      <th>VehBrand[B12]</th>\n",
+       "      <th>VehBrand[B13]</th>\n",
+       "      <th>VehBrand[B14]</th>\n",
+       "      <th>VehBrand[B2]</th>\n",
+       "      <th>VehBrand[B3]</th>\n",
+       "      <th>VehBrand[B4]</th>\n",
        "      <th>...</th>\n",
-       "      <th>VehAge__1</th>\n",
-       "      <th>VehAge__2</th>\n",
-       "      <th>VehPower__4</th>\n",
-       "      <th>VehPower__5</th>\n",
-       "      <th>VehPower__6</th>\n",
-       "      <th>VehPower__7</th>\n",
-       "      <th>VehPower__8</th>\n",
-       "      <th>VehPower__9</th>\n",
+       "      <th>VehAge[1]</th>\n",
+       "      <th>VehAge[2]</th>\n",
+       "      <th>VehPower[4]</th>\n",
+       "      <th>VehPower[5]</th>\n",
+       "      <th>VehPower[6]</th>\n",
+       "      <th>VehPower[7]</th>\n",
+       "      <th>VehPower[8]</th>\n",
+       "      <th>VehPower[9]</th>\n",
        "      <th>BonusMalus</th>\n",
        "      <th>Density</th>\n",
        "    </tr>\n",
@@ -1178,19 +1275,19 @@
        "</div>"
       ],
       "text/plain": [
-       "             intercept  VehBrand__B1  VehBrand__B10  VehBrand__B11  \\\n",
+       "             intercept  VehBrand[B1]  VehBrand[B10]  VehBrand[B11]  \\\n",
        "coefficient    2.88667     -0.064157            0.0       0.231868   \n",
        "\n",
-       "             VehBrand__B12  VehBrand__B13  VehBrand__B14  VehBrand__B2  \\\n",
+       "             VehBrand[B12]  VehBrand[B13]  VehBrand[B14]  VehBrand[B2]  \\\n",
        "coefficient      -0.211061       0.054979      -0.270346     -0.071453   \n",
        "\n",
-       "             VehBrand__B3  VehBrand__B4  ...  VehAge__1  VehAge__2  \\\n",
+       "             VehBrand[B3]  VehBrand[B4]  ...  VehAge[1]  VehAge[2]  \\\n",
        "coefficient       0.00291      0.059324  ...   0.008117  -0.229906   \n",
        "\n",
-       "             VehPower__4  VehPower__5  VehPower__6  VehPower__7  VehPower__8  \\\n",
+       "             VehPower[4]  VehPower[5]  VehPower[6]  VehPower[7]  VehPower[8]  \\\n",
        "coefficient    -0.111796    -0.123388     0.060757     0.005179    -0.021832   \n",
        "\n",
-       "             VehPower__9  BonusMalus   Density  \n",
+       "             VehPower[9]  BonusMalus   Density  \n",
        "coefficient     0.208158    0.032508  0.000002  \n",
        "\n",
        "[1 rows x 60 columns]"
@@ -1221,13 +1318,20 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:29.999716Z",
+     "iopub.status.busy": "2026-03-16T16:10:29.999621Z",
+     "iopub.status.idle": "2026-03-16T16:10:30.042393Z",
+     "shell.execute_reply": "2026-03-16T16:10:30.041988Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "training loss s_glm1: 73.91371104577475\n",
+      "training loss s_glm1: 73.91371104577468\n",
       "testing loss s_glm1:  72.35318912371723\n"
      ]
     }
@@ -1250,14 +1354,21 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-16T16:10:30.043803Z",
+     "iopub.status.busy": "2026-03-16T16:10:30.043715Z",
+     "iopub.status.idle": "2026-03-16T16:10:30.087940Z",
+     "shell.execute_reply": "2026-03-16T16:10:30.087586Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Total claim amount on train set, observed = 44594644.68, predicted = 45027861.66007367\n",
-      "Total claim amount on test set, observed = 4707551.37, predicted = 4999381.03386664\n"
+      "Total claim amount on test set, observed = 4707551.370000001, predicted = 4999381.033866642\n"
      ]
     }
    ],
@@ -1301,7 +1412,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.14.3"
   }
  },
  "nbformat": 4,