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        "## **Assignment #2: Classification, Regression, Clustering, Evaluation**"
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        "### **Overview**\n",
        "\n",
        "In this assignment, you'll level up your data science toolkit. While the first assignment focused on the data, on this one you will practice:\n",
        "\n",
        "- Classification models\n",
        "\n",
        "- Regression models\n",
        "\n",
        "- Feature Engineering\n",
        "\n",
        "- Evaluations\n",
        "\n",
        "You’ll go from raw data to insights by building a full modeling pipeline, enhancing your dataset, and training different models.\n",
        "\n",
        "This assignment will be completed individually."
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        "### **Objectives**\n",
        "\n",
        "You’ll gain hands-on experience in:\n",
        "- Evaluation\n",
        "- Classification\n",
        "- Regression\n",
        "- Dataset preparation\n",
        "- Explore various data hubs\n",
        "- Engineering meaningful features\n",
        "- Communicating findings clearly - visually and verbally\n",
        "\n",
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        "### **Submission Guidelines**\n",
        "\n",
        "1. Please note that this assignmnet must be submitted alone.\n",
        "2. Link to your HugingFace Model.\n",
        "\n",
        "Your HF model should include:\n",
        "- README file: explanations, visuals, insights, etc.\n",
        "- **Video**: Include the video of your presentation in the README file.\n",
        "- **Python Notebook**: upload a copy of this notebook, with all of your coding work. Do not submit a Colab link; include the `.ipynb` file in the HF model.\n",
        "- **ML Models:** Upload your models.\n",
        "\n",
        "Note: Students may be randomly chosen to present their work in a quick online session with the T.A., typically lasting ±10 minutes. Similar to Peer Review.\n",
        "\n",
        "<br><br>\n",
        "\n"
      ],
      "metadata": {
        "id": "MwRmaJBiIjMR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Evaluation Criteria**\n",
        "\n",
        "* **Data Handling & EDA (20%)**\n",
        "  Thoughtful and thorough data cleaning; handling of missing values, outliers, duplicates, and more; well-chosen visualizations; clear statistical summaries; use of EDA to guide modeling choices.\n",
        "\n",
        "* **Feature Engineering (20%)**\n",
        "  Creative and effective feature creation, transformation, encoding, selection, scaling, and more; integration of clustering results as features; clear explanation of feature choices and their impact.\n",
        "\n",
        "* **Model Training (20%)**\n",
        "  Appropriate selection of models; correct train/test split; reproducible code; logical modeling workflow with a solid baseline and improvements post-feature engineering. An iterative process.\n",
        "\n",
        "* **Evaluation & Interpretation (20%)**\n",
        "  Use of relevant evaluation metrics; structured model comparison; use of feature importance or visualizations to interpret results; clear discussion of what the model learned and how it performed.\n",
        "\n",
        "* **Presentation (20%)**\n",
        "  4–6 minute video with clear delivery; structured narrative; visuals that support the explanation; confident, professional communication of findings and lessons.\n",
        "\n",
        "* **Bonus (up to +10%)**\n",
        "  Extra work such as trying data science tools, creative visualizations, advanced hyper param tuning, interactive dashboards, and deeper business/ethical insights.\n",
        "\n",
        "* **Late Submission (-10% per day)**\n",
        "  Assignments submitted after the deadline will receive a 10% penalty per day.\n",
        "\n",
        "<br><br>"
      ],
      "metadata": {
        "id": "hD9SZmagIjOV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Additional Guidelines**\n",
        "\n",
        "- The first thing you should do is download a copy of this notebook to your drive.\n",
        "- Keep your dataset size manageable. If the dataset is too large, you can sample a subset.\n",
        "- Run on Colab (CPU is fine). Colab free is enough. No GPU needed.\n",
        "- You may use any Python package (scikit-learn, xgboost, lightgbm, catboost, etc.).\n",
        "- No SHAP required. Use `feature_importances`, and similar tools.\n",
        "- Make sure your results are reproducible (set **seeds** where needed).\n",
        "- Be thoughtful with your cluster features — only use them if they help!\n",
        "- Your presentation should tell a story; what worked, what didn’t, and why.\n",
        "- Be creative, but also rigorous."
      ],
      "metadata": {
        "id": "h3vpVHSxIUwI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Assignment High-level Flow"
      ],
      "metadata": {
        "id": "7lTH1B5b5c12"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "EK9fe2XygjgM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "6kUonEv8Ipkp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "imports"
      ],
      "metadata": {
        "id": "acyYQrhPdEhB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.io as pio\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import scipy.stats as stats\n",
        "import statsmodels.api as sm\n",
        "\n",
        "from statsmodels.formula.api import ols\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn import metrics\n",
        "from plotly.subplots import make_subplots\n",
        "from scipy.cluster.hierarchy import linkage, leaves_list\n",
        "from sklearn.feature_selection import f_classif, f_regression\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "\n",
        "%matplotlib inline\n",
        "sns.set_style('whitegrid')\n",
        "\n",
        "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
        "pd.set_option('display.max_columns', None)"
      ],
      "metadata": {
        "id": "H9ZazAMOc5jC"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set Seeds"
      ],
      "metadata": {
        "id": "TPaWKBWmdGNF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SEED = 42\n",
        "\n",
        "random.seed(SEED)\n",
        "np.random.seed(SEED)\n",
        "os.environ['PYTHONHASHSEED'] = str(SEED)"
      ],
      "metadata": {
        "id": "zBaCUY21dHeF"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For Jupyter Notebooks"
      ],
      "metadata": {
        "id": "INAizD1WeZcf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'"
      ],
      "metadata": {
        "id": "G0hg5eohd4s-"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Warnings"
      ],
      "metadata": {
        "id": "UwSXkPGvecLK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "Nk9C78G3d7vp"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br>"
      ],
      "metadata": {
        "id": "evFmlLbzdgBj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part 1: Select a Regression Dataset**\n",
        "\n",
        "1. Choose a numeric & categorical tabular dataset. If you prefer, you may use  open-source datasets; [Hugginface](https://huggingface.co/datasets?task_categories=task_categories:tabular-classification&sort=trending), [Kaggle](https://www.kaggle.com/datasets?tags=13302-Classification&minUsabilityRating=8.00+or+higher), etc.\n",
        "\n",
        "2. Avoid choosing a \"basic\"/\"small\" dataset.\n",
        "      - 10K rows and more.\n",
        "      - 15 features and more.\n",
        "      - Numeric & Categorial features are a must.\n",
        "\n",
        "3. The Label (target variable) is numeric.\n",
        "\n",
        "4. Please submit your dataset [here](https://forms.gle/YYiRLXJnbwUfwuwc7), to share it with the class so everyone can see.\n",
        "And make sure your chosen dataset is unique using this [link](https://docs.google.com/spreadsheets/d/1M8uojrzhSyVnOlSAJpzCKxrhWdzPR77k4x8Kxvr8VDk/edit?usp=sharing).\n",
        "\n",
        "      *Note: Due to their popularity, the following are datasets you may not choose.*\n",
        "      > - Iris dataset\n",
        "      > - Wine dataset\n",
        "      > - Titanic dataset\n",
        "      > - Boston Housing dataset\n",
        "\n",
        "5. Choose a dataset with a combination of numeric and textual values. This way you would have enough information to work on.\n",
        "\n",
        "6. Briefly describe your chosen dataset (source, size, features) and the question you want to answer."
      ],
      "metadata": {
        "id": "a_vsO0Q1IOMT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **About the Dataset**\n",
        "## *The Gladiators dataset, based on historical  and synthetic information altogether, is summarizing the statistics of the Gladiator fights of Ancient Greece.*\n",
        "## *Taken from Kaggle, the dataset has aproximately 10K rows, and 29 columns with numeric and textual values.*\n",
        "## We'll be studying the Gladiator's physical and mental features on fights, as well as external factors in the coliseum, while **the target variable would be the \"Wins\"**: what influences and helps the gladiators win the battles?*"
      ],
      "metadata": {
        "id": "WNmVu_DRlNDD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import kagglehub\n",
        "from kagglehub import KaggleDatasetAdapter\n",
        "from google.colab import files\n",
        "from google.colab import data_table\n",
        "data_table.disable_dataframe_formatter()\n",
        "\n",
        "\n",
        "#if needed to upload the Kaggle KPI, recomment the following line:\n",
        "#files.upload()\n",
        "\n",
        "file_path = \"gladiator_data.csv\"\n",
        "\n",
        "# Load the latest version\n",
        "df = kagglehub.load_dataset(\n",
        "  KaggleDatasetAdapter.PANDAS,\n",
        "  \"anthonytherrien/gladiator-combat-records-and-profiles-dataset\",\n",
        "  file_path\n",
        ")\n",
        "\n",
        "print(\"Hugging Face Dataset:\")\n",
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.width', 1000)\n",
        "df.head()"
      ],
      "metadata": {
        "id": "OI0MZzohKwfE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "43ee4ac5-4b3a-49ee-d204-5de15d0f7854"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading from https://www.kaggle.com/api/v1/datasets/download/anthonytherrien/gladiator-combat-records-and-profiles-dataset?dataset_version_number=16&file_name=gladiator_data.csv...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2.14M/2.14M [00:00<00:00, 35.4MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hugging Face Dataset:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Name  Age  Birth Year Origin  Height  Weight     Category  Wins  Losses Special Skills     Weapon of Choice Patron Wealth Equipment Quality  Public Favor Injury History  Mental Resilience Diet and Nutrition Tactical Knowledge Allegiance Network  Battle Experience Psychological Profile Health Status Personal Motivation Previous Occupation Training Intensity Battle Strategy Social Standing Crowd Appeal Techniques  Survived\n",
              "0       Aelius Verus   32          97   Gaul     195      85       Thraex    11       4         Novice  Sica (Curved Sword)           Low             Basic         0.842           High              1.005               Poor       Intermediate             Strong                 15               Fearful          Good               Glory          Unemployed             Medium        Balanced          Medium              Flamboyant     False\n",
              "1  Cocceius Galerius   20          32   Gaul     173      66  Hoplomachus     7       2          Speed               Dagger           Low             Basic         0.651            Low              5.930               Poor           Advanced           Moderate                  9               Fearful     Excellent              Wealth            Criminal               High        Balanced             Low                  Humble      True\n",
              "2      Pedius Furius   30          66   Gaul     170      67  Hoplomachus     6       0        Tactics               Dagger          High          Superior         0.594            Low              3.724          Excellent           Advanced             Strong                  6                 Stoic     Excellent             Freedom            Criminal                Low      Aggressive          Medium            Intimidating      True\n",
              "3  Maximian Maecenas   28          43   Gaul     189     104  Hoplomachus     6       2      Endurance               Dagger          High          Superior         0.541            Low              3.101          Excellent             Expert             Strong                  8           Calculative     Excellent            Survival             Laborer                Low        Balanced             Low             Charismatic     False\n",
              "4    Celsus Laronius   41         126   Rome     173      85  Hoplomachus    12       4         Novice               Dagger        Medium          Standard         0.762           High              4.336           Adequate       Intermediate             Strong                 16           Calculative          Fair            Survival          Unemployed                Low      Aggressive            High            Intimidating     False"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Birth Year</th>\n",
              "      <th>Origin</th>\n",
              "      <th>Height</th>\n",
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              "      <th>Losses</th>\n",
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              "      <th>Weapon of Choice</th>\n",
              "      <th>Patron Wealth</th>\n",
              "      <th>Equipment Quality</th>\n",
              "      <th>Public Favor</th>\n",
              "      <th>Injury History</th>\n",
              "      <th>Mental Resilience</th>\n",
              "      <th>Diet and Nutrition</th>\n",
              "      <th>Tactical Knowledge</th>\n",
              "      <th>Allegiance Network</th>\n",
              "      <th>Battle Experience</th>\n",
              "      <th>Psychological Profile</th>\n",
              "      <th>Health Status</th>\n",
              "      <th>Personal Motivation</th>\n",
              "      <th>Previous Occupation</th>\n",
              "      <th>Training Intensity</th>\n",
              "      <th>Battle Strategy</th>\n",
              "      <th>Social Standing</th>\n",
              "      <th>Crowd Appeal Techniques</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aelius Verus</td>\n",
              "      <td>32</td>\n",
              "      <td>97</td>\n",
              "      <td>Gaul</td>\n",
              "      <td>195</td>\n",
              "      <td>85</td>\n",
              "      <td>Thraex</td>\n",
              "      <td>11</td>\n",
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              "      <td>Novice</td>\n",
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              "      <td>Low</td>\n",
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              "      <td>0.842</td>\n",
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              "      <td>1.005</td>\n",
              "      <td>Poor</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Strong</td>\n",
              "      <td>15</td>\n",
              "      <td>Fearful</td>\n",
              "      <td>Good</td>\n",
              "      <td>Glory</td>\n",
              "      <td>Unemployed</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Balanced</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Flamboyant</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cocceius Galerius</td>\n",
              "      <td>20</td>\n",
              "      <td>32</td>\n",
              "      <td>Gaul</td>\n",
              "      <td>173</td>\n",
              "      <td>66</td>\n",
              "      <td>Hoplomachus</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>Speed</td>\n",
              "      <td>Dagger</td>\n",
              "      <td>Low</td>\n",
              "      <td>Basic</td>\n",
              "      <td>0.651</td>\n",
              "      <td>Low</td>\n",
              "      <td>5.930</td>\n",
              "      <td>Poor</td>\n",
              "      <td>Advanced</td>\n",
              "      <td>Moderate</td>\n",
              "      <td>9</td>\n",
              "      <td>Fearful</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>Wealth</td>\n",
              "      <td>Criminal</td>\n",
              "      <td>High</td>\n",
              "      <td>Balanced</td>\n",
              "      <td>Low</td>\n",
              "      <td>Humble</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Pedius Furius</td>\n",
              "      <td>30</td>\n",
              "      <td>66</td>\n",
              "      <td>Gaul</td>\n",
              "      <td>170</td>\n",
              "      <td>67</td>\n",
              "      <td>Hoplomachus</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>Tactics</td>\n",
              "      <td>Dagger</td>\n",
              "      <td>High</td>\n",
              "      <td>Superior</td>\n",
              "      <td>0.594</td>\n",
              "      <td>Low</td>\n",
              "      <td>3.724</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>Advanced</td>\n",
              "      <td>Strong</td>\n",
              "      <td>6</td>\n",
              "      <td>Stoic</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>Freedom</td>\n",
              "      <td>Criminal</td>\n",
              "      <td>Low</td>\n",
              "      <td>Aggressive</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Intimidating</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Maximian Maecenas</td>\n",
              "      <td>28</td>\n",
              "      <td>43</td>\n",
              "      <td>Gaul</td>\n",
              "      <td>189</td>\n",
              "      <td>104</td>\n",
              "      <td>Hoplomachus</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>Endurance</td>\n",
              "      <td>Dagger</td>\n",
              "      <td>High</td>\n",
              "      <td>Superior</td>\n",
              "      <td>0.541</td>\n",
              "      <td>Low</td>\n",
              "      <td>3.101</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>Expert</td>\n",
              "      <td>Strong</td>\n",
              "      <td>8</td>\n",
              "      <td>Calculative</td>\n",
              "      <td>Excellent</td>\n",
              "      <td>Survival</td>\n",
              "      <td>Laborer</td>\n",
              "      <td>Low</td>\n",
              "      <td>Balanced</td>\n",
              "      <td>Low</td>\n",
              "      <td>Charismatic</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Celsus Laronius</td>\n",
              "      <td>41</td>\n",
              "      <td>126</td>\n",
              "      <td>Rome</td>\n",
              "      <td>173</td>\n",
              "      <td>85</td>\n",
              "      <td>Hoplomachus</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>Novice</td>\n",
              "      <td>Dagger</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Standard</td>\n",
              "      <td>0.762</td>\n",
              "      <td>High</td>\n",
              "      <td>4.336</td>\n",
              "      <td>Adequate</td>\n",
              "      <td>Intermediate</td>\n",
              "      <td>Strong</td>\n",
              "      <td>16</td>\n",
              "      <td>Calculative</td>\n",
              "      <td>Fair</td>\n",
              "      <td>Survival</td>\n",
              "      <td>Unemployed</td>\n",
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              "      <td>Aggressive</td>\n",
              "      <td>High</td>\n",
              "      <td>Intimidating</td>\n",
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              "      spin 1s steps(1) infinite;\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import HfApi\n",
        "\n",
        "from huggingface_hub import notebook_login\n",
        "#notebook_login()\n",
        "\n",
        "# Your HF repo\n",
        "repo_id = \"Karish1/winning_model\"\n",
        "\n",
        "# Name of the file on HF\n",
        "dataset_filename = \"gladiator_data.csv\"\n",
        "\n",
        "api = HfApi()\n",
        "\n",
        "# 1. Check existing files in HF repo\n",
        "existing_files = api.list_repo_files(repo_id=repo_id)\n",
        "\n",
        "if dataset_filename in existing_files:\n",
        "    print(f\"Dataset '{dataset_filename}' already exists on HuggingFace. Upload skipped.\")\n",
        "else:\n",
        "    print(\"Dataset not found on HuggingFace. Uploading now...\")\n",
        "\n",
        "    # 2. Save the DataFrame as CSV locally\n",
        "    df.to_csv(dataset_filename, index=False)\n",
        "\n",
        "    # 3. Upload to HuggingFace\n",
        "    api.upload_file(\n",
        "        path_or_fileobj=dataset_filename,\n",
        "        path_in_repo=dataset_filename,\n",
        "        repo_id=repo_id\n",
        "    )\n",
        "\n",
        "    print(f\"Uploaded '{dataset_filename}' successfully!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34,
          "referenced_widgets": [
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        "id": "ToWlv4TjsA_R",
        "outputId": "5f99305c-a152-47ee-891b-16e9b1526211"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7206bdf58cb149f18f83071fc3ada58a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset 'gladiator_data.csv' already exists on HuggingFace. Upload skipped.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MANUAL_ENCODED_MAP = {\n",
        "    \"Allegiance Network\": {'Strong':3,'Moderate':2,'Weak':1},\n",
        "    \"Patron Wealth\": {'Low':1,'Medium':2,'High':3},\n",
        "    \"Equipment Quality\": {'Basic':1,'Standard':2,'Superior':3},\n",
        "    \"Injury History\": {'Low':1,'High':2},\n",
        "    \"Tactical Knowledge\": {'Basic':1,'Intermediate':2,'Advanced':3,'Expert':4},\n",
        "    \"Health Status\": {'Fair':1,'Good':2,'Excellent':3},\n",
        "    \"Training Intensity\": {'Low':1,'Medium':2,'High':3},\n",
        "    \"Social Standing\": {'Low':1,'Medium':2,'High':3},\n",
        "    \"Diet and Nutrition\": {'Poor':1,'Adequate':2,'Excellent':3},\n",
        "    \"Survived\": {False:0, True:1}\n",
        "}\n",
        "\n",
        "for col, mp in MANUAL_ENCODED_MAP.items():\n",
        "    df[col] = df[col].astype(\"object\").map(mp)"
      ],
      "metadata": {
        "id": "0Sj9_cW6uQ88"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "6FhGorzCunDu",
        "outputId": "f22bcf3d-60a7-4a20-8d60-f2bc580e181b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Name  Age  Birth Year Origin  Height  Weight     Category  Wins  Losses Special Skills     Weapon of Choice  Patron Wealth  Equipment Quality  Public Favor  Injury History  Mental Resilience  Diet and Nutrition  Tactical Knowledge  Allegiance Network  Battle Experience Psychological Profile  Health Status Personal Motivation Previous Occupation  Training Intensity Battle Strategy  Social Standing Crowd Appeal Techniques  Survived\n",
              "0       Aelius Verus   32          97   Gaul     195      85       Thraex    11       4         Novice  Sica (Curved Sword)              1                  1         0.842               2              1.005                   1                   2                   3                 15               Fearful              2               Glory          Unemployed                   2        Balanced                2              Flamboyant         0\n",
              "1  Cocceius Galerius   20          32   Gaul     173      66  Hoplomachus     7       2          Speed               Dagger              1                  1         0.651               1              5.930                   1                   3                   2                  9               Fearful              3              Wealth            Criminal                   3        Balanced                1                  Humble         1\n",
              "2      Pedius Furius   30          66   Gaul     170      67  Hoplomachus     6       0        Tactics               Dagger              3                  3         0.594               1              3.724                   3                   3                   3                  6                 Stoic              3             Freedom            Criminal                   1      Aggressive                2            Intimidating         1\n",
              "3  Maximian Maecenas   28          43   Gaul     189     104  Hoplomachus     6       2      Endurance               Dagger              3                  3         0.541               1              3.101                   3                   4                   3                  8           Calculative              3            Survival             Laborer                   1        Balanced                1             Charismatic         0\n",
              "4    Celsus Laronius   41         126   Rome     173      85  Hoplomachus    12       4         Novice               Dagger              2                  2         0.762               2              4.336                   2                   2                   3                 16           Calculative              1            Survival          Unemployed                   1      Aggressive                3            Intimidating         0"
            ],
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              "      <th>Allegiance Network</th>\n",
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              "      <td>Aelius Verus</td>\n",
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              "      <td>Cocceius Galerius</td>\n",
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              "      <td>Hoplomachus</td>\n",
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              "      <td>Dagger</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.651</td>\n",
              "      <td>1</td>\n",
              "      <td>5.930</td>\n",
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              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
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              "      <td>Wealth</td>\n",
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              "      <td>Balanced</td>\n",
              "      <td>1</td>\n",
              "      <td>Humble</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Pedius Furius</td>\n",
              "      <td>30</td>\n",
              "      <td>66</td>\n",
              "      <td>Gaul</td>\n",
              "      <td>170</td>\n",
              "      <td>67</td>\n",
              "      <td>Hoplomachus</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>Tactics</td>\n",
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              "      <td>0.594</td>\n",
              "      <td>1</td>\n",
              "      <td>3.724</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>Stoic</td>\n",
              "      <td>3</td>\n",
              "      <td>Freedom</td>\n",
              "      <td>Criminal</td>\n",
              "      <td>1</td>\n",
              "      <td>Aggressive</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Maximian Maecenas</td>\n",
              "      <td>28</td>\n",
              "      <td>43</td>\n",
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              "      <td>189</td>\n",
              "      <td>104</td>\n",
              "      <td>Hoplomachus</td>\n",
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              "      <td>Endurance</td>\n",
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              "      <td>0.541</td>\n",
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              "      <td>3.101</td>\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
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              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "          try {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "4t2QNyE6IPKS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part 2: Exploratory Data Analysis (EDA)**\n",
        "\n",
        "Use your EDA to tell the story of your data - highlight interesting patterns, anomalies, or relationships that lead you toward your classification goal. Ask interesting questions, and answer them.\n",
        "\n",
        "\n",
        "1. **Data Cleaning** : Check for missing values, duplicate entries, scaling/normalize issues,  parsing dates, fixing typos, or any inconsistencies.  Document how you address them.\n",
        "2. **Outlier Detection & Handling**: Identify outliers and decide whether to keep or remove them, providing a short justification.\n",
        "2. **Descriptive Statistics**: Summarize the data (e.g., mean, median, correlations) to reveal patterns.\n",
        "4. **Visualizations**: Use a set of  plots (e.g., histograms, scatter plots, box plots) to illustrate **key insights.** Label charts, axes, and legends clearly.\n",
        "\n",
        "Tip: not necessarily in this order."
      ],
      "metadata": {
        "id": "6eLmNWJJIPS0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *DATA CLEANING*"
      ],
      "metadata": {
        "id": "CYx0zZga4TJv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\n",
        "      f\"number of columns, rows: \\n{df.shape}\\n\"\n",
        "      f\"\\nnumber of null cells: \\n{df.isnull().sum()}\\n\"\n",
        "      f\"\\nnumber of '0' cells: \\n{(df == 0).sum()}\\n\"\n",
        "      f\"\\nnumber of duplicated rows: \\n{df.duplicated().sum()}\\n\"\n",
        "      \"\\nBottom line: There are no empty cells by mistake. The cells representing 0 are: \\n0 losses, 0 wins, 0 years of battle experience, and the bullian 'False' of the fighters that did not survive.\"\n",
        ")"
      ],
      "metadata": {
        "id": "fUEbi2wOhssS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c199011a-972d-4a07-b48c-29f26f87aca2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of columns, rows: \n",
            "(9976, 29)\n",
            "\n",
            "number of null cells: \n",
            "Name                       0\n",
            "Age                        0\n",
            "Birth Year                 0\n",
            "Origin                     0\n",
            "Height                     0\n",
            "Weight                     0\n",
            "Category                   0\n",
            "Wins                       0\n",
            "Losses                     0\n",
            "Special Skills             0\n",
            "Weapon of Choice           0\n",
            "Patron Wealth              0\n",
            "Equipment Quality          0\n",
            "Public Favor               0\n",
            "Injury History             0\n",
            "Mental Resilience          0\n",
            "Diet and Nutrition         0\n",
            "Tactical Knowledge         0\n",
            "Allegiance Network         0\n",
            "Battle Experience          0\n",
            "Psychological Profile      0\n",
            "Health Status              0\n",
            "Personal Motivation        0\n",
            "Previous Occupation        0\n",
            "Training Intensity         0\n",
            "Battle Strategy            0\n",
            "Social Standing            0\n",
            "Crowd Appeal Techniques    0\n",
            "Survived                   0\n",
            "dtype: int64\n",
            "\n",
            "number of '0' cells: \n",
            "Name                          0\n",
            "Age                           0\n",
            "Birth Year                    0\n",
            "Origin                        0\n",
            "Height                        0\n",
            "Weight                        0\n",
            "Category                      0\n",
            "Wins                         25\n",
            "Losses                     2548\n",
            "Special Skills                0\n",
            "Weapon of Choice              0\n",
            "Patron Wealth                 0\n",
            "Equipment Quality             0\n",
            "Public Favor                  0\n",
            "Injury History                0\n",
            "Mental Resilience             0\n",
            "Diet and Nutrition            0\n",
            "Tactical Knowledge            0\n",
            "Allegiance Network            0\n",
            "Battle Experience            25\n",
            "Psychological Profile         0\n",
            "Health Status                 0\n",
            "Personal Motivation           0\n",
            "Previous Occupation           0\n",
            "Training Intensity            0\n",
            "Battle Strategy               0\n",
            "Social Standing               0\n",
            "Crowd Appeal Techniques       0\n",
            "Survived                   5062\n",
            "dtype: int64\n",
            "\n",
            "number of duplicated rows: \n",
            "0\n",
            "\n",
            "Bottom line: There are no empty cells by mistake. The cells representing 0 are: \n",
            "0 losses, 0 wins, 0 years of battle experience, and the bullian 'False' of the fighters that did not survive.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *Outlier Detection & Handling*"
      ],
      "metadata": {
        "id": "aB0PfuOO4Zr8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### HEIGHT-WEIGHT OUTLIERS:"
      ],
      "metadata": {
        "id": "KV3fREYcjtC_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pio.renderers.default=\"colab\"\n",
        "\n",
        "df_before=df.copy()\n",
        "X=df_before[['Height']].values\n",
        "y=df_before['Weight'].values\n",
        "\n",
        "model_before=LinearRegression().fit(X,y)\n",
        "pred_before=model_before.predict(X)\n",
        "\n",
        "residuals=y-pred_before\n",
        "df_before['Weight_Residuals']=residuals\n",
        "\n",
        "thr=2*residuals.std()\n",
        "df_before['HeightWeight_Outlier']=np.abs(residuals)>thr\n",
        "outliers=df_before[df_before['HeightWeight_Outlier']]\n",
        "print(\"Outliers BEFORE:\",outliers.shape[0])\n",
        "display(outliers[['Name','Height','Weight','Weight_Residuals']])\n",
        "\n",
        "coef=np.polyfit(df_before[\"Height\"],df_before[\"Weight\"],1)\n",
        "df_before[\"Predicted_Weight_Before\"]=np.poly1d(coef)(df_before[\"Height\"])\n",
        "\n",
        "fig=px.scatter(df_before,x=\"Height\",y=\"Weight\",color=\"HeightWeight_Outlier\",\n",
        "               hover_name=\"Name\",\n",
        "               hover_data={\"Height\":True,\"Weight\":True,\"Category\":True,\n",
        "                           \"Battle Experience\":True,\"Wins\":True,\"Losses\":True,\n",
        "                           \"HeightWeight_Outlier\":True},\n",
        "               title=\"BEFORE Cleaning: Height–Weight Regression with Outliers\")\n",
        "fig.add_traces(px.line(x=df_before[\"Height\"].sort_values(),\n",
        "                       y=df_before[\"Predicted_Weight_Before\"].sort_values()).data)\n",
        "fig.update_layout(width=900,height=600)\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Amtopbzl4bc_",
        "outputId": "80664bd1-041d-4939-f37c-2dd192b29181"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outliers BEFORE: 478\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    Name  Height  Weight  Weight_Residuals\n",
              "5         Priscus Junius     166      41           -41.352\n",
              "96         Salvian Nerva     193      39           -41.578\n",
              "108    Saturninus Aelius     173     114            32.108\n",
              "115      Sedatius Marcus     180     114            32.568\n",
              "161       Ollius Albinus     183      45           -36.235\n",
              "...                  ...     ...     ...               ...\n",
              "9834   Antonius Geminius     163      37           -45.549\n",
              "9860  Bonifacius Quintus     175      46           -35.761\n",
              "9905    Cornutus Agrippa     175     114            32.239\n",
              "9929   Marcius Ravillius     167      36           -46.286\n",
              "9971  Sempronius Rufinus     186      47           -34.038\n",
              "\n",
              "[478 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5230f6f9-7696-4e56-bd38-459b38f067b8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Weight_Residuals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Priscus Junius</td>\n",
              "      <td>166</td>\n",
              "      <td>41</td>\n",
              "      <td>-41.352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Salvian Nerva</td>\n",
              "      <td>193</td>\n",
              "      <td>39</td>\n",
              "      <td>-41.578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>Saturninus Aelius</td>\n",
              "      <td>173</td>\n",
              "      <td>114</td>\n",
              "      <td>32.108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>Sedatius Marcus</td>\n",
              "      <td>180</td>\n",
              "      <td>114</td>\n",
              "      <td>32.568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>Ollius Albinus</td>\n",
              "      <td>183</td>\n",
              "      <td>45</td>\n",
              "      <td>-36.235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9834</th>\n",
              "      <td>Antonius Geminius</td>\n",
              "      <td>163</td>\n",
              "      <td>37</td>\n",
              "      <td>-45.549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9860</th>\n",
              "      <td>Bonifacius Quintus</td>\n",
              "      <td>175</td>\n",
              "      <td>46</td>\n",
              "      <td>-35.761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9905</th>\n",
              "      <td>Cornutus Agrippa</td>\n",
              "      <td>175</td>\n",
              "      <td>114</td>\n",
              "      <td>32.239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9929</th>\n",
              "      <td>Marcius Ravillius</td>\n",
              "      <td>167</td>\n",
              "      <td>36</td>\n",
              "      <td>-46.286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9971</th>\n",
              "      <td>Sempronius Rufinus</td>\n",
              "      <td>186</td>\n",
              "      <td>47</td>\n",
              "      <td>-34.038</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>478 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5230f6f9-7696-4e56-bd38-459b38f067b8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5230f6f9-7696-4e56-bd38-459b38f067b8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-e9c7ea00-8a2f-438d-9b0a-8b2bf4c970d4 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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Crispinus\",\"Publicius Orbilius\",\"Furius Flavius\",\"Livius Albinus\",\"Munatius Mallius\",\"Valentinian Felix\",\"Severian Mallius\",\"Maxentius Semellius\",\"Nasidius Nigidius\",\"Tiberius Marian\",\"Decentius Hostilius\",\"Aemilius Quinctilius\",\"Hilarianus Ravillius\",\"Cassius Cornelius\",\"Sulpicius Gabinius\",\"Gallus Paelignus\",\"Bonifacius Persius\",\"Sidonius Sittius\",\"Sergius Rullianus\",\"Lacerius Considius\",\"Paternus Fabrinius\",\"Burrienus Germanicus\",\"Mucius Iuventius\",\"Pomponius Junius\",\"Salinator Rufrius\",\"Papinius Vibius\",\"Terentius Numonius\",\"Domitian Zeno\",\"Vespasian Valerius\",\"Sempronius Maecilius\",\"Faustus Postumius\",\"Horatius Rebilus\",\"Marian Geta\",\"Numitorius Servius\",\"Petreius Betilienus\",\"Paulinus Cordius\",\"Vibius Florianus\",\"Baebius Eutropius\",\"Nasidienus Hostilius\",\"Egnatius Matinius\",\"Didius Camillius\",\"Nasidienus Curtius\",\"Terentius Fulvius\",\"Silanus Sempronius\",\"Bonifacius Domitius\",\"Betilienus Maximus\",\"Valentinian Genucius\",\"Caesennius Acilius\",\"Claudius Volumnius\",\"Laelius Rufius\",\"Volumnius Antonius\",\"Sergian Iunius\",\"Honorius Octavius\",\"Flavius Caninius\",\"Aquillius Maecenas\",\"Didius Cicero\",\"Aelius Tertius\",\"Gnaeus Fundanus\",\"Cantilius Catius\",\"Crispinus Suetonius\",\"Urbicus Lartius\",\"Urbicus Atilius\",\"Valerius Nasidius\",\"Petreius Drusus\",\"Gordian Suetonius\",\"Paconius Silvius\",\"Diocletian Crispinus\",\"Valentinian Sulpicius\",\"Seccius Probus\",\"Drusus Lartius\",\"Florianus Lucanus\",\"Icilius Hirrus\",\"Nasidienus Laronius\",\"Oppius Eutropius\",\"Laecanius Seneca\",\"Antonius Flavius\",\"Appius Livius\",\"Mallius Spurius\",\"Pupius Silanus\",\"Maecenas Aurelianus\",\"Sextius Hirrus\",\"Placidus Fannius\",\"Tertius Lentulus\",\"Scribonius Aurelianus\",\"Catius Festus\",\"Cicereius Nasennius\",\"Flaccus Numonius\",\"Rufrius Cornificius\",\"Laelius Sedatius\",\"Paelignus Caelian\",\"Plautius Servius\",\"Claudius Paulinus\",\"Sentius Caesennius\",\"Pettius Perperna\",\"Pompeius Celsus\",\"Drusus Pompeius\",\"Fulvius Cosconius\",\"Opimius Amulius\",\"Nazarius Maecilius\",\"Pasidienus Nasennius\",\"Murena Numonius\",\"Cinna Tacitus\",\"Caninius Faustus\",\"Fulcinia Matienus\",\"Quintus Granius\",\"Oppius Statilius\",\"Atilius Cassianus\",\"Candidus Maecenas\",\"Numitorius Rufinus\",\"Lartius Valerius\",\"Camillius Urbanus\",\"Paternus Laurentius\",\"Crispus Orchius\",\"Albinus Burrienus\",\"Maecilius Ravillius\",\"Justus Hostilius\",\"Gracchus Publius\",\"Ninnius Hadrian\",\"Plinius Mescinius\",\"Ravillius Nasennius\",\"Ostorius Pontidius\",\"Blasius Considius\",\"Octavenus Sedatius\",\"Longinius Verecundus\",\"Duronius Suetonius\",\"Vincentius Valerius\",\"Caecilius Cordius\",\"Agrippa Silvius\",\"Publicius Festinius\",\"Hilarianus Regulus\",\"Salvidienus Festinius\",\"Camillus Salvidienus\",\"Ravillius Marian\",\"Nigidius Cassio\",\"Aelius Regulus\",\"Ennius Pasidienus\",\"Amulius Domitian\",\"Ingenuus Silanus\",\"Ravillius Fannius\",\"Draco Vitus\",\"Sicinius Aelius\",\"Gracchus Laronius\",\"Appius Catius\",\"Geminius Sabucius\",\"Urbicus Hostilius\",\"Catilius Hostilius\",\"Hirrus Sempronius\",\"Egnatius Secundus\",\"Cestius Lollius\",\"Fidius Caninius\",\"Volumnius Ostorius\",\"Geta Diocletian\",\"Amulius Palpellius\",\"Junius Orbilius\",\"Gnaeus Sulpicius\",\"Pasidienus Silius\",\"Hilarianus Domitian\",\"Aelius Plinius\",\"Ninnius Nasennius\",\"Ovidius Claudius\",\"Cornificius Draco\",\"Maximian Terentius\",\"Gordian Cinna\",\"Servius Tacitus\",\"Cosconius Nerva\",\"Novellius Hostius\",\"Galerius Eutropius\",\"Marcus Urbanus\",\"Antonius Vitus\",\"Calventius Seccius\",\"Cornelius Cispius\",\"Ovidius Julius\",\"Ingenuus Zeno\",\"Salvidienus Ingenuus\",\"Genucius Novellius\",\"Pilus Valerius\",\"Genucius Rufrius\",\"Belisarius Stertinius\",\"Fundanus Valentinian\",\"Januarius Cyprian\",\"Balbinus Donatus\",\"Nonius Cyprian\",\"Mamilius Laberius\",\"Laelius Blasius\",\"Postumius Duronius\",\"Sertorius Didius\",\"Scribonius Silvius\",\"Laecanius Licinius\",\"Titus Petreius\",\"Domitian Priscus\",\"Severus Aulus\",\"Laronius Lucretius\",\"Castorius Maximian\",\"Quintus Scribonius\",\"Messalla Cethegus\",\"Zeno Varian\",\"Juvenalis Paternus\",\"Afranius Pontidius\",\"Aquillius Cominius\",\"Caelius Nerva\",\"Probus Mucius\",\"Semellius Lartius\",\"Crispus Segulius\",\"Pontidius Marcellus\",\"Quirinius Sornatius\",\"Bonifacius Poppaedius\",\"Papirius Magnentius\",\"Menenius Hordeonius\",\"Geta Opimius\",\"Claudius Munatius\",\"Claudius Lartius\",\"Hortensius Cispius\",\"Faustus Maxentius\",\"Manilius Quinctilius\",\"Decentius Sentius\",\"Verus Ollius\",\"Julius Secundus\",\"Martialis Paelignus\",\"Cinna Secundinius\",\"Lollius Servius\",\"Sedatius Marcellus\",\"Laberius Dacian\",\"Fannius Seneca\",\"Volumnius Titus\",\"Saturninus Gallus\",\"Paconius Secundus\",\"Stertinius Octavenus\",\"Clodius Gnaeus\",\"Marcellus Varro\",\"Octavius Pinarus\",\"Gabinius Petilius\",\"Salvian Petilius\",\"Ravillius Minicius\",\"Maximian Numerius\",\"Catius Nerva\",\"Geta Lucretius\",\"Camillius Menenius\",\"Stertinius Licinius\",\"Nasidienus Cestius\",\"Faustus Lucanus\",\"Vitus Caesennius\",\"Orestius Octavenus\",\"Marius Statilius\",\"Sabucius Sextius\",\"Considius Octavius\",\"Agrippa Laronius\",\"Numonius Numonius\",\"Gracchus Sestius\",\"Fabrinius Sextius\",\"Pinarus Rabirius\",\"Regulus Placidus\",\"Pilus Hortensius\",\"Cordius Cyprian\",\"Rufius Oppius\",\"Cethegus Caninius\",\"Fannius Cloelius\",\"Aurelianus Cilnius\",\"Januarius Albinus\",\"Cassianus Sentius\",\"Furius Festus\",\"Livius Anatolius\",\"Vibius Egnatuleius\",\"Honorius Sabucius\",\"Catius Pasidienus\",\"Hadrian Geminius\",\"Cicereius Nonius\",\"Maximus Eutropius\",\"Zeno Ofilius\",\"Draco Seccius\",\"Appius Caninius\",\"Paelignus Verus\",\"Camillus Gallus\",\"Gnaeus Cornelius\",\"Gabinius Agrippa\",\"Servilius Naevius\",\"Titus Cocceius\",\"Octavius Fundanus\",\"Antonius Donatus\",\"Nigidius Anatolius\",\"Caedicius Poppaedius\",\"Nautius Poppaedius\",\"Matienus Porcius\",\"Crispinus Tacitus\",\"Nasidienus Quinctilius\",\"Hordeonius Antonius\",\"Numicius Mallius\",\"Secundinius Maxentius\",\"Orestius Tacitus\",\"Sentius Terentius\",\"Salvidienus Persius\",\"Camillius Stertinius\",\"Minicius Laurentius\",\"Ollius Flaccus\",\"Aemilius Gratian\",\"Ninnius Novellius\",\"Ollius Livius\",\"Numitorius Ollius\",\"Cincinnatus Gnaeus\",\"Sornatius Laurentius\",\"Messalla Gallus\",\"Donatus Potitius\",\"Hilarianus Sergian\",\"Cosconius Lucanus\",\"Aquillius Varian\",\"Hostius Maximian\",\"Betilienus Pinarus\",\"Maxentius Mallius\",\"Caelius Felix\",\"Fulcinia Fidius\",\"Albinius Nasennius\",\"Catius Cosconius\",\"Rufrius Claudius\",\"Laberius Vincentius\",\"Sentius Semellius\",\"Ingenuus Semellius\",\"Sornatius Horatius\",\"Nigidius Junius\",\"Gordian Lentulus\",\"Fulcinia Fundanus\",\"Silvius Festus\",\"Plinius Rabirius\",\"Blasius Pacilius\",\"Caecilius Publius\",\"Sentius Decimus\",\"Balbus Catius\",\"Matienus Numerius\",\"Munatius Vincentius\",\"Geminius Tertius\",\"Maecilius Sergian\",\"Quinctilius Potitius\",\"Albinus Minicius\",\"Rufinus Oppius\",\"Zeno Cilnius\",\"Sextus Livius\",\"Lamponius Licinius\",\"Felix Sabucius\",\"Eutropius Memmius\",\"Calpurnius Cyprian\",\"Aelius Cassius\",\"Regulus Nerva\",\"Ollius Poppaedius\",\"Genucius Crispus\",\"Egnatius Cornificius\",\"Zeno Salvian\",\"Gaius Caninius\",\"Octavenus Orbilius\",\"Camillus Brennus\",\"Ennius Sestius\",\"Pinarus Catius\",\"Marcius Vitus\",\"Drusus Gnaeus\",\"Rufius Tarquinius\",\"Clodius Gabinius\",\"Caecilius Foslius\",\"Statilius Aemilius\",\"Petreius Vitalis\",\"Dacian Paternus\",\"Genucius Cestius\",\"Albinus Damasus\",\"Festus Geta\",\"Horatius Gaius\",\"Juvenalis Cornutus\",\"Primitivus Candidus\",\"Camillus Gabinius\",\"Castorius Opellius\",\"Crispinus Afranius\",\"Cassio Lucanus\",\"Brennus Munatius\",\"Ennius Pacilius\",\"Dacian Januarius\",\"Sepullius Julius\",\"Poppaedius Quintilianus\",\"Gaius Maecilius\",\"Vitus Cethegus\",\"Eutropius Ofilius\",\"Florianus Sallustius\",\"Nerva Oppius\",\"Papirius Vespasian\",\"Poppaedius Silvius\",\"Sextius Lentulus\",\"Licinius Camillius\",\"Honorius Laelius\",\"Varro Mamilius\",\"Sestius Quintilianus\",\"Stertinius Rufrius\",\"Hostius Valens\",\"Opellius Fabius\",\"Decimus Cassianus\",\"Verecundus Oppius\",\"Julius Iuventius\",\"Avidius Statilius\",\"Geminius Faustus\",\"Januarius Potitius\",\"Lucanus Vitalis\",\"Cassio Numicius\",\"Nasidius Fidius\",\"Petilius Faustus\",\"Flaccus Sentius\",\"Paconius Zeno\",\"Nonius Rullianus\",\"Granius Celsus\",\"Ovidius Primitivus\",\"Maximus Antonius\",\"Aquillius Opellius\",\"Balbinus Afranius\",\"Aelius Belisarius\",\"Aulus Didius\",\"Laurentius Probus\",\"Sabucius Florian\",\"Varro Matienus\",\"Sentius Sittius\",\"Cassianus Germanicus\",\"Nasidienus Gratian\",\"Plautius Geminius\",\"Sicinius Titus\",\"Ravillius Rufrius\",\"Silvius Aulus\",\"Lollius Blossius\",\"Camillus Eutropius\",\"Fabrinius Zeno\",\"Sextus Geta\",\"Aurelianus Cassius\",\"Felix Salinator\",\"Dacian Opimius\",\"Cispius Murena\",\"Maxentius Papirius\",\"Quintilianus Rullianus\",\"Hilarianus Clodius\",\"Hostilius Didius\",\"Livius Ostorius\",\"Perperna Justus\",\"Murena Lartius\",\"Percennius Rabirius\",\"Domitian Placidus\",\"Laronius Pontidius\",\"Pinarus Maecilius\",\"Sextus Nazarius\",\"Cornificius Oppius\",\"Severian Granius\",\"Pupius Ingenuus\",\"Nasidienus Iunius\",\"Considius Sentius\",\"Laetorius Numicius\",\"Camillus Servilius\",\"Fannius Norbanus\",\"Laelius Camillus\",\"Fabianus Vitalis\",\"Geta Pomponius\",\"Silvius Pomponius\",\"Vitus Licinius\",\"Duronius Foslius\",\"Maecilius Sertorius\",\"Gallus Rebilus\",\"Amulius Pontidius\",\"Matienus Numonius\",\"Hostius Servilius\",\"Rullianus Avidius\",\"Decentius Ennius\",\"Sornatius Florian\",\"Maecilius Bonifacius\",\"Latinius Caedicius\",\"Cato Gallus\",\"Laetorius Suetonius\",\"Minicius Segulius\",\"Cispius Maecenas\",\"Gratian Lacerius\",\"Maecilius Vincentius\",\"Ninnius Scribonius\",\"Petilius Castorius\",\"Lentulus Livius\",\"Segulius Varro\",\"Percennius Furius\",\"Calventius Germanicus\",\"Flavius Cassianus\",\"Modius Murena\",\"Bruttius Lentulus\",\"Cornelius Matinius\",\"Crispinus Hilarianus\",\"Verecundus Quintus\",\"Postumius Claudius\",\"Potitius Rabirius\",\"Salvius Nasennius\",\"Eborius Magnentius\",\"Aurelianus Saturninus\",\"Verecundus Segulius\",\"Ninnius Ostorius\",\"Sertorius Salvidienus\",\"Scribonius Pilus\",\"Vibius Pettius\",\"Florianus Ollius\",\"Numonius Coruncanius\",\"Bonifacius Felix\",\"Martialis Blossius\",\"Sextius Florianus\",\"Plautius Ravillius\",\"Paconius Hostius\",\"Claudius Cincinnatus\",\"Antonius Domitius\",\"Sextus Blasius\",\"Lacerius Cassio\",\"Severus Salvius\",\"Musonius Festus\",\"Sestius Cassio\",\"Terentius Mallius\",\"Musonius Granius\",\"Hadrian Opellius\",\"Matienus Sertorius\",\"Clodius Julius\",\"Pettius Aquillius\",\"Faustus Manius\",\"Sextius Maecenas\",\"Nigidius Cornutus\",\"Maxentius Tertius\",\"Pomponius Maxentius\",\"Persius Quintilianus\",\"Fundanus Paconius\",\"Pasidienus Ingenuus\",\"Cato Bonifacius\",\"Nigidius Tertius\",\"Titus Paconius\",\"Salvius Silius\",\"Albinus Sextus\",\"Hortensius Cinna\",\"Cinna Servius\",\"Verecundus Caesennius\",\"Nigidius Hordeonius\",\"Paconius Cethegus\",\"Numonius Junius\",\"Verecundus Menenius\",\"Egnatuleius Mallius\",\"Caecilius Petreius\",\"Cinna Curtius\",\"Pomponius Mescinius\",\"Albinius Balbus\",\"Tacitus Petilius\",\"Aulus Blossius\",\"Dacian Nazarius\",\"Flavius Cato\",\"Palpellius Magnentius\",\"Statilius Felix\",\"Cincinnatus Ollius\",\"Amulius Secundus\",\"Nero Camillius\",\"Sextus Publius\",\"Numonius Servilius\",\"Cispius Valerius\",\"Hilarianus Aelius\",\"Manilius Fannius\",\"Honorius Rufrius\",\"Priscus Marius\",\"Ovidius Horatius\",\"Caninius Cicereius\",\"Fulcinia Cilnius\",\"Galerius Cassius\",\"Geminius Didius\",\"Catilius Marian\",\"Camillius Sergius\",\"Eutropius Iuventius\",\"Cominius Aelius\",\"Nasennius Caecilius\",\"Vitus Curtius\",\"Plinius Cyprian\",\"Constantinus Ravillius\",\"Sextus Cassius\",\"Flavius Hilarianus\",\"Marcellus Seneca\",\"Messalla Plinius\",\"Festinius Gaius\",\"Cassio Titus\",\"Marcellus Burrienus\",\"Fabrinius Seccius\",\"Ovidius Cispius\",\"Papinius Coruncanius\",\"Nonius Petilius\",\"Opellius Cicereius\",\"Cicero Domitian\",\"Icilius Eborius\",\"Ravillius Licinius\",\"Herennius Priscus\",\"Naevius Junius\",\"Lucanus Hadrian\",\"Festus Cyprian\",\"Sidonius Caedicius\",\"Hostius Quinctilius\",\"Lentulus Hostius\",\"Probus Memmius\",\"Fundanus Pomponius\",\"Rullianus Pomponius\",\"Sidonius Cilnius\",\"Aemilius Placidus\",\"Fabrinius Probus\",\"Sestius Diocletian\",\"Latinius Iulius\",\"Tarquinius Calventius\",\"Mallius Quinctilius\",\"Crispinus Juvenalis\",\"Cocceius Gnaeus\",\"Fabius Ennius\",\"Verecundus Messalla\",\"Petreius Pasidienus\",\"Secundinius Perperna\",\"Poppaedius Lucanus\",\"Galerius Sestius\",\"Valentinian Galerius\",\"Sentius Pilus\",\"Lucanus Horatius\",\"Suetonius Quinctilius\",\"Justus Marcius\",\"Livius Saturninus\",\"Oppius Horatius\",\"Albinus Galerius\",\"Naevius Januarius\",\"Modius Zeno\",\"Rebilus Damasus\",\"Semellius Servius\",\"Geminius Regulus\",\"Lamponius Mamilius\",\"Lentulus Juvenalis\",\"Severus Nigidius\",\"Belisarius Vincentius\",\"Pilus Probus\",\"Crispus Betilienus\",\"Papirius Cordius\",\"Minicius Rullianus\",\"Antonius Cassius\",\"Camillius Lucanus\",\"Numitorius Perperna\",\"Saturninus Laronius\",\"Statilius Cordius\",\"Fabius Persius\",\"Laberius Iunius\",\"Urbicus Flavius\",\"Petreius Orbilius\",\"Genucius Pompeius\",\"Fulvius Rufinus\",\"Germanicus Pedius\",\"Ovidius Eborius\",\"Hilarianus Rabirius\",\"Quinctilius Acilius\",\"Granius Hilarianus\",\"Sornatius Cloelius\",\"Vitalis Salvius\",\"Laelius Furius\",\"Fulcinia Tarquinius\",\"Orestius Marius\",\"Servius Damasus\",\"Rullianus Memmius\",\"Laetorius Coruncanius\",\"Blaesus Cornelius\",\"Varian Lollius\",\"Severus Iuventius\",\"Antonius Cincinnatus\",\"Salvius Postumius\",\"Ogulnius Bonifacius\",\"Tertius Latinius\",\"Florian Cestius\",\"Gnaeus Ogulnius\",\"Constantinus Novellius\",\"Bruttius Hortensius\",\"Herennius Camillius\",\"Balbinus Rabirius\",\"Cosconius Lamponius\",\"Laecanius Coruncanius\",\"Scribonius Probus\",\"Sergius Decimus\",\"Mallius Valens\",\"Appius Considius\",\"Aurelianus Blasius\",\"Manilius Baebius\",\"Laelius Menenius\",\"Marian Aurelianus\",\"Petreius Flaccus\",\"Scribonius Hirrus\",\"Segulius Volumnius\",\"Severian Nautius\",\"Scribonius Flavius\",\"Severus Egnatuleius\",\"Quirinius Rutilius\",\"Pupius Paternus\",\"Urbanus Acilius\",\"Avidius Maecenas\",\"Pilus Stertinius\",\"Domitian Baebius\",\"Publius Quintus\",\"Modius Sergius\",\"Rebilus Silvius\",\"Pedius Hostilius\",\"Publicius Sornatius\",\"Nautius Blasius\",\"Sedatius Cornelius\",\"Ovidius Balbus\",\"Cassio Caelian\",\"Pupius Nasidienus\",\"Geminius Suetonius\",\"Verecundus Manius\",\"Lollius Nasidius\",\"Minicius Crispus\",\"Festinius Quirinius\",\"Segulius Maximian\",\"Rufrius Atilius\",\"Salinator Damasus\",\"Hilarianus Caelius\",\"Blaesus Fidius\",\"Mamilius Sidonius\",\"Maecenas Fabianus\",\"Mallius Marian\",\"Marcus Nero\",\"Sextius Nasennius\",\"Florianus Valens\",\"Potitius Urbicus\",\"Gratian Iuventius\",\"Munatius Candidus\",\"Terentius Atilius\",\"Oppius Sergius\",\"Domitian Fidius\",\"Hostius Maecenas\",\"Nero Martialis\",\"Blaesus Rufinus\",\"Cispius Caesennius\",\"Pasidienus Germanicus\",\"Spurius Calpurnius\",\"Hordeonius Priscus\",\"Laetorius Appius\",\"Avidius Considius\",\"Sestius Aulus\",\"Terentius Ostorius\",\"Modius Pacilius\",\"Herennius Titus\",\"Orestius Murena\",\"Fabius Sentius\",\"Eborius Licinius\",\"Calpurnius Claudius\",\"Iunius Sergian\",\"Cornutus Sempronius\",\"Varian Porcius\",\"Candidus Papinius\",\"Lucius Rutilius\",\"Fabianus Junius\",\"Herennius Sempronius\",\"Felix Cyprian\",\"Sulpicius Rutilius\",\"Junius Nautius\",\"Pedius Lucanus\",\"Herennius Nasidienus\",\"Paelignus Florian\",\"Silius Tertius\",\"Betilienus Sentius\",\"Afranius Brennus\",\"Celsus Paelignus\",\"Fundanus Gracchus\",\"Lollius Blasius\",\"Servius Pedius\",\"Seneca Probus\",\"Verecundus Pontidius\",\"Cordius Priscus\",\"Petilius Hortensius\",\"Tacitus Livius\",\"Aquillius Foslius\",\"Icilius Salvian\",\"Stertinius Terentius\",\"Decimus Laronius\",\"Fidenas Naevius\",\"Petilius Lentulus\",\"Publicius Marius\",\"Sextius Plinius\",\"Perperna Lucius\",\"Juvenalis Cocceius\",\"Lucius Granius\",\"Bonifacius Fannius\",\"Burrienus Hostius\",\"Baebius Galerius\",\"Salvian Cato\",\"Latinius Cominius\",\"Pacilius Bonifacius\",\"Sestius Betilienus\",\"Plautius Dacian\",\"Icilius Publicius\",\"Suetonius Martialis\",\"Opellius Spurius\",\"Numerius Juvenalis\",\"Diocletian Nerva\",\"Avidius Salvius\",\"Cordius Pettius\",\"Sittius Gratian\",\"Valens Galerius\",\"Cosconius Paconius\",\"Titus Cassius\",\"Quintus Sulpicius\",\"Paulinus Silanus\",\"Blasius Festus\",\"Sedatius Mescinius\",\"Cicero Sulpicius\",\"Silanus Fannius\",\"Urbicus Pedius\",\"Albinus Sepullius\",\"Sedatius Laurentius\",\"Cassius Lacerius\",\"Petreius Festus\",\"Sextus Bruttius\",\"Mallius Florianus\",\"Terentius Poppaedius\",\"Mamilius Novellius\",\"Nasidius Sittius\",\"Duronius Horatius\",\"Geminius Salvian\",\"Icilius Maximian\",\"Blossius Fundanus\",\"Nerva Fabrinius\",\"Hirrus Sextus\",\"Novellius Felix\",\"Candidus Numonius\",\"Blasius Gaius\",\"Munatius Ollius\",\"Flavius Tacitus\",\"Porcius Aurelianus\",\"Sergian Probus\",\"Mescinius Domitian\",\"Acilius Plinius\",\"Drusus Longinius\",\"Camillius Caedicius\",\"Salvian Saturninus\",\"Fundanus Appius\",\"Nasidienus Bruttius\",\"Oppius Marcus\",\"Cantilius Novellius\",\"Fidenas Varian\",\"Nazarius Maxentius\",\"Calventius Gaius\",\"Calventius Cassianus\",\"Geminius Cicereius\",\"Sornatius Furius\",\"Afranius Urbicus\",\"Faustus Herennius\",\"Orchius Drusus\",\"Claudius Cominius\",\"Aquillius Acilius\",\"Modius Flavius\",\"Bonifacius Nasidienus\",\"Hostius Statilius\",\"Lucius Sepullius\",\"Papirius Seccius\",\"Magnentius Clodius\",\"Cilnius Laurentius\",\"Vitalis Cispius\",\"Fidius Pupius\",\"Papirius Titus\",\"Fulcinia Vincentius\",\"Marius Gracchus\",\"Segulius Vespasian\",\"Acilius Fabrinius\",\"Sidonius Cloelius\",\"Catilius Dacian\",\"Florian Primitivus\",\"Plautius Atilius\",\"Pinarus Geminius\",\"Rufinus Cethegus\",\"Urbicus Maxentius\",\"Manius Maecilius\",\"Publius Menenius\",\"Tarquinius Zeno\",\"Plinius Plautius\",\"Fulcinia Tertius\",\"Castorius Egnatuleius\",\"Plautius Duronius\",\"Terentius Scribonius\",\"Januarius Honorius\",\"Nerva Icilius\",\"Blossius Vincentius\",\"Servilius Icilius\",\"Sextius Cassio\",\"Marcus Julius\",\"Paconius Severus\",\"Donatus Paelignus\",\"Fabrinius Varian\",\"Persius Blaesus\",\"Pasidienus Cispius\",\"Pacilius Tacitus\",\"Memmius Ollius\",\"Belisarius Martialis\",\"Numerius Marius\",\"Numerius Sergian\",\"Hostilius Ingenuus\",\"Fabius Castorius\",\"Caesennius Petilius\",\"Festus Vitalis\",\"Sedatius Aelius\",\"Sulpicius Silanus\",\"Semellius Considius\",\"Flaccus Sulpicius\",\"Cassius Laberius\",\"Regulus Iulius\",\"Acilius Sextius\",\"Sempronius Cominius\",\"Caedicius Mamilius\",\"Rebilus Dacian\",\"Paconius Cordius\",\"Genucius Priscus\",\"Atilius Menenius\",\"Claudius Pontidius\",\"Crispus Horatius\",\"Sestius Opimius\",\"Icilius Curtius\",\"Statilius Didius\",\"Nerva Ninnius\",\"Florian Valens\",\"Gallus Sidonius\",\"Cyprian Eutropius\",\"Egnatuleius Sergian\",\"Servius Murena\",\"Vitus Gracchus\",\"Silvius Belisarius\",\"Statilius Seccius\",\"Blasius Munatius\",\"Damasus Damasus\",\"Atilius Sallustius\",\"Sornatius Oppius\",\"Crispinus Blasius\",\"Numicius Florianus\",\"Papinius Hilarianus\",\"Nero Lartius\",\"Fidius Poppaedius\",\"Priscus Albinius\",\"Honorius Genucius\",\"Rullianus Paconius\",\"Ovidius Mucius\",\"Modius Caelian\",\"Considius Crispus\",\"Eutropius Cincinnatus\",\"Postumius Domitian\",\"Servilius Novellius\",\"Gallus Draco\",\"Dacian Antonius\",\"Cyprian Naevius\",\"Memmius Numitorius\",\"Sextius Gallus\",\"Felix Ollius\",\"Opimius Manius\",\"Lacerius Fabianus\",\"Nasennius Porcius\",\"Matinius Cinna\",\"Florianus Brennus\",\"Horatius Cinna\",\"Vitalis Minicius\",\"Aemilius Petreius\",\"Laurentius Nautius\",\"Marcius Ofilius\",\"Hirrus Opimius\",\"Blossius Matienus\",\"Sextus Novellius\",\"Murena Calpurnius\",\"Cato Germanicus\",\"Nero Atilius\",\"Plinius Cato\",\"Cornelius Decentius\",\"Valens Dacian\",\"Castorius Octavenus\",\"Nero Silanus\",\"Tarquinius Lartius\",\"Paulinus Sempronius\",\"Postumius Candidus\",\"Lucius Quirinius\",\"Regulus Festus\",\"Cilnius Sextus\",\"Percennius Stertinius\",\"Ollius Calventius\",\"Decentius Eutropius\",\"Valens Nerva\",\"Cicereius Pomponius\",\"Plautius Acilius\",\"Porcius Numonius\",\"Brennus Cilnius\",\"Nigidius Salvidienus\",\"Pontidius Saturninus\",\"Pettius Horatius\",\"Fabianus Gracchus\",\"Probus Cato\",\"Hadrian Eborius\",\"Livius Silanus\",\"Egnatius Nonius\",\"Tarquinius Ostorius\",\"Gracchus Flaccus\",\"Caesennius Diocletian\",\"Calpurnius Silius\",\"Caesennius Seneca\",\"Fabrinius Livius\",\"Ollius Nazarius\",\"Plautius Faustus\",\"Tarquinius Numicius\",\"Urbanus Constantinus\",\"Sextius Martialis\",\"Sittius Sidonius\",\"Seneca Silanus\",\"Tacitus Domitian\",\"Norbanus Cethegus\",\"Silanus Cethegus\",\"Iulius Stertinius\",\"Silanus Petreius\",\"Foslius Furius\",\"Justus Postumius\",\"Munatius Orchius\",\"Varian Matienus\",\"Numerius Festinius\",\"Marius Vincentius\",\"Sittius Blaesus\",\"Caelian Tacitus\",\"Martialis Pacilius\",\"Ennius Geminius\",\"Laetorius Cyprian\",\"Laurentius Sulpicius\",\"Longinius Cominius\",\"Sabucius Caninius\",\"Cornelius Junius\",\"Cinna Scribonius\",\"Fundanus Postumius\",\"Marcius Claudius\",\"Lollius Musonius\",\"Naevius Laetorius\",\"Egnatuleius Pomponius\",\"Sittius Januarius\",\"Calventius Festinius\",\"Iunius Perperna\",\"Acilius Rufius\",\"Lacerius Cassianus\",\"Pilus Naevius\",\"Opimius Urbanus\",\"Silanus Gnaeus\",\"Geminius Maecilius\",\"Numitorius Vibius\",\"Maecilius Martialis\",\"Maecenas Cassio\",\"Longinius Crispinus\",\"Terentius Valens\",\"Salvius Memmius\",\"Duronius Opimius\",\"Tiberius Rebilus\",\"Cordius Quintus\",\"Iuventius Fabrinius\",\"Faustus Seneca\",\"Papirius Cocceius\",\"Lucanus Nasidius\",\"Coruncanius Lucanus\",\"Salinator Balbinus\",\"Publicius Numerius\",\"Sidonius Festinius\",\"Sempronius Terentius\",\"Lamponius Opimius\",\"Primitivus Manius\",\"Nasidius Flavius\",\"Julius Probus\",\"Naevius Aquillius\",\"Placidus Rabirius\",\"Justus Seneca\",\"Aurelianus Albinus\",\"Petreius Cloelius\",\"Lucanus Decentius\",\"Constantinus Ogulnius\",\"Laronius Sextius\",\"Egnatius Paelignus\",\"Licinius Coruncanius\",\"Pius Gaius\",\"Marcius Orbilius\",\"Fabrinius Cloelius\",\"Catius Calventius\",\"Lucanus Lucretius\",\"Betilienus Albinus\",\"Spurius Gaius\",\"Ofilius Fabrinius\",\"Camillius Lollius\",\"Manilius Caelian\",\"Papirius Gallus\",\"Decimus Cocceius\",\"Quirinius Blossius\",\"Latinius Munatius\",\"Stertinius Orchius\",\"Bonifacius Cocceius\",\"Nautius Cocceius\",\"Cornelius Statilius\",\"Aquillius Vitalis\",\"Hirrus Camillius\",\"Coruncanius Mucius\",\"Constantinus Laurentius\",\"Lacerius Numerius\",\"Crispus Numerius\",\"Hortensius Plautius\",\"Paternus Rufius\",\"Sergius Burrienus\",\"Brennus Fulvius\",\"Caesennius Persius\",\"Cispius Camillius\",\"Gordian Camillus\",\"Camillus Varro\",\"Severus Mescinius\",\"Laecanius Terentius\",\"Justus Manius\",\"Perperna Salinator\",\"Aurelianus Damasus\",\"Mescinius Marcius\",\"Ofilius Geminius\",\"Placidus Pomponius\",\"Silanus Egnatius\",\"Fidenas Crispus\",\"Secundus Rebilus\",\"Numicius Decentius\",\"Statilius Cyprian\",\"Ravillius Mescinius\",\"Hirrus Salvian\",\"Aurelianus Silvius\",\"Nazarius Lollius\",\"Ovidius Laurentius\",\"Decimus Marcellus\",\"Rullianus Probus\",\"Sabucius Sestius\",\"Maximian Burrienus\",\"Cyprian Granius\",\"Hostilius Magnentius\",\"Pius Aelius\",\"Pontidius Gallus\",\"Marcellus Salvian\",\"Messalla Nasidienus\",\"Modius Cloelius\",\"Naevius Priscus\",\"Livius Caecilius\",\"Salinator Laronius\",\"Appius Sextius\",\"Ogulnius Ostorius\",\"Cinna Varro\",\"Baebius Sabucius\",\"Florianus Laetorius\",\"Caecilius Manilius\",\"Percennius Duronius\",\"Appius Longinius\",\"Ostorius Scribonius\",\"Salinator Granius\",\"Burrienus Tertius\",\"Papirius Domitius\",\"Latinius Lollius\",\"Albinus Cicereius\",\"Hordeonius Messalla\",\"Potitius Ofilius\",\"Titus Fulcinia\",\"Hadrian Petilius\",\"Sicinius Quirinius\",\"Martialis Juvenalis\",\"Afranius Numitorius\",\"Caedicius Foslius\",\"Livius Sedatius\",\"Mucius Saturninus\",\"Lucius Betilienus\",\"Baebius Publius\",\"Hirrus Felix\",\"Laecanius Anatolius\",\"Manius Secundus\",\"Ogulnius Paconius\",\"Cilnius Betilienus\",\"Sextus Orchius\",\"Nigidius Mescinius\",\"Aurelianus Marcus\",\"Sempronius Vincentius\",\"Geminius Horatius\",\"Balbinus Postumius\",\"Quintilianus Lentulus\",\"Secundus Fabianus\",\"Avidius Tiberius\",\"Nasennius Tertius\",\"Salvius Laetorius\",\"Stertinius Didius\",\"Pacilius Spurius\",\"Gratian Coruncanius\",\"Blaesus Baebius\",\"Opellius Orbilius\",\"Martialis Laronius\",\"Drusus Fabianus\",\"Varian Cassianus\",\"Saturninus Festus\",\"Poppaedius Ostorius\",\"Servilius Lucretius\",\"Poppaedius Pettius\",\"Caelian Opimius\",\"Ostorius Laronius\",\"Cethegus Munatius\",\"Cilnius Rufrius\",\"Mucius Menenius\",\"Amulius Caninius\",\"Appius Octavius\",\"Burrienus Caesennius\",\"Hostilius Eborius\",\"Vitus Plautius\",\"Pontidius Maximian\",\"Ovidius Cassianus\",\"Ninnius Poppaedius\",\"Agrippa Fundanus\",\"Sulpicius Cantilius\",\"Longinius Justus\",\"Icilius Marian\",\"Palpellius Seneca\",\"Pilus Secundinius\",\"Sepullius Galerius\",\"Verecundus Laronius\",\"Iulius Valens\",\"Bruttius Salvian\",\"Marcellus Eborius\",\"Cispius Cilnius\",\"Caelian Decentius\",\"Paulinus Draco\",\"Fabius Geta\",\"Nonius Probus\",\"Belisarius Caesennius\",\"Gracchus Celsus\",\"Papirius Nigidius\",\"Varro Pinarus\",\"Latinius Aquillius\",\"Maximian Ollius\",\"Quinctilius Nasidius\",\"Fundanus Persius\",\"Cyprian Foslius\",\"Priscus Felix\",\"Stertinius Gracchus\",\"Tarquinius Galerius\",\"Gordian Drusus\",\"Honorius Damasus\",\"Marius Ollius\",\"Egnatuleius Paelignus\",\"Marcius Coruncanius\",\"Sextus Octavius\",\"Ovidius Icilius\",\"Burrienus Perperna\",\"Balbinus Urbicus\",\"Laetorius Cornelius\",\"Furius Sidonius\",\"Cicereius Fabrinius\",\"Gratian Maecilius\",\"Camillius Rullianus\",\"Novellius Pinarus\",\"Cocceius Cordius\",\"Palpellius Sextius\",\"Eutropius Julius\",\"Pilus Murena\",\"Numonius Rabirius\",\"Lucretius Nonius\",\"Damasus Junius\",\"Rufius Celsus\",\"Cordius Publicius\",\"Blasius Statilius\",\"Cincinnatus Nonius\",\"Orchius Potitius\",\"Caedicius Cornelius\",\"Faustus Sornatius\",\"Salinator Hostius\",\"Antonius Silius\",\"Eutropius Cornificius\",\"Nasidienus Secundinius\",\"Sepullius Fulcinia\",\"Ennius Flaccus\",\"Seccius Pius\",\"Pasidienus Brennus\",\"Vespasian Valens\",\"Gallus Juvenalis\",\"Florian Lamponius\",\"Zeno Considius\",\"Iulius Percennius\",\"Primitivus Domitius\",\"Sergian Blasius\",\"Vitalis Cornutus\",\"Flaccus Marcellus\",\"Rebilus Pupius\",\"Claudius Scribonius\",\"Cinna Decentius\",\"Pacilius Orchius\",\"Porcius Claudius\",\"Caesennius Decentius\",\"Potitius Salinator\",\"Rebilus Ovidius\",\"Nerva Varro\",\"Florianus Regulus\",\"Tertius Laecanius\",\"Maecenas Marian\",\"Stertinius Hadrian\",\"Numitorius Rebilus\",\"Burrienus Seccius\",\"Sidonius Maximus\",\"Marian Silanus\",\"Cassius Hordeonius\",\"Quintilianus Laronius\",\"Pedius Orestius\",\"Dacian Semellius\",\"Acilius Pedius\",\"Quirinius Plautius\",\"Cominius Ofilius\",\"Paternus Manilius\",\"Hortensius Baebius\",\"Messalla Sepullius\",\"Salinator Brennus\",\"Marcellus Varian\",\"Sulpicius Papirius\",\"Bonifacius Tarquinius\",\"Germanicus Sabucius\",\"Statilius Cinna\",\"Cosconius Acilius\",\"Mallius Severian\",\"Gallus Pedius\",\"Cato Cyprian\",\"Papirius Norbanus\",\"Tiberius Salinator\",\"Tarquinius Nigidius\",\"Papinius Murena\",\"Petreius Candidus\",\"Marcellus Festus\",\"Titus Caninius\",\"Paulinus Catilius\",\"Marius Fidenas\",\"Granius Egnatuleius\",\"Papinius Volumnius\",\"Sentius Lucanus\",\"Potitius Musonius\",\"Cornutus Silanus\",\"Lucretius Marcius\",\"Priscus Florianus\",\"Norbanus Percennius\",\"Norbanus Curtius\",\"Porcius Nerva\",\"Spurius Valerius\",\"Appius Palpellius\",\"Maecilius Plinius\",\"Appius Plinius\",\"Sentius Fidius\",\"Rebilus Constantinus\",\"Constantinus Castorius\",\"Pasidienus Laetorius\",\"Agrippa Silanus\",\"Cornelius Mamilius\",\"Calventius Domitian\",\"Balbus Sidonius\",\"Maxentius Pius\",\"Quinctilius Gordian\",\"Postumius Secundus\",\"Florianus Primitivus\",\"Petreius Novellius\",\"Manius Sabucius\",\"Bruttius Mescinius\",\"Calventius Cantilius\",\"Nigidius Terentius\",\"Pomponius Modius\",\"Domitius Gracchus\",\"Severian Damasus\",\"Verecundus Hordeonius\",\"Pilus Numonius\",\"Fannius Gratian\",\"Baebius Sempronius\",\"Curtius Calpurnius\",\"Cloelius Lucretius\",\"Primitivus Porcius\",\"Martialis Caelian\",\"Domitius Ofilius\",\"Calpurnius Rutilius\",\"Perperna Ostorius\",\"Memmius Cethegus\",\"Latinius Eborius\",\"Suetonius Varian\",\"Geminius Semellius\",\"Sempronius Catilius\",\"Nasennius Ogulnius\",\"Hostius Cicereius\",\"Caesennius Sallustius\",\"Geminius Duronius\",\"Cordius Lucius\",\"Licinius Vitalis\",\"Ravillius Flavius\",\"Gallus Mallius\",\"Marcus Fannius\",\"Gracchus Julius\",\"Camillius Balbinus\",\"Cato Bruttius\",\"Cilnius Iunius\",\"Gabinius Latinius\",\"Laelius Sergius\",\"Sornatius Palpellius\",\"Florian Granius\",\"Aemilius Florian\",\"Rabirius Faustus\",\"Aulus Seneca\",\"Sittius Marcius\",\"Nero Sittius\",\"Lamponius Urbicus\",\"Iunius Foslius\",\"Sergius Geminius\",\"Munatius Primitivus\",\"Munatius Caedicius\",\"Albinius Norbanus\",\"Gracchus Pedius\",\"Pacilius Valens\",\"Salvius Pius\",\"Festinius Herennius\",\"Fannius Mescinius\",\"Norbanus Terentius\",\"Matienus Cassianus\",\"Gabinius Severus\",\"Lucius Valens\",\"Numonius Murena\",\"Antonius Felix\",\"Damasus Servilius\",\"Fabrinius Cestius\",\"Hortensius Mallius\",\"Seneca Norbanus\",\"Camillus Paternus\",\"Livius Porcius\",\"Salinator Lucius\",\"Ennius Nigidius\",\"Geta Nautius\",\"Cloelius Decimus\",\"Caecilius Salinator\",\"Seccius Hadrian\",\"Pinarus Granius\",\"Hadrian Licinius\",\"Aquillius Cornificius\",\"Ingenuus Priscus\",\"Cassianus Maximian\",\"Betilienus Paternus\",\"Cassio Paulinus\",\"Cosconius Belisarius\",\"Memmius Salvius\",\"Gratian Gratian\",\"Licinius Perperna\",\"Sempronius Vitalis\",\"Pupius Balbus\",\"Caelius Blossius\",\"Rufrius Mallius\",\"Fulcinia Potitius\",\"Sallustius Aelius\",\"Sergius Sabucius\",\"Sittius Iunius\",\"Maxentius Draco\",\"Salvius Sergian\",\"Pettius Camillus\",\"Foslius Cassianus\",\"Sedatius Decentius\",\"Murena Secundus\",\"Herennius Caedicius\",\"Manius Lamponius\",\"Gnaeus Pinarus\",\"Musonius Cassio\",\"Silanus Pompeius\",\"Scribonius Nautius\",\"Anatolius Cilnius\",\"Lucretius Cinna\",\"Novellius Placidus\",\"Ravillius Gallus\",\"Egnatuleius Palpellius\",\"Poppaedius Januarius\",\"Rufius Licinius\",\"Priscus Aquillius\",\"Scribonius Horatius\",\"Aurelianus Lamponius\",\"Fidius Sempronius\",\"Aelius Priscus\",\"Gnaeus Nazarius\",\"Tiberius Cassio\",\"Acilius Festinius\",\"Caelius Musonius\",\"Germanicus Aemilius\",\"Marcius Manius\",\"Naevius Agrippa\",\"Opellius Quintilianus\",\"Cordius Volumnius\",\"Papinius Vitalis\",\"Varian Aemilius\",\"Marcius Vibius\",\"Camillius Celsus\",\"Cocceius Burrienus\",\"Seccius Martialis\",\"Candidus Semellius\",\"Gratian Amulius\",\"Juvenalis Pinarus\",\"Caninius Salvidienus\",\"Calpurnius Maximian\",\"Claudius Duronius\",\"Januarius Scribonius\",\"Germanicus Florian\",\"Maximus Sidonius\",\"Balbus Latinius\",\"Amulius Valerius\",\"Egnatuleius Servius\",\"Probus Vibius\",\"Candidus Constantinus\",\"Pompeius Juvenalis\",\"Magnentius Coruncanius\",\"Afranius Ravillius\",\"Pasidienus Salinator\",\"Gracchus Porcius\",\"Didius Opimius\",\"Lucanus Cosconius\",\"Cassio Murena\",\"Regulus Lucius\",\"Gracchus Ennius\",\"Pius Maecenas\",\"Cornificius Quintilianus\",\"Calpurnius Quintilianus\",\"Marius Baebius\",\"Sergian Cicereius\",\"Numicius Iulius\",\"Baebius Vincentius\",\"Zeno Albinius\",\"Priscus Aelius\",\"Crispinus Postumius\",\"Domitian Eborius\",\"Marcus Antonius\",\"Junius Maecilius\",\"Gaius Sepullius\",\"Bonifacius Caedicius\",\"Valentinian Mescinius\",\"Cloelius Coruncanius\",\"Novellius Lucius\",\"Orchius Sentius\",\"Munatius Rufrius\",\"Magnentius Terentius\",\"Nerva Numonius\",\"Cassianus Secundus\",\"Fidius Matienus\",\"Maecilius Maxentius\",\"Terentius Cilnius\",\"Aquillius Silius\",\"Zeno Maxentius\",\"Aulus Gabinius\",\"Sulpicius Secundus\",\"Maximian Galerius\",\"Laelius Varian\",\"Cinna Manilius\",\"Aurelianus Lentulus\",\"Stertinius Servilius\",\"Draco Hadrian\",\"Quintus Blasius\",\"Gratian Nazarius\",\"Magnentius Maximian\",\"Fulcinia Ogulnius\",\"Spurius Naevius\",\"Lucretius Nasennius\",\"Octavenus Fidius\",\"Sicinius Pettius\",\"Justus Paternus\",\"Caedicius Mescinius\",\"Cocceius Quintus\",\"Pompeius Mamilius\",\"Pettius Sentius\",\"Numicius Marcellus\",\"Vespasian Fundanus\",\"Poppaedius Rullianus\",\"Geminius Cornificius\",\"Orestius Placidus\",\"Aemilius Severus\",\"Tertius Anatolius\",\"Bonifacius Hilarianus\",\"Ingenuus Quinctilius\",\"Pilus Ovidius\",\"Catilius Laberius\",\"Appius Ostorius\",\"Perperna Valens\",\"Sextus Perperna\",\"Cinna Longinius\",\"Sulpicius Marius\",\"Vitalis Lucius\",\"Cordius Gaius\",\"Eutropius Sallustius\",\"Paulinus Octavenus\",\"Persius Publius\",\"Fabrinius Duronius\",\"Sergius Gnaeus\",\"Menenius Honorius\",\"Semellius Rebilus\",\"Caesennius Octavenus\",\"Sempronius Sedatius\",\"Ravillius Papirius\",\"Mallius Ingenuus\",\"Cloelius Murena\",\"Manilius Festinius\",\"Sittius Curtius\",\"Cantilius Perperna\",\"Nasennius Juvenalis\",\"Menenius Modius\",\"Norbanus Pilus\",\"Geta Urbanus\",\"Palpellius Servilius\",\"Pupius Pilus\",\"Pius Hostius\",\"Lamponius Oppius\",\"Considius Rufrius\",\"Geta Didius\",\"Mescinius Caelian\",\"Hilarianus Cestius\",\"Ingenuus Faustus\",\"Sergius Aemilius\",\"Honorius Ennius\",\"Galerius Orestius\",\"Caninius Rufius\",\"Papirius Albinius\",\"Marcus Domitian\",\"Verecundus Florian\",\"Zeno Vitus\",\"Caecilius Iulius\",\"Opimius Petreius\",\"Terentius Sepullius\",\"Balbus Porcius\",\"Cassianus Paulinus\",\"Eutropius Ennius\",\"Pupius Lentulus\",\"Salvius Salinator\",\"Sidonius Laelius\",\"Granius Fidenas\",\"Numitorius Domitius\",\"Vitus Bonifacius\",\"Pilus Acilius\",\"Brennus Opellius\",\"Paelignus Potitius\",\"Laecanius Oppius\",\"Aurelianus Acilius\",\"Aurelianus Fundanus\",\"Magnentius Ravillius\",\"Lartius Secundinius\",\"Minicius Festinius\",\"Laecanius Ingenuus\",\"Gaius Marcellus\",\"Aquillius Cethegus\",\"Norbanus Manilius\",\"Priscus Munatius\",\"Antonius Gaius\",\"Sicinius Honorius\",\"Perperna Sempronius\",\"Naevius Maximian\",\"Octavius Aelius\",\"Varian Nonius\",\"Antonius Livius\",\"Flavius Quintilianus\",\"Balbus Magnentius\",\"Donatus Domitius\",\"Silanus Saturninus\",\"Valens Varian\",\"Zeno Castorius\",\"Appius Fabius\",\"Maximus Nigidius\",\"Didius Gallus\",\"Silvius Anatolius\",\"Avidius Musonius\",\"Zeno Anatolius\",\"Maecilius Silius\",\"Blasius Gordian\",\"Latinius Pinarus\",\"Tacitus Murena\",\"Sertorius Murena\",\"Semellius Lucretius\",\"Matinius Cassianus\",\"Cyprian Cato\",\"Urbanus Fidius\",\"Nazarius Fabianus\",\"Plinius Octavius\",\"Percennius Maximian\",\"Anatolius Fidius\",\"Afranius Modius\",\"Norbanus Laelius\",\"Brennus Nautius\",\"Murena Regulus\",\"Marius Nasidius\",\"Saturninus Caninius\",\"Camillius Mucius\",\"Albinus Plautius\",\"Blaesus Diocletian\",\"Laronius Opimius\",\"Ovidius Terentius\",\"Pinarus Fidenas\",\"Caninius Lollius\",\"Ingenuus Stertinius\",\"Iunius Nero\",\"Perperna Modius\",\"Rabirius Gnaeus\",\"Cornelius Silius\",\"Ovidius Albinius\",\"Servius Magnentius\",\"Nigidius Mucius\",\"Pius Titus\",\"Numerius Ogulnius\",\"Verecundus Spurius\",\"Secundus Rufrius\",\"Severian Ennius\",\"Genucius Lucanus\",\"Vincentius Geminius\",\"Rutilius Perperna\",\"Tacitus Fidius\",\"Seneca Diocletian\",\"Maecenas Publius\",\"Ingenuus Donatus\",\"Opellius Furius\",\"Vitus Juvenalis\",\"Rutilius Cincinnatus\",\"Urbicus Varian\",\"Sittius Laetorius\",\"Genucius Nigidius\",\"Porcius Januarius\",\"Rufinus Crispinus\",\"Vespasian Vitalis\",\"Nero Gabinius\",\"Rabirius Poppaedius\",\"Cincinnatus Numonius\",\"Marcus Sempronius\",\"Sidonius Caelian\",\"Januarius Quirinius\",\"Caesennius Sestius\",\"Porcius Albinus\",\"Maecilius Lucius\",\"Persius Cordius\",\"Florian Domitian\",\"Julius Cinna\",\"Damasus Hostilius\",\"Sittius Florian\",\"Aemilius Cincinnatus\",\"Marius Mamilius\",\"Suetonius Paconius\",\"Agrippa Sentius\",\"Vincentius Festus\",\"Celsus Cispius\",\"Lucretius Statilius\",\"Cassianus Nerva\",\"Hirrus Caedicius\",\"Priscus Cispius\",\"Salvidienus Drusus\",\"Foslius Felix\",\"Ogulnius Foslius\",\"Burrienus Silvius\",\"Severian Verus\",\"Postumius Cato\",\"Geta Lartius\",\"Munatius Granius\",\"Agrippa Lucius\",\"Paconius Laetorius\",\"Crispinus Sestius\",\"Poppaedius Cornificius\",\"Tarquinius Paelignus\",\"Duronius Ennius\",\"Manius Norbanus\",\"Fabius Rullianus\",\"Florian Caecilius\",\"Cassianus Papinius\",\"Orbilius Festinius\",\"Lentulus Candidus\",\"Silvius Caedicius\",\"Cornificius Marian\",\"Sergian Caesennius\",\"Tarquinius Livius\",\"Naevius Marian\",\"Varro Gallus\",\"Egnatuleius Aulus\",\"Ravillius Domitian\",\"Calpurnius Salvius\",\"Iulius Curtius\",\"Potitius Donatus\",\"Sertorius Galerius\",\"Hirrus Cincinnatus\",\"Horatius Postumius\",\"Baebius Junius\",\"Sornatius Pinarus\",\"Curtius Rufrius\",\"Rufius Orbilius\",\"Porcius Cispius\",\"Minicius Papinius\",\"Antonius Cornutus\",\"Salvidienus Julius\",\"Nasidienus Egnatuleius\",\"Primitivus Maximian\",\"Considius Burrienus\",\"Junius Salvidienus\",\"Mucius Crispus\",\"Sulpicius Pontidius\",\"Rebilus Opellius\",\"Afranius Lucretius\",\"Fulcinia Florian\",\"Memmius Novellius\",\"Ofilius Calpurnius\",\"Fabianus Maecenas\",\"Domitius Hirrus\",\"Claudius Marian\",\"Vitalis Sempronius\",\"Nasidius Celsus\",\"Aelius Pacilius\",\"Fulcinia Crispinus\",\"Percennius Diocletian\",\"Mamilius Suetonius\",\"Duronius Appius\",\"Aelius Primitivus\",\"Paelignus Seccius\",\"Celsus Cassius\",\"Musonius Cicero\",\"Gaius Pontidius\",\"Mallius Anatolius\",\"Urbanus Flavius\",\"Vitus Festus\",\"Tarquinius Lentulus\",\"Musonius Fabius\",\"Poppaedius Amulius\",\"Magnentius Pompeius\",\"Burrienus Granius\",\"Porcius Suetonius\",\"Cassianus Justus\",\"Cinna Foslius\",\"Publicius Petilius\",\"Ennius Cominius\",\"Flaccus Pompeius\",\"Nero Mescinius\",\"Scribonius Laelius\",\"Rufrius Verus\",\"Atilius Nasennius\",\"Damasus Cilnius\",\"Salinator Manilius\",\"Valerius Flaccus\",\"Ollius Suetonius\",\"Fannius Justus\",\"Maxentius Martialis\",\"Coruncanius Amulius\",\"Nasennius Secundinius\",\"Licinius Tacitus\",\"Cicero Messalla\",\"Sestius Iuventius\",\"Sornatius Valentinian\",\"Sittius Diocletian\",\"Galerius Cethegus\",\"Blaesus Paulinus\",\"Sextius Quintus\",\"Cestius Valens\",\"Januarius Caelian\",\"Sentius Nigidius\",\"Sextius Ogulnius\",\"Urbanus Quintus\",\"Publius Sergius\",\"Nigidius Geta\",\"Scribonius Eutropius\",\"Nonius Numicius\",\"Julius Nazarius\",\"Cosconius Rutilius\",\"Cyprian Decimus\",\"Antonius Menenius\",\"Bonifacius Laurentius\",\"Cordius Laecanius\",\"Lacerius Publicius\",\"Lucius Blasius\",\"Cethegus Nonius\",\"Antonius Marian\",\"Minicius Sertorius\",\"Diocletian Felix\",\"Verecundus Didius\",\"Lacerius Didius\",\"Vincentius Quirinius\",\"Juvenalis Pius\",\"Domitius Egnatius\",\"Egnatius Brennus\",\"Cornelius Urbanus\",\"Lollius Galerius\",\"Suetonius Iulius\",\"Gallus Murena\",\"Martialis Valerius\",\"Honorius Cominius\",\"Calventius Numitorius\",\"Silanus Damasus\",\"Horatius Blaesus\",\"Draco Constantinus\",\"Numicius Caelius\",\"Cloelius Pacilius\",\"Marian Numonius\",\"Herennius Plautius\",\"Quintilianus Pettius\",\"Maecilius Rullianus\",\"Betilienus Geta\",\"Quinctilius Festinius\",\"Laurentius Eborius\",\"Decentius Cyprian\",\"Gabinius Sittius\",\"Cilnius Icilius\",\"Postumius Quintus\",\"Gordian Crispus\",\"Genucius Maxentius\",\"Geminius Cosconius\",\"Servilius Agrippa\",\"Sulpicius Camillus\",\"Iuventius Ennius\",\"Opellius Varro\",\"Cornificius Orbilius\",\"Sentius Claudius\",\"Plinius Nazarius\",\"Vibius Fidius\",\"Maximian Munatius\",\"Cassianus Paternus\",\"Cestius Zeno\",\"Nerva Hordeonius\",\"Blaesus Cordius\",\"Sicinius Mallius\",\"Ostorius Sertorius\",\"Ravillius Persius\",\"Genucius Sornatius\",\"Rufrius Sextius\",\"Crispus Nazarius\",\"Poppaedius Cyprian\",\"Quinctilius Considius\",\"Caelian Crispinus\",\"Fabrinius Semellius\",\"Cosconius Ennius\",\"Cassio Bruttius\",\"Bonifacius Fidius\",\"Silvius Mamilius\",\"Cestius Messalla\",\"Cocceius Sornatius\",\"Brennus Crispus\",\"Varian Vespasian\",\"Plinius Sidonius\",\"Donatus Marian\",\"Orchius Icilius\",\"Junius Castorius\",\"Sidonius Stertinius\",\"Persius Bruttius\",\"Decimus Saturninus\",\"Sertorius Iuventius\",\"Severian Modius\",\"Verecundus Stertinius\",\"Suetonius Papirius\",\"Magnentius Fulcinia\",\"Gabinius Octavius\",\"Donatus Hostilius\",\"Pupius Festus\",\"Ennius Eborius\",\"Latinius Anatolius\",\"Germanicus Faustus\",\"Memmius Norbanus\",\"Iulius Terentius\",\"Martialis Cincinnatus\",\"Camillus Lamponius\",\"Lamponius Lentulus\",\"Orestius Blasius\",\"Nonius Fidenas\",\"Burrienus Laelius\",\"Ingenuus Egnatuleius\",\"Decentius Papinius\",\"Hostilius Horatius\",\"Naevius Hortensius\",\"Lucanus Junius\",\"Juvenalis Percennius\",\"Genucius Poppaedius\",\"Paternus Numerius\",\"Placidus Laronius\",\"Genucius Maecenas\",\"Caecilius Cyprian\",\"Hostilius Sextius\",\"Granius Ravillius\",\"Servilius Hadrian\",\"Maxentius Lentulus\",\"Nautius Cloelius\",\"Gratian Festus\",\"Poppaedius Sulpicius\",\"Octavenus Pedius\",\"Fabianus Statilius\",\"Maxentius Nazarius\",\"Caecilius Egnatuleius\",\"Constantinus Aemilius\",\"Hortensius Pacilius\",\"Porcius Candidus\",\"Murena Fabius\",\"Blaesus Festinius\",\"Martialis Brennus\",\"Ofilius Vitalis\",\"Servius Mallius\",\"Cassianus Cordius\",\"Spurius Livius\",\"Calventius Segulius\",\"Numicius Flaccus\",\"Rufius Latinius\",\"Juvenalis Dacian\",\"Cato Draco\",\"Cosconius Caninius\",\"Fabius Latinius\",\"Silanus Probus\",\"Horatius Granius\",\"Seccius Fabrinius\",\"Ostorius Blossius\",\"Persius Semellius\",\"Brennus Titus\",\"Agrippa Cestius\",\"Camillus Cominius\",\"Festinius Januarius\",\"Numerius Maxentius\",\"Antonius Matienus\",\"Marcus Tertius\",\"Curtius Fundanus\",\"Festus Naevius\",\"Pomponius Salvius\",\"Fundanus Vitus\",\"Quinctilius Numitorius\",\"Severian Iunius\",\"Potitius Sepullius\",\"Salvidienus Ofilius\",\"Servius Festinius\",\"Numicius Gnaeus\",\"Bruttius Caelian\",\"Persius Licinius\",\"Sabucius Segulius\",\"Aurelianus Genucius\",\"Martialis Sergian\",\"Candidus Rufinus\",\"Sempronius Sepullius\",\"Petilius Catius\",\"Valentinian Afranius\",\"Foslius Tarquinius\",\"Saturninus Brennus\",\"Priscus Eutropius\",\"Juvenalis Cornificius\",\"Aurelianus Sedatius\",\"Pedius Varian\",\"Fidius Iulius\",\"Maecilius Petreius\",\"Pinarus Orbilius\",\"Valens Terentius\",\"Hostius Maximus\",\"Primitivus Laelius\",\"Palpellius Horatius\",\"Bruttius Lollius\",\"Aquillius Rufrius\",\"Regulus Damasus\",\"Longinius Servilius\",\"Quintus Paelignus\",\"Nonius Aulus\",\"Hostilius Fabius\",\"Munatius Laelius\",\"Coruncanius Opellius\",\"Cicero Pontidius\",\"Diocletian Ostorius\",\"Regulus Probus\",\"Genucius Duronius\",\"Mamilius Caelius\",\"Matienus Cantilius\",\"Matinius Cantilius\",\"Herennius Caesennius\",\"Julius Ingenuus\",\"Numonius Afranius\",\"Honorius Calpurnius\",\"Sicinius Cornutus\",\"Porcius Martialis\",\"Sulpicius Valentinian\",\"Brennus Seneca\",\"Percennius Herennius\",\"Curtius Cinna\",\"Orchius Domitian\",\"Papirius Maxentius\",\"Maecilius Gracchus\",\"Claudius Salinator\",\"Martialis Gratian\",\"Julius Murena\",\"Honorius Numerius\",\"Nasidius Plinius\",\"Ogulnius Marius\",\"Ofilius Gallus\",\"Calventius Fabius\",\"Nasennius Musonius\",\"Persius Plautius\",\"Tarquinius Acilius\",\"Fabius Sicinius\",\"Salvius Magnentius\",\"Priscus Mucius\",\"Terentius Maxentius\",\"Severus Pacilius\",\"Caelian Zeno\",\"Murena Petreius\",\"Appius Iuventius\",\"Pompeius Longinius\",\"Hortensius Aulus\",\"Herennius Cestius\",\"Flavius Cocceius\",\"Silvius Sextius\",\"Naevius Caecilius\",\"Martialis Hadrian\",\"Domitian Maecenas\",\"Ogulnius Statilius\",\"Licinius Quintilianus\",\"Gordian Postumius\",\"Sepullius Flaccus\",\"Antonius Seneca\",\"Brennus Donatus\",\"Appius Sallustius\",\"Servius Papirius\",\"Lollius Sedatius\",\"Marius Ostorius\",\"Sepullius Valens\",\"Fulvius Faustus\",\"Numonius Iuventius\",\"Cassio Valerius\",\"Tarquinius Cocceius\",\"Cloelius Orbilius\",\"Horatius Placidus\",\"Statilius Cispius\",\"Antonius Seccius\",\"Silvius Zeno\",\"Naevius Ennius\",\"Draco Maecilius\",\"Lamponius Salinator\",\"Gratian Hortensius\",\"Candidus Nazarius\",\"Lucius Quinctilius\",\"Pasidienus Seneca\",\"Orchius Crispus\",\"Hilarianus Salinator\",\"Sextus Salinator\",\"Faustus Sestius\",\"Castorius Camillius\",\"Aquillius Musonius\",\"Menenius Paelignus\",\"Granius Sulpicius\",\"Pettius Lamponius\",\"Acilius Honorius\",\"Eborius Cantilius\",\"Crispus Caecilius\",\"Semellius Stertinius\",\"Agrippa Ravillius\",\"Laetorius Decimus\",\"Cloelius Matienus\",\"Horatius Munatius\",\"Terentius Sittius\",\"Sulpicius Nerva\",\"Sittius Placidus\",\"Castorius Statilius\",\"Germanicus Marcellus\",\"Caelius Damasus\",\"Sempronius Dacian\",\"Diocletian Marcus\",\"Cicereius Fundanus\",\"Messalla Ogulnius\",\"Aulus Gratian\",\"Justus Salvidienus\",\"Lacerius Petreius\",\"Primitivus Messalla\",\"Maximian Salvius\",\"Matienus Sempronius\",\"Foslius Orchius\",\"Semellius Julius\",\"Severus Octavenus\",\"Petilius Latinius\",\"Egnatuleius Musonius\",\"Domitius Lamponius\",\"Faustus Nasidienus\",\"Eutropius Catilius\",\"Rufrius Persius\",\"Calpurnius Severus\",\"Lucius Cato\",\"Januarius Paulinus\",\"Bruttius Rutilius\",\"Mescinius Appius\",\"Maecenas Murena\",\"Messalla Domitian\",\"Severus Memmius\",\"Numerius Coruncanius\",\"Valerius Numicius\",\"Cassio Sextius\",\"Maximus Balbus\",\"Tacitus Ovidius\",\"Fabianus Priscus\",\"Cassius Camillius\",\"Herennius Hostilius\",\"Camillus Sidonius\",\"Modius Nigidius\",\"Rullianus Appius\",\"Nasidienus Sertorius\",\"Sergian Sornatius\",\"Crispinus Murena\",\"Numicius Amulius\",\"Latinius Tertius\",\"Silius Rufrius\",\"Catius Gallus\",\"Germanicus Acilius\",\"Valentinian Camillius\",\"Castorius Pomponius\",\"Fabrinius Decimus\",\"Vitus Caecilius\",\"Festinius Tertius\",\"Celsus Marius\",\"Menenius Hilarianus\",\"Naevius Diocletian\",\"Camillus Laelius\",\"Aemilius Fulcinia\",\"Gabinius Herennius\",\"Iulius Sergius\",\"Candidus Ofilius\",\"Gabinius Zeno\",\"Herennius Postumius\",\"Magnentius Caelian\",\"Cincinnatus Fidius\",\"Claudius Nigidius\",\"Servius Vespasian\",\"Suetonius Maecenas\",\"Sextius Domitian\",\"Bruttius Norbanus\",\"Didius Maecenas\",\"Maxentius Zeno\",\"Numitorius Didius\",\"Regulus Blaesus\",\"Quintus Acilius\",\"Pontidius Vitus\",\"Salvidienus Munatius\",\"Rufius Longinius\",\"Paulinus Vibius\",\"Caesennius Fannius\",\"Postumius Laelius\",\"Cominius Lacerius\",\"Mallius Bonifacius\",\"Gallus Opellius\",\"Mucius Varian\",\"Laetorius Paulinus\",\"Terentius Baebius\",\"Sedatius Egnatuleius\",\"Lucretius Clodius\",\"Laronius Catius\",\"Plautius Matienus\",\"Ollius Amulius\",\"Baebius Salvian\",\"Suetonius Musonius\",\"Faustus Festinius\",\"Cosconius Rebilus\",\"Modius Mallius\",\"Felix Blasius\",\"Pasidienus Pompeius\",\"Postumius Catius\",\"Rebilus Seneca\",\"Varro Paulinus\",\"Catilius Catius\",\"Cicereius Marcellus\",\"Salvidienus Diocletian\",\"Constantinus Mallius\",\"Tiberius Sornatius\",\"Amulius Fulcinia\",\"Rufrius Tarquinius\",\"Pius Sergius\",\"Quirinius Placidus\",\"Mucius Fulcinia\",\"Publicius Marcellus\",\"Herennius Rufius\",\"Aquillius Poppaedius\",\"Lucanus Marcius\",\"Sextus Festinius\",\"Lollius Sidonius\",\"Opimius Marcus\",\"Cantilius Gracchus\",\"Publicius Modius\",\"Cornificius Fidenas\",\"Orchius Cassianus\",\"Modius Publicius\",\"Nasidienus Munatius\",\"Rufrius Faustus\",\"Orchius Laronius\",\"Pilus Opimius\",\"Decimus Felix\",\"Latinius Pompeius\",\"Tacitus Petreius\",\"Nero Foslius\",\"Numicius Opellius\",\"Felix Julius\",\"Fidius Brennus\",\"Lollius Hadrian\",\"Tiberius Gaius\",\"Sextius Sidonius\",\"Gabinius Maximian\",\"Quinctilius Petilius\",\"Secundinius Lollius\",\"Hortensius Honorius\",\"Menenius Sempronius\",\"Matienus Egnatius\",\"Orbilius Fidenas\",\"Sergian Latinius\",\"Nautius Publicius\",\"Galerius Cinna\",\"Caninius Blaesus\",\"Maecilius Pettius\",\"Tacitus Clodius\",\"Pinarus Pacilius\",\"Julius Marcellus\",\"Orbilius Sempronius\",\"Rufinus Honorius\",\"Mallius Manilius\",\"Horatius Crispinus\",\"Gabinius Fidenas\",\"Cinna Galerius\",\"Blaesus Dacian\",\"Messalla Geta\",\"Opellius Vitalis\",\"Dacian Silius\",\"Munatius Baebius\",\"Fannius Cispius\",\"Seneca Cornutus\",\"Sergius Numitorius\",\"Vitalis Faustus\",\"Cicero Regulus\",\"Anatolius Papinius\",\"Ingenuus Marius\",\"Sextus Fidius\",\"Gallus Marcius\",\"Licinius Sallustius\",\"Sergian Terentius\",\"Cocceius Publicius\",\"Maecilius Gnaeus\",\"Sallustius Marian\",\"Curtius Maximian\",\"Mucius Silvius\",\"Maecilius Quinctilius\",\"Cornificius Maecenas\",\"Maecilius Curtius\",\"Sextius Orestius\",\"Constantinus Geta\",\"Camillus Valentinian\",\"Hostius Galerius\",\"Laecanius Hilarianus\",\"Valerius Hordeonius\",\"Petilius Cosconius\",\"Antonius Octavius\",\"Albinus Servilius\",\"Sertorius Sertorius\",\"Claudius Cispius\",\"Secundus Pedius\",\"Primitivus Varian\",\"Silius Coruncanius\",\"Hirrus Tarquinius\",\"Memmius Hostilius\",\"Postumius Sepullius\",\"Suetonius Tiberius\",\"Sedatius Petreius\",\"Hirrus Betilienus\",\"Cassianus Iulius\",\"Matinius Varian\",\"Orbilius Cyprian\",\"Dacian Agrippa\",\"Volumnius Paelignus\",\"Ravillius Sestius\",\"Egnatius Hordeonius\",\"Catius Postumius\",\"Hortensius Cloelius\",\"Laelius Urbicus\",\"Crispus Sittius\",\"Hostius Norbanus\",\"Naevius Murena\",\"Galerius Opimius\",\"Silius Fabius\",\"Octavenus Longinius\",\"Draco Florian\",\"Silvius Cosconius\",\"Pupius Publicius\",\"Mallius Belisarius\",\"Ostorius Nonius\",\"Catius Geminius\",\"Lamponius Junius\",\"Festus Valens\",\"Antonius Caecilius\",\"Hilarianus Scribonius\",\"Avidius Urbanus\",\"Orchius Suetonius\",\"Januarius Fannius\",\"Baebius Caedicius\",\"Cinna Caelius\",\"Betilienus Marcellus\",\"Urbicus Suetonius\",\"Tiberius Fidius\",\"Hadrian Porcius\",\"Verus Decimus\",\"Regulus Maecilius\",\"Terentius Salinator\",\"Secundinius Balbus\",\"Felix Hordeonius\",\"Cornelius Furius\",\"Regulus Urbicus\",\"Cilnius Cicero\",\"Lucius Messalla\",\"Semellius Horatius\",\"Burrienus Spurius\",\"Caesennius Furius\",\"Paternus Sidonius\",\"Geta Hadrian\",\"Granius Norbanus\",\"Gnaeus Spurius\",\"Balbinus Tertius\",\"Calventius Celsus\",\"Lucretius Geminius\",\"Munatius Rebilus\",\"Laetorius Paelignus\",\"Fabianus Ostorius\",\"Cloelius Porcius\",\"Albinius Maxentius\",\"Saturninus Nasidius\",\"Nautius Publius\",\"Marcius Lentulus\",\"Coruncanius Decimus\",\"Novellius Servius\",\"Rutilius Manilius\",\"Pasidienus Severian\",\"Cato Maecenas\",\"Flaccus Marius\",\"Hilarianus Cantilius\",\"Calpurnius Saturninus\",\"Rabirius Amulius\",\"Herennius Lollius\",\"Bruttius Cilnius\",\"Marcus Volumnius\",\"Baebius Quinctilius\",\"Celsus Celsus\",\"Verus Memmius\",\"Coruncanius Iuventius\",\"Marcus Cilnius\",\"Nerva Norbanus\",\"Cicero Julius\",\"Aelius Ollius\",\"Constantinus Laberius\",\"Cocceius Cornutus\",\"Tarquinius Secundinius\",\"Geta Celsus\",\"Suetonius Rufius\",\"Seccius Silvius\",\"Valerius Cestius\",\"Pomponius Persius\",\"Numonius Sedatius\",\"Secundinius Claudius\",\"Papirius Balbinus\",\"Tiberius Orestius\",\"Juvenalis Cilnius\",\"Hirrus Sidonius\",\"Publius Suetonius\",\"Nero Blossius\",\"Statilius Numitorius\",\"Publicius Bruttius\",\"Vitus Rufrius\",\"Semellius Maximus\",\"Eborius Seneca\",\"Servius Silvius\",\"Sestius Pinarus\",\"Blaesus Acilius\",\"Titus Diocletian\",\"Gnaeus Sornatius\",\"Severian Crispinus\",\"Menenius Papinius\",\"Gnaeus Sergius\",\"Varro Publius\",\"Numerius Tarquinius\",\"Domitius Vitalis\",\"Calventius Antonius\",\"Cosconius Hadrian\",\"Eborius Manius\",\"Oppius Calventius\",\"Tacitus Castorius\",\"Sentius Manius\",\"Fabius Genucius\",\"Baebius Cominius\",\"Ogulnius Albinus\",\"Tacitus Rabirius\",\"Sentius Catilius\",\"Aelius Novellius\",\"Marius Servilius\",\"Marcius Vincentius\",\"Papinius Lentulus\",\"Tacitus Pasidienus\",\"Cethegus Cincinnatus\",\"Caelian Caelius\",\"Granius Calventius\",\"Candidus Tiberius\",\"Ogulnius Caedicius\",\"Juvenalis Foslius\",\"Vincentius Lacerius\",\"Nigidius Cornificius\",\"Memmius Duronius\",\"Nasidienus Pettius\",\"Paelignus Cornutus\",\"Nigidius Nero\",\"Flavius Verus\",\"Juvenalis Publicius\",\"Fidius Potitius\",\"Pettius Numonius\",\"Naevius Matienus\",\"Saturninus Maximian\",\"Maxentius Ollius\",\"Geta Anatolius\",\"Verus Flavius\",\"Caelian Nasennius\",\"Icilius Paelignus\",\"Cassio Balbus\",\"Sicinius Curtius\",\"Furius Ravillius\",\"Pedius Ravillius\",\"Flavius Sextus\",\"Juvenalis Minicius\",\"Marian Marian\",\"Junius Dacian\",\"Fundanus Sentius\",\"Pasidienus Matienus\",\"Sergius Tacitus\",\"Quinctilius Seccius\",\"Flaccus Quirinius\",\"Plautius Cominius\",\"Pupius Rabirius\",\"Cloelius Felix\",\"Coruncanius Afranius\",\"Petilius Sempronius\",\"Sulpicius Postumius\",\"Numitorius Pupius\",\"Volumnius Fannius\",\"Sempronius Candidus\",\"Ennius Julius\",\"Sergian Cato\",\"Festinius Maecenas\",\"Lollius Sestius\",\"Titus Mamilius\",\"Minicius Urbanus\",\"Marcus Gratian\",\"Zeno Pius\",\"Lucretius Albinius\",\"Justus Opimius\",\"Verecundus Maximus\",\"Paternus Germanicus\",\"Blossius Cispius\",\"Gnaeus Cato\",\"Volumnius Valentinian\",\"Lucius Caedicius\",\"Quirinius Marcus\",\"Lartius Calventius\",\"Sextus Seccius\",\"Nazarius Nero\",\"Iuventius Vincentius\",\"Publius Palpellius\",\"Manius Afranius\",\"Sestius Flavius\",\"Cassius Gabinius\",\"Domitian Verecundus\",\"Hortensius Salinator\",\"Quirinius Secundinius\",\"Iulius Honorius\",\"Longinius Camillus\",\"Verus Suetonius\",\"Hostius Tarquinius\",\"Cyprian Varian\",\"Salvius Livius\",\"Lamponius Tarquinius\",\"Servilius Caelius\",\"Cloelius Cornelius\",\"Fabianus Laelius\",\"Maecenas Iuventius\",\"Persius Numerius\",\"Catius Flavius\",\"Terentius Potitius\",\"Titus Verus\",\"Urbanus Januarius\",\"Aquillius Orchius\",\"Statilius Egnatuleius\",\"Festus Sestius\",\"Mucius Manilius\",\"Bruttius Perperna\",\"Cicero Numicius\",\"Sentius Cornificius\",\"Magnentius Dacian\",\"Pettius Junius\",\"Catius Fundanus\",\"Didius Sidonius\",\"Cato Vitalis\",\"Sextus Ofilius\",\"Ninnius Fidenas\",\"Bonifacius Lacerius\",\"Nero Valentinian\",\"Salvian Numonius\",\"Catius Aelius\",\"Balbus Lartius\",\"Regulus Vitalis\",\"Pettius Octavenus\",\"Musonius Juvenalis\",\"Publicius Ogulnius\",\"Marius Memmius\",\"Cyprian Iuventius\",\"Marcius Blossius\",\"Paternus Mescinius\",\"Sergian Ennius\",\"Gabinius Quintus\",\"Oppius Cilnius\",\"Sittius Vitus\",\"Laelius Flavius\",\"Marius Maximus\",\"Blaesus Honorius\",\"Felix Valerius\",\"Paulinus Horatius\",\"Draco Flavius\",\"Numonius Clodius\",\"Palpellius Laurentius\",\"Balbus Maecenas\",\"Ostorius Blasius\",\"Lucius Lartius\",\"Iuventius Memmius\",\"Sittius Nasidienus\",\"Januarius Caecilius\",\"Vitalis Donatus\",\"Hilarianus Damasus\",\"Pomponius Drusus\",\"Didius Sestius\",\"Aulus Sedatius\",\"Junius Cicero\",\"Salvius Regulus\",\"Diocletian Messalla\",\"Nasidius Gaius\",\"Hirrus Decimus\",\"Lentulus Blasius\",\"Geminius Caelian\",\"Matinius Cicereius\",\"Appius Paternus\",\"Lacerius Gabinius\",\"Nasidienus Afranius\",\"Nasidienus Fabrinius\",\"Maxentius Plinius\",\"Germanicus Regulus\",\"Munatius Petilius\",\"Paternus Publius\",\"Domitius Cornificius\",\"Norbanus Placidus\",\"Appius Blaesus\",\"Sicinius Palpellius\",\"Laelius Probus\",\"Pilus Terentius\",\"Pasidienus Salvius\",\"Draco Fundanus\",\"Fulvius Sittius\",\"Cornelius Numonius\",\"Numitorius Ostorius\",\"Cispius Cicero\",\"Valerius Sergius\",\"Marcius Papinius\",\"Sestius Fulvius\",\"Quinctilius Appius\",\"Plinius Suetonius\",\"Gallus Secundus\",\"Urbanus Papinius\",\"Festinius Caecilius\",\"Primitivus Sergian\",\"Percennius Felix\",\"Sabucius Messalla\",\"Fulvius Cispius\",\"Terentius Lollius\",\"Catilius Afranius\",\"Opellius Fundanus\",\"Sempronius Curtius\",\"Secundus Martialis\",\"Aemilius Persius\",\"Ninnius Sergian\",\"Norbanus Furius\",\"Musonius Ofilius\",\"Belisarius Latinius\",\"Matinius Ogulnius\",\"Mamilius Blaesus\",\"Paulinus Tacitus\",\"Sallustius Verecundus\",\"Secundus Sedatius\",\"Cyprian Severus\",\"Camillus Fulvius\",\"Icilius Iunius\",\"Decimus Cantilius\",\"Persius Galerius\",\"Tertius Paulinus\",\"Novellius Blossius\",\"Sicinius Cordius\",\"Marcellus Dacian\",\"Albinus Valentinian\",\"Nautius Pupius\",\"Paelignus Vincentius\",\"Sertorius Maximus\",\"Fannius Regulus\",\"Valentinian Tertius\",\"Fulvius Dacian\",\"Severus Statilius\",\"Ninnius Vespasian\",\"Quirinius Regulus\",\"Sergian Faustus\",\"Maximus Fidius\",\"Aemilius Segulius\",\"Salvian Rufius\",\"Pettius Seccius\",\"Hadrian Modius\",\"Aelius Mescinius\",\"Betilienus Vincentius\",\"Cosconius Salvidienus\",\"Sabucius Orestius\",\"Caelius Urbanus\",\"Afranius Vincentius\",\"Calpurnius Sentius\",\"Aelius Sabucius\",\"Volumnius Vespasian\",\"Fidenas Pasidienus\",\"Numicius Cosconius\",\"Tiberius Caninius\",\"Ingenuus Sallustius\",\"Stertinius Agrippa\",\"Publicius Iunius\",\"Florian Fidenas\",\"Pasidienus Ofilius\",\"Egnatius Justus\",\"Pupius Longinius\",\"Fundanus Justus\",\"Papinius Atilius\",\"Valentinian Caelian\",\"Manius Vitalis\",\"Caelius Quintus\",\"Laberius Egnatuleius\",\"Cocceius Verecundus\",\"Pius Cincinnatus\",\"Laurentius Considius\",\"Cincinnatus Decentius\",\"Salvian Gnaeus\",\"Novellius Curtius\",\"Rufrius Nerva\",\"Novellius Nigidius\",\"Gallus Blasius\",\"Secundinius Pacilius\",\"Florian Calpurnius\",\"Sestius Juvenalis\",\"Valens Cloelius\",\"Octavenus Lucius\",\"Novellius Oppius\",\"Licinius Vitus\",\"Festus Maximian\",\"Messalla Cassio\",\"Iuventius Secundus\",\"Laetorius Opimius\",\"Herennius Tiberius\",\"Julius Postumius\",\"Terentius Tacitus\",\"Gaius Laronius\",\"Fabianus Placidus\",\"Vitus Balbus\",\"Sabucius Munatius\",\"Aurelianus Quintus\",\"Suetonius Varro\",\"Paternus Ingenuus\",\"Sornatius Ollius\",\"Modius Blossius\",\"Semellius Regulus\",\"Iulius Cantilius\",\"Primitivus Lucretius\",\"Granius Cordius\",\"Terentius Ennius\",\"Hortensius Vincentius\",\"Cornutus Mucius\",\"Hadrian Nasidius\",\"Caelian Vibius\",\"Balbus Coruncanius\",\"Anatolius Decentius\",\"Cosconius Ravillius\",\"Sornatius Caecilius\",\"Agrippa Blasius\",\"Laronius Papirius\",\"Titus Pontidius\",\"Regulus Catilius\",\"Agrippa Cornelius\",\"Caelian Florian\",\"Plautius Salinator\",\"Manius Ollius\",\"Silanus Sextius\",\"Segulius Ollius\",\"Domitian Caelius\",\"Maecenas Sittius\",\"Fabius Longinius\",\"Amulius Januarius\",\"Tacitus Porcius\",\"Lartius Longinius\",\"Messalla Flaccus\",\"Varro Cordius\",\"Diocletian Hortensius\",\"Camillus Atilius\",\"Maxentius Naevius\",\"Curtius Laurentius\",\"Livius Hortensius\",\"Gnaeus Semellius\",\"Paulinus Matinius\",\"Considius Novellius\",\"Seccius Pontidius\",\"Crispus Ofilius\",\"Octavius Fidenas\",\"Regulus Domitian\",\"Pupius Mallius\",\"Pasidienus Valentinian\",\"Pettius Minicius\",\"Gnaeus Modius\",\"Aurelianus Rullianus\",\"Menenius Priscus\",\"Papirius Vitus\",\"Belisarius Livius\",\"Furius Sextius\",\"Tarquinius Poppaedius\",\"Agrippa Crispus\",\"Manius Numonius\",\"Octavenus Lartius\",\"Urbanus Cornificius\",\"Betilienus Verus\",\"Murena Nazarius\",\"Valentinian Sempronius\",\"Manilius Latinius\",\"Belisarius Rufrius\",\"Marcius Caninius\",\"Ennius Sidonius\",\"Mucius Atilius\",\"Herennius Nasidius\",\"Eborius Caelian\",\"Orchius Paconius\",\"Longinius Placidus\",\"Pompeius Flavius\",\"Martialis Galerius\",\"Iunius Marcius\",\"Nonius Numonius\",\"Hilarianus Musonius\",\"Nasidius Martialis\",\"Vitalis Rutilius\",\"Mallius Palpellius\",\"Petreius Publicius\",\"Sittius Perperna\",\"Decentius Pupius\",\"Octavius Sepullius\",\"Laetorius Duronius\",\"Ovidius Sedatius\",\"Probus Laelius\",\"Caedicius Calpurnius\",\"Licinius Pilus\",\"Atilius Foslius\",\"Fulvius Tertius\",\"Acilius Lucretius\",\"Fannius Paulinus\",\"Atilius Suetonius\",\"Matienus Calpurnius\",\"Manilius Blossius\",\"Gratian Sulpicius\",\"Regulus Livius\",\"Quinctilius Herennius\",\"Celsus Caecilius\",\"Mescinius Cassio\",\"Numitorius Herennius\",\"Titus Fidius\",\"Sicinius Paulinus\",\"Sicinius Avidius\",\"Bruttius Baebius\",\"Silanus Caelian\",\"Gordian Papirius\",\"Cestius Sextus\",\"Calpurnius Drusus\",\"Perperna Fulcinia\",\"Urbanus Cicero\",\"Naevius Marcus\",\"Memmius Celsus\",\"Hirrus Novellius\",\"Fabius Palpellius\",\"Publicius Latinius\",\"Pilus Laurentius\",\"Fidenas Stertinius\",\"Hostilius Gnaeus\",\"Gordian Dacian\",\"Volumnius Seneca\",\"Drusus Galerius\",\"Ollius Seccius\",\"Cassius Hostius\",\"Oppius Poppaedius\",\"Cispius Pasidienus\",\"Belisarius Vitus\",\"Segulius Nautius\",\"Porcius Germanicus\",\"Severus Fabianus\",\"Manius Quirinius\",\"Faustus Laelius\",\"Petilius Nerva\",\"Octavius Regulus\",\"Cestius Blaesus\",\"Juvenalis Cloelius\",\"Lentulus Iunius\",\"Papirius Aurelianus\",\"Calpurnius Rufinus\",\"Mamilius Gaius\",\"Longinius Felix\",\"Nero Hadrian\",\"Amulius Longinius\",\"Sextus Festus\",\"Balbinus Egnatius\",\"Junius Menenius\",\"Sallustius Magnentius\",\"Aemilius Numitorius\",\"Minicius Cornelius\",\"Norbanus Valerius\",\"Rullianus Urbanus\",\"Quirinius Seccius\",\"Fulcinia Egnatius\",\"Poppaedius Blossius\",\"Sedatius Sidonius\",\"Maecenas Faustus\",\"Lartius Granius\",\"Antonius Statilius\",\"Albinus Livius\",\"Decentius Caelian\",\"Granius Castorius\",\"Betilienus Licinius\",\"Salvian Cominius\",\"Acilius Acilius\",\"Pius Eutropius\",\"Pupius Poppaedius\",\"Suetonius Norbanus\",\"Tacitus Laelius\",\"Januarius Maximus\",\"Atilius Festus\",\"Diocletian Geminius\",\"Ennius Honorius\",\"Sextus Quintus\",\"Norbanus Pius\",\"Lucanus Hilarianus\",\"Ollius Galerius\",\"Nasidius Zeno\",\"Fundanus Urbicus\",\"Domitian Tiberius\",\"Horatius Iuventius\",\"Decentius Gallus\",\"Manius Dacian\",\"Cilnius Secundinius\",\"Gratian Blasius\",\"Caelian Octavius\",\"Marcius Sabucius\",\"Norbanus Candidus\",\"Eutropius Belisarius\",\"Numonius Plautius\",\"Fidenas Segulius\",\"Claudius Manilius\",\"Burrienus Honorius\",\"Lentulus Gordian\",\"Fulvius Varian\",\"Decimus Cincinnatus\",\"Lollius Nero\",\"Herennius Catius\",\"Gracchus Caedicius\",\"Murena Lamponius\",\"Rutilius Nazarius\",\"Catius Galerius\",\"Valerius Cassio\",\"Paternus Afranius\",\"Duronius Sergian\",\"Acilius Seneca\",\"Publicius Magnentius\",\"Lacerius Bonifacius\",\"Geminius Silvius\",\"Castorius Murena\",\"Cassianus Ostorius\",\"Gratian Octavius\",\"Gratian Lucius\",\"Maecilius Memmius\",\"Sallustius Quirinius\",\"Festinius Tarquinius\",\"Urbanus Matinius\",\"Geta Laelius\",\"Fabius Cassianus\",\"Fabrinius Sicinius\",\"Maximian Granius\",\"Cocceius Maximus\",\"Geta Fidius\",\"Egnatius Considius\",\"Lucius Geminius\",\"Probus Bonifacius\",\"Silanus Dacian\",\"Blaesus Sedatius\",\"Albinius Granius\",\"Fidenas Papinius\",\"Pedius Caninius\",\"Verus Rebilus\",\"Lucanus Brennus\",\"Camillius Hordeonius\",\"Numitorius Catius\",\"Opimius Vincentius\",\"Caelian Felix\",\"Vibius Severus\",\"Lollius Crispinus\",\"Fidius Egnatuleius\",\"Foslius Ennius\",\"Ollius Perperna\",\"Quintus Ofilius\",\"Donatus Murena\",\"Cinna Gordian\",\"Probus Marcus\",\"Cosconius Hordeonius\",\"Cinna Nerva\",\"Cyprian Paconius\",\"Rebilus Bruttius\",\"Servilius Volumnius\",\"Iunius Cassius\",\"Lentulus Sempronius\",\"Semellius Cantilius\",\"Caesennius Musonius\",\"Octavenus Agrippa\",\"Perperna Pupius\",\"Donatus Manilius\",\"Urbicus Sepullius\",\"Honorius Donatus\",\"Marian Florian\",\"Licinius Publicius\",\"Laecanius Musonius\",\"Tarquinius Numerius\",\"Regulus Constantinus\",\"Rabirius Cosconius\",\"Iuventius Opimius\",\"Sempronius Palpellius\",\"Celsus Marcellus\",\"Poppaedius Florianus\",\"Blasius Bonifacius\",\"Agrippa Sempronius\",\"Mallius Novellius\",\"Sempronius Gallus\",\"Ostorius Opellius\",\"Numerius Fidenas\",\"Lucanus Clodius\",\"Damasus Gaius\",\"Albinius Caesennius\",\"Vitus Magnentius\",\"Silanus Sedatius\",\"Pinarus Eborius\",\"Decimus Suetonius\",\"Bonifacius Laronius\",\"Rebilus Octavius\",\"Hostius Sittius\",\"Domitian Camillus\",\"Aurelianus Aurelianus\",\"Papirius Matienus\",\"Constantinus Probus\",\"Nigidius Juvenalis\",\"Belisarius Fidenas\",\"Lucretius Quinctilius\",\"Lacerius Vitus\",\"Donatus Palpellius\",\"Coruncanius Longinius\",\"Cyprian Paulinus\",\"Betilienus Silius\",\"Cicereius Sestius\",\"Fannius Orestius\",\"Seneca Aulus\",\"Quintilianus Varro\",\"Laberius Nasidienus\",\"Tacitus Flaccus\",\"Crispus Ollius\",\"Candidus Lucanus\",\"Geminius Maximus\",\"Cassius Eutropius\",\"Servius Agrippa\",\"Vespasian Flavius\",\"Didius Manilius\",\"Iunius Lamponius\",\"Hortensius Festinius\",\"Belisarius Blaesus\",\"Gaius Junius\",\"Considius Pasidienus\",\"Damasus Egnatuleius\",\"Nasidius Hostilius\",\"Januarius Damasus\",\"Sextius Genucius\",\"Maecenas Hadrian\",\"Decentius Publicius\",\"Norbanus Constantinus\",\"Nonius Varro\",\"Papinius Duronius\",\"Lollius Naevius\",\"Cornificius Varro\",\"Afranius Horatius\",\"Domitius Sertorius\",\"Manius Magnentius\",\"Salvius Foslius\",\"Festus Modius\",\"Hostilius Caelian\",\"Saturninus Statilius\",\"Maxentius Gratian\",\"Urbicus Nasidius\",\"Julius Paconius\",\"Geta Balbus\",\"Verecundus Bonifacius\",\"Cethegus Clodius\",\"Numonius Hirrus\",\"Lamponius Hordeonius\",\"Blossius Lucretius\",\"Cato Probus\",\"Numonius Zeno\",\"Balbus Nonius\",\"Clodius Nasidienus\",\"Felix Agrippa\",\"Laronius Caelian\",\"Marian Fannius\",\"Primitivus Nigidius\",\"Postumius Salinator\",\"Ofilius Paconius\",\"Pomponius Gabinius\",\"Agrippa Nasidius\",\"Sextius Flaccus\",\"Lamponius Fidenas\",\"Justus Oppius\",\"Matinius Sextius\",\"Vitus Galerius\",\"Cincinnatus Icilius\",\"Priscus Caelian\",\"Seccius Considius\",\"Seneca Sornatius\",\"Ostorius Fulcinia\",\"Julius Julius\",\"Cornificius Lollius\",\"Cicero Cocceius\",\"Minicius Nasidienus\",\"Vibius Cantilius\",\"Galerius Ogulnius\",\"Cinna Nazarius\",\"Nonius Ofilius\",\"Avidius Plautius\",\"Cicereius Semellius\",\"Urbanus Musonius\",\"Blasius Laetorius\",\"Icilius Longinius\",\"Considius Potitius\",\"Caedicius Porcius\",\"Sulpicius Donatus\",\"Spurius Cassianus\",\"Salvian Drusus\",\"Salvius Pilus\",\"Aquillius Sulpicius\",\"Cominius Albinus\",\"Catius Paternus\",\"Modius Aelius\",\"Salvidienus Sestius\",\"Lartius Sidonius\",\"Lucanus Sestius\",\"Anatolius Egnatuleius\",\"Terentius Verecundus\",\"Statilius Blaesus\",\"Didius Mescinius\",\"Duronius Fulcinia\",\"Genucius Lentulus\",\"Diocletian Lentulus\",\"Secundus Cato\",\"Priscus Maxentius\",\"Hostius Tertius\",\"Quintilianus Rabirius\",\"Rufinus Paulinus\",\"Rabirius Secundus\",\"Nonius Sallustius\",\"Mescinius Rufinus\",\"Mescinius Plautius\",\"Faustus Ravillius\",\"Numitorius Aemilius\",\"Pinarus Silius\",\"Vitus Egnatuleius\",\"Statilius Mamilius\",\"Agrippa Opellius\",\"Orbilius Manilius\",\"Varian Horatius\",\"Ingenuus Furius\",\"Regulus Opimius\",\"Genucius Decimus\",\"Probus Caecilius\",\"Octavenus Balbinus\",\"Longinius Salvidienus\",\"Livius Camillius\",\"Ingenuus Numicius\",\"Norbanus Rullianus\",\"Blaesus Avidius\",\"Caelius Calventius\",\"Salvius Licinius\",\"Caecilius Fidenas\",\"Iunius Modius\",\"Quirinius Draco\",\"Appius Albinus\",\"Cato Sepullius\",\"Fabrinius Aulus\",\"Iulius Didius\",\"Burrienus Salinator\",\"Probus Catius\",\"Tertius Festus\",\"Maximian Iunius\",\"Paulinus Lartius\",\"Rufius Pacilius\",\"Castorius Duronius\",\"Verus Calventius\",\"Petreius Nazarius\",\"Geminius Cethegus\",\"Candidus Cicero\",\"Lucretius Honorius\",\"Gracchus Cornificius\",\"Belisarius Postumius\",\"Tacitus Pomponius\",\"Aelius Galerius\",\"Valentinian Celsus\",\"Laelius Fabianus\",\"Probus Blossius\",\"Publicius Opimius\",\"Festus Iunius\",\"Cornelius Galerius\",\"Plinius Appius\",\"Sertorius Geta\",\"Rabirius Herennius\",\"Cassio Mucius\",\"Lucanus Hostilius\",\"Ostorius Cassianus\",\"Munatius Papirius\",\"Cornutus Persius\",\"Mamilius Perperna\",\"Granius Florianus\",\"Germanicus Salvian\",\"Ollius Afranius\",\"Baebius Pacilius\",\"Hilarianus Tiberius\",\"Numerius Caelius\",\"Cestius Titus\",\"Rebilus Pilus\",\"Galerius Primitivus\",\"Spurius Salinator\",\"Domitius Musonius\",\"Sedatius Licinius\",\"Clodius Cassius\",\"Porcius Lucretius\",\"Pedius Rullianus\",\"Messalla Cocceius\",\"Rufrius Cyprian\",\"Laelius Maecenas\",\"Scribonius Cicero\",\"Pupius Sergian\",\"Blaesus Latinius\",\"Crispus Blasius\",\"Rufinus Plinius\",\"Memmius Sepullius\",\"Pomponius Vincentius\",\"Cyprian Salvian\",\"Valens Suetonius\",\"Aurelianus Amulius\",\"Matienus Sallustius\",\"Spurius Mescinius\",\"Atilius Paternus\",\"Sicinius Fabius\",\"Silvius Blossius\",\"Messalla Verus\",\"Pacilius Caedicius\",\"Marcellus Quirinius\",\"Herennius Publius\",\"Fulvius Pupius\",\"Pompeius Marcellus\",\"Sentius Dacian\",\"Fabianus Cicero\",\"Amulius Probus\",\"Nasidienus Papinius\",\"Quintus Magnentius\",\"Valens Rufrius\",\"Sedatius Donatus\",\"Crispinus Cestius\",\"Pompeius Quirinius\",\"Fabius Vibius\",\"Valens Caelius\",\"Blasius Fulcinia\",\"Licinius Gracchus\",\"Caelius Dacian\",\"Valerius Sextus\",\"Damasus Sextius\",\"Paelignus Varian\",\"Geminius Menenius\",\"Papirius Vitalis\",\"Nero Ennius\",\"Servilius Persius\",\"Appius Silanus\",\"Duronius Decentius\",\"Sidonius Fulvius\",\"Nasennius Zeno\",\"Nero Maximian\",\"Nasennius Gnaeus\",\"Hirrus Lucretius\",\"Ingenuus Latinius\",\"Bruttius Acilius\",\"Ostorius Lartius\",\"Fabius Aulus\",\"Eborius Statilius\",\"Marcius Duronius\",\"Probus Oppius\",\"Damasus Murena\",\"Geta Icilius\",\"Terentius Cosconius\",\"Gratian Horatius\",\"Burrienus Ovidius\",\"Hortensius Antonius\",\"Manilius Horatius\",\"Iuventius Persius\",\"Cominius Aquillius\",\"Mescinius Hirrus\",\"Marian Bruttius\",\"Aurelianus Agrippa\",\"Silanus Maxentius\",\"Cispius Sidonius\",\"Icilius Draco\",\"Quintus Pomponius\",\"Priscus Scribonius\",\"Hordeonius Caedicius\",\"Segulius Granius\",\"Laronius Salinator\",\"Cato Ingenuus\",\"Claudius Caecilius\",\"Lentulus Constantinus\",\"Sabucius Anatolius\",\"Salvian Licinius\",\"Salvius Tiberius\",\"Plautius Paulinus\",\"Decimus Sepullius\",\"Salvius Catius\",\"Hostius Sestius\",\"Avidius Semellius\",\"Sestius Lucretius\",\"Domitius Maximian\",\"Salvius Cassianus\",\"Orbilius Ostorius\",\"Appius Ennius\",\"Egnatuleius Egnatius\",\"Afranius Zeno\",\"Blasius Brennus\",\"Camillus Anatolius\",\"Vibius Novellius\",\"Drusus Justus\",\"Cethegus Damasus\",\"Aquillius Rufius\",\"Galerius Laronius\",\"Lamponius Ravillius\",\"Salvidienus Anatolius\",\"Orbilius Furius\",\"Maximian Gnaeus\",\"Salvian Laurentius\",\"Belisarius Maximian\",\"Numicius Memmius\",\"Appius Geta\",\"Silanus Cestius\",\"Caninius Nasennius\",\"Sextus Persius\",\"Fidius Caelius\",\"Stertinius Gabinius\",\"Manilius Volumnius\",\"Domitian Hortensius\",\"Sidonius Afranius\",\"Scribonius Claudius\",\"Paelignus Secundinius\",\"Constantinus Quinctilius\",\"Publicius Cicero\",\"Cassius Sidonius\",\"Menenius Silanus\",\"Sergian Tacitus\",\"Rullianus Domitius\",\"Sempronius Cassianus\",\"Geta Pinarus\",\"Fidius Probus\",\"Sornatius Calpurnius\",\"Valens Marcus\",\"Cloelius Albinius\",\"Geminius Laetorius\",\"Postumius Rufinus\",\"Maecilius Mamilius\",\"Potitius Numicius\",\"Caecilius Geminius\",\"Novellius Sergian\",\"Cominius Faustus\",\"Paelignus Caesennius\",\"Egnatius Cispius\",\"Opellius Rullianus\",\"Maecilius Caecilius\",\"Servilius Balbus\",\"Silanus Vitus\",\"Fulcinia Paternus\",\"Didius Festus\",\"Catilius Caelius\",\"Numonius Diocletian\",\"Germanicus Balbus\",\"Paulinus Fabrinius\",\"Minicius Considius\",\"Icilius Gratian\",\"Naevius Sentius\",\"Icilius Plautius\",\"Petreius Vincentius\",\"Ostorius Cornificius\",\"Gratian Minicius\",\"Geminius Aurelianus\",\"Cornificius Papinius\",\"Pilus Sertorius\",\"Mallius Geminius\",\"Musonius Junius\",\"Celsus Memmius\",\"Icilius Volumnius\",\"Octavius Sicinius\",\"Catilius Fabianus\",\"Brennus Salvius\",\"Maximus Cordius\",\"Crispus Lamponius\",\"Gnaeus Verus\",\"Flavius Severus\",\"Fundanus Sallustius\",\"Salvius Lentulus\",\"Julius Marius\",\"Priscus Quintilianus\",\"Eborius Felix\",\"Catius Sextus\",\"Sicinius Hordeonius\",\"Gracchus Placidus\",\"Servilius Verecundus\",\"Vitus Paelignus\",\"Junius Pompeius\",\"Munatius Urbanus\",\"Ninnius Afranius\",\"Martialis Numitorius\",\"Seccius Marian\",\"Julius Fidius\",\"Opellius Munatius\",\"Cassio Marcellus\",\"Bonifacius Pinarus\",\"Pettius Opimius\",\"Pompeius Palpellius\",\"Mallius Gallus\",\"Cassio Opimius\",\"Donatus Sentius\",\"Egnatuleius Minicius\",\"Furius Cantilius\",\"Vincentius Rutilius\",\"Herennius Didius\",\"Pinarus Antonius\",\"Galerius Stertinius\",\"Catilius Sulpicius\",\"Curtius Sestius\",\"Suetonius Domitian\",\"Maxentius Sextus\",\"Lartius Silanus\",\"Cinna Pontidius\",\"Numerius Ovidius\",\"Rullianus Quirinius\",\"Gabinius Laelius\",\"Vibius Cassianus\",\"Fulcinia Lartius\",\"Caedicius Menenius\",\"Papirius Pompeius\",\"Fulvius Potitius\",\"Cordius Honorius\",\"Cornelius Verus\",\"Publius Papirius\",\"Severian Cassio\",\"Opimius Nasennius\",\"Galerius Ravillius\",\"Hordeonius Catilius\",\"Nazarius Nasennius\",\"Musonius Cassianus\",\"Mallius Didius\",\"Laecanius Cicero\",\"Cinna Marian\",\"Pedius Regulus\",\"Florian Urbicus\",\"Salvidienus Rabirius\",\"Faustus Celsus\",\"Orchius Cornutus\",\"Orestius Valentinian\",\"Minicius Gaius\",\"Magnentius Bonifacius\",\"Lartius Sertorius\",\"Caelian Salinator\",\"Tarquinius Licinius\",\"Sestius Geminius\",\"Hilarianus Severian\",\"Pinarus Mamilius\",\"Ostorius Fabius\",\"Gratian Genucius\",\"Hadrian Iulius\",\"Herennius Albinius\",\"Maximian Balbus\",\"Lamponius Menenius\",\"Pacilius Avidius\",\"Menenius Blaesus\",\"Vespasian Primitivus\",\"Silanus Verecundus\",\"Matinius Betilienus\",\"Cinna Icilius\",\"Lollius Laelius\",\"Silanus Salvidienus\",\"Perperna Zeno\",\"Opellius Dacian\",\"Sittius Magnentius\",\"Lacerius Rabirius\",\"Caedicius Varian\",\"Rabirius Fidius\",\"Sextus Lucanus\",\"Camillius Germanicus\",\"Domitian Aquillius\",\"Maximus Caecilius\",\"Numonius Fidenas\",\"Sergius Segulius\",\"Lartius Ovidius\",\"Oppius Catilius\",\"Blossius Gratian\",\"Lamponius Afranius\",\"Bonifacius Maximian\",\"Servilius Nasidienus\",\"Nasennius Julius\",\"Avidius Bonifacius\",\"Quinctilius Ingenuus\",\"Blossius Festus\",\"Licinius Secundinius\",\"Caelian Calpurnius\",\"Didius Rebilus\",\"Valentinian Honorius\",\"Semellius Numerius\",\"Cornutus Laecanius\",\"Calpurnius Fidenas\",\"Marius Martialis\",\"Bonifacius Tacitus\",\"Julius Maximus\",\"Agrippa Fabius\",\"Silanus Hostius\",\"Atilius Felix\",\"Sertorius Cassio\",\"Claudius Blossius\",\"Lollius Cilnius\",\"Magnentius Pettius\",\"Didius Laberius\",\"Hortensius Claudius\",\"Claudius Rullianus\",\"Gabinius Candidus\",\"Genucius Fannius\",\"Blossius Cato\",\"Pompeius Orchius\",\"Salinator Mallius\",\"Antonius Salinator\",\"Crispinus Iulius\",\"Silvius Pasidienus\",\"Cato Tarquinius\",\"Clodius Plinius\",\"Pilus Fabianus\",\"Ovidius Geminius\",\"Manius Tiberius\",\"Perperna Gabinius\",\"Drusus Nonius\",\"Matienus Diocletian\",\"Afranius Agrippa\",\"Blaesus Sicinius\",\"Festus Geminius\",\"Zeno Gracchus\",\"Nero Junius\",\"Servius Laecanius\",\"Opellius Eutropius\",\"Quinctilius Cethegus\",\"Vitalis Acilius\",\"Postumius Blasius\",\"Sepullius Tertius\",\"Vitalis Egnatius\",\"Cassio Cethegus\",\"Rutilius Camillus\",\"Pius Publicius\",\"Messalla Geminius\",\"Caelian Pettius\",\"Sextus Dacian\",\"Licinius Silanus\",\"Constantinus Nazarius\",\"Hortensius Castorius\",\"Hordeonius Cordius\",\"Sestius Sidonius\",\"Vitalis Betilienus\",\"Publicius Appius\",\"Cassio Agrippa\",\"Maximian Cocceius\",\"Pinarus Minicius\",\"Marian Marcus\",\"Sextius Julius\",\"Balbinus Publius\",\"Lucanus Cyprian\",\"Licinius Sittius\",\"Quinctilius Quintilianus\",\"Diocletian Cosconius\",\"Matinius Florian\",\"Diocletian Verecundus\",\"Cordius Belisarius\",\"Fulcinia Clodius\",\"Decimus Icilius\",\"Postumius Fundanus\",\"Vitus Donatus\",\"Pomponius Tarquinius\",\"Blasius Albinus\",\"Marcius Tarquinius\",\"Latinius Gnaeus\",\"Nonius Dacian\",\"Paelignus Clodius\",\"Urbanus Gabinius\",\"Verecundus Albinius\",\"Ollius Marcellus\",\"Perperna Vibius\",\"Matienus Sidonius\",\"Valentinian Scribonius\",\"Maximus Rabirius\",\"Seccius Gnaeus\",\"Urbanus Lucius\",\"Candidus Fannius\",\"Donatus Sempronius\",\"Silanus Amulius\",\"Mescinius Fannius\",\"Gratian Pacilius\",\"Nonius Honorius\",\"Appius Nonius\",\"Sornatius Rebilus\",\"Salvian Hilarianus\",\"Octavenus Quinctilius\",\"Mescinius Cornutus\",\"Cornutus Cato\",\"Valentinian Belisarius\",\"Publicius Paternus\",\"Festus Januarius\",\"Ninnius Marian\",\"Segulius Publicius\",\"Donatus Paconius\",\"Cordius Sallustius\",\"Plinius Florianus\",\"Sornatius Pontidius\",\"Potitius Caedicius\",\"Seccius Quirinius\",\"Volumnius Sittius\",\"Octavenus Acilius\",\"Laberius Balbinus\",\"Valerius Domitian\",\"Ogulnius Cordius\",\"Albinius Sabucius\",\"Cato Terentius\",\"Secundus Valerius\",\"Rufrius Gnaeus\",\"Amulius Pinarus\",\"Seneca Secundinius\",\"Modius Statilius\",\"Avidius Ovidius\",\"Decentius Tertius\",\"Gallus Fidius\",\"Caedicius Cassianus\",\"Petilius Rufrius\",\"Cato Cordius\",\"Laecanius Numicius\",\"Ollius Antonius\",\"Lucretius Salvidienus\",\"Calpurnius Livius\",\"Flaccus Gnaeus\",\"Honorius Florian\",\"Cominius Antonius\",\"Hilarianus Numitorius\",\"Plautius Cordius\",\"Cilnius Laberius\",\"Postumius Florianus\",\"Zeno Nero\",\"Modius Porcius\",\"Sempronius Honorius\",\"Caecilius Draco\",\"Crispinus Sidonius\",\"Saturninus Cosconius\",\"Cominius Sentius\",\"Rufrius Eutropius\",\"Secundus Valentinian\",\"Cornelius Pilus\",\"Cato Caecilius\",\"Rabirius Sentius\",\"Camillius Pinarus\",\"Menenius Rufinus\",\"Tacitus Florianus\",\"Sergius Papinius\",\"Geta Ingenuus\",\"Volumnius Marius\",\"Papinius Maecenas\",\"Caninius Publicius\",\"Marcellus Ninnius\",\"Flaccus Iunius\",\"Magnentius Maecenas\",\"Lucanus Sentius\",\"Paconius Balbinus\",\"Appius Granius\",\"Atilius Balbus\",\"Flaccus Baebius\",\"Nasidienus Nazarius\",\"Seneca Calventius\",\"Salvian Cyprian\",\"Aemilius Lucius\",\"Burrienus Mallius\",\"Draco Musonius\",\"Celsus Sergius\",\"Fundanus Ravillius\",\"Petilius Petreius\",\"Seccius Horatius\",\"Nero Silvius\",\"Donatus Atilius\",\"Statilius Rufinus\",\"Zeno Cassio\",\"Calventius Caedicius\",\"Quinctilius Longinius\",\"Gaius Rullianus\",\"Naevius Juvenalis\",\"Nasidienus Sicinius\",\"Silanus Celsus\",\"Amulius Iuventius\",\"Statilius Fundanus\",\"Perperna Felix\",\"Cestius Scribonius\",\"Maximus Foslius\",\"Candidus Germanicus\",\"Orestius Catius\",\"Placidus Aquillius\",\"Sidonius Novellius\",\"Plinius Sittius\",\"Valens Duronius\",\"Domitius Urbanus\",\"Maecenas Tarquinius\",\"Curtius Magnentius\",\"Ovidius Lucius\",\"Norbanus Claudius\",\"Festus Blossius\",\"Servilius Titus\",\"Sestius Cyprian\",\"Hortensius Sulpicius\",\"Iulius Numonius\",\"Sextius Aulus\",\"Balbinus Caedicius\",\"Percennius Nasennius\",\"Sittius Domitius\",\"Cornelius Vitalis\",\"Urbanus Atilius\",\"Fundanus Pupius\",\"Primitivus Sidonius\",\"Damasus Cassius\",\"Sergian Verus\",\"Calpurnius Florianus\",\"Cosconius Laberius\",\"Antonius Egnatuleius\",\"Junius Herennius\",\"Hordeonius Sallustius\",\"Gaius Latinius\",\"Gracchus Messalla\",\"Octavenus Percennius\",\"Cordius Calventius\",\"Vincentius Rufinus\",\"Caecilius Blaesus\",\"Florian Ollius\",\"Pompeius Numonius\",\"Secundinius Vibius\",\"Camillius Petreius\",\"Poppaedius Cassianus\",\"Catius Cornificius\",\"Iulius Cethegus\",\"Valentinian Lamponius\",\"Ovidius Atilius\",\"Silius Opimius\",\"Lartius Draco\",\"Gabinius Calventius\",\"Sertorius Longinius\",\"Lacerius Ninnius\",\"Papirius Hortensius\",\"Maecilius Probus\",\"Lollius Iulius\",\"Probus Varro\",\"Fabianus Germanicus\",\"Orbilius Baebius\",\"Pius Vibius\",\"Sidonius Seccius\",\"Novellius Cassianus\",\"Cicero Salvius\",\"Urbanus Hortensius\",\"Porcius Ogulnius\",\"Calpurnius Octavius\",\"Belisarius Ogulnius\",\"Nazarius Pasidienus\",\"Rebilus Granius\",\"Tacitus Cassius\",\"Minicius Juvenalis\",\"Sallustius Laurentius\",\"Martialis Domitius\",\"Cassianus Nasidius\",\"Maecenas Salinator\",\"Ennius Egnatuleius\",\"Cocceius Palpellius\",\"Pasidienus Probus\",\"Furius Sentius\",\"Munatius Donatus\",\"Vincentius Duronius\",\"Calpurnius Pettius\",\"Cassio Numitorius\",\"Placidus Flaccus\",\"Sidonius Didius\",\"Hortensius Manilius\",\"Iulius Volumnius\",\"Blaesus Egnatius\",\"Pontidius Quintilianus\",\"Orestius Blossius\",\"Severian Palpellius\",\"Porcius Pupius\",\"Fabianus Cocceius\",\"Petreius Nasidienus\",\"Januarius Martialis\",\"Volumnius Publius\",\"Memmius Flavius\",\"Messalla Mucius\",\"Iulius Octavenus\",\"Sidonius Volumnius\",\"Secundus Flaccus\",\"Manilius Nerva\",\"Marcellus Placidus\",\"Caedicius Maxentius\",\"Quinctilius Laetorius\",\"Pinarus Petilius\",\"Urbanus Juvenalis\",\"Laetorius Pettius\",\"Vibius Vibius\",\"Crispinus Servilius\",\"Ovidius Orestius\",\"Perperna Furius\",\"Cantilius Palpellius\",\"Volumnius Mamilius\",\"Sestius Iulius\",\"Aemilius Lartius\",\"Nerva Balbus\",\"Foslius Oppius\",\"Geminius Blaesus\",\"Albinius Papinius\",\"Ravillius Numonius\",\"Lucanus Orchius\",\"Felix Anatolius\",\"Damasus Longinius\",\"Cincinnatus Agrippa\",\"Modius Aurelianus\",\"Regulus Varian\",\"Manilius Germanicus\",\"Lacerius Rutilius\",\"Salinator Manius\",\"Publicius Cyprian\",\"Sextius Considius\",\"Secundus Placidus\",\"Percennius Cincinnatus\",\"Servius Marian\",\"Silvius Mallius\",\"Novellius Fabius\",\"Octavenus Mucius\",\"Bonifacius Egnatuleius\",\"Murena Gallus\",\"Gnaeus Paelignus\",\"Cosconius Silvius\",\"Curtius Titus\",\"Octavenus Manilius\",\"Licinius Porcius\",\"Aelius Candidus\",\"Postumius Decentius\",\"Nasidius Menenius\",\"Lacerius Celsus\",\"Numicius Betilienus\",\"Mescinius Hordeonius\",\"Latinius Silanus\",\"Pius Maxentius\",\"Aelius Salvius\",\"Rufrius Salinator\",\"Poppaedius Juvenalis\",\"Probus Furius\",\"Opellius Antonius\",\"Lartius Eborius\",\"Papinius Pomponius\",\"Faustus Sepullius\",\"Caedicius Fabianus\",\"Eborius Hadrian\",\"Camillius Norbanus\",\"Gaius Vincentius\",\"Antonius Drusus\",\"Cinna Caninius\",\"Albinus Rufius\",\"Sedatius Pedius\",\"Verus Ofilius\",\"Furius Marcius\",\"Urbicus Manius\",\"Rufius Martialis\",\"Juvenalis Cordius\",\"Musonius Cornelius\",\"Catilius Hirrus\",\"Rabirius Gallus\",\"Marcus Cornificius\",\"Fabianus Albinus\",\"Hostius Bonifacius\",\"Foslius Zeno\",\"Probus Nigidius\",\"Matienus Ennius\",\"Festus Nasennius\",\"Poppaedius Anatolius\",\"Hilarianus Modius\",\"Draco Terentius\",\"Rufinus Curtius\",\"Musonius Festinius\",\"Memmius Nasidius\",\"Hordeonius Pacilius\",\"Ravillius Severian\",\"Fannius Hordeonius\",\"Spurius Segulius\",\"Marian Cato\",\"Sergius Silius\",\"Pedius Icilius\",\"Lucanus Balbus\",\"Ogulnius Petilius\",\"Fannius Egnatius\",\"Laecanius Valens\",\"Terentius Ravillius\",\"Caecilius Varro\",\"Sallustius Hilarianus\",\"Pedius Vibius\",\"Oppius Martialis\",\"Quirinius Fabius\",\"Lamponius Nautius\",\"Fulvius Latinius\",\"Secundinius Numitorius\",\"Albinius Cestius\",\"Ollius Coruncanius\",\"Sulpicius Caesennius\",\"Marian Vincentius\",\"Orbilius Novellius\",\"Amulius Sergian\",\"Draco Cincinnatus\",\"Nonius Verus\",\"Sornatius Gordian\",\"Nero Nasidius\",\"Laurentius Flavius\",\"Semellius Florianus\",\"Hortensius Opimius\",\"Lucius Considius\",\"Iulius Gordian\",\"Gabinius Spurius\",\"Lucanus Fulcinia\",\"Ollius Geta\",\"Sertorius Pettius\",\"Tiberius Munatius\",\"Sextus Verecundus\",\"Ogulnius Julius\",\"Brennus Tacitus\",\"Calpurnius Diocletian\",\"Avidius Modius\",\"Mallius Nonius\",\"Octavenus Volumnius\",\"Aemilius Livius\",\"Cicero Vitus\",\"Januarius Pacilius\",\"Salvian Oppius\",\"Marcus Belisarius\",\"Icilius Flaccus\",\"Hostius Salinator\",\"Pontidius Iunius\",\"Statilius Agrippa\",\"Opimius Brennus\",\"Lucanus Iunius\",\"Terentius Pilus\",\"Paulinus Anatolius\",\"Tertius Gallus\",\"Ostorius Laberius\",\"Potitius Florian\",\"Paconius Cato\",\"Opimius Octavius\",\"Opellius Ollius\",\"Bruttius Numerius\",\"Furius Musonius\",\"Antonius Hilarianus\",\"Rufius Messalla\",\"Plautius Opellius\",\"Bruttius Pettius\",\"Cispius Laecanius\",\"Nerva Afranius\",\"Lucanus Albinius\",\"Maecilius Geta\",\"Florianus Octavenus\",\"Tiberius Maecilius\",\"Blaesus Cornutus\",\"Papirius Lucretius\",\"Ofilius Percennius\",\"Hordeonius Octavius\",\"Nero Severus\",\"Murena Martialis\",\"Bonifacius Mucius\",\"Lucretius Hordeonius\",\"Gratian Laetorius\",\"Seneca Lucretius\",\"Albinus Hilarianus\",\"Fabianus Lucius\",\"Papinius Suetonius\",\"Drusus Menenius\",\"Fidius Vincentius\",\"Rufrius Belisarius\",\"Sidonius Hadrian\",\"Egnatius Blasius\",\"Afranius Cinna\",\"Lentulus Cethegus\",\"Cinna Iunius\",\"Calventius Rufius\",\"Orestius Domitius\",\"Plinius Licinius\",\"Suetonius Damasus\",\"Mescinius Brennus\",\"Servilius Honorius\",\"Domitius Sepullius\",\"Sergius Cispius\",\"Aelius Acilius\",\"Pettius Festinius\",\"Egnatius Varro\",\"Draco Probus\",\"Postumius Lollius\",\"Nasidius Orestius\",\"Donatus Tacitus\",\"Balbus Fabrinius\",\"Minicius Decimus\",\"Cloelius Rufrius\",\"Caelius Iunius\",\"Fidenas Marcus\",\"Baebius Caninius\",\"Geminius Ofilius\",\"Octavius Fidius\",\"Varian Granius\",\"Curtius Valens\",\"Sulpicius Numitorius\",\"Gordian Cocceius\",\"Silius Lollius\",\"Sentius Pasidienus\",\"Laronius Matienus\",\"Salvius Pedius\",\"Segulius Salvidienus\",\"Quintilianus Opellius\",\"Afranius Cassianus\",\"Genucius Germanicus\",\"Semellius Murena\",\"Cyprian Rufrius\",\"Pacilius Nazarius\",\"Balbus Marian\",\"Blossius Gallus\",\"Matinius Paternus\",\"Nasidienus Hostius\",\"Hadrian Novellius\",\"Ostorius Donatus\",\"Plinius Mucius\",\"Festus Furius\",\"Herennius Nerva\",\"Cosconius Eutropius\",\"Fabius Sergius\",\"Camillius Ingenuus\",\"Mescinius Lentulus\",\"Primitivus Semellius\",\"Camillus Priscus\",\"Novellius Laecanius\",\"Hostius Albinius\",\"Porcius Cinna\",\"Januarius Cocceius\",\"Terentius Quirinius\",\"Hostius Cornelius\",\"Albinus Junius\",\"Cinna Avidius\",\"Placidus Cyprian\",\"Zeno Persius\",\"Burrienus Sepullius\",\"Sergius Aulus\",\"Calventius Fabrinius\",\"Sallustius Salinator\",\"Geta Iulius\",\"Laronius Numicius\",\"Cominius Opellius\",\"Acilius Constantinus\",\"Cethegus Mamilius\",\"Terentius Valerius\",\"Cantilius Magnentius\",\"Justus Sextus\",\"Faustus Longinius\",\"Gracchus Camillius\",\"Opellius Coruncanius\",\"Donatus Minicius\",\"Lacerius Calpurnius\",\"Lartius Cyprian\",\"Fidenas Cinna\",\"Coruncanius Aulus\",\"Iunius Laetorius\",\"Cicero Valens\",\"Laelius Maxentius\",\"Matienus Murena\",\"Foslius Foslius\",\"Lucretius Urbicus\",\"Manilius Silvius\",\"Primitivus Sextus\",\"Mallius Clodius\",\"Avidius Segulius\",\"Gratian Sallustius\",\"Fabius Pettius\",\"Valens Appius\",\"Caesennius Clodius\",\"Flaccus Rufinus\",\"Secundus Balbinus\",\"Modius Calpurnius\",\"Orchius Sittius\",\"Nonius Nonius\",\"Cestius Ostorius\",\"Sepullius Quinctilius\",\"Lucius Laberius\",\"Marius Lacerius\",\"Nazarius Catius\",\"Sicinius Salinator\",\"Paternus Lartius\",\"Cyprian Calventius\",\"Anatolius Blossius\",\"Ennius Gaius\",\"Sergian Geminius\",\"Fabianus Servilius\",\"Damasus Brennus\",\"Catius Rufrius\",\"Seccius Mallius\",\"Pius Sicinius\",\"Secundinius Camillius\",\"Pasidienus Cornutus\",\"Laecanius Caesennius\",\"Caedicius Cassius\",\"Munatius Fabius\",\"Latinius Papinius\",\"Gordian Laetorius\",\"Nasennius Rufrius\",\"Severian Valentinian\",\"Julius Lollius\",\"Nasennius Maxentius\",\"Naevius Avidius\",\"Oppius Rutilius\",\"Postumius Appius\",\"Betilienus Plinius\",\"Naevius Sedatius\",\"Tacitus Seccius\",\"Albinius Gallus\",\"Aemilius Duronius\",\"Sentius Titus\",\"Matinius Numonius\",\"Lollius Rebilus\",\"Calpurnius Tacitus\",\"Laetorius Hostilius\",\"Geminius Caecilius\",\"Cornelius Domitian\",\"Icilius Rufinus\",\"Volumnius Faustus\",\"Flaccus Sergius\",\"Secundinius Titus\",\"Marius Vitalis\",\"Agrippa Salvian\",\"Pompeius Aemilius\",\"Ennius Geta\",\"Salvius Fannius\",\"Lucretius Cordius\",\"Vespasian Laelius\",\"Poppaedius Eborius\",\"Domitian Novellius\",\"Lentulus Nigidius\",\"Manius Plinius\",\"Cocceius Poppaedius\",\"Castorius Vitalis\",\"Menenius Spurius\",\"Gallus Gaius\",\"Galerius Crispinus\",\"Calventius Volumnius\",\"Nasennius Mucius\",\"Furius Mamilius\",\"Laecanius Pontidius\",\"Blaesus Camillius\",\"Pupius Musonius\",\"Cordius Nasidienus\",\"Verecundus Considius\",\"Maecilius Nasidius\",\"Caelius Sempronius\",\"Modius Sepullius\",\"Primitivus Modius\",\"Rufinus Manius\",\"Maecenas Eutropius\",\"Dacian Petreius\",\"Considius Sepullius\",\"Placidus Egnatius\",\"Sicinius Granius\",\"Cincinnatus Numitorius\",\"Fundanus Genucius\",\"Licinius Didius\",\"Fabianus Salinator\",\"Tertius Quirinius\",\"Betilienus Maecenas\",\"Lamponius Mallius\",\"Petilius Cornutus\",\"Sentius Sepullius\",\"Hilarianus Lamponius\",\"Minicius Salvidienus\",\"Silanus Appius\",\"Marian Rufrius\",\"Marian Caelius\",\"Nazarius Longinius\",\"Blaesus Castorius\",\"Didius Januarius\",\"Caedicius Sabucius\",\"Mescinius Placidus\",\"Salinator Cornutus\",\"Gracchus Dacian\",\"Agrippa Pettius\",\"Albinius Pomponius\",\"Fulvius Betilienus\",\"Furius Januarius\",\"Pompeius Numicius\",\"Anatolius Laronius\",\"Fulcinia Pilus\",\"Rutilius Cassianus\",\"Tacitus Ennius\",\"Scribonius Decentius\",\"Numicius Paconius\",\"Magnentius Caecilius\",\"Matienus Betilienus\",\"Orestius Honorius\",\"Hostilius Sentius\",\"Julius Vitalis\",\"Hilarianus Nasennius\",\"Albinius Placidus\",\"Cicero Longinius\",\"Musonius Herennius\",\"Scribonius Cinna\",\"Sicinius Lucanus\",\"Priscus Burrienus\",\"Paconius Octavius\",\"Curtius Blaesus\",\"Quirinius Castorius\",\"Domitian Bruttius\",\"Servilius Castorius\",\"Domitius Fundanus\",\"Sedatius Norbanus\",\"Perperna Pomponius\",\"Cispius Numonius\",\"Laurentius Maximus\",\"Domitian Gordian\",\"Secundinius Calventius\",\"Eutropius Ravillius\",\"Gracchus Varro\",\"Acilius Paulinus\",\"Silanus Honorius\",\"Varian Severian\",\"Aelius Hordeonius\",\"Paulinus Marcus\",\"Quinctilius Tarquinius\",\"Numitorius Albinius\",\"Agrippa Festinius\",\"Duronius Paconius\",\"Percennius Gaius\",\"Numicius Servius\",\"Hostilius Maximus\",\"Cincinnatus Nazarius\",\"Nautius Flaccus\",\"Geta Numerius\",\"Festinius Sertorius\",\"Papinius Furius\",\"Vitus Potitius\",\"Placidus Maecilius\",\"Mamilius Sergius\",\"Zeno Pasidienus\",\"Memmius Eborius\",\"Egnatius Ovidius\",\"Secundus Sallustius\",\"Rufinus Sextus\",\"Cornificius Caninius\",\"Menenius Aurelianus\",\"Novellius Persius\",\"Eborius Sextus\",\"Numerius Iunius\",\"Lollius Octavius\",\"Sergius Cethegus\",\"Furius Agrippa\",\"Crispus Sicinius\",\"Fannius Petilius\",\"Nazarius Percennius\",\"Nasidius Terentius\",\"Domitian Sergius\",\"Cicereius Percennius\",\"Rabirius Quinctilius\",\"Octavenus Ninnius\",\"Betilienus Nasennius\",\"Mucius Cassius\",\"Caecilius Rufius\",\"Lollius Hirrus\",\"Geta Fidenas\",\"Mucius Nasennius\",\"Geta Rufinus\",\"Paulinus Gabinius\",\"Nazarius Severian\",\"Valerius Atilius\",\"Magnentius Galerius\",\"Marcius Verecundus\",\"Marcius Curtius\",\"Gnaeus Minicius\",\"Murena Quinctilius\",\"Nasidienus Felix\",\"Matienus Bruttius\",\"Pasidienus Caedicius\",\"Decimus Vibius\",\"Naevius Lollius\",\"Opellius Seneca\",\"Dacian Lucius\",\"Severian Drusus\",\"Januarius Festinius\",\"Maecenas Oppius\",\"Nasidienus Eborius\",\"Sallustius Bruttius\",\"Cosconius Opimius\",\"Cocceius Faustus\",\"Pacilius Geminius\",\"Sallustius Petilius\",\"Papirius Fundanus\",\"Laurentius Marcellus\",\"Caesennius Sornatius\",\"Gratian Marcus\",\"Ingenuus Cethegus\",\"Lentulus Statilius\",\"Cominius Lentulus\",\"Gaius Mucius\",\"Claudius Magnentius\",\"Fundanus Segulius\",\"Pupius Donatus\",\"Ostorius Nasennius\",\"Geta Catius\",\"Murena Cato\",\"Gratian Draco\",\"Eutropius Festus\",\"Vitalis Claudius\",\"Celsus Cornutus\",\"Marcius Segulius\",\"Matinius Minicius\",\"Novellius Messalla\",\"Cordius Potitius\",\"Stertinius Manius\",\"Balbinus Octavius\",\"Drusus Numonius\",\"Opimius Geminius\",\"Eborius Pasidienus\",\"Tacitus Caelius\",\"Statilius Cincinnatus\",\"Gallus Didius\",\"Orestius Caelius\",\"Coruncanius Hadrian\",\"Vespasian Nautius\",\"Palpellius Cincinnatus\",\"Gracchus Vitalis\",\"Hilarianus Zeno\",\"Cethegus Geminius\",\"Cethegus Rullianus\",\"Sertorius Rufrius\",\"Mucius Clodius\",\"Secundinius Cestius\",\"Horatius Candidus\",\"Camillius Felix\",\"Sedatius Rufrius\",\"Norbanus Januarius\",\"Plautius Salvius\",\"Licinius Afranius\",\"Blaesus Geta\",\"Antonius Papinius\",\"Tarquinius Norbanus\",\"Avidius Caedicius\",\"Aquillius Octavius\",\"Duronius Priscus\",\"Iulius Marius\",\"Albinius Lacerius\",\"Cloelius Flaccus\",\"Cassianus Geminius\",\"Marcus Cicero\",\"Quinctilius Draco\",\"Varian Mamilius\",\"Ostorius Burrienus\",\"Ostorius Florian\",\"Hadrian Cyprian\",\"Sergian Rufinus\",\"Seneca Pettius\",\"Iuventius Hordeonius\",\"Quintilianus Eborius\",\"Gratian Maecenas\",\"Seneca Musonius\",\"Maecilius Rufius\",\"Rabirius Pompeius\",\"Gabinius Mucius\",\"Rufinus Verecundus\",\"Plautius Honorius\",\"Titus Pasidienus\",\"Calventius Pomponius\",\"Foslius Gabinius\",\"Egnatuleius Maximus\",\"Zeno Lartius\",\"Cinna Tarquinius\",\"Valentinian Bonifacius\",\"Rullianus Pontidius\",\"Memmius Balbus\",\"Mucius Didius\",\"Clodius Cornutus\",\"Fundanus Lucius\",\"Decimus Terentius\",\"Anatolius Bonifacius\",\"Numonius Publius\",\"Paternus Aquillius\",\"Numerius Decimus\",\"Urbicus Caninius\",\"Sornatius Catius\",\"Vibius Germanicus\",\"Quinctilius Publius\",\"Opellius Fidenas\",\"Suetonius Plinius\",\"Marcius Pupius\",\"Silvius Plautius\",\"Perperna Domitian\",\"Tacitus Fannius\",\"Spurius Coruncanius\",\"Paelignus Gratian\",\"Eborius Caedicius\",\"Caedicius Semellius\",\"Suetonius Cethegus\",\"Rufrius Balbus\",\"Justus Segulius\",\"Gratian Iulius\",\"Perperna Petilius\",\"Candidus Iuventius\",\"Varro Priscus\",\"Musonius Sextus\",\"Amulius Maximus\",\"Cincinnatus Sextius\",\"Petreius Mescinius\",\"Vespasian Numerius\",\"Hostilius Ofilius\",\"Honorius Naevius\",\"Probus Januarius\",\"Diocletian Cloelius\",\"Caelius Horatius\",\"Aelius Draco\",\"Florianus Cornificius\",\"Numerius Dacian\",\"Nonius Cispius\",\"Musonius Catilius\",\"Laelius Cornelius\",\"Fannius Claudius\",\"Cispius Domitian\",\"Cethegus Fidius\",\"Licinius Seccius\",\"Ollius Appius\",\"Gaius Nonius\",\"Cordius Aurelianus\",\"Urbicus Urbanus\",\"Papinius Ninnius\",\"Suetonius Hordeonius\",\"Urbanus Felix\",\"Octavius Caninius\",\"Cestius Blossius\",\"Vincentius Marcius\",\"Gallus Lucius\",\"Pilus Lartius\",\"Licinius Balbus\",\"Balbinus Terentius\",\"Furius Sempronius\",\"Blasius Stertinius\",\"Afranius Cornutus\",\"Primitivus Decentius\",\"Caelius Decimus\",\"Fabianus Cicereius\",\"Sestius Silvius\",\"Fulvius Suetonius\",\"Sepullius Duronius\",\"Didius Germanicus\",\"Herennius Bruttius\",\"Maecenas Menenius\",\"Matinius Cilnius\",\"Cornificius Matienus\",\"Junius Vitalis\",\"Sulpicius Sedatius\",\"Publicius Ovidius\",\"Verecundus Fannius\",\"Calpurnius Postumius\",\"Agrippa Pinarus\",\"Nigidius Saturninus\",\"Fabius Camillius\",\"Lentulus Fabius\",\"Nazarius Silanus\",\"Octavenus Seneca\",\"Catius Icilius\",\"Cordius Pacilius\",\"Marcus Latinius\",\"Marcus Laurentius\",\"Lacerius Papirius\",\"Celsus Novellius\",\"Minicius Memmius\",\"Silvius Tertius\",\"Numicius Fulcinia\",\"Oppius Papinius\",\"Sertorius Maecilius\",\"Claudius Lacerius\",\"Papinius Magnentius\",\"Sidonius Spurius\",\"Poppaedius Ofilius\",\"Curtius Rufius\",\"Flavius Paelignus\",\"Nasidienus Ogulnius\",\"Sextius Faustus\",\"Pupius Sicinius\",\"Magnentius Sentius\",\"Maximian Vitalis\",\"Norbanus Nasennius\",\"Cassius Postumius\",\"Festus Nigidius\",\"Messalla Vespasian\",\"Salinator Tacitus\",\"Menenius Maecenas\",\"Potitius Hordeonius\",\"Constantinus Festinius\",\"Salvius Rullianus\",\"Candidus Saturninus\",\"Paternus Manius\",\"Castorius Cordius\",\"Rebilus Marcellus\",\"Laberius Damasus\",\"Donatus Cosconius\",\"Domitius Aelius\",\"Cicero Florian\",\"Paternus Gnaeus\",\"Caesennius Perperna\",\"Foslius Aulus\",\"Draco Secundus\",\"Plautius Pilus\",\"Lentulus Probus\",\"Rullianus Ingenuus\",\"Betilienus Lartius\",\"Gabinius Fulcinia\",\"Urbanus Maximus\",\"Tertius Ninnius\",\"Decimus Acilius\",\"Varro Maximus\",\"Numerius Didius\",\"Dacian Aurelianus\",\"Pontidius Didius\",\"Ovidius Pettius\",\"Segulius Fidius\",\"Plinius Damasus\",\"Valens Sicinius\",\"Numonius Lartius\",\"Bruttius Modius\",\"Curtius Pius\",\"Licinius Hordeonius\",\"Pedius Maximus\",\"Nonius Modius\",\"Rebilus Potitius\",\"Statilius Drusus\",\"Nasennius Severus\",\"Quintus Didius\",\"Fannius Iulius\",\"Ravillius Paulinus\",\"Iunius Afranius\",\"Nero Ostorius\",\"Sempronius Genucius\",\"Longinius Sextus\",\"Sentius Scribonius\",\"Domitius Primitivus\",\"Juvenalis Mescinius\",\"Pupius Maecilius\",\"Blaesus Opimius\",\"Pettius Suetonius\",\"Granius Vincentius\",\"Candidus Nigidius\",\"Honorius Publicius\",\"Constantinus Gracchus\",\"Fulvius Crispinus\",\"Pilus Lacerius\",\"Probus Martialis\",\"Eborius Genucius\",\"Salvidienus Hilarianus\",\"Cestius Livius\",\"Quirinius Sertorius\",\"Albinus Musonius\",\"Rullianus Silius\",\"Icilius Sallustius\",\"Candidus Poppaedius\",\"Opimius Salinator\",\"Aulus Iuventius\",\"Cilnius Egnatuleius\",\"Cethegus Cominius\",\"Justus Laecanius\",\"Volumnius Egnatuleius\",\"Quintus Gracchus\",\"Valentinian Decentius\",\"Coruncanius Cato\",\"Genucius Orbilius\",\"Anatolius Pettius\",\"Ogulnius Vespasian\",\"Candidus Postumius\",\"Regulus Burrienus\",\"Aulus Orbilius\",\"Salvian Aquillius\",\"Pettius Cicereius\",\"Bonifacius Camillus\",\"Lacerius Sedatius\",\"Menenius Novellius\",\"Claudius Granius\",\"Baebius Tiberius\",\"Salvian Lucanus\",\"Cestius Ollius\",\"Volumnius Pupius\",\"Castorius Junius\",\"Sabucius Laelius\",\"Stertinius Betilienus\",\"Duronius Sentius\",\"Tiberius Didius\",\"Juvenalis Ovidius\",\"Munatius Norbanus\",\"Valerius Persius\",\"Priscus Januarius\",\"Cocceius Geta\",\"Celsus Cinna\",\"Felix Avidius\",\"Sextius Musonius\",\"Verus Papinius\",\"Caelian Aquillius\",\"Plautius Numerius\",\"Semellius Petilius\",\"Diocletian Coruncanius\",\"Quintus Burrienus\",\"Quirinius Geta\",\"Modius Severian\",\"Juvenalis Anatolius\",\"Cestius Gaius\",\"Camillus Florianus\",\"Pontidius Nerva\",\"Nerva Burrienus\",\"Cornificius Ollius\",\"Menenius Cornelius\",\"Seccius Marcus\",\"Quinctilius Latinius\",\"Lucius Publicius\",\"Munatius Horatius\",\"Mallius Fulvius\",\"Sergian Cassianus\",\"Pettius Domitius\",\"Sergius Paelignus\",\"Gracchus Opimius\",\"Pacilius Antonius\",\"Salinator Cassio\",\"Dacian Priscus\",\"Tertius Murena\",\"Murena Cassio\",\"Numicius Lucius\",\"Maximus Latinius\",\"Valens Donatus\",\"Publicius Domitius\",\"Varro Sittius\",\"Albinius Novellius\",\"Rufinus Hostilius\",\"Octavius Numerius\",\"Priscus Urbanus\",\"Cincinnatus Varro\",\"Tiberius Nasidius\",\"Modius Donatus\",\"Cosconius Cocceius\",\"Laronius Gratian\",\"Festus Gracchus\",\"Sergius Curtius\",\"Sertorius Pinarus\",\"Aemilius Furius\",\"Memmius Nerva\",\"Lollius Marcus\",\"Cornificius Egnatius\",\"Latinius Fulcinia\",\"Poppaedius Sallustius\",\"Felix Terentius\",\"Baebius Pasidienus\",\"Blossius Secundus\",\"Galerius Norbanus\",\"Antonius Numerius\",\"Cloelius Januarius\",\"Flavius Rutilius\",\"Salvidienus Felix\",\"Munatius Curtius\",\"Segulius Octavius\",\"Seccius Aelius\",\"Blaesus Severian\",\"Plinius Orbilius\",\"Hordeonius Papinius\",\"Sittius Valens\",\"Afranius Caelian\",\"Avidius Egnatius\",\"Sedatius Salvidienus\",\"Geminius Nonius\",\"Quintilianus Sittius\",\"Gabinius Nigidius\",\"Dacian Primitivus\",\"Seccius Gallus\",\"Octavius Saturninus\",\"Naevius Calpurnius\",\"Sittius Caecilius\",\"Terentius Numitorius\",\"Bruttius Herennius\",\"Albinius Marcius\",\"Numitorius Stertinius\",\"Nazarius Valerius\",\"Genucius Longinius\",\"Ogulnius Maximian\",\"Crispus Nasennius\",\"Martialis Lollius\",\"Herennius Pinarus\",\"Fabrinius Secundus\",\"Messalla Aemilius\",\"Balbinus Mamilius\",\"Cethegus Priscus\",\"Betilienus Nero\",\"Candidus Memmius\",\"Foslius Brennus\",\"Primitivus Cicereius\",\"Martialis Festus\",\"Bruttius Maecenas\",\"Brennus Sentius\",\"Calpurnius Ostorius\",\"Nasennius Fulvius\",\"Cosconius Lartius\",\"Rabirius Minicius\",\"Antonius Marcius\",\"Mucius Pettius\",\"Draco Calventius\",\"Tiberius Oppius\",\"Geminius Sidonius\",\"Laronius Mallius\",\"Albinius Geta\",\"Crispinus Caecilius\",\"Cato Silius\",\"Musonius Sempronius\",\"Cestius Nautius\",\"Latinius Nautius\",\"Cinna Furius\",\"Gaius Severus\",\"Maximian Sittius\",\"Nero Perperna\",\"Nazarius Lentulus\",\"Calventius Cornificius\",\"Cornelius Ovidius\",\"Nerva Pilus\",\"Sedatius Severus\",\"Gratian Paulinus\",\"Cordius Considius\",\"Hortensius Severian\",\"Juvenalis Vitus\",\"Salvian Mamilius\",\"Valerius Fabrinius\",\"Anatolius Camillus\",\"Verus Celsus\",\"Candidus Ravillius\",\"Duronius Laecanius\",\"Foslius Diocletian\",\"Hostius Silanus\",\"Coruncanius Licinius\",\"Suetonius Belisarius\",\"Faustus Memmius\",\"Caesennius Camillus\",\"Gallus Balbinus\",\"Justus Priscus\",\"Laetorius Lartius\",\"Ovidius Duronius\",\"Anatolius Gordian\",\"Gallus Candidus\",\"Salvian Numicius\",\"Mescinius Postumius\",\"Sicinius Longinius\",\"Minicius Quintus\",\"Tarquinius Avidius\",\"Claudius Quintilianus\",\"Lucretius Modius\",\"Celsus Menenius\",\"Numitorius Fulcinia\",\"Tacitus Latinius\",\"Atilius Poppaedius\",\"Camillus Scribonius\",\"Cassius Catilius\",\"Cassianus Novellius\",\"Egnatuleius Petilius\",\"Caecilius Fabrinius\",\"Lucretius Tacitus\",\"Salvian Ostorius\",\"Sergian Albinius\",\"Decimus Mescinius\",\"Baebius Valens\",\"Papirius Germanicus\",\"Papinius Placidus\",\"Didius Cordius\",\"Pontidius Primitivus\",\"Quinctilius Aquillius\",\"Lartius Saturninus\",\"Laetorius Cispius\",\"Marcius Catius\",\"Silanus Potitius\",\"Opellius Valentinian\",\"Sempronius Belisarius\",\"Cilnius Sepullius\",\"Blossius Rutilius\",\"Faustus Matienus\",\"Ostorius Latinius\",\"Albinus Statilius\",\"Nigidius Memmius\",\"Caedicius Hordeonius\",\"Primitivus Iulius\",\"Horatius Gnaeus\",\"Pompeius Secundinius\",\"Opimius Julius\",\"Fidenas Pius\",\"Maecenas Quirinius\",\"Pettius Afranius\",\"Eutropius Paelignus\",\"Zeno Segulius\",\"Rufrius Aquillius\",\"Murena Vibius\",\"Palpellius Granius\",\"Livius Cassianus\",\"Ogulnius Didius\",\"Cilnius Mescinius\",\"Musonius Marian\",\"Foslius Cispius\",\"Maximus Egnatuleius\",\"Tarquinius Menenius\",\"Gaius Domitian\",\"Claudius Urbicus\",\"Cilnius Cosconius\",\"Aelius Tiberius\",\"Quintus Cassianus\",\"Germanicus Salvidienus\",\"Mallius Festinius\",\"Sergius Drusus\",\"Furius Pomponius\",\"Vibius Geta\",\"Cassius Pedius\",\"Seccius Caelian\",\"Severus Aelius\",\"Probus Terentius\",\"Porcius Rebilus\",\"Albinus Scribonius\",\"Blossius Naevius\",\"Lollius Silius\",\"Nasidienus Cilnius\",\"Ovidius Magnentius\",\"Sestius Baebius\",\"Maximus Celsus\",\"Flavius Silanus\",\"Albinus Julius\",\"Manius Valens\",\"Musonius Maecenas\",\"Severian Candidus\",\"Herennius Calpurnius\",\"Vitalis Ravillius\",\"Salvidienus Porcius\",\"Belisarius Pasidienus\",\"Drusus Egnatius\",\"Martialis Hostius\",\"Paelignus Valentinian\",\"Considius Paconius\",\"Domitius Lucretius\",\"Salvius Numicius\",\"Januarius Ovidius\",\"Valens Pedius\",\"Licinius Januarius\",\"Festinius Maximus\",\"Vibius Opellius\",\"Publius Eutropius\",\"Sittius Marian\",\"Ovidius Secundinius\",\"Urbanus Memmius\",\"Valentinian Musonius\",\"Paternus Silius\",\"Cinna Coruncanius\",\"Brennus Sestius\",\"Nero Fulcinia\",\"Magnentius Icilius\",\"Salvian Servilius\",\"Cassianus Agrippa\",\"Vespasian Marian\",\"Caninius Severus\",\"Aurelianus Memmius\",\"Catius Valerius\",\"Spurius Aulus\",\"Nasidius Camillius\",\"Hadrian Memmius\",\"Nazarius Felix\",\"Marcellus Bonifacius\",\"Terentius Sornatius\",\"Modius Didius\",\"Cloelius Ofilius\",\"Norbanus Pasidienus\",\"Sempronius Publius\",\"Ninnius Petilius\",\"Cincinnatus Festus\",\"Caecilius Cilnius\",\"Opimius Numicius\",\"Cosconius Mescinius\",\"Calpurnius Murena\",\"Julius Aulus\",\"Germanicus Silanus\",\"Fidenas Fabius\",\"Servius Cocceius\",\"Nautius Damasus\",\"Secundinius Cinna\",\"Cornutus Perperna\",\"Egnatius Cethegus\",\"Iunius Flavius\",\"Cosconius Ninnius\",\"Potitius Blossius\",\"Vespasian Silanus\",\"Tarquinius Rabirius\",\"Marcellus Secundus\",\"Martialis Granius\",\"Hilarianus Festinius\",\"Pilus Novellius\",\"Justus Ninnius\",\"Genucius Gordian\",\"Sentius Vespasian\",\"Numicius Pomponius\",\"Perperna Manius\",\"Laecanius Iuventius\",\"Primitivus Foslius\",\"Nautius Cilnius\",\"Priscus Sidonius\",\"Gracchus Fidenas\",\"Iulius Valerius\",\"Sulpicius Marcus\",\"Lucius Nonius\",\"Clodius Postumius\",\"Crispus Ennius\",\"Salvidienus Zeno\",\"Salinator Salvian\",\"Fundanus Festus\",\"Potitius Drusus\",\"Nonius Drusus\",\"Salinator Sergian\",\"Granius Draco\",\"Rufius Camillius\",\"Papinius Gordian\",\"Numicius Duronius\",\"Damasus Paternus\",\"Marcus Lucretius\",\"Genucius Opellius\",\"Silanus Musonius\",\"Lacerius Furius\",\"Pius Licinius\",\"Cassianus Verecundus\",\"Papirius Claudius\",\"Duronius Cassio\",\"Festus Pasidienus\",\"Paulinus Sabucius\",\"Valentinian Ollius\",\"Secundus Camillus\",\"Eborius Domitian\",\"Caelian Castorius\",\"Percennius Cicero\",\"Salvian Cantilius\",\"Longinius Drusus\",\"Marcus Iuventius\",\"Cornelius Aelius\",\"Cornelius Laronius\",\"Hordeonius Geminius\",\"Paternus Palpellius\",\"Octavenus Primitivus\",\"Amulius Gabinius\",\"Numicius Placidus\",\"Palpellius Marian\",\"Fundanus Iulius\",\"Servius Marcellus\",\"Cicero Maximus\",\"Marcellus Blasius\",\"Justus Duronius\",\"Paconius Egnatius\",\"Herennius Amulius\",\"Ninnius Statilius\",\"Domitian Fundanus\",\"Bruttius Valens\",\"Cincinnatus Segulius\",\"Eutropius Ollius\",\"Gaius Octavius\",\"Germanicus Sedatius\",\"Tertius Mucius\",\"Burrienus Pinarus\",\"Sittius Volumnius\",\"Numicius Tiberius\",\"Maximian Camillius\",\"Cassius Probus\",\"Draco Fannius\",\"Nasidienus Germanicus\",\"Magnentius Octavius\",\"Coruncanius Fidius\",\"Rabirius Egnatius\",\"Laronius Petilius\",\"Icilius Vibius\",\"Gratian Aelius\",\"Geta Flaccus\",\"Nerva Avidius\",\"Spurius Diocletian\",\"Eborius Urbanus\",\"Sicinius Matinius\",\"Nero Fundanus\",\"Nasennius Orbilius\",\"Pacilius Pacilius\",\"Festus Pilus\",\"Lamponius Maximus\",\"Germanicus Dacian\",\"Laetorius Rufrius\",\"Plinius Vitus\",\"Maximus Perperna\",\"Novellius Publicius\",\"Varian Diocletian\",\"Potitius Maxentius\",\"Egnatius Norbanus\",\"Maecenas Aulus\",\"Maximian Iulius\",\"Cominius Novellius\",\"Atilius Iunius\",\"Probus Quintus\",\"Cordius Nonius\",\"Marcellus Fulcinia\",\"Octavius Tiberius\",\"Publius Orestius\",\"Lentulus Pacilius\",\"Aulus Publius\",\"Germanicus Salinator\",\"Lacerius Caelius\",\"Catius Fabrinius\",\"Sextius Domitius\",\"Verus Octavenus\",\"Varro Germanicus\",\"Aelius Quirinius\",\"Vespasian Verecundus\",\"Manilius Icilius\",\"Bonifacius Geta\",\"Ogulnius Papinius\",\"Galerius Rebilus\",\"Domitius Rufinus\",\"Matinius Sentius\",\"Galerius Seccius\",\"Probus Marian\",\"Novellius Flavius\",\"Mamilius Fabianus\",\"Decimus Seneca\",\"Rufrius Amulius\",\"Nasidius Postumius\",\"Petilius Cominius\",\"Baebius Hilarianus\",\"Mamilius Priscus\",\"Bruttius Iulius\",\"Marius Maxentius\",\"Laetorius Maecenas\",\"Manilius Cassianus\",\"Tiberius Quinctilius\",\"Hostius Matienus\",\"Saturninus Probus\",\"Acilius Fannius\",\"Manius Furius\",\"Urbanus Marius\",\"Dacian Cestius\",\"Agrippa Secundus\",\"Gaius Burrienus\",\"Salvidienus Messalla\",\"Paelignus Gabinius\",\"Amulius Balbinus\",\"Silvius Caelius\",\"Sicinius Sentius\",\"Lamponius Bruttius\",\"Munatius Mamilius\",\"Laronius Sedatius\",\"Florianus Cassius\",\"Porcius Cincinnatus\",\"Gordian Ofilius\",\"Palpellius Caedicius\",\"Nautius Agrippa\",\"Salvidienus Gaius\",\"Fidius Salinator\",\"Drusus Cispius\",\"Honorius Fulcinia\",\"Valerius Secundus\",\"Hostilius Salvian\",\"Cinna Decimus\",\"Seccius Primitivus\",\"Menenius Felix\",\"Pettius Laberius\",\"Laurentius Rufrius\",\"Fidius Murena\",\"Munatius Iuventius\",\"Rufrius Castorius\",\"Crispus Silvius\",\"Catilius Galerius\",\"Vincentius Tarquinius\",\"Manius Sextus\",\"Rufius Seneca\",\"Nasidienus Balbus\",\"Longinius Baebius\",\"Verus Lartius\",\"Nasennius Rabirius\",\"Germanicus Gratian\",\"Petreius Pinarus\",\"Cinna Camillus\",\"Aquillius Numitorius\",\"Laelius Lamponius\",\"Curtius Orchius\",\"Pacilius Pedius\",\"Justus Porcius\",\"Nerva Marian\",\"Sulpicius Paconius\",\"Manilius Hordeonius\",\"Ovidius Egnatius\",\"Constantinus Minicius\",\"Munatius Quirinius\",\"Fidius Sittius\",\"Sentius Candidus\",\"Flavius Zeno\",\"Pilus Cornutus\",\"Curtius Fabrinius\",\"Bruttius Tarquinius\",\"Pontidius Donatus\",\"Cornelius Nigidius\",\"Messalla Publius\",\"Eborius Eutropius\",\"Bonifacius Avidius\",\"Rufinus Lamponius\",\"Licinius Anatolius\",\"Lollius Fabrinius\",\"Lartius Paternus\",\"Justus Perperna\",\"Regulus Memmius\",\"Eutropius Gabinius\",\"Crispinus Ennius\",\"Atilius Numerius\",\"Memmius Quintus\",\"Rufius Horatius\",\"Cato Decentius\",\"Sallustius Pacilius\",\"Castorius Sedatius\",\"Baebius Calventius\",\"Rullianus Baebius\",\"Quirinius Ogulnius\",\"Curtius Gallus\",\"Memmius Junius\",\"Lucretius Icilius\",\"Sittius Minicius\",\"Tacitus Gabinius\",\"Pasidienus Poppaedius\",\"Eutropius Cassius\",\"Persius Orestius\",\"Vibius Licinius\",\"Pupius Burrienus\",\"Secundus Foslius\",\"Manius Saturninus\",\"Maxentius Salvidienus\",\"Salvidienus Mucius\",\"Latinius Gratian\",\"Ingenuus Opimius\",\"Marius Varian\",\"Persius Acilius\",\"Nero Hostius\",\"Vibius Lamponius\",\"Cassianus Afranius\",\"Amulius Fundanus\",\"Fidius Publicius\",\"Cornutus Rufinus\",\"Hirrus Minicius\",\"Varro Anatolius\",\"Mucius Nazarius\",\"Sergian Foslius\",\"Icilius Semellius\",\"Numonius Opellius\",\"Burrienus Castorius\",\"Domitius Didius\",\"Justus Lucius\",\"Maecenas Tertius\",\"Belisarius Manilius\",\"Seccius Caecilius\",\"Calpurnius Stertinius\",\"Spurius Constantinus\",\"Quinctilius Palpellius\",\"Plinius Rutilius\",\"Considius Maxentius\",\"Valerius Pius\",\"Diocletian Volumnius\",\"Titus Latinius\",\"Numitorius Hadrian\",\"Pinarus Marcellus\",\"Memmius Sempronius\",\"Varro Flaccus\",\"Rabirius Justus\",\"Cornutus Papirius\",\"Domitian Decimus\",\"Tiberius Mamilius\",\"Pacilius Decimus\",\"Rabirius Decentius\",\"Flaccus Numerius\",\"Festus Pius\",\"Octavius Fabianus\",\"Lucanus Matinius\",\"Memmius Cornelius\",\"Burrienus Nero\",\"Aemilius Varian\",\"Postumius Varian\",\"Nero Balbus\",\"Silanus Rutilius\",\"Urbanus Quirinius\",\"Urbanus Latinius\",\"Seccius Lentulus\",\"Lucretius Nerva\",\"Iunius Lartius\",\"Persius Egnatius\",\"Paternus Cicereius\",\"Eborius Perperna\",\"Coruncanius Bruttius\",\"Paconius Petreius\",\"Lucanus Secundinius\",\"Saturninus Paternus\",\"Considius Horatius\",\"Laurentius Rufius\",\"Nasidius Probus\",\"Verecundus Appius\",\"Avidius Gordian\",\"Eborius Norbanus\",\"Opimius Cloelius\",\"Curtius Sulpicius\",\"Marcius Sepullius\",\"Maecilius Longinius\",\"Salvidienus Aemilius\",\"Marcus Minicius\",\"Geta Rufius\",\"Ninnius Sextus\",\"Numitorius Cyprian\",\"Persius Florianus\",\"Honorius Verecundus\",\"Hilarianus Afranius\",\"Varian Coruncanius\",\"Gracchus Munatius\",\"Burrienus Fabrinius\",\"Scribonius Cicereius\",\"Orestius Albinius\",\"Percennius Acilius\",\"Nigidius Manilius\",\"Postumius Sextus\",\"Statilius Livius\",\"Florianus Gnaeus\",\"Opimius Fulcinia\",\"Aquillius Blasius\",\"Nazarius Paelignus\",\"Cicero Pomponius\",\"Verus Priscus\",\"Cloelius Diocletian\",\"Catius Ingenuus\",\"Sentius Verecundus\",\"Sepullius Pinarus\",\"Mamilius Menenius\",\"Marian Laronius\",\"Pettius Matienus\",\"Coruncanius Rabirius\",\"Nasidius Draco\",\"Nonius Pettius\",\"Fulcinia Fabius\",\"Urbicus Albinius\",\"Cornutus Petreius\",\"Belisarius Acilius\",\"Aquillius Tertius\",\"Seccius Germanicus\",\"Scribonius Sicinius\",\"Severian Laurentius\",\"Sallustius Considius\",\"Castorius Salvidienus\",\"Aulus Aurelianus\",\"Tarquinius Catius\",\"Decimus Pacilius\",\"Papirius Pontidius\",\"Gratian Manius\",\"Nero Iuventius\",\"Camillius Suetonius\",\"Celsus Domitius\",\"Severus Rullianus\",\"Cornelius Lucius\",\"Catius Granius\",\"Rufrius Novellius\",\"Belisarius Cethegus\",\"Nigidius Sentius\",\"Lamponius Ninnius\",\"Murena Maecenas\",\"Marcus Appius\",\"Cornutus Cassianus\",\"Vitalis Geta\",\"Fabianus Hadrian\",\"Ostorius Licinius\",\"Cispius Claudius\",\"Dacian Belisarius\",\"Ollius Numitorius\",\"Naevius Vincentius\",\"Caesennius Cassianus\",\"Fulcinia Numicius\",\"Martialis Cantilius\",\"Pettius Marcius\",\"Flavius Pompeius\",\"Lucretius Matienus\",\"Modius Verecundus\",\"Urbicus Persius\",\"Sentius Fannius\",\"Quintus Sextius\",\"Fulvius Iuventius\",\"Lollius Gratian\",\"Laronius Pasidienus\",\"Crispinus Laecanius\",\"Mescinius Pontidius\",\"Rullianus Maecenas\",\"Nigidius Curtius\",\"Perperna Coruncanius\",\"Valens Decimus\",\"Cincinnatus Lacerius\",\"Florian Potitius\",\"Cicereius Fulcinia\",\"Blossius Cethegus\",\"Persius Probus\",\"Cato Fundanus\",\"Faustus Caedicius\",\"Matinius Sornatius\",\"Aurelianus Appius\",\"Laurentius Sicinius\",\"Varian Mescinius\",\"Catilius Livius\",\"Lartius Maxentius\",\"Lollius Candidus\",\"Nasennius Nazarius\",\"Sedatius Laberius\",\"Pomponius Sulpicius\",\"Cloelius Curtius\",\"Rufinus Servius\",\"Magnentius Ennius\",\"Marcius Munatius\",\"Cloelius Clodius\",\"Quinctilius Severian\",\"Lucanus Sempronius\",\"Genucius Octavius\",\"Sornatius Salinator\",\"Octavenus Caelian\",\"Festinius Cordius\",\"Herennius Segulius\",\"Petilius Icilius\",\"Burrienus Vitalis\",\"Menenius Donatus\",\"Cestius Albinius\",\"Pasidienus Semellius\",\"Sittius Sestius\",\"Aulus Munatius\",\"Hostilius Stertinius\",\"Servilius Vincentius\",\"Faustus Livius\",\"Appius Ninnius\",\"Flavius Celsus\",\"Decentius Foslius\",\"Fundanus Sestius\",\"Silius Fabrinius\",\"Salinator Mamilius\",\"Geta Nasennius\",\"Livius Novellius\",\"Numerius Orchius\",\"Horatius Maximus\",\"Laronius Paconius\",\"Tiberius Cinna\",\"Regulus Junius\",\"Numitorius Blasius\",\"Mescinius Persius\",\"Valentinian Catius\",\"Coruncanius Nigidius\",\"Memmius Herennius\",\"Diocletian Urbicus\",\"Ravillius Nautius\",\"Pupius Norbanus\",\"Memmius Belisarius\",\"Geminius Paternus\",\"Primitivus Varro\",\"Silius Pomponius\",\"Avidius Naevius\",\"Maximian Probus\",\"Mallius Nazarius\",\"Cornelius Servius\",\"Festinius Sabucius\",\"Castorius Cispius\",\"Nazarius Granius\",\"Plinius Fundanus\",\"Servius Stertinius\",\"Antonius Cosconius\",\"Cloelius Damasus\",\"Antonius Blossius\",\"Verecundus Eborius\",\"Caelian Brennus\",\"Sedatius Probus\",\"Palpellius Lucretius\",\"Orbilius Rufius\",\"Primitivus Secundus\",\"Catilius Aurelianus\",\"Pupius Sergius\",\"Cornificius Crispinus\",\"Catilius Placidus\",\"Zeno Flavius\",\"Sepullius Camillius\",\"Stertinius Catilius\",\"Numitorius Numonius\",\"Drusus Pontidius\",\"Orbilius Lucanus\",\"Damasus Tertius\",\"Fabrinius Palpellius\",\"Aemilius Cocceius\",\"Nasidienus Albinus\",\"Laelius Segulius\",\"Camillus Maecenas\",\"Considius Priscus\",\"Naevius Pettius\",\"Ravillius Mallius\",\"Seccius Blossius\",\"Pinarus Plautius\",\"Aulus Petilius\",\"Cantilius Laberius\",\"Ollius Sedatius\",\"Vincentius Donatus\",\"Silvius Coruncanius\",\"Cassio Verecundus\",\"Stertinius Sornatius\",\"Ingenuus Martialis\",\"Fidius Verecundus\",\"Sallustius Iunius\",\"Ofilius Aurelianus\",\"Volumnius Cicero\",\"Rebilus Gnaeus\",\"Maximian Domitian\",\"Appius Anatolius\",\"Gabinius Cominius\",\"Catilius Cato\",\"Lamponius Laetorius\",\"Afranius Florian\",\"Caecilius Urbanus\",\"Calpurnius Pilus\",\"Primitivus Cassianus\",\"Paulinus Minicius\",\"Blasius Albinius\",\"Pompeius Blasius\",\"Hostius Silvius\",\"Sulpicius Segulius\",\"Menenius Verecundus\",\"Cyprian Belisarius\",\"Lamponius Fundanus\",\"Horatius Nonius\",\"Diocletian Laronius\",\"Pius Iuventius\",\"Munatius Valerius\",\"Sallustius Matinius\",\"Orbilius Octavenus\",\"Secundinius Musonius\",\"Maxentius Perperna\",\"Honorius Caninius\",\"Hordeonius Pinarus\",\"Pacilius Paulinus\",\"Cominius Ostorius\",\"Decimus Laberius\",\"Gordian Aurelianus\",\"Seneca Justus\",\"Domitius Constantinus\",\"Lartius Baebius\",\"Sabucius Didius\",\"Ingenuus Cassianus\",\"Potitius Opimius\",\"Caecilius Vincentius\",\"Juvenalis Justus\",\"Faustus Suetonius\",\"Vibius Iulius\",\"Seneca Seccius\",\"Ogulnius Aquillius\",\"Marcus Naevius\",\"Sidonius Geta\",\"Menenius Laurentius\",\"Sertorius Numicius\",\"Numerius Hadrian\",\"Numonius Cicero\",\"Nazarius Cosconius\",\"Servilius Juvenalis\",\"Publius Priscus\",\"Domitian Marcius\",\"Petreius Fabianus\",\"Paulinus Secundinius\",\"Ollius Munatius\",\"Naevius Betilienus\",\"Varian Pius\",\"Marian Pomponius\",\"Regulus Fabrinius\",\"Duronius Sornatius\",\"Marcus Paternus\",\"Fulcinia Celsus\",\"Hortensius Caedicius\",\"Junius Genucius\",\"Manius Sicinius\",\"Paconius Albinius\",\"Quintus Nasidius\",\"Ingenuus Vespasian\",\"Rufinus Genucius\",\"Rufius Cincinnatus\",\"Titus Numonius\",\"Cyprian Pedius\",\"Julius Justus\",\"Licinius Nasidienus\",\"Porcius Gracchus\",\"Cornificius Foslius\",\"Blasius Atilius\",\"Terentius Juvenalis\",\"Balbinus Suetonius\",\"Sabucius Camillius\",\"Semellius Genucius\",\"Maximian Aurelianus\",\"Flavius Maecilius\",\"Bruttius Caedicius\",\"Camillius Lacerius\",\"Ravillius Honorius\",\"Stertinius Laronius\",\"Camillius Regulus\",\"Orchius Ollius\",\"Cloelius Opellius\",\"Blaesus Cocceius\",\"Honorius Longinius\",\"Tarquinius Cominius\",\"Aquillius Sergian\",\"Cilnius Decimus\",\"Pupius Petilius\",\"Numonius Rufinus\",\"Blaesus Suetonius\",\"Percennius Caelius\",\"Honorius Postumius\",\"Cassius Fabrinius\",\"Lucanus Ravillius\",\"Lacerius Ennius\",\"Iunius Marius\",\"Sicinius Cassianus\",\"Balbinus Manilius\",\"Cicereius Hostilius\",\"Lollius Rutilius\",\"Aurelianus Caelius\",\"Hostilius Crispus\",\"Fundanus Hordeonius\",\"Cilnius Stertinius\",\"Caedicius Cosconius\",\"Salinator Probus\",\"Festinius Constantinus\",\"Priscus Gratian\",\"Sallustius Albinius\",\"Tertius Sextius\",\"Livius Paternus\",\"Verus Octavius\",\"Tertius Naevius\",\"Ovidius Gnaeus\",\"Rufius Lartius\",\"Maximus Seneca\",\"Primitivus Cinna\",\"Aurelianus Nerva\",\"Urbicus Primitivus\",\"Cato Marcus\",\"Donatus Seneca\",\"Pomponius Spurius\",\"Flavius Stertinius\",\"Varian Latinius\",\"Draco Balbus\",\"Pedius Germanicus\",\"Granius Servilius\",\"Atilius Paconius\",\"Eutropius Norbanus\",\"Maecilius Plautius\",\"Lucius Cosconius\",\"Sabucius Marcius\",\"Blaesus Sempronius\",\"Fabianus Verecundus\",\"Silvius Scribonius\",\"Orestius Orbilius\",\"Cornutus Gracchus\",\"Silanus Fundanus\",\"Latinius Avidius\",\"Florian Donatus\",\"Paelignus Pompeius\",\"Caninius Caninius\",\"Cilnius Juvenalis\",\"Maximus Caelian\",\"Murena Gratian\",\"Valens Balbinus\",\"Florian Fundanus\",\"Cyprian Baebius\",\"Honorius Laberius\",\"Cestius Decentius\",\"Constantinus Poppaedius\",\"Festinius Sestius\",\"Lucretius Gaius\",\"Decentius Gratian\",\"Lacerius Flaccus\",\"Albinus Sulpicius\",\"Fidenas Aelius\",\"Sempronius Herennius\",\"Tertius Rullianus\",\"Cestius Valerius\",\"Gnaeus Titus\",\"Sidonius Aemilius\",\"Ollius Festus\",\"Januarius Valens\",\"Icilius Crispinus\",\"Felix Plautius\",\"Florian Florian\",\"Mallius Nigidius\",\"Hadrian Pius\",\"Spurius Cordius\",\"Salvian Potitius\",\"Icilius Marcus\",\"Paelignus Ravillius\",\"Octavenus Sempronius\",\"Festinius Fabrinius\",\"Castorius Fulcinia\",\"Nasennius Laelius\",\"Festinius Salvidienus\",\"Nautius Camillus\",\"Quirinius Caedicius\",\"Ennius Publius\",\"Damasus Hadrian\",\"Mucius Furius\",\"Varian Laurentius\",\"Silius Vespasian\",\"Ennius Mamilius\",\"Nerva Numerius\",\"Cornutus Eborius\",\"Vitus Nerva\",\"Terentius Cato\",\"Avidius Aquillius\",\"Iulius Clodius\",\"Herennius Mamilius\",\"Constantinus Ollius\",\"Horatius Novellius\",\"Silvius Orestius\",\"Juvenalis Caedicius\",\"Sestius Bruttius\",\"Plinius Sepullius\",\"Hilarianus Sidonius\",\"Cyprian Valerius\",\"Cassianus Primitivus\",\"Severian Sentius\",\"Cyprian Nigidius\",\"Minicius Secundinius\",\"Regulus Sicinius\",\"Tertius Marcus\",\"Cornutus Flaccus\",\"Pontidius Hortensius\",\"Gracchus Cornutus\",\"Maecilius Burrienus\",\"Camillius Opellius\",\"Lucanus Maecenas\",\"Laetorius Cocceius\",\"Sextus Decimus\",\"Placidus Segulius\",\"Maximus Terentius\",\"Secundus Fabrinius\",\"Cassio Cornificius\",\"Balbus Petilius\",\"Pius Servius\",\"Quirinius Gabinius\",\"Atilius Cornificius\",\"Vitus Fabianus\",\"Lacerius Albinus\",\"Eborius Baebius\",\"Maximus Menenius\",\"Constantinus Verus\",\"Antonius Rebilus\",\"Silius Avidius\",\"Afranius Justus\",\"Gabinius Cordius\",\"Varro Mescinius\",\"Plinius Avidius\",\"Blaesus Mucius\",\"Antonius Paelignus\",\"Cassio Numonius\",\"Lamponius Aquillius\",\"Lucretius Laecanius\",\"Fulcinia Varro\",\"Sextus Longinius\",\"Sittius Vincentius\",\"Cornelius Oppius\",\"Orbilius Blossius\",\"Camillius Geminius\",\"Nonius Fidius\",\"Albinius Ravillius\",\"Messalla Tiberius\",\"Magnentius Sicinius\",\"Curtius Fulcinia\",\"Ogulnius Novellius\",\"Saturninus Servilius\",\"Cornelius Rullianus\",\"Balbus Lentulus\",\"Maximian Sallustius\",\"Decentius Norbanus\",\"Musonius Gallus\",\"Fabrinius Mescinius\",\"Pettius Gaius\",\"Felix Papirius\",\"Pompeius Modius\",\"Appius Silvius\",\"Donatus Servius\",\"Opimius Januarius\",\"Aelius Pettius\",\"Tacitus Decentius\",\"Geminius Vitalis\",\"Urbanus Nazarius\",\"Nerva Plinius\",\"Suetonius Porcius\",\"Anatolius Caelian\",\"Cordius Percennius\",\"Sextius Fabius\",\"Acilius Rufrius\",\"Avidius Calventius\",\"Balbinus Flaccus\",\"Sittius Seccius\",\"Celsus Semellius\",\"Anatolius Fabrinius\",\"Segulius Egnatuleius\",\"Dacian Rullianus\",\"Cassius Sedatius\",\"Nasidius Nero\",\"Hortensius Sedatius\",\"Agrippa Pompeius\",\"Antonius Marcellus\",\"Aemilius Porcius\",\"Catius Gratian\",\"Iulius Geta\",\"Cosconius Castorius\",\"Aquillius Numicius\",\"Priscus Postumius\",\"Geminius Clodius\",\"Cornelius Fidenas\",\"Celsus Licinius\",\"Celsus Florianus\",\"Domitian Tertius\",\"Crispus Calventius\",\"Castorius Pius\",\"Norbanus Cassianus\",\"Vitus Rebilus\",\"Matinius Hortensius\",\"Burrienus Rufrius\",\"Fannius Albinius\",\"Caedicius Martialis\",\"Appius Marcius\",\"Galerius Lucanus\",\"Sittius Priscus\",\"Fannius Secundinius\",\"Fundanus Numitorius\",\"Laurentius Maecilius\",\"Geta Constantinus\",\"Sertorius Nasidius\",\"Cantilius Longinius\",\"Decimus Sulpicius\",\"Catilius Scribonius\",\"Paternus Crispinus\",\"Nerva Publius\",\"Nazarius Segulius\",\"Cestius Stertinius\",\"Egnatuleius Considius\",\"Ogulnius Lollius\",\"Messalla Afranius\",\"Licinius Acilius\",\"Publicius Quinctilius\",\"Rullianus Hordeonius\",\"Flaccus Clodius\",\"Avidius Drusus\",\"Naevius Pius\",\"Blossius Duronius\",\"Cincinnatus Festinius\",\"Publicius Fundanus\",\"Minicius Pacilius\",\"Vitalis Foslius\",\"Numitorius Volumnius\",\"Anatolius Opimius\",\"Cinna Quintus\",\"Lartius Blaesus\",\"Nonius Valerius\",\"Pontidius Nigidius\",\"Afranius Norbanus\",\"Ollius Plinius\",\"Germanicus Junius\",\"Silanus Paternus\",\"Sicinius Celsus\",\"Aurelianus Ofilius\",\"Tertius Decentius\",\"Volumnius Mescinius\",\"Geta Hilarianus\",\"Blaesus Camillus\",\"Cato Acilius\",\"Regulus Brennus\",\"Silvius Rebilus\",\"Varian Cornelius\",\"Cicereius Egnatuleius\",\"Papinius Papinius\",\"Opimius Opimius\",\"Saturninus Mamilius\",\"Primitivus Paternus\",\"Atilius Rabirius\",\"Opimius Cantilius\",\"Blasius Probus\",\"Nonius Cicereius\",\"Ostorius Draco\",\"Festinius Domitian\",\"Titus Pomponius\",\"Secundus Papinius\",\"Lacerius Pilus\",\"Cestius Quinctilius\",\"Manius Galerius\",\"Pasidienus Ovidius\",\"Paconius Novellius\",\"Oppius Maecilius\",\"Cordius Sextius\",\"Gratian Eutropius\",\"Julius Palpellius\",\"Tiberius Pontidius\",\"Appius Nero\",\"Maecilius Lentulus\",\"Icilius Calpurnius\",\"Blasius Juvenalis\",\"Maecilius Donatus\",\"Orchius Fulvius\",\"Maximus Sertorius\",\"Cicero Fannius\",\"Herennius Suetonius\",\"Ollius Longinius\",\"Celsus Sidonius\",\"Nazarius Afranius\",\"Januarius Ogulnius\",\"Honorius Plinius\",\"Appius Valentinian\",\"Ollius Tarquinius\",\"Honorius Zeno\",\"Salinator Appius\",\"Candidus Musonius\",\"Nasidius Granius\",\"Ogulnius Nonius\",\"Zeno Gordian\",\"Gordian Florian\",\"Severian Sestius\",\"Salvidienus Agrippa\",\"Vincentius Rufius\",\"Cantilius Papirius\",\"Anatolius Segulius\",\"Ravillius Magnentius\",\"Paulinus Brennus\",\"Germanicus Pompeius\",\"Lamponius Diocletian\",\"Latinius Lucretius\",\"Camillus Ofilius\",\"Juvenalis Iuventius\",\"Livius Appius\",\"Mescinius Magnentius\",\"Spurius Amulius\",\"Hadrian Maxentius\",\"Suetonius Blossius\",\"Pompeius Pasidienus\",\"Honorius Sestius\",\"Cethegus Julius\",\"Nero Cornelius\",\"Laurentius Crispus\",\"Marian Iunius\",\"Lacerius Damasus\",\"Festus Damasus\",\"Damasus Didius\",\"Ninnius Manilius\",\"Sulpicius Severian\",\"Candidus Plautius\",\"Cispius Secundus\",\"Afranius Valerius\",\"Decimus Claudius\",\"Herennius Cicereius\",\"Appius Marcellus\",\"Manius Pontidius\",\"Nonius Pacilius\",\"Pontidius Gaius\",\"Baebius Pompeius\",\"Florian Rullianus\",\"Silvius Maximus\",\"Livius Cicereius\",\"Sabucius Fundanus\",\"Nautius Quintilianus\",\"Sergius Cornelius\",\"Fannius Ingenuus\",\"Festinius Pilus\",\"Horatius Sittius\",\"Clodius Oppius\",\"Ogulnius Vitus\",\"Fundanus Menenius\",\"Ofilius Vitus\",\"Decentius Spurius\",\"Marius Sabucius\",\"Cordius Mallius\",\"Antonius Mamilius\",\"Cicero Volumnius\",\"Considius Varian\",\"Laurentius Nigidius\",\"Petilius Hostius\",\"Menenius Probus\",\"Ostorius Sempronius\",\"Latinius Naevius\",\"Secundus Octavenus\",\"Balbus Balbinus\",\"Orbilius Postumius\",\"Cordius Appius\",\"Sicinius Geta\",\"Petreius Catius\",\"Pontidius Cyprian\",\"Honorius Horatius\",\"Secundus Pacilius\",\"Albinius Sittius\",\"Caesennius Mescinius\",\"Laetorius Baebius\",\"Cosconius Sicinius\",\"Hadrian Marcus\",\"Blossius Caelius\",\"Naevius Acilius\",\"Hilarianus Florianus\",\"Curtius Lollius\",\"Poppaedius Pomponius\",\"Porcius Fulcinia\",\"Diocletian Publius\",\"Aulus Nero\",\"Segulius Florianus\",\"Marcellus Rufinus\",\"Marian Persius\",\"Statilius Curtius\",\"Aulus Numonius\",\"Perperna Nazarius\",\"Cinna Laronius\",\"Octavenus Suetonius\",\"Naevius Catilius\",\"Calventius Gratian\",\"Varro Caelian\",\"Cornelius Percennius\",\"Sextius Pompeius\",\"Semellius Laberius\",\"Florian Pompeius\",\"Severus Paconius\",\"Sempronius Musonius\",\"Sepullius Marian\",\"Baebius Sestius\",\"Icilius Quinctilius\",\"Ingenuus Primitivus\",\"Pontidius Matienus\",\"Tacitus Pacilius\",\"Memmius Betilienus\",\"Naevius Paternus\",\"Caesennius Coruncanius\",\"Claudius Ollius\",\"Publius Pontidius\",\"Pomponius Granius\",\"Pius Salinator\",\"Dacian Baebius\",\"Palpellius Paternus\",\"Sedatius Postumius\",\"Pasidienus Longinius\",\"Pinarus Lucius\",\"Juvenalis Calpurnius\",\"Caecilius Crispinus\",\"Hilarianus Fabianus\",\"Aquillius Ostorius\",\"Mamilius Latinius\",\"Messalla Sentius\",\"Scribonius Sornatius\",\"Cantilius Placidus\",\"Sittius Coruncanius\",\"Nero Diocletian\",\"Aelius Terentius\",\"Varro Lartius\",\"Catius Matienus\",\"Orchius Marcius\",\"Percennius Hortensius\",\"Eborius Sicinius\",\"Eborius Silvius\",\"Valentinian Calventius\",\"Paelignus Statilius\",\"Postumius Nonius\",\"Quirinius Antonius\",\"Hostius Pilus\",\"Quirinius Appius\",\"Salvidienus Licinius\",\"Ninnius Vitalis\",\"Dacian Eborius\",\"Caecilius Primitivus\",\"Catius Cyprian\",\"Laelius Maximian\",\"Maximus Gordian\",\"Paelignus Rufinus\",\"Volumnius Fabius\",\"Rabirius Valentinian\",\"Manius Valentinian\",\"Quintus Pompeius\",\"Pomponius Petilius\",\"Cornificius Castorius\",\"Varian Fulvius\",\"Latinius Statilius\",\"Laecanius Longinius\",\"Perperna Saturninus\",\"Furius Catilius\",\"Bonifacius Segulius\",\"Claudius Pasidienus\",\"Modius Eutropius\",\"Diocletian Palpellius\",\"Gracchus Marcus\",\"Secundus Tacitus\",\"Semellius Celsus\",\"Nonius Memmius\",\"Horatius Secundus\",\"Caelian Nerva\",\"Maxentius Secundinius\",\"Sempronius Iuventius\",\"Maxentius Fannius\",\"Nerva Nasidienus\",\"Flaccus Severus\",\"Amulius Martialis\",\"Hostius Perperna\",\"Paternus Oppius\",\"Lollius Memmius\",\"Scribonius Rutilius\",\"Porcius Numitorius\",\"Silanus Ravillius\",\"Salinator Balbus\",\"Sabucius Gallus\",\"Considius Domitius\",\"Hostilius Numonius\",\"Maximus Geta\",\"Galerius Gratian\",\"Domitius Postumius\",\"Granius Appius\",\"Caedicius Cassio\",\"Caedicius Lamponius\",\"Julius Belisarius\",\"Memmius Aurelianus\",\"Publius Lucretius\",\"Gracchus Ninnius\",\"Cosconius Urbanus\",\"Lucius Regulus\",\"Aemilius Licinius\",\"Camillius Severus\",\"Gabinius Publicius\",\"Camillius Mallius\",\"Damasus Gratian\",\"Lacerius Magnentius\",\"Nazarius Cornificius\",\"Maecilius Vitus\",\"Varro Considius\",\"Murena Paulinus\",\"Norbanus Pettius\",\"Sestius Aemilius\",\"Vespasian Petilius\",\"Nigidius Pius\",\"Verus Cassius\",\"Iunius Licinius\",\"Paelignus Gnaeus\",\"Ostorius Aulus\",\"Iunius Ingenuus\",\"Paulinus Egnatius\",\"Valerius Duronius\",\"Decimus Paulinus\",\"Rutilius Anatolius\",\"Rullianus Catius\",\"Hordeonius Sentius\",\"Scribonius Blasius\",\"Maxentius Maecilius\",\"Pinarus Urbicus\",\"Lucretius Maecilius\",\"Pompeius Ogulnius\",\"Drusus Gratian\",\"Publicius Publicius\",\"Camillus Valerius\",\"Lentulus Numitorius\",\"Segulius Spurius\",\"Fabius Iulius\",\"Pinarus Terentius\",\"Anatolius Oppius\",\"Publicius Zeno\",\"Cicereius Caelian\",\"Pius Severian\",\"Matinius Nonius\",\"Justus Cilnius\",\"Fannius Catilius\",\"Gallus Varro\",\"Flavius Manius\",\"Volumnius Florian\",\"Lamponius Severian\",\"Paulinus Aulus\",\"Appius Juvenalis\",\"Cilnius Hirrus\",\"Petreius Pilus\",\"Silvius Secundinius\",\"Fannius Diocletian\",\"Galerius Fannius\",\"Poppaedius Cincinnatus\",\"Verecundus Maecilius\",\"Varro Catilius\",\"Mucius Maecenas\",\"Ovidius Marian\",\"Antonius Regulus\",\"Sergian Amulius\",\"Fundanus Caedicius\",\"Martialis Ostorius\",\"Magnentius Juvenalis\",\"Verecundus Manilius\",\"Marcellus Palpellius\",\"Eutropius Nasidius\",\"Festinius Priscus\",\"Porcius Sergius\",\"Scribonius Laetorius\",\"Messalla Ofilius\",\"Marian Varian\",\"Sergius Laelius\",\"Nasidienus Vitalis\",\"Draco Silius\",\"Sulpicius Pettius\",\"Severian Papinius\",\"Nigidius Statilius\",\"Ogulnius Cassio\",\"Fulcinia Mucius\",\"Sestius Sulpicius\",\"Hordeonius Coruncanius\",\"Caelian Messalla\",\"Numicius Caesennius\",\"Hilarianus Potitius\",\"Titus Cloelius\",\"Coruncanius Mescinius\",\"Drusus Mescinius\",\"Silvius Sergian\",\"Florian Baebius\",\"Sertorius Servilius\",\"Eborius Plinius\",\"Blasius Publicius\",\"Caedicius Catilius\",\"Sepullius Hostilius\",\"Pompeius Volumnius\",\"Mamilius Sempronius\",\"Matinius Rabirius\",\"Volumnius Martialis\",\"Marcellus Cinna\",\"Silius Maecilius\",\"Sentius Sextius\",\"Memmius Acilius\",\"Blasius Hadrian\",\"Vitus Icilius\",\"Severian Titus\",\"Draco Lacerius\",\"Calventius Salvius\",\"Vitalis Valens\",\"Maecenas Mescinius\",\"Maximus Nasidienus\",\"Tertius Persius\",\"Afranius Orbilius\",\"Balbinus Opellius\",\"Rullianus Blossius\",\"Iulius Spurius\",\"Icilius Calventius\",\"Crispinus Sempronius\",\"Numicius Hordeonius\",\"Matienus Nigidius\",\"Eutropius Sulpicius\",\"Fabius Rufrius\",\"Cantilius Potitius\",\"Betilienus Palpellius\",\"Bonifacius Sextus\",\"Bonifacius Gnaeus\",\"Quirinius Cestius\",\"Saturninus Cocceius\",\"Lucanus Suetonius\",\"Aelius Donatus\",\"Tacitus Cato\",\"Sergian Oppius\",\"Terentius Servilius\",\"Salvidienus Ostorius\",\"Hortensius Marcellus\",\"Gaius Geta\",\"Minicius Afranius\",\"Quinctilius Hostilius\",\"Aurelianus Servius\",\"Eborius Priscus\",\"Geta Latinius\",\"Suetonius Aurelianus\",\"Nautius Horatius\",\"Mescinius Severus\",\"Cicero Amulius\",\"Seccius Octavenus\",\"Lucretius Nautius\",\"Pasidienus Claudius\",\"Paulinus Norbanus\",\"Marcus Opellius\",\"Sidonius Castorius\",\"Ollius Severian\",\"Calventius Calventius\",\"Cassio Secundinius\",\"Munatius Dacian\",\"Albinius Murena\",\"Blossius Pasidienus\",\"Petreius Cicereius\",\"Furius Tertius\",\"Ingenuus Betilienus\",\"Pasidienus Laecanius\",\"Cornutus Nerva\",\"Balbinus Spurius\",\"Marcius Avidius\",\"Statilius Clodius\",\"Damasus Cispius\",\"Curtius Petilius\",\"Drusus Quinctilius\",\"Cominius Sidonius\",\"Didius Anatolius\",\"Paconius Nasidienus\",\"Salinator Sergius\",\"Pius Urbicus\",\"Aulus Secundinius\",\"Gallus Pupius\",\"Magnentius Lentulus\",\"Rufius Caelian\",\"Juvenalis Betilienus\",\"Aelius Egnatius\",\"Magnentius Cinna\",\"Petilius Celsus\",\"Poppaedius Maximian\",\"Severus Musonius\",\"Minicius Caecilius\",\"Aelius Urbicus\",\"Pompeius Laberius\",\"Maximian Cinna\",\"Quirinius Galerius\",\"Cassius Severian\",\"Fabrinius Claudius\",\"Murena Cassianus\",\"Caninius Bonifacius\",\"Brennus Priscus\",\"Postumius Marian\",\"Cominius Lartius\",\"Cilnius Varro\",\"Sabucius Cassio\",\"Celsus Hilarianus\",\"Scribonius Sallustius\",\"Cicereius Iulius\",\"Ingenuus Constantinus\",\"Pettius Hadrian\",\"Florian Festus\",\"Ovidius Foslius\",\"Germanicus Severus\",\"Genucius Florianus\",\"Gordian Sepullius\",\"Horatius Sedatius\",\"Laetorius Considius\",\"Egnatius Novellius\",\"Numonius Cato\",\"Afranius Rullianus\",\"Saturninus Silanus\",\"Caecilius Postumius\",\"Calpurnius Constantinus\",\"Anatolius Blaesus\",\"Laurentius Catilius\",\"Seneca Regulus\",\"Herennius Pupius\",\"Pasidienus Albinius\",\"Fidenas Longinius\",\"Mallius Lollius\",\"Paternus Cilnius\",\"Pacilius Duronius\",\"Hostilius Nasennius\",\"Quintus Vespasian\",\"Cestius Geminius\",\"Salvius Lamponius\",\"Cicereius Iunius\",\"Scribonius Primitivus\",\"Amulius Furius\",\"Sextius Gratian\",\"Julius Cassio\",\"Geta Segulius\",\"Caesennius Laecanius\",\"Festus Seccius\",\"Palpellius Iuventius\",\"Anatolius Cassius\",\"Cassio Pilus\",\"Rutilius Ollius\",\"Tacitus Genucius\",\"Volumnius Honorius\",\"Gallus Geta\",\"Maecenas Verecundus\",\"Placidus Messalla\",\"Saturninus Rufrius\",\"Cantilius Laronius\",\"Sertorius Porcius\",\"Severian Catius\",\"Agrippa Cassianus\",\"Acilius Cestius\",\"Foslius Cominius\",\"Claudius Segulius\",\"Paulinus Avidius\",\"Postumius Urbanus\",\"Ingenuus Cyprian\",\"Valentinian Licinius\",\"Cicereius Gabinius\",\"Nasennius Geminius\",\"Albinus Aelius\",\"Mallius Flavius\",\"Egnatuleius Herennius\",\"Hilarianus Opellius\",\"Titus Quirinius\",\"Saturninus Sabucius\",\"Lucanus Paelignus\",\"Foslius Papirius\",\"Salvius Numerius\",\"Paelignus Orestius\",\"Sentius Murena\",\"Sallustius Lucius\",\"Fundanus Silvius\",\"Petilius Foslius\",\"Atilius Rufinus\",\"Fabius Memmius\",\"Minicius Laronius\",\"Sabucius Plinius\",\"Calpurnius Granius\",\"Considius Flavius\",\"Diocletian Marcellus\",\"Vincentius Mamilius\",\"Martialis Catilius\",\"Nasidius Calpurnius\",\"Betilienus Faustus\",\"Mallius Ovidius\",\"Salinator Nigidius\",\"Minicius Oppius\",\"Segulius Modius\",\"Hilarianus Sentius\",\"Hortensius Genucius\",\"Clodius Paternus\",\"Longinius Lacerius\",\"Petilius Calventius\",\"Paelignus Camillius\",\"Urbicus Belisarius\",\"Julius Spurius\",\"Galerius Quirinius\",\"Pedius Blossius\",\"Manilius Hostilius\",\"Hostius Belisarius\",\"Octavenus Paconius\",\"Naevius Laelius\",\"Quinctilius Secundinius\",\"Hostius Orbilius\",\"Faustus Cassius\",\"Pupius Sestius\",\"Titus Salvius\",\"Ingenuus Lentulus\",\"Appius Maximus\",\"Rufinus Sedatius\",\"Claudius Quirinius\",\"Celsus Herennius\",\"Pedius Constantinus\",\"Sulpicius Fulcinia\",\"Cassius Persius\",\"Sertorius Plautius\",\"Gordian Candidus\",\"Furius Zeno\",\"Duronius Eutropius\",\"Sertorius Sicinius\",\"Acilius Fulvius\",\"Cincinnatus Foslius\",\"Appius Foslius\",\"Valentinian Balbinus\",\"Lartius Rufius\",\"Maecilius Verus\",\"Fannius Marcus\",\"Manius Catius\",\"Pompeius Caecilius\",\"Nasidius Burrienus\",\"Faustus Camillius\",\"Gratian Catius\",\"Aurelianus Mucius\",\"Hordeonius Rufinus\",\"Varro Nasennius\",\"Genucius Cornutus\",\"Sulpicius Baebius\",\"Januarius Hostius\",\"Gabinius Geta\",\"Ogulnius Pettius\",\"Drusus Tiberius\",\"Terentius Quintus\",\"Oppius Spurius\",\"Genucius Silius\",\"Fidius Nigidius\",\"Cassius Titus\",\"Icilius Placidus\",\"Regulus Plinius\",\"Numitorius Bruttius\",\"Varian Volumnius\",\"Sepullius Memmius\",\"Blaesus Marcius\",\"Rabirius Probus\",\"Plinius Brennus\",\"Caedicius Salvian\",\"Salvius Maecilius\",\"Florianus Pompeius\",\"Livius Antonius\",\"Verecundus Octavenus\",\"Gordian Caelian\",\"Coruncanius Matienus\",\"Urbicus Sabucius\",\"Longinius Sulpicius\",\"Sextius Aemilius\",\"Amulius Balbus\",\"Stertinius Lucius\",\"Maximian Gallus\",\"Licinius Varro\",\"Sergius Naevius\",\"Horatius Hordeonius\",\"Sabucius Nasennius\",\"Coruncanius Opimius\",\"Sedatius Ennius\",\"Lucius Urbicus\",\"Saturninus Vincentius\",\"Numitorius Seccius\",\"Lartius Priscus\",\"Percennius Pontidius\",\"Betilienus Hirrus\",\"Aelius Cato\",\"Hostilius Ogulnius\",\"Florianus Pius\",\"Pedius Salvius\",\"Fulvius Cethegus\",\"Cordius Laelius\",\"Laetorius Rebilus\",\"Ennius Cicero\",\"Tarquinius Hostius\",\"Terentius Manius\",\"Curtius Hordeonius\",\"Acilius Orchius\",\"Draco Iunius\",\"Hadrian Lollius\",\"Baebius Considius\",\"Sidonius Albinus\",\"Sornatius Ingenuus\",\"Nonius Saturninus\",\"Gnaeus Silius\",\"Martialis Licinius\",\"Sidonius Decimus\",\"Mucius Valens\",\"Albinus Duronius\",\"Oppius Rufius\",\"Iuventius Egnatuleius\",\"Regulus Nasennius\",\"Blossius Fidenas\",\"Cantilius Fannius\",\"Maximus Curtius\",\"Volumnius Balbus\",\"Nasennius Primitivus\",\"Verus Volumnius\",\"Hadrian Hirrus\",\"Ninnius Marcellus\",\"Nasidienus Licinius\",\"Munatius Nasennius\",\"Severian Horatius\",\"Livius Herennius\",\"Lucius Drusus\",\"Servilius Iulius\",\"Caninius Caecilius\",\"Mamilius Cordius\",\"Blaesus Blasius\",\"Livius Sallustius\",\"Hirrus Cicereius\",\"Rufinus Amulius\",\"Festinius Longinius\",\"Damasus Servius\",\"Silanus Volumnius\",\"Orbilius Opimius\",\"Baebius Postumius\",\"Nautius Numerius\",\"Seneca Constantinus\",\"Hadrian Gnaeus\",\"Silanus Pomponius\",\"Lucius Juvenalis\",\"Matinius Crispinus\",\"Drusus Valentinian\",\"Pontidius Lentulus\",\"Juvenalis Semellius\",\"Varian Orestius\",\"Egnatius Mamilius\",\"Lucretius Regulus\",\"Laecanius Sulpicius\",\"Germanicus Fidius\",\"Sedatius Bonifacius\",\"Marcius Cloelius\",\"Marian Opellius\",\"Semellius Duronius\",\"Valentinian Servius\",\"Postumius Publicius\",\"Maecilius Cinna\",\"Marius Cestius\",\"Laberius Cyprian\",\"Cethegus Vincentius\",\"Lartius Naevius\",\"Iulius Pomponius\",\"Hadrian Palpellius\",\"Sextius Rufrius\",\"Balbinus Sornatius\",\"Primitivus Hostius\",\"Florianus Minicius\",\"Catilius Acilius\",\"Iulius Publicius\",\"Semellius Marian\",\"Clodius Statilius\",\"Rutilius Sornatius\",\"Zeno Cethegus\",\"Salvian Orbilius\",\"Petreius Cethegus\",\"Sextius Festus\",\"Cincinnatus Marcellus\",\"Paelignus Albinus\",\"Priscus Fidenas\",\"Celsus Balbinus\",\"Cosconius Maecilius\",\"Volumnius Salinator\",\"Pacilius Rullianus\",\"Rufrius Geta\",\"Januarius Perperna\",\"Quirinius Sextus\",\"Maximus Fundanus\",\"Sicinius Hirrus\",\"Avidius Sextius\",\"Plinius Fabius\",\"Marius Titus\",\"Castorius Cassio\",\"Sicinius Vincentius\",\"Segulius Considius\",\"Ogulnius Lucius\",\"Manius Blossius\",\"Acilius Atilius\",\"Castorius Lucretius\",\"Manius Foslius\",\"Matinius Postumius\",\"Fabianus Calventius\",\"Aulus Sidonius\",\"Cornificius Nigidius\",\"Paconius Rufrius\",\"Curtius Junius\",\"Severian Potitius\",\"Petilius Paelignus\",\"Lartius Gordian\",\"Ingenuus Matienus\",\"Nonius Papirius\",\"Aulus Manius\",\"Fundanus Papirius\",\"Geta Suetonius\",\"Papirius Ofilius\",\"Plautius Lentulus\",\"Drusus Quintilianus\",\"Sextius Albinius\",\"Dacian Verecundus\",\"Urbicus Cestius\",\"Oppius Matinius\",\"Salvian Aemilius\",\"Numitorius Nonius\",\"Cordius Marius\",\"Blasius Calventius\",\"Sabucius Potitius\",\"Albinus Vitus\",\"Quinctilius Nigidius\",\"Lucius Perperna\",\"Orbilius Didius\",\"Sulpicius Percennius\",\"Duronius Nasennius\",\"Cato Ravillius\",\"Marian Fabianus\",\"Justus Verecundus\",\"Amulius Ravillius\",\"Aelius Petilius\",\"Hostilius Norbanus\",\"Marcius Ollius\",\"Hirrus Volumnius\",\"Ingenuus Laetorius\",\"Rufrius Titus\",\"Constantinus Saturninus\",\"Mescinius Primitivus\",\"Cassio Albinus\",\"Hadrian Blaesus\",\"Sertorius Marcellus\",\"Oppius Domitius\",\"Nasidius Gordian\",\"Germanicus Marius\",\"Hordeonius Cincinnatus\",\"Maecilius Laberius\",\"Fidius Vespasian\",\"Anatolius Horatius\",\"Marius Eutropius\",\"Celsus Seccius\",\"Magnentius Decimus\",\"Gabinius Castorius\",\"Albinus Ninnius\",\"Fulcinia Laberius\",\"Albinus Salvius\",\"Caedicius Papirius\",\"Blossius Novellius\",\"Latinius Sextus\",\"Modius Gaius\",\"Sergian Murena\",\"Cestius Magnentius\",\"Iuventius Vitalis\",\"Iuventius Maecilius\",\"Eutropius Calventius\",\"Nero Messalla\",\"Pontidius Orbilius\",\"Regulus Petreius\",\"Magnentius Sempronius\",\"Varro Gnaeus\",\"Egnatuleius Vibius\",\"Florianus Gratian\",\"Tiberius Maxentius\",\"Nasidienus Laurentius\",\"Numonius Titus\",\"Laecanius Statilius\",\"Foslius Sentius\",\"Bonifacius Cominius\",\"Laetorius Priscus\",\"Sertorius Fannius\",\"Cordius Primitivus\",\"Camillus Ennius\",\"Nautius Marius\",\"Aulus Damasus\",\"Rullianus Draco\",\"Albinus Nasidienus\",\"Percennius Segulius\",\"Donatus Crispinus\",\"Sicinius Stertinius\",\"Marcellus Vibius\",\"Catius Balbus\",\"Tarquinius Lucanus\",\"Appius Caelius\",\"Caesennius Cato\",\"Pasidienus Acilius\",\"Valerius Caninius\",\"Postumius Anatolius\",\"Bruttius Afranius\",\"Laronius Poppaedius\",\"Numerius Pettius\",\"Palpellius Primitivus\",\"Fabianus Curtius\",\"Calventius Probus\",\"Hostius Terentius\",\"Rebilus Sestius\",\"Secundinius Foslius\",\"Nazarius Cicero\",\"Pedius Munatius\",\"Caelius Geta\",\"Gnaeus Domitian\",\"Gabinius Oppius\",\"Albinus Amulius\",\"Icilius Juvenalis\",\"Menenius Octavenus\",\"Maecenas Laelius\",\"Domitian Fabius\",\"Paternus Tertius\",\"Nerva Antonius\",\"Pupius Aemilius\",\"Catius Vespasian\",\"Blossius Palpellius\",\"Geminius Flaccus\",\"Eutropius Pedius\",\"Drusus Gordian\",\"Longinius Orestius\",\"Anatolius Minicius\",\"Varro Blossius\",\"Sergius Damasus\",\"Brennus Florian\",\"Pinarus Caesennius\",\"Fidius Sulpicius\",\"Foslius Fabrinius\",\"Aemilius Sextus\",\"Acilius Hadrian\",\"Pompeius Bonifacius\",\"Nautius Regulus\",\"Sergius Silvius\",\"Severus Laetorius\",\"Magnentius Urbanus\",\"Mallius Geta\",\"Clodius Lacerius\",\"Sabucius Claudius\",\"Terentius Cocceius\",\"Nasidienus Ovidius\",\"Cassio Pius\",\"Cispius Lamponius\",\"Urbanus Sicinius\",\"Salvian Sepullius\",\"Gabinius Lucretius\",\"Octavius Cincinnatus\",\"Pettius Hilarianus\",\"Octavenus Cilnius\",\"Flavius Laecanius\",\"Sextus Egnatius\",\"Balbus Sertorius\",\"Munatius Rutilius\",\"Fidius Rebilus\",\"Percennius Geminius\",\"Salinator Nasidienus\",\"Poppaedius Drusus\",\"Nasidienus Fidenas\",\"Nasidienus Sextus\",\"Drusus Laelius\",\"Cilnius Marcus\",\"Priscus Titus\",\"Cloelius Opimius\",\"Opellius Hirrus\",\"Modius Betilienus\",\"Sentius Curtius\",\"Nasennius Salinator\",\"Horatius Sentius\",\"Bonifacius Musonius\",\"Flaccus Petilius\",\"Varian Sergius\",\"Norbanus Cispius\",\"Messalla Herennius\",\"Quinctilius Egnatius\",\"Marian Albinus\",\"Horatius Naevius\",\"Numerius Hordeonius\",\"Caesennius Brennus\",\"Severian Nigidius\",\"Seneca Rufinus\",\"Florian Pomponius\",\"Sextus Honorius\",\"Laetorius Fabius\",\"Balbinus Matinius\",\"Cicereius Lamponius\",\"Laelius Laronius\",\"Laetorius Iunius\",\"Sicinius Sertorius\",\"Brennus Nonius\",\"Porcius Messalla\",\"Baebius Rufrius\",\"Felix Servius\",\"Matienus Laronius\",\"Opimius Rebilus\",\"Rufius Numonius\",\"Lacerius Geminius\",\"Placidus Catius\",\"Aelius Rabirius\",\"Cestius Numitorius\",\"Valentinian Nerva\",\"Genucius Maecilius\",\"Crispus Rebilus\",\"Ogulnius Fabius\",\"Catius Gracchus\",\"Urbanus Ingenuus\",\"Marius Valens\",\"Servilius Marcellus\",\"Numitorius Laronius\",\"Sextus Ostorius\",\"Tertius Severus\",\"Afranius Novellius\",\"Varro Cethegus\",\"Pompeius Octavenus\",\"Quirinius Brennus\",\"Vitalis Orbilius\",\"Sextius Gnaeus\",\"Gaius Brennus\",\"Postumius Draco\",\"Damasus Marian\",\"Cloelius Petilius\",\"Tiberius Sestius\",\"Balbus Modius\",\"Semellius Musonius\",\"Valens Eutropius\",\"Festinius Sicinius\",\"Geta Verus\",\"Maxentius Laurentius\",\"Hostilius Egnatuleius\",\"Juvenalis Caecilius\",\"Geminius Calventius\",\"Quintus Ovidius\",\"Gnaeus Sertorius\",\"Plautius Sergius\",\"Laecanius Nasidienus\",\"Felix Servilius\",\"Musonius Titus\",\"Pius Marcellus\",\"Memmius Licinius\",\"Nigidius Fabianus\",\"Cantilius Fidenas\",\"Rutilius Martialis\",\"Seneca Paternus\",\"Norbanus Lucanus\",\"Camillus Suetonius\",\"Albinius Vitus\",\"Cordius Terentius\",\"Sedatius Rufinus\",\"Catius Potitius\",\"Secundinius Hadrian\",\"Latinius Octavius\",\"Calventius Rebilus\",\"Rufius Silvius\",\"Marcus Magnentius\",\"Numerius Menenius\",\"Hortensius Duronius\",\"Aelius Ostorius\",\"Lacerius Murena\",\"Pomponius Cyprian\",\"Antonius Servius\",\"Numitorius Gallus\",\"Salvius Paulinus\",\"Quintus Sentius\",\"Gallus Bruttius\",\"Felix Cornificius\",\"Ollius Publicius\",\"Petilius Domitius\",\"Afranius Orestius\",\"Bonifacius Plinius\",\"Cloelius Ovidius\",\"Betilienus Caedicius\",\"Fidenas Verus\",\"Aurelianus Cantilius\",\"Salvian Poppaedius\",\"Agrippa Pedius\",\"Ofilius Nasennius\",\"Severian Bruttius\",\"Fulvius Atilius\",\"Betilienus Sallustius\",\"Pupius Anatolius\",\"Agrippa Vincentius\",\"Cominius Fidenas\",\"Plinius Fabianus\",\"Agrippa Atilius\",\"Minicius Papirius\",\"Verecundus Brennus\",\"Pasidienus Rufinus\",\"Rufinus Cloelius\",\"Naevius Fidenas\",\"Caesennius Postumius\",\"Regulus Volumnius\",\"Laronius Scribonius\",\"Palpellius Fulvius\",\"Tacitus Munatius\",\"Magnentius Hostilius\",\"Scribonius Gallus\",\"Catius Seccius\",\"Atilius Longinius\",\"Sornatius Flavius\",\"Ennius Sextus\",\"Acilius Justus\",\"Poppaedius Pilus\",\"Fulcinia Secundinius\",\"Severian Caesennius\",\"Ravillius Didius\",\"Lollius Maximus\",\"Festinius Duronius\",\"Matinius Nasennius\",\"Pomponius Livius\",\"Cornelius Opellius\",\"Sentius Laetorius\",\"Juvenalis Segulius\",\"Vitalis Silanus\",\"Porcius Didius\",\"Silanus Sestius\",\"Titus Sulpicius\",\"Baebius Cicero\",\"Pettius Palpellius\",\"Domitian Crispus\",\"Cornelius Cinna\",\"Lollius Varro\",\"Sicinius Betilienus\",\"Foslius Atilius\",\"Aulus Palpellius\",\"Gratian Pius\",\"Cestius Aulus\",\"Vincentius Florianus\",\"Saturninus Publicius\",\"Juvenalis Eborius\",\"Curtius Licinius\",\"Flaccus Lollius\",\"Egnatius Atilius\",\"Matinius Probus\",\"Cantilius Servilius\",\"Segulius Lartius\",\"Caedicius Albinus\",\"Opellius Salinator\",\"Herennius Galerius\",\"Amulius Gaius\",\"Donatus Geminius\",\"Hadrian Duronius\",\"Geta Placidus\",\"Rufius Agrippa\",\"Florianus Hostilius\",\"Terentius Sextus\",\"Orbilius Drusus\",\"Opellius Ogulnius\",\"Iuventius Pomponius\",\"Crispinus Volumnius\",\"Marcellus Sidonius\",\"Rabirius Laetorius\",\"Silius Clodius\",\"Hortensius Fulvius\",\"Vespasian Aulus\",\"Sergian Semellius\",\"Didius Albinus\",\"Afranius Rabirius\",\"Ninnius Amulius\",\"Salinator Nautius\",\"Norbanus Scribonius\",\"Rullianus Lamponius\",\"Crispus Gabinius\",\"Statilius Pacilius\",\"Mucius Cyprian\",\"Crispus Cornelius\",\"Gordian Albinius\",\"Modius Vespasian\",\"Nerva Gnaeus\",\"Sornatius Opimius\",\"Celsus Belisarius\",\"Paelignus Placidus\",\"Fidenas Flavius\",\"Marius Appius\",\"Cethegus Persius\",\"Sicinius Ennius\",\"Probus Orchius\",\"Cinna Rufrius\",\"Laelius Silius\",\"Pomponius Cilnius\",\"Nonius Laronius\",\"Germanicus Suetonius\",\"Pius Opimius\",\"Naevius Pinarus\",\"Cantilius Florianus\",\"Januarius Faustus\",\"Hostilius Cocceius\",\"Poppaedius Cominius\",\"Titus Nasennius\",\"Genucius Vibius\",\"Foslius Cyprian\",\"Novellius Statilius\",\"Galerius Naevius\",\"Claudius Sextius\",\"Menenius Furius\",\"Caedicius Felix\",\"Varro Florian\",\"Gaius Varian\",\"Minicius Ennius\",\"Gallus Plautius\",\"Ovidius Burrienus\",\"Balbus Varro\",\"Cethegus Vibius\",\"Sornatius Diocletian\",\"Blossius Salinator\",\"Burrienus Caelius\",\"Sergius Florian\",\"Granius Dacian\",\"Maximian Petreius\",\"Valerius Numonius\",\"Gracchus Lucretius\",\"Pinarus Albinius\",\"Caelius Salvian\",\"Cispius Minicius\",\"Valerius Domitius\",\"Murena Novellius\",\"Octavenus Quintus\",\"Atilius Florian\",\"Lucanus Florian\",\"Duronius Nasidius\",\"Clodius Plautius\",\"Ostorius Manius\",\"Sallustius Secundinius\",\"Cyprian Octavius\",\"Plinius Agrippa\",\"Albinius Bonifacius\",\"Opimius Pinarus\",\"Hadrian Regulus\",\"Nonius Ravillius\",\"Paelignus Latinius\",\"Nasidius Marian\",\"Priscus Cominius\",\"Gratian Petilius\",\"Cornificius Cilnius\",\"Zeno Tiberius\",\"Candidus Laecanius\",\"Eutropius Afranius\",\"Octavius Sentius\",\"Caelian Domitian\",\"Nautius Appius\",\"Antonius Cordius\",\"Laronius Gordian\",\"Iuventius Amulius\",\"Pomponius Opellius\",\"Vitus Orestius\",\"Calpurnius Egnatius\",\"Laronius Laronius\",\"Cinna Considius\",\"Matinius Calventius\",\"Norbanus Betilienus\",\"Terentius Urbicus\",\"Aemilius Celsus\",\"Cornelius Sextus\",\"Catius Plinius\",\"Primitivus Gabinius\",\"Drusus Furius\",\"Gracchus Drusus\",\"Paelignus Junius\",\"Iulius Porcius\",\"Martialis Lacerius\",\"Sestius Poppaedius\",\"Placidus Numitorius\",\"Donatus Petilius\",\"Salinator Gallus\",\"Tarquinius Vincentius\",\"Memmius Rabirius\",\"Ovidius Varro\",\"Tarquinius Iunius\",\"Longinius Junius\",\"Zeno Festinius\",\"Quirinius Flavius\",\"Januarius Titus\",\"Cato Lollius\",\"Ninnius Varian\",\"Livius Orchius\",\"Eborius Egnatius\",\"Statilius Sestius\",\"Pedius Rufius\",\"Petilius Porcius\",\"Secundus Julius\",\"Scribonius Livius\",\"Menenius Hirrus\",\"Papirius Coruncanius\",\"Maximus Festinius\",\"Pupius Cloelius\",\"Aquillius Quintilianus\",\"Lentulus Lucretius\",\"Placidus Oppius\",\"Atilius Tertius\",\"Silvius Persius\",\"Egnatius Ollius\",\"Varro Memmius\",\"Marcius Laberius\",\"Pinarus Cestius\",\"Aelius Quintilianus\",\"Paulinus Tarquinius\",\"Gabinius Maecilius\",\"Urbanus Mucius\",\"Felix Sicinius\",\"Acilius Palpellius\",\"Potitius Gordian\",\"Blasius Manilius\",\"Gabinius Verecundus\",\"Hadrian Munatius\",\"Marcellus Potitius\",\"Cassius Fulvius\",\"Cilnius Gracchus\",\"Pius Tertius\",\"Fabius Nonius\",\"Urbicus Cethegus\",\"Rebilus Blossius\",\"Percennius Livius\",\"Fulcinia Quintilianus\",\"Caesennius Calventius\",\"Eutropius Donatus\",\"Valentinian Poppaedius\",\"Caesennius Nerva\",\"Camillus Quirinius\",\"Nasennius Numicius\",\"Acilius Silvius\",\"Servius Castorius\",\"Palpellius Secundus\",\"Calpurnius Paulinus\",\"Ovidius Maximus\",\"Silanus Oppius\",\"Pasidienus Cicereius\",\"Lucanus Salvian\",\"Sextus Maximus\",\"Quintilianus Pomponius\",\"Paconius Avidius\",\"Ofilius Pedius\",\"Caninius Priscus\",\"Brennus Sidonius\",\"Mamilius Ingenuus\",\"Rebilus Laronius\",\"Amulius Marian\",\"Aelius Vitus\",\"Maecenas Publicius\",\"Fabianus Quirinius\",\"Acilius Semellius\",\"Lucius Caecilius\",\"Horatius Petreius\",\"Cornelius Albinius\",\"Cestius Maximian\",\"Gracchus Vitus\",\"Silius Secundus\",\"Mallius Cicero\",\"Fidenas Crispinus\",\"Eutropius Fabrinius\",\"Hostius Lollius\",\"Maecenas Salvian\",\"Verus Germanicus\",\"Flaccus Camillus\",\"Acilius Numonius\",\"Ovidius Marcius\",\"Vincentius Catilius\",\"Lucius Hirrus\",\"Caesennius Domitius\",\"Octavius Cosconius\",\"Paternus Valens\",\"Vitalis Tacitus\",\"Constantinus Munatius\",\"Sextius Cinna\",\"Quinctilius Flaccus\",\"Fidius Valens\",\"Blasius Paconius\",\"Publicius Hilarianus\",\"Ingenuus Segulius\",\"Lamponius Rebilus\",\"Furius Aulus\",\"Salvidienus Baebius\",\"Hostilius Dacian\",\"Sidonius Severus\",\"Maxentius Titus\",\"Menenius Appius\",\"Claudius Gracchus\",\"Justus Cantilius\",\"Blasius Martialis\",\"Domitius Tarquinius\",\"Caninius Livius\",\"Secundus Sergius\",\"Valens Munatius\",\"Justus Atilius\",\"Tertius Lartius\",\"Plautius Rufrius\",\"Vitus Laberius\",\"Nerva Octavius\",\"Servius Camillus\",\"Diocletian Titus\",\"Secundus Orbilius\",\"Draco Coruncanius\",\"Fabius Nasidienus\",\"Ovidius Cyprian\",\"Rufrius Balbinus\",\"Minicius Nazarius\",\"Perperna Lacerius\",\"Cordius Caelian\",\"Salvius Domitius\",\"Varro Porcius\",\"Sempronius Sittius\",\"Crispinus Faustus\",\"Acilius Porcius\",\"Primitivus Albinus\",\"Rutilius Sestius\",\"Manius Avidius\",\"Vibius Opimius\",\"Catilius Nasennius\",\"Cornelius Orbilius\",\"Publius Clodius\",\"Caelius Florian\",\"Zeno Pinarus\",\"Fidenas Nautius\",\"Musonius Regulus\",\"Januarius Nonius\",\"Vitus Severus\",\"Manilius Cosconius\",\"Cosconius Junius\",\"Postumius Sittius\",\"Vibius Salvidienus\",\"Claudius Herennius\",\"Cosconius Maxentius\",\"Vibius Appius\",\"Lamponius Minicius\",\"Flaccus Silvius\",\"Juvenalis Manilius\",\"Tarquinius Aquillius\",\"Geminius Modius\",\"Seneca Egnatius\",\"Afranius Sepullius\",\"Verus Hostilius\",\"Avidius Fidenas\",\"Damasus Aulus\",\"Sedatius Cornificius\",\"Salvidienus Primitivus\",\"Lartius Murena\",\"Sergius Blaesus\",\"Donatus Fidenas\",\"Iunius Verecundus\",\"Pontidius Eutropius\",\"Lucius Domitius\",\"Camillius Caecilius\",\"Nautius Atilius\",\"Pius Gabinius\",\"Calpurnius Hostius\",\"Severian Segulius\",\"Pompeius Tarquinius\",\"Laurentius Burrienus\",\"Pius Secundus\",\"Semellius Anatolius\",\"Pettius Silanus\",\"Verecundus Matienus\",\"Rufinus Agrippa\",\"Caninius Albinus\",\"Faustus Cloelius\",\"Pedius Hortensius\",\"Maecenas Salvidienus\",\"Nasidius Gnaeus\",\"Varro Granius\",\"Appius Publicius\",\"Domitius Cincinnatus\",\"Didius Afranius\",\"Nero Rufius\",\"Caecilius Pedius\",\"Opellius Magnentius\",\"Catius Scribonius\",\"Seccius Sextius\",\"Licinius Menenius\",\"Porcius Junius\",\"Modius Nautius\",\"Cassio Lentulus\",\"Numerius Pius\",\"Papinius Cornificius\",\"Severus Gratian\",\"Nero Persius\",\"Hilarianus Acilius\",\"Gratian Caninius\",\"Donatus Rutilius\",\"Hilarianus Constantinus\",\"Antonius Spurius\",\"Gordian Terentius\",\"Cispius Aemilius\",\"Tacitus Maecenas\",\"Laberius Fidius\",\"Petilius Numitorius\",\"Volumnius Manilius\",\"Granius Maecilius\",\"Menenius Plautius\",\"Cicero Granius\",\"Ostorius Nasidius\",\"Marcellus Quintilianus\",\"Ninnius Minicius\",\"Ingenuus Calpurnius\",\"Postumius Germanicus\",\"Servilius Palpellius\",\"Didius Novellius\",\"Terentius Volumnius\",\"Postumius Albinus\",\"Clodius Ovidius\",\"Fabianus Numicius\",\"Fulvius Laelius\",\"Laecanius Valentinian\",\"Festinius Faustus\",\"Nero Pilus\",\"Vespasian Foslius\",\"Murena Oppius\",\"Fannius Gaius\",\"Messalla Cosconius\",\"Papinius Crispinus\",\"Scribonius Pompeius\",\"Sallustius Vespasian\",\"Urbicus Constantinus\",\"Blaesus Cato\",\"Cicero Geminius\",\"Lucius Cispius\",\"Rufius Faustus\",\"Hostilius Sittius\",\"Flaccus Laurentius\",\"Cloelius Nasidienus\",\"Persius Laberius\",\"Pilus Gratian\",\"Papirius Quintilianus\",\"Sidonius Fannius\",\"Sepullius Martialis\",\"Latinius Gallus\",\"Nasidius Lucius\",\"Tacitus Gracchus\",\"Acilius Nerva\",\"Geminius Aemilius\",\"Varian Nasennius\",\"Quintilianus Ostorius\",\"Rutilius Caelian\",\"Burrienus Nonius\",\"Spurius Scribonius\",\"Duronius Lucretius\",\"Percennius Salvian\",\"Lamponius Volumnius\",\"Secundinius Pupius\",\"Statilius Ollius\",\"Coruncanius Salvidienus\",\"Gabinius Catilius\",\"Nasidius Regulus\",\"Pilus Publicius\",\"Salvius Felix\",\"Galerius Burrienus\",\"Blaesus Hirrus\",\"Herennius Pompeius\",\"Nerva Egnatuleius\",\"Antonius Junius\",\"Faustus Quirinius\",\"Silanus Ninnius\",\"Musonius Probus\",\"Nasennius Albinus\",\"Laurentius Porcius\",\"Pilus Ingenuus\",\"Memmius Opellius\",\"Cordius Sertorius\",\"Seccius Varian\",\"Aulus Hostius\",\"Cincinnatus Sepullius\",\"Hortensius Vitus\",\"Sedatius Florian\",\"Foslius Catius\",\"Icilius Pomponius\",\"Cominius Fabrinius\",\"Quintus Iunius\",\"Marcus Servius\",\"Caedicius Constantinus\",\"Coruncanius Laecanius\",\"Vespasian Manilius\",\"Baebius Constantinus\",\"Vitus Probus\",\"Laecanius Priscus\",\"Urbanus Cassianus\",\"Palpellius Cassianus\",\"Spurius Pacilius\",\"Bruttius Blasius\",\"Honorius Hilarianus\",\"Cato Pompeius\",\"Valentinian Caesennius\",\"Cosconius Pettius\",\"Camillius Cethegus\",\"Januarius Ollius\",\"Scribonius Segulius\",\"Caesennius Varro\",\"Numerius Candidus\",\"Ollius Rufrius\",\"Publicius Sidonius\",\"Aelius Hostilius\",\"Florian Cornelius\",\"Gaius Caecilius\",\"Verus Cantilius\",\"Opellius Fulvius\",\"Rabirius Memmius\",\"Constantinus Orestius\",\"Plinius Numonius\",\"Nautius Duronius\",\"Severian Eborius\",\"Opimius Ovidius\",\"Sergian Festus\",\"Curtius Gordian\",\"Semellius Balbinus\",\"Cordius Camillius\",\"Mucius Quintilianus\",\"Paelignus Egnatuleius\",\"Tacitus Persius\",\"Diocletian Laberius\",\"Sempronius Anatolius\",\"Atilius Minicius\",\"Pettius Probus\",\"Modius Decimus\",\"Lamponius Nazarius\",\"Brennus Cispius\",\"Naevius Valerius\",\"Hadrian Hadrian\",\"Martialis Caninius\",\"Gallus Blaesus\",\"Rufrius Statilius\",\"Considius Tertius\",\"Duronius Rullianus\",\"Belisarius Numicius\",\"Icilius Rebilus\",\"Spurius Valens\",\"Nasidius Tarquinius\",\"Mamilius Bonifacius\",\"Norbanus Salvidienus\",\"Seneca Cocceius\",\"Laberius Caecilius\",\"Sergian Cispius\",\"Florian Marcellus\",\"Florian Caninius\",\"Vitus Placidus\",\"Laurentius Sornatius\",\"Bonifacius Julius\",\"Cethegus Amulius\",\"Marcus Herennius\",\"Camillus Didius\",\"Dacian Pilus\",\"Sidonius Granius\",\"Baebius Sentius\",\"Pomponius Tertius\",\"Lollius Dacian\",\"Fulcinia Marian\",\"Hortensius Domitius\",\"Sulpicius Rufrius\",\"Atilius Curtius\",\"Agrippa Afranius\",\"Sittius Sallustius\",\"Murena Cicereius\",\"Nazarius Donatus\",\"Longinius Plautius\",\"Atilius Lucanus\",\"Novellius Paulinus\",\"Rullianus Fundanus\",\"Fundanus Valens\",\"Atilius Valerius\",\"Caedicius Spurius\",\"Segulius Sedatius\",\"Pompeius Lucius\",\"Priscus Salinator\",\"Pedius Urbicus\",\"Pomponius Fulcinia\",\"Laelius Donatus\",\"Curtius Nasidius\",\"Horatius Anatolius\",\"Bonifacius Octavius\",\"Fabrinius Crispus\",\"Egnatuleius Varro\",\"Aulus Horatius\",\"Gordian Festus\",\"Servius Lentulus\",\"Octavius Varro\",\"Caedicius Balbus\",\"Oppius Claudius\",\"Junius Cocceius\",\"Fidius Maecilius\",\"Laetorius Lucretius\",\"Appius Iulius\",\"Didius Gnaeus\",\"Camillus Plinius\",\"Fabius Domitius\",\"Clodius Iulius\",\"Foslius Balbinus\",\"Blossius Aquillius\",\"Nasidius Quirinius\",\"Vincentius Calpurnius\",\"Saturninus Potitius\",\"Naevius Honorius\",\"Regulus Marius\",\"Livius Blasius\",\"Cicero Postumius\",\"Pilus Plautius\",\"Mescinius Considius\",\"Cominius Nigidius\",\"Maximus Domitian\",\"Mallius Genucius\",\"Geta Secundus\",\"Pupius Aurelianus\",\"Ostorius Vincentius\",\"Livius Orestius\",\"Statilius Festinius\",\"Novellius Cyprian\",\"Pontidius Draco\",\"Belisarius Caninius\",\"Servius Maecenas\",\"Latinius Laronius\",\"Cincinnatus Gratian\",\"Domitian Sepullius\",\"Lollius Numitorius\",\"Crispus Balbinus\",\"Numerius Ollius\",\"Petreius Laecanius\",\"Verus Primitivus\",\"Novellius Pettius\",\"Laurentius Sergius\",\"Maximus Oppius\",\"Candidus Damasus\",\"Servilius Latinius\",\"Novellius Betilienus\",\"Cominius Aurelianus\",\"Aquillius Verus\",\"Murena Silanus\",\"Musonius Mamilius\",\"Brennus Rebilus\",\"Numonius Maecenas\",\"Pacilius Maxentius\",\"Celsus Lucretius\",\"Laronius Mamilius\",\"Laberius Terentius\",\"Hadrian Naevius\",\"Caedicius Agrippa\",\"Gracchus Hostilius\",\"Faustus Primitivus\",\"Gordian Laurentius\",\"Germanicus Hirrus\",\"Munatius Magnentius\",\"Fundanus Latinius\",\"Gracchus Atilius\",\"Cocceius Novellius\",\"Papirius Mamilius\",\"Aemilius Paconius\",\"Cilnius Mallius\",\"Crispus Hostilius\",\"Opimius Nasidienus\",\"Maximian Severus\",\"Nasidienus Sextius\",\"Silanus Laetorius\",\"Betilienus Fundanus\",\"Tacitus Festinius\",\"Hostilius Seneca\",\"Scribonius Musonius\",\"Minicius Vibius\",\"Marian Mamilius\",\"Sallustius Papirius\",\"Urbicus Decentius\",\"Memmius Sextius\",\"Cicereius Orestius\",\"Flaccus Paelignus\",\"Plinius Plinius\",\"Lentulus Salinator\",\"Cassius Marcius\",\"Rufrius Caelian\",\"Rufrius Damasus\",\"Paconius Fidenas\",\"Caedicius Urbicus\",\"Quinctilius Severus\",\"Cassio Valentinian\",\"Marcellus Silanus\",\"Plinius Cilnius\",\"Plautius Tarquinius\",\"Clodius Hostilius\",\"Maecenas Egnatuleius\",\"Rabirius Eborius\",\"Domitian Ostorius\",\"Fidenas Hilarianus\",\"Marius Numitorius\",\"Geminius Paelignus\",\"Cestius Vibius\",\"Laurentius Blossius\",\"Cocceius Flaccus\",\"Blossius Coruncanius\",\"Sicinius Eborius\",\"Vincentius Verus\",\"Aurelianus Verecundus\",\"Aquillius Terentius\",\"Regulus Menenius\",\"Pasidienus Eutropius\",\"Calpurnius Iulius\",\"Mescinius Mucius\",\"Paulinus Aemilius\",\"Mallius Cominius\",\"Fannius Betilienus\",\"Cornificius Gordian\",\"Rufinus Cantilius\",\"Bruttius Paelignus\",\"Marcius Rullianus\",\"Nasidius Cicereius\",\"Camillius Catius\",\"Camillus Secundus\",\"Fabrinius Anatolius\",\"Seneca Pasidienus\",\"Gaius Cinna\",\"Sedatius Sertorius\",\"Numonius Bonifacius\",\"Caninius Julius\",\"Mallius Sertorius\",\"Iunius Percennius\",\"Aurelianus Decentius\",\"Betilienus Pupius\",\"Pompeius Sepullius\",\"Nautius Domitian\",\"Opellius Domitian\",\"Hostilius Acilius\",\"Foslius Latinius\",\"Cassianus Ravillius\",\"Catilius Betilienus\",\"Juvenalis Domitius\",\"Mescinius Salvidienus\",\"Fundanus Scribonius\",\"Pacilius Marcellus\",\"Julius Junius\",\"Maecilius Cordius\",\"Fannius Flavius\",\"Germanicus Cincinnatus\",\"Pompeius Valerius\",\"Domitius Papirius\",\"Hostilius Hadrian\",\"Novellius Germanicus\",\"Flavius Segulius\",\"Memmius Publius\",\"Segulius Lacerius\",\"Laelius Salvian\",\"Rufinus Iunius\",\"Zeno Laetorius\",\"Vitalis Blasius\",\"Maecilius Volumnius\",\"Fabianus Cilnius\",\"Secundus Quintilianus\",\"Rufius Juvenalis\",\"Catilius Sertorius\",\"Albinius Curtius\",\"Plinius Lentulus\",\"Ingenuus Marcellus\",\"Duronius Silvius\",\"Egnatuleius Valerius\",\"Lollius Orestius\",\"Sestius Sicinius\",\"Pius Fabianus\",\"Decentius Granius\",\"Novellius Rufrius\",\"Brennus Hostilius\",\"Numicius Pilus\",\"Stertinius Cocceius\",\"Januarius Germanicus\",\"Poppaedius Agrippa\",\"Crispinus Persius\",\"Menenius Lucius\",\"Mescinius Nasidienus\",\"Bonifacius Sextius\",\"Domitian Aemilius\",\"Draco Seneca\",\"Betilienus Magnentius\",\"Bruttius Sextius\",\"Florianus Constantinus\",\"Cornelius Avidius\",\"Silius Dacian\",\"Statilius Nautius\",\"Porcius Quinctilius\",\"Festinius Scribonius\",\"Dacian Salvius\",\"Cornificius Cordius\",\"Ingenuus Pilus\",\"Seneca Vincentius\",\"Naevius Mallius\",\"Lucretius Longinius\",\"Marcus Flavius\",\"Florianus Balbus\",\"Rabirius Sidonius\",\"Fundanus Galerius\",\"Sornatius Nazarius\",\"Bruttius Suetonius\",\"Modius Valens\",\"Rufrius Segulius\",\"Draco Licinius\",\"Cosconius Donatus\",\"Laurentius Ingenuus\",\"Gratian Nero\",\"Juvenalis Nerva\",\"Rebilus Numicius\",\"Nigidius Cethegus\",\"Januarius Numitorius\",\"Poppaedius Julius\",\"Salvidienus Naevius\",\"Cloelius Octavius\",\"Mamilius Sextius\",\"Quintilianus Catius\",\"Modius Tacitus\",\"Pompeius Vespasian\",\"Castorius Cloelius\",\"Sidonius Paelignus\",\"Castorius Orestius\",\"Florianus Laecanius\",\"Herennius Sallustius\",\"Cantilius Fabrinius\",\"Sextius Iuventius\",\"Geta Palpellius\",\"Regulus Mucius\",\"Sicinius Fabrinius\",\"Marcus Aurelianus\",\"Cato Maxentius\",\"Duronius Marcius\",\"Didius Licinius\",\"Egnatuleius Sabucius\",\"Gaius Manius\",\"Numicius Quintus\",\"Poppaedius Opimius\",\"Honorius Gnaeus\",\"Domitius Cloelius\",\"Diocletian Verus\",\"Tiberius Perperna\",\"Ennius Cornutus\",\"Naevius Geminius\",\"Cocceius Livius\",\"Baebius Honorius\",\"Valerius Vitalis\",\"Juvenalis Magnentius\",\"Laronius Calpurnius\",\"Caecilius Latinius\",\"Gordian Rufius\",\"Memmius Januarius\",\"Geta Fabius\",\"Naevius Paulinus\",\"Constantinus Gaius\",\"Pacilius Lamponius\",\"Decimus Persius\",\"Blossius Lamponius\",\"Nautius Hostilius\",\"Claudius Rufinus\",\"Antonius Segulius\",\"Cicereius Rutilius\",\"Avidius Palpellius\",\"Probus Paulinus\",\"Amulius Fannius\",\"Anatolius Valentinian\",\"Plinius Camillus\",\"Calpurnius Publius\",\"Lucanus Didius\",\"Vitalis Icilius\",\"Laronius Nasidienus\",\"Salvidienus Maecilius\",\"Mescinius Numerius\",\"Mescinius Baebius\",\"Percennius Placidus\",\"Castorius Paulinus\",\"Maecenas Lucius\",\"Fidius Numicius\",\"Numitorius Pomponius\",\"Rebilus Statilius\",\"Nautius Munatius\",\"Fulvius Messalla\",\"Numonius Severus\",\"Amulius Bonifacius\",\"Cornelius Lamponius\",\"Poppaedius Bonifacius\",\"Honorius Rufius\",\"Gaius Regulus\",\"Draco Gallus\",\"Florian Marcus\",\"Gnaeus Tarquinius\",\"Verecundus Cornelius\",\"Cicero Geta\",\"Rufinus Egnatius\",\"Coruncanius Cethegus\",\"Calventius Publius\",\"Marian Cocceius\",\"Aquillius Pettius\",\"Domitius Pinarus\",\"Matinius Lucanus\",\"Cornificius Appius\",\"Manilius Lucanus\",\"Valentinian Laronius\",\"Caninius Draco\",\"Paternus Junius\",\"Dacian Celsus\",\"Horatius Aelius\",\"Vitus Sextius\",\"Servilius Ingenuus\",\"Burrienus Titus\",\"Draco Ollius\",\"Aquillius Ovidius\",\"Iuventius Gnaeus\",\"Nasennius Caelian\",\"Justus Petreius\",\"Bonifacius Regulus\",\"Manilius Drusus\",\"Cestius Priscus\",\"Lucanus Magnentius\",\"Rufrius Cloelius\",\"Rufius Severian\",\"Tertius Publius\",\"Cosconius Honorius\",\"Cincinnatus Hirrus\",\"Festinius Aemilius\",\"Cicereius Appius\",\"Cloelius Maecenas\",\"Damasus Martialis\",\"Seccius Candidus\",\"Manius Amulius\",\"Quinctilius Cyprian\",\"Anatolius Urbicus\",\"Ollius Florian\",\"Octavius Priscus\",\"Egnatius Quintilianus\",\"Cethegus Fulvius\",\"Nazarius Ennius\",\"Didius Perperna\",\"Atilius Cornelius\",\"Papinius Tacitus\",\"Mescinius Gracchus\",\"Norbanus Cassio\",\"Rebilus Regulus\",\"Festus Maxentius\",\"Coruncanius Sulpicius\",\"Cordius Valens\",\"Caelius Lentulus\",\"Lucanus Aelius\",\"Fulvius Menenius\",\"Rufrius Pedius\",\"Fannius Manilius\",\"Spurius Porcius\",\"Munatius Cassius\",\"Murena Blasius\",\"Gratian Duronius\",\"Statilius Publicius\",\"Fundanus Petreius\",\"Iunius Icilius\",\"Titus Aulus\",\"Bonifacius Coruncanius\",\"Tarquinius Pinarus\",\"Claudius Publicius\",\"Camillius Tacitus\",\"Rufinus Donatus\",\"Ninnius Cominius\",\"Modius Orchius\",\"Drusus Nasennius\",\"Iunius Vincentius\",\"Lartius Caelian\",\"Avidius Silvius\",\"Donatus Icilius\",\"Silanus Aurelianus\",\"Antonius Anatolius\",\"Mescinius Ofilius\",\"Festinius Publicius\",\"Fidenas Matienus\",\"Ninnius Nero\",\"Rutilius Cloelius\",\"Sentius Aulus\",\"Mallius Herennius\",\"Nerva Candidus\",\"Servius Paulinus\",\"Gabinius Gabinius\",\"Petreius Sepullius\",\"Percennius Ingenuus\",\"Egnatuleius Salvius\",\"Severus Hirrus\",\"Afranius Paulinus\",\"Titus Nasidienus\",\"Hilarianus Maximus\",\"Tertius Caninius\",\"Iuventius Matienus\",\"Valentinian Severian\",\"Pontidius Cilnius\",\"Geta Licinius\",\"Novellius Numitorius\",\"Valens Florianus\",\"Laetorius Laetorius\",\"Octavenus Camillus\",\"Balbinus Fabianus\",\"Drusus Spurius\",\"Matienus Hordeonius\",\"Laronius Magnentius\",\"Cominius Sedatius\",\"Silius Lucretius\",\"Pupius Gabinius\",\"Lartius Quirinius\",\"Cassio Orestius\",\"Lucretius Manilius\",\"Furius Secundus\",\"Sergius Nonius\",\"Iuventius Caninius\",\"Egnatius Salinator\",\"Scribonius Balbus\",\"Lamponius Rabirius\",\"Papinius Pius\",\"Marius Candidus\",\"Claudius Latinius\",\"Valentinian Minicius\",\"Crispinus Decentius\",\"Manius Fabius\",\"Balbinus Perperna\",\"Caesennius Fabrinius\",\"Matienus Livius\",\"Egnatius Numerius\",\"Paelignus Paternus\",\"Sextius Varian\",\"Paulinus Mamilius\",\"Cinna Sicinius\",\"Minicius Porcius\",\"Sempronius Paulinus\",\"Horatius Persius\",\"Vitalis Cicereius\",\"Camillus Furius\",\"Nazarius Anatolius\",\"Festinius Gordian\",\"Seccius Valerius\",\"Sestius Burrienus\",\"Agrippa Furius\",\"Celsus Laecanius\",\"Cocceius Percennius\",\"Naevius Caesennius\",\"Sestius Orbilius\",\"Pompeius Castorius\",\"Volumnius Cicereius\",\"Geta Damasus\",\"Manilius Suetonius\",\"Lentulus Severian\",\"Nerva Bruttius\",\"Bruttius Urbanus\",\"Percennius Iunius\",\"Porcius Ingenuus\",\"Cassio Petreius\",\"Statilius Terentius\",\"Papinius Domitian\",\"Sepullius Lentulus\",\"Cordius Blossius\",\"Laronius Fabianus\",\"Naevius Segulius\",\"Hostius Ogulnius\",\"Fabrinius Lucretius\",\"Brennus Silius\",\"Curtius Betilienus\",\"Cocceius Quintilianus\",\"Matienus Titus\",\"Pacilius Cinna\",\"Caelian Plautius\",\"Manilius Sertorius\",\"Iunius Fundanus\",\"Quirinius Sempronius\",\"Modius Castorius\",\"Licinius Fulcinia\",\"Vitalis Catius\",\"Potitius Crispus\",\"Aulus Fabianus\",\"Honorius Munatius\",\"Burrienus Rutilius\",\"Fulvius Camillus\",\"Galerius Egnatius\",\"Cantilius Maximian\",\"Quirinius Faustus\",\"Clodius Percennius\",\"Maecenas Maecilius\",\"Maximus Probus\",\"Saturninus Seneca\",\"Albinus Numicius\",\"Statilius Porcius\",\"Bonifacius Memmius\",\"Ravillius Cethegus\",\"Caninius Geminius\",\"Orchius Albinus\",\"Sergian Albinus\",\"Papirius Cethegus\",\"Domitius Octavenus\",\"Gordian Damasus\",\"Belisarius Constantinus\",\"Novellius Hirrus\",\"Nasennius Magnentius\",\"Juvenalis Marcus\",\"Candidus Severian\",\"Manius Regulus\",\"Curtius Cornutus\",\"Marcus Agrippa\",\"Verus Anatolius\",\"Papinius Cicero\",\"Verus Calpurnius\",\"Geta Cloelius\",\"Fidenas Cloelius\",\"Germanicus Cispius\",\"Sallustius Afranius\",\"Cicero Pedius\",\"Blasius Cilnius\",\"Fidius Atilius\",\"Menenius Iunius\",\"Geminius Fidenas\",\"Considius Marian\",\"Calventius Pacilius\",\"Fabius Nigidius\",\"Cilnius Tertius\",\"Segulius Messalla\",\"Julius Plautius\",\"Nazarius Pacilius\",\"Palpellius Amulius\",\"Hordeonius Vitus\",\"Laecanius Nasennius\",\"Sergius Lentulus\",\"Verecundus Norbanus\",\"Quinctilius Flavius\",\"Caesennius Salvius\",\"Pius Agrippa\",\"Granius Brennus\",\"Martialis Norbanus\",\"Verus Justus\",\"Hostilius Verecundus\",\"Pomponius Naevius\",\"Secundus Hortensius\",\"Damasus Saturninus\",\"Minicius Vespasian\",\"Matienus Martialis\",\"Honorius Ingenuus\",\"Albinius Caninius\",\"Papinius Ostorius\",\"Vitus Sabucius\",\"Norbanus Albinius\",\"Regulus Paulinus\",\"Opimius Albinius\",\"Sabucius Cornificius\",\"Flavius Palpellius\",\"Calpurnius Curtius\",\"Damasus Antonius\",\"Ingenuus Silius\",\"Hostilius Festinius\",\"Considius Papirius\",\"Cassio Sergian\",\"Horatius Lentulus\",\"Blaesus Justus\",\"Laelius Burrienus\",\"Valerius Verus\",\"Domitius Cispius\",\"Rufinus Coruncanius\",\"Felix Lollius\",\"Celsus Perperna\",\"Paulinus Publicius\",\"Junius Tiberius\",\"Manilius Julius\",\"Cornificius Musonius\",\"Priscus Sextus\",\"Gabinius Marcus\",\"Cincinnatus Nasennius\",\"Placidus Semellius\",\"Cassius Cassio\",\"Belisarius Laecanius\",\"Probus Numerius\",\"Fabrinius Gaius\",\"Maxentius Festus\",\"Curtius Egnatius\",\"Ingenuus Pius\",\"Servilius Rebilus\",\"Sidonius Decentius\",\"Poppaedius Draco\",\"Cicereius Curtius\",\"Laelius Crispus\",\"Lollius Amulius\",\"Paternus Sicinius\",\"Matienus Gallus\",\"Ollius Percennius\",\"Didius Rufinus\",\"Volumnius Bruttius\",\"Sentius Poppaedius\",\"Rutilius Lollius\",\"Albinius Fabianus\",\"Segulius Secundinius\",\"Gaius Petilius\",\"Camillus Geta\",\"Servius Pasidienus\",\"Tertius Palpellius\",\"Salvian Suetonius\",\"Matienus Laecanius\",\"Bruttius Seneca\",\"Opimius Numerius\",\"Ravillius Florianus\",\"Silvius Junius\",\"Cato Afranius\",\"Domitius Cyprian\",\"Rullianus Flaccus\",\"Vitus Bruttius\",\"Marcus Fabius\",\"Verecundus Varro\",\"Porcius Numicius\",\"Clodius Salvidienus\",\"Orbilius Vitus\",\"Modius Secundus\",\"Blaesus Aurelianus\",\"Percennius Laecanius\",\"Probus Maximian\",\"Orchius Anatolius\",\"Gaius Fannius\",\"Sornatius Galerius\",\"Papirius Bonifacius\",\"Eborius Numonius\",\"Sextus Burrienus\",\"Pilus Aurelianus\",\"Tacitus Marius\",\"Cocceius Sergius\",\"Albinius Celsus\",\"Murena Hilarianus\",\"Nautius Palpellius\",\"Didius Eborius\",\"Porcius Lucanus\",\"Latinius Constantinus\",\"Sergian Lartius\",\"Lucanus Fannius\",\"Mescinius Sabucius\",\"Urbicus Lucius\",\"Nasennius Draco\",\"Licinius Blasius\",\"Laronius Titus\",\"Caelian Horatius\",\"Terentius Cethegus\",\"Pius Rufius\",\"Geta Cassio\",\"Blaesus Papinius\",\"Aurelianus Eborius\",\"Volumnius Nero\",\"Vitalis Fulcinia\",\"Sertorius Domitius\",\"Salvidienus Egnatius\",\"Fabrinius Blaesus\",\"Albinius Marcus\",\"Ennius Fabianus\",\"Valerius Betilienus\",\"Festus Cispius\",\"Plautius Gabinius\",\"Gratian Julius\",\"Placidus Sextus\",\"Hordeonius Domitius\",\"Statilius Ninnius\",\"Saturninus Aurelianus\",\"Urbanus Vibius\",\"Pius Cassianus\",\"Gabinius Terentius\",\"Hostilius Cestius\",\"Maecenas Decentius\",\"Albinius Fulcinia\",\"Zeno Paternus\",\"Aquillius Furius\",\"Festinius Nazarius\",\"Novellius Valerius\",\"Manius Cinna\",\"Gaius Atilius\",\"Sestius Bonifacius\",\"Aurelianus Egnatuleius\",\"Gordian Fulcinia\",\"Domitian Blossius\",\"Gallus Fundanus\",\"Memmius Sornatius\",\"Severus Ollius\",\"Percennius Hadrian\",\"Hostius Mamilius\",\"Nonius Seneca\",\"Atilius Cantilius\",\"Rufinus Sabucius\",\"Cassio Fabianus\",\"Fulvius Valens\",\"Semellius Gallus\",\"Pettius Atilius\",\"Cassio Memmius\",\"Rufius Duronius\",\"Titus Fabrinius\",\"Pedius Paternus\",\"Iunius Hostius\",\"Genucius Burrienus\",\"Icilius Seneca\",\"Germanicus Ninnius\",\"Cilnius Florianus\",\"Hilarianus Salvius\",\"Fabrinius Cispius\",\"Rebilus Cato\",\"Cassianus Egnatius\",\"Donatus Fundanus\",\"Silanus Pasidienus\",\"Cicero Catilius\",\"Vincentius Hortensius\",\"Latinius Papirius\",\"Cornificius Primitivus\",\"Lollius Catius\",\"Aurelianus Baebius\",\"Numitorius Saturninus\",\"Cocceius Nasidius\",\"Secundinius Publius\",\"Festinius Cosconius\",\"Paconius Atilius\",\"Granius Marcus\",\"Ravillius Diocletian\",\"Papinius Crispus\",\"Mamilius Festus\",\"Munatius Cocceius\",\"Modius Marius\",\"Cicereius Matinius\",\"Ninnius Fabius\",\"Fidius Sextius\",\"Sentius Bruttius\",\"Cicereius Caesennius\",\"Gallus Ostorius\",\"Pomponius Fulvius\",\"Pupius Orestius\",\"Crispus Maecilius\",\"Ostorius Priscus\",\"Papirius Caelian\",\"Pinarus Ninnius\",\"Minicius Rutilius\",\"Aquillius Plautius\",\"Orchius Memmius\",\"Statilius Nonius\",\"Furius Gnaeus\",\"Spurius Ostorius\",\"Pupius Regulus\",\"Rutilius Hordeonius\",\"Blossius Nasennius\",\"Quirinius Candidus\",\"Persius Festus\",\"Paconius Oppius\",\"Statilius Paelignus\",\"Silius Faustus\",\"Laurentius Laurentius\",\"Scribonius Herennius\",\"Perperna Regulus\",\"Volumnius Dacian\",\"Agrippa Sallustius\",\"Camillus Festinius\",\"Fabrinius Valerius\",\"Statilius Nazarius\",\"Egnatius Sergius\",\"Pasidienus Novellius\",\"Furius Sestius\",\"Orbilius Statilius\",\"Salvian Minicius\",\"Justus Vitalis\",\"Servilius Faustus\",\"Germanicus Caelius\",\"Orbilius Vibius\",\"Licinius Sepullius\",\"Caelius Sepullius\",\"Lentulus Calventius\",\"Gratian Volumnius\",\"Maecenas Cato\",\"Mamilius Orchius\",\"Orchius Plinius\",\"Novellius Sergius\",\"Lamponius Pasidienus\",\"Cocceius Valerius\",\"Pasidienus Sallustius\",\"Caninius Petilius\",\"Rabirius Sergius\",\"Sallustius Orbilius\",\"Cilnius Valens\",\"Crispinus Varian\",\"Salvius Eutropius\",\"Ravillius Celsus\",\"Sepullius Fundanus\",\"Avidius Pacilius\",\"Considius Octavenus\",\"Fidenas Geta\",\"Cornificius Horatius\",\"Horatius Servius\",\"Blaesus Musonius\",\"Calpurnius Anatolius\",\"Pomponius Salinator\",\"Cato Latinius\",\"Avidius Cornificius\",\"Probus Octavius\",\"Sepullius Aelius\",\"Caninius Clodius\",\"Amulius Gracchus\",\"Clodius Sergian\",\"Constantinus Cocceius\",\"Maximus Fulvius\",\"Octavius Januarius\",\"Nasidius Balbinus\",\"Festinius Maxentius\",\"Mamilius Hostius\",\"Longinius Rufius\",\"Atilius Sergius\",\"Octavius Orbilius\",\"Licinius Marius\",\"Honorius Sextius\",\"Urbicus Sergian\",\"Belisarius Cassio\",\"Ollius Julius\",\"Agrippa Diocletian\",\"Dacian Vibius\",\"Numerius Paternus\",\"Perperna Nonius\",\"Fulcinia Valerius\",\"Caecilius Camillius\",\"Orbilius Marian\",\"Balbus Nautius\",\"Tacitus Minicius\",\"Hadrian Rebilus\",\"Orbilius Orchius\",\"Terentius Tiberius\",\"Furius Manilius\",\"Scribonius Crispinus\",\"Decimus Fannius\",\"Zeno Perperna\",\"Rufius Verus\",\"Novellius Fulvius\",\"Germanicus Calpurnius\",\"Cominius Postumius\",\"Cethegus Castorius\",\"Munatius Marian\",\"Calpurnius Flaccus\",\"Fulvius Orbilius\",\"Catilius Lamponius\",\"Longinius Norbanus\",\"Valens Mucius\",\"Numicius Honorius\",\"Petreius Gordian\",\"Castorius Servilius\",\"Norbanus Germanicus\",\"Drusus Vitus\",\"Cyprian Numitorius\",\"Florian Gabinius\",\"Castorius Sertorius\",\"Menenius Sedatius\",\"Vitalis Ingenuus\",\"Atilius Considius\",\"Icilius Coruncanius\",\"Fabius Ingenuus\",\"Decentius Cominius\",\"Lamponius Acilius\",\"Iunius Tiberius\",\"Cinna Nonius\",\"Opellius Rutilius\",\"Saturninus Cloelius\",\"Anatolius Matienus\",\"Placidus Cantilius\",\"Maximus Camillus\",\"Placidus Junius\",\"Draco Acilius\",\"Opimius Gracchus\",\"Marius Lamponius\",\"Livius Cordius\",\"Numitorius Constantinus\",\"Nonius Statilius\",\"Honorius Burrienus\",\"Cantilius Didius\",\"Paelignus Galerius\",\"Quirinius Hostilius\",\"Drusus Granius\",\"Valentinian Geminius\",\"Cyprian Cosconius\",\"Perperna Ollius\",\"Ofilius Lollius\",\"Acilius Cicereius\",\"Sempronius Coruncanius\",\"Sedatius Tiberius\",\"Caecilius Caninius\",\"Albinus Munatius\",\"Cicereius Sulpicius\",\"Pasidienus Vitalis\",\"Egnatuleius Probus\",\"Terentius Pinarus\",\"Pontidius Valentinian\",\"Agrippa Vespasian\",\"Felix Vincentius\",\"Nasidius Fulcinia\",\"Crispinus Lucanus\",\"Faustus Domitian\",\"Semellius Volumnius\",\"Gracchus Justus\",\"Sittius Pontidius\",\"Plinius Draco\",\"Silanus Atilius\",\"Aulus Stertinius\",\"Hortensius Matinius\",\"Fulcinia Opimius\",\"Poppaedius Valens\",\"Marcus Numonius\",\"Numitorius Percennius\",\"Cicereius Maximus\",\"Laelius Baebius\",\"Hortensius Egnatius\",\"Cordius Hadrian\",\"Ovidius Titus\",\"Volumnius Sertorius\",\"Nonius Ingenuus\",\"Plautius Foslius\",\"Silvius Aurelianus\",\"Cosconius Pius\",\"Cincinnatus Cassianus\",\"Honorius Vitus\",\"Cordius Gordian\",\"Blaesus Maximian\",\"Paternus Cicero\",\"Caelian Vitus\",\"Volumnius Junius\",\"Orestius Brennus\",\"Stertinius Salvidienus\",\"Ennius Betilienus\",\"Persius Quinctilius\",\"Genucius Damasus\",\"Longinius Laelius\",\"Caesennius Flaccus\",\"Gracchus Maximus\",\"Fabius Aquillius\",\"Catilius Domitius\",\"Cosconius Marius\",\"Cicereius Sallustius\",\"Potitius Gracchus\",\"Cornelius Cicero\",\"Pasidienus Cocceius\",\"Cyprian Hostius\",\"Seneca Januarius\",\"Caecilius Blasius\",\"Iulius Gracchus\",\"Blossius Domitian\",\"Ofilius Laberius\",\"Egnatuleius Numitorius\",\"Salvidienus Hostilius\",\"Aelius Marcellus\",\"Blasius Marcius\",\"Cocceius Camillus\",\"Opellius Cantilius\",\"Quintilianus Silius\",\"Numitorius Segulius\",\"Scribonius Duronius\",\"Clodius Caecilius\",\"Lucanus Domitius\",\"Menenius Coruncanius\",\"Lartius Hadrian\",\"Publicius Valens\",\"Scribonius Baebius\",\"Lucretius Silanus\",\"Balbus Seneca\",\"Lucretius Valentinian\",\"Germanicus Vincentius\",\"Herennius Balbus\",\"Curtius Menenius\",\"Ninnius Cinna\",\"Drusus Albinius\",\"Numonius Numerius\",\"Tiberius Rullianus\",\"Egnatuleius Maximian\",\"Rebilus Aelius\",\"Ravillius Rufinus\",\"Caelian Belisarius\",\"Petreius Quintus\",\"Considius Verecundus\",\"Plautius Junius\",\"Cicereius Anatolius\",\"Calpurnius Cloelius\",\"Gaius Rebilus\",\"Damasus Paconius\",\"Cassio Cocceius\",\"Suetonius Verecundus\",\"Constantinus Balbinus\",\"Plautius Probus\",\"Sertorius Quintilianus\",\"Catius Vitus\",\"Lucius Gnaeus\",\"Lucretius Potitius\",\"Ovidius Brennus\",\"Diocletian Nigidius\",\"Sempronius Ofilius\",\"Gnaeus Pacilius\",\"Matinius Iulius\",\"Aemilius Scribonius\",\"Laelius Ninnius\",\"Statilius Foslius\",\"Cornutus Quinctilius\",\"Plautius Hadrian\",\"Domitius Seneca\",\"Nigidius Crispus\",\"Numitorius Tacitus\",\"Urbicus Ollius\",\"Hostilius Paconius\",\"Calpurnius Appius\",\"Hortensius Verus\",\"Sedatius Minicius\",\"Salvian Duronius\",\"Laberius Eborius\",\"Laelius Munatius\",\"Stertinius Sittius\",\"Lartius Numitorius\",\"Pasidienus Numonius\",\"Nasidius Fulvius\",\"Silvius Acilius\",\"Festus Sicinius\",\"Orbilius Nazarius\",\"Orestius Martialis\",\"Cominius Valens\",\"Paternus Quirinius\",\"Marcius Pompeius\",\"Vitus Sornatius\",\"Silanus Juvenalis\",\"Primitivus Salvius\",\"Januarius Gratian\",\"Lucius Tiberius\",\"Furius Cilnius\",\"Pacilius Severian\",\"Laelius Cocceius\",\"Rebilus Iuventius\",\"Constantinus Curtius\",\"Livius Tertius\",\"Pupius Afranius\",\"Potitius Castorius\",\"Probus Rullianus\",\"Amulius Rabirius\",\"Pasidienus Fulcinia\",\"Icilius Honorius\",\"Seccius Fidenas\",\"Cincinnatus Geminius\",\"Ravillius Claudius\",\"Maecenas Quinctilius\",\"Granius Spurius\",\"Castorius Cato\",\"Pacilius Opimius\",\"Castorius Baebius\",\"Munatius Damasus\",\"Sergius Fabianus\",\"Paelignus Modius\",\"Verecundus Iunius\",\"Pilus Florianus\",\"Fannius Baebius\",\"Pilus Potitius\",\"Publicius Perperna\",\"Cilnius Vincentius\",\"Hostilius Novellius\",\"Aulus Zeno\",\"Caelius Januarius\",\"Fidenas Nonius\",\"Bonifacius Junius\",\"Norbanus Lamponius\",\"Camillius Nasidius\",\"Egnatius Nasidius\",\"Segulius Perperna\",\"Juvenalis Silius\",\"Eutropius Fabianus\",\"Persius Minicius\",\"Perperna Fulvius\",\"Pinarus Blasius\",\"Statilius Maecilius\",\"Silanus Hadrian\",\"Poppaedius Balbus\",\"Balbinus Marcius\",\"Appius Sergian\",\"Tiberius Secundinius\",\"Iuventius Florianus\",\"Magnentius Betilienus\",\"Foslius Rabirius\",\"Caelius Cominius\",\"Orbilius Pomponius\",\"Nero Aquillius\",\"Junius Horatius\",\"Marcus Genucius\",\"Aquillius Celsus\",\"Considius Gabinius\",\"Cethegus Vitalis\",\"Rullianus Afranius\",\"Icilius Decimus\",\"Laecanius Tertius\",\"Spurius Sicinius\",\"Fulvius Papirius\",\"Fabianus Maximian\",\"Perperna Sittius\",\"Longinius Manius\",\"Flavius Cosconius\",\"Iuventius Martialis\",\"Cestius Domitian\",\"Sertorius Rebilus\",\"Eutropius Crispus\",\"Sentius Balbus\",\"Granius Hadrian\",\"Plautius Caesennius\",\"Tertius Severian\",\"Perperna Duronius\",\"Valentinian Nasidienus\",\"Sornatius Blaesus\",\"Drusus Orbilius\",\"Cominius Mucius\",\"Caecilius Servilius\",\"Laronius Honorius\",\"Lucanus Pedius\",\"Blaesus Silvius\",\"Sextius Catilius\",\"Quintus Ingenuus\",\"Candidus Verus\",\"Persius Juvenalis\",\"Afranius Foslius\",\"Murena Ingenuus\",\"Celsus Nautius\",\"Longinius Valerius\",\"Aurelianus Sextius\",\"Paternus Julius\",\"Valerius Eborius\",\"Matienus Flavius\",\"Foslius Lucanus\",\"Quintilianus Vitalis\",\"Regulus Agrippa\",\"Regulus Fannius\",\"Calpurnius Plinius\",\"Lartius Hostius\",\"Hilarianus Urbicus\",\"Hilarianus Pupius\",\"Balbus Rebilus\",\"Papirius Constantinus\",\"Bruttius Persius\",\"Appius Aurelianus\",\"Stertinius Januarius\",\"Diocletian Ninnius\",\"Maximus Gnaeus\",\"Messalla Longinius\",\"Hostius Cinna\",\"Festinius Iulius\",\"Sergius Publicius\",\"Memmius Albinius\",\"Nasidius Stertinius\",\"Fidius Cassio\",\"Cornificius Blaesus\",\"Gracchus Anatolius\",\"Cato Novellius\",\"Mucius Mescinius\",\"Valentinian Cestius\",\"Opimius Celsus\",\"Valerius Paelignus\",\"Licinius Cosconius\",\"Cassio Rabirius\",\"Gratian Galerius\",\"Hordeonius Geta\",\"Sornatius Cominius\",\"Betilienus Regulus\",\"Crispus Catius\",\"Sempronius Rullianus\",\"Paelignus Decimus\",\"Fabius Gratian\",\"Domitius Manilius\",\"Varro Papirius\",\"Caelius Cassius\",\"Silius Nero\",\"Quintus Hadrian\",\"Stertinius Gallus\",\"Aulus Rullianus\",\"Cyprian Statilius\",\"Cocceius Egnatius\",\"Calpurnius Papirius\",\"Varian Maecilius\",\"Lacerius Blossius\",\"Octavenus Quintilianus\",\"Hortensius Quintilianus\",\"Cicereius Orchius\",\"Ennius Hostilius\",\"Iulius Lollius\",\"Flavius Sidonius\",\"Palpellius Silvius\",\"Blasius Numitorius\",\"Icilius Modius\",\"Gracchus Cinna\",\"Calpurnius Sallustius\",\"Tertius Vitalis\",\"Numicius Cyprian\",\"Foslius Hostilius\",\"Martialis Percennius\",\"Quintus Maecenas\",\"Plinius Ingenuus\",\"Oppius Domitian\",\"Sallustius Draco\",\"Longinius Matinius\",\"Eutropius Octavenus\",\"Rufinus Varro\",\"Sicinius Pinarus\",\"Salinator Pedius\",\"Fulvius Numonius\",\"Matinius Burrienus\",\"Felix Nautius\",\"Aemilius Hortensius\",\"Cornutus Galerius\",\"Memmius Laronius\",\"Titus Ravillius\",\"Octavenus Calventius\",\"Avidius Pupius\",\"Catilius Drusus\",\"Varian Pomponius\",\"Geminius Primitivus\",\"Ovidius Vincentius\",\"Memmius Maxentius\",\"Primitivus Blasius\",\"Diocletian Lacerius\",\"Caninius Fidius\",\"Sornatius Acilius\",\"Persius Fidius\",\"Sextius Caninius\",\"Varro Geta\",\"Ogulnius Servius\",\"Valens Pompeius\",\"Nerva Maximian\",\"Fidius Vitus\",\"Varian Marcius\",\"Maxentius Publicius\",\"Blasius Orbilius\",\"Catius Manilius\",\"Sextus Gaius\",\"Lucius Sabucius\",\"Didius Mucius\",\"Rufrius Valerius\",\"Norbanus Papinius\",\"Menenius Balbinus\",\"Verecundus Mucius\",\"Tertius Priscus\",\"Maecilius Numitorius\",\"Semellius Statilius\",\"Bonifacius Ollius\",\"Caninius Gabinius\",\"Paternus Eborius\",\"Paternus Cispius\",\"Latinius Felix\",\"Stertinius Ostorius\",\"Caedicius Paconius\",\"Salvian Pedius\",\"Sextius Lacerius\",\"Verus Cordius\",\"Sallustius Persius\",\"Laecanius Afranius\",\"Laetorius Servilius\",\"Numerius Rufrius\",\"Vespasian Cyprian\",\"Pomponius Fidius\",\"Munatius Plinius\",\"Sempronius Lucretius\",\"Curtius Gaius\",\"Petilius Sertorius\",\"Sulpicius Diocletian\",\"Nasidienus Eutropius\",\"Varro Sallustius\",\"Valentinian Lucretius\",\"Tarquinius Drusus\",\"Papinius Albinus\",\"Laurentius Quinctilius\",\"Statilius Valentinian\",\"Servilius Cordius\",\"Januarius Albinius\",\"Laecanius Poppaedius\",\"Vibius Orestius\",\"Postumius Burrienus\",\"Coruncanius Cassianus\",\"Rabirius Albinus\",\"Iulius Plinius\",\"Sextus Cicereius\",\"Tertius Verus\",\"Cornutus Atilius\",\"Volumnius Perperna\",\"Cantilius Flavius\",\"Suetonius Messalla\",\"Caelius Aelius\",\"Sepullius Blaesus\",\"Messalla Octavenus\",\"Diocletian Calventius\",\"Cornelius Quintus\",\"Sextus Numicius\",\"Ofilius Saturninus\",\"Antonius Sallustius\",\"Fulcinia Baebius\",\"Camillus Salvian\",\"Herennius Regulus\",\"Octavius Sextus\",\"Aurelianus Egnatius\",\"Seneca Latinius\",\"Papinius Seccius\",\"Quintus Geminius\",\"Gabinius Coruncanius\",\"Salvian Opimius\",\"Albinius Manilius\",\"Aquillius Urbicus\",\"Fabius Geminius\",\"Juvenalis Tiberius\",\"Aemilius Vibius\",\"Pacilius Quirinius\",\"Baebius Magnentius\",\"Hordeonius Amulius\",\"Cornificius Manius\",\"Camillus Germanicus\",\"Foslius Marcellus\",\"Aulus Considius\",\"Iunius Gracchus\",\"Drusus Maximian\",\"Magnentius Sulpicius\",\"Gracchus Duronius\",\"Octavius Crispinus\",\"Catius Aurelianus\",\"Lucius Florian\",\"Caninius Duronius\",\"Spurius Poppaedius\",\"Porcius Avidius\",\"Sepullius Sempronius\",\"Considius Albinius\",\"Octavius Placidus\",\"Galerius Probus\",\"Sidonius Vincentius\",\"Gallus Maecenas\",\"Murena Valens\",\"Iuventius Balbinus\",\"Servilius Crispus\",\"Seccius Calventius\",\"Cicereius Sertorius\",\"Mallius Lacerius\",\"Cassianus Publius\",\"Cyprian Quintus\",\"Manilius Gordian\",\"Castorius Gallus\",\"Januarius Cato\",\"Naevius Papinius\",\"Donatus Lartius\",\"Junius Faustus\",\"Juvenalis Priscus\",\"Galerius Calventius\",\"Menenius Germanicus\",\"Didius Matinius\",\"Marius Faustus\",\"Matienus Maecenas\",\"Cassius Sittius\",\"Laelius Nautius\",\"Laurentius Livius\",\"Potitius Egnatuleius\",\"Tarquinius Lacerius\",\"Severus Publius\",\"Cocceius Cloelius\",\"Blossius Fabrinius\",\"Numerius Valentinian\",\"Lucretius Secundinius\",\"Quintus Eborius\",\"Murena Laelius\",\"Sallustius Amulius\",\"Manius Menenius\",\"Spurius Lucretius\",\"Atilius Regulus\",\"Cominius Semellius\",\"Stertinius Matinius\",\"Iunius Porcius\",\"Honorius Justus\",\"Percennius Cethegus\",\"Crispinus Cocceius\",\"Sulpicius Magnentius\",\"Cincinnatus Messalla\",\"Sergian Lollius\",\"Musonius Pettius\",\"Laetorius Sallustius\",\"Gnaeus Albinus\",\"Sempronius Cinna\",\"Cassianus Lucretius\",\"Vincentius Minicius\",\"Januarius Cassius\",\"Aulus Rebilus\",\"Cato Pupius\",\"Pontidius Hostius\",\"Vitus Eborius\",\"Verecundus Quintilianus\",\"Eutropius Amulius\",\"Maecilius Maximus\",\"Minicius Cornutus\",\"Maxentius Coruncanius\",\"Cornutus Gordian\",\"Atilius Afranius\",\"Gallus Fannius\",\"Bruttius Messalla\",\"Aemilius Diocletian\",\"Hortensius Maximus\",\"Paternus Tarquinius\",\"Marcellus Eutropius\",\"Musonius Gnaeus\",\"Maximian Suetonius\",\"Novellius Camillius\",\"Paternus Potitius\",\"Munatius Atilius\",\"Pompeius Licinius\",\"Camillus Sulpicius\",\"Cethegus Nazarius\",\"Genucius Cassio\",\"Draco Domitian\",\"Ofilius Hortensius\",\"Cocceius Secundus\",\"Papinius Rufius\",\"Januarius Cilnius\",\"Laelius Catilius\",\"Petilius Florian\",\"Octavenus Crispinus\",\"Primitivus Tarquinius\",\"Iulius Postumius\",\"Marius Secundinius\",\"Bruttius Donatus\",\"Ogulnius Fabrinius\",\"Constantinus Gordian\",\"Pomponius Sergius\",\"Gnaeus Salvidienus\",\"Quinctilius Titus\",\"Valerius Acilius\",\"Damasus Minicius\",\"Maecilius Mucius\",\"Sempronius Quirinius\",\"Aquillius Blossius\",\"Hilarianus Albinius\",\"Calpurnius Quirinius\",\"Ofilius Mamilius\",\"Verus Draco\",\"Orchius Salinator\",\"Genucius Laetorius\",\"Sergian Tarquinius\",\"Cassio Manius\",\"Clodius Draco\",\"Eutropius Mescinius\",\"Hortensius Pontidius\",\"Hordeonius Sergian\",\"Sallustius Menenius\",\"Sertorius Domitian\",\"Gratian Ostorius\",\"Servilius Cicereius\",\"Zeno Mescinius\",\"Hordeonius Salinator\",\"Nazarius Fabius\",\"Granius Julius\",\"Gabinius Poppaedius\",\"Saturninus Laetorius\",\"Fundanus Statilius\",\"Vibius Numerius\",\"Sextius Acilius\",\"Quirinius Sicinius\",\"Seccius Pedius\",\"Calpurnius Gaius\",\"Egnatuleius Fundanus\",\"Petreius Gabinius\",\"Gordian Petreius\",\"Priscus Castorius\",\"Damasus Quintilianus\",\"Draco Florianus\",\"Paelignus Anatolius\",\"Caesennius Papinius\",\"Faustus Percennius\",\"Cispius Caedicius\",\"Oppius Paulinus\",\"Lucius Egnatuleius\",\"Sicinius Anatolius\",\"Cato Pilus\",\"Florian Pasidienus\",\"Constantinus Hadrian\",\"Lucretius Ovidius\",\"Perperna Fabrinius\",\"Vitus Cispius\",\"Geta Appius\",\"Varro Marian\",\"Cassianus Gracchus\",\"Sulpicius Sextius\",\"Quintilianus Sallustius\",\"Latinius Didius\",\"Tarquinius Silius\",\"Galerius Aulus\",\"Gratian Constantinus\",\"Crispinus Salvian\",\"Foslius Opimius\",\"Octavenus Ravillius\",\"Hordeonius Constantinus\",\"Seccius Quinctilius\",\"Ovidius Licinius\",\"Hortensius Catius\",\"Florianus Opimius\",\"Nero Crispinus\",\"Cantilius Horatius\",\"Stertinius Segulius\",\"Secundinius Juvenalis\",\"Papirius Tarquinius\",\"Cincinnatus Orchius\",\"Naevius Marcellus\",\"Gnaeus Flaccus\",\"Julius Varro\",\"Catilius Geta\",\"Sextus Hilarianus\",\"Cicereius Minicius\",\"Flaccus Donatus\",\"Catilius Mamilius\",\"Marcellus Norbanus\",\"Ogulnius Laronius\",\"Fabius Fundanus\",\"Nasidienus Pacilius\",\"Musonius Dacian\",\"Vibius Domitian\",\"Mescinius Vibius\",\"Brennus Scribonius\",\"Gabinius Stertinius\",\"Statilius Mallius\",\"Hordeonius Salvius\",\"Pomponius Rufius\",\"Matinius Numerius\",\"Caelius Varian\",\"Gnaeus Lartius\",\"Maecilius Seneca\",\"Lartius Lollius\",\"Fabius Camillus\",\"Fidenas Festus\",\"Vibius Silanus\",\"Tiberius Vitalis\",\"Geta Cosconius\",\"Cosconius Constantinus\",\"Mescinius Rabirius\",\"Quintilianus Severus\",\"Cornelius Memmius\",\"Percennius Seccius\",\"Maximian Florianus\",\"Cilnius Suetonius\",\"Nasidius Salinator\",\"Opimius Egnatuleius\",\"Servius Sidonius\",\"Matinius Orbilius\",\"Minicius Petreius\",\"Iuventius Aulus\",\"Balbinus Aemilius\",\"Probus Herennius\",\"Cicereius Numerius\",\"Eutropius Balbus\",\"Agrippa Considius\",\"Decentius Caecilius\",\"Manilius Acilius\",\"Lentulus Avidius\",\"Secundus Faustus\",\"Ravillius Salvian\",\"Gordian Iunius\",\"Laetorius Lollius\",\"Plinius Laelius\",\"Sextus Rullianus\",\"Cato Domitius\",\"Valens Sextus\",\"Germanicus Manilius\",\"Cethegus Camillius\",\"Fabrinius Belisarius\",\"Honorius Sulpicius\",\"Servilius Mescinius\",\"Flaccus Lacerius\",\"Pilus Honorius\",\"Cispius Rufrius\",\"Honorius Pilus\",\"Salinator Publicius\",\"Atilius Damasus\",\"Aulus Segulius\",\"Poppaedius Burrienus\",\"Herennius Fabius\",\"Quirinius Orestius\",\"Lartius Iulius\",\"Marius Sextus\",\"Ovidius Blasius\",\"Nerva Nasidius\",\"Herennius Hadrian\",\"Rufrius Marcus\",\"Orestius Plinius\",\"Pomponius Curtius\",\"Flaccus Cicereius\",\"Martialis Modius\",\"Poppaedius Aquillius\",\"Caesennius Festinius\",\"Sergian Hilarianus\",\"Egnatius Quinctilius\",\"Florian Nerva\",\"Severian Manius\",\"Hirrus Bonifacius\",\"Cornelius Tarquinius\",\"Sidonius Salvidienus\",\"Caesennius Norbanus\",\"Galerius Verus\",\"Festus Vitus\",\"Paelignus Servilius\",\"Maximian Servius\",\"Donatus Drusus\",\"Norbanus Antonius\",\"Claudius Fidenas\",\"Hilarianus Galerius\",\"Servius Quintus\",\"Cilnius Lamponius\",\"Paternus Claudius\",\"Cinna Sittius\",\"Gnaeus Ravillius\",\"Semellius Nasidius\",\"Lucretius Calventius\",\"Bruttius Cordius\",\"Iuventius Tarquinius\",\"Paelignus Cyprian\",\"Plautius Flavius\",\"Ennius Minicius\",\"Marius Cantilius\",\"Donatus Festinius\",\"Ravillius Vitus\",\"Catius Rutilius\",\"Ostorius Tiberius\",\"Geminius Diocletian\",\"Candidus Verecundus\",\"Segulius Paelignus\",\"Vincentius Terentius\",\"Hortensius Blaesus\",\"Flaccus Silius\",\"Galerius Drusus\",\"Fulvius Plinius\",\"Terentius Gaius\",\"Magnentius Caelius\",\"Munatius Varro\",\"Varian Cicereius\",\"Plautius Camillius\",\"Crispinus Scribonius\",\"Secundus Lamponius\",\"Numonius Acilius\",\"Icilius Lucanus\",\"Rufinus Sulpicius\",\"Iuventius Aquillius\",\"Cinna Geta\",\"Secundus Hostilius\",\"Placidus Marian\",\"Camillus Avidius\",\"Ingenuus Postumius\",\"Vitalis Cordius\",\"Norbanus Florianus\",\"Iulius Mucius\",\"Foslius Lartius\",\"Octavius Sornatius\",\"Tiberius Draco\",\"Salinator Lentulus\",\"Orestius Lollius\",\"Norbanus Mallius\",\"Hadrian Camillius\",\"Postumius Mallius\",\"Junius Marcellus\",\"Fabianus Lucanus\",\"Sergius Licinius\",\"Persius Granius\",\"Potitius Rufinus\",\"Fabianus Balbus\",\"Iunius Mamilius\",\"Matienus Numitorius\",\"Camillus Rufius\",\"Orestius Verus\",\"Camillus Placidus\",\"Hostius Ovidius\",\"Marcus Orbilius\",\"Gallus Modius\",\"Plinius Fulvius\",\"Palpellius Foslius\",\"Gnaeus Orbilius\",\"Balbus Aemilius\",\"Pupius Mucius\",\"Laberius Plautius\",\"Decimus Matienus\",\"Burrienus Nasennius\",\"Hadrian Messalla\",\"Servilius Vitalis\",\"Iuventius Sallustius\",\"Papirius Numonius\",\"Germanicus Rufrius\",\"Pomponius Mucius\",\"Fulcinia Galerius\",\"Cispius Crispus\",\"Florianus Pacilius\",\"Sempronius Segulius\",\"Poppaedius Blasius\",\"Poppaedius Vitalis\",\"Mallius Drusus\",\"Quirinius Sallustius\",\"Salinator Paulinus\",\"Vibius Avidius\",\"Petreius Pettius\",\"Cornificius Naevius\",\"Sentius Cordius\",\"Germanicus Papinius\",\"Cornelius Fulcinia\",\"Gracchus Caelius\",\"Laelius Catius\",\"Gracchus Catilius\",\"Seccius Paconius\",\"Cassio Afranius\",\"Palpellius Faustus\",\"Murena Caecilius\",\"Laurentius Pompeius\",\"Naevius Cincinnatus\",\"Egnatuleius Ovidius\",\"Regulus Quintilianus\",\"Crispinus Constantinus\",\"Curtius Maxentius\",\"Paulinus Ogulnius\",\"Sestius Caelius\",\"Albinius Salvidienus\",\"Varian Fulcinia\",\"Germanicus Segulius\",\"Considius Duronius\",\"Nasennius Mescinius\",\"Lartius Bonifacius\",\"Castorius Rufius\",\"Salinator Tarquinius\",\"Aurelianus Fulvius\",\"Seneca Hirrus\",\"Sabucius Manius\",\"Decimus Blasius\",\"Aelius Segulius\",\"Faustus Novellius\",\"Licinius Urbicus\",\"Semellius Lacerius\",\"Rabirius Opellius\",\"Pacilius Draco\",\"Fabianus Laronius\",\"Musonius Cloelius\",\"Opellius Lollius\",\"Fulvius Blossius\",\"Amulius Aulus\",\"Vincentius Seneca\",\"Paelignus Catius\",\"Hostilius Sidonius\",\"Sornatius Cassianus\",\"Geminius Amulius\",\"Hostilius Atilius\",\"Numicius Petilius\",\"Brennus Sergius\",\"Maxentius Lucretius\",\"Licinius Norbanus\",\"Nigidius Sedatius\",\"Gnaeus 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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "import numpy as np\n",
        "\n",
        "# 1. Fit initial regression and compute residual outliers\n",
        "m0 = LinearRegression().fit(df_before[['Height']], df_before['Weight'])\n",
        "res = df_before['Weight'] - m0.predict(df_before[['Height']])\n",
        "res_flag = np.abs(res) > 2 * res.std()\n",
        "\n",
        "# 2. IQR outliers\n",
        "Q1, Q3 = df_before['Weight'].quantile([.25, .75])\n",
        "IQR = Q3 - Q1\n",
        "iqr_flag = (df_before['Weight'] < Q1 - 1.5*IQR) | (df_before['Weight'] > Q3 + 1.5*IQR)\n",
        "\n",
        "# 3. Final flag (NOT saved to df)\n",
        "flag = res_flag | iqr_flag\n",
        "print(\"Total HW outliers:\", flag.sum())\n",
        "\n",
        "# 4. Clean regression on non-outliers\n",
        "clean_model = LinearRegression().fit(df_before.loc[~flag, ['Height']],\n",
        "                                    df_before.loc[~flag, 'Weight'])\n",
        "\n",
        "# 5. Create cleaned Weight series (no columns added to df)\n",
        "Weight_clean = df_before['Weight'].copy()\n",
        "Weight_clean[flag] = clean_model.predict(df_before.loc[flag, ['Height']])\n",
        "\n",
        "# 6. After-regression plot\n",
        "coef=np.polyfit(df[\"Height\"],df[\"Weight\"],1)\n",
        "fn=np.poly1d(coef)\n",
        "pred_after=fn(df[\"Height\"])\n",
        "\n",
        "fig=px.scatter(df,x=\"Height\",y=\"Weight\",hover_name=\"Name\",\n",
        "               title=\"AFTER Cleaning: Height–Weight Regression\")\n",
        "fig.add_trace(go.Scatter(x=df[\"Height\"].sort_values(),\n",
        "                         y=np.sort(pred_after),\n",
        "                         mode=\"lines\",name=\"Regression\",line=dict(color=\"green\")))\n",
        "fig.update_layout(width=900,height=600); fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "4TSSwMiDi0yv",
        "outputId": "64191d4f-635c-4ffe-b09a-53881b69c4d5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total HW outliers: 478\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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Cornelius\",\"Sulpicius Gabinius\",\"Gallus Paelignus\",\"Bonifacius Persius\",\"Sidonius Sittius\",\"Sergius Rullianus\",\"Lacerius Considius\",\"Paternus Fabrinius\",\"Burrienus Germanicus\",\"Mucius Iuventius\",\"Pomponius Junius\",\"Salinator Rufrius\",\"Papinius Vibius\",\"Terentius Numonius\",\"Domitian Zeno\",\"Ollius Albinus\",\"Vespasian Valerius\",\"Sempronius Maecilius\",\"Faustus Postumius\",\"Horatius Rebilus\",\"Marian Geta\",\"Numitorius Servius\",\"Petreius Betilienus\",\"Paulinus Cordius\",\"Vibius Florianus\",\"Baebius Eutropius\",\"Nasidienus Hostilius\",\"Egnatius Matinius\",\"Didius Camillius\",\"Nasidienus Curtius\",\"Terentius Fulvius\",\"Silanus Sempronius\",\"Bonifacius Domitius\",\"Betilienus Maximus\",\"Valentinian Genucius\",\"Caesennius Acilius\",\"Claudius Volumnius\",\"Laelius Rufius\",\"Volumnius Antonius\",\"Sergian Iunius\",\"Honorius Octavius\",\"Flavius Caninius\",\"Aquillius Maecenas\",\"Didius Cicero\",\"Aelius Tertius\",\"Gnaeus Fundanus\",\"Cantilius Catius\",\"Crispinus Suetonius\",\"Urbicus Lartius\",\"Urbicus Atilius\",\"Valerius Nasidius\",\"Petreius Drusus\",\"Gordian Suetonius\",\"Paconius Silvius\",\"Quinctilius Pontidius\",\"Diocletian Crispinus\",\"Valentinian Sulpicius\",\"Maecenas Ollius\",\"Seccius Probus\",\"Drusus Lartius\",\"Florianus Lucanus\",\"Icilius Hirrus\",\"Nasidienus Laronius\",\"Oppius Eutropius\",\"Laecanius Seneca\",\"Antonius Flavius\",\"Appius Livius\",\"Mallius Spurius\",\"Fabianus Sergian\",\"Pupius Silanus\",\"Maecenas Aurelianus\",\"Sextius Hirrus\",\"Placidus Fannius\",\"Tertius Lentulus\",\"Scribonius Aurelianus\",\"Catius Festus\",\"Cicereius Nasennius\",\"Flaccus Numonius\",\"Rufrius Cornificius\",\"Laelius Sedatius\",\"Paelignus Caelian\",\"Plautius Servius\",\"Claudius Paulinus\",\"Sentius Caesennius\",\"Pettius Perperna\",\"Minicius Lollius\",\"Pompeius Celsus\",\"Drusus Pompeius\",\"Fulvius Cosconius\",\"Opimius Amulius\",\"Nazarius Maecilius\",\"Pasidienus Nasennius\",\"Murena Numonius\",\"Cinna Tacitus\",\"Caninius Faustus\",\"Fulcinia Matienus\",\"Quintus Granius\",\"Oppius Statilius\",\"Atilius Cassianus\",\"Candidus Maecenas\",\"Numitorius Rufinus\",\"Januarius Domitian\",\"Lartius Valerius\",\"Maximus Marian\",\"Camillius Urbanus\",\"Paternus Laurentius\",\"Crispus Orchius\",\"Albinus Burrienus\",\"Maecilius Ravillius\",\"Justus Hostilius\",\"Pettius Blaesus\",\"Gracchus Publius\",\"Ninnius Hadrian\",\"Plinius Mescinius\",\"Ravillius Nasennius\",\"Ostorius Pontidius\",\"Blasius Considius\",\"Octavenus Sedatius\",\"Longinius Verecundus\",\"Duronius Suetonius\",\"Titus Orbilius\",\"Vincentius Valerius\",\"Caecilius Cordius\",\"Agrippa Silvius\",\"Publicius Festinius\",\"Hilarianus Regulus\",\"Salvidienus Festinius\",\"Camillus Salvidienus\",\"Ravillius Marian\",\"Nigidius Cassio\",\"Aelius Regulus\",\"Ennius Pasidienus\",\"Amulius Domitian\",\"Ingenuus Silanus\",\"Ravillius Fannius\",\"Draco Vitus\",\"Sicinius Aelius\",\"Gracchus Laronius\",\"Appius Catius\",\"Geminius Sabucius\",\"Urbicus Hostilius\",\"Catilius Hostilius\",\"Hirrus Sempronius\",\"Egnatius Secundus\",\"Cestius Lollius\",\"Fidius Caninius\",\"Volumnius Ostorius\",\"Geta Diocletian\",\"Amulius Palpellius\",\"Junius Orbilius\",\"Gnaeus Sulpicius\",\"Pasidienus Silius\",\"Hilarianus Domitian\",\"Aelius Plinius\",\"Ninnius Nasennius\",\"Ovidius Claudius\",\"Cornificius Draco\",\"Maximian Terentius\",\"Gordian Cinna\",\"Servius Tacitus\",\"Cosconius Nerva\",\"Novellius Hostius\",\"Galerius Eutropius\",\"Marcus Urbanus\",\"Messalla Camillius\",\"Antonius Vitus\",\"Calventius Seccius\",\"Cornelius Cispius\",\"Ovidius Julius\",\"Ingenuus Zeno\",\"Salvidienus Ingenuus\",\"Genucius Novellius\",\"Pilus Valerius\",\"Genucius Rufrius\",\"Belisarius Stertinius\",\"Fundanus Valentinian\",\"Januarius Cyprian\",\"Balbinus Donatus\",\"Nonius Cyprian\",\"Mamilius Laberius\",\"Laelius Blasius\",\"Postumius Duronius\",\"Sertorius Didius\",\"Scribonius Silvius\",\"Laecanius Licinius\",\"Titus Petreius\",\"Domitian Priscus\",\"Severus Aulus\",\"Laronius Lucretius\",\"Castorius Maximian\",\"Quintus Scribonius\",\"Messalla Cethegus\",\"Zeno Varian\",\"Constantinus Rufrius\",\"Juvenalis Paternus\",\"Afranius Pontidius\",\"Aquillius Cominius\",\"Caelius Nerva\",\"Probus Mucius\",\"Fabianus Fidius\",\"Semellius Lartius\",\"Crispus Segulius\",\"Pontidius Marcellus\",\"Quirinius Sornatius\",\"Bonifacius Poppaedius\",\"Papirius Magnentius\",\"Menenius Hordeonius\",\"Geta Opimius\",\"Claudius Munatius\",\"Claudius Lartius\",\"Hortensius Cispius\",\"Faustus Maxentius\",\"Manilius Quinctilius\",\"Decentius Sentius\",\"Verus Ollius\",\"Julius Secundus\",\"Martialis Paelignus\",\"Cinna Secundinius\",\"Lollius Servius\",\"Sedatius Marcellus\",\"Laberius Dacian\",\"Fannius Seneca\",\"Volumnius Titus\",\"Saturninus Gallus\",\"Paconius Secundus\",\"Stertinius Octavenus\",\"Clodius Gnaeus\",\"Marcellus Varro\",\"Octavius Pinarus\",\"Gabinius Petilius\",\"Salvian Petilius\",\"Ravillius Minicius\",\"Clodius Flaccus\",\"Maximian Numerius\",\"Catius Nerva\",\"Geta Lucretius\",\"Camillius Menenius\",\"Stertinius Licinius\",\"Nasidienus Cestius\",\"Faustus Lucanus\",\"Vitus Caesennius\",\"Orestius Octavenus\",\"Marius Statilius\",\"Sabucius Sextius\",\"Considius Octavius\",\"Agrippa Laronius\",\"Numonius Numonius\",\"Gracchus Sestius\",\"Fabrinius Sextius\",\"Pinarus Rabirius\",\"Regulus Placidus\",\"Pilus Hortensius\",\"Cordius Cyprian\",\"Rufius Oppius\",\"Cethegus Caninius\",\"Fannius Cloelius\",\"Aurelianus Cilnius\",\"Januarius Albinus\",\"Cassianus Sentius\",\"Furius Festus\",\"Livius Anatolius\",\"Vibius Egnatuleius\",\"Honorius Sabucius\",\"Catius Pasidienus\",\"Hadrian Geminius\",\"Cicereius Nonius\",\"Maximus Eutropius\",\"Zeno Ofilius\",\"Draco Seccius\",\"Appius Caninius\",\"Paelignus Verus\",\"Camillus Gallus\",\"Gnaeus Cornelius\",\"Gabinius Agrippa\",\"Servilius Naevius\",\"Titus Cocceius\",\"Octavius Fundanus\",\"Antonius Donatus\",\"Nigidius Anatolius\",\"Caedicius Poppaedius\",\"Nautius Poppaedius\",\"Matienus Porcius\",\"Crispinus Tacitus\",\"Nasidienus Quinctilius\",\"Hordeonius Antonius\",\"Numicius Mallius\",\"Secundinius Maxentius\",\"Orestius Tacitus\",\"Sentius Terentius\",\"Salvidienus Persius\",\"Camillius Stertinius\",\"Minicius Laurentius\",\"Ollius Flaccus\",\"Aemilius Gratian\",\"Pilus Salinator\",\"Ninnius Novellius\",\"Ollius Livius\",\"Numitorius Ollius\",\"Constantinus Sextus\",\"Cincinnatus Gnaeus\",\"Sornatius Laurentius\",\"Messalla Gallus\",\"Donatus Potitius\",\"Hilarianus Sergian\",\"Cosconius Lucanus\",\"Aquillius Varian\",\"Hostius Maximian\",\"Betilienus Pinarus\",\"Maxentius Mallius\",\"Caelius Felix\",\"Caedicius Antonius\",\"Fulcinia Fidius\",\"Albinius Nasennius\",\"Catius Cosconius\",\"Rufrius Claudius\",\"Laberius Vincentius\",\"Sentius Semellius\",\"Ingenuus Semellius\",\"Sornatius Horatius\",\"Nigidius Junius\",\"Gordian Lentulus\",\"Fulcinia Fundanus\",\"Silvius Festus\",\"Plinius Rabirius\",\"Porcius Julius\",\"Blasius Pacilius\",\"Caecilius Publius\",\"Sentius Decimus\",\"Balbus Catius\",\"Matienus Numerius\",\"Munatius Vincentius\",\"Geminius Tertius\",\"Maecilius Sergian\",\"Quinctilius Potitius\",\"Albinus Minicius\",\"Rufinus Oppius\",\"Zeno Cilnius\",\"Sextus Livius\",\"Lamponius Licinius\",\"Palpellius Tacitus\",\"Felix Sabucius\",\"Eutropius Memmius\",\"Calpurnius Cyprian\",\"Aelius Cassius\",\"Regulus Nerva\",\"Ollius Poppaedius\",\"Genucius Crispus\",\"Egnatius Cornificius\",\"Zeno Salvian\",\"Gaius Caninius\",\"Octavenus Orbilius\",\"Camillus Brennus\",\"Ennius Sestius\",\"Pinarus Catius\",\"Marcius Vitus\",\"Drusus Gnaeus\",\"Rufius Tarquinius\",\"Clodius Gabinius\",\"Caecilius Foslius\",\"Statilius Aemilius\",\"Petreius Vitalis\",\"Dacian Paternus\",\"Genucius Cestius\",\"Albinus Damasus\",\"Festus Geta\",\"Horatius Gaius\",\"Juvenalis Cornutus\",\"Primitivus Candidus\",\"Camillus Gabinius\",\"Castorius Opellius\",\"Crispinus Afranius\",\"Albinius Servilius\",\"Cassio Lucanus\",\"Brennus Munatius\",\"Ennius Pacilius\",\"Dacian Januarius\",\"Sepullius Julius\",\"Poppaedius Quintilianus\",\"Gaius Maecilius\",\"Vitus Cethegus\",\"Eutropius Ofilius\",\"Florianus Sallustius\",\"Nerva Oppius\",\"Papirius Vespasian\",\"Poppaedius Silvius\",\"Sextius Lentulus\",\"Licinius Camillius\",\"Honorius Laelius\",\"Varro Mamilius\",\"Sestius Quintilianus\",\"Stertinius Rufrius\",\"Salvian Ninnius\",\"Hostius Valens\",\"Opellius Fabius\",\"Decimus Cassianus\",\"Verecundus Oppius\",\"Julius Iuventius\",\"Avidius Statilius\",\"Geminius Faustus\",\"Januarius Potitius\",\"Lucanus Vitalis\",\"Cassio Numicius\",\"Nasidius Fidius\",\"Petilius Faustus\",\"Flaccus Sentius\",\"Paconius Zeno\",\"Nonius Rullianus\",\"Granius Celsus\",\"Ovidius Primitivus\",\"Maximus Antonius\",\"Aquillius Opellius\",\"Balbinus Afranius\",\"Aelius Belisarius\",\"Aulus Didius\",\"Laurentius Probus\",\"Sabucius Florian\",\"Varro Matienus\",\"Sentius Sittius\",\"Cassianus Germanicus\",\"Nasidienus Gratian\",\"Plautius Geminius\",\"Sicinius Titus\",\"Ravillius Rufrius\",\"Silvius Aulus\",\"Lollius Blossius\",\"Camillus Eutropius\",\"Fabrinius Zeno\",\"Sextus Geta\",\"Aurelianus Cassius\",\"Felix Salinator\",\"Dacian Opimius\",\"Furius Ninnius\",\"Cispius Murena\",\"Maxentius Papirius\",\"Quintilianus Rullianus\",\"Hilarianus Clodius\",\"Hostilius Didius\",\"Livius Ostorius\",\"Perperna Justus\",\"Murena Lartius\",\"Percennius Rabirius\",\"Domitian Placidus\",\"Laronius Pontidius\",\"Pinarus Maecilius\",\"Varian Geminius\",\"Sextus Nazarius\",\"Cornificius Oppius\",\"Severian Granius\",\"Pupius Ingenuus\",\"Nasidienus Iunius\",\"Considius Sentius\",\"Laetorius Numicius\",\"Camillus Servilius\",\"Fannius Norbanus\",\"Laelius Camillus\",\"Fabianus Vitalis\",\"Geta Pomponius\",\"Silvius Pomponius\",\"Vitus Licinius\",\"Suetonius Primitivus\",\"Duronius Foslius\",\"Maecilius Sertorius\",\"Gallus Rebilus\",\"Amulius Pontidius\",\"Matienus Numonius\",\"Hostius Servilius\",\"Rullianus Avidius\",\"Decentius Ennius\",\"Sornatius Florian\",\"Maecilius Bonifacius\",\"Latinius Caedicius\",\"Cato Gallus\",\"Laetorius Suetonius\",\"Minicius Segulius\",\"Cispius Maecenas\",\"Gratian Lacerius\",\"Maecilius Vincentius\",\"Ninnius Scribonius\",\"Petilius Castorius\",\"Lentulus Livius\",\"Segulius Varro\",\"Percennius Furius\",\"Calventius Germanicus\",\"Flavius Cassianus\",\"Aelius Fulvius\",\"Modius Murena\",\"Bruttius Lentulus\",\"Cornelius Matinius\",\"Crispinus Hilarianus\",\"Verecundus Quintus\",\"Postumius Claudius\",\"Potitius Rabirius\",\"Salvius Nasennius\",\"Eborius Magnentius\",\"Aurelianus Saturninus\",\"Verecundus Segulius\",\"Ninnius Ostorius\",\"Sertorius Salvidienus\",\"Scribonius Pilus\",\"Vibius Pettius\",\"Florianus Ollius\",\"Numonius Coruncanius\",\"Bonifacius Felix\",\"Martialis Blossius\",\"Sextius Florianus\",\"Numitorius Ovidius\",\"Plautius Ravillius\",\"Paconius Hostius\",\"Claudius Cincinnatus\",\"Antonius Domitius\",\"Sextus Blasius\",\"Lacerius Cassio\",\"Severus Salvius\",\"Musonius Festus\",\"Sestius Cassio\",\"Terentius Mallius\",\"Musonius Granius\",\"Hadrian Opellius\",\"Matienus Sertorius\",\"Clodius Julius\",\"Pettius Aquillius\",\"Faustus Manius\",\"Sextius Maecenas\",\"Nigidius Cornutus\",\"Maxentius Tertius\",\"Pomponius Maxentius\",\"Persius Quintilianus\",\"Fundanus Paconius\",\"Pasidienus Ingenuus\",\"Cato Bonifacius\",\"Nigidius Tertius\",\"Titus Paconius\",\"Salvius Silius\",\"Albinus Sextus\",\"Hortensius Cinna\",\"Florianus Herennius\",\"Cinna Servius\",\"Verecundus Caesennius\",\"Nigidius Hordeonius\",\"Paconius Cethegus\",\"Numonius Junius\",\"Verecundus Menenius\",\"Egnatuleius Mallius\",\"Caecilius Petreius\",\"Cinna Curtius\",\"Pomponius Mescinius\",\"Albinius Balbus\",\"Tacitus Petilius\",\"Aulus Blossius\",\"Dacian Nazarius\",\"Flavius Cato\",\"Palpellius Magnentius\",\"Statilius Felix\",\"Cincinnatus Ollius\",\"Amulius Secundus\",\"Nero Camillius\",\"Sextus Publius\",\"Numonius Servilius\",\"Cispius Valerius\",\"Hilarianus Aelius\",\"Manilius Fannius\",\"Honorius Rufrius\",\"Priscus Marius\",\"Ovidius Horatius\",\"Caninius Cicereius\",\"Fulcinia Cilnius\",\"Galerius Cassius\",\"Geminius Didius\",\"Catilius Marian\",\"Camillius Sergius\",\"Eutropius Iuventius\",\"Cominius Aelius\",\"Nasennius Caecilius\",\"Vitus Curtius\",\"Plinius Cyprian\",\"Constantinus Ravillius\",\"Sextus Cassius\",\"Flavius Hilarianus\",\"Marcellus Seneca\",\"Messalla Plinius\",\"Festinius Gaius\",\"Cassio Titus\",\"Marcellus Burrienus\",\"Fabrinius Seccius\",\"Ovidius Cispius\",\"Papinius Coruncanius\",\"Nonius Petilius\",\"Opellius Cicereius\",\"Cicero Domitian\",\"Icilius Eborius\",\"Ravillius Licinius\",\"Herennius Priscus\",\"Naevius Junius\",\"Lucanus Hadrian\",\"Festus Cyprian\",\"Sidonius Caedicius\",\"Stertinius Laberius\",\"Hostius Quinctilius\",\"Lentulus Hostius\",\"Probus Memmius\",\"Fundanus Pomponius\",\"Rullianus Pomponius\",\"Sidonius Cilnius\",\"Aemilius Placidus\",\"Fabrinius Probus\",\"Sestius Diocletian\",\"Latinius Iulius\",\"Tarquinius Calventius\",\"Mallius Quinctilius\",\"Crispinus Juvenalis\",\"Cocceius Gnaeus\",\"Fabius Ennius\",\"Verecundus Messalla\",\"Petreius Pasidienus\",\"Secundinius Perperna\",\"Poppaedius Lucanus\",\"Galerius Sestius\",\"Valentinian Galerius\",\"Sentius Pilus\",\"Lucanus Horatius\",\"Suetonius Quinctilius\",\"Justus Marcius\",\"Livius Saturninus\",\"Oppius Horatius\",\"Cominius Sicinius\",\"Gallus Ogulnius\",\"Albinus Galerius\",\"Naevius Januarius\",\"Modius Zeno\",\"Rebilus Damasus\",\"Semellius Servius\",\"Geminius Regulus\",\"Lamponius Mamilius\",\"Lentulus Juvenalis\",\"Severus Nigidius\",\"Belisarius Vincentius\",\"Pilus Probus\",\"Crispus Betilienus\",\"Papirius Cordius\",\"Minicius Rullianus\",\"Antonius Cassius\",\"Camillius Lucanus\",\"Numitorius Perperna\",\"Saturninus Laronius\",\"Statilius Cordius\",\"Fabius Persius\",\"Laberius Iunius\",\"Urbicus Flavius\",\"Petreius Orbilius\",\"Genucius Pompeius\",\"Fulvius Rufinus\",\"Germanicus Pedius\",\"Ovidius Eborius\",\"Hilarianus Rabirius\",\"Quinctilius Acilius\",\"Granius Hilarianus\",\"Sornatius Cloelius\",\"Vitalis Salvius\",\"Laelius Furius\",\"Fulcinia Tarquinius\",\"Orestius Marius\",\"Servius Damasus\",\"Rullianus Memmius\",\"Laetorius Coruncanius\",\"Blaesus Cornelius\",\"Varian Lollius\",\"Severus Iuventius\",\"Antonius Cincinnatus\",\"Salvius Postumius\",\"Ogulnius Bonifacius\",\"Tertius Latinius\",\"Florian Cestius\",\"Gnaeus Ogulnius\",\"Constantinus Novellius\",\"Bruttius Hortensius\",\"Herennius Camillius\",\"Balbinus Rabirius\",\"Cosconius Lamponius\",\"Laecanius Coruncanius\",\"Scribonius Probus\",\"Sergius Decimus\",\"Mallius Valens\",\"Appius Considius\",\"Aurelianus Blasius\",\"Manilius Baebius\",\"Laelius Menenius\",\"Marian Aurelianus\",\"Gracchus Didius\",\"Petreius Flaccus\",\"Scribonius Hirrus\",\"Segulius Volumnius\",\"Severian Nautius\",\"Scribonius Flavius\",\"Severus Egnatuleius\",\"Quirinius Rutilius\",\"Pupius Paternus\",\"Urbanus Acilius\",\"Avidius Maecenas\",\"Pilus Stertinius\",\"Domitian Baebius\",\"Publius Quintus\",\"Modius Sergius\",\"Rebilus Silvius\",\"Pedius Hostilius\",\"Publicius Sornatius\",\"Nautius Blasius\",\"Sedatius Cornelius\",\"Ovidius Balbus\",\"Hilarianus Cethegus\",\"Cassio Caelian\",\"Pupius Nasidienus\",\"Geminius Suetonius\",\"Verecundus Manius\",\"Lollius Nasidius\",\"Minicius Crispus\",\"Festinius Quirinius\",\"Segulius Maximian\",\"Rufrius Atilius\",\"Plinius Vitalis\",\"Salinator Damasus\",\"Hilarianus Caelius\",\"Blaesus Fidius\",\"Mamilius Sidonius\",\"Maecenas Fabianus\",\"Mallius Marian\",\"Marcus Nero\",\"Ollius Orbilius\",\"Sextius Nasennius\",\"Florianus Valens\",\"Potitius Urbicus\",\"Gratian Iuventius\",\"Munatius Candidus\",\"Terentius Atilius\",\"Oppius Sergius\",\"Domitian Fidius\",\"Hostius Maecenas\",\"Faustus Pinarus\",\"Nero Martialis\",\"Blaesus Rufinus\",\"Cispius Caesennius\",\"Pasidienus Germanicus\",\"Spurius Calpurnius\",\"Hordeonius Priscus\",\"Laetorius Appius\",\"Avidius Considius\",\"Oppius Antonius\",\"Sestius Aulus\",\"Terentius Ostorius\",\"Modius Pacilius\",\"Herennius Titus\",\"Orestius Murena\",\"Fabius Sentius\",\"Eborius Licinius\",\"Calpurnius Claudius\",\"Iunius Sergian\",\"Cornutus Sempronius\",\"Varian Porcius\",\"Candidus Papinius\",\"Lucius Rutilius\",\"Fabianus Junius\",\"Herennius Sempronius\",\"Felix Cyprian\",\"Sulpicius Rutilius\",\"Junius Nautius\",\"Pedius Lucanus\",\"Herennius Nasidienus\",\"Paelignus Florian\",\"Silius Tertius\",\"Betilienus Sentius\",\"Afranius Brennus\",\"Celsus Paelignus\",\"Fundanus Gracchus\",\"Lollius Blasius\",\"Servius Pedius\",\"Rebilus Mescinius\",\"Seneca Probus\",\"Verecundus Pontidius\",\"Cordius Priscus\",\"Petilius Hortensius\",\"Tacitus Livius\",\"Aquillius Foslius\",\"Icilius Salvian\",\"Stertinius Terentius\",\"Decimus Laronius\",\"Fidenas Naevius\",\"Petilius Lentulus\",\"Publicius Marius\",\"Sextius Plinius\",\"Perperna Lucius\",\"Juvenalis Cocceius\",\"Lucius Granius\",\"Bonifacius Fannius\",\"Burrienus Hostius\",\"Baebius Galerius\",\"Salvian Cato\",\"Latinius Cominius\",\"Pacilius Bonifacius\",\"Sestius Betilienus\",\"Plautius Dacian\",\"Icilius Publicius\",\"Suetonius Martialis\",\"Opellius Spurius\",\"Numerius Juvenalis\",\"Diocletian Nerva\",\"Avidius Salvius\",\"Cordius Pettius\",\"Sittius Gratian\",\"Valens Galerius\",\"Diocletian Clodius\",\"Cosconius Paconius\",\"Titus Cassius\",\"Tertius Rebilus\",\"Quintus Sulpicius\",\"Paulinus Silanus\",\"Blasius Festus\",\"Sedatius Mescinius\",\"Cicero Sulpicius\",\"Silanus Fannius\",\"Urbicus Pedius\",\"Albinus Sepullius\",\"Sedatius Laurentius\",\"Cassius Lacerius\",\"Petreius Festus\",\"Sextus Bruttius\",\"Mallius Florianus\",\"Terentius Poppaedius\",\"Mamilius Novellius\",\"Nasidius Sittius\",\"Duronius Horatius\",\"Geminius Salvian\",\"Icilius Maximian\",\"Blossius Fundanus\",\"Nerva Fabrinius\",\"Hirrus Sextus\",\"Novellius Felix\",\"Candidus Numonius\",\"Blasius Gaius\",\"Munatius Ollius\",\"Flavius Tacitus\",\"Porcius Aurelianus\",\"Sergian Probus\",\"Mescinius Domitian\",\"Acilius Plinius\",\"Drusus Longinius\",\"Camillius Caedicius\",\"Salvian Saturninus\",\"Fundanus Appius\",\"Nasidienus Bruttius\",\"Oppius Marcus\",\"Cantilius Novellius\",\"Fidenas Varian\",\"Nazarius Maxentius\",\"Calventius Gaius\",\"Calventius Cassianus\",\"Geminius Cicereius\",\"Sornatius Furius\",\"Afranius Urbicus\",\"Faustus Herennius\",\"Orchius Drusus\",\"Claudius Cominius\",\"Aquillius Acilius\",\"Modius Flavius\",\"Bonifacius Nasidienus\",\"Hostius Statilius\",\"Lucius Sepullius\",\"Papirius Seccius\",\"Magnentius Clodius\",\"Cilnius Laurentius\",\"Vitalis Cispius\",\"Fidius Pupius\",\"Papirius Titus\",\"Fulcinia Vincentius\",\"Marius Gracchus\",\"Segulius Vespasian\",\"Acilius Fabrinius\",\"Sidonius Cloelius\",\"Catilius Dacian\",\"Florian Primitivus\",\"Plautius Atilius\",\"Pinarus Geminius\",\"Rufinus Cethegus\",\"Urbicus Maxentius\",\"Manius Maecilius\",\"Publius Menenius\",\"Tarquinius Zeno\",\"Plinius Plautius\",\"Fulcinia Tertius\",\"Castorius Egnatuleius\",\"Zeno Valerius\",\"Plautius Duronius\",\"Terentius Scribonius\",\"Januarius Honorius\",\"Nerva Icilius\",\"Blossius Vincentius\",\"Servilius Icilius\",\"Sextius Cassio\",\"Herennius Numonius\",\"Marcus Julius\",\"Paconius Severus\",\"Donatus Paelignus\",\"Fabrinius Varian\",\"Persius Blaesus\",\"Pasidienus Cispius\",\"Pacilius Tacitus\",\"Memmius Ollius\",\"Belisarius Martialis\",\"Numerius Marius\",\"Numerius Sergian\",\"Hostilius Ingenuus\",\"Fabius Castorius\",\"Caesennius Petilius\",\"Festus Vitalis\",\"Sedatius Aelius\",\"Sulpicius Silanus\",\"Semellius Considius\",\"Flaccus Sulpicius\",\"Cassius Laberius\",\"Regulus Iulius\",\"Suetonius Octavius\",\"Acilius Sextius\",\"Sempronius Cominius\",\"Caedicius Mamilius\",\"Rebilus Dacian\",\"Paconius Cordius\",\"Genucius Priscus\",\"Atilius Menenius\",\"Claudius Pontidius\",\"Crispus Horatius\",\"Sestius Opimius\",\"Icilius Curtius\",\"Statilius Didius\",\"Nerva Ninnius\",\"Florian Valens\",\"Gallus Sidonius\",\"Cyprian Eutropius\",\"Egnatuleius Sergian\",\"Sestius Ninnius\",\"Servius Murena\",\"Vitus Gracchus\",\"Silvius Belisarius\",\"Statilius Seccius\",\"Blasius Munatius\",\"Damasus Damasus\",\"Atilius Sallustius\",\"Sornatius Oppius\",\"Crispinus Blasius\",\"Numicius Florianus\",\"Papinius Hilarianus\",\"Nero Lartius\",\"Fidius Poppaedius\",\"Priscus Albinius\",\"Honorius Genucius\",\"Rullianus Paconius\",\"Ovidius Mucius\",\"Modius Caelian\",\"Considius Crispus\",\"Eutropius Cincinnatus\",\"Postumius Domitian\",\"Stertinius Pontidius\",\"Servilius Novellius\",\"Gallus Draco\",\"Dacian Antonius\",\"Cyprian Naevius\",\"Memmius Numitorius\",\"Sextius Gallus\",\"Felix Ollius\",\"Opimius Manius\",\"Lacerius Fabianus\",\"Nasennius Porcius\",\"Matinius Cinna\",\"Florianus Brennus\",\"Horatius Cinna\",\"Vitalis Minicius\",\"Aemilius Petreius\",\"Laurentius Nautius\",\"Marcius Ofilius\",\"Hirrus Opimius\",\"Blossius Matienus\",\"Sextus Novellius\",\"Murena Calpurnius\",\"Cato Germanicus\",\"Nero Atilius\",\"Plinius Cato\",\"Cornelius Decentius\",\"Valens Dacian\",\"Castorius Octavenus\",\"Nero Silanus\",\"Tarquinius Lartius\",\"Paulinus Sempronius\",\"Postumius Candidus\",\"Cloelius Cethegus\",\"Lucius Quirinius\",\"Regulus Festus\",\"Cilnius Sextus\",\"Betilienus Plautius\",\"Percennius Stertinius\",\"Decentius Oppius\",\"Ollius Calventius\",\"Decentius Eutropius\",\"Valens Nerva\",\"Cicereius Pomponius\",\"Plautius Acilius\",\"Porcius Numonius\",\"Brennus Cilnius\",\"Nigidius Salvidienus\",\"Pontidius Saturninus\",\"Pettius Horatius\",\"Fabianus Gracchus\",\"Rebilus Antonius\",\"Probus Cato\",\"Hadrian Eborius\",\"Livius Silanus\",\"Egnatius Nonius\",\"Tarquinius Ostorius\",\"Gracchus Flaccus\",\"Caesennius Diocletian\",\"Calpurnius Silius\",\"Caesennius Seneca\",\"Fabrinius Livius\",\"Ollius Nazarius\",\"Plautius Faustus\",\"Tarquinius Numicius\",\"Urbanus Constantinus\",\"Sextius Martialis\",\"Sittius Sidonius\",\"Seneca Silanus\",\"Tacitus Domitian\",\"Norbanus Cethegus\",\"Silanus Cethegus\",\"Iulius Stertinius\",\"Silanus Petreius\",\"Foslius Furius\",\"Justus Postumius\",\"Munatius Orchius\",\"Varian Matienus\",\"Numerius Festinius\",\"Marius Vincentius\",\"Sittius Blaesus\",\"Caelian Tacitus\",\"Martialis Pacilius\",\"Ennius Geminius\",\"Laetorius Cyprian\",\"Laurentius Sulpicius\",\"Longinius Cominius\",\"Sabucius Caninius\",\"Cornelius Junius\",\"Cinna Scribonius\",\"Fundanus Postumius\",\"Marcius Claudius\",\"Lollius Musonius\",\"Naevius Laetorius\",\"Egnatuleius Pomponius\",\"Sittius Januarius\",\"Calventius Festinius\",\"Iunius Perperna\",\"Acilius Rufius\",\"Lacerius Cassianus\",\"Pilus Naevius\",\"Opimius Urbanus\",\"Silanus Gnaeus\",\"Geminius Maecilius\",\"Numitorius Vibius\",\"Maecilius Martialis\",\"Maecenas Cassio\",\"Longinius Crispinus\",\"Terentius Valens\",\"Salvius Memmius\",\"Duronius Opimius\",\"Tiberius Rebilus\",\"Cordius Quintus\",\"Iuventius Fabrinius\",\"Ollius Opellius\",\"Faustus Seneca\",\"Papirius Cocceius\",\"Aurelianus Sidonius\",\"Lucanus Nasidius\",\"Coruncanius Lucanus\",\"Salinator Balbinus\",\"Publicius Numerius\",\"Sidonius Festinius\",\"Sempronius Terentius\",\"Lamponius Opimius\",\"Sornatius Varian\",\"Primitivus Manius\",\"Nasidius Flavius\",\"Julius Probus\",\"Naevius Aquillius\",\"Placidus Rabirius\",\"Justus Seneca\",\"Aurelianus Albinus\",\"Petreius Cloelius\",\"Lucanus Decentius\",\"Constantinus Ogulnius\",\"Laronius Sextius\",\"Egnatius Paelignus\",\"Licinius Coruncanius\",\"Pius Gaius\",\"Marcius Orbilius\",\"Fabrinius Cloelius\",\"Catius Calventius\",\"Lucanus Lucretius\",\"Betilienus Albinus\",\"Spurius Gaius\",\"Ofilius Fabrinius\",\"Camillius Lollius\",\"Manilius Caelian\",\"Papirius Gallus\",\"Decimus Cocceius\",\"Quirinius Blossius\",\"Latinius Munatius\",\"Stertinius Orchius\",\"Bonifacius Cocceius\",\"Nautius Cocceius\",\"Cornelius Statilius\",\"Aquillius Vitalis\",\"Hirrus Camillius\",\"Coruncanius Mucius\",\"Constantinus Laurentius\",\"Lacerius Numerius\",\"Crispus Numerius\",\"Hortensius Plautius\",\"Paternus Rufius\",\"Sergius Burrienus\",\"Brennus Fulvius\",\"Caesennius Persius\",\"Cispius Camillius\",\"Gordian Camillus\",\"Camillus Varro\",\"Severus Mescinius\",\"Laecanius Terentius\",\"Justus Manius\",\"Perperna Salinator\",\"Aurelianus Damasus\",\"Mescinius Marcius\",\"Ofilius Geminius\",\"Placidus Pomponius\",\"Silanus Egnatius\",\"Fidenas Crispus\",\"Secundus Rebilus\",\"Numicius Decentius\",\"Statilius Cyprian\",\"Ravillius Mescinius\",\"Hirrus Salvian\",\"Aurelianus Silvius\",\"Nazarius Lollius\",\"Ovidius Laurentius\",\"Decimus Marcellus\",\"Rullianus Probus\",\"Sabucius Sestius\",\"Maximian Burrienus\",\"Cyprian Granius\",\"Hostilius Magnentius\",\"Pius Aelius\",\"Pontidius Gallus\",\"Marcellus Salvian\",\"Messalla Nasidienus\",\"Modius Cloelius\",\"Naevius Priscus\",\"Livius Caecilius\",\"Salinator Laronius\",\"Appius Sextius\",\"Ogulnius Ostorius\",\"Cinna Varro\",\"Papinius Valerius\",\"Baebius Sabucius\",\"Florianus Laetorius\",\"Caecilius Manilius\",\"Percennius Duronius\",\"Papirius Rullianus\",\"Appius Longinius\",\"Ostorius Scribonius\",\"Salinator Granius\",\"Nonius Nerva\",\"Burrienus Tertius\",\"Papirius Domitius\",\"Latinius Lollius\",\"Cominius Terentius\",\"Albinus Cicereius\",\"Hordeonius Messalla\",\"Potitius Ofilius\",\"Titus Fulcinia\",\"Hadrian Petilius\",\"Sicinius Quirinius\",\"Martialis Juvenalis\",\"Afranius Numitorius\",\"Caedicius Foslius\",\"Livius Sedatius\",\"Mucius Saturninus\",\"Lucius Betilienus\",\"Baebius Publius\",\"Hirrus Felix\",\"Laecanius Anatolius\",\"Manius Secundus\",\"Ogulnius Paconius\",\"Cilnius Betilienus\",\"Sextus Orchius\",\"Nigidius Mescinius\",\"Aurelianus Marcus\",\"Sempronius Vincentius\",\"Geminius Horatius\",\"Balbinus Postumius\",\"Mucius Rufrius\",\"Quintilianus Lentulus\",\"Secundus Fabianus\",\"Avidius Tiberius\",\"Papirius Crispus\",\"Nasennius Tertius\",\"Salvius Laetorius\",\"Stertinius Didius\",\"Pacilius Spurius\",\"Gratian Coruncanius\",\"Blaesus Baebius\",\"Suetonius Ingenuus\",\"Opellius Orbilius\",\"Martialis Laronius\",\"Drusus Fabianus\",\"Varian Cassianus\",\"Saturninus Festus\",\"Poppaedius Ostorius\",\"Servilius Lucretius\",\"Poppaedius Pettius\",\"Caelian Opimius\",\"Ostorius Laronius\",\"Cethegus Munatius\",\"Cilnius Rufrius\",\"Mucius Menenius\",\"Amulius Candidus\",\"Amulius Caninius\",\"Appius Octavius\",\"Burrienus Caesennius\",\"Hostilius Eborius\",\"Vitus Plautius\",\"Pontidius Maximian\",\"Ovidius Cassianus\",\"Ninnius Poppaedius\",\"Agrippa Fundanus\",\"Sulpicius Cantilius\",\"Longinius Justus\",\"Icilius Marian\",\"Palpellius Seneca\",\"Pilus Secundinius\",\"Sepullius Galerius\",\"Verecundus Laronius\",\"Iulius Valens\",\"Bruttius Salvian\",\"Marcellus Eborius\",\"Cispius Cilnius\",\"Caelian Decentius\",\"Paulinus Draco\",\"Fabius Geta\",\"Nonius Probus\",\"Belisarius Caesennius\",\"Gracchus Celsus\",\"Papirius Nigidius\",\"Varro Pinarus\",\"Latinius Aquillius\",\"Laecanius Caecilius\",\"Maximian Ollius\",\"Quinctilius Nasidius\",\"Fundanus Persius\",\"Cyprian Foslius\",\"Priscus Felix\",\"Stertinius Gracchus\",\"Tarquinius Galerius\",\"Gordian Drusus\",\"Honorius Damasus\",\"Marius Ollius\",\"Egnatuleius Paelignus\",\"Marcius Coruncanius\",\"Sextus Octavius\",\"Ovidius Icilius\",\"Burrienus Perperna\",\"Balbinus Urbicus\",\"Laetorius Cornelius\",\"Catius Cato\",\"Furius Sidonius\",\"Cicereius Fabrinius\",\"Gratian Maecilius\",\"Camillius Rullianus\",\"Novellius Pinarus\",\"Cocceius Cordius\",\"Palpellius Sextius\",\"Eutropius Julius\",\"Pilus Murena\",\"Numonius Rabirius\",\"Lucretius Nonius\",\"Damasus Junius\",\"Rufius Celsus\",\"Blossius Orchius\",\"Cordius Publicius\",\"Blasius Statilius\",\"Cincinnatus Nonius\",\"Orchius Potitius\",\"Caedicius Cornelius\",\"Faustus Sornatius\",\"Salinator Hostius\",\"Antonius Silius\",\"Eutropius Cornificius\",\"Nasidienus Secundinius\",\"Sepullius Fulcinia\",\"Ennius Flaccus\",\"Seccius Pius\",\"Pasidienus Brennus\",\"Vespasian Valens\",\"Gallus Juvenalis\",\"Florian Lamponius\",\"Zeno Considius\",\"Iulius Percennius\",\"Primitivus Domitius\",\"Magnentius Sidonius\",\"Sergian Blasius\",\"Vitalis Cornutus\",\"Menenius Antonius\",\"Flaccus Marcellus\",\"Rebilus Pupius\",\"Claudius Scribonius\",\"Cinna Decentius\",\"Pacilius Orchius\",\"Porcius Claudius\",\"Caesennius Decentius\",\"Potitius Salinator\",\"Rebilus Ovidius\",\"Nerva Varro\",\"Florianus Regulus\",\"Tertius Laecanius\",\"Persius Domitius\",\"Maecenas Marian\",\"Stertinius Hadrian\",\"Numitorius Rebilus\",\"Burrienus Seccius\",\"Sidonius Maximus\",\"Marian Silanus\",\"Cassius Hordeonius\",\"Quintilianus Laronius\",\"Pedius Orestius\",\"Maximus Pius\",\"Dacian Semellius\",\"Acilius Pedius\",\"Quirinius Plautius\",\"Cominius Ofilius\",\"Paternus Manilius\",\"Hortensius Baebius\",\"Messalla Sepullius\",\"Salinator Brennus\",\"Marcellus Varian\",\"Sulpicius Papirius\",\"Bonifacius Tarquinius\",\"Germanicus Sabucius\",\"Statilius Cinna\",\"Cosconius Acilius\",\"Mallius Severian\",\"Gallus Pedius\",\"Cato Cyprian\",\"Papirius Norbanus\",\"Tiberius Salinator\",\"Tarquinius Nigidius\",\"Papinius Murena\",\"Petreius Candidus\",\"Marcellus Festus\",\"Titus Caninius\",\"Paulinus Catilius\",\"Marius Fidenas\",\"Granius Egnatuleius\",\"Papinius Volumnius\",\"Sentius Lucanus\",\"Potitius Musonius\",\"Cornutus Silanus\",\"Lucretius Marcius\",\"Priscus Florianus\",\"Norbanus Percennius\",\"Norbanus Curtius\",\"Porcius Nerva\",\"Spurius Valerius\",\"Appius Palpellius\",\"Maecilius Plinius\",\"Appius Plinius\",\"Sentius Fidius\",\"Rebilus Constantinus\",\"Constantinus Castorius\",\"Pasidienus Laetorius\",\"Sedatius Considius\",\"Agrippa Silanus\",\"Cornelius Mamilius\",\"Calventius Domitian\",\"Balbus Sidonius\",\"Maxentius Pius\",\"Quinctilius Gordian\",\"Postumius Secundus\",\"Florianus Primitivus\",\"Petreius Novellius\",\"Manius Sabucius\",\"Bruttius Mescinius\",\"Calventius Cantilius\",\"Nigidius Terentius\",\"Pomponius Modius\",\"Domitius Gracchus\",\"Severian Damasus\",\"Verecundus Hordeonius\",\"Pilus Numonius\",\"Fannius Gratian\",\"Baebius Sempronius\",\"Curtius Calpurnius\",\"Cloelius Lucretius\",\"Primitivus Porcius\",\"Burrienus Catilius\",\"Martialis Caelian\",\"Domitius Ofilius\",\"Calpurnius Rutilius\",\"Perperna Ostorius\",\"Memmius Cethegus\",\"Latinius Eborius\",\"Suetonius Varian\",\"Geminius Semellius\",\"Sempronius Catilius\",\"Nasennius Ogulnius\",\"Hostius Cicereius\",\"Caesennius Sallustius\",\"Geminius Duronius\",\"Cordius Lucius\",\"Licinius Vitalis\",\"Ravillius Flavius\",\"Gallus Mallius\",\"Marcus Fannius\",\"Gracchus Julius\",\"Camillius Balbinus\",\"Cato Bruttius\",\"Cilnius Iunius\",\"Gabinius Latinius\",\"Laelius Sergius\",\"Sornatius Palpellius\",\"Florian Granius\",\"Aemilius Florian\",\"Rabirius Faustus\",\"Aulus Seneca\",\"Sittius Marcius\",\"Nero Sittius\",\"Lamponius Urbicus\",\"Iunius Foslius\",\"Sergius Geminius\",\"Munatius Primitivus\",\"Munatius Caedicius\",\"Albinius Norbanus\",\"Gracchus Pedius\",\"Pacilius Valens\",\"Salvius Pius\",\"Festinius Herennius\",\"Fannius Mescinius\",\"Norbanus Terentius\",\"Matienus Cassianus\",\"Gabinius Severus\",\"Lucius Valens\",\"Numonius Murena\",\"Antonius Felix\",\"Damasus Servilius\",\"Fabrinius Cestius\",\"Hortensius Mallius\",\"Seneca Norbanus\",\"Camillus Paternus\",\"Livius Porcius\",\"Salinator Lucius\",\"Ennius Nigidius\",\"Geta Nautius\",\"Cloelius Decimus\",\"Caecilius Salinator\",\"Seccius Hadrian\",\"Pinarus Granius\",\"Hadrian Licinius\",\"Aquillius Cornificius\",\"Ingenuus Priscus\",\"Cassianus Maximian\",\"Vibius Lucanus\",\"Betilienus Paternus\",\"Cassio Paulinus\",\"Cosconius Belisarius\",\"Memmius Salvius\",\"Gratian Gratian\",\"Licinius Perperna\",\"Sempronius Vitalis\",\"Pupius Balbus\",\"Caelius Blossius\",\"Rufrius Mallius\",\"Fulcinia Potitius\",\"Sallustius Aelius\",\"Sergius Sabucius\",\"Sittius Iunius\",\"Maxentius Draco\",\"Salvius Sergian\",\"Pettius Camillus\",\"Foslius Cassianus\",\"Sedatius Decentius\",\"Murena Secundus\",\"Herennius Caedicius\",\"Manius Lamponius\",\"Gnaeus Pinarus\",\"Musonius Cassio\",\"Silanus Pompeius\",\"Scribonius Nautius\",\"Anatolius Cilnius\",\"Lucretius Cinna\",\"Novellius Placidus\",\"Ravillius Gallus\",\"Egnatuleius Palpellius\",\"Poppaedius Januarius\",\"Rufius Licinius\",\"Priscus Aquillius\",\"Scribonius Horatius\",\"Aurelianus Lamponius\",\"Fidius Sempronius\",\"Aelius Priscus\",\"Gnaeus Nazarius\",\"Paulinus Messalla\",\"Tiberius Cassio\",\"Acilius Festinius\",\"Caelius Musonius\",\"Germanicus Aemilius\",\"Marcius Manius\",\"Naevius Agrippa\",\"Iunius Iuventius\",\"Opellius Quintilianus\",\"Cordius Volumnius\",\"Papinius Vitalis\",\"Varian Aemilius\",\"Marcius Vibius\",\"Camillius Celsus\",\"Cocceius Burrienus\",\"Seccius Martialis\",\"Candidus Semellius\",\"Gratian Amulius\",\"Juvenalis Pinarus\",\"Caninius Salvidienus\",\"Calpurnius Maximian\",\"Claudius Duronius\",\"Januarius Scribonius\",\"Germanicus Florian\",\"Maximus Sidonius\",\"Balbus Latinius\",\"Amulius Valerius\",\"Egnatuleius Servius\",\"Probus Vibius\",\"Candidus Constantinus\",\"Pompeius Juvenalis\",\"Magnentius Coruncanius\",\"Afranius Ravillius\",\"Pasidienus Salinator\",\"Gracchus Porcius\",\"Didius Opimius\",\"Lucanus Cosconius\",\"Cassio Murena\",\"Regulus Lucius\",\"Gracchus Ennius\",\"Pius Maecenas\",\"Cornificius Quintilianus\",\"Calpurnius Quintilianus\",\"Marius Baebius\",\"Sergian Cicereius\",\"Numicius Iulius\",\"Baebius Vincentius\",\"Zeno Albinius\",\"Priscus Aelius\",\"Crispinus Postumius\",\"Domitian Eborius\",\"Marcus Antonius\",\"Junius Maecilius\",\"Gaius Sepullius\",\"Bonifacius Caedicius\",\"Valentinian Mescinius\",\"Cloelius Coruncanius\",\"Novellius Lucius\",\"Orchius Sentius\",\"Munatius Rufrius\",\"Magnentius Terentius\",\"Nerva Numonius\",\"Cassianus Secundus\",\"Fidius Matienus\",\"Maecilius Maxentius\",\"Terentius Cilnius\",\"Aquillius Silius\",\"Zeno Maxentius\",\"Aulus Gabinius\",\"Sulpicius Secundus\",\"Maximian Galerius\",\"Laelius Varian\",\"Cinna Manilius\",\"Aurelianus Lentulus\",\"Stertinius Servilius\",\"Draco Hadrian\",\"Quintus Blasius\",\"Gratian Nazarius\",\"Magnentius Maximian\",\"Fulcinia Ogulnius\",\"Spurius Naevius\",\"Lucretius Nasennius\",\"Octavenus Fidius\",\"Sicinius Pettius\",\"Justus Paternus\",\"Caedicius Mescinius\",\"Cocceius Quintus\",\"Pompeius Mamilius\",\"Pettius Sentius\",\"Numicius Marcellus\",\"Vespasian Fundanus\",\"Poppaedius Rullianus\",\"Geminius Cornificius\",\"Orestius Placidus\",\"Aemilius Severus\",\"Tertius Anatolius\",\"Bonifacius Hilarianus\",\"Ingenuus Quinctilius\",\"Pilus Ovidius\",\"Catilius Laberius\",\"Appius Ostorius\",\"Perperna Valens\",\"Sextus Perperna\",\"Cinna Longinius\",\"Sulpicius Marius\",\"Vitalis Lucius\",\"Cordius Gaius\",\"Eutropius Sallustius\",\"Paulinus Octavenus\",\"Julius Cloelius\",\"Persius Publius\",\"Fabrinius Duronius\",\"Sergius Gnaeus\",\"Menenius Honorius\",\"Semellius Rebilus\",\"Caesennius Octavenus\",\"Sempronius Sedatius\",\"Ravillius Papirius\",\"Mallius Ingenuus\",\"Cloelius Murena\",\"Manilius Festinius\",\"Sittius Curtius\",\"Cantilius Perperna\",\"Nasennius Juvenalis\",\"Menenius Modius\",\"Norbanus Pilus\",\"Geta Urbanus\",\"Palpellius Servilius\",\"Pupius Pilus\",\"Pius Hostius\",\"Lamponius Oppius\",\"Probus Ennius\",\"Considius Rufrius\",\"Geta Didius\",\"Mescinius Caelian\",\"Hilarianus Cestius\",\"Ingenuus Faustus\",\"Sergius Aemilius\",\"Honorius Ennius\",\"Galerius Orestius\",\"Laelius Laetorius\",\"Caninius Rufius\",\"Papirius Albinius\",\"Marcus Domitian\",\"Verecundus Florian\",\"Zeno Vitus\",\"Numicius Didius\",\"Caecilius Iulius\",\"Opimius Petreius\",\"Terentius Sepullius\",\"Balbus Porcius\",\"Cassianus Paulinus\",\"Probus Orbilius\",\"Eutropius Ennius\",\"Pupius Lentulus\",\"Salvius Salinator\",\"Sidonius Laelius\",\"Granius Fidenas\",\"Numitorius Domitius\",\"Vitus Bonifacius\",\"Pilus Acilius\",\"Brennus Opellius\",\"Paelignus Potitius\",\"Laecanius Oppius\",\"Aurelianus Acilius\",\"Calventius Quintilianus\",\"Aurelianus Fundanus\",\"Magnentius Ravillius\",\"Lartius Secundinius\",\"Minicius Festinius\",\"Laecanius Ingenuus\",\"Gaius Marcellus\",\"Aquillius Cethegus\",\"Norbanus Manilius\",\"Priscus Munatius\",\"Antonius Gaius\",\"Sicinius Honorius\",\"Perperna Sempronius\",\"Cestius Antonius\",\"Naevius Maximian\",\"Octavius Aelius\",\"Varian Nonius\",\"Antonius Livius\",\"Flavius Quintilianus\",\"Balbus Magnentius\",\"Donatus Domitius\",\"Silanus Saturninus\",\"Valens Varian\",\"Zeno Castorius\",\"Appius Fabius\",\"Maximus Nigidius\",\"Didius Gallus\",\"Silvius Anatolius\",\"Avidius Musonius\",\"Zeno Anatolius\",\"Maecilius Silius\",\"Blasius Gordian\",\"Latinius Pinarus\",\"Tacitus Murena\",\"Sertorius Murena\",\"Semellius Lucretius\",\"Matinius Cassianus\",\"Cyprian Cato\",\"Urbanus Fidius\",\"Nazarius Fabianus\",\"Plinius Octavius\",\"Percennius Maximian\",\"Anatolius Fidius\",\"Afranius Modius\",\"Norbanus Laelius\",\"Brennus Nautius\",\"Mescinius Opellius\",\"Murena Regulus\",\"Marius Nasidius\",\"Saturninus Caninius\",\"Camillius Mucius\",\"Albinus Plautius\",\"Blaesus Diocletian\",\"Laronius Opimius\",\"Ovidius Terentius\",\"Pinarus Fidenas\",\"Caninius Lollius\",\"Ingenuus Stertinius\",\"Iunius Nero\",\"Perperna Modius\",\"Rabirius Gnaeus\",\"Cornelius Silius\",\"Ovidius Albinius\",\"Servius Magnentius\",\"Nigidius Mucius\",\"Pius Titus\",\"Numerius Ogulnius\",\"Verecundus Spurius\",\"Secundus Rufrius\",\"Severian Ennius\",\"Genucius Lucanus\",\"Vincentius Geminius\",\"Rutilius Perperna\",\"Tacitus Fidius\",\"Seneca Diocletian\",\"Nonius Cethegus\",\"Maecenas Publius\",\"Ingenuus Donatus\",\"Opellius Furius\",\"Vitus Juvenalis\",\"Rutilius Cincinnatus\",\"Urbicus Varian\",\"Sittius Laetorius\",\"Genucius Nigidius\",\"Agrippa Juvenalis\",\"Porcius Januarius\",\"Rufinus Crispinus\",\"Vespasian Vitalis\",\"Nero Gabinius\",\"Rabirius Poppaedius\",\"Varro Appius\",\"Cincinnatus Numonius\",\"Marcus Sempronius\",\"Sidonius Caelian\",\"Januarius Quirinius\",\"Caesennius Sestius\",\"Porcius Albinus\",\"Maecilius Lucius\",\"Persius Cordius\",\"Florian Domitian\",\"Julius Cinna\",\"Sergian Damasus\",\"Damasus Hostilius\",\"Sittius Florian\",\"Aemilius Cincinnatus\",\"Marius Mamilius\",\"Suetonius Paconius\",\"Agrippa Sentius\",\"Vincentius Festus\",\"Plinius Gaius\",\"Celsus Cispius\",\"Lucretius Statilius\",\"Cassianus Nerva\",\"Hirrus Caedicius\",\"Priscus Cispius\",\"Salvidienus Drusus\",\"Foslius Felix\",\"Ogulnius Foslius\",\"Burrienus Silvius\",\"Severian Verus\",\"Postumius Cato\",\"Geta Lartius\",\"Munatius Granius\",\"Agrippa Lucius\",\"Paconius Laetorius\",\"Crispinus Sestius\",\"Poppaedius Cornificius\",\"Tarquinius Paelignus\",\"Duronius Ennius\",\"Manius Norbanus\",\"Fabius Rullianus\",\"Silvius Nerva\",\"Aulus Nautius\",\"Florian Caecilius\",\"Cassianus Papinius\",\"Orbilius Festinius\",\"Lentulus Candidus\",\"Lucanus Musonius\",\"Silvius Caedicius\",\"Cornificius Marian\",\"Sergian Caesennius\",\"Tarquinius Livius\",\"Naevius Marian\",\"Varro Gallus\",\"Egnatuleius Aulus\",\"Ravillius Domitian\",\"Calpurnius Salvius\",\"Iulius Curtius\",\"Potitius Donatus\",\"Sertorius Galerius\",\"Hirrus Cincinnatus\",\"Horatius Postumius\",\"Baebius Junius\",\"Sornatius Pinarus\",\"Curtius Rufrius\",\"Rufius Orbilius\",\"Porcius Cispius\",\"Minicius Papinius\",\"Antonius Cornutus\",\"Salvidienus Julius\",\"Nasidienus Egnatuleius\",\"Primitivus Maximian\",\"Considius Burrienus\",\"Junius Salvidienus\",\"Mucius Crispus\",\"Sulpicius Pontidius\",\"Rebilus Opellius\",\"Afranius Lucretius\",\"Fulcinia Florian\",\"Memmius Novellius\",\"Ofilius Calpurnius\",\"Fabianus Maecenas\",\"Domitius Hirrus\",\"Claudius Marian\",\"Vitalis Sempronius\",\"Nasidius Celsus\",\"Aelius Pacilius\",\"Fulcinia Crispinus\",\"Percennius Diocletian\",\"Mamilius Suetonius\",\"Duronius Appius\",\"Aelius Primitivus\",\"Paelignus Seccius\",\"Celsus Cassius\",\"Musonius Cicero\",\"Gaius Pontidius\",\"Mallius Anatolius\",\"Urbanus Flavius\",\"Vitus Festus\",\"Tarquinius Lentulus\",\"Musonius Fabius\",\"Poppaedius Amulius\",\"Magnentius Pompeius\",\"Burrienus Granius\",\"Porcius Suetonius\",\"Cassianus Justus\",\"Cinna Foslius\",\"Publicius Petilius\",\"Ennius Cominius\",\"Flaccus Pompeius\",\"Nero Mescinius\",\"Scribonius Laelius\",\"Rufrius Verus\",\"Atilius Nasennius\",\"Damasus Cilnius\",\"Salinator Manilius\",\"Valerius Flaccus\",\"Ollius Suetonius\",\"Fannius Justus\",\"Maxentius Martialis\",\"Festus Honorius\",\"Coruncanius Amulius\",\"Nasennius Secundinius\",\"Licinius Tacitus\",\"Cicero Messalla\",\"Sestius Iuventius\",\"Sornatius Valentinian\",\"Sittius Diocletian\",\"Galerius Cethegus\",\"Blaesus Paulinus\",\"Sextius Quintus\",\"Cestius Valens\",\"Januarius Caelian\",\"Sentius Nigidius\",\"Sextius Ogulnius\",\"Urbanus Quintus\",\"Sabucius Maximian\",\"Publius Sergius\",\"Nigidius Geta\",\"Scribonius Eutropius\",\"Nonius Numicius\",\"Julius Nazarius\",\"Cosconius Rutilius\",\"Cyprian Decimus\",\"Antonius Menenius\",\"Bonifacius Laurentius\",\"Cordius Laecanius\",\"Lacerius Publicius\",\"Lucius Blasius\",\"Cethegus Nonius\",\"Silanus Lucretius\",\"Antonius Marian\",\"Minicius Sertorius\",\"Diocletian Felix\",\"Verecundus Didius\",\"Lacerius Didius\",\"Vincentius Quirinius\",\"Juvenalis Pius\",\"Urbicus Salvian\",\"Domitius Egnatius\",\"Egnatius Brennus\",\"Cornelius Urbanus\",\"Lollius Galerius\",\"Suetonius Iulius\",\"Gallus Murena\",\"Martialis Valerius\",\"Honorius Cominius\",\"Calventius Numitorius\",\"Silanus Damasus\",\"Horatius Blaesus\",\"Draco Constantinus\",\"Numicius Caelius\",\"Cloelius Pacilius\",\"Marian Numonius\",\"Herennius Plautius\",\"Quintilianus Pettius\",\"Maecilius Rullianus\",\"Betilienus Geta\",\"Quinctilius Festinius\",\"Laurentius Eborius\",\"Decentius Cyprian\",\"Gabinius Sittius\",\"Cilnius Icilius\",\"Postumius Quintus\",\"Gordian Crispus\",\"Sicinius Mucius\",\"Genucius Maxentius\",\"Festinius Nasennius\",\"Geminius Cosconius\",\"Servilius Agrippa\",\"Sulpicius Camillus\",\"Iuventius Ennius\",\"Opellius Varro\",\"Cornificius Orbilius\",\"Sentius Claudius\",\"Plinius Nazarius\",\"Vibius Fidius\",\"Maximian Munatius\",\"Cassianus Paternus\",\"Cestius Zeno\",\"Nerva Hordeonius\",\"Blaesus Cordius\",\"Sicinius Mallius\",\"Ostorius Sertorius\",\"Ravillius Persius\",\"Genucius Sornatius\",\"Rufrius Sextius\",\"Crispus Nazarius\",\"Poppaedius Cyprian\",\"Quinctilius Considius\",\"Caelian Crispinus\",\"Fabrinius Semellius\",\"Cosconius Ennius\",\"Cassio Bruttius\",\"Bonifacius Fidius\",\"Silvius Mamilius\",\"Cestius Messalla\",\"Cocceius Sornatius\",\"Brennus Crispus\",\"Varian Vespasian\",\"Plinius Sidonius\",\"Donatus Marian\",\"Orchius Icilius\",\"Junius Castorius\",\"Sidonius Stertinius\",\"Persius Bruttius\",\"Decimus Saturninus\",\"Sertorius Iuventius\",\"Severian Modius\",\"Verecundus Stertinius\",\"Priscus Secundus\",\"Suetonius Papirius\",\"Magnentius Fulcinia\",\"Gabinius Octavius\",\"Donatus Hostilius\",\"Pupius Festus\",\"Ennius Eborius\",\"Latinius Anatolius\",\"Germanicus Faustus\",\"Memmius Norbanus\",\"Iulius Terentius\",\"Martialis Cincinnatus\",\"Camillus Lamponius\",\"Lamponius Lentulus\",\"Orestius Blasius\",\"Nonius Fidenas\",\"Burrienus Laelius\",\"Ingenuus Egnatuleius\",\"Decentius Papinius\",\"Hostilius Horatius\",\"Naevius Hortensius\",\"Lucanus Junius\",\"Juvenalis Percennius\",\"Celsus Nero\",\"Genucius Poppaedius\",\"Paternus Numerius\",\"Placidus Laronius\",\"Genucius Maecenas\",\"Salvidienus Gordian\",\"Caecilius Cyprian\",\"Hostilius Sextius\",\"Granius Ravillius\",\"Servilius Hadrian\",\"Maxentius Lentulus\",\"Nautius Cloelius\",\"Gratian Festus\",\"Poppaedius Sulpicius\",\"Octavenus Pedius\",\"Fabianus Statilius\",\"Maxentius Nazarius\",\"Caecilius Egnatuleius\",\"Constantinus Aemilius\",\"Hortensius Pacilius\",\"Porcius Candidus\",\"Murena Fabius\",\"Blaesus Festinius\",\"Martialis Brennus\",\"Ofilius Vitalis\",\"Servius Mallius\",\"Cassianus Cordius\",\"Spurius Livius\",\"Calventius Segulius\",\"Numicius Flaccus\",\"Rufius Latinius\",\"Juvenalis Dacian\",\"Cato Draco\",\"Cosconius Caninius\",\"Fabius Latinius\",\"Silanus Probus\",\"Horatius Granius\",\"Seccius Fabrinius\",\"Ostorius Blossius\",\"Persius Semellius\",\"Brennus Titus\",\"Agrippa Cestius\",\"Camillus Cominius\",\"Festinius Januarius\",\"Numerius Maxentius\",\"Antonius Matienus\",\"Marcus Tertius\",\"Curtius Fundanus\",\"Festus Naevius\",\"Pomponius Salvius\",\"Fundanus Vitus\",\"Quinctilius Numitorius\",\"Severian Iunius\",\"Coruncanius Crispus\",\"Potitius Sepullius\",\"Salvidienus Ofilius\",\"Servius Festinius\",\"Numicius Gnaeus\",\"Bruttius Caelian\",\"Persius Licinius\",\"Sabucius Segulius\",\"Aurelianus Genucius\",\"Martialis Sergian\",\"Iuventius Orchius\",\"Festinius Pius\",\"Candidus Rufinus\",\"Sempronius Sepullius\",\"Petilius Catius\",\"Valentinian Afranius\",\"Foslius Tarquinius\",\"Magnentius Sergius\",\"Saturninus Brennus\",\"Priscus Eutropius\",\"Juvenalis Cornificius\",\"Aurelianus Sedatius\",\"Pedius Varian\",\"Fidius Iulius\",\"Maecilius Petreius\",\"Pinarus Orbilius\",\"Valens Terentius\",\"Hostius Maximus\",\"Primitivus Laelius\",\"Palpellius Horatius\",\"Bruttius Lollius\",\"Aquillius Rufrius\",\"Regulus Damasus\",\"Longinius Servilius\",\"Quintus Paelignus\",\"Nonius Aulus\",\"Hostilius Fabius\",\"Munatius Laelius\",\"Coruncanius Opellius\",\"Cicero Pontidius\",\"Diocletian Ostorius\",\"Regulus Probus\",\"Genucius Duronius\",\"Mamilius Caelius\",\"Matienus Cantilius\",\"Matinius Cantilius\",\"Herennius Caesennius\",\"Julius Ingenuus\",\"Numonius Afranius\",\"Honorius Calpurnius\",\"Sicinius Cornutus\",\"Porcius Martialis\",\"Sulpicius Valentinian\",\"Brennus Seneca\",\"Percennius Herennius\",\"Curtius Cinna\",\"Orchius Domitian\",\"Papirius Maxentius\",\"Maecilius Gracchus\",\"Claudius Salinator\",\"Martialis Gratian\",\"Julius Murena\",\"Honorius Numerius\",\"Nasidius Plinius\",\"Ogulnius Marius\",\"Ofilius Gallus\",\"Calventius Fabius\",\"Nasennius Musonius\",\"Persius Plautius\",\"Tarquinius Acilius\",\"Petreius Postumius\",\"Fabius Sicinius\",\"Salvius Magnentius\",\"Priscus Mucius\",\"Matienus Latinius\",\"Terentius Maxentius\",\"Severus Pacilius\",\"Caelian Zeno\",\"Murena Petreius\",\"Minicius Fannius\",\"Vitalis Oppius\",\"Appius Iuventius\",\"Pompeius Longinius\",\"Hortensius Aulus\",\"Herennius Cestius\",\"Agrippa Placidus\",\"Flavius Cocceius\",\"Silvius Sextius\",\"Naevius Caecilius\",\"Martialis Hadrian\",\"Domitian Maecenas\",\"Ogulnius Statilius\",\"Licinius Quintilianus\",\"Gordian Postumius\",\"Sepullius Flaccus\",\"Antonius Seneca\",\"Brennus Donatus\",\"Appius Sallustius\",\"Servius Papirius\",\"Lollius Sedatius\",\"Marius Ostorius\",\"Sepullius Valens\",\"Fulvius Faustus\",\"Numonius Iuventius\",\"Lucanus Licinius\",\"Zeno Honorius\",\"Cassio Valerius\",\"Tarquinius Cocceius\",\"Cloelius Orbilius\",\"Horatius Placidus\",\"Statilius Cispius\",\"Antonius Seccius\",\"Silvius Zeno\",\"Naevius Ennius\",\"Draco Maecilius\",\"Lamponius Salinator\",\"Gratian Hortensius\",\"Gnaeus Matienus\",\"Candidus Nazarius\",\"Lucius Quinctilius\",\"Manius Postumius\",\"Pasidienus Seneca\",\"Orchius Crispus\",\"Hilarianus Salinator\",\"Sextus Salinator\",\"Faustus Sestius\",\"Castorius Camillius\",\"Aquillius Musonius\",\"Menenius Paelignus\",\"Granius Sulpicius\",\"Pettius Lamponius\",\"Acilius Honorius\",\"Eborius Cantilius\",\"Crispus Caecilius\",\"Rufinus Clodius\",\"Semellius Stertinius\",\"Agrippa Ravillius\",\"Laetorius Decimus\",\"Cloelius Matienus\",\"Horatius Munatius\",\"Terentius Sittius\",\"Sulpicius Nerva\",\"Sittius Placidus\",\"Castorius Statilius\",\"Germanicus Marcellus\",\"Caelius Damasus\",\"Sempronius Dacian\",\"Diocletian Marcus\",\"Cicereius Fundanus\",\"Messalla Ogulnius\",\"Aulus Gratian\",\"Justus Salvidienus\",\"Lacerius Petreius\",\"Primitivus Messalla\",\"Maximian Salvius\",\"Matienus Sempronius\",\"Foslius Orchius\",\"Semellius Julius\",\"Severus Octavenus\",\"Petilius Latinius\",\"Egnatuleius Musonius\",\"Domitius Lamponius\",\"Faustus Nasidienus\",\"Eutropius Catilius\",\"Rufrius Persius\",\"Calpurnius Severus\",\"Lucius Cato\",\"Januarius Paulinus\",\"Bruttius Rutilius\",\"Mescinius Appius\",\"Maecenas Murena\",\"Messalla Domitian\",\"Severus Memmius\",\"Numerius Coruncanius\",\"Valerius Numicius\",\"Cassio Sextius\",\"Maximus Balbus\",\"Numitorius Brennus\",\"Tacitus Ovidius\",\"Valerius Zeno\",\"Fabianus Priscus\",\"Cassius Camillius\",\"Herennius Hostilius\",\"Camillus Sidonius\",\"Modius Nigidius\",\"Rullianus Appius\",\"Nasidienus Sertorius\",\"Sergian Sornatius\",\"Maecilius Acilius\",\"Crispinus Murena\",\"Numicius Amulius\",\"Latinius Tertius\",\"Silius Rufrius\",\"Catius Gallus\",\"Germanicus Acilius\",\"Valentinian Camillius\",\"Castorius Pomponius\",\"Fabrinius Decimus\",\"Opellius Saturninus\",\"Vitus Caecilius\",\"Festinius Tertius\",\"Celsus Marius\",\"Menenius Hilarianus\",\"Naevius Diocletian\",\"Camillus Laelius\",\"Aemilius Fulcinia\",\"Gabinius Herennius\",\"Iulius Sergius\",\"Candidus Ofilius\",\"Gabinius Zeno\",\"Herennius Postumius\",\"Magnentius Caelian\",\"Cincinnatus Fidius\",\"Claudius Nigidius\",\"Servius Vespasian\",\"Maecenas Regulus\",\"Suetonius Maecenas\",\"Sextius Domitian\",\"Bruttius Norbanus\",\"Didius Maecenas\",\"Maxentius Zeno\",\"Numitorius Didius\",\"Regulus Blaesus\",\"Quintus Acilius\",\"Pontidius Vitus\",\"Salvidienus Munatius\",\"Rufius Longinius\",\"Paulinus Vibius\",\"Caesennius Fannius\",\"Postumius Laelius\",\"Cominius Lacerius\",\"Mallius Bonifacius\",\"Gallus Opellius\",\"Mucius Varian\",\"Laetorius Paulinus\",\"Terentius Baebius\",\"Sedatius Egnatuleius\",\"Lucretius Clodius\",\"Laronius Catius\",\"Plautius Matienus\",\"Ollius Amulius\",\"Baebius Salvian\",\"Suetonius Musonius\",\"Faustus Festinius\",\"Cosconius Rebilus\",\"Modius Mallius\",\"Felix Blasius\",\"Pasidienus Pompeius\",\"Postumius Catius\",\"Rebilus Seneca\",\"Varro Paulinus\",\"Catilius Catius\",\"Cicereius Marcellus\",\"Salvidienus Diocletian\",\"Constantinus Mallius\",\"Tiberius Sornatius\",\"Maximus Ninnius\",\"Amulius Fulcinia\",\"Rufrius Tarquinius\",\"Pius Sergius\",\"Quirinius Placidus\",\"Mucius Fulcinia\",\"Publicius Marcellus\",\"Herennius Rufius\",\"Aquillius Poppaedius\",\"Lucanus Marcius\",\"Sextus Festinius\",\"Lollius Sidonius\",\"Opimius Marcus\",\"Cantilius Gracchus\",\"Publicius Modius\",\"Cornificius Fidenas\",\"Orchius Cassianus\",\"Modius Publicius\",\"Nasidienus Munatius\",\"Rufrius Faustus\",\"Orchius Laronius\",\"Pilus Opimius\",\"Decimus Felix\",\"Latinius Pompeius\",\"Tacitus Petreius\",\"Nero Foslius\",\"Numicius Opellius\",\"Aelius Hostius\",\"Felix Julius\",\"Fidius Brennus\",\"Lollius Hadrian\",\"Tiberius Gaius\",\"Sextius Sidonius\",\"Gabinius Maximian\",\"Quinctilius Petilius\",\"Secundinius Lollius\",\"Hortensius Honorius\",\"Menenius Sempronius\",\"Matienus Egnatius\",\"Orbilius Fidenas\",\"Sergian Latinius\",\"Nautius Publicius\",\"Galerius Cinna\",\"Caninius Blaesus\",\"Maecilius Pettius\",\"Paelignus Vitus\",\"Tacitus Clodius\",\"Pinarus Pacilius\",\"Julius Marcellus\",\"Orbilius Sempronius\",\"Rufinus Honorius\",\"Mallius Manilius\",\"Horatius Crispinus\",\"Gabinius Fidenas\",\"Blaesus Salvius\",\"Cinna Galerius\",\"Blaesus Dacian\",\"Paelignus Manius\",\"Messalla Geta\",\"Opellius Vitalis\",\"Dacian Silius\",\"Munatius Baebius\",\"Fannius Cispius\",\"Seneca Cornutus\",\"Sergius Numitorius\",\"Vitalis Faustus\",\"Cicero Regulus\",\"Anatolius Papinius\",\"Ingenuus Marius\",\"Sextus Fidius\",\"Gallus Marcius\",\"Quirinius Crispinus\",\"Licinius Sallustius\",\"Sergian Terentius\",\"Cocceius Publicius\",\"Maecilius Gnaeus\",\"Sallustius Marian\",\"Curtius Maximian\",\"Mucius Silvius\",\"Maecilius Quinctilius\",\"Cornificius Maecenas\",\"Maecilius Curtius\",\"Sextius Orestius\",\"Constantinus Geta\",\"Camillus Valentinian\",\"Hostius Galerius\",\"Quinctilius Stertinius\",\"Laecanius Hilarianus\",\"Valerius Hordeonius\",\"Petilius Cosconius\",\"Antonius Octavius\",\"Albinus Servilius\",\"Sertorius Sertorius\",\"Claudius Cispius\",\"Secundus Pedius\",\"Primitivus Varian\",\"Silius Coruncanius\",\"Hirrus Tarquinius\",\"Memmius Hostilius\",\"Postumius Sepullius\",\"Suetonius Tiberius\",\"Sedatius Petreius\",\"Hirrus Betilienus\",\"Cassianus Iulius\",\"Matinius Varian\",\"Orbilius Cyprian\",\"Dacian Agrippa\",\"Volumnius Paelignus\",\"Ravillius Sestius\",\"Egnatius Hordeonius\",\"Oppius Sextus\",\"Catius Postumius\",\"Hortensius Cloelius\",\"Laelius Urbicus\",\"Crispus Sittius\",\"Hostius Norbanus\",\"Naevius Murena\",\"Galerius Opimius\",\"Silius Fabius\",\"Octavenus Longinius\",\"Draco Florian\",\"Silvius Cosconius\",\"Pupius Publicius\",\"Mallius Belisarius\",\"Ostorius Nonius\",\"Catius Geminius\",\"Egnatius Domitian\",\"Lamponius Junius\",\"Festus Valens\",\"Antonius Caecilius\",\"Hilarianus Scribonius\",\"Avidius Urbanus\",\"Orchius Suetonius\",\"Januarius Fannius\",\"Baebius Caedicius\",\"Cinna Caelius\",\"Betilienus Marcellus\",\"Urbicus Suetonius\",\"Tiberius Fidius\",\"Hadrian Porcius\",\"Verus Decimus\",\"Regulus Maecilius\",\"Terentius Salinator\",\"Secundinius Balbus\",\"Felix Hordeonius\",\"Cornelius Furius\",\"Sittius Cincinnatus\",\"Regulus Urbicus\",\"Cilnius Cicero\",\"Lucius Messalla\",\"Semellius Horatius\",\"Burrienus Spurius\",\"Caesennius Furius\",\"Paternus Sidonius\",\"Geta Hadrian\",\"Granius Norbanus\",\"Gnaeus Spurius\",\"Balbinus Tertius\",\"Calventius Celsus\",\"Lucretius Geminius\",\"Munatius Rebilus\",\"Laetorius Paelignus\",\"Fabianus Ostorius\",\"Cloelius Porcius\",\"Albinius Maxentius\",\"Saturninus Nasidius\",\"Nautius Publius\",\"Marcius Lentulus\",\"Coruncanius Decimus\",\"Novellius Servius\",\"Rutilius Manilius\",\"Pasidienus Severian\",\"Cato Maecenas\",\"Flaccus Marius\",\"Hilarianus Cantilius\",\"Calpurnius Saturninus\",\"Rabirius Amulius\",\"Herennius Lollius\",\"Bruttius Cilnius\",\"Marcus Volumnius\",\"Baebius Quinctilius\",\"Celsus Celsus\",\"Verus Memmius\",\"Egnatius Dacian\",\"Coruncanius Iuventius\",\"Marcus Cilnius\",\"Nerva Norbanus\",\"Cicero Julius\",\"Aelius Ollius\",\"Constantinus Laberius\",\"Cocceius Cornutus\",\"Tarquinius Secundinius\",\"Geta Celsus\",\"Suetonius Rufius\",\"Seccius Silvius\",\"Valerius Cestius\",\"Pomponius Persius\",\"Numonius Sedatius\",\"Secundinius Claudius\",\"Papirius Balbinus\",\"Tiberius Orestius\",\"Juvenalis Cilnius\",\"Celsus Secundus\",\"Hirrus Sidonius\",\"Publius Suetonius\",\"Nero Blossius\",\"Statilius Numitorius\",\"Publicius Bruttius\",\"Hortensius Draco\",\"Vitus Rufrius\",\"Semellius Maximus\",\"Eborius Seneca\",\"Servius Silvius\",\"Sestius Pinarus\",\"Blaesus Acilius\",\"Titus Diocletian\",\"Gnaeus Sornatius\",\"Severian Crispinus\",\"Menenius Papinius\",\"Gnaeus Sergius\",\"Varro Publius\",\"Numerius Tarquinius\",\"Domitius Vitalis\",\"Calventius Antonius\",\"Cosconius Hadrian\",\"Eborius Manius\",\"Oppius Calventius\",\"Tacitus Castorius\",\"Sentius Manius\",\"Fabius Genucius\",\"Baebius Cominius\",\"Ogulnius Albinus\",\"Tacitus Rabirius\",\"Sentius Catilius\",\"Aelius Novellius\",\"Marius Servilius\",\"Marcius Vincentius\",\"Papinius Lentulus\",\"Tacitus Pasidienus\",\"Cethegus Cincinnatus\",\"Caelian Caelius\",\"Granius Calventius\",\"Candidus Tiberius\",\"Ogulnius Caedicius\",\"Juvenalis Foslius\",\"Vincentius Lacerius\",\"Nigidius Cornificius\",\"Memmius Duronius\",\"Nasidienus Pettius\",\"Julius Caedicius\",\"Paelignus Cornutus\",\"Nigidius Nero\",\"Flavius Verus\",\"Juvenalis Publicius\",\"Fidius Potitius\",\"Pettius Numonius\",\"Naevius Matienus\",\"Saturninus Maximian\",\"Maxentius Ollius\",\"Geta Anatolius\",\"Verus Flavius\",\"Ravillius Martialis\",\"Caelian Nasennius\",\"Icilius Paelignus\",\"Cassio Balbus\",\"Sicinius Curtius\",\"Furius Ravillius\",\"Pedius Ravillius\",\"Flavius Sextus\",\"Juvenalis Minicius\",\"Marian Marian\",\"Junius Dacian\",\"Fundanus Sentius\",\"Pasidienus Matienus\",\"Sergius Tacitus\",\"Quinctilius Seccius\",\"Ovidius Verus\",\"Flaccus Quirinius\",\"Plautius Cominius\",\"Pupius Rabirius\",\"Cloelius Felix\",\"Coruncanius Afranius\",\"Petilius Sempronius\",\"Sulpicius Postumius\",\"Numitorius Pupius\",\"Volumnius Fannius\",\"Sempronius Candidus\",\"Ennius Julius\",\"Sergian Cato\",\"Festinius Maecenas\",\"Lollius Sestius\",\"Titus Mamilius\",\"Minicius Urbanus\",\"Pupius Blasius\",\"Marcus Gratian\",\"Potitius Tiberius\",\"Zeno Pius\",\"Lucretius Albinius\",\"Justus Opimius\",\"Verecundus Maximus\",\"Paternus Germanicus\",\"Blossius Cispius\",\"Gnaeus Cato\",\"Volumnius Valentinian\",\"Lucius Caedicius\",\"Quirinius Marcus\",\"Lartius Calventius\",\"Sextus Seccius\",\"Nazarius Nero\",\"Iuventius Vincentius\",\"Publius Palpellius\",\"Manius Afranius\",\"Sestius Flavius\",\"Cassius Gabinius\",\"Domitian Verecundus\",\"Hortensius Salinator\",\"Quirinius Secundinius\",\"Iulius Honorius\",\"Longinius Camillus\",\"Verus Suetonius\",\"Hostius Tarquinius\",\"Cyprian Varian\",\"Salvius Livius\",\"Lamponius Tarquinius\",\"Servilius Caelius\",\"Cloelius Cornelius\",\"Fabianus Laelius\",\"Maecenas Iuventius\",\"Persius Numerius\",\"Catius Flavius\",\"Terentius Potitius\",\"Titus Verus\",\"Urbanus Januarius\",\"Aquillius Orchius\",\"Statilius Egnatuleius\",\"Festus Sestius\",\"Mucius Manilius\",\"Bruttius Perperna\",\"Cicero Numicius\",\"Sentius Cornificius\",\"Magnentius Dacian\",\"Pettius Junius\",\"Catius Fundanus\",\"Didius Sidonius\",\"Cato Vitalis\",\"Sextus Ofilius\",\"Ninnius Fidenas\",\"Bonifacius Lacerius\",\"Nero Valentinian\",\"Salvian Numonius\",\"Catius Aelius\",\"Balbus Lartius\",\"Regulus Vitalis\",\"Pettius Octavenus\",\"Musonius Juvenalis\",\"Laecanius Calpurnius\",\"Zeno Didius\",\"Publicius Ogulnius\",\"Marius Memmius\",\"Cyprian Iuventius\",\"Marcius Blossius\",\"Paternus Mescinius\",\"Sergian Ennius\",\"Publius Candidus\",\"Gabinius Quintus\",\"Oppius Cilnius\",\"Sittius Vitus\",\"Laelius Flavius\",\"Marius Maximus\",\"Blaesus Honorius\",\"Felix Valerius\",\"Paulinus Horatius\",\"Draco Flavius\",\"Numonius Clodius\",\"Palpellius Laurentius\",\"Balbus Maecenas\",\"Ostorius Blasius\",\"Lucius Lartius\",\"Iuventius Memmius\",\"Sittius Nasidienus\",\"Januarius Caecilius\",\"Vitalis Donatus\",\"Saturninus Pasidienus\",\"Hilarianus Damasus\",\"Pomponius Drusus\",\"Didius Sestius\",\"Aulus Sedatius\",\"Junius Cicero\",\"Salvius Regulus\",\"Diocletian Messalla\",\"Nasidius Gaius\",\"Hirrus Decimus\",\"Lentulus Blasius\",\"Geminius Caelian\",\"Matinius Cicereius\",\"Appius Paternus\",\"Lacerius Gabinius\",\"Nasidienus Afranius\",\"Nasidienus Fabrinius\",\"Maxentius Plinius\",\"Germanicus Regulus\",\"Munatius Petilius\",\"Paternus Publius\",\"Domitius Cornificius\",\"Norbanus Placidus\",\"Appius Blaesus\",\"Sicinius Palpellius\",\"Laelius Probus\",\"Pilus Terentius\",\"Pasidienus Salvius\",\"Draco Fundanus\",\"Fulvius Sittius\",\"Cornelius Numonius\",\"Numitorius Ostorius\",\"Cispius Cicero\",\"Valerius Sergius\",\"Marcius Papinius\",\"Sestius Fulvius\",\"Quinctilius Appius\",\"Plinius Suetonius\",\"Gallus Secundus\",\"Urbanus Papinius\",\"Festinius Caecilius\",\"Primitivus Sergian\",\"Percennius Felix\",\"Sabucius Messalla\",\"Fulvius Cispius\",\"Terentius Lollius\",\"Catilius Afranius\",\"Opellius Fundanus\",\"Sempronius Curtius\",\"Secundus Martialis\",\"Aemilius Persius\",\"Ninnius Sergian\",\"Norbanus Furius\",\"Musonius Ofilius\",\"Belisarius Latinius\",\"Matinius Ogulnius\",\"Mamilius Blaesus\",\"Paulinus Tacitus\",\"Sallustius Verecundus\",\"Secundus Sedatius\",\"Cyprian Severus\",\"Pius Tarquinius\",\"Camillus Fulvius\",\"Icilius Iunius\",\"Decimus Cantilius\",\"Persius Galerius\",\"Tertius Paulinus\",\"Novellius Blossius\",\"Sicinius Cordius\",\"Marcellus Dacian\",\"Albinus Valentinian\",\"Nautius Pupius\",\"Paelignus Vincentius\",\"Sertorius Maximus\",\"Fannius Regulus\",\"Valentinian Tertius\",\"Duronius Livius\",\"Fulvius Dacian\",\"Severus Statilius\",\"Ninnius Vespasian\",\"Quirinius Regulus\",\"Sergian Faustus\",\"Maximus Fidius\",\"Aemilius Segulius\",\"Salvian Rufius\",\"Pettius Seccius\",\"Hadrian Modius\",\"Aelius Mescinius\",\"Betilienus Vincentius\",\"Cosconius Salvidienus\",\"Sabucius Orestius\",\"Caelius Urbanus\",\"Afranius Vincentius\",\"Calpurnius Sentius\",\"Aelius Sabucius\",\"Volumnius Vespasian\",\"Fidenas Pasidienus\",\"Numicius Cosconius\",\"Genucius Rullianus\",\"Tiberius Caninius\",\"Ingenuus Sallustius\",\"Stertinius Agrippa\",\"Publicius Iunius\",\"Florian Fidenas\",\"Pasidienus Ofilius\",\"Egnatius Justus\",\"Pupius Longinius\",\"Fundanus Justus\",\"Papinius Atilius\",\"Valentinian Caelian\",\"Manius Vitalis\",\"Caelius Quintus\",\"Laberius Egnatuleius\",\"Cocceius Verecundus\",\"Pius Cincinnatus\",\"Laurentius Considius\",\"Cincinnatus Decentius\",\"Salvian Gnaeus\",\"Novellius Curtius\",\"Rufrius Nerva\",\"Novellius Nigidius\",\"Gallus Blasius\",\"Secundinius Pacilius\",\"Florian Calpurnius\",\"Sestius Juvenalis\",\"Valens Cloelius\",\"Octavenus Lucius\",\"Novellius Oppius\",\"Pius Amulius\",\"Licinius Vitus\",\"Festus Maximian\",\"Messalla Cassio\",\"Iuventius Secundus\",\"Laetorius Opimius\",\"Herennius Tiberius\",\"Camillus Papirius\",\"Julius Postumius\",\"Terentius Tacitus\",\"Gaius Laronius\",\"Fabianus Placidus\",\"Vitus Balbus\",\"Sabucius Munatius\",\"Aurelianus Quintus\",\"Suetonius Varro\",\"Paternus Ingenuus\",\"Valentinian Fulvius\",\"Sornatius Ollius\",\"Modius Blossius\",\"Semellius Regulus\",\"Iulius Cantilius\",\"Primitivus Lucretius\",\"Granius Cordius\",\"Terentius Ennius\",\"Hortensius Vincentius\",\"Cornutus Mucius\",\"Hadrian Nasidius\",\"Caelian Vibius\",\"Balbus Coruncanius\",\"Anatolius Decentius\",\"Cosconius Ravillius\",\"Sornatius Caecilius\",\"Agrippa Blasius\",\"Maximian Sestius\",\"Laronius Papirius\",\"Titus Pontidius\",\"Regulus Catilius\",\"Agrippa Cornelius\",\"Caelian Florian\",\"Plautius Salinator\",\"Manius Ollius\",\"Silanus Sextius\",\"Segulius Ollius\",\"Domitian Caelius\",\"Maecenas Sittius\",\"Fabius Longinius\",\"Amulius Januarius\",\"Tacitus Porcius\",\"Lartius Longinius\",\"Messalla Flaccus\",\"Varro Cordius\",\"Diocletian Hortensius\",\"Camillus Atilius\",\"Maxentius Naevius\",\"Curtius Laurentius\",\"Livius Hortensius\",\"Gnaeus Semellius\",\"Paulinus Matinius\",\"Considius Novellius\",\"Seccius Pontidius\",\"Crispus Ofilius\",\"Octavius Fidenas\",\"Regulus Domitian\",\"Pupius Mallius\",\"Pasidienus Valentinian\",\"Pettius Minicius\",\"Gnaeus Modius\",\"Aurelianus Rullianus\",\"Menenius Priscus\",\"Papirius Vitus\",\"Belisarius Livius\",\"Furius Sextius\",\"Tarquinius Poppaedius\",\"Agrippa Crispus\",\"Manius Numonius\",\"Octavenus Lartius\",\"Urbanus Cornificius\",\"Betilienus Verus\",\"Murena Nazarius\",\"Valentinian Sempronius\",\"Manilius Latinius\",\"Belisarius Rufrius\",\"Marcius Caninius\",\"Ennius Sidonius\",\"Mucius Atilius\",\"Herennius Nasidius\",\"Eborius Caelian\",\"Orchius Paconius\",\"Longinius Placidus\",\"Pompeius Flavius\",\"Martialis Galerius\",\"Iunius Marcius\",\"Nonius Numonius\",\"Hilarianus Musonius\",\"Nasidius Martialis\",\"Vitalis Rutilius\",\"Mallius Palpellius\",\"Petreius Publicius\",\"Sittius Perperna\",\"Seneca Pedius\",\"Decentius Pupius\",\"Octavius Sepullius\",\"Laetorius Duronius\",\"Ovidius Sedatius\",\"Probus Laelius\",\"Caedicius Calpurnius\",\"Licinius Pilus\",\"Atilius Foslius\",\"Fulvius Tertius\",\"Acilius Lucretius\",\"Fannius Paulinus\",\"Atilius Suetonius\",\"Matienus Calpurnius\",\"Manilius Blossius\",\"Gratian Sulpicius\",\"Modius Bruttius\",\"Regulus Livius\",\"Quinctilius Herennius\",\"Celsus Caecilius\",\"Mescinius Cassio\",\"Numitorius Herennius\",\"Titus Fidius\",\"Sicinius Paulinus\",\"Sicinius Avidius\",\"Bruttius Baebius\",\"Silanus Caelian\",\"Gordian Papirius\",\"Cestius Sextus\",\"Calpurnius Drusus\",\"Perperna Fulcinia\",\"Urbanus Cicero\",\"Naevius Marcus\",\"Memmius Celsus\",\"Hirrus Novellius\",\"Fabius Palpellius\",\"Publicius Latinius\",\"Pilus Laurentius\",\"Fidenas Stertinius\",\"Hostilius Gnaeus\",\"Gordian Dacian\",\"Volumnius Seneca\",\"Drusus Galerius\",\"Ollius Seccius\",\"Aurelianus Festus\",\"Cassius Hostius\",\"Oppius Poppaedius\",\"Cispius Pasidienus\",\"Belisarius Vitus\",\"Segulius Nautius\",\"Porcius Germanicus\",\"Severus Fabianus\",\"Manius Quirinius\",\"Faustus Laelius\",\"Petilius Nerva\",\"Octavius Regulus\",\"Cestius Blaesus\",\"Juvenalis Cloelius\",\"Lentulus Iunius\",\"Papirius Aurelianus\",\"Calpurnius Rufinus\",\"Mamilius Gaius\",\"Longinius Felix\",\"Nero Hadrian\",\"Amulius Longinius\",\"Sextus Festus\",\"Balbinus Egnatius\",\"Junius Menenius\",\"Sallustius Magnentius\",\"Aemilius Numitorius\",\"Minicius Cornelius\",\"Norbanus Valerius\",\"Rullianus Urbanus\",\"Quirinius Seccius\",\"Fulcinia Egnatius\",\"Silanus Verus\",\"Poppaedius Blossius\",\"Sedatius Sidonius\",\"Maecenas Faustus\",\"Lartius Granius\",\"Scribonius Pinarus\",\"Antonius Statilius\",\"Albinus Livius\",\"Decentius Caelian\",\"Granius Castorius\",\"Betilienus Licinius\",\"Salvian Cominius\",\"Lucanus Papirius\",\"Acilius Acilius\",\"Pius Eutropius\",\"Pupius Poppaedius\",\"Suetonius Norbanus\",\"Tacitus Laelius\",\"Januarius Maximus\",\"Atilius Festus\",\"Diocletian Geminius\",\"Ennius Honorius\",\"Sextus Quintus\",\"Norbanus Pius\",\"Lucanus Hilarianus\",\"Ollius Galerius\",\"Nasidius Zeno\",\"Fidenas Decimus\",\"Fundanus Urbicus\",\"Domitian Tiberius\",\"Horatius Iuventius\",\"Decentius Gallus\",\"Manius Dacian\",\"Cilnius Secundinius\",\"Sestius Camillius\",\"Gratian Blasius\",\"Caelian Octavius\",\"Marcius Sabucius\",\"Norbanus Candidus\",\"Eutropius Belisarius\",\"Numonius Plautius\",\"Fidenas Segulius\",\"Claudius Manilius\",\"Burrienus Honorius\",\"Lentulus Gordian\",\"Fulvius Varian\",\"Magnentius Hirrus\",\"Decimus Cincinnatus\",\"Lollius Nero\",\"Herennius Catius\",\"Gracchus Caedicius\",\"Murena Lamponius\",\"Rutilius Nazarius\",\"Catius Galerius\",\"Valerius Cassio\",\"Paternus Afranius\",\"Duronius Sergian\",\"Acilius Seneca\",\"Cilnius Avidius\",\"Publicius Magnentius\",\"Lacerius Bonifacius\",\"Geminius Silvius\",\"Castorius Murena\",\"Cassianus Ostorius\",\"Gratian Octavius\",\"Gratian Lucius\",\"Nigidius Granius\",\"Maecilius Memmius\",\"Sallustius Quirinius\",\"Festinius Tarquinius\",\"Urbanus Matinius\",\"Geta Laelius\",\"Fabius Cassianus\",\"Fabrinius Sicinius\",\"Maximian Granius\",\"Cocceius Maximus\",\"Geta Fidius\",\"Egnatius Considius\",\"Lucius Geminius\",\"Probus Bonifacius\",\"Silanus Dacian\",\"Blaesus Sedatius\",\"Albinius Granius\",\"Fidenas Papinius\",\"Pedius Caninius\",\"Verus Rebilus\",\"Lucanus Brennus\",\"Camillius Hordeonius\",\"Numitorius Catius\",\"Opimius Vincentius\",\"Caelian Felix\",\"Vibius Severus\",\"Lollius Crispinus\",\"Fidius Egnatuleius\",\"Foslius Ennius\",\"Ollius Perperna\",\"Quintus Ofilius\",\"Donatus Murena\",\"Cinna Gordian\",\"Probus Marcus\",\"Cosconius Hordeonius\",\"Cinna Nerva\",\"Cyprian Paconius\",\"Rebilus Bruttius\",\"Servilius Volumnius\",\"Iunius Cassius\",\"Lentulus Sempronius\",\"Semellius Cantilius\",\"Caesennius Musonius\",\"Octavenus Agrippa\",\"Perperna Pupius\",\"Donatus Manilius\",\"Urbicus Sepullius\",\"Honorius Donatus\",\"Marian Florian\",\"Licinius Publicius\",\"Laecanius Musonius\",\"Tarquinius Numerius\",\"Regulus Constantinus\",\"Rabirius Cosconius\",\"Iuventius Opimius\",\"Sempronius Palpellius\",\"Celsus Marcellus\",\"Poppaedius Florianus\",\"Blasius Bonifacius\",\"Agrippa Sempronius\",\"Mallius Novellius\",\"Sempronius Gallus\",\"Ostorius Opellius\",\"Numerius Fidenas\",\"Lucanus Clodius\",\"Damasus Gaius\",\"Albinius Caesennius\",\"Vitus Magnentius\",\"Silanus Sedatius\",\"Laberius Pacilius\",\"Pinarus Eborius\",\"Decimus Suetonius\",\"Bonifacius Laronius\",\"Rebilus Octavius\",\"Hostius Sittius\",\"Domitian Camillus\",\"Aurelianus Aurelianus\",\"Papirius Matienus\",\"Constantinus Probus\",\"Nigidius Juvenalis\",\"Belisarius Fidenas\",\"Lucretius Quinctilius\",\"Lacerius Vitus\",\"Donatus Palpellius\",\"Coruncanius Longinius\",\"Cyprian Paulinus\",\"Betilienus Silius\",\"Cicereius Sestius\",\"Fannius Orestius\",\"Orbilius Catius\",\"Seneca Aulus\",\"Quintilianus Varro\",\"Laberius Nasidienus\",\"Tacitus Flaccus\",\"Crispus Ollius\",\"Caelian Opellius\",\"Candidus Lucanus\",\"Geminius Maximus\",\"Cassius Eutropius\",\"Servius Agrippa\",\"Vespasian Flavius\",\"Didius Manilius\",\"Iunius Lamponius\",\"Hortensius Festinius\",\"Belisarius Blaesus\",\"Gaius Junius\",\"Considius Pasidienus\",\"Damasus Egnatuleius\",\"Nasidius Hostilius\",\"Januarius Damasus\",\"Sextius Genucius\",\"Maecenas Hadrian\",\"Decentius Publicius\",\"Opellius Hortensius\",\"Norbanus Constantinus\",\"Nonius Varro\",\"Papinius Duronius\",\"Lollius Naevius\",\"Cornificius Varro\",\"Afranius Horatius\",\"Domitius Sertorius\",\"Manius Magnentius\",\"Rabirius Castorius\",\"Salvius Foslius\",\"Festus Modius\",\"Hostilius Caelian\",\"Saturninus Statilius\",\"Maxentius Gratian\",\"Urbicus Nasidius\",\"Julius Paconius\",\"Geta Balbus\",\"Verecundus Bonifacius\",\"Cethegus Clodius\",\"Numonius Hirrus\",\"Lamponius Hordeonius\",\"Blossius Lucretius\",\"Cato Probus\",\"Numonius Zeno\",\"Balbus Nonius\",\"Clodius Nasidienus\",\"Felix Agrippa\",\"Laronius Caelian\",\"Marian Fannius\",\"Primitivus Nigidius\",\"Postumius Salinator\",\"Ofilius Paconius\",\"Pomponius Gabinius\",\"Agrippa Nasidius\",\"Sextius Flaccus\",\"Lamponius Fidenas\",\"Justus Oppius\",\"Matinius Sextius\",\"Vitus Galerius\",\"Cincinnatus Icilius\",\"Priscus Caelian\",\"Seccius Considius\",\"Numonius Ogulnius\",\"Seneca Sornatius\",\"Ostorius Fulcinia\",\"Julius Julius\",\"Cornificius Lollius\",\"Cicero Cocceius\",\"Minicius Nasidienus\",\"Vibius Cantilius\",\"Galerius Ogulnius\",\"Cinna Nazarius\",\"Nonius Ofilius\",\"Avidius Plautius\",\"Sentius Fabius\",\"Cicereius Semellius\",\"Urbanus Musonius\",\"Blasius Laetorius\",\"Icilius Longinius\",\"Considius Potitius\",\"Caedicius Porcius\",\"Sulpicius Donatus\",\"Spurius Cassianus\",\"Salvian Drusus\",\"Salvius Pilus\",\"Aquillius Sulpicius\",\"Cominius Albinus\",\"Catius Paternus\",\"Modius Aelius\",\"Salvidienus Sestius\",\"Lartius Sidonius\",\"Lucanus Sestius\",\"Anatolius Egnatuleius\",\"Terentius Verecundus\",\"Statilius Blaesus\",\"Didius Mescinius\",\"Duronius Fulcinia\",\"Genucius Lentulus\",\"Diocletian Lentulus\",\"Secundus Cato\",\"Priscus Maxentius\",\"Hostius Tertius\",\"Quintilianus Rabirius\",\"Rufinus Paulinus\",\"Rabirius Secundus\",\"Brennus Duronius\",\"Nonius Sallustius\",\"Mescinius Rufinus\",\"Mescinius Plautius\",\"Faustus Ravillius\",\"Numitorius Aemilius\",\"Pinarus Silius\",\"Vitus Egnatuleius\",\"Statilius Mamilius\",\"Agrippa Opellius\",\"Orbilius Manilius\",\"Germanicus Felix\",\"Varian Horatius\",\"Ingenuus Furius\",\"Regulus Opimius\",\"Genucius Decimus\",\"Probus Caecilius\",\"Octavenus Balbinus\",\"Longinius Salvidienus\",\"Livius Camillius\",\"Ingenuus Numicius\",\"Norbanus Rullianus\",\"Blaesus Avidius\",\"Caelius Calventius\",\"Salvius Licinius\",\"Caecilius Fidenas\",\"Iunius Modius\",\"Quirinius Draco\",\"Appius Albinus\",\"Cato Sepullius\",\"Fabrinius Aulus\",\"Iulius Didius\",\"Cassio Fabrinius\",\"Naevius Lacerius\",\"Burrienus Salinator\",\"Probus Catius\",\"Tertius Festus\",\"Maximian Iunius\",\"Paulinus Lartius\",\"Camillus Camillius\",\"Rufius Pacilius\",\"Castorius Duronius\",\"Verus Calventius\",\"Petreius Nazarius\",\"Galerius Hilarianus\",\"Geminius Cethegus\",\"Betilienus Draco\",\"Fidenas Maximus\",\"Candidus Cicero\",\"Lucretius Honorius\",\"Gracchus Cornificius\",\"Belisarius Postumius\",\"Tacitus Pomponius\",\"Aelius Galerius\",\"Valentinian Celsus\",\"Laelius Fabianus\",\"Probus Blossius\",\"Publicius Opimius\",\"Festus Iunius\",\"Cornelius Galerius\",\"Plinius Appius\",\"Sertorius Geta\",\"Rabirius Herennius\",\"Cassio Mucius\",\"Lucanus Hostilius\",\"Ostorius Cassianus\",\"Munatius Papirius\",\"Cornutus Persius\",\"Mamilius Perperna\",\"Granius Florianus\",\"Germanicus Salvian\",\"Ollius Afranius\",\"Baebius Pacilius\",\"Vespasian Sicinius\",\"Hilarianus Tiberius\",\"Numerius Caelius\",\"Cestius Titus\",\"Rebilus Pilus\",\"Galerius Primitivus\",\"Spurius Salinator\",\"Domitius Musonius\",\"Sedatius Licinius\",\"Modius Geminius\",\"Clodius Cassius\",\"Porcius Lucretius\",\"Pedius Rullianus\",\"Messalla Cocceius\",\"Rufrius Cyprian\",\"Laelius Maecenas\",\"Scribonius Cicero\",\"Pupius Sergian\",\"Blaesus Latinius\",\"Crispus Blasius\",\"Rufinus Plinius\",\"Memmius Sepullius\",\"Pomponius Vincentius\",\"Cyprian Salvian\",\"Valens Suetonius\",\"Aurelianus Amulius\",\"Matienus Sallustius\",\"Spurius Mescinius\",\"Atilius Paternus\",\"Sicinius Fabius\",\"Silvius Blossius\",\"Messalla Verus\",\"Pacilius Caedicius\",\"Scribonius Tacitus\",\"Marcellus Quirinius\",\"Herennius Publius\",\"Fulvius Pupius\",\"Pompeius Marcellus\",\"Sentius Dacian\",\"Fabianus Cicero\",\"Amulius Probus\",\"Nasidienus Papinius\",\"Quintus Magnentius\",\"Valens Rufrius\",\"Sedatius Donatus\",\"Crispinus Cestius\",\"Pompeius Quirinius\",\"Fabius Vibius\",\"Valens Caelius\",\"Blasius Fulcinia\",\"Licinius Gracchus\",\"Caelius Dacian\",\"Valerius Sextus\",\"Damasus Sextius\",\"Paelignus Varian\",\"Geminius Menenius\",\"Papirius Vitalis\",\"Nero Ennius\",\"Servilius Persius\",\"Appius Silanus\",\"Duronius Decentius\",\"Sidonius Fulvius\",\"Saturninus Felix\",\"Nasennius Zeno\",\"Nero Maximian\",\"Matienus Perperna\",\"Nasennius Gnaeus\",\"Hirrus Lucretius\",\"Ingenuus Latinius\",\"Bruttius Acilius\",\"Ostorius Lartius\",\"Fabius Aulus\",\"Eborius Statilius\",\"Marcius Duronius\",\"Probus Oppius\",\"Damasus Murena\",\"Geta Icilius\",\"Terentius Cosconius\",\"Gratian Horatius\",\"Burrienus Ovidius\",\"Hortensius Antonius\",\"Manilius Horatius\",\"Iuventius Persius\",\"Cominius Aquillius\",\"Mescinius Hirrus\",\"Marian Bruttius\",\"Aurelianus Agrippa\",\"Silanus Maxentius\",\"Cispius Sidonius\",\"Icilius Draco\",\"Quintus Pomponius\",\"Priscus Scribonius\",\"Hordeonius Caedicius\",\"Segulius Granius\",\"Laronius Salinator\",\"Cato Ingenuus\",\"Claudius Caecilius\",\"Lentulus Constantinus\",\"Sabucius Anatolius\",\"Salvian Licinius\",\"Salvius Tiberius\",\"Livius Fidenas\",\"Plautius Paulinus\",\"Decimus Sepullius\",\"Salvius Catius\",\"Hostius Sestius\",\"Avidius Semellius\",\"Sestius Lucretius\",\"Domitius Maximian\",\"Salvius Cassianus\",\"Orbilius Ostorius\",\"Appius Ennius\",\"Egnatuleius Egnatius\",\"Afranius Zeno\",\"Blasius Brennus\",\"Camillus Anatolius\",\"Vibius Novellius\",\"Drusus Justus\",\"Cethegus Damasus\",\"Aquillius Rufius\",\"Galerius Laronius\",\"Lamponius Ravillius\",\"Salvidienus Anatolius\",\"Orbilius Furius\",\"Maximian Gnaeus\",\"Salvian Laurentius\",\"Belisarius Maximian\",\"Numicius Memmius\",\"Appius Geta\",\"Silanus Cestius\",\"Caninius Nasennius\",\"Sextus Persius\",\"Fidius Caelius\",\"Stertinius Gabinius\",\"Manilius Volumnius\",\"Domitian Hortensius\",\"Sidonius Afranius\",\"Junius Scribonius\",\"Scribonius Claudius\",\"Paelignus Secundinius\",\"Constantinus Quinctilius\",\"Publicius Cicero\",\"Cassius Sidonius\",\"Menenius Silanus\",\"Sergian Tacitus\",\"Rullianus Domitius\",\"Sempronius Cassianus\",\"Geta Pinarus\",\"Fidius Probus\",\"Sornatius Calpurnius\",\"Valens Marcus\",\"Cloelius Albinius\",\"Geminius Laetorius\",\"Postumius Rufinus\",\"Maecilius Mamilius\",\"Potitius Numicius\",\"Caecilius Geminius\",\"Novellius Sergian\",\"Cominius Faustus\",\"Paelignus Caesennius\",\"Egnatius Cispius\",\"Opellius Rullianus\",\"Maecilius Caecilius\",\"Servilius Balbus\",\"Cassio Considius\",\"Silanus Vitus\",\"Fulcinia Paternus\",\"Didius Festus\",\"Catilius Caelius\",\"Numonius Diocletian\",\"Germanicus Balbus\",\"Paulinus Fabrinius\",\"Minicius Considius\",\"Icilius Gratian\",\"Naevius Sentius\",\"Icilius Plautius\",\"Petreius Vincentius\",\"Ostorius Cornificius\",\"Gratian Minicius\",\"Lacerius Verecundus\",\"Geminius Aurelianus\",\"Rufrius Licinius\",\"Cornificius Papinius\",\"Pilus Sertorius\",\"Mallius Geminius\",\"Musonius Junius\",\"Celsus Memmius\",\"Icilius Volumnius\",\"Octavius Sicinius\",\"Catilius Fabianus\",\"Herennius Verecundus\",\"Brennus Salvius\",\"Maximus Cordius\",\"Crispus Lamponius\",\"Gnaeus Verus\",\"Sidonius Urbanus\",\"Flavius Severus\",\"Magnentius Pinarus\",\"Fundanus Sallustius\",\"Salvius Lentulus\",\"Genucius Plinius\",\"Julius Marius\",\"Priscus Quintilianus\",\"Eborius Felix\",\"Catius Sextus\",\"Sicinius Hordeonius\",\"Gracchus Placidus\",\"Servilius Verecundus\",\"Vitus Paelignus\",\"Junius Pompeius\",\"Munatius Urbanus\",\"Ninnius Afranius\",\"Martialis Numitorius\",\"Seccius Marian\",\"Julius Fidius\",\"Opellius Munatius\",\"Cassio Marcellus\",\"Bonifacius Pinarus\",\"Pettius Opimius\",\"Pompeius Palpellius\",\"Mallius Gallus\",\"Cassio Opimius\",\"Donatus Sentius\",\"Egnatuleius Minicius\",\"Furius Cantilius\",\"Vincentius Rutilius\",\"Herennius Didius\",\"Pinarus Antonius\",\"Ravillius Constantinus\",\"Galerius Stertinius\",\"Catilius Sulpicius\",\"Curtius Sestius\",\"Suetonius Domitian\",\"Maxentius Sextus\",\"Lartius Silanus\",\"Oppius Bonifacius\",\"Cinna Pontidius\",\"Numerius Ovidius\",\"Rullianus Quirinius\",\"Gabinius Laelius\",\"Castorius Camillus\",\"Vibius Cassianus\",\"Fulcinia Lartius\",\"Caedicius Menenius\",\"Papirius Pompeius\",\"Fulvius Potitius\",\"Cordius Honorius\",\"Cornelius Verus\",\"Publius Papirius\",\"Severian Cassio\",\"Opimius Nasennius\",\"Galerius Ravillius\",\"Hordeonius Catilius\",\"Nazarius Nasennius\",\"Musonius Cassianus\",\"Mallius Didius\",\"Laecanius Cicero\",\"Cinna Marian\",\"Pedius Regulus\",\"Florian Urbicus\",\"Salvidienus Rabirius\",\"Faustus Celsus\",\"Orchius Cornutus\",\"Orestius Valentinian\",\"Minicius Gaius\",\"Magnentius Bonifacius\",\"Lartius Sertorius\",\"Caelian Salinator\",\"Tarquinius Licinius\",\"Sestius Geminius\",\"Hilarianus Severian\",\"Pinarus Mamilius\",\"Ostorius Fabius\",\"Gratian Genucius\",\"Hadrian Iulius\",\"Herennius Albinius\",\"Maximian Balbus\",\"Lamponius Menenius\",\"Pacilius Avidius\",\"Menenius Blaesus\",\"Vespasian Primitivus\",\"Silanus Verecundus\",\"Matinius Betilienus\",\"Cinna Icilius\",\"Lollius Laelius\",\"Silanus Salvidienus\",\"Perperna Zeno\",\"Opellius Dacian\",\"Sittius Magnentius\",\"Lacerius Rabirius\",\"Caedicius Varian\",\"Rabirius Fidius\",\"Sextus Lucanus\",\"Camillius Germanicus\",\"Domitian Aquillius\",\"Maximus Caecilius\",\"Numonius Fidenas\",\"Sergius Segulius\",\"Lartius Ovidius\",\"Oppius Catilius\",\"Blossius Gratian\",\"Lamponius Afranius\",\"Bonifacius Maximian\",\"Servilius Nasidienus\",\"Nasennius Julius\",\"Avidius Bonifacius\",\"Quinctilius Ingenuus\",\"Blossius Festus\",\"Licinius Secundinius\",\"Caelian Calpurnius\",\"Didius Rebilus\",\"Valentinian Honorius\",\"Semellius Numerius\",\"Cornutus Laecanius\",\"Calpurnius Fidenas\",\"Marius Martialis\",\"Bonifacius Tacitus\",\"Julius Maximus\",\"Agrippa Fabius\",\"Silanus Hostius\",\"Atilius Felix\",\"Sertorius Cassio\",\"Claudius Blossius\",\"Lollius Cilnius\",\"Magnentius Pettius\",\"Didius Laberius\",\"Hortensius Claudius\",\"Claudius Rullianus\",\"Gabinius Candidus\",\"Genucius Fannius\",\"Blossius Cato\",\"Pompeius Orchius\",\"Salinator Mallius\",\"Antonius Salinator\",\"Crispinus Iulius\",\"Silvius Pasidienus\",\"Cato Tarquinius\",\"Clodius Plinius\",\"Pilus Fabianus\",\"Ovidius Geminius\",\"Manius Tiberius\",\"Perperna Gabinius\",\"Drusus Nonius\",\"Matienus Diocletian\",\"Afranius Agrippa\",\"Blaesus Sicinius\",\"Festus Geminius\",\"Numerius Hostilius\",\"Zeno Gracchus\",\"Nero Junius\",\"Servius Laecanius\",\"Oppius Valerius\",\"Opellius Eutropius\",\"Quinctilius Cethegus\",\"Vitalis Acilius\",\"Postumius Blasius\",\"Sepullius Tertius\",\"Vitalis Egnatius\",\"Cassio Cethegus\",\"Rutilius Camillus\",\"Pius Publicius\",\"Messalla Geminius\",\"Caelian Pettius\",\"Sextus Dacian\",\"Licinius Silanus\",\"Constantinus Nazarius\",\"Hortensius Castorius\",\"Hordeonius Cordius\",\"Sestius Sidonius\",\"Vitalis Betilienus\",\"Publicius Appius\",\"Cassio Agrippa\",\"Maximian Cocceius\",\"Pinarus Minicius\",\"Marian Marcus\",\"Sextius Julius\",\"Balbinus Publius\",\"Lucanus Cyprian\",\"Caelius Cassio\",\"Licinius Sittius\",\"Quinctilius Quintilianus\",\"Diocletian Cosconius\",\"Matinius Florian\",\"Diocletian Verecundus\",\"Cordius Belisarius\",\"Fulcinia Clodius\",\"Decimus Icilius\",\"Postumius Fundanus\",\"Vitus Donatus\",\"Pomponius Tarquinius\",\"Blasius Albinus\",\"Marcius Tarquinius\",\"Latinius Gnaeus\",\"Nonius Dacian\",\"Paelignus Clodius\",\"Ostorius Cyprian\",\"Urbanus Gabinius\",\"Maximian Magnentius\",\"Verecundus Albinius\",\"Ollius Marcellus\",\"Perperna Vibius\",\"Matienus Sidonius\",\"Valentinian Scribonius\",\"Maximus Rabirius\",\"Seccius Gnaeus\",\"Urbanus Lucius\",\"Candidus Fannius\",\"Donatus Sempronius\",\"Silanus Amulius\",\"Mescinius Fannius\",\"Gratian Pacilius\",\"Nonius Honorius\",\"Appius Nonius\",\"Sornatius Rebilus\",\"Salvian Hilarianus\",\"Octavenus Quinctilius\",\"Mescinius Cornutus\",\"Cornutus Cato\",\"Valentinian Belisarius\",\"Publicius Paternus\",\"Festus Januarius\",\"Ninnius Marian\",\"Segulius Publicius\",\"Donatus Paconius\",\"Cordius Sallustius\",\"Plinius Florianus\",\"Sornatius Pontidius\",\"Potitius Caedicius\",\"Seccius Quirinius\",\"Volumnius Sittius\",\"Octavenus Acilius\",\"Laberius Balbinus\",\"Valerius Domitian\",\"Ogulnius Cordius\",\"Albinius Sabucius\",\"Cato Terentius\",\"Secundus Valerius\",\"Rufrius Gnaeus\",\"Amulius Pinarus\",\"Seneca Secundinius\",\"Modius Statilius\",\"Avidius Ovidius\",\"Decentius Tertius\",\"Gallus Fidius\",\"Caedicius Cassianus\",\"Petilius Rufrius\",\"Cato Cordius\",\"Laecanius Numicius\",\"Ollius Antonius\",\"Lucretius Salvidienus\",\"Calpurnius Livius\",\"Flaccus Gnaeus\",\"Honorius Florian\",\"Cominius Antonius\",\"Hilarianus Numitorius\",\"Plautius Cordius\",\"Cilnius Laberius\",\"Postumius Florianus\",\"Zeno Nero\",\"Modius Porcius\",\"Sempronius Honorius\",\"Caecilius Draco\",\"Crispinus Sidonius\",\"Saturninus Cosconius\",\"Cominius Sentius\",\"Rufrius Eutropius\",\"Secundus Valentinian\",\"Cornelius Pilus\",\"Cato Caecilius\",\"Rabirius Sentius\",\"Camillius Pinarus\",\"Menenius Rufinus\",\"Tacitus Florianus\",\"Sergius Papinius\",\"Geta Ingenuus\",\"Volumnius Marius\",\"Papinius Maecenas\",\"Caninius Publicius\",\"Marcellus Ninnius\",\"Iuventius Camillius\",\"Flaccus Iunius\",\"Magnentius Maecenas\",\"Lucanus Sentius\",\"Paconius Balbinus\",\"Appius Granius\",\"Atilius Balbus\",\"Flaccus Baebius\",\"Nasidienus Nazarius\",\"Seneca Calventius\",\"Salvian Cyprian\",\"Ogulnius Flavius\",\"Cicero Cominius\",\"Aemilius Lucius\",\"Burrienus Mallius\",\"Draco Musonius\",\"Celsus Sergius\",\"Fundanus Ravillius\",\"Petilius Petreius\",\"Seccius Horatius\",\"Nero Silvius\",\"Donatus Atilius\",\"Statilius Rufinus\",\"Zeno Cassio\",\"Calventius Caedicius\",\"Quinctilius Longinius\",\"Gaius Rullianus\",\"Naevius Juvenalis\",\"Nasidienus Sicinius\",\"Silanus Celsus\",\"Amulius Iuventius\",\"Statilius Fundanus\",\"Perperna Felix\",\"Fabianus Julius\",\"Cestius Scribonius\",\"Maximus Foslius\",\"Candidus Germanicus\",\"Orestius Catius\",\"Placidus Aquillius\",\"Sidonius Novellius\",\"Plinius Sittius\",\"Valens Duronius\",\"Domitius Urbanus\",\"Maecenas Tarquinius\",\"Curtius Magnentius\",\"Ovidius Lucius\",\"Norbanus Claudius\",\"Festus Blossius\",\"Servilius Titus\",\"Sestius Cyprian\",\"Hortensius Sulpicius\",\"Iulius Numonius\",\"Sextius Aulus\",\"Balbinus Caedicius\",\"Percennius Nasennius\",\"Sittius Domitius\",\"Cornelius Vitalis\",\"Urbanus Atilius\",\"Fundanus Pupius\",\"Primitivus Sidonius\",\"Damasus Cassius\",\"Sergian Verus\",\"Calpurnius Florianus\",\"Cosconius Laberius\",\"Antonius Egnatuleius\",\"Junius Herennius\",\"Hordeonius Sallustius\",\"Gaius Latinius\",\"Gracchus Messalla\",\"Octavenus Percennius\",\"Cordius Calventius\",\"Vincentius Rufinus\",\"Caecilius Blaesus\",\"Florian Ollius\",\"Pompeius Numonius\",\"Secundinius Vibius\",\"Camillius Petreius\",\"Poppaedius Cassianus\",\"Catius Cornificius\",\"Marius Seccius\",\"Iulius Cethegus\",\"Valentinian Lamponius\",\"Ovidius Atilius\",\"Silius Opimius\",\"Lartius Draco\",\"Gabinius Calventius\",\"Sertorius Longinius\",\"Lacerius Ninnius\",\"Papirius Hortensius\",\"Maecilius Probus\",\"Fidenas Cosconius\",\"Lollius Iulius\",\"Probus Varro\",\"Galerius Sicinius\",\"Fabianus Germanicus\",\"Orbilius Baebius\",\"Pius Vibius\",\"Sidonius Seccius\",\"Novellius Cassianus\",\"Cicero Salvius\",\"Urbanus Hortensius\",\"Porcius Ogulnius\",\"Calpurnius Octavius\",\"Belisarius Ogulnius\",\"Nazarius Pasidienus\",\"Rebilus Granius\",\"Tacitus Cassius\",\"Minicius Juvenalis\",\"Sallustius Laurentius\",\"Martialis Domitius\",\"Cassianus Nasidius\",\"Maecenas Salinator\",\"Ennius Egnatuleius\",\"Cocceius Palpellius\",\"Pasidienus Probus\",\"Furius Sentius\",\"Munatius Donatus\",\"Vincentius Duronius\",\"Calpurnius Pettius\",\"Cassio Numitorius\",\"Placidus Flaccus\",\"Sidonius Didius\",\"Hortensius Manilius\",\"Iulius Volumnius\",\"Blaesus Egnatius\",\"Pontidius Quintilianus\",\"Orestius Blossius\",\"Severian Palpellius\",\"Porcius Pupius\",\"Fabianus Cocceius\",\"Petreius Nasidienus\",\"Januarius Martialis\",\"Volumnius Publius\",\"Memmius Flavius\",\"Messalla Mucius\",\"Iulius Octavenus\",\"Sidonius Volumnius\",\"Secundus Flaccus\",\"Manilius Nerva\",\"Marcellus Placidus\",\"Caedicius Maxentius\",\"Quinctilius Laetorius\",\"Pinarus Petilius\",\"Urbanus Juvenalis\",\"Laetorius Pettius\",\"Vibius Vibius\",\"Crispinus Servilius\",\"Ovidius Orestius\",\"Perperna Furius\",\"Cantilius Palpellius\",\"Volumnius Mamilius\",\"Sestius Iulius\",\"Aemilius Lartius\",\"Nerva Balbus\",\"Foslius Oppius\",\"Geminius Blaesus\",\"Albinius Papinius\",\"Ravillius Numonius\",\"Lucanus Orchius\",\"Felix Anatolius\",\"Damasus Longinius\",\"Cincinnatus Agrippa\",\"Modius Aurelianus\",\"Regulus Varian\",\"Manilius Germanicus\",\"Lacerius Rutilius\",\"Salinator Manius\",\"Publicius Cyprian\",\"Sextius Considius\",\"Secundus Placidus\",\"Percennius Cincinnatus\",\"Servius Marian\",\"Silvius Mallius\",\"Novellius Fabius\",\"Octavenus Mucius\",\"Bonifacius Egnatuleius\",\"Murena Gallus\",\"Gnaeus Paelignus\",\"Cosconius Silvius\",\"Curtius Titus\",\"Octavenus Manilius\",\"Licinius Porcius\",\"Aelius Candidus\",\"Postumius Decentius\",\"Nasidius Menenius\",\"Clodius Petreius\",\"Lacerius Celsus\",\"Numicius Betilienus\",\"Mescinius Hordeonius\",\"Latinius Silanus\",\"Pius Maxentius\",\"Aelius Salvius\",\"Rufrius Salinator\",\"Poppaedius Juvenalis\",\"Probus Furius\",\"Opellius Antonius\",\"Lartius Eborius\",\"Papinius Pomponius\",\"Faustus Sepullius\",\"Caedicius Fabianus\",\"Eborius Hadrian\",\"Camillius Norbanus\",\"Gaius Vincentius\",\"Antonius Drusus\",\"Cinna Caninius\",\"Albinus Rufius\",\"Sedatius Pedius\",\"Verus Ofilius\",\"Furius Marcius\",\"Urbicus Manius\",\"Rufius Martialis\",\"Juvenalis Cordius\",\"Musonius Cornelius\",\"Catilius Hirrus\",\"Rabirius Gallus\",\"Marcus Cornificius\",\"Fabianus Albinus\",\"Hostius Bonifacius\",\"Foslius Zeno\",\"Probus Nigidius\",\"Matienus Ennius\",\"Festus Nasennius\",\"Poppaedius Anatolius\",\"Hilarianus Modius\",\"Draco Terentius\",\"Rufinus Curtius\",\"Musonius Festinius\",\"Memmius Nasidius\",\"Hordeonius Pacilius\",\"Ravillius Severian\",\"Fannius Hordeonius\",\"Spurius Segulius\",\"Marian Cato\",\"Sergius Silius\",\"Pedius Icilius\",\"Lucanus Balbus\",\"Ogulnius Petilius\",\"Fannius Egnatius\",\"Laecanius Valens\",\"Terentius Ravillius\",\"Caecilius Varro\",\"Sallustius Hilarianus\",\"Pedius Vibius\",\"Oppius Martialis\",\"Quirinius Fabius\",\"Lamponius Nautius\",\"Fulvius Latinius\",\"Secundinius Numitorius\",\"Albinius Cestius\",\"Ollius Coruncanius\",\"Sulpicius Caesennius\",\"Urbanus Primitivus\",\"Marian Vincentius\",\"Orbilius Novellius\",\"Amulius Sergian\",\"Draco Cincinnatus\",\"Nonius Verus\",\"Sornatius Gordian\",\"Nero Nasidius\",\"Laurentius Flavius\",\"Semellius Florianus\",\"Hortensius Opimius\",\"Lucius Considius\",\"Iulius Gordian\",\"Gabinius Spurius\",\"Lucanus Fulcinia\",\"Ollius Geta\",\"Sertorius Pettius\",\"Tiberius Munatius\",\"Sextus Verecundus\",\"Ogulnius Julius\",\"Brennus Tacitus\",\"Calpurnius Diocletian\",\"Avidius Modius\",\"Mallius Nonius\",\"Octavenus Volumnius\",\"Aemilius Livius\",\"Cicero Vitus\",\"Januarius Pacilius\",\"Salvian Oppius\",\"Marcus Belisarius\",\"Festinius Galerius\",\"Icilius Flaccus\",\"Octavenus Fundanus\",\"Hostius Salinator\",\"Pontidius Iunius\",\"Statilius Agrippa\",\"Opimius Brennus\",\"Lucanus Iunius\",\"Terentius Pilus\",\"Paulinus Anatolius\",\"Tertius Gallus\",\"Ostorius Laberius\",\"Potitius Florian\",\"Paconius Cato\",\"Opimius Octavius\",\"Opellius Ollius\",\"Bruttius Numerius\",\"Furius Musonius\",\"Antonius Hilarianus\",\"Rufius Messalla\",\"Plautius Opellius\",\"Bruttius Pettius\",\"Cispius Laecanius\",\"Nerva Afranius\",\"Lucanus Albinius\",\"Maecilius Geta\",\"Florianus Octavenus\",\"Tiberius Maecilius\",\"Blaesus Cornutus\",\"Papirius Lucretius\",\"Ofilius Percennius\",\"Hordeonius Octavius\",\"Nero Severus\",\"Murena Martialis\",\"Bonifacius Mucius\",\"Lucretius Hordeonius\",\"Gratian Laetorius\",\"Seneca Lucretius\",\"Albinus Hilarianus\",\"Fabianus Lucius\",\"Papinius Suetonius\",\"Drusus Menenius\",\"Fidius Vincentius\",\"Rufrius Belisarius\",\"Sidonius Hadrian\",\"Egnatius Blasius\",\"Licinius Sergius\",\"Afranius Cinna\",\"Lentulus Cethegus\",\"Cinna Iunius\",\"Calventius Rufius\",\"Orestius Domitius\",\"Plinius Licinius\",\"Suetonius Damasus\",\"Mescinius Brennus\",\"Servilius Honorius\",\"Domitius Sepullius\",\"Sergius Cispius\",\"Aelius Acilius\",\"Pettius Festinius\",\"Egnatius Varro\",\"Draco Probus\",\"Postumius Lollius\",\"Nasidius Orestius\",\"Donatus Tacitus\",\"Balbus Fabrinius\",\"Minicius Decimus\",\"Cloelius Rufrius\",\"Caelius Iunius\",\"Fidenas Marcus\",\"Baebius Caninius\",\"Geminius Ofilius\",\"Octavius Fidius\",\"Varian Granius\",\"Curtius Valens\",\"Sulpicius Numitorius\",\"Gordian Cocceius\",\"Silius Lollius\",\"Sentius Pasidienus\",\"Laronius Matienus\",\"Salvius Pedius\",\"Segulius Salvidienus\",\"Quintilianus Opellius\",\"Afranius Cassianus\",\"Genucius Germanicus\",\"Semellius Murena\",\"Dacian Vitalis\",\"Cyprian Rufrius\",\"Pacilius Nazarius\",\"Balbus Marian\",\"Blossius Gallus\",\"Matinius Paternus\",\"Nasidienus Hostius\",\"Hadrian Novellius\",\"Ostorius Donatus\",\"Plinius Mucius\",\"Festus Furius\",\"Herennius Nerva\",\"Cosconius Eutropius\",\"Fabius Sergius\",\"Camillius Ingenuus\",\"Mescinius Lentulus\",\"Primitivus Semellius\",\"Silvius Egnatuleius\",\"Camillus Priscus\",\"Novellius Laecanius\",\"Hostius Albinius\",\"Porcius Cinna\",\"Januarius Cocceius\",\"Terentius Quirinius\",\"Hostius Cornelius\",\"Albinus Junius\",\"Cinna Avidius\",\"Placidus Cyprian\",\"Zeno Persius\",\"Burrienus Sepullius\",\"Sergius Aulus\",\"Calventius Fabrinius\",\"Sallustius Salinator\",\"Geta Iulius\",\"Laronius Numicius\",\"Cominius Opellius\",\"Acilius Constantinus\",\"Cethegus Mamilius\",\"Terentius Valerius\",\"Cantilius Magnentius\",\"Justus Sextus\",\"Faustus Longinius\",\"Gracchus Camillius\",\"Opellius Coruncanius\",\"Donatus Minicius\",\"Lacerius Calpurnius\",\"Lartius Cyprian\",\"Fidenas Cinna\",\"Coruncanius Aulus\",\"Iunius Laetorius\",\"Cicero Valens\",\"Laelius Maxentius\",\"Matienus Murena\",\"Foslius Foslius\",\"Lucretius Urbicus\",\"Manilius Silvius\",\"Primitivus Sextus\",\"Mallius Clodius\",\"Avidius Segulius\",\"Gratian Sallustius\",\"Fabius Pettius\",\"Valens Appius\",\"Caesennius Clodius\",\"Flaccus Rufinus\",\"Secundus Balbinus\",\"Modius Calpurnius\",\"Orchius Sittius\",\"Nonius Nonius\",\"Cestius Ostorius\",\"Sepullius Quinctilius\",\"Lucius Laberius\",\"Marius Lacerius\",\"Nazarius Catius\",\"Sicinius Salinator\",\"Paternus Lartius\",\"Sextius Verecundus\",\"Cyprian Calventius\",\"Numerius Iuventius\",\"Anatolius Blossius\",\"Ennius Gaius\",\"Sergian Geminius\",\"Fabianus Servilius\",\"Damasus Brennus\",\"Catius Rufrius\",\"Seccius Mallius\",\"Pius Sicinius\",\"Secundinius Camillius\",\"Pasidienus Cornutus\",\"Laecanius Caesennius\",\"Caedicius Cassius\",\"Munatius Fabius\",\"Latinius Papinius\",\"Gordian Laetorius\",\"Nasennius Rufrius\",\"Severian Valentinian\",\"Julius Lollius\",\"Nasennius Maxentius\",\"Naevius Avidius\",\"Oppius Rutilius\",\"Postumius Appius\",\"Betilienus Plinius\",\"Secundus Claudius\",\"Naevius Sedatius\",\"Tacitus Seccius\",\"Albinius Gallus\",\"Paulinus Hordeonius\",\"Aemilius Duronius\",\"Sentius Titus\",\"Matinius Numonius\",\"Cethegus Regulus\",\"Lollius Rebilus\",\"Calpurnius Tacitus\",\"Laetorius Hostilius\",\"Geminius Caecilius\",\"Cornelius Domitian\",\"Icilius Rufinus\",\"Volumnius Faustus\",\"Flaccus Sergius\",\"Secundinius Titus\",\"Marius Vitalis\",\"Agrippa Salvian\",\"Pompeius Aemilius\",\"Eborius Sergian\",\"Ennius Geta\",\"Salvius Fannius\",\"Cato Pius\",\"Lucretius Cordius\",\"Vespasian Laelius\",\"Poppaedius Eborius\",\"Domitian Novellius\",\"Lentulus Nigidius\",\"Manius Plinius\",\"Cocceius Poppaedius\",\"Castorius Vitalis\",\"Menenius Spurius\",\"Gallus Gaius\",\"Galerius Crispinus\",\"Calventius Volumnius\",\"Nasennius Mucius\",\"Furius Mamilius\",\"Laecanius Pontidius\",\"Blaesus Camillius\",\"Pupius Musonius\",\"Cordius Nasidienus\",\"Verecundus Considius\",\"Maecilius Nasidius\",\"Postumius Constantinus\",\"Caelius Sempronius\",\"Modius Sepullius\",\"Primitivus Modius\",\"Rufinus Manius\",\"Maecenas Eutropius\",\"Dacian Petreius\",\"Considius Sepullius\",\"Placidus Egnatius\",\"Sicinius Granius\",\"Cincinnatus Numitorius\",\"Fundanus Genucius\",\"Licinius Didius\",\"Decentius Pomponius\",\"Fabianus Salinator\",\"Tertius Quirinius\",\"Betilienus Maecenas\",\"Lamponius Mallius\",\"Petilius Cornutus\",\"Sentius Sepullius\",\"Hilarianus Lamponius\",\"Minicius Salvidienus\",\"Silanus Appius\",\"Marian Rufrius\",\"Marian Caelius\",\"Nazarius Longinius\",\"Bruttius Rullianus\",\"Decentius Cicero\",\"Blaesus Castorius\",\"Didius Januarius\",\"Lucius Terentius\",\"Caedicius Sabucius\",\"Mescinius Placidus\",\"Salinator Cornutus\",\"Gracchus Dacian\",\"Agrippa Pettius\",\"Albinius Pomponius\",\"Fulvius Betilienus\",\"Furius Januarius\",\"Pompeius Numicius\",\"Anatolius Laronius\",\"Fulcinia Pilus\",\"Rutilius Cassianus\",\"Tacitus Ennius\",\"Scribonius Decentius\",\"Numicius Paconius\",\"Magnentius Caecilius\",\"Matienus Betilienus\",\"Orestius Honorius\",\"Hostilius Sentius\",\"Julius Vitalis\",\"Hilarianus Nasennius\",\"Albinius Placidus\",\"Cicero Longinius\",\"Musonius Herennius\",\"Scribonius Cinna\",\"Sicinius Lucanus\",\"Priscus Burrienus\",\"Paconius Octavius\",\"Curtius Blaesus\",\"Quirinius Castorius\",\"Domitian Bruttius\",\"Servilius Castorius\",\"Domitius Fundanus\",\"Sedatius Norbanus\",\"Perperna Pomponius\",\"Cispius Numonius\",\"Laurentius Maximus\",\"Domitian Gordian\",\"Secundinius Calventius\",\"Eutropius Ravillius\",\"Gracchus Varro\",\"Acilius Paulinus\",\"Silanus Honorius\",\"Varian Severian\",\"Seccius Persius\",\"Aelius Hordeonius\",\"Paulinus Marcus\",\"Quinctilius Tarquinius\",\"Numitorius Albinius\",\"Agrippa Festinius\",\"Duronius Paconius\",\"Maximian Caelius\",\"Percennius Gaius\",\"Numicius Servius\",\"Hostilius Maximus\",\"Cincinnatus Nazarius\",\"Nautius Flaccus\",\"Geta Numerius\",\"Festinius Sertorius\",\"Papinius Furius\",\"Vitus Potitius\",\"Placidus Maecilius\",\"Mamilius Sergius\",\"Zeno Pasidienus\",\"Memmius Eborius\",\"Egnatius Ovidius\",\"Secundus Sallustius\",\"Rufinus Sextus\",\"Cornificius Caninius\",\"Rufrius Silanus\",\"Menenius Aurelianus\",\"Novellius Persius\",\"Eborius Sextus\",\"Numerius Iunius\",\"Lollius Octavius\",\"Sergius Cethegus\",\"Furius Agrippa\",\"Crispus Sicinius\",\"Fannius Petilius\",\"Nazarius Percennius\",\"Nasidius Terentius\",\"Domitian Sergius\",\"Cicereius Percennius\",\"Rabirius Quinctilius\",\"Octavenus Ninnius\",\"Betilienus Nasennius\",\"Mucius Cassius\",\"Caecilius Rufius\",\"Lollius Hirrus\",\"Geta Fidenas\",\"Mucius Nasennius\",\"Geta Rufinus\",\"Paulinus Gabinius\",\"Nazarius Severian\",\"Valerius Atilius\",\"Magnentius Galerius\",\"Clodius Cicereius\",\"Marcius Verecundus\",\"Marcius Curtius\",\"Gnaeus Minicius\",\"Curtius Juvenalis\",\"Murena Quinctilius\",\"Nasidienus Felix\",\"Matienus Bruttius\",\"Pasidienus Caedicius\",\"Decimus Vibius\",\"Naevius Lollius\",\"Opellius Seneca\",\"Dacian Lucius\",\"Severian Drusus\",\"Januarius Festinius\",\"Maecenas Oppius\",\"Nasidienus Eborius\",\"Sallustius Bruttius\",\"Cosconius Opimius\",\"Cocceius Faustus\",\"Pacilius Geminius\",\"Sallustius Petilius\",\"Papirius Fundanus\",\"Laurentius Marcellus\",\"Caesennius Sornatius\",\"Gratian Marcus\",\"Ingenuus Cethegus\",\"Lentulus Statilius\",\"Cominius Lentulus\",\"Gaius Mucius\",\"Claudius Magnentius\",\"Fundanus Segulius\",\"Pupius Donatus\",\"Ostorius Nasennius\",\"Geta Catius\",\"Murena Cato\",\"Gratian Draco\",\"Eutropius Festus\",\"Vitalis Claudius\",\"Celsus Cornutus\",\"Marcius Segulius\",\"Matinius Minicius\",\"Novellius Messalla\",\"Cordius Potitius\",\"Stertinius Manius\",\"Balbinus Octavius\",\"Drusus Numonius\",\"Opimius Geminius\",\"Eborius Pasidienus\",\"Tacitus Caelius\",\"Statilius Cincinnatus\",\"Gallus Didius\",\"Orestius Caelius\",\"Nasidius Magnentius\",\"Coruncanius Hadrian\",\"Vespasian Nautius\",\"Palpellius Cincinnatus\",\"Gracchus Vitalis\",\"Nasidienus Rullianus\",\"Hilarianus Zeno\",\"Cethegus Geminius\",\"Cethegus Rullianus\",\"Sertorius Rufrius\",\"Mucius Clodius\",\"Secundinius Cestius\",\"Horatius Candidus\",\"Camillius Felix\",\"Sedatius Rufrius\",\"Norbanus Januarius\",\"Plautius Salvius\",\"Licinius Afranius\",\"Blaesus Geta\",\"Antonius Papinius\",\"Tarquinius Norbanus\",\"Avidius Caedicius\",\"Aquillius Octavius\",\"Duronius Priscus\",\"Iulius Marius\",\"Albinius Lacerius\",\"Cloelius Flaccus\",\"Cassianus Geminius\",\"Marcus Cicero\",\"Quinctilius Draco\",\"Varian Mamilius\",\"Ostorius Burrienus\",\"Ostorius Florian\",\"Hadrian Cyprian\",\"Sergian Rufinus\",\"Seneca Pettius\",\"Iuventius Hordeonius\",\"Quintilianus Eborius\",\"Gratian Maecenas\",\"Seneca Musonius\",\"Maecilius Rufius\",\"Rabirius Pompeius\",\"Gabinius Mucius\",\"Rufinus Verecundus\",\"Plautius Honorius\",\"Titus Pasidienus\",\"Pilus Nasennius\",\"Calventius Pomponius\",\"Foslius Gabinius\",\"Egnatuleius Maximus\",\"Zeno Lartius\",\"Cinna Tarquinius\",\"Valentinian Bonifacius\",\"Rullianus Pontidius\",\"Memmius Balbus\",\"Mucius Didius\",\"Clodius Cornutus\",\"Latinius Duronius\",\"Fundanus Lucius\",\"Decimus Terentius\",\"Anatolius Bonifacius\",\"Numonius Publius\",\"Paternus Aquillius\",\"Numerius Decimus\",\"Urbicus Caninius\",\"Sornatius Catius\",\"Vibius Germanicus\",\"Quinctilius Publius\",\"Opellius Fidenas\",\"Suetonius Plinius\",\"Marcius Pupius\",\"Silvius Plautius\",\"Perperna Domitian\",\"Tacitus Fannius\",\"Spurius Coruncanius\",\"Paelignus Gratian\",\"Eborius Caedicius\",\"Caedicius Semellius\",\"Sabucius Bonifacius\",\"Suetonius Cethegus\",\"Rufrius Balbus\",\"Justus Segulius\",\"Gratian Iulius\",\"Perperna Petilius\",\"Candidus Iuventius\",\"Varro Priscus\",\"Musonius Sextus\",\"Amulius Maximus\",\"Cincinnatus Sextius\",\"Petreius Mescinius\",\"Vespasian Numerius\",\"Hostilius Ofilius\",\"Honorius Naevius\",\"Probus Januarius\",\"Diocletian Cloelius\",\"Caelius Horatius\",\"Aelius Draco\",\"Florianus Cornificius\",\"Numerius Dacian\",\"Nonius Cispius\",\"Musonius Catilius\",\"Laelius Cornelius\",\"Fannius Claudius\",\"Cispius Domitian\",\"Cethegus Fidius\",\"Licinius Seccius\",\"Ollius Appius\",\"Gaius Nonius\",\"Cordius Aurelianus\",\"Urbicus Urbanus\",\"Papinius Ninnius\",\"Suetonius Hordeonius\",\"Urbanus Felix\",\"Octavius Caninius\",\"Cestius Blossius\",\"Vincentius Marcius\",\"Gallus Lucius\",\"Percennius Gratian\",\"Pilus Lartius\",\"Licinius Balbus\",\"Balbinus Terentius\",\"Furius Sempronius\",\"Blasius Stertinius\",\"Afranius Cornutus\",\"Primitivus Decentius\",\"Caelius Decimus\",\"Fabianus Cicereius\",\"Sestius Silvius\",\"Fulvius Suetonius\",\"Sepullius Duronius\",\"Didius Germanicus\",\"Herennius Bruttius\",\"Maecenas Menenius\",\"Matinius Cilnius\",\"Cornificius Matienus\",\"Junius Vitalis\",\"Sulpicius Sedatius\",\"Publicius Ovidius\",\"Betilienus Ogulnius\",\"Verecundus Fannius\",\"Calpurnius Postumius\",\"Agrippa Pinarus\",\"Nigidius Saturninus\",\"Fabius Camillius\",\"Lentulus Fabius\",\"Nazarius Silanus\",\"Octavenus Seneca\",\"Catius Icilius\",\"Cordius Pacilius\",\"Sallustius Festus\",\"Marcus Latinius\",\"Marcus Laurentius\",\"Lacerius Papirius\",\"Celsus Novellius\",\"Minicius Memmius\",\"Silvius Tertius\",\"Numicius Fulcinia\",\"Sextius Terentius\",\"Oppius Papinius\",\"Sertorius Maecilius\",\"Claudius Lacerius\",\"Papinius Magnentius\",\"Sidonius Spurius\",\"Poppaedius Ofilius\",\"Curtius Rufius\",\"Flavius Paelignus\",\"Nasidienus Ogulnius\",\"Sextius Faustus\",\"Pupius Sicinius\",\"Magnentius Sentius\",\"Maximian Vitalis\",\"Norbanus Nasennius\",\"Cassius Postumius\",\"Festus Nigidius\",\"Messalla Vespasian\",\"Salinator Tacitus\",\"Menenius Maecenas\",\"Potitius Hordeonius\",\"Constantinus Festinius\",\"Salvius Rullianus\",\"Candidus Saturninus\",\"Paternus Manius\",\"Castorius Cordius\",\"Rebilus Marcellus\",\"Laberius Damasus\",\"Donatus Cosconius\",\"Poppaedius Flaccus\",\"Domitius Aelius\",\"Cicero Florian\",\"Paternus Gnaeus\",\"Caesennius Perperna\",\"Foslius Aulus\",\"Draco Secundus\",\"Plautius Pilus\",\"Lentulus Probus\",\"Rullianus Ingenuus\",\"Betilienus Lartius\",\"Gabinius Fulcinia\",\"Urbanus Maximus\",\"Tertius Ninnius\",\"Decimus Acilius\",\"Varro Maximus\",\"Numerius Didius\",\"Dacian Aurelianus\",\"Pontidius Didius\",\"Ovidius Pettius\",\"Segulius Fidius\",\"Plinius Damasus\",\"Valens Sicinius\",\"Numonius Lartius\",\"Bruttius Modius\",\"Curtius Pius\",\"Licinius Hordeonius\",\"Pedius Maximus\",\"Nonius Modius\",\"Rebilus Potitius\",\"Statilius Drusus\",\"Nasennius Severus\",\"Quintus Didius\",\"Fannius Iulius\",\"Ravillius Paulinus\",\"Iunius Afranius\",\"Nero Ostorius\",\"Sempronius Genucius\",\"Longinius Sextus\",\"Menenius Aulus\",\"Sentius Scribonius\",\"Domitius Primitivus\",\"Juvenalis Mescinius\",\"Pupius Maecilius\",\"Blaesus Opimius\",\"Pettius Suetonius\",\"Granius Vincentius\",\"Candidus Nigidius\",\"Honorius Publicius\",\"Constantinus Gracchus\",\"Fulvius Crispinus\",\"Pilus Lacerius\",\"Probus Martialis\",\"Eborius Genucius\",\"Salvidienus Hilarianus\",\"Cestius Livius\",\"Quirinius Sertorius\",\"Albinus Musonius\",\"Rullianus Silius\",\"Icilius Sallustius\",\"Candidus Poppaedius\",\"Opimius Salinator\",\"Aulus Iuventius\",\"Cilnius Egnatuleius\",\"Cethegus Cominius\",\"Justus Laecanius\",\"Volumnius Egnatuleius\",\"Quintus Gracchus\",\"Valentinian Decentius\",\"Coruncanius Cato\",\"Genucius Orbilius\",\"Anatolius Pettius\",\"Ogulnius Vespasian\",\"Candidus Postumius\",\"Regulus Burrienus\",\"Aulus Orbilius\",\"Salvian Aquillius\",\"Pettius Cicereius\",\"Bonifacius Camillus\",\"Lacerius Sedatius\",\"Menenius Novellius\",\"Claudius Granius\",\"Baebius Tiberius\",\"Salvian Lucanus\",\"Cestius Ollius\",\"Volumnius Pupius\",\"Castorius Junius\",\"Sabucius Laelius\",\"Stertinius Betilienus\",\"Duronius Sentius\",\"Tiberius Didius\",\"Juvenalis Ovidius\",\"Munatius Norbanus\",\"Valerius Persius\",\"Priscus Januarius\",\"Cocceius Geta\",\"Celsus Cinna\",\"Felix Avidius\",\"Sextius Musonius\",\"Verus Papinius\",\"Caelian Aquillius\",\"Plautius Numerius\",\"Semellius Petilius\",\"Diocletian Coruncanius\",\"Quintus Burrienus\",\"Quirinius Geta\",\"Modius Severian\",\"Juvenalis Anatolius\",\"Cestius Gaius\",\"Camillus Florianus\",\"Pontidius Nerva\",\"Nerva Burrienus\",\"Cornificius Ollius\",\"Menenius Cornelius\",\"Seccius Marcus\",\"Quinctilius Latinius\",\"Lucius Publicius\",\"Munatius Horatius\",\"Mallius Fulvius\",\"Sergian Cassianus\",\"Pettius Domitius\",\"Cicereius Statilius\",\"Sergius Paelignus\",\"Gracchus Opimius\",\"Pacilius Antonius\",\"Salinator Cassio\",\"Dacian Priscus\",\"Tertius Murena\",\"Murena Cassio\",\"Numicius Lucius\",\"Maximus Latinius\",\"Cornificius Cyprian\",\"Valens Donatus\",\"Publicius Domitius\",\"Varro Sittius\",\"Albinius Novellius\",\"Rufinus Hostilius\",\"Octavius Numerius\",\"Priscus Urbanus\",\"Cincinnatus Varro\",\"Tiberius Nasidius\",\"Modius Donatus\",\"Cosconius Cocceius\",\"Laronius Gratian\",\"Festus Gracchus\",\"Sergius Curtius\",\"Sertorius Pinarus\",\"Aemilius Furius\",\"Memmius Nerva\",\"Lollius Marcus\",\"Cornificius Egnatius\",\"Latinius Fulcinia\",\"Poppaedius Sallustius\",\"Felix Terentius\",\"Baebius Pasidienus\",\"Blossius Secundus\",\"Galerius Norbanus\",\"Antonius Numerius\",\"Cloelius Januarius\",\"Flavius Rutilius\",\"Salvidienus Felix\",\"Munatius Curtius\",\"Segulius Octavius\",\"Seccius Aelius\",\"Blaesus Severian\",\"Plinius Orbilius\",\"Hordeonius Papinius\",\"Sittius Valens\",\"Afranius Caelian\",\"Avidius Egnatius\",\"Sedatius Salvidienus\",\"Geminius Nonius\",\"Quintilianus Sittius\",\"Gabinius Nigidius\",\"Dacian Primitivus\",\"Seccius Gallus\",\"Octavius Saturninus\",\"Naevius Calpurnius\",\"Sittius Caecilius\",\"Terentius Numitorius\",\"Bruttius Herennius\",\"Albinius Marcius\",\"Numitorius Stertinius\",\"Nazarius Valerius\",\"Genucius Longinius\",\"Caedicius Sempronius\",\"Ogulnius Maximian\",\"Crispus Nasennius\",\"Martialis Lollius\",\"Herennius Pinarus\",\"Fabrinius Secundus\",\"Messalla Aemilius\",\"Balbinus Mamilius\",\"Cethegus Priscus\",\"Betilienus Nero\",\"Candidus Memmius\",\"Foslius Brennus\",\"Primitivus Cicereius\",\"Martialis Festus\",\"Bruttius Maecenas\",\"Brennus Sentius\",\"Calpurnius Ostorius\",\"Nasennius Fulvius\",\"Cosconius Lartius\",\"Rabirius Minicius\",\"Antonius Marcius\",\"Mucius Pettius\",\"Draco Calventius\",\"Tiberius Oppius\",\"Geminius Sidonius\",\"Laronius Mallius\",\"Albinius Geta\",\"Crispinus Caecilius\",\"Cato Silius\",\"Numitorius Sentius\",\"Musonius Sempronius\",\"Cestius Nautius\",\"Latinius Nautius\",\"Cinna Furius\",\"Gaius Severus\",\"Maximian Sittius\",\"Nero Perperna\",\"Nazarius Lentulus\",\"Calventius Cornificius\",\"Cassio Severus\",\"Cornelius Ovidius\",\"Nerva Pilus\",\"Sedatius Severus\",\"Gratian Paulinus\",\"Cordius Considius\",\"Hortensius Severian\",\"Juvenalis Vitus\",\"Salvian Mamilius\",\"Valerius Fabrinius\",\"Anatolius Camillus\",\"Verus Celsus\",\"Candidus Ravillius\",\"Duronius Laecanius\",\"Foslius Diocletian\",\"Hostius Silanus\",\"Coruncanius Licinius\",\"Suetonius Belisarius\",\"Faustus Memmius\",\"Caesennius Camillus\",\"Gallus Balbinus\",\"Cincinnatus Cicereius\",\"Justus Priscus\",\"Laetorius Lartius\",\"Considius Manilius\",\"Ovidius Duronius\",\"Anatolius Gordian\",\"Gallus Candidus\",\"Salvian Numicius\",\"Mescinius Postumius\",\"Sicinius Longinius\",\"Minicius Quintus\",\"Lacerius Salvian\",\"Tarquinius Avidius\",\"Claudius Quintilianus\",\"Lucretius Modius\",\"Celsus Menenius\",\"Numitorius Fulcinia\",\"Tacitus Latinius\",\"Fidenas Cocceius\",\"Atilius Poppaedius\",\"Camillus Scribonius\",\"Cassius Catilius\",\"Cassianus Novellius\",\"Egnatuleius Petilius\",\"Caecilius Fabrinius\",\"Lucretius Tacitus\",\"Salvian Ostorius\",\"Sergian Albinius\",\"Decimus Mescinius\",\"Baebius Valens\",\"Papirius Germanicus\",\"Papinius Placidus\",\"Didius Cordius\",\"Pontidius Primitivus\",\"Quinctilius Aquillius\",\"Lartius Saturninus\",\"Laetorius Cispius\",\"Marcius Catius\",\"Silanus Potitius\",\"Opellius Valentinian\",\"Sempronius Belisarius\",\"Cilnius Sepullius\",\"Blossius Rutilius\",\"Faustus Matienus\",\"Ostorius Latinius\",\"Albinus Statilius\",\"Nigidius Memmius\",\"Caedicius Hordeonius\",\"Primitivus Iulius\",\"Horatius Gnaeus\",\"Pompeius Secundinius\",\"Opimius Julius\",\"Fidenas Pius\",\"Maecenas Quirinius\",\"Pettius Afranius\",\"Eutropius Paelignus\",\"Zeno Segulius\",\"Rufrius Aquillius\",\"Murena Vibius\",\"Palpellius Granius\",\"Livius Cassianus\",\"Ogulnius Didius\",\"Cilnius Mescinius\",\"Musonius Marian\",\"Foslius Cispius\",\"Maximus Egnatuleius\",\"Tarquinius Menenius\",\"Gaius Domitian\",\"Claudius Urbicus\",\"Severian Munatius\",\"Cilnius Cosconius\",\"Aelius Tiberius\",\"Quintus Cassianus\",\"Germanicus Salvidienus\",\"Mallius Festinius\",\"Sergius Drusus\",\"Furius Pomponius\",\"Vibius Geta\",\"Cassius Pedius\",\"Seccius Caelian\",\"Severus Aelius\",\"Probus Terentius\",\"Porcius Rebilus\",\"Albinus Scribonius\",\"Blossius Naevius\",\"Lollius Silius\",\"Nasidienus Cilnius\",\"Ovidius Magnentius\",\"Sestius Baebius\",\"Maximus Celsus\",\"Flavius Silanus\",\"Albinus Julius\",\"Manius Valens\",\"Musonius Maecenas\",\"Severian Candidus\",\"Herennius Calpurnius\",\"Vitalis Ravillius\",\"Salvidienus Porcius\",\"Belisarius Pasidienus\",\"Drusus Egnatius\",\"Martialis Hostius\",\"Paelignus Valentinian\",\"Considius Paconius\",\"Domitius Lucretius\",\"Salvius Numicius\",\"Januarius Ovidius\",\"Valens Pedius\",\"Licinius Januarius\",\"Festinius Maximus\",\"Fulcinia Verus\",\"Vibius Opellius\",\"Publius Eutropius\",\"Sittius Marian\",\"Ovidius Secundinius\",\"Urbanus Memmius\",\"Marcus Blasius\",\"Valentinian Musonius\",\"Paternus Silius\",\"Cinna Coruncanius\",\"Brennus Sestius\",\"Nero Fulcinia\",\"Magnentius Icilius\",\"Salvian Servilius\",\"Cassianus Agrippa\",\"Vespasian Marian\",\"Caninius Severus\",\"Aurelianus Memmius\",\"Catius Valerius\",\"Spurius Aulus\",\"Nasidius Camillius\",\"Hadrian Memmius\",\"Nazarius Felix\",\"Marcellus Bonifacius\",\"Terentius Sornatius\",\"Modius Didius\",\"Cloelius Ofilius\",\"Norbanus Pasidienus\",\"Plautius Appius\",\"Sempronius Publius\",\"Ninnius Petilius\",\"Cincinnatus Festus\",\"Caecilius Cilnius\",\"Opimius Numicius\",\"Cosconius Mescinius\",\"Calpurnius Murena\",\"Julius Aulus\",\"Germanicus Silanus\",\"Fidenas Fabius\",\"Servius Cocceius\",\"Nautius Damasus\",\"Secundinius Cinna\",\"Cornutus Perperna\",\"Egnatius Cethegus\",\"Iunius Flavius\",\"Cosconius Ninnius\",\"Potitius Blossius\",\"Vespasian Silanus\",\"Tarquinius Rabirius\",\"Marcellus Secundus\",\"Martialis Granius\",\"Hilarianus Festinius\",\"Pilus Novellius\",\"Justus Ninnius\",\"Valentinian Paelignus\",\"Genucius Gordian\",\"Sentius Vespasian\",\"Numicius Pomponius\",\"Perperna Manius\",\"Laecanius Iuventius\",\"Primitivus Foslius\",\"Nautius Cilnius\",\"Nero Rufrius\",\"Priscus Sidonius\",\"Gracchus Fidenas\",\"Iulius Valerius\",\"Sulpicius Marcus\",\"Lucius Nonius\",\"Clodius Postumius\",\"Crispus Ennius\",\"Salvidienus Zeno\",\"Salinator Salvian\",\"Fundanus Festus\",\"Potitius Drusus\",\"Nonius Drusus\",\"Salinator Sergian\",\"Granius Draco\",\"Rufius Camillius\",\"Manilius Camillius\",\"Papinius Gordian\",\"Numicius Duronius\",\"Damasus Paternus\",\"Marcus Lucretius\",\"Genucius Opellius\",\"Silanus Musonius\",\"Lacerius Furius\",\"Pius Licinius\",\"Cassianus Verecundus\",\"Papirius Claudius\",\"Duronius Cassio\",\"Festus Pasidienus\",\"Paulinus Sabucius\",\"Valentinian Ollius\",\"Secundus Camillus\",\"Eborius Domitian\",\"Caelian Castorius\",\"Percennius Cicero\",\"Salvian Cantilius\",\"Longinius Drusus\",\"Marcus Iuventius\",\"Cornelius Aelius\",\"Cornelius Laronius\",\"Hordeonius Geminius\",\"Paternus Palpellius\",\"Octavenus Primitivus\",\"Amulius Gabinius\",\"Numicius Placidus\",\"Palpellius Marian\",\"Fundanus Iulius\",\"Servius Marcellus\",\"Cicero Maximus\",\"Marcellus Blasius\",\"Justus Duronius\",\"Paconius Egnatius\",\"Herennius Amulius\",\"Ninnius Statilius\",\"Domitian Fundanus\",\"Bruttius Valens\",\"Cincinnatus Segulius\",\"Eutropius Ollius\",\"Gaius Octavius\",\"Germanicus Sedatius\",\"Tertius Mucius\",\"Burrienus Pinarus\",\"Sittius Volumnius\",\"Numicius Tiberius\",\"Maximian Camillius\",\"Cassius Probus\",\"Draco Fannius\",\"Nasidienus Germanicus\",\"Magnentius Octavius\",\"Coruncanius Fidius\",\"Rabirius Egnatius\",\"Laronius Petilius\",\"Icilius Vibius\",\"Gratian Aelius\",\"Geta Flaccus\",\"Nerva Avidius\",\"Spurius Diocletian\",\"Eborius Urbanus\",\"Sicinius Matinius\",\"Nero Fundanus\",\"Nasennius Orbilius\",\"Pacilius Pacilius\",\"Festus Pilus\",\"Lamponius Maximus\",\"Germanicus Dacian\",\"Laetorius Rufrius\",\"Pacilius Placidus\",\"Plinius Vitus\",\"Maximus Perperna\",\"Novellius Publicius\",\"Varian Diocletian\",\"Potitius Maxentius\",\"Egnatius Norbanus\",\"Maecenas Aulus\",\"Maximian Iulius\",\"Cominius Novellius\",\"Atilius Iunius\",\"Probus Quintus\",\"Cordius Nonius\",\"Marcellus Fulcinia\",\"Octavius Tiberius\",\"Publius Orestius\",\"Lentulus Pacilius\",\"Aulus Publius\",\"Germanicus Salinator\",\"Lacerius Caelius\",\"Catius Fabrinius\",\"Sextius Domitius\",\"Verus Octavenus\",\"Varro Germanicus\",\"Aelius Quirinius\",\"Vespasian Verecundus\",\"Manilius Icilius\",\"Bonifacius Geta\",\"Ogulnius Papinius\",\"Galerius Rebilus\",\"Domitius Rufinus\",\"Matinius Sentius\",\"Galerius Seccius\",\"Probus Marian\",\"Rufinus Blossius\",\"Novellius Flavius\",\"Mamilius Fabianus\",\"Decimus Seneca\",\"Rufrius Amulius\",\"Nasidius Postumius\",\"Petilius Cominius\",\"Baebius Hilarianus\",\"Mamilius Priscus\",\"Bruttius Iulius\",\"Marius Maxentius\",\"Laetorius Maecenas\",\"Manilius Cassianus\",\"Tiberius Quinctilius\",\"Hostius Matienus\",\"Saturninus Probus\",\"Acilius Fannius\",\"Manius Furius\",\"Urbanus Marius\",\"Dacian Cestius\",\"Agrippa Secundus\",\"Gaius Burrienus\",\"Salvidienus Messalla\",\"Paelignus Gabinius\",\"Amulius Balbinus\",\"Silvius Caelius\",\"Sicinius Sentius\",\"Lamponius Bruttius\",\"Munatius Mamilius\",\"Laronius Sedatius\",\"Florianus Cassius\",\"Porcius Cincinnatus\",\"Gordian Ofilius\",\"Palpellius Caedicius\",\"Nautius Agrippa\",\"Maecenas Cispius\",\"Salvidienus Gaius\",\"Fidius Salinator\",\"Drusus Cispius\",\"Honorius Fulcinia\",\"Valerius Secundus\",\"Hostilius Salvian\",\"Cinna Decimus\",\"Seccius Primitivus\",\"Menenius Felix\",\"Pettius Laberius\",\"Laurentius Rufrius\",\"Fidius Murena\",\"Munatius Iuventius\",\"Rufrius Castorius\",\"Crispus Silvius\",\"Catilius Galerius\",\"Vincentius Tarquinius\",\"Manius Sextus\",\"Rufius Seneca\",\"Nasidienus Balbus\",\"Longinius Baebius\",\"Verus Lartius\",\"Nasennius Rabirius\",\"Germanicus Gratian\",\"Petreius Pinarus\",\"Cinna Camillus\",\"Aquillius Numitorius\",\"Laelius Lamponius\",\"Curtius Orchius\",\"Pacilius Pedius\",\"Justus Porcius\",\"Nerva Marian\",\"Sulpicius Paconius\",\"Manilius Hordeonius\",\"Ovidius Egnatius\",\"Vibius Varro\",\"Constantinus Minicius\",\"Munatius Quirinius\",\"Segulius Festinius\",\"Fidius Sittius\",\"Sentius Candidus\",\"Flavius Zeno\",\"Pilus Cornutus\",\"Curtius Fabrinius\",\"Bruttius Tarquinius\",\"Pontidius Donatus\",\"Cornelius Nigidius\",\"Pompeius Matienus\",\"Messalla Publius\",\"Eborius Eutropius\",\"Bonifacius Avidius\",\"Rufinus Lamponius\",\"Licinius Anatolius\",\"Lollius Fabrinius\",\"Lartius Paternus\",\"Justus Perperna\",\"Regulus Memmius\",\"Eutropius Gabinius\",\"Crispinus Ennius\",\"Atilius Numerius\",\"Memmius Quintus\",\"Rufius Horatius\",\"Cato Decentius\",\"Sallustius Pacilius\",\"Castorius Sedatius\",\"Baebius Calventius\",\"Rullianus Baebius\",\"Quirinius Ogulnius\",\"Curtius Gallus\",\"Ogulnius Silanus\",\"Memmius Junius\",\"Lucretius Icilius\",\"Sittius Minicius\",\"Tacitus Gabinius\",\"Pasidienus Poppaedius\",\"Eutropius Cassius\",\"Persius Orestius\",\"Vibius Licinius\",\"Pupius Burrienus\",\"Secundus Foslius\",\"Manius Saturninus\",\"Maxentius Salvidienus\",\"Salvidienus Mucius\",\"Latinius Gratian\",\"Ingenuus Opimius\",\"Marius Varian\",\"Persius Acilius\",\"Nero Hostius\",\"Vibius Lamponius\",\"Cassianus Afranius\",\"Amulius Fundanus\",\"Fidius Publicius\",\"Cornutus Rufinus\",\"Hirrus Minicius\",\"Varro Anatolius\",\"Opellius Postumius\",\"Mucius Nazarius\",\"Caelian Pomponius\",\"Sergian Foslius\",\"Icilius Semellius\",\"Numonius Opellius\",\"Burrienus Castorius\",\"Domitius Didius\",\"Justus Lucius\",\"Maecenas Tertius\",\"Belisarius Manilius\",\"Seccius Caecilius\",\"Calpurnius Stertinius\",\"Nautius Sergius\",\"Spurius Constantinus\",\"Quinctilius Palpellius\",\"Plinius Rutilius\",\"Considius Maxentius\",\"Valerius Pius\",\"Diocletian Volumnius\",\"Titus Latinius\",\"Balbinus Ogulnius\",\"Numitorius Hadrian\",\"Urbanus Orchius\",\"Pinarus Marcellus\",\"Memmius Sempronius\",\"Varro Flaccus\",\"Rabirius Justus\",\"Cornutus Papirius\",\"Domitian Decimus\",\"Tiberius Mamilius\",\"Pacilius Decimus\",\"Rabirius Decentius\",\"Flaccus Numerius\",\"Festus Pius\",\"Octavius Fabianus\",\"Lucanus Matinius\",\"Memmius Cornelius\",\"Burrienus Nero\",\"Aemilius Varian\",\"Postumius Varian\",\"Nero Balbus\",\"Vincentius Florian\",\"Silanus Rutilius\",\"Urbanus Quirinius\",\"Urbanus Latinius\",\"Seccius Lentulus\",\"Lucretius Nerva\",\"Iunius Lartius\",\"Persius Egnatius\",\"Paternus Cicereius\",\"Eborius Perperna\",\"Coruncanius Bruttius\",\"Paconius Petreius\",\"Lucanus Secundinius\",\"Saturninus Paternus\",\"Considius Horatius\",\"Laurentius Rufius\",\"Nasidius Probus\",\"Verecundus Appius\",\"Avidius Gordian\",\"Eborius Norbanus\",\"Opimius Cloelius\",\"Curtius Sulpicius\",\"Marcius Sepullius\",\"Maecilius Longinius\",\"Salvidienus Aemilius\",\"Marcus Minicius\",\"Geta Rufius\",\"Ninnius Sextus\",\"Numitorius Cyprian\",\"Persius Florianus\",\"Honorius Verecundus\",\"Severian Fabius\",\"Hilarianus Afranius\",\"Decentius Plinius\",\"Varian Coruncanius\",\"Gracchus Munatius\",\"Burrienus Fabrinius\",\"Scribonius Cicereius\",\"Orestius Albinius\",\"Percennius Acilius\",\"Nigidius Manilius\",\"Postumius Sextus\",\"Statilius Livius\",\"Florianus Gnaeus\",\"Opimius Fulcinia\",\"Aquillius Blasius\",\"Nazarius Paelignus\",\"Cicero Pomponius\",\"Verus Priscus\",\"Cloelius Diocletian\",\"Catius Ingenuus\",\"Sentius Verecundus\",\"Sepullius Pinarus\",\"Mamilius Menenius\",\"Marian Laronius\",\"Pettius Matienus\",\"Coruncanius Rabirius\",\"Nasidius Draco\",\"Nonius Pettius\",\"Fulcinia Fabius\",\"Urbicus Albinius\",\"Cornutus Petreius\",\"Belisarius Acilius\",\"Aquillius Tertius\",\"Seccius Germanicus\",\"Persius Gratian\",\"Scribonius Sicinius\",\"Severian Laurentius\",\"Sallustius Considius\",\"Castorius Salvidienus\",\"Aulus Aurelianus\",\"Tarquinius Catius\",\"Decimus Pacilius\",\"Papirius Pontidius\",\"Gratian Manius\",\"Nero Iuventius\",\"Camillius Suetonius\",\"Celsus Domitius\",\"Severus Rullianus\",\"Cornelius Lucius\",\"Catius Granius\",\"Rufrius Novellius\",\"Belisarius Cethegus\",\"Maximus Rutilius\",\"Nigidius Sentius\",\"Lamponius Ninnius\",\"Murena Maecenas\",\"Marcus Appius\",\"Cornutus Cassianus\",\"Vitalis Geta\",\"Fabianus Hadrian\",\"Rebilus Pontidius\",\"Ostorius Licinius\",\"Cispius Claudius\",\"Dacian Belisarius\",\"Ollius Numitorius\",\"Naevius Vincentius\",\"Caesennius Cassianus\",\"Fulcinia Numicius\",\"Martialis Cantilius\",\"Pettius Marcius\",\"Flavius Pompeius\",\"Lucretius Matienus\",\"Modius Verecundus\",\"Urbicus Persius\",\"Sentius Fannius\",\"Quintus Sextius\",\"Fulvius Iuventius\",\"Gordian Sergian\",\"Lollius Gratian\",\"Laronius Pasidienus\",\"Crispinus Laecanius\",\"Mescinius Pontidius\",\"Rullianus Maecenas\",\"Nigidius Curtius\",\"Perperna Coruncanius\",\"Valens Decimus\",\"Cincinnatus Lacerius\",\"Florian Potitius\",\"Cicereius Fulcinia\",\"Blossius Cethegus\",\"Persius Probus\",\"Cato Fundanus\",\"Faustus Caedicius\",\"Matinius Sornatius\",\"Aurelianus Appius\",\"Laurentius Sicinius\",\"Varian Mescinius\",\"Catilius Livius\",\"Lartius Maxentius\",\"Lollius Candidus\",\"Nasennius Nazarius\",\"Sedatius Laberius\",\"Pomponius Sulpicius\",\"Cloelius Curtius\",\"Rufinus Servius\",\"Hordeonius Verus\",\"Magnentius Ennius\",\"Marcius Munatius\",\"Cloelius Clodius\",\"Quinctilius Severian\",\"Lucanus Sempronius\",\"Catius Valentinian\",\"Genucius Octavius\",\"Sornatius Salinator\",\"Octavenus Caelian\",\"Festinius Cordius\",\"Herennius Segulius\",\"Petilius Icilius\",\"Burrienus Vitalis\",\"Menenius Donatus\",\"Cestius Albinius\",\"Palpellius Clodius\",\"Pasidienus Semellius\",\"Sittius Sestius\",\"Aulus Munatius\",\"Hostilius Stertinius\",\"Servilius Vincentius\",\"Faustus Livius\",\"Appius Ninnius\",\"Flavius Celsus\",\"Decentius Foslius\",\"Fundanus Sestius\",\"Silius Fabrinius\",\"Salinator Mamilius\",\"Geta Nasennius\",\"Livius Novellius\",\"Numerius Orchius\",\"Horatius Maximus\",\"Laronius Paconius\",\"Tiberius Cinna\",\"Regulus Junius\",\"Numitorius Blasius\",\"Mescinius Persius\",\"Festus Secundus\",\"Valentinian Catius\",\"Coruncanius Nigidius\",\"Memmius Herennius\",\"Diocletian Urbicus\",\"Ravillius Nautius\",\"Pupius Norbanus\",\"Memmius Belisarius\",\"Geminius Paternus\",\"Primitivus Varro\",\"Silius Pomponius\",\"Avidius Naevius\",\"Maximian Probus\",\"Mallius Nazarius\",\"Cornelius Servius\",\"Festinius Sabucius\",\"Castorius Cispius\",\"Nazarius Granius\",\"Plinius Fundanus\",\"Servius Stertinius\",\"Antonius Cosconius\",\"Cloelius Damasus\",\"Antonius Blossius\",\"Verecundus Eborius\",\"Caelian Brennus\",\"Sedatius Probus\",\"Palpellius Lucretius\",\"Orbilius Rufius\",\"Primitivus Secundus\",\"Catilius Aurelianus\",\"Pupius Sergius\",\"Cornificius Crispinus\",\"Catilius Placidus\",\"Zeno Flavius\",\"Sepullius Camillius\",\"Stertinius Catilius\",\"Numitorius Numonius\",\"Catius Juvenalis\",\"Drusus Pontidius\",\"Orbilius Lucanus\",\"Damasus Tertius\",\"Fabrinius Palpellius\",\"Aemilius Cocceius\",\"Nasidienus Albinus\",\"Laelius Segulius\",\"Camillus Maecenas\",\"Considius Priscus\",\"Naevius Pettius\",\"Ravillius Mallius\",\"Seccius Blossius\",\"Pinarus Plautius\",\"Aulus Petilius\",\"Ostorius Plinius\",\"Cantilius Laberius\",\"Ollius Sedatius\",\"Vincentius Donatus\",\"Silvius Coruncanius\",\"Cassio Verecundus\",\"Stertinius Sornatius\",\"Ingenuus Martialis\",\"Fidius Verecundus\",\"Sallustius Iunius\",\"Ofilius Aurelianus\",\"Volumnius Cicero\",\"Rebilus Gnaeus\",\"Maximian Domitian\",\"Appius Anatolius\",\"Gabinius Cominius\",\"Catilius Cato\",\"Lamponius Laetorius\",\"Afranius Florian\",\"Caecilius Urbanus\",\"Calpurnius Pilus\",\"Primitivus Cassianus\",\"Castorius Nautius\",\"Paulinus Minicius\",\"Blasius Albinius\",\"Pompeius Blasius\",\"Hostius Silvius\",\"Sulpicius Segulius\",\"Menenius Verecundus\",\"Cyprian Belisarius\",\"Lamponius Fundanus\",\"Horatius Nonius\",\"Diocletian Laronius\",\"Pius Iuventius\",\"Munatius Valerius\",\"Sallustius Matinius\",\"Saturninus Nazarius\",\"Orbilius Octavenus\",\"Secundinius Musonius\",\"Maxentius Perperna\",\"Honorius Caninius\",\"Hordeonius Pinarus\",\"Pacilius Paulinus\",\"Cominius Ostorius\",\"Decimus Laberius\",\"Gordian Aurelianus\",\"Seneca Justus\",\"Domitius Constantinus\",\"Lartius Baebius\",\"Sabucius Didius\",\"Ingenuus Cassianus\",\"Potitius Opimius\",\"Caecilius Vincentius\",\"Juvenalis Justus\",\"Faustus Suetonius\",\"Martialis Clodius\",\"Vibius Iulius\",\"Seneca Seccius\",\"Ogulnius Aquillius\",\"Marcus Naevius\",\"Sidonius Geta\",\"Menenius Laurentius\",\"Sertorius Numicius\",\"Numerius Hadrian\",\"Numonius Cicero\",\"Nazarius Cosconius\",\"Servilius Juvenalis\",\"Publius Priscus\",\"Domitian Marcius\",\"Petreius Fabianus\",\"Paulinus Secundinius\",\"Ollius Munatius\",\"Naevius Betilienus\",\"Varian Pius\",\"Marian Pomponius\",\"Regulus Fabrinius\",\"Duronius Sornatius\",\"Marcus Paternus\",\"Fulcinia Celsus\",\"Hortensius Caedicius\",\"Junius Genucius\",\"Manius Sicinius\",\"Paconius Albinius\",\"Quintus Nasidius\",\"Ingenuus Vespasian\",\"Rufinus Genucius\",\"Rufius Cincinnatus\",\"Titus Numonius\",\"Cyprian Pedius\",\"Julius Justus\",\"Licinius Nasidienus\",\"Porcius Gracchus\",\"Cornificius Foslius\",\"Blasius Atilius\",\"Terentius Juvenalis\",\"Balbinus Suetonius\",\"Sabucius Camillius\",\"Semellius Genucius\",\"Maximian Aurelianus\",\"Flavius Maecilius\",\"Bruttius Caedicius\",\"Camillius Lacerius\",\"Ravillius Honorius\",\"Stertinius Laronius\",\"Camillius Regulus\",\"Cassius Paternus\",\"Orchius Ollius\",\"Cloelius Opellius\",\"Blaesus Cocceius\",\"Acilius Tiberius\",\"Honorius Longinius\",\"Tarquinius Cominius\",\"Aquillius Sergian\",\"Cilnius Decimus\",\"Pupius Petilius\",\"Numonius Rufinus\",\"Blaesus Suetonius\",\"Percennius Caelius\",\"Honorius Postumius\",\"Cassius Fabrinius\",\"Lucanus Ravillius\",\"Lacerius Ennius\",\"Iunius Marius\",\"Sicinius Cassianus\",\"Balbinus Manilius\",\"Cicereius Hostilius\",\"Lollius Rutilius\",\"Aurelianus Caelius\",\"Hostilius Crispus\",\"Fundanus Hordeonius\",\"Sornatius Calventius\",\"Cilnius Stertinius\",\"Caedicius Cosconius\",\"Salinator Probus\",\"Festinius Constantinus\",\"Laronius Balbinus\",\"Priscus Gratian\",\"Calventius Verus\",\"Sallustius Albinius\",\"Tertius Sextius\",\"Livius Paternus\",\"Verus Octavius\",\"Tertius Naevius\",\"Ovidius Gnaeus\",\"Rufius Lartius\",\"Maximus Seneca\",\"Primitivus Cinna\",\"Aurelianus Nerva\",\"Urbicus Primitivus\",\"Cato Marcus\",\"Donatus Seneca\",\"Pomponius Spurius\",\"Flavius Stertinius\",\"Varian Latinius\",\"Draco Balbus\",\"Pedius Germanicus\",\"Granius Servilius\",\"Atilius Paconius\",\"Eutropius Norbanus\",\"Maecilius Plautius\",\"Lucius Cosconius\",\"Sabucius Marcius\",\"Blaesus Sempronius\",\"Fabianus Verecundus\",\"Silvius Scribonius\",\"Orestius Orbilius\",\"Cornutus Gracchus\",\"Silanus Fundanus\",\"Latinius Avidius\",\"Florian Donatus\",\"Paelignus Pompeius\",\"Caninius Caninius\",\"Severian Fulvius\",\"Cilnius Juvenalis\",\"Maximus Caelian\",\"Murena Gratian\",\"Valens Balbinus\",\"Florian Fundanus\",\"Cyprian Baebius\",\"Honorius Laberius\",\"Cestius Decentius\",\"Constantinus Poppaedius\",\"Festinius Sestius\",\"Lucretius Gaius\",\"Decentius Gratian\",\"Lacerius Flaccus\",\"Albinus Sulpicius\",\"Fidenas Aelius\",\"Sempronius Herennius\",\"Tertius Rullianus\",\"Cestius Valerius\",\"Gnaeus Titus\",\"Sidonius Aemilius\",\"Ollius Festus\",\"Januarius Valens\",\"Icilius Crispinus\",\"Felix Plautius\",\"Florian Florian\",\"Mallius Nigidius\",\"Hadrian Pius\",\"Spurius Cordius\",\"Salvian Potitius\",\"Icilius Marcus\",\"Paelignus Ravillius\",\"Octavenus Sempronius\",\"Festinius Fabrinius\",\"Castorius Fulcinia\",\"Nasennius Laelius\",\"Latinius Oppius\",\"Festinius Salvidienus\",\"Nautius Camillus\",\"Quirinius Caedicius\",\"Ennius Publius\",\"Damasus Hadrian\",\"Mucius Furius\",\"Varian Laurentius\",\"Silius Vespasian\",\"Ennius Mamilius\",\"Nerva Numerius\",\"Cornutus Eborius\",\"Vitus Nerva\",\"Terentius Cato\",\"Avidius Aquillius\",\"Iulius Clodius\",\"Herennius Mamilius\",\"Constantinus Ollius\",\"Horatius Novellius\",\"Silvius Orestius\",\"Juvenalis Caedicius\",\"Sestius Bruttius\",\"Plinius Sepullius\",\"Hilarianus Sidonius\",\"Cyprian Valerius\",\"Cassianus Primitivus\",\"Severian Sentius\",\"Cyprian Nigidius\",\"Minicius Secundinius\",\"Afranius Hostilius\",\"Regulus Sicinius\",\"Tertius Marcus\",\"Cornutus Flaccus\",\"Pontidius Hortensius\",\"Gracchus Cornutus\",\"Maecilius Burrienus\",\"Camillius Opellius\",\"Lucanus Maecenas\",\"Laetorius Cocceius\",\"Sextus Decimus\",\"Placidus Segulius\",\"Maximus Terentius\",\"Secundus Fabrinius\",\"Cassio Cornificius\",\"Balbus Petilius\",\"Pius Servius\",\"Quirinius Gabinius\",\"Atilius Cornificius\",\"Vitus Fabianus\",\"Lacerius Albinus\",\"Eborius Baebius\",\"Maximus Menenius\",\"Constantinus Verus\",\"Antonius Rebilus\",\"Silius Avidius\",\"Afranius Justus\",\"Gabinius Cordius\",\"Varro Mescinius\",\"Pacilius Nero\",\"Plinius Avidius\",\"Blaesus Mucius\",\"Antonius Paelignus\",\"Cassio Numonius\",\"Lamponius Aquillius\",\"Lucretius Laecanius\",\"Fulcinia Varro\",\"Sextus Longinius\",\"Sittius Vincentius\",\"Cornelius Oppius\",\"Orbilius Blossius\",\"Camillius Geminius\",\"Nonius Fidius\",\"Albinius Ravillius\",\"Messalla Tiberius\",\"Magnentius Sicinius\",\"Curtius Fulcinia\",\"Ogulnius Novellius\",\"Saturninus Servilius\",\"Cornelius Rullianus\",\"Balbus Lentulus\",\"Maximian Sallustius\",\"Decentius Norbanus\",\"Musonius Gallus\",\"Fabrinius Mescinius\",\"Pettius Gaius\",\"Felix Papirius\",\"Pompeius Modius\",\"Appius Silvius\",\"Donatus Servius\",\"Opimius Januarius\",\"Aelius Pettius\",\"Tacitus Decentius\",\"Pomponius Acilius\",\"Geminius Vitalis\",\"Urbanus Nazarius\",\"Nerva Plinius\",\"Suetonius Porcius\",\"Anatolius Caelian\",\"Cordius Percennius\",\"Sextius Fabius\",\"Acilius Rufrius\",\"Avidius Calventius\",\"Balbinus Flaccus\",\"Sittius Seccius\",\"Celsus Semellius\",\"Anatolius Fabrinius\",\"Segulius Egnatuleius\",\"Dacian Rullianus\",\"Cassius Sedatius\",\"Nasidius Nero\",\"Hortensius Sedatius\",\"Agrippa Pompeius\",\"Antonius Marcellus\",\"Aemilius Porcius\",\"Catius Gratian\",\"Iulius Geta\",\"Cosconius Castorius\",\"Aquillius Numicius\",\"Priscus Postumius\",\"Egnatius Clodius\",\"Numicius Livius\",\"Geminius Clodius\",\"Cornelius Fidenas\",\"Celsus Licinius\",\"Celsus Florianus\",\"Domitian Tertius\",\"Crispus Calventius\",\"Castorius Pius\",\"Norbanus Cassianus\",\"Pompeius Sestius\",\"Vitus Rebilus\",\"Matinius Hortensius\",\"Burrienus Rufrius\",\"Fannius Albinius\",\"Caedicius Martialis\",\"Appius Marcius\",\"Galerius Lucanus\",\"Sittius Priscus\",\"Fannius Secundinius\",\"Fundanus Numitorius\",\"Laurentius Maecilius\",\"Geta Constantinus\",\"Sertorius Nasidius\",\"Cantilius Longinius\",\"Amulius Iunius\",\"Decimus Sulpicius\",\"Catilius Scribonius\",\"Paternus Crispinus\",\"Nerva Publius\",\"Nazarius Segulius\",\"Cestius Stertinius\",\"Egnatuleius Considius\",\"Ogulnius Lollius\",\"Messalla Afranius\",\"Licinius Acilius\",\"Publicius Quinctilius\",\"Rullianus Hordeonius\",\"Flaccus Clodius\",\"Avidius Drusus\",\"Naevius Pius\",\"Blossius Duronius\",\"Cincinnatus Festinius\",\"Publicius Fundanus\",\"Potitius Justus\",\"Minicius Pacilius\",\"Vitalis Foslius\",\"Numitorius Volumnius\",\"Anatolius Opimius\",\"Cinna Quintus\",\"Lartius Blaesus\",\"Nonius Valerius\",\"Pontidius Nigidius\",\"Afranius Norbanus\",\"Ollius Plinius\",\"Germanicus Junius\",\"Silanus Paternus\",\"Sicinius Celsus\",\"Aurelianus Ofilius\",\"Tertius Decentius\",\"Volumnius Mescinius\",\"Geta Hilarianus\",\"Blaesus Camillus\",\"Cato Acilius\",\"Regulus Brennus\",\"Silvius Rebilus\",\"Varian Cornelius\",\"Cicereius Egnatuleius\",\"Papinius Papinius\",\"Opimius Opimius\",\"Saturninus Mamilius\",\"Primitivus Paternus\",\"Atilius Rabirius\",\"Opimius Cantilius\",\"Blasius Probus\",\"Nonius Cicereius\",\"Ostorius Draco\",\"Festinius Domitian\",\"Titus Pomponius\",\"Secundus Papinius\",\"Lacerius Pilus\",\"Cestius Quinctilius\",\"Manius Galerius\",\"Pasidienus Ovidius\",\"Paconius Novellius\",\"Oppius Maecilius\",\"Cordius Sextius\",\"Gratian Eutropius\",\"Julius Palpellius\",\"Tiberius Pontidius\",\"Appius Nero\",\"Maecilius Lentulus\",\"Icilius Calpurnius\",\"Blasius Juvenalis\",\"Maecilius Donatus\",\"Orchius Fulvius\",\"Maximus Sertorius\",\"Cicero Fannius\",\"Herennius Suetonius\",\"Ollius Longinius\",\"Celsus Sidonius\",\"Nazarius Afranius\",\"Januarius Ogulnius\",\"Honorius Plinius\",\"Appius Valentinian\",\"Ollius Tarquinius\",\"Honorius Zeno\",\"Salinator Appius\",\"Candidus Musonius\",\"Nasidius Granius\",\"Ogulnius Nonius\",\"Zeno Gordian\",\"Gordian Florian\",\"Severian Sestius\",\"Salvidienus Agrippa\",\"Vincentius Rufius\",\"Cantilius Papirius\",\"Anatolius Segulius\",\"Ravillius Magnentius\",\"Paulinus Brennus\",\"Germanicus Pompeius\",\"Lamponius Diocletian\",\"Latinius Lucretius\",\"Camillus Ofilius\",\"Juvenalis Iuventius\",\"Livius Appius\",\"Mescinius Magnentius\",\"Spurius Amulius\",\"Hadrian Maxentius\",\"Suetonius Blossius\",\"Pompeius Pasidienus\",\"Honorius Sestius\",\"Cethegus Julius\",\"Nero Cornelius\",\"Laurentius Crispus\",\"Marian Iunius\",\"Decimus Camillus\",\"Lacerius Damasus\",\"Festus Damasus\",\"Damasus Didius\",\"Ninnius Manilius\",\"Sulpicius Severian\",\"Candidus Plautius\",\"Cispius Secundus\",\"Pius Regulus\",\"Afranius Valerius\",\"Cordius Vitus\",\"Decimus Claudius\",\"Herennius Cicereius\",\"Appius Marcellus\",\"Manius Pontidius\",\"Nonius Pacilius\",\"Pontidius Gaius\",\"Baebius Pompeius\",\"Florian Rullianus\",\"Silvius Maximus\",\"Livius Cicereius\",\"Sabucius Fundanus\",\"Nautius Quintilianus\",\"Sergius Cornelius\",\"Fannius Ingenuus\",\"Festinius Pilus\",\"Horatius Sittius\",\"Clodius Oppius\",\"Ogulnius Vitus\",\"Fundanus Menenius\",\"Ofilius Vitus\",\"Decentius Spurius\",\"Marius Sabucius\",\"Cordius Mallius\",\"Antonius Mamilius\",\"Cicero Volumnius\",\"Considius Varian\",\"Laurentius Nigidius\",\"Petilius Hostius\",\"Menenius Probus\",\"Ostorius Sempronius\",\"Latinius Naevius\",\"Secundus Octavenus\",\"Balbus Balbinus\",\"Orbilius Postumius\",\"Cordius Appius\",\"Sicinius Geta\",\"Petreius Catius\",\"Pontidius Cyprian\",\"Honorius Horatius\",\"Secundus Pacilius\",\"Albinius Sittius\",\"Caesennius Mescinius\",\"Laetorius Baebius\",\"Cosconius Sicinius\",\"Hadrian Marcus\",\"Blossius Caelius\",\"Naevius Acilius\",\"Hilarianus Florianus\",\"Curtius Lollius\",\"Poppaedius Pomponius\",\"Porcius Fulcinia\",\"Diocletian Publius\",\"Aulus Nero\",\"Segulius Florianus\",\"Marcellus Rufinus\",\"Marian Persius\",\"Statilius Curtius\",\"Aulus Numonius\",\"Perperna Nazarius\",\"Cinna Laronius\",\"Octavenus Suetonius\",\"Naevius Catilius\",\"Calventius Gratian\",\"Varro Caelian\",\"Cornelius Percennius\",\"Sextius Pompeius\",\"Semellius Laberius\",\"Florian Pompeius\",\"Severus Paconius\",\"Sempronius Musonius\",\"Sepullius Marian\",\"Baebius Sestius\",\"Icilius Quinctilius\",\"Ingenuus Primitivus\",\"Pontidius Matienus\",\"Tacitus Pacilius\",\"Memmius Betilienus\",\"Naevius Paternus\",\"Caesennius Coruncanius\",\"Claudius Ollius\",\"Publius Pontidius\",\"Pomponius Granius\",\"Pius Salinator\",\"Sextius Plautius\",\"Suetonius Stertinius\",\"Statilius Pompeius\",\"Dacian Baebius\",\"Palpellius Paternus\",\"Sedatius Postumius\",\"Pasidienus Longinius\",\"Pinarus Lucius\",\"Juvenalis Calpurnius\",\"Caecilius Crispinus\",\"Hilarianus Fabianus\",\"Aquillius Ostorius\",\"Mamilius Latinius\",\"Messalla Sentius\",\"Scribonius Sornatius\",\"Cantilius Placidus\",\"Sittius Coruncanius\",\"Nero Diocletian\",\"Aelius Terentius\",\"Varro Lartius\",\"Catius Matienus\",\"Orchius Marcius\",\"Percennius Hortensius\",\"Eborius Sicinius\",\"Eborius Silvius\",\"Valentinian Calventius\",\"Paelignus Statilius\",\"Postumius Nonius\",\"Quirinius Antonius\",\"Hostius Pilus\",\"Quirinius Appius\",\"Salvidienus Licinius\",\"Ninnius Vitalis\",\"Dacian Eborius\",\"Caecilius Primitivus\",\"Catius Cyprian\",\"Laelius Maximian\",\"Maximus Gordian\",\"Paelignus Rufinus\",\"Volumnius Fabius\",\"Rabirius Valentinian\",\"Manius Valentinian\",\"Quintus Pompeius\",\"Pomponius Petilius\",\"Cornificius Castorius\",\"Varian Fulvius\",\"Latinius Statilius\",\"Laecanius Longinius\",\"Perperna Saturninus\",\"Furius Catilius\",\"Bonifacius Segulius\",\"Claudius Pasidienus\",\"Modius Eutropius\",\"Diocletian Palpellius\",\"Gracchus Marcus\",\"Secundus Tacitus\",\"Semellius Celsus\",\"Nonius Memmius\",\"Horatius Secundus\",\"Caelian Nerva\",\"Maxentius Secundinius\",\"Sempronius Iuventius\",\"Maxentius Fannius\",\"Nerva Nasidienus\",\"Flaccus Severus\",\"Amulius Martialis\",\"Hostius Perperna\",\"Paternus Oppius\",\"Lollius Memmius\",\"Scribonius Rutilius\",\"Porcius Numitorius\",\"Silanus Ravillius\",\"Salinator Balbus\",\"Sabucius Gallus\",\"Considius Domitius\",\"Hostilius Numonius\",\"Maximus Geta\",\"Galerius Gratian\",\"Domitius Postumius\",\"Granius Appius\",\"Caedicius Cassio\",\"Caedicius Lamponius\",\"Julius Belisarius\",\"Memmius Aurelianus\",\"Publius Lucretius\",\"Gracchus Ninnius\",\"Cosconius Urbanus\",\"Lucius Regulus\",\"Aemilius Licinius\",\"Camillius Severus\",\"Gabinius Publicius\",\"Camillius Mallius\",\"Damasus Gratian\",\"Lacerius Magnentius\",\"Nazarius Cornificius\",\"Maecilius Vitus\",\"Varro Considius\",\"Murena Paulinus\",\"Norbanus Pettius\",\"Sestius Aemilius\",\"Vespasian Petilius\",\"Nigidius Pius\",\"Verus Cassius\",\"Iunius Licinius\",\"Paelignus Gnaeus\",\"Ostorius Aulus\",\"Iunius Ingenuus\",\"Paulinus Egnatius\",\"Valerius Duronius\",\"Decimus Paulinus\",\"Rutilius Anatolius\",\"Rullianus Catius\",\"Hordeonius Sentius\",\"Scribonius Blasius\",\"Maxentius Maecilius\",\"Pinarus Urbicus\",\"Lucretius Maecilius\",\"Pompeius Ogulnius\",\"Drusus Gratian\",\"Publicius Publicius\",\"Camillus Valerius\",\"Lentulus Numitorius\",\"Segulius Spurius\",\"Suetonius Matinius\",\"Fabius Iulius\",\"Pinarus Terentius\",\"Anatolius Oppius\",\"Publicius Zeno\",\"Cicereius Caelian\",\"Pius Severian\",\"Matinius Nonius\",\"Justus Cilnius\",\"Fannius Catilius\",\"Gallus Varro\",\"Flavius Manius\",\"Volumnius Florian\",\"Lamponius Severian\",\"Paulinus Aulus\",\"Appius Juvenalis\",\"Cilnius Hirrus\",\"Petreius Pilus\",\"Silvius Secundinius\",\"Fannius Diocletian\",\"Galerius Fannius\",\"Poppaedius Cincinnatus\",\"Verecundus Maecilius\",\"Varro Catilius\",\"Mucius Maecenas\",\"Ovidius Marian\",\"Antonius Regulus\",\"Sergian Amulius\",\"Fundanus Caedicius\",\"Cocceius Genucius\",\"Martialis Ostorius\",\"Magnentius Juvenalis\",\"Verecundus Manilius\",\"Marcellus Palpellius\",\"Maximian Sentius\",\"Eutropius Nasidius\",\"Festinius Priscus\",\"Porcius Sergius\",\"Scribonius Laetorius\",\"Messalla Ofilius\",\"Marian Varian\",\"Sergius Laelius\",\"Nasidienus Vitalis\",\"Draco Silius\",\"Sulpicius Pettius\",\"Severian Papinius\",\"Nigidius Statilius\",\"Ogulnius Cassio\",\"Fulcinia Mucius\",\"Sulpicius Mallius\",\"Sestius Sulpicius\",\"Hordeonius Coruncanius\",\"Caelian Messalla\",\"Numicius Caesennius\",\"Hilarianus Potitius\",\"Titus Cloelius\",\"Junius Lollius\",\"Coruncanius Mescinius\",\"Drusus Mescinius\",\"Silvius Sergian\",\"Florian Baebius\",\"Sertorius Servilius\",\"Eborius Plinius\",\"Blasius Publicius\",\"Caedicius Catilius\",\"Sepullius Hostilius\",\"Pompeius Volumnius\",\"Mamilius Sempronius\",\"Matinius Rabirius\",\"Volumnius Martialis\",\"Marcellus Cinna\",\"Silius Maecilius\",\"Sentius Sextius\",\"Memmius Acilius\",\"Blasius Hadrian\",\"Vitus Icilius\",\"Severian Titus\",\"Draco Lacerius\",\"Calventius Salvius\",\"Vitalis Valens\",\"Maecenas Mescinius\",\"Maximus Nasidienus\",\"Tertius Persius\",\"Afranius Orbilius\",\"Balbinus Opellius\",\"Rullianus Blossius\",\"Iulius Spurius\",\"Icilius Calventius\",\"Crispinus Sempronius\",\"Numicius Hordeonius\",\"Matienus Nigidius\",\"Eutropius Sulpicius\",\"Fabius Rufrius\",\"Cantilius Potitius\",\"Betilienus Palpellius\",\"Bonifacius Sextus\",\"Bonifacius Gnaeus\",\"Quirinius Cestius\",\"Saturninus Cocceius\",\"Lucanus Suetonius\",\"Aelius Donatus\",\"Tacitus Cato\",\"Sergian Oppius\",\"Terentius Servilius\",\"Salvidienus Ostorius\",\"Hortensius Marcellus\",\"Gaius Geta\",\"Minicius Afranius\",\"Quinctilius Hostilius\",\"Aurelianus Servius\",\"Saturninus Duronius\",\"Gratian Ogulnius\",\"Eborius Priscus\",\"Geta Latinius\",\"Suetonius Aurelianus\",\"Nautius Horatius\",\"Mescinius Severus\",\"Cicero Amulius\",\"Seccius Octavenus\",\"Lucretius Nautius\",\"Gratian Semellius\",\"Pasidienus Claudius\",\"Paulinus Norbanus\",\"Marcus Opellius\",\"Sidonius Castorius\",\"Ollius Severian\",\"Calventius Calventius\",\"Cassio Secundinius\",\"Munatius Dacian\",\"Marcus Laberius\",\"Albinius Murena\",\"Blossius Pasidienus\",\"Petreius Cicereius\",\"Furius Tertius\",\"Ingenuus Betilienus\",\"Pasidienus Laecanius\",\"Cornutus Nerva\",\"Gratian Fabius\",\"Balbinus Spurius\",\"Marcius Avidius\",\"Statilius Clodius\",\"Novellius Celsus\",\"Damasus Cispius\",\"Curtius Petilius\",\"Drusus Quinctilius\",\"Cominius Sidonius\",\"Didius Anatolius\",\"Paconius Nasidienus\",\"Salinator Sergius\",\"Pius Urbicus\",\"Aulus Secundinius\",\"Gallus Pupius\",\"Magnentius Lentulus\",\"Rufius Caelian\",\"Juvenalis Betilienus\",\"Aelius Egnatius\",\"Magnentius Cinna\",\"Petilius Celsus\",\"Poppaedius Maximian\",\"Nautius Avidius\",\"Severus Musonius\",\"Minicius Caecilius\",\"Aelius Urbicus\",\"Pompeius Laberius\",\"Maximian Cinna\",\"Quirinius Galerius\",\"Orchius Rufinus\",\"Cassius Severian\",\"Fabrinius Claudius\",\"Murena Cassianus\",\"Caninius Bonifacius\",\"Brennus Priscus\",\"Draco Ravillius\",\"Postumius Marian\",\"Cominius Lartius\",\"Cilnius Varro\",\"Sabucius Cassio\",\"Celsus Hilarianus\",\"Scribonius Sallustius\",\"Cicereius Iulius\",\"Ingenuus Constantinus\",\"Pettius Hadrian\",\"Gnaeus Laurentius\",\"Florian Festus\",\"Ovidius Foslius\",\"Germanicus Severus\",\"Genucius Florianus\",\"Gordian Sepullius\",\"Nero Icilius\",\"Horatius Sedatius\",\"Laetorius Considius\",\"Egnatius Novellius\",\"Numonius Cato\",\"Afranius Rullianus\",\"Saturninus Silanus\",\"Caecilius Postumius\",\"Calpurnius Constantinus\",\"Anatolius Blaesus\",\"Laurentius Catilius\",\"Seneca Regulus\",\"Herennius Pupius\",\"Pasidienus Albinius\",\"Fidenas Longinius\",\"Mallius Lollius\",\"Paternus Cilnius\",\"Laelius Pasidienus\",\"Pacilius Duronius\",\"Hostilius Nasennius\",\"Quintus Vespasian\",\"Cestius Geminius\",\"Perperna Sornatius\",\"Salvius Lamponius\",\"Cicereius Iunius\",\"Scribonius Primitivus\",\"Amulius Furius\",\"Sextius Gratian\",\"Julius Cassio\",\"Geta Segulius\",\"Caesennius Laecanius\",\"Festus Seccius\",\"Plautius Quintilianus\",\"Palpellius Iuventius\",\"Anatolius Cassius\",\"Cassio Pilus\",\"Rutilius Ollius\",\"Tacitus Genucius\",\"Volumnius Honorius\",\"Gallus Geta\",\"Maecenas Verecundus\",\"Placidus Messalla\",\"Saturninus Rufrius\",\"Cantilius Laronius\",\"Sertorius Porcius\",\"Severian Catius\",\"Crispinus Rebilus\",\"Agrippa Cassianus\",\"Acilius Cestius\",\"Poppaedius Palpellius\",\"Foslius Cominius\",\"Claudius Segulius\",\"Paulinus Avidius\",\"Postumius Urbanus\",\"Ingenuus Cyprian\",\"Valentinian Licinius\",\"Cicereius Gabinius\",\"Celsus Bruttius\",\"Agrippa Hortensius\",\"Nasennius Geminius\",\"Albinus Aelius\",\"Mallius Flavius\",\"Egnatuleius Herennius\",\"Hilarianus Opellius\",\"Titus Quirinius\",\"Saturninus Sabucius\",\"Lucanus Paelignus\",\"Foslius Papirius\",\"Salvius Numerius\",\"Paelignus Orestius\",\"Sentius Murena\",\"Sallustius Lucius\",\"Fundanus Silvius\",\"Petilius Foslius\",\"Atilius Rufinus\",\"Fabius Memmius\",\"Publicius Orchius\",\"Minicius Laronius\",\"Sabucius Plinius\",\"Calpurnius Granius\",\"Considius Flavius\",\"Diocletian Marcellus\",\"Vincentius Mamilius\",\"Martialis Catilius\",\"Nasidius Calpurnius\",\"Betilienus Faustus\",\"Mallius Ovidius\",\"Magnentius Donatus\",\"Salinator Nigidius\",\"Minicius Oppius\",\"Segulius Modius\",\"Hilarianus Sentius\",\"Catilius Salinator\",\"Hortensius Genucius\",\"Clodius Paternus\",\"Longinius Lacerius\",\"Petilius Calventius\",\"Paelignus Camillius\",\"Petreius Maecilius\",\"Urbicus Belisarius\",\"Julius Spurius\",\"Galerius Quirinius\",\"Pedius Blossius\",\"Manilius Hostilius\",\"Hostius Belisarius\",\"Octavenus Paconius\",\"Naevius Laelius\",\"Quinctilius Secundinius\",\"Hostius Orbilius\",\"Faustus Cassius\",\"Pupius Sestius\",\"Titus Salvius\",\"Ingenuus Lentulus\",\"Appius Maximus\",\"Rufinus Sedatius\",\"Claudius Quirinius\",\"Celsus Herennius\",\"Pedius Constantinus\",\"Sulpicius Fulcinia\",\"Cassius Persius\",\"Sertorius Plautius\",\"Gordian Candidus\",\"Furius Zeno\",\"Duronius Eutropius\",\"Sertorius Sicinius\",\"Acilius Fulvius\",\"Cincinnatus Foslius\",\"Appius Foslius\",\"Valentinian Balbinus\",\"Lartius Rufius\",\"Maecilius Verus\",\"Fannius Marcus\",\"Manius Catius\",\"Pompeius Caecilius\",\"Nasidius Burrienus\",\"Faustus Camillius\",\"Gratian Catius\",\"Aurelianus Mucius\",\"Hordeonius Rufinus\",\"Varro Nasennius\",\"Genucius Cornutus\",\"Sulpicius Baebius\",\"Januarius Hostius\",\"Gabinius Geta\",\"Ogulnius Pettius\",\"Drusus Tiberius\",\"Terentius Quintus\",\"Oppius Spurius\",\"Genucius Silius\",\"Fidius Nigidius\",\"Cassius Titus\",\"Icilius Placidus\",\"Regulus Plinius\",\"Numitorius Bruttius\",\"Varian Volumnius\",\"Sepullius Memmius\",\"Blaesus Marcius\",\"Rabirius Probus\",\"Plinius Brennus\",\"Caedicius Salvian\",\"Salvius Maecilius\",\"Florianus Pompeius\",\"Livius Antonius\",\"Verecundus Octavenus\",\"Gordian Caelian\",\"Coruncanius Matienus\",\"Urbicus Sabucius\",\"Longinius Sulpicius\",\"Sextius Aemilius\",\"Plautius Quirinius\",\"Amulius Balbus\",\"Stertinius Lucius\",\"Maximian Gallus\",\"Licinius Varro\",\"Sergius Naevius\",\"Amulius Cloelius\",\"Horatius Hordeonius\",\"Sabucius Nasennius\",\"Coruncanius Opimius\",\"Sedatius Ennius\",\"Lucius Urbicus\",\"Saturninus Vincentius\",\"Numitorius Seccius\",\"Lartius Priscus\",\"Paulinus Pilus\",\"Percennius Pontidius\",\"Betilienus Hirrus\",\"Aelius Cato\",\"Hostilius Ogulnius\",\"Norbanus Sentius\",\"Florianus Pius\",\"Pedius Salvius\",\"Fulvius Cethegus\",\"Cordius Laelius\",\"Laetorius Rebilus\",\"Ennius Cicero\",\"Tarquinius Hostius\",\"Terentius Manius\",\"Curtius Hordeonius\",\"Acilius Orchius\",\"Draco Iunius\",\"Hadrian Lollius\",\"Baebius Considius\",\"Sidonius Albinus\",\"Sornatius Ingenuus\",\"Nonius Saturninus\",\"Gnaeus Silius\",\"Martialis Licinius\",\"Sidonius Decimus\",\"Mucius Valens\",\"Albinus Duronius\",\"Rufius Lucius\",\"Oppius Rufius\",\"Camillus Albinus\",\"Iuventius Egnatuleius\",\"Regulus Nasennius\",\"Blossius Fidenas\",\"Cantilius Fannius\",\"Maximus Curtius\",\"Volumnius Balbus\",\"Nasennius Primitivus\",\"Verus Volumnius\",\"Hadrian Hirrus\",\"Ninnius Marcellus\",\"Nasidienus Licinius\",\"Modius Florianus\",\"Munatius Nasennius\",\"Octavenus Caecilius\",\"Severian Horatius\",\"Livius Herennius\",\"Lucius Drusus\",\"Servilius Iulius\",\"Caninius Caecilius\",\"Mamilius Cordius\",\"Blaesus Blasius\",\"Livius Sallustius\",\"Hirrus Cicereius\",\"Rufinus Amulius\",\"Festinius Longinius\",\"Damasus Servius\",\"Silanus Volumnius\",\"Orbilius Opimius\",\"Baebius Postumius\",\"Nautius Numerius\",\"Seneca Constantinus\",\"Hadrian Gnaeus\",\"Silanus Pomponius\",\"Lucius Juvenalis\",\"Matinius Crispinus\",\"Drusus Valentinian\",\"Pontidius Lentulus\",\"Juvenalis Semellius\",\"Varian Orestius\",\"Egnatius Mamilius\",\"Lucretius Regulus\",\"Laecanius Sulpicius\",\"Germanicus Fidius\",\"Sedatius Bonifacius\",\"Marcius Cloelius\",\"Marian Opellius\",\"Semellius Duronius\",\"Valentinian Servius\",\"Postumius Publicius\",\"Maecilius Cinna\",\"Marius Cestius\",\"Laberius Cyprian\",\"Cethegus Vincentius\",\"Lartius Naevius\",\"Iulius Pomponius\",\"Hadrian Palpellius\",\"Sextius Rufrius\",\"Balbinus Sornatius\",\"Primitivus Hostius\",\"Florianus Minicius\",\"Catilius Acilius\",\"Iulius Publicius\",\"Semellius Marian\",\"Clodius Statilius\",\"Rutilius Sornatius\",\"Zeno Cethegus\",\"Salvian Orbilius\",\"Petreius Cethegus\",\"Sextius Festus\",\"Cincinnatus Marcellus\",\"Paelignus Albinus\",\"Priscus Fidenas\",\"Celsus Balbinus\",\"Aquillius Draco\",\"Balbus Numicius\",\"Cosconius Maecilius\",\"Volumnius Salinator\",\"Pacilius Rullianus\",\"Rufrius Geta\",\"Januarius Perperna\",\"Quirinius Sextus\",\"Maximus Fundanus\",\"Sicinius Hirrus\",\"Avidius Sextius\",\"Plinius Fabius\",\"Marius Titus\",\"Castorius Cassio\",\"Sicinius Vincentius\",\"Segulius Considius\",\"Ogulnius Lucius\",\"Manius Blossius\",\"Acilius Atilius\",\"Castorius Lucretius\",\"Manius Foslius\",\"Matinius Postumius\",\"Fabianus Calventius\",\"Aulus Sidonius\",\"Cornificius Nigidius\",\"Paconius Rufrius\",\"Curtius Junius\",\"Severian Potitius\",\"Petilius Paelignus\",\"Lartius Gordian\",\"Ingenuus Matienus\",\"Nonius Papirius\",\"Aulus Manius\",\"Fundanus Papirius\",\"Geta Suetonius\",\"Papirius Ofilius\",\"Publicius Bonifacius\",\"Plautius Lentulus\",\"Marcus Rufius\",\"Drusus Quintilianus\",\"Sextius Albinius\",\"Dacian Verecundus\",\"Urbicus Cestius\",\"Oppius Matinius\",\"Salvian Aemilius\",\"Numitorius Nonius\",\"Cordius Marius\",\"Blasius Calventius\",\"Sabucius Potitius\",\"Albinus Vitus\",\"Quinctilius Nigidius\",\"Lucius Perperna\",\"Orbilius Didius\",\"Sulpicius Percennius\",\"Duronius Nasennius\",\"Cato Ravillius\",\"Marian Fabianus\",\"Justus Verecundus\",\"Amulius Ravillius\",\"Aelius Petilius\",\"Hostilius Norbanus\",\"Marcius Ollius\",\"Hirrus Volumnius\",\"Ingenuus Laetorius\",\"Rufrius Titus\",\"Constantinus Saturninus\",\"Mescinius Primitivus\",\"Cassio Albinus\",\"Hadrian Blaesus\",\"Sertorius Marcellus\",\"Oppius Domitius\",\"Nasidius Gordian\",\"Germanicus Marius\",\"Hordeonius Cincinnatus\",\"Maecilius Laberius\",\"Fidius Vespasian\",\"Anatolius Horatius\",\"Marius Eutropius\",\"Celsus Seccius\",\"Magnentius Decimus\",\"Probus Pedius\",\"Gabinius Castorius\",\"Albinus Ninnius\",\"Fulcinia Laberius\",\"Albinus Salvius\",\"Caedicius Papirius\",\"Blossius Novellius\",\"Latinius Sextus\",\"Modius Gaius\",\"Sergian Murena\",\"Cestius Magnentius\",\"Iuventius Vitalis\",\"Iuventius Maecilius\",\"Eutropius Calventius\",\"Nero Messalla\",\"Pontidius Orbilius\",\"Regulus Petreius\",\"Magnentius Sempronius\",\"Varro Gnaeus\",\"Egnatuleius Vibius\",\"Florianus Gratian\",\"Tiberius Maxentius\",\"Nasidienus Laurentius\",\"Numonius Titus\",\"Laecanius Statilius\",\"Foslius Sentius\",\"Bonifacius Cominius\",\"Laetorius Priscus\",\"Sertorius Fannius\",\"Clodius Petilius\",\"Cordius Primitivus\",\"Camillus Ennius\",\"Egnatius Gracchus\",\"Nautius Marius\",\"Aulus Damasus\",\"Rullianus Draco\",\"Albinus Nasidienus\",\"Percennius Segulius\",\"Donatus Crispinus\",\"Murena Quirinius\",\"Sicinius Stertinius\",\"Marcellus Vibius\",\"Catius Balbus\",\"Tarquinius Lucanus\",\"Appius Caelius\",\"Caesennius Cato\",\"Pasidienus Acilius\",\"Valerius Caninius\",\"Postumius Anatolius\",\"Bruttius Afranius\",\"Laronius Poppaedius\",\"Numerius Pettius\",\"Palpellius Primitivus\",\"Fabianus Curtius\",\"Calventius Probus\",\"Hostius Terentius\",\"Rebilus Sestius\",\"Secundinius Foslius\",\"Nazarius Cicero\",\"Priscus Ollius\",\"Pedius Munatius\",\"Caelius Geta\",\"Gnaeus Domitian\",\"Gabinius Oppius\",\"Albinus Amulius\",\"Icilius Juvenalis\",\"Menenius Octavenus\",\"Maecenas Laelius\",\"Domitian Fabius\",\"Paternus Tertius\",\"Nerva Antonius\",\"Pupius Aemilius\",\"Catius Vespasian\",\"Felix Rufrius\",\"Blossius Palpellius\",\"Caelius Paelignus\",\"Geminius Flaccus\",\"Eutropius Pedius\",\"Drusus Gordian\",\"Longinius Orestius\",\"Anatolius Minicius\",\"Varro Blossius\",\"Sergius Damasus\",\"Brennus Florian\",\"Pinarus Caesennius\",\"Fidius Sulpicius\",\"Foslius Fabrinius\",\"Aemilius Sextus\",\"Acilius Hadrian\",\"Pompeius Bonifacius\",\"Nautius Regulus\",\"Sergius Silvius\",\"Severus Laetorius\",\"Magnentius Urbanus\",\"Mallius Geta\",\"Clodius Lacerius\",\"Sabucius Claudius\",\"Terentius Cocceius\",\"Nasidienus Ovidius\",\"Cassio Pius\",\"Cispius Lamponius\",\"Urbanus Sicinius\",\"Salvian Sepullius\",\"Gabinius Lucretius\",\"Octavius Cincinnatus\",\"Pettius Hilarianus\",\"Octavenus Cilnius\",\"Flavius Laecanius\",\"Sextus Egnatius\",\"Balbus Sertorius\",\"Munatius Rutilius\",\"Fidius Rebilus\",\"Percennius Geminius\",\"Salinator Nasidienus\",\"Poppaedius Drusus\",\"Nasidienus Fidenas\",\"Nasidienus Sextus\",\"Drusus Laelius\",\"Cilnius Marcus\",\"Priscus Titus\",\"Cloelius Opimius\",\"Opellius Hirrus\",\"Modius Betilienus\",\"Sentius Curtius\",\"Nasennius Salinator\",\"Horatius Sentius\",\"Bonifacius Musonius\",\"Flaccus Petilius\",\"Varian Sergius\",\"Norbanus Cispius\",\"Messalla Herennius\",\"Quinctilius Egnatius\",\"Marian Albinus\",\"Horatius Naevius\",\"Numerius Hordeonius\",\"Caesennius Brennus\",\"Severian Nigidius\",\"Seneca Rufinus\",\"Florian Pomponius\",\"Sextus Honorius\",\"Laetorius Fabius\",\"Balbinus Matinius\",\"Cicereius Lamponius\",\"Laelius Laronius\",\"Laetorius Iunius\",\"Sicinius Sertorius\",\"Brennus Nonius\",\"Porcius Messalla\",\"Baebius Rufrius\",\"Felix Servius\",\"Matienus Laronius\",\"Opimius Rebilus\",\"Rufius Numonius\",\"Cinna Munatius\",\"Lacerius Geminius\",\"Rabirius Anatolius\",\"Placidus Catius\",\"Aelius Rabirius\",\"Cestius Numitorius\",\"Valentinian Nerva\",\"Genucius Maecilius\",\"Crispus Rebilus\",\"Ogulnius Fabius\",\"Catius Gracchus\",\"Urbanus Ingenuus\",\"Marius Valens\",\"Servilius Marcellus\",\"Numitorius Laronius\",\"Sextus Ostorius\",\"Tertius Severus\",\"Afranius Novellius\",\"Varro Cethegus\",\"Pompeius Octavenus\",\"Quirinius Brennus\",\"Vitalis Orbilius\",\"Sextius Gnaeus\",\"Gaius Brennus\",\"Postumius Draco\",\"Damasus Marian\",\"Cloelius Petilius\",\"Tiberius Sestius\",\"Laronius Balbus\",\"Balbus Modius\",\"Semellius Musonius\",\"Valens Eutropius\",\"Festinius Sicinius\",\"Geta Verus\",\"Maxentius Laurentius\",\"Hostilius Egnatuleius\",\"Juvenalis Caecilius\",\"Domitius Valentinian\",\"Geminius Calventius\",\"Quintus Ovidius\",\"Gnaeus Sertorius\",\"Plautius Sergius\",\"Laecanius Nasidienus\",\"Felix Servilius\",\"Musonius Titus\",\"Pius Marcellus\",\"Memmius Licinius\",\"Nigidius Fabianus\",\"Cantilius Fidenas\",\"Rutilius Martialis\",\"Seneca Paternus\",\"Norbanus Lucanus\",\"Camillus Suetonius\",\"Albinius Vitus\",\"Cordius Terentius\",\"Sedatius Rufinus\",\"Catius Potitius\",\"Secundinius Hadrian\",\"Latinius Octavius\",\"Calventius Rebilus\",\"Rufius Silvius\",\"Marcus Magnentius\",\"Numerius Menenius\",\"Hortensius Duronius\",\"Aelius Ostorius\",\"Lacerius Murena\",\"Pomponius Cyprian\",\"Antonius Servius\",\"Quintilianus Naevius\",\"Numitorius Gallus\",\"Salvius Paulinus\",\"Quintus Sentius\",\"Gallus Bruttius\",\"Felix Cornificius\",\"Ollius Publicius\",\"Petilius Domitius\",\"Afranius Orestius\",\"Bonifacius Plinius\",\"Cloelius Ovidius\",\"Betilienus Caedicius\",\"Fidenas Verus\",\"Aurelianus Cantilius\",\"Salvian Poppaedius\",\"Agrippa Pedius\",\"Ofilius Nasennius\",\"Severian Bruttius\",\"Fulvius Atilius\",\"Betilienus Sallustius\",\"Pupius Anatolius\",\"Agrippa Vincentius\",\"Cominius Fidenas\",\"Plinius Fabianus\",\"Agrippa Atilius\",\"Minicius Papirius\",\"Verecundus Brennus\",\"Pasidienus Rufinus\",\"Rufinus Cloelius\",\"Naevius Fidenas\",\"Caesennius Postumius\",\"Regulus Volumnius\",\"Laronius Scribonius\",\"Palpellius Fulvius\",\"Tacitus Munatius\",\"Magnentius Hostilius\",\"Scribonius Gallus\",\"Catius Seccius\",\"Atilius Longinius\",\"Sornatius Flavius\",\"Ennius Sextus\",\"Acilius Justus\",\"Poppaedius Pilus\",\"Fulcinia Secundinius\",\"Severian Caesennius\",\"Ravillius Didius\",\"Quintilianus Afranius\",\"Lollius Maximus\",\"Festinius Duronius\",\"Matinius Nasennius\",\"Pomponius Livius\",\"Cornelius Opellius\",\"Sentius Laetorius\",\"Juvenalis Segulius\",\"Vitalis Silanus\",\"Porcius Didius\",\"Silanus Sestius\",\"Titus Sulpicius\",\"Baebius Cicero\",\"Pettius Palpellius\",\"Domitian Crispus\",\"Cornelius Cinna\",\"Lollius Varro\",\"Sicinius Betilienus\",\"Foslius Atilius\",\"Aulus Palpellius\",\"Gratian Pius\",\"Cestius Aulus\",\"Florian Brennus\",\"Vincentius Florianus\",\"Saturninus Publicius\",\"Juvenalis Eborius\",\"Curtius Licinius\",\"Flaccus Lollius\",\"Egnatius Atilius\",\"Matinius Probus\",\"Cantilius Servilius\",\"Segulius Lartius\",\"Caedicius Albinus\",\"Opellius Salinator\",\"Herennius Galerius\",\"Amulius Gaius\",\"Donatus Geminius\",\"Hadrian Duronius\",\"Geta Placidus\",\"Rufius Agrippa\",\"Florianus Hostilius\",\"Terentius Sextus\",\"Orbilius Drusus\",\"Opellius Ogulnius\",\"Iuventius Pomponius\",\"Crispinus Volumnius\",\"Marcellus Sidonius\",\"Rabirius Laetorius\",\"Silius Clodius\",\"Caelius Verus\",\"Hortensius Fulvius\",\"Vespasian Aulus\",\"Sergian Semellius\",\"Didius Albinus\",\"Afranius Rabirius\",\"Ninnius Amulius\",\"Salinator Nautius\",\"Icilius Munatius\",\"Norbanus Scribonius\",\"Rullianus Lamponius\",\"Crispus Gabinius\",\"Statilius Pacilius\",\"Mucius Cyprian\",\"Crispus Cornelius\",\"Gordian Albinius\",\"Modius Vespasian\",\"Nerva Gnaeus\",\"Sornatius Opimius\",\"Celsus Belisarius\",\"Paelignus Placidus\",\"Fidenas Flavius\",\"Marius Appius\",\"Cethegus Persius\",\"Nautius Decentius\",\"Sicinius Ennius\",\"Probus Orchius\",\"Cinna Rufrius\",\"Laelius Silius\",\"Pomponius Cilnius\",\"Nonius Laronius\",\"Germanicus Suetonius\",\"Pius Opimius\",\"Silvius Seneca\",\"Naevius Pinarus\",\"Cantilius Florianus\",\"Januarius Faustus\",\"Hostilius Cocceius\",\"Poppaedius Cominius\",\"Titus Nasennius\",\"Genucius Vibius\",\"Egnatuleius Nasidienus\",\"Foslius Cyprian\",\"Novellius Statilius\",\"Galerius Naevius\",\"Claudius Sextius\",\"Menenius Furius\",\"Caedicius Felix\",\"Varro Florian\",\"Gaius Varian\",\"Minicius Ennius\",\"Gallus Plautius\",\"Ovidius Burrienus\",\"Balbus Varro\",\"Cethegus Vibius\",\"Sornatius Diocletian\",\"Blossius Salinator\",\"Burrienus Caelius\",\"Sergius Florian\",\"Granius Dacian\",\"Maximian Petreius\",\"Valerius Numonius\",\"Gracchus Lucretius\",\"Pinarus Albinius\",\"Caelius Salvian\",\"Cispius Minicius\",\"Valerius Domitius\",\"Murena Novellius\",\"Octavenus Quintus\",\"Atilius Florian\",\"Lucanus Florian\",\"Duronius Nasidius\",\"Clodius Plautius\",\"Ostorius Manius\",\"Sallustius Secundinius\",\"Cyprian Octavius\",\"Plinius Agrippa\",\"Albinius Bonifacius\",\"Opimius Pinarus\",\"Numonius Horatius\",\"Hadrian Regulus\",\"Nonius Ravillius\",\"Paelignus Latinius\",\"Nasidius Marian\",\"Priscus Cominius\",\"Gratian Petilius\",\"Cornificius Cilnius\",\"Zeno Tiberius\",\"Candidus Laecanius\",\"Eutropius Afranius\",\"Octavius Sentius\",\"Caelian Domitian\",\"Nautius Appius\",\"Antonius Cordius\",\"Laronius Gordian\",\"Iuventius Amulius\",\"Pomponius Opellius\",\"Vitus Orestius\",\"Calpurnius Egnatius\",\"Laronius Laronius\",\"Cassius Regulus\",\"Cinna Considius\",\"Matinius Calventius\",\"Norbanus Betilienus\",\"Terentius Urbicus\",\"Aemilius Celsus\",\"Cornelius Sextus\",\"Catius Plinius\",\"Primitivus Gabinius\",\"Drusus Furius\",\"Gracchus Drusus\",\"Paelignus Junius\",\"Iulius Porcius\",\"Martialis Lacerius\",\"Sestius Poppaedius\",\"Placidus Numitorius\",\"Donatus Petilius\",\"Salinator Gallus\",\"Tarquinius Vincentius\",\"Memmius Rabirius\",\"Ovidius Varro\",\"Tarquinius Iunius\",\"Longinius Junius\",\"Zeno Festinius\",\"Quirinius Flavius\",\"Januarius Titus\",\"Cato Lollius\",\"Domitian Decentius\",\"Ninnius Varian\",\"Livius Orchius\",\"Eborius Egnatius\",\"Statilius Sestius\",\"Pedius Rufius\",\"Petilius Porcius\",\"Secundus Julius\",\"Scribonius Livius\",\"Menenius Hirrus\",\"Papirius Coruncanius\",\"Maximus Festinius\",\"Pupius Cloelius\",\"Aquillius Quintilianus\",\"Lentulus Lucretius\",\"Valentinian Maximus\",\"Placidus Oppius\",\"Atilius Tertius\",\"Silvius Persius\",\"Egnatius Ollius\",\"Varro Memmius\",\"Marcius Laberius\",\"Pinarus Cestius\",\"Aelius Quintilianus\",\"Paulinus Tarquinius\",\"Gabinius Maecilius\",\"Urbanus Mucius\",\"Felix Sicinius\",\"Acilius Palpellius\",\"Castorius Papirius\",\"Potitius Gordian\",\"Blasius Manilius\",\"Gabinius Verecundus\",\"Hadrian Munatius\",\"Marcellus Potitius\",\"Cassius Fulvius\",\"Cilnius Gracchus\",\"Pius Tertius\",\"Fabius Nonius\",\"Urbicus Cethegus\",\"Rebilus Blossius\",\"Percennius Livius\",\"Pompeius Bruttius\",\"Fulcinia Quintilianus\",\"Caesennius Calventius\",\"Eutropius Donatus\",\"Valentinian Poppaedius\",\"Caesennius Nerva\",\"Camillus Quirinius\",\"Nasennius Numicius\",\"Acilius Silvius\",\"Servius Castorius\",\"Palpellius Secundus\",\"Calpurnius Paulinus\",\"Ovidius Maximus\",\"Silanus Oppius\",\"Pasidienus Cicereius\",\"Lucanus Salvian\",\"Sextus Maximus\",\"Quintilianus Pomponius\",\"Paconius Avidius\",\"Ofilius Pedius\",\"Caninius Priscus\",\"Brennus Sidonius\",\"Mamilius Ingenuus\",\"Rebilus Laronius\",\"Amulius Marian\",\"Aelius Vitus\",\"Maecenas Publicius\",\"Fabianus Quirinius\",\"Acilius Semellius\",\"Lucius Caecilius\",\"Horatius Petreius\",\"Cornelius Albinius\",\"Cestius Maximian\",\"Gracchus Vitus\",\"Silius Secundus\",\"Mallius Cicero\",\"Fidenas Crispinus\",\"Eutropius Fabrinius\",\"Hostius Lollius\",\"Maecenas Salvian\",\"Verus Germanicus\",\"Flaccus Camillus\",\"Acilius Numonius\",\"Ovidius Marcius\",\"Vincentius Catilius\",\"Lucius Hirrus\",\"Caesennius Domitius\",\"Octavius Cosconius\",\"Paternus Valens\",\"Vitalis Tacitus\",\"Saturninus Bruttius\",\"Constantinus Munatius\",\"Sextius Cinna\",\"Quinctilius Flaccus\",\"Fidius Valens\",\"Blasius Paconius\",\"Publicius Hilarianus\",\"Ingenuus Segulius\",\"Lamponius Rebilus\",\"Furius Aulus\",\"Salvidienus Baebius\",\"Hostilius Dacian\",\"Sidonius Severus\",\"Maxentius Titus\",\"Menenius Appius\",\"Claudius Gracchus\",\"Justus Cantilius\",\"Blasius Martialis\",\"Domitius Tarquinius\",\"Caninius Livius\",\"Secundus Sergius\",\"Geta Valerius\",\"Valens Munatius\",\"Justus Atilius\",\"Tertius Lartius\",\"Plautius Rufrius\",\"Vitus Laberius\",\"Nerva Octavius\",\"Servius Camillus\",\"Diocletian Titus\",\"Secundus Orbilius\",\"Draco Coruncanius\",\"Fabius Nasidienus\",\"Ovidius Cyprian\",\"Nerva Coruncanius\",\"Rufrius Balbinus\",\"Minicius Nazarius\",\"Perperna Lacerius\",\"Cordius Caelian\",\"Salvius Domitius\",\"Varro Porcius\",\"Sempronius Sittius\",\"Crispinus Faustus\",\"Acilius Porcius\",\"Primitivus Albinus\",\"Rutilius Sestius\",\"Manius Avidius\",\"Vibius Opimius\",\"Catilius Nasennius\",\"Cornelius Orbilius\",\"Publius Clodius\",\"Verus Latinius\",\"Caelius Florian\",\"Zeno Pinarus\",\"Fidenas Nautius\",\"Musonius Regulus\",\"Januarius Nonius\",\"Vitus Severus\",\"Manilius Cosconius\",\"Cosconius Junius\",\"Postumius Sittius\",\"Vibius Salvidienus\",\"Claudius Herennius\",\"Cosconius Maxentius\",\"Vibius Appius\",\"Lamponius Minicius\",\"Flaccus Silvius\",\"Juvenalis Manilius\",\"Tarquinius Aquillius\",\"Geminius Modius\",\"Seneca Egnatius\",\"Afranius Sepullius\",\"Verus Hostilius\",\"Avidius Fidenas\",\"Damasus Aulus\",\"Sedatius Cornificius\",\"Salvidienus Primitivus\",\"Lartius Murena\",\"Sergius Blaesus\",\"Donatus Fidenas\",\"Iunius Verecundus\",\"Pontidius Eutropius\",\"Lucius Domitius\",\"Camillius Caecilius\",\"Nautius Atilius\",\"Pius Gabinius\",\"Calpurnius Hostius\",\"Severian Segulius\",\"Pompeius Tarquinius\",\"Laurentius Burrienus\",\"Pius Secundus\",\"Semellius Anatolius\",\"Horatius Cicereius\",\"Pettius Silanus\",\"Verecundus Matienus\",\"Rufinus Agrippa\",\"Caninius Albinus\",\"Faustus Cloelius\",\"Pedius Hortensius\",\"Maecenas Salvidienus\",\"Nasidius Gnaeus\",\"Varro Granius\",\"Appius Publicius\",\"Domitius Cincinnatus\",\"Fidius Maximus\",\"Didius Afranius\",\"Nero Rufius\",\"Caecilius Pedius\",\"Opellius Magnentius\",\"Catius Scribonius\",\"Seccius Sextius\",\"Licinius Menenius\",\"Porcius Junius\",\"Modius Nautius\",\"Cassio Lentulus\",\"Numerius Pius\",\"Papinius Cornificius\",\"Severus Gratian\",\"Nero Persius\",\"Hilarianus Acilius\",\"Gratian Caninius\",\"Donatus Rutilius\",\"Hilarianus Constantinus\",\"Antonius Spurius\",\"Paternus Seccius\",\"Gordian Terentius\",\"Cispius Aemilius\",\"Tacitus Maecenas\",\"Laberius Fidius\",\"Petilius Numitorius\",\"Volumnius Manilius\",\"Granius Maecilius\",\"Menenius Plautius\",\"Cicero Granius\",\"Ostorius Nasidius\",\"Marcellus Quintilianus\",\"Ninnius Minicius\",\"Ingenuus Calpurnius\",\"Postumius Germanicus\",\"Servilius Palpellius\",\"Didius Novellius\",\"Terentius Volumnius\",\"Postumius Albinus\",\"Clodius Ovidius\",\"Fabianus Numicius\",\"Fulvius Laelius\",\"Laecanius Valentinian\",\"Festinius Faustus\",\"Nero Pilus\",\"Vespasian Foslius\",\"Murena Oppius\",\"Fannius Gaius\",\"Messalla Cosconius\",\"Papinius Crispinus\",\"Scribonius Pompeius\",\"Sallustius Vespasian\",\"Urbicus Constantinus\",\"Blaesus Cato\",\"Cicero Geminius\",\"Lucius Cispius\",\"Rufius Faustus\",\"Hostilius Sittius\",\"Flaccus Laurentius\",\"Vitalis Lollius\",\"Cloelius Nasidienus\",\"Persius Laberius\",\"Gnaeus Sempronius\",\"Pilus Gratian\",\"Papirius Quintilianus\",\"Sidonius Fannius\",\"Sepullius Martialis\",\"Latinius Gallus\",\"Nasidius Lucius\",\"Tacitus Gracchus\",\"Acilius Nerva\",\"Geminius Aemilius\",\"Varian Nasennius\",\"Servilius Gracchus\",\"Quintilianus Ostorius\",\"Rutilius Caelian\",\"Burrienus Nonius\",\"Spurius Scribonius\",\"Duronius Lucretius\",\"Percennius Salvian\",\"Lamponius Volumnius\",\"Secundinius Pupius\",\"Statilius Ollius\",\"Coruncanius Salvidienus\",\"Gabinius Catilius\",\"Nasidius Regulus\",\"Pilus Publicius\",\"Salvius Felix\",\"Galerius Burrienus\",\"Blaesus Hirrus\",\"Herennius Pompeius\",\"Nerva Egnatuleius\",\"Antonius Junius\",\"Faustus Quirinius\",\"Silanus Ninnius\",\"Musonius Probus\",\"Nasennius Albinus\",\"Laurentius Porcius\",\"Pilus Ingenuus\",\"Memmius Opellius\",\"Cordius Sertorius\",\"Blasius Blossius\",\"Seccius Varian\",\"Aulus Hostius\",\"Cincinnatus Sepullius\",\"Hortensius Vitus\",\"Sedatius Florian\",\"Foslius Catius\",\"Fulcinia Scribonius\",\"Icilius Pomponius\",\"Cominius Fabrinius\",\"Quintus Iunius\",\"Marcus Servius\",\"Caedicius Constantinus\",\"Coruncanius Laecanius\",\"Vespasian Manilius\",\"Baebius Constantinus\",\"Vitus Probus\",\"Laecanius Priscus\",\"Urbanus Cassianus\",\"Palpellius Cassianus\",\"Spurius Pacilius\",\"Bruttius Blasius\",\"Honorius Hilarianus\",\"Cato Pompeius\",\"Valentinian Caesennius\",\"Cosconius Pettius\",\"Camillius Cethegus\",\"Januarius Ollius\",\"Scribonius Segulius\",\"Paelignus Draco\",\"Caesennius Varro\",\"Numerius Candidus\",\"Ollius Rufrius\",\"Publicius Sidonius\",\"Aelius Hostilius\",\"Florian Cornelius\",\"Gaius Caecilius\",\"Verus Cantilius\",\"Opellius Fulvius\",\"Rabirius Memmius\",\"Constantinus Orestius\",\"Plinius Numonius\",\"Nautius Duronius\",\"Severian Eborius\",\"Pompeius Fulvius\",\"Opimius Ovidius\",\"Sergian Festus\",\"Curtius Gordian\",\"Semellius Balbinus\",\"Cordius Camillius\",\"Mucius Quintilianus\",\"Paelignus Egnatuleius\",\"Tacitus Persius\",\"Diocletian Laberius\",\"Sempronius Anatolius\",\"Atilius Minicius\",\"Pettius Probus\",\"Modius Decimus\",\"Lamponius Nazarius\",\"Brennus Cispius\",\"Naevius Valerius\",\"Hadrian Hadrian\",\"Martialis Caninius\",\"Gallus Blaesus\",\"Rufrius Statilius\",\"Considius Tertius\",\"Duronius Rullianus\",\"Belisarius Numicius\",\"Icilius Rebilus\",\"Spurius Valens\",\"Nasidius Tarquinius\",\"Mamilius Bonifacius\",\"Norbanus Salvidienus\",\"Seneca Cocceius\",\"Laberius Caecilius\",\"Sergian Cispius\",\"Florian Marcellus\",\"Florian Caninius\",\"Vitus Placidus\",\"Sergius Eborius\",\"Laurentius Sornatius\",\"Bonifacius Julius\",\"Cethegus Amulius\",\"Marcus Herennius\",\"Camillus Didius\",\"Dacian Pilus\",\"Sidonius Granius\",\"Baebius Sentius\",\"Pomponius Tertius\",\"Lollius Dacian\",\"Fulcinia Marian\",\"Hortensius Domitius\",\"Sulpicius Rufrius\",\"Atilius Curtius\",\"Agrippa Afranius\",\"Sittius Sallustius\",\"Murena Cicereius\",\"Nazarius Donatus\",\"Longinius Plautius\",\"Atilius Lucanus\",\"Novellius Paulinus\",\"Rullianus Fundanus\",\"Fundanus Valens\",\"Atilius Valerius\",\"Caedicius Spurius\",\"Segulius Sedatius\",\"Pompeius Lucius\",\"Priscus Salinator\",\"Pedius Urbicus\",\"Pomponius Fulcinia\",\"Porcius Pacilius\",\"Laelius Donatus\",\"Curtius Nasidius\",\"Horatius Anatolius\",\"Bonifacius Octavius\",\"Fabrinius Crispus\",\"Egnatuleius Varro\",\"Aulus Horatius\",\"Gordian Festus\",\"Servius Lentulus\",\"Octavius Varro\",\"Caedicius Balbus\",\"Oppius Claudius\",\"Junius Cocceius\",\"Fidius Maecilius\",\"Laetorius Lucretius\",\"Appius Iulius\",\"Didius Gnaeus\",\"Camillus Plinius\",\"Fabius Domitius\",\"Clodius Iulius\",\"Foslius Balbinus\",\"Blossius Aquillius\",\"Nasidius Quirinius\",\"Vincentius Calpurnius\",\"Saturninus Potitius\",\"Naevius Honorius\",\"Regulus Marius\",\"Livius Blasius\",\"Cicero Postumius\",\"Pilus Plautius\",\"Mescinius Considius\",\"Cominius Nigidius\",\"Maximus Domitian\",\"Mallius Genucius\",\"Geta Secundus\",\"Pupius Aurelianus\",\"Ostorius Vincentius\",\"Lollius Licinius\",\"Livius Orestius\",\"Statilius Festinius\",\"Novellius Cyprian\",\"Pontidius Draco\",\"Belisarius Caninius\",\"Servius Maecenas\",\"Latinius Laronius\",\"Cincinnatus Gratian\",\"Domitian Sepullius\",\"Lollius Numitorius\",\"Crispus Balbinus\",\"Numerius Ollius\",\"Petreius Laecanius\",\"Verus Primitivus\",\"Novellius Pettius\",\"Laurentius Sergius\",\"Maximus Oppius\",\"Candidus Damasus\",\"Servilius Latinius\",\"Novellius Betilienus\",\"Cominius Aurelianus\",\"Aquillius Verus\",\"Murena Silanus\",\"Musonius Mamilius\",\"Brennus Rebilus\",\"Numonius Maecenas\",\"Pacilius Maxentius\",\"Balbinus Fidius\",\"Celsus Lucretius\",\"Laronius Mamilius\",\"Laberius Terentius\",\"Hadrian Naevius\",\"Caedicius Agrippa\",\"Gracchus Hostilius\",\"Faustus Primitivus\",\"Gordian Laurentius\",\"Germanicus Hirrus\",\"Munatius Magnentius\",\"Fundanus Latinius\",\"Gracchus Atilius\",\"Cocceius Novellius\",\"Papirius Mamilius\",\"Aemilius Paconius\",\"Cilnius Mallius\",\"Crispus Hostilius\",\"Opimius Nasidienus\",\"Maximian Severus\",\"Nasidienus Sextius\",\"Silanus Laetorius\",\"Betilienus Fundanus\",\"Tacitus Festinius\",\"Hostilius Seneca\",\"Scribonius Musonius\",\"Minicius Vibius\",\"Marian Mamilius\",\"Sallustius Papirius\",\"Urbicus Decentius\",\"Memmius Sextius\",\"Cicereius Orestius\",\"Marius Oppius\",\"Flaccus Paelignus\",\"Plinius Plinius\",\"Lentulus Salinator\",\"Cassius Marcius\",\"Fidius Maximian\",\"Rufrius Caelian\",\"Rufrius Damasus\",\"Paconius Fidenas\",\"Caedicius Urbicus\",\"Quinctilius Severus\",\"Cassio Valentinian\",\"Marcellus Silanus\",\"Plinius Cilnius\",\"Plautius Tarquinius\",\"Clodius Hostilius\",\"Maecenas Egnatuleius\",\"Rabirius Eborius\",\"Domitian Ostorius\",\"Fidenas Hilarianus\",\"Marius Numitorius\",\"Geminius Paelignus\",\"Cestius Vibius\",\"Laurentius Blossius\",\"Cocceius Flaccus\",\"Blossius Coruncanius\",\"Valens Sestius\",\"Sicinius Eborius\",\"Vincentius Verus\",\"Aurelianus Verecundus\",\"Aquillius Terentius\",\"Regulus Menenius\",\"Pasidienus Eutropius\",\"Calpurnius Iulius\",\"Mescinius Mucius\",\"Paulinus Aemilius\",\"Constantinus Cassianus\",\"Mallius Cominius\",\"Fannius Betilienus\",\"Cornificius Gordian\",\"Rufinus Cantilius\",\"Bruttius Paelignus\",\"Marcius Rullianus\",\"Nasidius Cicereius\",\"Camillius Catius\",\"Camillus Secundus\",\"Fabrinius Anatolius\",\"Seneca Pasidienus\",\"Gaius Cinna\",\"Sedatius Sertorius\",\"Numonius Bonifacius\",\"Caninius Julius\",\"Mallius Sertorius\",\"Iunius Percennius\",\"Aurelianus Decentius\",\"Cispius Sertorius\",\"Betilienus Pupius\",\"Pompeius Sepullius\",\"Nautius Domitian\",\"Curtius Munatius\",\"Opellius Domitian\",\"Hostilius Acilius\",\"Foslius Latinius\",\"Cassianus Ravillius\",\"Catilius Betilienus\",\"Juvenalis Domitius\",\"Mescinius Salvidienus\",\"Fundanus Scribonius\",\"Pacilius Marcellus\",\"Julius Junius\",\"Maecilius Cordius\",\"Fannius Flavius\",\"Germanicus Cincinnatus\",\"Pompeius Valerius\",\"Domitius Papirius\",\"Hostilius Hadrian\",\"Novellius Germanicus\",\"Flavius Segulius\",\"Memmius Publius\",\"Segulius Lacerius\",\"Laelius Salvian\",\"Rufinus Iunius\",\"Zeno Laetorius\",\"Vitalis Blasius\",\"Maecilius Volumnius\",\"Fabianus Cilnius\",\"Fulcinia Festus\",\"Secundus Quintilianus\",\"Rufius Juvenalis\",\"Catilius Sertorius\",\"Albinius Curtius\",\"Plinius Lentulus\",\"Ingenuus Marcellus\",\"Sepullius Tacitus\",\"Duronius Silvius\",\"Egnatuleius Valerius\",\"Lollius Orestius\",\"Sestius Sicinius\",\"Pius Fabianus\",\"Decentius Granius\",\"Longinius Quintus\",\"Novellius Rufrius\",\"Brennus Hostilius\",\"Numicius Pilus\",\"Stertinius Cocceius\",\"Januarius Germanicus\",\"Poppaedius Agrippa\",\"Crispinus Persius\",\"Menenius Lucius\",\"Mescinius Nasidienus\",\"Bonifacius Sextius\",\"Domitian Aemilius\",\"Draco Seneca\",\"Betilienus Magnentius\",\"Bruttius Sextius\",\"Florianus Constantinus\",\"Cornelius Avidius\",\"Silius Dacian\",\"Statilius Nautius\",\"Porcius Quinctilius\",\"Festinius Scribonius\",\"Dacian Salvius\",\"Cornificius Cordius\",\"Ingenuus Pilus\",\"Seneca Vincentius\",\"Naevius Mallius\",\"Lucretius Longinius\",\"Marcus Flavius\",\"Duronius Tiberius\",\"Florianus Balbus\",\"Rabirius Sidonius\",\"Fundanus Galerius\",\"Sornatius Nazarius\",\"Bruttius Suetonius\",\"Modius Valens\",\"Rufrius Segulius\",\"Draco Licinius\",\"Cosconius Donatus\",\"Laurentius Ingenuus\",\"Gratian Nero\",\"Juvenalis Nerva\",\"Rebilus Numicius\",\"Nigidius Cethegus\",\"Januarius Numitorius\",\"Poppaedius Julius\",\"Salvidienus Naevius\",\"Cloelius Octavius\",\"Mamilius Sextius\",\"Quintilianus Catius\",\"Modius Tacitus\",\"Pompeius Vespasian\",\"Castorius Cloelius\",\"Sidonius Paelignus\",\"Castorius Orestius\",\"Florianus Laecanius\",\"Herennius Sallustius\",\"Cantilius Fabrinius\",\"Sextius Iuventius\",\"Geta Palpellius\",\"Regulus Mucius\",\"Sicinius Fabrinius\",\"Marcus Aurelianus\",\"Cato Maxentius\",\"Duronius Marcius\",\"Didius Licinius\",\"Egnatuleius Sabucius\",\"Gaius Manius\",\"Numicius Quintus\",\"Poppaedius Opimius\",\"Honorius Gnaeus\",\"Domitius Cloelius\",\"Diocletian Verus\",\"Tiberius Perperna\",\"Ennius Cornutus\",\"Naevius Geminius\",\"Cocceius Livius\",\"Baebius Honorius\",\"Valerius Vitalis\",\"Juvenalis Magnentius\",\"Laronius Calpurnius\",\"Cominius Quintilianus\",\"Caecilius Latinius\",\"Gordian Rufius\",\"Silvius Marcius\",\"Memmius Januarius\",\"Geta Fabius\",\"Naevius Paulinus\",\"Constantinus Gaius\",\"Pacilius Lamponius\",\"Decimus Persius\",\"Blossius Lamponius\",\"Iulius Fabianus\",\"Nautius Hostilius\",\"Claudius Rufinus\",\"Antonius Segulius\",\"Cicereius Rutilius\",\"Avidius Palpellius\",\"Probus Paulinus\",\"Amulius Fannius\",\"Anatolius Valentinian\",\"Plinius Camillus\",\"Calpurnius Publius\",\"Lucanus Didius\",\"Vitalis Icilius\",\"Laronius Nasidienus\",\"Salvidienus Maecilius\",\"Mescinius Numerius\",\"Mescinius Baebius\",\"Percennius Placidus\",\"Castorius Paulinus\",\"Regulus Martialis\",\"Maecenas Lucius\",\"Horatius Albinus\",\"Fidius Numicius\",\"Hortensius Lucius\",\"Numitorius Pomponius\",\"Rebilus Statilius\",\"Nautius Munatius\",\"Fulvius Messalla\",\"Numonius Severus\",\"Amulius Bonifacius\",\"Cornelius Lamponius\",\"Poppaedius Bonifacius\",\"Honorius Rufius\",\"Gaius Regulus\",\"Draco Gallus\",\"Florian Marcus\",\"Gnaeus Tarquinius\",\"Verecundus Cornelius\",\"Cicero Geta\",\"Rufinus Egnatius\",\"Coruncanius Cethegus\",\"Calventius Publius\",\"Marian Cocceius\",\"Aquillius Pettius\",\"Domitius Pinarus\",\"Matinius Lucanus\",\"Cornificius Appius\",\"Manilius Lucanus\",\"Valentinian Laronius\",\"Caninius Draco\",\"Paternus Junius\",\"Dacian Celsus\",\"Horatius Aelius\",\"Vitus Sextius\",\"Servilius Ingenuus\",\"Burrienus Titus\",\"Draco Ollius\",\"Aquillius Ovidius\",\"Iuventius Gnaeus\",\"Nasennius Caelian\",\"Justus Petreius\",\"Bonifacius Regulus\",\"Manilius Drusus\",\"Cestius Priscus\",\"Lucanus Magnentius\",\"Rufrius Cloelius\",\"Rufius Severian\",\"Tertius Publius\",\"Iunius Florianus\",\"Cosconius Honorius\",\"Cincinnatus Hirrus\",\"Festinius Aemilius\",\"Cicereius Appius\",\"Cloelius Maecenas\",\"Damasus Martialis\",\"Seccius Candidus\",\"Manius Amulius\",\"Quinctilius Cyprian\",\"Anatolius Urbicus\",\"Ollius Florian\",\"Octavius Priscus\",\"Egnatius Quintilianus\",\"Cethegus Fulvius\",\"Nazarius Ennius\",\"Didius Perperna\",\"Atilius Cornelius\",\"Papinius Tacitus\",\"Mescinius Gracchus\",\"Norbanus Cassio\",\"Rebilus Regulus\",\"Festus Maxentius\",\"Coruncanius Sulpicius\",\"Cordius Valens\",\"Caelius Lentulus\",\"Lucanus Aelius\",\"Fulvius Menenius\",\"Rufrius Pedius\",\"Fannius Manilius\",\"Spurius Porcius\",\"Munatius Cassius\",\"Murena Blasius\",\"Gratian Duronius\",\"Statilius Publicius\",\"Fundanus Petreius\",\"Iunius Icilius\",\"Titus Aulus\",\"Bonifacius Coruncanius\",\"Tarquinius Pinarus\",\"Claudius Publicius\",\"Camillius Tacitus\",\"Rufinus Donatus\",\"Ninnius Cominius\",\"Modius Orchius\",\"Drusus Nasennius\",\"Iunius Vincentius\",\"Lartius Caelian\",\"Avidius Silvius\",\"Donatus Icilius\",\"Iunius Plinius\",\"Silanus Aurelianus\",\"Ennius Saturninus\",\"Antonius Anatolius\",\"Mescinius Ofilius\",\"Festinius Publicius\",\"Fidenas Matienus\",\"Ninnius Nero\",\"Rutilius Cloelius\",\"Sentius Aulus\",\"Julius Cornutus\",\"Mallius Herennius\",\"Nerva Candidus\",\"Servius Paulinus\",\"Gabinius Gabinius\",\"Petreius Sepullius\",\"Percennius Ingenuus\",\"Egnatuleius Salvius\",\"Severus Hirrus\",\"Afranius Paulinus\",\"Titus Nasidienus\",\"Hilarianus Maximus\",\"Tertius Caninius\",\"Iuventius Matienus\",\"Valentinian Severian\",\"Pontidius Cilnius\",\"Paconius Cicereius\",\"Geta Licinius\",\"Novellius Numitorius\",\"Valens Florianus\",\"Laetorius Laetorius\",\"Octavenus Camillus\",\"Balbinus Fabianus\",\"Drusus Spurius\",\"Matienus Hordeonius\",\"Maecilius Paternus\",\"Laronius Magnentius\",\"Cominius Sedatius\",\"Silius Lucretius\",\"Pupius Gabinius\",\"Lartius Quirinius\",\"Cassio Orestius\",\"Lucretius Manilius\",\"Furius Secundus\",\"Calpurnius Cestius\",\"Sergius Nonius\",\"Iuventius Caninius\",\"Egnatius Salinator\",\"Scribonius Balbus\",\"Lamponius Rabirius\",\"Papinius Pius\",\"Marius Candidus\",\"Claudius Latinius\",\"Valentinian Minicius\",\"Crispinus Decentius\",\"Manius Fabius\",\"Balbinus Perperna\",\"Caesennius Fabrinius\",\"Matienus Livius\",\"Egnatius Numerius\",\"Paelignus Paternus\",\"Sextius Varian\",\"Paulinus Mamilius\",\"Cinna Sicinius\",\"Minicius Porcius\",\"Sempronius Paulinus\",\"Horatius Persius\",\"Vitalis Cicereius\",\"Camillus Furius\",\"Nazarius Anatolius\",\"Festinius Gordian\",\"Seccius Valerius\",\"Sestius Burrienus\",\"Agrippa Furius\",\"Celsus Laecanius\",\"Cocceius Percennius\",\"Naevius Caesennius\",\"Sestius Orbilius\",\"Pompeius Castorius\",\"Volumnius Cicereius\",\"Geta Damasus\",\"Manilius Suetonius\",\"Lentulus Severian\",\"Nerva Bruttius\",\"Bruttius Urbanus\",\"Percennius Iunius\",\"Porcius Ingenuus\",\"Cassio Petreius\",\"Statilius Terentius\",\"Papinius Domitian\",\"Sepullius Lentulus\",\"Cordius Blossius\",\"Laronius Fabianus\",\"Naevius Segulius\",\"Hostius Ogulnius\",\"Fabrinius Lucretius\",\"Brennus Silius\",\"Curtius Betilienus\",\"Cocceius Quintilianus\",\"Matienus Titus\",\"Pacilius Cinna\",\"Caelian Plautius\",\"Manilius Sertorius\",\"Eutropius Cocceius\",\"Iunius Fundanus\",\"Quirinius Sempronius\",\"Modius Castorius\",\"Licinius Fulcinia\",\"Vitalis Catius\",\"Potitius Crispus\",\"Gnaeus Tiberius\",\"Aulus Fabianus\",\"Honorius Munatius\",\"Burrienus Rutilius\",\"Fulvius Camillus\",\"Galerius Egnatius\",\"Cantilius Maximian\",\"Quirinius Faustus\",\"Clodius Percennius\",\"Maecenas Maecilius\",\"Maximus Probus\",\"Saturninus Seneca\",\"Albinus Numicius\",\"Statilius Porcius\",\"Bonifacius Memmius\",\"Ravillius Cethegus\",\"Caninius Geminius\",\"Orchius Albinus\",\"Sergian Albinus\",\"Papirius Cethegus\",\"Domitius Octavenus\",\"Gordian Damasus\",\"Belisarius Constantinus\",\"Novellius Hirrus\",\"Nasennius Magnentius\",\"Juvenalis Marcus\",\"Candidus Severian\",\"Manius Regulus\",\"Curtius Cornutus\",\"Marcus Agrippa\",\"Verus Anatolius\",\"Papinius Cicero\",\"Verus Calpurnius\",\"Geta Cloelius\",\"Fidenas Cloelius\",\"Germanicus Cispius\",\"Sallustius Afranius\",\"Cicero Pedius\",\"Blasius Cilnius\",\"Fidius Atilius\",\"Menenius Iunius\",\"Geminius Fidenas\",\"Considius Marian\",\"Calventius Pacilius\",\"Fabius Nigidius\",\"Cilnius Tertius\",\"Segulius Messalla\",\"Julius Plautius\",\"Nazarius Pacilius\",\"Palpellius Amulius\",\"Hordeonius Vitus\",\"Laecanius Nasennius\",\"Sergius Lentulus\",\"Verecundus Norbanus\",\"Quinctilius Flavius\",\"Caesennius Salvius\",\"Pius Agrippa\",\"Januarius Hadrian\",\"Granius Brennus\",\"Martialis Norbanus\",\"Verus Justus\",\"Hostilius Verecundus\",\"Pomponius Naevius\",\"Secundus Hortensius\",\"Damasus Saturninus\",\"Minicius Vespasian\",\"Matienus Martialis\",\"Honorius Ingenuus\",\"Albinius Caninius\",\"Papinius Ostorius\",\"Vitus Sabucius\",\"Norbanus Albinius\",\"Regulus Paulinus\",\"Opimius Albinius\",\"Sabucius Cornificius\",\"Flavius Palpellius\",\"Calpurnius Curtius\",\"Cloelius Mamilius\",\"Damasus Antonius\",\"Ingenuus Silius\",\"Hostilius Festinius\",\"Considius Papirius\",\"Cassio Sergian\",\"Horatius Lentulus\",\"Blaesus Justus\",\"Laelius Burrienus\",\"Valerius Verus\",\"Domitius Cispius\",\"Rufinus Coruncanius\",\"Felix Lollius\",\"Celsus Perperna\",\"Paulinus Publicius\",\"Junius Tiberius\",\"Manilius Julius\",\"Cornificius Musonius\",\"Priscus Sextus\",\"Gabinius Marcus\",\"Cincinnatus Nasennius\",\"Placidus Semellius\",\"Cassius Cassio\",\"Belisarius Laecanius\",\"Probus Numerius\",\"Fabrinius Gaius\",\"Maxentius Festus\",\"Curtius Egnatius\",\"Ingenuus Pius\",\"Servilius Rebilus\",\"Sidonius Decentius\",\"Poppaedius Draco\",\"Cicereius Curtius\",\"Laelius Crispus\",\"Lollius Amulius\",\"Paternus Sicinius\",\"Matienus Gallus\",\"Ollius Percennius\",\"Didius Rufinus\",\"Volumnius Bruttius\",\"Sentius Poppaedius\",\"Rutilius Lollius\",\"Albinius Fabianus\",\"Segulius Secundinius\",\"Gaius Petilius\",\"Camillus Geta\",\"Servius Pasidienus\",\"Tertius Palpellius\",\"Salvian Suetonius\",\"Matienus Laecanius\",\"Bruttius Seneca\",\"Opimius Numerius\",\"Castorius Nerva\",\"Ravillius Florianus\",\"Silvius Junius\",\"Cato Afranius\",\"Domitius Cyprian\",\"Rullianus Flaccus\",\"Vitus Bruttius\",\"Marcus Fabius\",\"Verecundus Varro\",\"Porcius Numicius\",\"Clodius Salvidienus\",\"Orbilius Vitus\",\"Modius Secundus\",\"Blaesus Aurelianus\",\"Percennius Laecanius\",\"Probus Maximian\",\"Orchius Anatolius\",\"Gaius Fannius\",\"Sornatius Galerius\",\"Papirius Bonifacius\",\"Eborius Numonius\",\"Sextus Burrienus\",\"Pilus Aurelianus\",\"Tacitus Marius\",\"Cocceius Sergius\",\"Albinius Celsus\",\"Murena Hilarianus\",\"Nautius Palpellius\",\"Didius Eborius\",\"Porcius Lucanus\",\"Servilius Dacian\",\"Latinius Constantinus\",\"Sergian Lartius\",\"Lucanus Fannius\",\"Mescinius Sabucius\",\"Urbicus Lucius\",\"Nasennius Draco\",\"Licinius Blasius\",\"Laronius Titus\",\"Caelian Horatius\",\"Terentius Cethegus\",\"Pius Rufius\",\"Scribonius Caelius\",\"Geta Cassio\",\"Blaesus Papinius\",\"Aurelianus Eborius\",\"Volumnius Nero\",\"Vitalis Fulcinia\",\"Sertorius Domitius\",\"Salvidienus Egnatius\",\"Fabrinius Blaesus\",\"Albinius Marcus\",\"Ennius Fabianus\",\"Valerius Betilienus\",\"Festus Cispius\",\"Plautius Gabinius\",\"Gratian Julius\",\"Placidus Sextus\",\"Hordeonius Domitius\",\"Statilius Ninnius\",\"Saturninus Aurelianus\",\"Urbanus Vibius\",\"Pius Cassianus\",\"Gabinius Terentius\",\"Hostilius Cestius\",\"Maecenas Decentius\",\"Albinius Fulcinia\",\"Zeno Paternus\",\"Aquillius Furius\",\"Festinius Nazarius\",\"Novellius Valerius\",\"Manius Cinna\",\"Gaius Atilius\",\"Sestius Bonifacius\",\"Aurelianus Egnatuleius\",\"Gordian Fulcinia\",\"Domitian Blossius\",\"Gallus Fundanus\",\"Memmius Sornatius\",\"Severus Ollius\",\"Percennius Hadrian\",\"Hostius Mamilius\",\"Nonius Seneca\",\"Atilius Cantilius\",\"Rufinus Sabucius\",\"Cassio Fabianus\",\"Fulvius Valens\",\"Semellius Gallus\",\"Pettius Atilius\",\"Cassio Memmius\",\"Rufius Duronius\",\"Titus Fabrinius\",\"Pedius Paternus\",\"Iunius Hostius\",\"Genucius Burrienus\",\"Icilius Seneca\",\"Germanicus Ninnius\",\"Cilnius Florianus\",\"Hilarianus Salvius\",\"Fabrinius Cispius\",\"Rebilus Cato\",\"Cassianus Egnatius\",\"Donatus Fundanus\",\"Silanus Pasidienus\",\"Cicero Catilius\",\"Vincentius Hortensius\",\"Latinius Papirius\",\"Cornificius Primitivus\",\"Lollius Catius\",\"Aurelianus Baebius\",\"Numitorius Saturninus\",\"Cocceius Nasidius\",\"Murena Aelius\",\"Secundinius Publius\",\"Festinius Cosconius\",\"Paconius Atilius\",\"Granius Marcus\",\"Ravillius Diocletian\",\"Papinius Crispus\",\"Mamilius Festus\",\"Munatius Cocceius\",\"Modius Marius\",\"Cicereius Matinius\",\"Ninnius Fabius\",\"Mamilius Honorius\",\"Fidius Sextius\",\"Lamponius Egnatuleius\",\"Sentius Bruttius\",\"Cicereius Caesennius\",\"Gallus Ostorius\",\"Pomponius Fulvius\",\"Pupius Orestius\",\"Crispus Maecilius\",\"Ostorius Priscus\",\"Papirius Caelian\",\"Sergian Rufius\",\"Pinarus Ninnius\",\"Minicius Rutilius\",\"Aquillius Plautius\",\"Orchius Memmius\",\"Statilius Nonius\",\"Furius Gnaeus\",\"Spurius Ostorius\",\"Pupius Regulus\",\"Rutilius Hordeonius\",\"Blossius Nasennius\",\"Quirinius Candidus\",\"Persius Festus\",\"Paconius Oppius\",\"Statilius Paelignus\",\"Silius Faustus\",\"Laurentius Laurentius\",\"Scribonius Herennius\",\"Zeno Curtius\",\"Perperna Regulus\",\"Volumnius Dacian\",\"Agrippa Sallustius\",\"Camillus Festinius\",\"Fabrinius Valerius\",\"Statilius Nazarius\",\"Egnatius Sergius\",\"Pasidienus Novellius\",\"Furius Sestius\",\"Orbilius Statilius\",\"Salvian Minicius\",\"Justus Vitalis\",\"Servilius Faustus\",\"Germanicus Caelius\",\"Orbilius Vibius\",\"Licinius Sepullius\",\"Caelius Sepullius\",\"Lentulus Calventius\",\"Gratian Volumnius\",\"Maecenas Cato\",\"Mamilius Orchius\",\"Orchius Plinius\",\"Novellius Sergius\",\"Lamponius Pasidienus\",\"Cocceius Valerius\",\"Pasidienus Sallustius\",\"Caninius Petilius\",\"Rabirius Sergius\",\"Sallustius Orbilius\",\"Cilnius Valens\",\"Crispinus Varian\",\"Salvius Eutropius\",\"Ravillius Celsus\",\"Sepullius Fundanus\",\"Avidius Pacilius\",\"Considius Octavenus\",\"Fidenas Geta\",\"Cornificius Horatius\",\"Horatius Servius\",\"Blaesus Musonius\",\"Calpurnius Anatolius\",\"Pomponius Salinator\",\"Cato Latinius\",\"Avidius Cornificius\",\"Probus Octavius\",\"Sepullius Aelius\",\"Caninius Clodius\",\"Amulius Gracchus\",\"Clodius Sergian\",\"Constantinus Cocceius\",\"Maximus Fulvius\",\"Octavius Januarius\",\"Nasidius Balbinus\",\"Festinius Maxentius\",\"Mamilius Hostius\",\"Longinius Rufius\",\"Atilius Sergius\",\"Octavius Orbilius\",\"Licinius Marius\",\"Honorius Sextius\",\"Urbicus Sergian\",\"Belisarius Cassio\",\"Felix Murena\",\"Ollius Julius\",\"Agrippa Diocletian\",\"Dacian Vibius\",\"Numerius Paternus\",\"Perperna Nonius\",\"Fulcinia Valerius\",\"Cosconius Fannius\",\"Caecilius Camillius\",\"Orbilius Marian\",\"Balbus Nautius\",\"Tacitus Minicius\",\"Hadrian Rebilus\",\"Orbilius Orchius\",\"Terentius Tiberius\",\"Furius Manilius\",\"Scribonius Crispinus\",\"Decimus Fannius\",\"Zeno Perperna\",\"Rufius Verus\",\"Novellius Fulvius\",\"Germanicus Calpurnius\",\"Cominius Postumius\",\"Cethegus Castorius\",\"Munatius Marian\",\"Calpurnius Flaccus\",\"Fulvius Orbilius\",\"Catilius Lamponius\",\"Longinius Norbanus\",\"Valens Mucius\",\"Numicius Honorius\",\"Petreius Gordian\",\"Castorius Servilius\",\"Norbanus Germanicus\",\"Drusus Vitus\",\"Cyprian Numitorius\",\"Amulius Laberius\",\"Florian Gabinius\",\"Castorius Sertorius\",\"Menenius Sedatius\",\"Vitalis Ingenuus\",\"Atilius Considius\",\"Icilius Coruncanius\",\"Fabius Ingenuus\",\"Decentius Cominius\",\"Lamponius Acilius\",\"Iunius Tiberius\",\"Cinna Nonius\",\"Opellius Rutilius\",\"Saturninus Cloelius\",\"Anatolius Matienus\",\"Placidus Cantilius\",\"Maximus Camillus\",\"Placidus Junius\",\"Draco Acilius\",\"Opimius Gracchus\",\"Marius Lamponius\",\"Crispus Marian\",\"Livius Cordius\",\"Numitorius Constantinus\",\"Nonius Statilius\",\"Honorius Burrienus\",\"Cantilius Didius\",\"Paelignus Galerius\",\"Quirinius Hostilius\",\"Drusus Granius\",\"Valentinian Geminius\",\"Cyprian Cosconius\",\"Perperna Ollius\",\"Ofilius Lollius\",\"Candidus Cornificius\",\"Acilius Cicereius\",\"Caedicius Cyprian\",\"Sempronius Coruncanius\",\"Sedatius Tiberius\",\"Caecilius Caninius\",\"Albinus Munatius\",\"Cicereius Sulpicius\",\"Pasidienus Vitalis\",\"Egnatuleius Probus\",\"Terentius Pinarus\",\"Pontidius Valentinian\",\"Agrippa Vespasian\",\"Felix Vincentius\",\"Nasidius Fulcinia\",\"Crispinus Lucanus\",\"Faustus Domitian\",\"Semellius Volumnius\",\"Gracchus Justus\",\"Sittius Pontidius\",\"Plinius Draco\",\"Silanus Atilius\",\"Aulus Stertinius\",\"Hortensius Matinius\",\"Fulcinia Opimius\",\"Poppaedius Valens\",\"Marcus Numonius\",\"Numitorius Percennius\",\"Varro Pomponius\",\"Cicereius Maximus\",\"Laelius Baebius\",\"Hortensius Egnatius\",\"Cordius Hadrian\",\"Ovidius Titus\",\"Volumnius Sertorius\",\"Nonius Ingenuus\",\"Plautius Foslius\",\"Silvius Aurelianus\",\"Cosconius Pius\",\"Cincinnatus Cassianus\",\"Honorius Vitus\",\"Cordius Gordian\",\"Blaesus Maximian\",\"Paternus Cicero\",\"Caelian Vitus\",\"Volumnius Junius\",\"Orestius Brennus\",\"Stertinius Salvidienus\",\"Ennius Betilienus\",\"Persius Quinctilius\",\"Genucius Damasus\",\"Longinius Laelius\",\"Caesennius Flaccus\",\"Gracchus Maximus\",\"Fabius Aquillius\",\"Catilius Domitius\",\"Cosconius Marius\",\"Cicereius Sallustius\",\"Potitius Gracchus\",\"Cornelius Cicero\",\"Pasidienus Cocceius\",\"Cyprian Hostius\",\"Seneca Januarius\",\"Caecilius Blasius\",\"Iulius Gracchus\",\"Blossius Domitian\",\"Ofilius Laberius\",\"Egnatuleius Numitorius\",\"Salvidienus Hostilius\",\"Aelius Marcellus\",\"Blasius Marcius\",\"Cocceius Camillus\",\"Opellius Cantilius\",\"Quintilianus Silius\",\"Numitorius Segulius\",\"Scribonius Duronius\",\"Clodius Caecilius\",\"Lucanus Domitius\",\"Menenius Coruncanius\",\"Lartius Hadrian\",\"Publicius Valens\",\"Scribonius Baebius\",\"Lucretius Silanus\",\"Balbus Seneca\",\"Lucretius Valentinian\",\"Germanicus Vincentius\",\"Herennius Balbus\",\"Curtius Menenius\",\"Ninnius Cinna\",\"Drusus Albinius\",\"Numonius Numerius\",\"Tiberius Rullianus\",\"Egnatuleius Maximian\",\"Rebilus Aelius\",\"Ravillius Rufinus\",\"Caelian Belisarius\",\"Petreius Quintus\",\"Considius Verecundus\",\"Plautius Junius\",\"Cicereius Anatolius\",\"Calpurnius Cloelius\",\"Gaius Rebilus\",\"Damasus Paconius\",\"Cassio Cocceius\",\"Suetonius Verecundus\",\"Constantinus Balbinus\",\"Plautius Probus\",\"Sertorius Quintilianus\",\"Catius Vitus\",\"Camillus Severian\",\"Lucius Gnaeus\",\"Lucretius Potitius\",\"Ovidius Brennus\",\"Diocletian Nigidius\",\"Sempronius Ofilius\",\"Laronius Paternus\",\"Petreius Appius\",\"Gnaeus Pacilius\",\"Matinius Iulius\",\"Aemilius Scribonius\",\"Laelius Ninnius\",\"Statilius Foslius\",\"Cornutus Quinctilius\",\"Plautius Hadrian\",\"Domitius Seneca\",\"Nigidius Crispus\",\"Numitorius Tacitus\",\"Urbicus Ollius\",\"Hostilius Paconius\",\"Postumius Baebius\",\"Calpurnius Appius\",\"Hortensius Verus\",\"Sedatius Minicius\",\"Salvian Duronius\",\"Laberius Eborius\",\"Laelius Munatius\",\"Stertinius Sittius\",\"Lartius Numitorius\",\"Pasidienus Numonius\",\"Decimus Tiberius\",\"Nasidius Fulvius\",\"Silvius Acilius\",\"Festus Sicinius\",\"Orbilius Nazarius\",\"Orestius Martialis\",\"Cominius Valens\",\"Paternus Quirinius\",\"Marcius Pompeius\",\"Vitus Sornatius\",\"Silanus Juvenalis\",\"Primitivus Salvius\",\"Januarius Gratian\",\"Lucius Tiberius\",\"Furius Cilnius\",\"Pacilius Severian\",\"Laelius Cocceius\",\"Rebilus Iuventius\",\"Constantinus Curtius\",\"Livius Tertius\",\"Pupius Afranius\",\"Potitius Castorius\",\"Probus Rullianus\",\"Amulius Rabirius\",\"Pasidienus Fulcinia\",\"Icilius Honorius\",\"Seccius Fidenas\",\"Cincinnatus Geminius\",\"Ravillius Claudius\",\"Maecenas Quinctilius\",\"Granius Spurius\",\"Castorius Cato\",\"Pacilius Opimius\",\"Castorius Baebius\",\"Munatius Damasus\",\"Sergius Fabianus\",\"Paelignus Modius\",\"Verecundus Iunius\",\"Pilus Florianus\",\"Fannius Baebius\",\"Pilus Potitius\",\"Publicius Perperna\",\"Cilnius Vincentius\",\"Hostilius Novellius\",\"Aulus Zeno\",\"Caelius Januarius\",\"Fidenas Nonius\",\"Bonifacius Junius\",\"Norbanus Lamponius\",\"Norbanus Salvian\",\"Camillius Nasidius\",\"Egnatius Nasidius\",\"Segulius Perperna\",\"Juvenalis Silius\",\"Eutropius Fabianus\",\"Persius Minicius\",\"Perperna Fulvius\",\"Pinarus Blasius\",\"Statilius Maecilius\",\"Silanus Hadrian\",\"Persius Orchius\",\"Poppaedius Balbus\",\"Balbinus Marcius\",\"Appius Sergian\",\"Tiberius Secundinius\",\"Iuventius Florianus\",\"Magnentius Betilienus\",\"Foslius Rabirius\",\"Caelius Cominius\",\"Orbilius Pomponius\",\"Nero Aquillius\",\"Junius Horatius\",\"Varro Ofilius\",\"Marcus Genucius\",\"Aquillius Celsus\",\"Considius Gabinius\",\"Cethegus Vitalis\",\"Rullianus Afranius\",\"Pupius Mescinius\",\"Icilius Decimus\",\"Laecanius Tertius\",\"Spurius Sicinius\",\"Fulvius Papirius\",\"Fabianus Maximian\",\"Perperna Sittius\",\"Longinius Manius\",\"Flavius Cosconius\",\"Iuventius Martialis\",\"Fulvius Licinius\",\"Cestius Domitian\",\"Sertorius Rebilus\",\"Eutropius Crispus\",\"Sentius Balbus\",\"Granius Hadrian\",\"Plautius Caesennius\",\"Tertius Severian\",\"Perperna Duronius\",\"Valentinian Nasidienus\",\"Sornatius Blaesus\",\"Drusus Orbilius\",\"Cominius Mucius\",\"Caecilius Servilius\",\"Laronius Honorius\",\"Lucanus Pedius\",\"Blaesus Silvius\",\"Sextius Catilius\",\"Quintus Ingenuus\",\"Candidus Verus\",\"Persius Juvenalis\",\"Afranius Foslius\",\"Murena Ingenuus\",\"Celsus Nautius\",\"Longinius Valerius\",\"Aurelianus Sextius\",\"Paternus Julius\",\"Valerius Eborius\",\"Matienus Flavius\",\"Foslius Lucanus\",\"Quintilianus Vitalis\",\"Regulus Agrippa\",\"Regulus Fannius\",\"Calpurnius Plinius\",\"Lartius Hostius\",\"Hilarianus Urbicus\",\"Hilarianus Pupius\",\"Balbus Rebilus\",\"Papirius Constantinus\",\"Bruttius Persius\",\"Appius Aurelianus\",\"Stertinius Januarius\",\"Diocletian Ninnius\",\"Maximus Gnaeus\",\"Messalla Longinius\",\"Hostius Cinna\",\"Festinius Iulius\",\"Sergius Publicius\",\"Memmius Albinius\",\"Nasidius Stertinius\",\"Cicereius Rufrius\",\"Fidius Cassio\",\"Cornificius Blaesus\",\"Gracchus Anatolius\",\"Cato Novellius\",\"Mucius Mescinius\",\"Valentinian Cestius\",\"Opimius Celsus\",\"Musonius Balbus\",\"Valerius Paelignus\",\"Licinius Cosconius\",\"Cassio Rabirius\",\"Gratian Galerius\",\"Hordeonius Geta\",\"Sornatius Cominius\",\"Betilienus Regulus\",\"Crispus Catius\",\"Sempronius Rullianus\",\"Paelignus Decimus\",\"Fabius Gratian\",\"Domitius Manilius\",\"Varro Papirius\",\"Caelius Cassius\",\"Silius Nero\",\"Quintus Hadrian\",\"Stertinius Gallus\",\"Aulus Rullianus\",\"Cyprian Statilius\",\"Cocceius Egnatius\",\"Calpurnius Papirius\",\"Varian Maecilius\",\"Varian Secundinius\",\"Lacerius Blossius\",\"Octavenus Quintilianus\",\"Hortensius Quintilianus\",\"Cicereius Orchius\",\"Ennius Hostilius\",\"Iulius Lollius\",\"Flavius Sidonius\",\"Palpellius Silvius\",\"Blasius Numitorius\",\"Dacian Octavenus\",\"Icilius Modius\",\"Gracchus Cinna\",\"Calpurnius Sallustius\",\"Tertius Vitalis\",\"Numicius Cyprian\",\"Foslius Hostilius\",\"Martialis Percennius\",\"Quintus Maecenas\",\"Plinius Ingenuus\",\"Oppius Domitian\",\"Sallustius Draco\",\"Longinius Matinius\",\"Eutropius Octavenus\",\"Rufinus Varro\",\"Sicinius Pinarus\",\"Salinator Pedius\",\"Fulvius Numonius\",\"Matinius Burrienus\",\"Felix Nautius\",\"Aemilius Hortensius\",\"Cornutus Galerius\",\"Memmius Laronius\",\"Titus Ravillius\",\"Octavenus Calventius\",\"Avidius Pupius\",\"Catilius Drusus\",\"Varian Pomponius\",\"Geminius Primitivus\",\"Ovidius Vincentius\",\"Memmius Maxentius\",\"Primitivus Blasius\",\"Diocletian Lacerius\",\"Caninius Fidius\",\"Sornatius Acilius\",\"Persius Fidius\",\"Sextius Caninius\",\"Varro Geta\",\"Ogulnius Servius\",\"Valens Pompeius\",\"Nerva Maximian\",\"Fidius Vitus\",\"Varian Marcius\",\"Maxentius Publicius\",\"Blasius Orbilius\",\"Catius Manilius\",\"Sextus Gaius\",\"Lucius Sabucius\",\"Didius Mucius\",\"Paconius Norbanus\",\"Rufrius Valerius\",\"Norbanus Papinius\",\"Geminius Blossius\",\"Menenius Balbinus\",\"Verecundus Mucius\",\"Tertius Priscus\",\"Maecilius Numitorius\",\"Semellius Statilius\",\"Bonifacius Ollius\",\"Caninius Gabinius\",\"Paternus Eborius\",\"Paternus Cispius\",\"Latinius Felix\",\"Stertinius Ostorius\",\"Caedicius Paconius\",\"Salvian Pedius\",\"Sextius Lacerius\",\"Verus Cordius\",\"Sallustius Persius\",\"Laecanius Afranius\",\"Laetorius Servilius\",\"Numerius Rufrius\",\"Vespasian Cyprian\",\"Sepullius Scribonius\",\"Pomponius Fidius\",\"Munatius Plinius\",\"Sempronius Lucretius\",\"Curtius Gaius\",\"Petilius Sertorius\",\"Sulpicius Diocletian\",\"Nasidienus Eutropius\",\"Pasidienus Considius\",\"Varro Sallustius\",\"Valentinian Lucretius\",\"Tarquinius Drusus\",\"Papinius Albinus\",\"Laurentius Quinctilius\",\"Statilius Valentinian\",\"Servilius Cordius\",\"Januarius Albinius\",\"Laecanius Poppaedius\",\"Hostilius Modius\",\"Vibius Orestius\",\"Postumius Burrienus\",\"Coruncanius Cassianus\",\"Rabirius Albinus\",\"Iulius Plinius\",\"Sextus Cicereius\",\"Tertius Verus\",\"Cornutus Atilius\",\"Volumnius Perperna\",\"Cantilius Flavius\",\"Suetonius Messalla\",\"Caelius Aelius\",\"Sepullius Blaesus\",\"Messalla Octavenus\",\"Diocletian Calventius\",\"Cornelius Quintus\",\"Sextus Numicius\",\"Ofilius Saturninus\",\"Antonius Sallustius\",\"Fulcinia Baebius\",\"Camillus Salvian\",\"Herennius Regulus\",\"Octavius Sextus\",\"Aurelianus Egnatius\",\"Seneca Latinius\",\"Papinius Seccius\",\"Quintus Geminius\",\"Gabinius Coruncanius\",\"Salvian Opimius\",\"Albinius Manilius\",\"Aquillius Urbicus\",\"Fabius Geminius\",\"Juvenalis Tiberius\",\"Aemilius Vibius\",\"Pacilius Quirinius\",\"Baebius Magnentius\",\"Hordeonius Amulius\",\"Cornificius Manius\",\"Camillus Germanicus\",\"Foslius Marcellus\",\"Aulus Considius\",\"Iunius Gracchus\",\"Drusus Maximian\",\"Magnentius Sulpicius\",\"Gracchus Duronius\",\"Octavius Crispinus\",\"Catius Aurelianus\",\"Lucius Florian\",\"Caninius Duronius\",\"Spurius Poppaedius\",\"Porcius Avidius\",\"Sepullius Sempronius\",\"Considius Albinius\",\"Octavius Placidus\",\"Galerius Probus\",\"Sidonius Vincentius\",\"Gallus Maecenas\",\"Murena Valens\",\"Iuventius Balbinus\",\"Servilius Crispus\",\"Seccius Calventius\",\"Cicereius Sertorius\",\"Mallius Lacerius\",\"Cassianus Publius\",\"Cyprian Quintus\",\"Manilius Gordian\",\"Castorius Gallus\",\"Januarius Cato\",\"Naevius Papinius\",\"Donatus Lartius\",\"Junius Faustus\",\"Juvenalis Priscus\",\"Galerius Calventius\",\"Menenius Germanicus\",\"Didius Matinius\",\"Marius Faustus\",\"Matienus Maecenas\",\"Cassius Sittius\",\"Laelius Nautius\",\"Laurentius Livius\",\"Potitius Egnatuleius\",\"Tarquinius Lacerius\",\"Severus Publius\",\"Cocceius Cloelius\",\"Blossius Fabrinius\",\"Numerius Valentinian\",\"Lucretius Secundinius\",\"Quintus Eborius\",\"Murena Laelius\",\"Vitus Foslius\",\"Sallustius Amulius\",\"Manius Menenius\",\"Spurius Lucretius\",\"Atilius Regulus\",\"Cominius Semellius\",\"Stertinius Matinius\",\"Iunius Porcius\",\"Honorius Justus\",\"Opimius Gallus\",\"Percennius Cethegus\",\"Crispinus Cocceius\",\"Sulpicius Magnentius\",\"Cincinnatus Messalla\",\"Seccius Felix\",\"Sergian Lollius\",\"Musonius Pettius\",\"Laetorius Sallustius\",\"Gnaeus Albinus\",\"Sempronius Cinna\",\"Cassianus Lucretius\",\"Vincentius Minicius\",\"Januarius Cassius\",\"Aulus Rebilus\",\"Cato Pupius\",\"Pontidius Hostius\",\"Vitus Eborius\",\"Verecundus Quintilianus\",\"Eutropius Amulius\",\"Maecilius Maximus\",\"Minicius Cornutus\",\"Maxentius Coruncanius\",\"Cornutus Gordian\",\"Atilius Afranius\",\"Gallus Fannius\",\"Bruttius Messalla\",\"Aemilius Diocletian\",\"Hortensius Maximus\",\"Paternus Tarquinius\",\"Marcellus Eutropius\",\"Musonius Gnaeus\",\"Maximian Suetonius\",\"Novellius Camillius\",\"Paternus Potitius\",\"Munatius Atilius\",\"Pompeius Licinius\",\"Camillus Sulpicius\",\"Cethegus Nazarius\",\"Genucius Cassio\",\"Draco Domitian\",\"Ofilius Hortensius\",\"Cocceius Secundus\",\"Papinius Rufius\",\"Januarius Cilnius\",\"Laelius 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    {
      "cell_type": "code",
      "source": [
        "fig_age = make_subplots(rows=2, cols=1,shared_xaxes=True,vertical_spacing=0.07,subplot_titles=(\"Age Distribution\", \"Age Boxplot - in search for Outliers\"))\n",
        "# Histogram\n",
        "fig_age.add_trace(go.Histogram(x=df[\"Age\"],nbinsx=40,marker_color=\"#4C78A8\",opacity=0.75),row=1, col=1)\n",
        "# Boxplot\n",
        "fig_age.add_trace(go.Box(x=df[\"Age\"],marker_color=\"#E45756\",boxmean=True,hoverinfo=\"skip\"),row=2, col=1)\n",
        "# Layout\n",
        "fig_age.update_layout(height=700,width=1000,template=\"simple_white\",showlegend=False,title=\"Age Distribution\")\n",
        "fig_age.update_xaxes(title=\"Grladiator's age\", row=2, col=1)\n",
        "fig_age.update_yaxes(title=\"Number of Gladiators\", row=1, col=1)\n",
        "\n",
        "fig_age.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "dJN06N-ig5xW",
        "outputId": "c7c32873-5172-41f2-b251-3f1f66e6b775"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                            \n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *Number of fights per gladiator:*"
      ],
      "metadata": {
        "id": "K_KgcpBSpr91"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = make_subplots(rows=2, cols=1,shared_xaxes=True,vertical_spacing=0.07,subplot_titles=(\"Blue - Fight Count Distribution\", \"Red - Fight Count Boxplot: in search for Outliers\"))\n",
        "# --- Histogram ---\n",
        "fig.add_trace(go.Histogram(x=df[\"Battle Experience\"],nbinsx=40,marker_color=\"#4C78A8\",opacity=0.75),row=1, col=1)\n",
        "# --- Boxplot ---\n",
        "fig.add_trace(go.Box(x=df[\"Battle Experience\"],marker_color=\"#E45756\",boxmean=True,hoverinfo=\"skip\"),row=2, col=1)\n",
        "# Layout & Axis Labels\n",
        "fig.update_layout(height=700,width=1000,template=\"simple_white\",showlegend=False,title=dict(text=\"Fights per Gladiator\",font=dict(size=20)),)\n",
        "fig.update_xaxes(title_text=\"Battle Experience (Total Fights each Gladiator had)\",row=2, col=1,tickfont=dict(size=12))\n",
        "fig.update_yaxes(title_text=\"Number of Gladiators\",row=1, col=1,tickfont=dict(size=12))\n",
        "fig.update_yaxes(title_text=\"\", row=2, col=1)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "6081wUNGfDWI",
        "outputId": "37920b51-b146-4a5a-bfbe-05981e713f71"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *Wins and losses distributions:*"
      ],
      "metadata": {
        "id": "Wmv3s_0xqUQ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig=make_subplots(rows=2,cols=2,\n",
        "    subplot_titles=(\"Wins Distribution\",\"Losses Distribution\",\n",
        "                    \"Wins Outliers\",\"Losses Outliers\"),\n",
        "    vertical_spacing=0.12,horizontal_spacing=0.1)\n",
        "\n",
        "fig.add_trace(go.Histogram(x=df[\"Wins\"],nbinsx=40,marker_color=\"#4C78A8\",opacity=.75),1,1)\n",
        "fig.add_trace(go.Histogram(x=df[\"Losses\"],nbinsx=40,marker_color=\"#E45756\",opacity=.75),1,2)\n",
        "\n",
        "fig.add_trace(go.Box(x=df[\"Wins\"],marker_color=\"#4C78A8\",boxmean=True,hoverinfo=\"skip\"),2,1)\n",
        "fig.add_trace(go.Box(x=df[\"Losses\"],marker_color=\"#E45756\",boxmean=True,hoverinfo=\"skip\"),2,2)\n",
        "\n",
        "fig.update_layout(height=800,width=1200,template=\"simple_white\",\n",
        "                  showlegend=False,title=\"Wins & Losses: Distributions and Outliers\")\n",
        "\n",
        "fig.update_xaxes(title=\"Wins\",row=2,col=1)\n",
        "fig.update_xaxes(title=\"Losses\",row=2,col=2)\n",
        "fig.update_yaxes(title=\"Number of Gladiators\",row=1,col=1)\n",
        "fig.update_yaxes(title=\"Number of Gladiators\",row=1,col=2)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "w10w5i8BhBYJ",
        "outputId": "5b1ec476-e6d1-47c7-8696-888dee1f1993"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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11,5,9,7,13,3,3,6,3,11,9,14,11,11,11,5,7,10,10,2,3,7,3,8,10,6,9,14,10,4,6,9,2,1,3,7,11,2,12,11,11,19,11,4,4,7,12,11,7,9,10,10,12,9,13,6,6,9,8,17,5,4,12,8,5,6,11,2,6,7,3,8,9,7,8,15,8,9,7,6,7,4,3,6,15,6,8,6,5,9,10,1,7,6,8,2,7,13,12,3,8,8,10,13,7,5,11,11,6,9,4,5,7,5,8,15,6,6,7,10,7,8,15,5,8,13,6,8,13,14,8,4,2,8,4,12,7,6,9,6,7,6,9,8,6,4,12,11,10,16,0,10,7,9,2,8,11,9,9,5,5,4,14,7,19,9,14,6,3,9,8,12,4,8,7,15,14,6,7,9,6,8,8,8,6,8,4,6,12,7,11,6,7,7,8,12,15,12,9,6,13,7,19,12,2,4,7,12,5,10,4,10,9,11,10,11,7,2,9,8,3,9,9,6,8,6,4,9,5,11,5,9,9,10,11,3,6,15,11,10,5,9,7,2,12,16,5,11,7,5,8,14,8,9,3,10,5,7,5,3,7,2,9,11,8,3,3,6,2,8,10,6,10,4,4,6,6,10,2,2,3,11,11,5,10,9,2,8,6,5,14,9,11,6,10,9,5,6,8,13,6,5,3,3,4,9,9,11,6,11,6,4,13,5,9,13,8,8,4,12,12,5,10,7,12,10,17,9,6,1,10,2,1,6,13,16,2,7,10,4,10,9,6,5,8,7,9,16,13,3,7,6,2,9,16,8,11,6,14,11,7,8,6,4,9,5,10,3,10,8,11,4,8,10,7,9,16,3,7,12,6,14,8,7,11,4,6,5,4,2,9,3,10,11,6,10,4,16,1,7,7,5,11,8,7,19,3,3,3,13,10,10,5,13,4,10,11,10,9,9,11,10,2,13,11,7,4,5,13,7,5,2,8,10,14,11,8,9,8,6,8,5,12,6,11,9,5,8,8,7,7,11,9,18,5,4,17,5,10,5,4,8,12,8,1,2,4,10,13,3,9,8,8,8,8,4,3,5,7,2,8,8,6,5,9,8,12,1,10,4,2,10,8,4,13,5,12,2,7,6,9,5,10,3,7,7,7,10,8,12,8,12,10,10,14,14,8,11,9,7,13,5,9,14,9,4,9,4,4,8,9,6,7,17,9,1,8,11,5,2,4,4,8,8,9,8,6,11,7,10,6,5,9,7,5,12,12,9,7,6,6,8,11,10,4,4,7,10,3,8,8,12,4,6,8,5,6,10,5,0,7,12,5,8,4,7,9,9,10,8,7,15,9,5,9,9,7,8,8,12,11,3,14,12,2,7,4,14,14,3,3,8,8,6,3,5,8,6,9,7,12,12,5,3,6,8,8,9,5,4,10,8,8,15,13,6,9,9,3,5,6,4,8,14,4,6,4,7,6,15,9,15,8,17,12,6,9,10,8,4,5,11,6,5,6,4,13,7,3,11,6,13,7,5,4,9,17,11,6,7,6,5,9,9,9,3,3,6,5,5,9,5,0,4,12,10,4,9,3,9,1,4,2,11,8,7,9,8,13,6,9,9,9,9,10,9,2,9,6,9,2,7,4,12,7,13,6,12,17,10,10,7,8,7,10,13,5,3,4,3,6,8,4,6,8,11,5,6,11,8,5,4,4,8,6,7,12,6,11,3,2,11,7,8,3,8,11,11,7,12,9,7,6,1,6,6,5,12,10,5,8,6,15,4,7,5,10,9,10,8,6,5,7,9,6,3,7,9,7,2,12,8,3,8,5,8,7,9,9,9,10,7,3,6,9,15,4,10,7,12,8,4,7,7,16,15,5,14,7,8,11,13,11,5,8,1,6,4,7,10,7,6,8,10,6,5,2,7,6,10,1,13,6,6,13,7,3,9,11,7,7,14,7,10,6,11,5,3,5,9,10,4,7,4,7,4,10,8,7,14,6,9,6,13,7,10,6,7,4,4,5,8,4,7,10,11,10,8,10,6,5,7,10,15,5,7,15,11,10,12,8,9,5,9,9,4,16,6,2,5,13,5,1,11,11,3,15,10,6,7,7,11,3,8,5,9,4,13,22,12,7,3,13,8,6,9,4,8,11,6,5,4,5,5,11,4,12,6,7,10,14,3,13,6,8,11,5,9,6,4,6,9,15,4,6,10,8,9,8,9,12,7,7,12,8,1,7,10,14,6,12,14,2,8,8,6,8,7,5,6,12,8,8,5,2,7,2,5,1,11,19,4,14,7,9,9,6,3,7,5,2,2,11,9,7,4,12,3,7,7,6,6,9,8,14,12,12,13,5,10,9,8,7,9,9,4,6,8,9,8,11,3,7,5,4,7,12,13,6,13,3,16,6,11,6,9,8,8,8,5,5,9,3,9,11,6,12,11,4,13,12,18,8,5,3,2,12,13,6,11,11,4,13,5,12,3,13,2,6,4,14,5,15,12,9,11,10,4,6,7,8,10,11,7,12,8,7,4,8,3,3,7,5,11,8,8,18,3,3,9,5,6,8,6,3,4,8,12,12,7,6,4,3,2,2,3,3,6,12,3,3,14,8,10,15,12,10,8,7,4,5,12,15,9,5,9,6,7,9,7,10,15,11,14,3,12,10,9,7,4,10,6,9,6,10,10,6,11,6,4,14,4,5,8,12,13,8,5,8,4,8,4,3,3,8,7,10,13,4,5,0,8,11,4,9,10,4,13,11,8,9,6,7,6,8,11,6,13,5,3,5,10,8,16,6,6,6,4,8,9,10,6,7,5,3,6,14,9,8,4,3,10,5,10,9,10,10,7,7,3,5,6,20,14,8,14,9,8,7,3,7,18,2,6,10,5,7,3,12,4,12,6,12,3,13,8,15,9,8,9,8,12,16,8,10,13,15,11,8,12,12,14,7,2,14,6,12,9,4,8,6,4,11,14,5,9,16,7,6,7,4,5,7,5,16,2,7,13,5,7,7,8,10,13,10,9,9,9,6,5,4,6,6,5,12,3,12,9,10,13,9,6,8,6,5,6,3,12,9,6,2,5,13,4,8,5,8,9,6,4,4,10,18,7,14,11,12,4,9,6,7,5,6,10,8,13,8,5,6,5,6,2,2,11,5,10,4,2,3,10,6,7,9,15,12,5,5,5,2,2,7,21,12,3,6,18,5,6,12,12,5,5,10,3,8,13,6,10,9,4,4,6,4,4,5,8,6,5,14,9,7,7,6,4,3,12,13,3,6,6,8,5,11,8,9,3,5,9,5,12,6,7,7,4,8,3,4,9,5,9,10,12,17,4,8,12,0,11,11,3,8,3,8,4,3,6,4,8,4,7,8,7,9,3,8,12,14,10,8,16,3,7,4,8,4,4,6,7,5,10,12,6,7,11,15,6,8,8,7,6,7,6,5,9,11,12,7,11,5,9,8,10,9,8,8,7,12,16,2,6,6,8,8,12,14,13,9,13,4,10,12,10,10,9,7,3,7,13,8,8,6,5,10,3,8,3,8,6,4,8,11,6,3,8,7,5,3,6,3,8,5,10,3,14,10,8,11,5,8,8,13,5,10,7,5,5,9,11,20,3,5,10,5,11,1,9,14,5,12,2,9,5,4,5,9,7,2,17,5,4,12,9,11,6,7,14,15,10,7,3,19,12,5,5,2,6,9,7,9,13,4,12,4,5,5,6,7,1,3,6,9,7,4,6,8,6,7,3,5,7,4,6,6,14,13,8,10,3,2,12,8,11,8,3,8,3,4,7,8,12,7,4,14,7,7,4,6,4,8,2,10,9,2,10,8,18,8,7,13,2,11,7,7,4,6,9,3,8,13,11,9,4,3,3,6,10,5,5,9,13,8,6,7,9,10,5,7,7,5,9,4,5,10,9,10,8,8,8,9,7,9,9,6,4,9,5,11,3,7,10,8,6,6,8,9,6,1,5,6,11,7,9,14,6,4,6,7,9,9,3,11,4,7,11,10,13,11,7,12,8,7,9,7,18,5,5,11,6,8,14,8,10,11,6,3,12,5,7,11,16,5,7,9,10,8,3,3,8,5,12,7,3,7,10,13,6,11,10,14,12,10,14,6,9,10,3,4,8,9,4,8,8,10,14,12,7,5,7,6,14,8,12,11,11,7,12,5,4,9,8,6,3,7,6,9,12,7,3,1,12,11,11,6,4,8,7,9,7,4,4,9,6,6,6,11,4,10,8,9,6,2,8,2,13,10,7,9,2,8,10,12,9,11,6,6,6,12,12,2,8,5,9,6,6,7,6,6,4,1,5,4,5,7,5,8,15,8,12,8,9,11,8,5,10,2,3,15,18,6,10,6,8,3,12,4,11,10,9,4,9,12,22,11,9,5,11,3,8,5,13,6,4,4,10,5,7,8,12,13,7,8,9,10,7,8,8,11,4,8,10,5,4,8,7,12,9,12,13,16,8,8,3,6,8,10,4,5,9,4,6,11,6,4,6,15,2,8,4,11,11,6,11,5,8,3,13,8,15,8,3,9,7,9,4,15,11,6,8,9,9,7,9,7,14,7,9,19,14,7,6,4,10,6,8,9,12,14,6,11,4,11,16,5,12,7,8,7,5,6,10,7,16,3,10,8,5,4,1,10,8,8,8,7,14,8,12,4,8,6,6,9,8,18,12,7,6,10,3,10,4,3,8,7,11,8,6,8,8,7,10,4,9,2,12,3,6,8,14,5,7,9,5,3,8,8,2,7,7,5,9,10,10,11,8,11,10,15,7,12,2,4,6,18,5,19,4,6,6,8,9,8,8,10,14,6,5,7,4,12,2,11,13,7,10,6,5,14,13,5,6,7,5,4,13,10,6,6,12,9,4,5,11,8,11,14,4,4,2,8,5,5,2,7,7,10,6,6,9,10,9,4,9,6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14,13,6,13,10,15,9,11,12,4,10,9,9,11,6,8,7,12,8,11,5,8,4,5,9,7,11,5,4,8,11,14,8,12,7,15,2,12,10,8,1,14,5,9,16,10,13,5,3,5,7,2,4,8,7,5,6,9,8,8,12,9,2,12,14,8,9,8,4,5,11,15,12,11,6,6,6,8,9,12,8,7,7,6,13,6,8,9,8,9,8,3,7,10,13,9,11,7,9,4,8,2,8,12,8,8,8,9,1,8,7,9,4,9,0,5,7,5,4,7,1,7,7,12,12,12,10,11,11,7,5,12,6,6,10,4,5,4,3,10,8,0,9,13,7,16,9,12,10,4,18,5,10,12,8,9,7,7,8,2,9,6,8,5,3,8,3,11,9,6,17,5,9,2,5,10,4,10,10,5,15,7,3,11,9,5,4,5,7,5,9,11,7,10,1,7,6,5,6,7,9,2,17,13,4,9,7,6,8,9,7,7,1,10,5,5,6,8,2,7,10,13,5,6,9,4,3,8,6,10,3,11,3,8,8,11,3,8,12,2,7,15,6,12,13,10,16,10,11,8,3,11,6,13,1,2,9,15,12,9,7,10,2,4,4,8,6,11,7,2,4,13,8,10,3,7,6,2,14,10,11,4,11,3,8,12,13,7,8,9,6,8,12,4,6,4,4,6,13,10,9,8,4,5,3,11,6,8,7,5,7,4,14,11,7,6,7,4,13,6,11,4,4,5,6,10,7,6,11,13,4,12,12,7,11,3,5,6,10,6,16,3,14,5,7,13,8,13,5,9,2,7,7,3,9,5,7,6,3,9,8,4,10,5,9,6,11,5,9,5,6,7,13,7,8,4,9,8,6,7,7,7,6,6,9,12,10,6,12,5,4,6,8,3,5,8,5,5,5,5,13,6,14,4,4,10,9,16,11,9,7,2,13,6,11,9,7,3,8,7,5,5,4,6,6,14,5,3,8,11,11,8,4,5,7,6,6,13,9,14,5,5,9,9,4,10,10,3,10,10,9,7,5,9,3,8,14,12,6,13,4,6,13,9,9,7,2,7,6,11,7,14,5,4,14,6,4,10,7,9,7,5,3,4,8,8,10,7,9,4,9,8,14,8,9,10,12,7,6,8,7,7,15,11,9,6,13,15,8,10,2,10,7,2,6,9,7,5,12,11,7,9,11,5,10,6,9,7,8,15,4,17,11,10,10,9,11,3,9,7,2,5,6,6,11,8,9,8,7,7,3,9,5,5,10,11,5,10,4,4,4,9,5,13,14,8,7,6,3,10,7,7,15,5,12,4,6,9,8,2,6,6,8,5,4,12,15,12,5,13,8,10,6,14,7,6,9,9,13,4,14,13,5,10,11,6,8,3,7,1,5,6,6,5,6,13,6,7,9,6,14,8,14,9,6,9,6,10,10,13,5,12,9,7,9,17,12,7,13,9,1,18,12,10,9,4,7,7,7,7,9,9,15,14,5,3,5,5,10,15,4,9,8,7,7,5,14,9,5,13,6,7,7,14,7,7,4,5,5,8,8,11,9,9,12,9,10,7,10,6,6,4,7,14,8,9,8,9,11,12,6,18,7,8,5,5,5,13,8,2,5,5,8,8,13,8,6,8,7,3,5,7,3,4,12,5,5,6,11,8,2,13,7,2,7,5,6,6,3,6,8,7,10,7,10,15,3,9,5,2,7,5,8,6,6,7,7,7,7,6,9,12,4,9,10,14,8,6,8,8,10,9,13,8,4,6,15,7,8,11,3,8,9,7,6,1,8,8,5,11,10,10,11,7,5,4,9,8,6,7,10,3,6,12,16,5,11,4,1,13,13,4,3,7,5,4,3,12,5,4,13,11,14,13,4,13,5,6,8,9,11,3,8,9,6,5,7,7,2,13,9,4,4,8,10,5,11,10,12,5,11,11,6,6,9,19,7,8,2,8,11,4,1,8,12,5,5,6,11,6,7,4,6,3,9,8,8,3,6,3,1,5,6,6,18,6,8,14,6,6,8,6,7,4,8,6,9,4,3,11,15,6,4,9,7,11,9,12,7,10,6,9,9,8,15,8,9,13,10,5,10,8,13,3,13,2,11,11,3,13,11,9,4,13,4,14,5,13,7,16,5,7,6,2,9,8,5,7,11,13,10,17,9,3,9,11,8,9,10,6,5,8,4,5,5,9,2,11,8,10,12,3,5,7,7,9,8,15,8,6,10,10,4,3,6,13,10,10,6,11,11,7,9,2,9,6,13,6,11,7,8,6,3,9,9,6,8,5,8,7,13,6,6,6,3,6,4,7,6,11,10,9,8,7,12,10,5,7,3,6,8,12,6,7,13,8,8,4,4,12,1,4,12,5,7,9,8,14,7,7,4,9,8,6,8,5,10,9,8,4,2,7,7,6,10,7,11,9,10,13,5,9,12,7,4,12,4,3,11,6,8,8,9,11,12,9,4,8,9,5,7,9,6,11,10,11,15,9,6,10,4,1,16,16,7,11,5,7,7,7,17,6,13,11,4,7,5,6,10,9,12,10,9,12,5,4,7,4,9,5,6,12,5,6,5,9,6,3,5,15,8,4,13,14,12,7,6,10,7,4,1,6,15,12,8,16,14,6,7,9,0,9,10,4,12,13,3,15,13,5,6,7,10,3,5,11,11,10,7,5,4,5,20,6,9,14,14,15,3,10,6,11,7,2,11,10,7,7,7,6,5,8,4,9,5,8,15,9,14,9,12,8,15,2,12,3,4,9,4,6,10,4,15,6,11,6,12,7,11,15,13,7,3,8,9,8,7,15,11,7,8,5,11,10,8,10,7,4,6,11,2,6,8,6,13,6,15,3,6,9,7,11,4,5,10,6,10,11,8,13,8,4,17,6,6,3,13,8,7,9,7,4,7,8,7,7,8,7,2,11,11,11,7,3,6,7,5,4,7,5,9,11,5,9,5,11,16,11,11,5,7,8,13,15,9,10,5,11,10,4,9,7,4,8,3,3,8,3,10,2,9,20,5,10,9,3,12,8,13,10,9,13,8,11,11,14,12,12,14,14,6,4,9,7,9,6,4,3,5,9,6,14,13,10,6,14,7,4,4,6,8,17,8,16,15,5,10,13,8,9,5,3,3,9,5,6,11,6,12,3,8,2,6,8,7,7,8,7,8,3,5,11,7,7,6,2,1,9,3,3,12,1,8,1,4,5,11,11,10,12,15,3,6,6,5,8,15,10,9,11,4,11,9,11,5,6,5,15,14,13,8,9,9,17,2,8,2,8,4,13,16,6,12,13,2,6,7,10,5,10,6,6,13,7,4,0,7,14,3,11,12,12,8,10,7,3,10,10,5,7,12,8,14,9,9,5,9,5,9,5,4,14,6,4,5,5,12,11,9,7,5,2,4,12,15,12,7,7,10,6,5,7,9,7,4,14,7,6,7,10,3,6,5,7,4,6,4,7,6,9,9,3,7,9,13,3,6,4,8,6,4,4,6,14,9,12,13,11,8,4,6,12,2,8,5,10,7,11,2,8,9,5,2,7,6,9,5,7,9,4,8,12,4,8,1,3,10,6,9,6,9,7,11,11,0,6,6,8,8,4,8,4,10,3,4,7,10,5,6,8,18,4,9,16,7,8,4,10,10,4,6,7,13,12,10,5,5,7,5,12,8,11,3,15,12,8,7,14,13,5,8,1,0,7,10,13,13,9,7,13,5,5,8,15,4,7,8,6,6,11,6,8,5,9,18,8,9,4,12,6,9,9,5,4,2,7,10,13,8,9,8,4,5,8,15,2,6,8,5,15,12,10,9,7,12,4,8,4,4,8,6,7,6,15,6,9,10,10,11,6,8,4,5,10,7,6,3,14,8,6,8,5,13,6,4,4,13,8,6,7,8,11,9,5,8,8,12,8,7,3,9,10,8,9,6,7,4,10,10,10,13,7,2,8,17,4,11,5,7,12,4,7,8,13,6,5,8,16,6,16,7,6,8,13,6,14,10,10,6,9,10,9,14,9,15,5,6,10,3,6,13,9,15,7,5,14,6,7,5,9,12,2,7,10,11,7,11,3,11,9,8,8,9,7,5,8,15,5,11,1,10,9,7,7,9,9,4,4,8,13,8,9,3,4,5,10,8,8,9,8,7,11,6,8,3,5,7,8,2,7,5,6,7,2,4,5,11,6,12,4,5,5,9,8,8,3,13,9,5,11,20,9,14,5,9,5,5,10,14,5,11,7,7,10,5,9,6,7,7,6,8,8,4,9,8,3,5,11,7,10,7,11,9,8,5,10,7,6,6,14,12,7,6,14,12,11,8,3,4,13,7,12,7,2,8,2,5,11,2,11,11,8,3,5,3,9,6,7,5,10,9,8,9,7,3,7,12,6,1,6,4,17,6,11,9,2,3,7,8,5,11,7,6,12,15,9,4,4,12,10,6,18,7,8,12,3,6,10,9,2,14,7,8,10,13,6,11,9,9,4,8,2,4,5,10,4,15,10,8,7,9,7,8,10,5,8,9,15,8,9,9,9,9,9,2,9,4,9,3,7,9,3,6,8,8,6,5,13,11,4,16,7,14,6,13,13,3,5,10,6,9,4,3,13,8,6,9,5,6,8,13,13,8,14,6,9,9,10,9,7,9,5,2,6,7,5,10,6,7,8,15,6,6,3,9,4,2,6,10,19,11,11,7,8,10,4,9,9,13,7,9,8,5,5,10,5,7,7,8,6,6,3,7,5,1,7,8,12,5,4,5,10,7,8,5,14,10,18,3,8,8,12,6,7,7,4,13,6,9,6,11,7,10,5,8,0,9,9,14,7,7,2,14,8,7,4,5,5,8,6,11,3,16,4,4,4,7,5,10,9,5,12,13,7,7,6,8,4,6,8,9,12,6,10,5,5,16,9,6,8,6,2,11,5,10,7,4,4,2,8,6,5,8,3,10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        "num_df=df.select_dtypes(include=[\"int64\",\"float64\"])\n",
        "corr=num_df.corr()\n",
        "\n",
        "Z=linkage(corr,method=\"ward\")\n",
        "idx=leaves_list(Z)\n",
        "corr_clustered=corr.iloc[idx,:].iloc[:,idx]\n",
        "\n",
        "fig=px.imshow(corr_clustered,aspect=\"auto\",color_continuous_scale=\"RdBu_r\",\n",
        "              title=\"Clustered Correlation Heatmap\")\n",
        "fig.update_layout(width=1400,height=1000,margin=dict(l=200,b=200),\n",
        "                  title=dict(font=dict(size=24)))\n",
        "fig.update_xaxes(tickfont=dict(size=14),tickangle=45)\n",
        "fig.update_yaxes(tickfont=dict(size=12))\n",
        "fig.show()"
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        "outputId": "eca9087f-3921-44d0-b364-1fe6d615bb23"
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      "execution_count": 15,
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      "source": [
        "### **Research:** Pose relevant questions about your dataset, then answer them using visual elements (e.g. charts or plots) to provide clear insights.\n",
        "\n",
        "For example, in the 2nd lecture the entire class took a survey. Then, we talked about the collected data and desplayed the collected data using the right **plots** - Lines, Bars, Hist, Pie, Map, HeatMap, Area, Time, etc.\n",
        "\n",
        "An aditional more specific example, would be the questions we asked during the recitation on the `Titanic` dataset:\n",
        "  - \"Did survival rates differ by gender?\"\n",
        "  - \"Was passenger class related to survival?\"\n",
        "  - \"What was the age distribution of survivors vs. non-survivors?\"\n",
        "  - \"Did embarking location (port) have any effect on survival?\"    \n",
        "  \n",
        "And how we answered those questions using **plots**.\n",
        "\n",
        "The idea is to pose questions that can uncover patterns, correlations, or anomalies in your dataset, then back those up with clean, insightful visualizations."
      ],
      "metadata": {
        "id": "lo68PsjTK0_j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temp_df = df.copy()\n",
        "temp_df[\"Battle Experience\"] = temp_df[\"Wins\"] + temp_df[\"Losses\"]\n",
        "temp_df[\"Win_Rate\"] = temp_df[\"Wins\"] / temp_df[\"Battle Experience\"]\n",
        "\n",
        "# Remove fighters with 0 fights to avoid division by zero\n",
        "temp_df = temp_df[temp_df[\"Battle Experience\"] > 0]\n",
        "\n",
        "corr_rate = temp_df[\"Battle Experience\"].corr(temp_df[\"Win_Rate\"])\n",
        "print(\"Correlation between Experience and Win Rate:\", corr_rate)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NCdHccVELENr",
        "outputId": "ca750fb9-6415-46ae-d3dd-700d574db4f2"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation between Experience and Win Rate: -0.48973832328336325\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Battle Experince as a very strong predictor of wins:"
      ],
      "metadata": {
        "id": "4ZDVOuooKZaR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Features and target\n",
        "X = temp_df[[\"Battle Experience\"]]\n",
        "y = temp_df[\"Wins\"]\n",
        "\n",
        "# --- Fit Linear Regression ---\n",
        "model = LinearRegression()\n",
        "model.fit(X, y)\n",
        "\n",
        "intercept = model.intercept_\n",
        "slope = model.coef_[0]\n",
        "r2 = model.score(X, y)\n",
        "\n",
        "print(\"=== Linear Regression: Wins ~ Total Fights ===\")\n",
        "print(f\"Intercept (β0): {intercept:.4f}\")\n",
        "print(f\"Slope (β1): {slope:.4f}\")\n",
        "print(f\"R²: {r2:.4f}\")\n",
        "\n",
        "# --- Create prediction line for plotting ---\n",
        "temp_df[\"Predicted_Wins\"] = model.predict(X)\n",
        "\n",
        "# --- Plot: Scatter + Regression Line ---\n",
        "fig = px.scatter(\n",
        "    temp_df,\n",
        "    x=\"Battle Experience\",\n",
        "    y=\"Wins\",\n",
        "    title=\"Linear Regression: Wins vs Battle Experience\",\n",
        "    labels={\"Total Fights (Battle Experience)\", \"Wins\"},\n",
        "    opacity=0.7,\n",
        ")\n",
        "\n",
        "fig.add_traces(\n",
        "    px.line(\n",
        "        temp_df,\n",
        "        x=\"Battle Experience\",\n",
        "        y=\"Predicted_Wins\",\n",
        "    ).data\n",
        ")\n",
        "\n",
        "fig.update_layout(width=900, height=600)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        },
        "id": "kpvogg2jIwMI",
        "outputId": "ae905d0a-647d-47a8-8d64-114a71affef4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Linear Regression: Wins ~ Total Fights ===\n",
            "Intercept (β0): 1.0285\n",
            "Slope (β1): 0.7121\n",
            "R²: 0.9096\n"
          ]
        },
        {
          "output_type": "display_data",
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10,5,6,4,6,9,3,9,7,4,4,10,10,4,12,5,5,8,5,13,9,10,8,2,9,7,10,6,6,12,9,3,15,5,5,9,14,5,8,8,8,10,12,6,12,9,11,9,6,5,13,10,10,6,7,10,13,7,6,5,4,10,7,6,13,10,9,8,7,10,5,13,14,8,5,10,3,8,13,15,18,2,10,8,6,11,6,7,6,11,5,14,10,5,10,9,15,8,13,1,9,8,8,8,10,11,2,8,7,11,5,6,7,7,14,5,9,6,9,8,12,9,2,6,10,5,7,5,5,8,8,9,6,10,5,3,4,12,9,8,8,9,4,8,4,4,6,4,3,3,8,5,9,10,8,7,4,5,3,2,6,8,5,15,8,5,8,12,2,6,15,5,9,5,10,22,6,9,5,6,7,2,7,11,7,15,13,6,14,12,8,4,10,8,14,13,10,8,8,7,11,6,10,5,6,7,5,9,13,6,8,12,11,7,17,9,2,4,4,8,4,1,9,13,7,9,6,5,3,11,9,6,7,4,12,8,3,6,4,9,7,8,10,4,9,5,5,13,4,9,6,8,9,8,7,17,7,16,14,9,3,11,4,8,20,6,6,7,9,6,6,6,7,3,7,5,6,9,8,8,9,4,10,13,16,9,5,5,8,9,5,7,3,10,11,3,11,7,14,5,10,8,7,13,4,6,5,8,5,2,2,7,20,6,5,8,11,7,13,8,5,11,4,11,4,15,14,5,8,7,6,11,15,5,11,3,10,8,7,5,10,10,4,6,9,5,2,4,9,3,12,8,14,12,9,2,4,6,9,9,11,7,5,4,4,10,10,15,7,7,8,6,9,8,13,8,3,3,3,9,4,1,9,15,5,5,12,10,6,9,8,10,4,8,7,12,8,16,6,13,15,11,11,5,11,6,6,12,6,10,3,8,10,10,9,11,10,2,18,8,8,10,5,4,6,8,5,5,14,14,5,13,9,8,6,6,10,4,5,5,11,5,11,6,4,5,3,7,4,7,8,7,5,6,6,10,7,10,4,4,4,7,10,8,8,4,7,7,5,7,7,4,5,10,3,16,13,8,4,4,2,9,4,3,4,9,5,9,11,5,10,8,9,4,4,8,12,10,17,7,10,8,7,11,12,8,9,5,9,6,4,11,8,9,7,6,3,7,6,5,8,12,6,7,11,8,6,6,8,5,14,10,12,13,9,12,11,8,12,14,3,10,4,6,10,7,7,6,5,6,4,6,7,8,13,9,9,12,8,14,11,3,13,7,5,7,7,5,6,10,7,12,7,4,12,11,7,14,5,3,6,4,3,7,11,12,12,6,9,12,6,15,13,6,5,9,7,6,6,5,2,3,6,5,11,5,1,6,11,8,4,6,2,13,16,7,8,5,4,4,12,11,5,8,10,10,3,10,8,6,14,4,10,11,7,10,5,6,4,2,9,5,12,6,12,5,5,3,4,11,6,11,11,7,4,9,3,5,12,15,9,5,4,9,8,4,13,13,12,9,5,7,4,7,11,4,4,9,2,6,8,7,10,11,5,8,11,7,13,11,9,4,8,10,7,7,4,11,3,8,10,7,17,8,5,6,11,4,5,14,14,7,10,7,6,17,13,10,7,3,5,14,11,9,10,9,9,6,9,4,4,10,7,9,7,7,8,6,7,6,16,5,9,13,10,11,4,10,14,8,13,6,6,5,5,3,11,6,14,8,4,8,8,5,4,11,12,15,6,15,7,5,8,7,3,12,8,11,7,12,7,8,6,6,8,9,14,7,13,4,7,14,10,9,2,4,13,13,2,9,4,5,10,3,11,10,6,5,6,9,7,11,11,7,6,7,6,8,3,3,8,4,7,7,4,13,4,16,13,2,7,7,4,9,6,7,9,6,12,8,12,17,4,8,5,8,3,2,11,10,9,6,7,10,4,12,5,4,15,1,5,5,6,3,6,8,13,6,6,10,6,12,22,13,14,5,8,9,9,5,11,9,6,7,6,4,2,8,12,10,7,6,4,10,7,7,4,6,8,8,10,9,4,8,9,9,3,7,22,10,8,10,12,5,3,8,10,9,5,8,9,4,10,7,5,5,7,7,13,3,5,14,6,12,9,2,5,1,5,10,9,10,13,10,11,8,5,5,13,10,3,14,16,11,10,12,11,10,9,9,4,5,3,4,4,5,10,5,2,9,5,11,10,5,3,8,8,10,3,8,6,18,6,4,12,3,7,10,8,3,12,10,4,9,3,7,15,13,10,19,5,7,11,7,3,15,3,7,15,12,12,3,9,11,8,13,12,8,6,10,5,6,3,7,4,16,9,5,4,10,4,6,11,11,3,6,6,1,8,8,4,10,10,7,5,3,2,4,5,9,7,6,8,9,7,11,9,12,6,2,11,10,6,3,10,9,8,9,6,9,5,8,5,9,6,6,6,5,8,4,11,5,8,5,5,6,8,3,8,8,5,9,7,6,5,9,9,10,10,9,4,5,14,10,7,9,4,15,9,9,9,8,8,20,3,4,3,4,7,6,5,13,5,2,14,8,9,7,2,8,7,8,6,6,10,10,4,8,8,11,11,10,3,8,13,8,7,15,5,5,11,4,7,7,7,6,11,3,13,7,8,6,8,7,9,16,7,7,10,12,7,4,15,7,7,6,10,13,4,7,6,3,9,7,4,12,10,9,12,9,8,10,6,5,3,7,2,7,6,14,13,9,7,8,7,5,3,8,11,7,9,5,14,11,10,9,3,4,10,8,5,9,2,7,5,7,14,7,7,7,4,9,6,6,7,9,13,7,11,11,5,13,5,7,2,7,12,7,8,7,4,19,14,8,6,6,7,9,7,11,12,8,8,17,9,6,11,3,11,10,11,7,4,7,6,9,9,4,11,14,9,7,6,10,9,1,18,3,7,9,12,13,5,9,7,5,8,4,5,15,3,12,6,10,4,3,16,8,7,8,6,2,5,10,8,4,8,10,3,9,6,9,9,1,7,3,8,6,9,6,10,12,6,1,5,6,6,10,11,14,13,10,9,9,10,11,14,5,12,15,5,11,4,4,11,14,6,12,6,9,8,8,14,3,8,4,4,5,13,5,12,12,1,4,11,10,14,9,4,7,5,3,7,14,2,7,3,5,15,7,5,4,11,6,6,5,7,3,6,5,5,10,3,15,6,7,8,12,15,5,11,3,7,4,10,4,9,6,4,6,6,2,12,9,3,11,13,6,5,5,1,10,6,5,12,1,4,12,8,10,5,9,9,12,9,5,5,3,3,8,9,9,7,9,4,5,6,12,8,3,12,3,10,7,12,14,5,1,6,12,8,10,14,4,10,8,11,13,9,6,11,8,8,8,5,9,4,2,5,6,5,16,2,12,6,9,9,8,7,10,5,4,17,9,12,9,7,7,4,12,7,6,10,5,5,10,7,9,10,4,8,7,16,6,5,3,12,9,2,8,2,10,6,5,10,5,9,12,11,9,6,8,9,2,8,10,1,5,12,12,2,5,6,5,4,3,8,1,8,7,3,9,14,5,7,11,11,5,8,16,9,7,14,15,5,12,11,7,14,4,6,10,8,8,9,12,4,8,6,10,9,6,4,6,7,10,12,10,13,15,9,8,9,5,11,5,8,6,11,14,1,11,6,8,11,10,3,2,10,8,7,16,10,8,7,6,11,11,8,2,4,8,11,8,9,10,15,7,3,9,6,11,8,12,9,16,6,8,9,6,7,6,11,6,8,5,16,7,11,1,7,11,4,5,6,6,2,7,3,8,16,5,10,6,16,10,4,11,8,4,11,14,13,12,7,10,9,4,6,8,5,11,9,6,9,5,5,6,9,10,6,8,7,5,4,16,5,6,11,8,12,8,10,5,5,12,2,8,5,13,9,6,7,8,3,8,9,2,6,8,11,10,4,5,7,8,17,7,4,5,12,4,10,7,4,7,10,11,11,8,3,13,6,4,6,8,7,7,13,11,6,5,5,11,11,11,6,3,6,9,13,13,13,4,11,14,9,6,9,10,17,9,12,10,8,5,13,5,11,13,9,8,5,10,5,8,9,4,9,2,2,5,11,7,11,10,4,3,5,10,5,6,9,4,9,5,8,10,13,6,7,5,10,9,10,7,7,2,9,5,5,9,12,6,11,14,8,2,6,11,7,9,11,9,9,10,3,6,11,5,13,10,10,10,10,5,9,14,4,10,12,4,5,8,7,11,4,6,7,9,9,6,3,4,21,3,8,12,7,6,6,18,7,7,7,9,8,14,13,8,3,7,7,8,6,7,11,6,21,6,10,6,5,5,12,4,2,7,4,18,12,4,3,8,8,8,10,5,10,7,4,12,9,7,10,8,9,15,10,10,11,7,8,15,5,14,3,6,10,12,2,1,3,11,9,2,4,13,18,14,11,12,7,9,5,7,8,9,4,9,10,10,7,5,6,4,7,6,3,11,4,11,12,6,2,6,13,9,10,12,11,6,5,4,3,6,6,8,3,9,10,11,7,6,5,4,7,15,5,5,3,10,15,10,8,7,6,7,9,8,7,10,9,4,10,10,10,8,7,11,10,3,9,5,2,3,7,11,11,5,9,7,13,3,3,6,3,11,9,14,11,11,11,5,7,10,10,2,3,7,3,8,10,6,9,14,10,4,6,9,2,1,3,7,11,2,12,11,11,19,11,4,4,7,12,11,7,9,10,10,12,9,13,6,6,9,8,17,5,4,12,8,5,6,11,2,6,7,3,8,9,7,8,15,8,9,7,6,7,4,3,6,15,6,8,6,5,9,10,1,7,6,8,2,7,13,12,3,8,8,10,13,7,5,11,11,6,9,4,5,7,5,8,15,6,6,7,10,7,8,15,5,8,13,6,8,13,14,8,4,2,8,4,12,7,6,9,6,7,6,9,8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              "                            \n",
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *Did the Gladiators with most battle experience continued to fight beacuse they always won? - No.*"
      ],
      "metadata": {
        "id": "fEfOF03nr5QS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Top 10 gladiators by total fights\n",
        "top10 = (temp_df.sort_values(\"Battle Experience\", ascending=False).head(10))\n",
        "# Prepare data for plotting\n",
        "plot_df = top10.melt(id_vars=[\"Name\", \"Battle Experience\"],value_vars=[\"Wins\", \"Losses\"],var_name=\"Metric\",value_name=\"Count\")\n",
        "# Plot\n",
        "fig = px.bar(plot_df,x=\"Name\",y=\"Count\",color=\"Metric\",barmode=\"group\",title=\"Top 10 Gladiators by Total Fights: Wins vs Losses\",labels={\"Count\": \"Number of Fights\"},template=\"simple_white\")\n",
        "\n",
        "fig.update_layout(width=1200,height=600,xaxis_tickangle=45)\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "_LddEtOZoJjU",
        "outputId": "26b778e0-40df-4ef0-8bbf-947edbf32ebf"
      },
      "execution_count": 18,
      "outputs": [
        {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### WHEN WERE THE GLADIATOR FIGHTS THE MOST POPLUAR?"
      ],
      "metadata": {
        "id": "fEL1kbfWNcXt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Centuries column\n",
        "def assign_century(year):\n",
        "    if 1 <= year <= 100:\n",
        "        return \"1st_Century\"\n",
        "    elif 101 <= year <= 200:\n",
        "        return \"2nd_Century\"\n",
        "    elif 201 <= year <= 300:\n",
        "        return \"3rd_Century\"\n",
        "    elif 301 <= year <= 400:\n",
        "        return \"4th_Century\"\n",
        "    else:\n",
        "        return \"Other\"\n",
        "\n",
        "df[\"Century\"] = df[\"Birth Year\"].apply(assign_century)"
      ],
      "metadata": {
        "id": "hg1IsNLyhs8e"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
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      "source": [
        "century_table = df[\"Century\"].value_counts().reindex(\n",
        "    [\"1st_Century\", \"2nd_Century\", \"3rd_Century\", \"4th_Century\"],\n",
        "    fill_value=0\n",
        ").reset_index(name=\"Count\")\n",
        "\n",
        "century_table.columns = [\"Century\", \"Count\"]\n",
        "fig = px.bar(\n",
        "    century_table,\n",
        "    x=\"Century\",\n",
        "    y=\"Count\",\n",
        "    text=\"Count\",\n",
        "    title=\"Number of Gladiators Born per Century\",\n",
        "    labels={\"Century\": \"Century\", \"Count\": \"Number of Gladiators\"},\n",
        "    color=\"Century\"\n",
        ")\n",
        "\n",
        "fig.update_traces(textposition=\"outside\")\n",
        "fig.update_layout(width=800, height=500)\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
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          "height": 517
        },
        "id": "x7BLRV9QJz8H",
        "outputId": "a3297490-3cfa-417b-9191-d15494c67a79"
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      "execution_count": 20,
      "outputs": [
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              "                            \n",
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
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              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **CHECKING THE CATEGORICAL VALUES:**"
      ],
      "metadata": {
        "id": "QsUYBTn_nPq8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *WAS THERE ANY IMPORTANCE FOR THE ORIGIN OF THE FIGHTER?*"
      ],
      "metadata": {
        "id": "u8BY2UnLm9SO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Compute averages and totals\n",
        "avg_wins = df.groupby(\"Origin\")[\"Wins\"].mean()\n",
        "total_wins = df.groupby(\"Origin\")[\"Wins\"].sum()\n",
        "\n",
        "# 2. Create subplot figure: 1 row, 2 columns\n",
        "fig = make_subplots(\n",
        "    rows=1, cols=2,\n",
        "    subplot_titles=(\"Average Wins per Origin\", \"Total Wins per Origin\")\n",
        ")\n",
        "\n",
        "# --- Left plot: Average Wins ---\n",
        "fig.add_trace(\n",
        "    go.Bar(\n",
        "        x=avg_wins.index,\n",
        "        y=avg_wins.values,\n",
        "        text=avg_wins.round(2),\n",
        "        textposition=\"outside\",\n",
        "        name=\"Average Wins\"\n",
        "    ),\n",
        "    row=1, col=1\n",
        ")\n",
        "\n",
        "# --- Right plot: Total Wins ---\n",
        "fig.add_trace(\n",
        "    go.Bar(\n",
        "        x=total_wins.index,\n",
        "        y=total_wins.values,\n",
        "        text=total_wins,\n",
        "        textposition=\"outside\",\n",
        "        name=\"Total Wins\"\n",
        "    ),\n",
        "    row=1, col=2\n",
        ")\n",
        "\n",
        "# Layout adjustments\n",
        "fig.update_layout(\n",
        "    width=1100,\n",
        "    height=550,\n",
        "    showlegend=False\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "WjGv84UFmw9n",
        "outputId": "23475533-36e8-4701-9bf2-22fb7d3dbc4c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</html>"
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *WICH CATEGORY OF FIGHTERS WAS THE MOST POPULAR, WHICH HAD THE MOST WINS?*"
      ],
      "metadata": {
        "id": "ceZL_idz3ZTZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Compute averages and totals\n",
        "avg_skills = df.groupby(\"Special Skills\")[\"Wins\"].mean()\n",
        "total_skills = df.groupby(\"Special Skills\")[\"Wins\"].sum()\n",
        "\n",
        "# 2. Create subplot figure: 1 row, 2 columns\n",
        "fig = make_subplots(\n",
        "    rows=1, cols=2,\n",
        "    subplot_titles=(\"Average Wins per Special Skills\", \"Total Wins per Special Skills\")\n",
        ")\n",
        "\n",
        "# --- Left plot: Average Skills ---\n",
        "fig.add_trace(\n",
        "    go.Bar(\n",
        "        x=avg_skills.index,\n",
        "        y=avg_skills.values,\n",
        "        text=avg_skills.round(2),\n",
        "        textposition=\"outside\",\n",
        "        name=\"Average Wins\"\n",
        "    ),\n",
        "    row=1, col=1\n",
        ")\n",
        "\n",
        "# --- Right plot: Total Wins ---\n",
        "fig.add_trace(\n",
        "    go.Bar(\n",
        "        x=total_skills.index,\n",
        "        y=total_skills.values,\n",
        "        text=total_skills,\n",
        "        textposition=\"outside\",\n",
        "        name=\"Total Wins\"\n",
        "    ),\n",
        "    row=1, col=2\n",
        ")\n",
        "\n",
        "# Layout adjustments\n",
        "fig.update_layout(\n",
        "    width=1100,\n",
        "    height=550,\n",
        "    showlegend=False\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
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        "id": "BzB9G-EgoruR",
        "outputId": "6d2156a5-764f-4daf-c402-7df42ac5b0e4",
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          "base_uri": "https://localhost:8080/",
          "height": 567
        }
      },
      "execution_count": 22,
      "outputs": [
        {
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          "data": {
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    {
      "cell_type": "code",
      "source": [
        "# 1. Compute averages and totals\n",
        "avg_losses = df.groupby(\"Special Skills\")[\"Losses\"].mean()\n",
        "total_losses = df.groupby(\"Special Skills\")[\"Losses\"].sum()\n",
        "\n",
        "# 2. Create subplot figure: 1 row, 2 columns\n",
        "fig = make_subplots(\n",
        "    rows=1, cols=2,\n",
        "    subplot_titles=(\"Average Losses per Special Skills\", \"Total Losses per Special Skills\")\n",
        ")\n",
        "\n",
        "# --- Left plot: Average Losses ---\n",
        "fig.add_trace(\n",
        "    go.Bar(\n",
        "        x=avg_losses.index,\n",
        "        y=avg_losses.values,\n",
        "        text=avg_losses.round(2),\n",
        "        textposition=\"outside\",\n",
        "        name=\"Average Losses\"\n",
        "    ),\n",
        "    row=1, col=1\n",
        ")\n",
        "\n",
        "# --- Right plot: Total Losses ---\n",
        "fig.add_trace(\n",
        "    go.Bar(\n",
        "        x=total_losses.index,\n",
        "        y=total_losses.values,\n",
        "        text=total_losses,\n",
        "        textposition=\"outside\",\n",
        "        name=\"Total Losses\"\n",
        "    ),\n",
        "    row=1, col=2\n",
        ")\n",
        "\n",
        "# Layout adjustments\n",
        "fig.update_layout(\n",
        "    width=1100,\n",
        "    height=550,\n",
        "    showlegend=False\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "uecEtX5upmmc",
        "outputId": "43454263-0b11-465d-c67e-04f2e4fb280a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **CATEGORICAL FEATURES:**"
      ],
      "metadata": {
        "id": "ZiqP_w0OwbFN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *Categorical Features Influence on Wins and Losses*"
      ],
      "metadata": {
        "id": "FcuNU3Kssl5B"
      }
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    {
      "cell_type": "code",
      "source": [
        "cat_features=[\n",
        "    \"Psychological Profile\",\"Weapon of Choice\",\"Personal Motivation\",\n",
        "    \"Previous Occupation\",\"Battle Strategy\",\"Crowd Appeal Techniques\"\n",
        "]\n",
        "\n",
        "enc=OneHotEncoder(drop=\"first\",sparse_output=False)\n",
        "X=enc.fit_transform(df[cat_features])\n",
        "names=enc.get_feature_names_out(cat_features)\n",
        "\n",
        "Fw,_=f_regression(X,df[\"Wins\"])\n",
        "Fl,_=f_regression(X,df[\"Losses\"])\n",
        "\n",
        "anova=pd.DataFrame({\n",
        "    \"Feature\":[n.split(\"_\")[0] for n in names],\n",
        "    \"F_wins\":Fw,\"F_losses\":Fl\n",
        "}).groupby(\"Feature\").mean().reset_index()\n",
        "\n",
        "long=anova.melt(\"Feature\",[\"F_wins\",\"F_losses\"],\"Metric\",\"F_value\")\n",
        "\n",
        "fig=px.bar(long,x=\"Feature\",y=\"F_value\",color=\"Metric\",barmode=\"group\",title=\"ANOVA F-Statistics by Feature <br>(Wins vs Losses: Effect Strength)\",labels={\"F_value\":\"F-Statistic\"},template=\"simple_white\")\n",
        "fig.update_layout(width=1000,height=600,xaxis_tickangle=-45)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "BRo78muRKLqc",
        "outputId": "00b07dfd-7582-4505-fbbd-3f7764c1530e"
      },
      "execution_count": 24,
      "outputs": [
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          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# *Crowd Appeal & Public Favor:*"
      ],
      "metadata": {
        "id": "6Fqj0J3FwOV7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define pastel colors\n",
        "pastel_colors = [\"#a8dadc\", \"#ffd6a5\", \"#cdb4db\", \"#bde0fe\"]\n",
        "\n",
        "means = df.groupby(\"Crowd Appeal Techniques\")[\"Public Favor\"].mean().round(2).reset_index()\n",
        "fig = px.bar(\n",
        "    means,x=\"Crowd Appeal Techniques\",y=\"Public Favor\",text=\"Public Favor\",title=\"Average Public Favor by Crowd Appeal Technique\",template=\"simple_white\", color=\"Crowd Appeal Techniques\", color_discrete_sequence=pastel_colors,)\n",
        "fig.update_traces(textposition=\"outside\")\n",
        "fig.update_layout(width=1100, height=600)\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "uKTfl40AQnFC",
        "outputId": "9118e4d3-7420-4b2e-9b6e-fbd59c79827f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *Previous Occupation & Public Favor?*"
      ],
      "metadata": {
        "id": "O6_0ieTHkm14"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define strong colors\n",
        "strong_colors = [\"#457b9d\", \"#e76f51\", \"#6d597a\", \"#2a9d8f\"]\n",
        "\n",
        "means = df.groupby(\"Previous Occupation\")[\"Public Favor\"].mean().round(2).reset_index()\n",
        "fig = px.bar(\n",
        "    means,x=\"Previous Occupation\",y=\"Public Favor\",text=\"Public Favor\",title=\"Average Public Favor by Crowd Appeal Technique\",template=\"simple_white\", color=\"Previous Occupation\", color_discrete_sequence=strong_colors,)\n",
        "fig.update_traces(textposition=\"outside\")\n",
        "fig.update_layout(width=1100, height=600)\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "PLknxmcPj8kl",
        "outputId": "c20a46b5-7df2-42cd-b86c-90f7ede2a299"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "zrIvgbNoIPU6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part 3: Define and Train a baseline model**\n",
        "\n",
        "1. **Regression Goal**: Clearly state the problem you’re addressing.\n",
        "\n",
        "2. **Feature Selection**: Identify the features that seem most relevant. It’s fine to start with all features if you’re unsure.\n",
        "\n",
        "3. **Train-Test Split**: Partition your data into training, and testing sets. Use simple sampling. Quick reminder - when using ramdom - Use `Seed`!\n",
        "\n",
        "4. **Model Training**: For simplicity, start with default parameters on a `Linear Regression` model, using scikit-learn. Focus on establishing a baseline.\n",
        "\n",
        "5. **Model Evaluation**: Present straightforward metrics such as MAE, MSE, RMSE, R2, etc.\n",
        "\n",
        "6. **Insights**: Summarize the model’s performance with visuals.\n",
        "\n",
        "7. **Feature Importance:** Explain & Visualize  `feature importance` by looking on the `coefficients` of the Linear Regression model .\n",
        "\n",
        "<br>\n",
        "\n",
        "*FYI: Sections 5 and 6 will be repeated throughout your work.*"
      ],
      "metadata": {
        "id": "TxKHPqppIPZT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**When will the Gladiator most likely win the fight?**"
      ],
      "metadata": {
        "id": "EfniaMYBK6sK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##*The Features to be most interested in are:*\n",
        "##*1. Battle Experience (Important feature!- not to be used in the linear model)*\n",
        "##*2. Public Favor*\n",
        "##*2. Crowd Appeal Techniques*\n",
        "##*4. Previous Occupation of the Gladiator*"
      ],
      "metadata": {
        "id": "sj9LKoUamJRJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *Encoding of the categorical columns and linear model training:*\n",
        "After running the linear model, the predictions were 100% accurate, which was caused by a strong correlation between the number of fights (Battle Experience) and the number of wins. Thus, i decided not to use the Battle Experince in the Linear Model Predictions. \"Losses\" will be the new interesting feature.\n",
        "The experience-wins function is a positive function with lowering slope, which means that the Gladiator, averagely, wins more when he fights more, but becomes less effective as a function of time.\n",
        "Previous results:\n",
        "\n",
        "=== BASELINE LINEAR REGRESSION RESULTS ===\n",
        "\n",
        "MAE:  0.0000\n",
        "\n",
        "MSE:  0.0000\n",
        "\n",
        "RMSE: 0.0000\n",
        "\n",
        "R²:   1.0000"
      ],
      "metadata": {
        "id": "6JX3nEDK2aIQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install category_encoders\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from category_encoders import TargetEncoder"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "1taybVwTinI1",
        "outputId": "32a06a15-ade1-44a9-fb5c-bcf423f39288"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting category_encoders\n",
            "  Downloading category_encoders-2.9.0-py3-none-any.whl.metadata (7.9 kB)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.12/dist-packages (from category_encoders) (2.0.2)\n",
            "Requirement already satisfied: pandas>=1.0.5 in /usr/local/lib/python3.12/dist-packages (from category_encoders) (2.2.2)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.12/dist-packages (from category_encoders) (1.0.2)\n",
            "Requirement already satisfied: scikit-learn>=1.6.0 in /usr/local/lib/python3.12/dist-packages (from category_encoders) (1.6.1)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.12/dist-packages (from category_encoders) (1.16.3)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.12/dist-packages (from category_encoders) (0.14.6)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas>=1.0.5->category_encoders) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas>=1.0.5->category_encoders) (2025.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas>=1.0.5->category_encoders) (2025.2)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn>=1.6.0->category_encoders) (1.5.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn>=1.6.0->category_encoders) (3.6.0)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.12/dist-packages (from statsmodels>=0.9.0->category_encoders) (25.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas>=1.0.5->category_encoders) (1.17.0)\n",
            "Downloading category_encoders-2.9.0-py3-none-any.whl (85 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.9/85.9 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: category_encoders\n",
            "Successfully installed category_encoders-2.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SEED = 42\n",
        "TARGET = \"Wins\"\n",
        "DROP_COLS = [\"Name\", \"Birth Year\"]   # columns not used for ML\n",
        "\n",
        "y = df[TARGET]                             # this is the target the model learns to predict\n",
        "X = df.drop(columns=[TARGET] + DROP_COLS)  # everything else becomes input features except Gladiator's names\n",
        "\n",
        "# 1. Train–test split (80/20)\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.2, random_state=SEED\n",
        ")"
      ],
      "metadata": {
        "id": "OyhekCdZhT6C"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define categorical columns\n",
        "CAT_COLS = [\n",
        "    \"Origin\", \"Category\", \"Special Skills\", \"Weapon of Choice\",\n",
        "    \"Psychological Profile\", \"Personal Motivation\",\n",
        "    \"Previous Occupation\", \"Battle Strategy\",\n",
        "    \"Crowd Appeal Techniques\", \"Century\"\n",
        "\n",
        "]\n",
        "# ---- K-FOLD TARGET ENCODING ----\n",
        "encoder = TargetEncoder(cols=CAT_COLS)\n",
        "\n",
        "# Fit on training data ONLY\n",
        "encoder.fit(X_train[CAT_COLS], y_train)\n",
        "\n",
        "# Transform both datasets\n",
        "X_train_encoded = X_train.copy()\n",
        "X_test_encoded = X_test.copy()\n",
        "\n",
        "X_train_encoded[CAT_COLS] = encoder.transform(X_train[CAT_COLS])\n",
        "X_test_encoded[CAT_COLS]  = encoder.transform(X_test[CAT_COLS])\n",
        "\n",
        "# Now X_train_encoded + X_test_encoded have ONLY numeric versions of categorical features that were in X_train and X_test"
      ],
      "metadata": {
        "id": "BG5urlOYp2Nm"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "wlkyqLJi_yuD",
        "outputId": "272f0c31-00f8-4f7a-cc1a-a32ded0d2465"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age Origin  Height  Weight     Category  Losses Special Skills     Weapon of Choice  Patron Wealth  Equipment Quality  Public Favor  Injury History  Mental Resilience  Diet and Nutrition  Tactical Knowledge  Allegiance Network  Battle Experience Psychological Profile  Health Status Personal Motivation Previous Occupation  Training Intensity Battle Strategy  Social Standing Crowd Appeal Techniques  Survived      Century\n",
              "0   32   Gaul     195      85       Thraex       4         Novice  Sica (Curved Sword)              1                  1         0.842               2              1.005                   1                   2                   3                 15               Fearful              2               Glory          Unemployed                   2        Balanced                2              Flamboyant         0  1st_Century\n",
              "1   20   Gaul     173      66  Hoplomachus       2          Speed               Dagger              1                  1         0.651               1              5.930                   1                   3                   2                  9               Fearful              3              Wealth            Criminal                   3        Balanced                1                  Humble         1  1st_Century\n",
              "2   30   Gaul     170      67  Hoplomachus       0        Tactics               Dagger              3                  3         0.594               1              3.724                   3                   3                   3                  6                 Stoic              3             Freedom            Criminal                   1      Aggressive                2            Intimidating         1  1st_Century\n",
              "3   28   Gaul     189     104  Hoplomachus       2      Endurance               Dagger              3                  3         0.541               1              3.101                   3                   4                   3                  8           Calculative              3            Survival             Laborer                   1        Balanced                1             Charismatic         0  1st_Century\n",
              "4   41   Rome     173      85  Hoplomachus       4         Novice               Dagger              2                  2         0.762               2              4.336                   2                   2                   3                 16           Calculative              1            Survival          Unemployed                   1      Aggressive                3            Intimidating         0  2nd_Century"
            ],
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              "      <th>Age</th>\n",
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              "      <th>Diet and Nutrition</th>\n",
              "      <th>Tactical Knowledge</th>\n",
              "      <th>Allegiance Network</th>\n",
              "      <th>Battle Experience</th>\n",
              "      <th>Psychological Profile</th>\n",
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              "      <td>32</td>\n",
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              "      <td>Glory</td>\n",
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              "      <td>2</td>\n",
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              "      <td>Flamboyant</td>\n",
              "      <td>0</td>\n",
              "      <td>1st_Century</td>\n",
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              "      <th>1</th>\n",
              "      <td>20</td>\n",
              "      <td>Gaul</td>\n",
              "      <td>173</td>\n",
              "      <td>66</td>\n",
              "      <td>Hoplomachus</td>\n",
              "      <td>2</td>\n",
              "      <td>Speed</td>\n",
              "      <td>Dagger</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.651</td>\n",
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              "      <td>5.930</td>\n",
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              "      <td>9</td>\n",
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              "      <td>Wealth</td>\n",
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              "      <td>3</td>\n",
              "      <td>Balanced</td>\n",
              "      <td>1</td>\n",
              "      <td>Humble</td>\n",
              "      <td>1</td>\n",
              "      <td>1st_Century</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>30</td>\n",
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              "      <td>0.594</td>\n",
              "      <td>1</td>\n",
              "      <td>3.724</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>Stoic</td>\n",
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              "      <td>Freedom</td>\n",
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              "      <td>Aggressive</td>\n",
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              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>Gaul</td>\n",
              "      <td>189</td>\n",
              "      <td>104</td>\n",
              "      <td>Hoplomachus</td>\n",
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              "      <td>Endurance</td>\n",
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              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0.541</td>\n",
              "      <td>1</td>\n",
              "      <td>3.101</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>Calculative</td>\n",
              "      <td>3</td>\n",
              "      <td>Survival</td>\n",
              "      <td>Laborer</td>\n",
              "      <td>1</td>\n",
              "      <td>Balanced</td>\n",
              "      <td>1</td>\n",
              "      <td>Charismatic</td>\n",
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              "      <th>4</th>\n",
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              "      <td>16</td>\n",
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              "      <td>1</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-061e44fe-0289-4c26-96dd-074c7aaf4327');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "                'suggestCharts', [key], {});\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Initialize Linear Regression with default parameters\n",
        "baseline_reg = LinearRegression(\n",
        "    fit_intercept=True,   # learn the intercept\n",
        "    copy_X=True,          # copy input data\n",
        "    n_jobs=None,          # single-threaded\n",
        "    positive=False        # allow negative coefficients\n",
        ")\n",
        "\n",
        "# 2. Train the model\n",
        "baseline_reg.fit(X_train_encoded, y_train)\n",
        "\n",
        "print(\"Baseline Linear Regression model trained successfully.\")\n",
        "\n",
        "baseline_reg = LinearRegression()\n",
        "baseline_reg.fit(X_train_encoded, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "txZQuQ6w0Ums",
        "outputId": "8010e007-ff03-488c-b7b3-d8f7d55bea73"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Baseline Linear Regression model trained successfully.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "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>LinearRegression()</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>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Generate predictions on the test set\n",
        "y_pred = baseline_reg.predict(X_test_encoded)\n",
        "\n",
        "# 2. Compute evaluation metrics\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "rmse = mse ** 0.5\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "# 3. Print results\n",
        "print(\"=== BASELINE LINEAR REGRESSION RESULTS ===\")\n",
        "print(f\"MAE:  {mae:.4f}\")\n",
        "print(f\"MSE:  {mse:.4f}\")\n",
        "print(f\"RMSE: {rmse:.4f}\")\n",
        "print(f\"R²:   {r2:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gO7vWhiuhkQU",
        "outputId": "fedd1216-9c30-4eca-995c-e9861ba154e5"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== BASELINE LINEAR REGRESSION RESULTS ===\n",
            "MAE:  0.0000\n",
            "MSE:  0.0000\n",
            "RMSE: 0.0000\n",
            "R²:   1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build results DF\n",
        "res = pd.DataFrame({\"Actual\": y_test, \"Predicted\": y_pred})\n",
        "res[\"Residuals\"] = res[\"Actual\"] - res[\"Predicted\"]\n",
        "\n",
        "PLOT_W = 700\n",
        "PLOT_H = 350\n",
        "\n",
        "# 1. Predicted vs Actual\n",
        "px.scatter(\n",
        "    res, x=\"Actual\", y=\"Predicted\",\n",
        "    title=\"Predicted vs Actual\",\n",
        "    template=\"simple_white\", opacity=0.7, trendline=\"ols\",\n",
        "    width=PLOT_W, height=PLOT_H,\n",
        "    color_discrete_sequence=[\"#2ca02c\"]   # BLUE\n",
        ").show()\n",
        "\n",
        "# 2. Tail Squared Error Plot\n",
        "sq_errors = (y_test - y_pred)**2\n",
        "\n",
        "px.line(\n",
        "    sq_errors.sort_values().reset_index(drop=True),\n",
        "    title=\"Tail Plot of Squared Prediction Errors<br><sub>Shorter tail → fewer outliers</sub>\",\n",
        "    labels={\"value\": \"Squared Error\", \"index\": \"Sorted Samples\"},\n",
        "    template=\"simple_white\",\n",
        "    width=PLOT_W, height=PLOT_H,\n",
        "    color_discrete_sequence=[\"#ff7f0e\"]\n",
        ").show()\n",
        "\n",
        "#  3. Residual Histogram\n",
        "px.histogram(\n",
        "    res, x=\"Residuals\", nbins=20,\n",
        "    title=\"Residuals Distribution<br><sub>Negative = overpredicted, Positive = underpredicted</sub>\",\n",
        "    template=\"simple_white\",\n",
        "    width=PLOT_W, height=PLOT_H,\n",
        "    color_discrete_sequence=[\"#d62728\"]\n",
        ").show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xOIUtuqeRA69",
        "outputId": "d5e54030-8afd-4b75-cbe2-cf2b5508856d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "# Get coefficients into a dataframe\n",
        "coef_df = pd.DataFrame({\n",
        "    \"Feature\": X_train_encoded.columns.tolist(),\n",
        "    \"Coefficient\": baseline_reg.coef_\n",
        "})\n",
        "\n",
        "# Sort by absolute importance\n",
        "coef_df[\"AbsCoef\"] = coef_df[\"Coefficient\"].abs()\n",
        "coef_df = coef_df.sort_values(\"AbsCoef\", ascending=False)\n",
        "\n",
        "# Show table\n",
        "display(coef_df)\n",
        "\n",
        "# Plot\n",
        "px.bar(\n",
        "    coef_df,\n",
        "    x=\"AbsCoef\",\n",
        "    y=\"Feature\",\n",
        "    orientation=\"h\",\n",
        "    title=\"Feature Importance (Linear Regression Coefficients)\",\n",
        "    labels={\"AbsCoef\": \"Absolute Coefficient\"},\n",
        "    template=\"simple_white\",\n",
        "    width=700,\n",
        "    height=500\n",
        ").show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KouQYjylV5KU",
        "outputId": "a4c21402-bc1a-4bd3-c371-6af34912ae73"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                    Feature  Coefficient  AbsCoef\n",
              "5                    Losses       -1.000    1.000\n",
              "16        Battle Experience        1.000    1.000\n",
              "10             Public Favor        0.000    0.000\n",
              "1                    Origin       -0.000    0.000\n",
              "4                  Category       -0.000    0.000\n",
              "11           Injury History        0.000    0.000\n",
              "18            Health Status       -0.000    0.000\n",
              "7          Weapon of Choice        0.000    0.000\n",
              "17    Psychological Profile       -0.000    0.000\n",
              "19      Personal Motivation       -0.000    0.000\n",
              "2                    Height        0.000    0.000\n",
              "9         Equipment Quality        0.000    0.000\n",
              "22          Battle Strategy       -0.000    0.000\n",
              "23          Social Standing        0.000    0.000\n",
              "25                 Survived       -0.000    0.000\n",
              "13       Diet and Nutrition       -0.000    0.000\n",
              "20      Previous Occupation       -0.000    0.000\n",
              "0                       Age       -0.000    0.000\n",
              "15       Allegiance Network       -0.000    0.000\n",
              "24  Crowd Appeal Techniques        0.000    0.000\n",
              "6            Special Skills       -0.000    0.000\n",
              "12        Mental Resilience        0.000    0.000\n",
              "26                  Century        0.000    0.000\n",
              "14       Tactical Knowledge       -0.000    0.000\n",
              "3                    Weight       -0.000    0.000\n",
              "8             Patron Wealth        0.000    0.000\n",
              "21       Training Intensity       -0.000    0.000"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Coefficient</th>\n",
              "      <th>AbsCoef</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Losses</td>\n",
              "      <td>-1.000</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Battle Experience</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Public Favor</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Origin</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Category</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Injury History</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Health Status</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Weapon of Choice</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Psychological Profile</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Personal Motivation</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Height</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Equipment Quality</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Battle Strategy</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Social Standing</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Survived</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Diet and Nutrition</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Previous Occupation</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Age</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Allegiance Network</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Crowd Appeal Techniques</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Special Skills</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Mental Resilience</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Century</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Tactical Knowledge</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Weight</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Patron Wealth</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Training Intensity</td>\n",
              "      <td>-0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "summary": "{\n  \"name\": \"coef_df\",\n  \"rows\": 27,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"Psychological Profile\",\n          \"Social Standing\",\n          \"Personal Motivation\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coefficient\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2773500981126146,\n        \"min\": -1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          -4.531965080989409e-16,\n          2.0122792321330962e-16,\n          -4.285851187835199e-16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AbsCoef\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2668802563418118,\n        \"min\": 6.938893903907228e-18,\n        \"max\": 1.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          4.285851187835199e-16,\n          9.820112589215945e-17,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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    {
      "cell_type": "code",
      "source": [
        "X_train_encoded.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "vZnppvS_1u5n",
        "outputId": "6e4bde79-2624-4db5-c643-496bfb7c9342"
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      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Age   Origin   Height   Weight  Category   Losses  Special Skills  Weapon of Choice  Patron Wealth  Equipment Quality  Public Favor  Injury History  Mental Resilience  Diet and Nutrition  Tactical Knowledge  Allegiance Network  Battle Experience  Psychological Profile  Health Status  Personal Motivation  Previous Occupation  Training Intensity  Battle Strategy  Social Standing  Crowd Appeal Techniques  Survived  Century\n",
              "count 7980.000 7980.000 7980.000 7980.000  7980.000 7980.000        7980.000          7980.000       7980.000           7980.000      7980.000        7980.000           7980.000            7980.000            7980.000            7980.000           7980.000               7980.000       7980.000             7980.000             7980.000            7980.000         7980.000         7980.000                 7980.000  7980.000 7980.000\n",
              "mean    31.421    7.832  175.447   81.731     7.832    1.725           7.832             7.832          2.016              2.016         0.696           1.204              4.531               2.016               2.795               2.651              9.557                  7.832          2.564                7.832                7.832               1.832            7.832            2.113                    7.832     0.493    7.832\n",
              "std      8.096    0.112   10.184   15.957     0.101    1.711           0.195             0.056          0.817              0.817         0.139           0.403              2.307               0.817               0.955               0.481              4.712                  0.145          0.641                0.075                0.754               0.552            0.123            0.653                    0.873     0.500    0.212\n",
              "min     18.000    7.647  141.000   19.000     7.701    0.000           7.586             7.743          1.000              1.000         0.299           1.000              0.851               1.000               1.000               1.000              0.000                  7.658          1.000                7.736                5.126               1.000            7.732            1.000                    7.318     0.000    7.078\n",
              "25%     24.000    7.745  169.000   71.000     7.735    0.000           7.586             7.811          1.000              1.000         0.590           1.000              2.685               1.000               2.000               2.000              6.000                  7.675          2.000                7.736                7.808               1.000            7.732            2.000                    7.318     0.000    7.568\n",
              "50%     31.000    7.894  175.000   82.000     7.801    1.000           7.942             7.817          2.000              2.000         0.689           1.000              4.482               2.000               3.000               3.000              9.000                  7.868          3.000                7.805                7.948               2.000            7.732            2.000                    7.537     0.000    7.977\n",
              "75%     38.000    7.941  182.000   92.000     7.884    3.000           7.995             7.817          3.000              3.000         0.799           1.000              6.188               3.000               4.000               3.000             12.000                  8.009          3.000                7.898                8.121               2.000            7.902            3.000                    7.714     1.000    7.977\n",
              "max     45.000    7.958  215.000  139.000     8.003   14.000           8.081             7.950          3.000              3.000         1.124           2.000             11.479               3.000               4.000               3.000             34.000                  8.009          3.000                7.962                8.298               3.000            8.124            3.000                   10.129     1.000    7.977"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Origin</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Category</th>\n",
              "      <th>Losses</th>\n",
              "      <th>Special Skills</th>\n",
              "      <th>Weapon of Choice</th>\n",
              "      <th>Patron Wealth</th>\n",
              "      <th>Equipment Quality</th>\n",
              "      <th>Public Favor</th>\n",
              "      <th>Injury History</th>\n",
              "      <th>Mental Resilience</th>\n",
              "      <th>Diet and Nutrition</th>\n",
              "      <th>Tactical Knowledge</th>\n",
              "      <th>Allegiance Network</th>\n",
              "      <th>Battle Experience</th>\n",
              "      <th>Psychological Profile</th>\n",
              "      <th>Health Status</th>\n",
              "      <th>Personal Motivation</th>\n",
              "      <th>Previous Occupation</th>\n",
              "      <th>Training Intensity</th>\n",
              "      <th>Battle Strategy</th>\n",
              "      <th>Social Standing</th>\n",
              "      <th>Crowd Appeal Techniques</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Century</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "      <td>7980.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>31.421</td>\n",
              "      <td>7.832</td>\n",
              "      <td>175.447</td>\n",
              "      <td>81.731</td>\n",
              "      <td>7.832</td>\n",
              "      <td>1.725</td>\n",
              "      <td>7.832</td>\n",
              "      <td>7.832</td>\n",
              "      <td>2.016</td>\n",
              "      <td>2.016</td>\n",
              "      <td>0.696</td>\n",
              "      <td>1.204</td>\n",
              "      <td>4.531</td>\n",
              "      <td>2.016</td>\n",
              "      <td>2.795</td>\n",
              "      <td>2.651</td>\n",
              "      <td>9.557</td>\n",
              "      <td>7.832</td>\n",
              "      <td>2.564</td>\n",
              "      <td>7.832</td>\n",
              "      <td>7.832</td>\n",
              "      <td>1.832</td>\n",
              "      <td>7.832</td>\n",
              "      <td>2.113</td>\n",
              "      <td>7.832</td>\n",
              "      <td>0.493</td>\n",
              "      <td>7.832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.096</td>\n",
              "      <td>0.112</td>\n",
              "      <td>10.184</td>\n",
              "      <td>15.957</td>\n",
              "      <td>0.101</td>\n",
              "      <td>1.711</td>\n",
              "      <td>0.195</td>\n",
              "      <td>0.056</td>\n",
              "      <td>0.817</td>\n",
              "      <td>0.817</td>\n",
              "      <td>0.139</td>\n",
              "      <td>0.403</td>\n",
              "      <td>2.307</td>\n",
              "      <td>0.817</td>\n",
              "      <td>0.955</td>\n",
              "      <td>0.481</td>\n",
              "      <td>4.712</td>\n",
              "      <td>0.145</td>\n",
              "      <td>0.641</td>\n",
              "      <td>0.075</td>\n",
              "      <td>0.754</td>\n",
              "      <td>0.552</td>\n",
              "      <td>0.123</td>\n",
              "      <td>0.653</td>\n",
              "      <td>0.873</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000</td>\n",
              "      <td>7.647</td>\n",
              "      <td>141.000</td>\n",
              "      <td>19.000</td>\n",
              "      <td>7.701</td>\n",
              "      <td>0.000</td>\n",
              "      <td>7.586</td>\n",
              "      <td>7.743</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.299</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.851</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>7.658</td>\n",
              "      <td>1.000</td>\n",
              "      <td>7.736</td>\n",
              "      <td>5.126</td>\n",
              "      <td>1.000</td>\n",
              "      <td>7.732</td>\n",
              "      <td>1.000</td>\n",
              "      <td>7.318</td>\n",
              "      <td>0.000</td>\n",
              "      <td>7.078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>24.000</td>\n",
              "      <td>7.745</td>\n",
              "      <td>169.000</td>\n",
              "      <td>71.000</td>\n",
              "      <td>7.735</td>\n",
              "      <td>0.000</td>\n",
              "      <td>7.586</td>\n",
              "      <td>7.811</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.590</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2.685</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>6.000</td>\n",
              "      <td>7.675</td>\n",
              "      <td>2.000</td>\n",
              "      <td>7.736</td>\n",
              "      <td>7.808</td>\n",
              "      <td>1.000</td>\n",
              "      <td>7.732</td>\n",
              "      <td>2.000</td>\n",
              "      <td>7.318</td>\n",
              "      <td>0.000</td>\n",
              "      <td>7.568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>31.000</td>\n",
              "      <td>7.894</td>\n",
              "      <td>175.000</td>\n",
              "      <td>82.000</td>\n",
              "      <td>7.801</td>\n",
              "      <td>1.000</td>\n",
              "      <td>7.942</td>\n",
              "      <td>7.817</td>\n",
              "      <td>2.000</td>\n",
              "      <td>2.000</td>\n",
              "      <td>0.689</td>\n",
              "      <td>1.000</td>\n",
              "      <td>4.482</td>\n",
              "      <td>2.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>9.000</td>\n",
              "      <td>7.868</td>\n",
              "      <td>3.000</td>\n",
              "      <td>7.805</td>\n",
              "      <td>7.948</td>\n",
              "      <td>2.000</td>\n",
              "      <td>7.732</td>\n",
              "      <td>2.000</td>\n",
              "      <td>7.537</td>\n",
              "      <td>0.000</td>\n",
              "      <td>7.977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>38.000</td>\n",
              "      <td>7.941</td>\n",
              "      <td>182.000</td>\n",
              "      <td>92.000</td>\n",
              "      <td>7.884</td>\n",
              "      <td>3.000</td>\n",
              "      <td>7.995</td>\n",
              "      <td>7.817</td>\n",
              "      <td>3.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>0.799</td>\n",
              "      <td>1.000</td>\n",
              "      <td>6.188</td>\n",
              "      <td>3.000</td>\n",
              "      <td>4.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>12.000</td>\n",
              "      <td>8.009</td>\n",
              "      <td>3.000</td>\n",
              "      <td>7.898</td>\n",
              "      <td>8.121</td>\n",
              "      <td>2.000</td>\n",
              "      <td>7.902</td>\n",
              "      <td>3.000</td>\n",
              "      <td>7.714</td>\n",
              "      <td>1.000</td>\n",
              "      <td>7.977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>45.000</td>\n",
              "      <td>7.958</td>\n",
              "      <td>215.000</td>\n",
              "      <td>139.000</td>\n",
              "      <td>8.003</td>\n",
              "      <td>14.000</td>\n",
              "      <td>8.081</td>\n",
              "      <td>7.950</td>\n",
              "      <td>3.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>1.124</td>\n",
              "      <td>2.000</td>\n",
              "      <td>11.479</td>\n",
              "      <td>3.000</td>\n",
              "      <td>4.000</td>\n",
              "      <td>3.000</td>\n",
              "      <td>34.000</td>\n",
              "      <td>8.009</td>\n",
              "      <td>3.000</td>\n",
              "      <td>7.962</td>\n",
              "      <td>8.298</td>\n",
              "      <td>3.000</td>\n",
              "      <td>8.124</td>\n",
              "      <td>3.000</td>\n",
              "      <td>10.129</td>\n",
              "      <td>1.000</td>\n",
              "      <td>7.977</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "    .colab-df-buttons div {\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-577f7371-6406-4d5f-bfbb-3c9427ed604f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-a9ade4ca-d924-484d-b1c2-34e3d56dd96d\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a9ade4ca-d924-484d-b1c2-34e3d56dd96d')\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      --hover-fill-color: #174EA6;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-a9ade4ca-d924-484d-b1c2-34e3d56dd96d button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_encoded.nunique()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 931
        },
        "id": "XaswAvKz1wtS",
        "outputId": "503a7eb8-e7ec-4bf0-d929-a49323641be5"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                          28\n",
              "Origin                        6\n",
              "Height                       72\n",
              "Weight                      108\n",
              "Category                      6\n",
              "Losses                       15\n",
              "Special Skills                6\n",
              "Weapon of Choice              6\n",
              "Patron Wealth                 3\n",
              "Equipment Quality             3\n",
              "Public Favor               7980\n",
              "Injury History                2\n",
              "Mental Resilience          7980\n",
              "Diet and Nutrition            3\n",
              "Tactical Knowledge            4\n",
              "Allegiance Network            3\n",
              "Battle Experience            32\n",
              "Psychological Profile         4\n",
              "Health Status                 3\n",
              "Personal Motivation           5\n",
              "Previous Occupation           5\n",
              "Training Intensity            3\n",
              "Battle Strategy               3\n",
              "Social Standing               3\n",
              "Crowd Appeal Techniques       4\n",
              "Survived                      2\n",
              "Century                       4\n",
              "dtype: int64"
            ],
            "text/html": [
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              "      <td>28</td>\n",
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              "      <td>72</td>\n",
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              "      <th>Category</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Losses</th>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
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              "      <th>Special Skills</th>\n",
              "      <td>6</td>\n",
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              "      <th>Weapon of Choice</th>\n",
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              "      <td>7980</td>\n",
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              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mental Resilience</th>\n",
              "      <td>7980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Diet and Nutrition</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tactical Knowledge</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Allegiance Network</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Battle Experience</th>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Psychological Profile</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Health Status</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Personal Motivation</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Previous Occupation</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Training Intensity</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Battle Strategy</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Social Standing</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Crowd Appeal Techniques</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Century</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "hkerNEsMIPbK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part 4: Feature Engineering**\n",
        "\n",
        "* Create, transform, scale, or extract new features; encoding categoricals, polynomial features, PCA, etc.\n",
        "*TIP: use sklearn's tools, such as Scalar, One-Hot, etc.*\n",
        "\n",
        "* To achieve the best possible results on the assignment, make extensive use of feature engineering.\n",
        "\n",
        "* Use a `Clustring Model` to create a new feature."
      ],
      "metadata": {
        "id": "wnzIOsrRVc_i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfc = df.copy()"
      ],
      "metadata": {
        "id": "JtPubizm403C"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-fuzzy\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.cluster import DBSCAN\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.mixture import GaussianMixture\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.metrics import silhouette_samples, silhouette_score\n",
        "from sklearn.metrics import davies_bouldin_score\n",
        "from pandas.plotting import parallel_coordinates\n",
        "\n",
        "import skfuzzy as fuzz\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Np81o1Y4SR_",
        "outputId": "28d85963-2d76-4f1a-edd5-57dcc7dfb077"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scikit-fuzzy\n",
            "  Downloading scikit_fuzzy-0.5.0-py2.py3-none-any.whl.metadata (2.6 kB)\n",
            "Downloading scikit_fuzzy-0.5.0-py2.py3-none-any.whl (920 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m920.8/920.8 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: scikit-fuzzy\n",
            "Successfully installed scikit-fuzzy-0.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfc = df.copy()   # <-- use dfc instead of df\n",
        "\n",
        "SEED = 42\n",
        "TARGET = \"Wins\"\n",
        "\n",
        "DROP_COLS = [\"Name\", \"Birth Year\", \"Battle Experience\"]\n",
        "\n",
        "MANUAL_ENCODED_COLS = [\n",
        "    \"Allegiance Network\", \"Patron Wealth\", \"Equipment Quality\",\n",
        "    \"Injury History\", \"Tactical Knowledge\", \"Health Status\",\n",
        "    \"Training Intensity\", \"Social Standing\",\n",
        "    \"Diet and Nutrition\", \"Survived\"\n",
        "]\n",
        "\n",
        "CAT_COLS = [\n",
        "    \"Origin\", \"Category\", \"Special Skills\", \"Weapon of Choice\",\n",
        "    \"Psychological Profile\", \"Personal Motivation\",\n",
        "    \"Previous Occupation\", \"Battle Strategy\",\n",
        "    \"Crowd Appeal Techniques\", \"Century\"\n",
        "]\n",
        "\n",
        "# IMPORTANT: use dfc instead of df\n",
        "y = dfc[TARGET]\n",
        "X = dfc.drop(columns=[TARGET] + DROP_COLS)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.2, random_state=SEED\n",
        ")"
      ],
      "metadata": {
        "id": "3dbil20LFyK7"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Numerical without battle experience because too   high correlation with Target value Wins\n",
        "NUMERICAL = [\n",
        "    \"Age\",\n",
        "    \"Birth Year\",\n",
        "    \"Height\",\n",
        "    \"Weight\",\n",
        "    \"Wins\",\n",
        "    \"Losses\",\n",
        "    \"Public Favor\",\n",
        "    \"Mental Resilience\"\n",
        "]\n",
        "\n",
        "# Correct TargetEncoder initialization\n",
        "encoder = TargetEncoder(cols=CAT_COLS)\n",
        "encoder.fit(X_train[CAT_COLS], y_train)\n",
        "\n",
        "X_train_enc = X_train.copy()\n",
        "X_test_enc  = X_test.copy()\n",
        "\n",
        "X_train_enc[CAT_COLS] = encoder.transform(X_train[CAT_COLS])\n",
        "X_test_enc[CAT_COLS]  = encoder.transform(X_test[CAT_COLS])\n",
        "\n",
        "# IMPORTANT: use dfc instead of df\n",
        "df_num = dfc[NUMERICAL]\n",
        "df_num.skew().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "bxjEncU2BuDa",
        "outputId": "99894e8d-02e4-4f33-bc28-356099f4216c"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Losses               1.433\n",
              "Birth Year           1.248\n",
              "Wins                 0.513\n",
              "Mental Resilience    0.251\n",
              "Public Favor         0.128\n",
              "Age                  0.013\n",
              "Height               0.004\n",
              "Weight              -0.002\n",
              "dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Losses</th>\n",
              "      <td>1.433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Birth Year</th>\n",
              "      <td>1.248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wins</th>\n",
              "      <td>0.513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mental Resilience</th>\n",
              "      <td>0.251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Public Favor</th>\n",
              "      <td>0.128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Height</th>\n",
              "      <td>0.004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>-0.002</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### *Mathematical changes:*"
      ],
      "metadata": {
        "id": "1kTBom9gENcy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfc[\"log_Losses\"] = np.log1p(dfc[\"Losses\"])"
      ],
      "metadata": {
        "id": "aHBqsYseD9Gx"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### *Creating Features:*"
      ],
      "metadata": {
        "id": "h9DKB1oib_O_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def add_engineered_features(df_in: pd.DataFrame) -> pd.DataFrame:\n",
        "    df_fe = df_in.copy()\n",
        "\n",
        "    # ---- Binning ----\n",
        "    df_fe[\"Age_Group\"] = pd.cut(\n",
        "        df_fe[\"Age\"],\n",
        "        bins=[0, 25, 35, 50, 120],\n",
        "        labels=[0, 1, 2, 3]\n",
        "    ).astype(int)\n",
        "\n",
        "    # BMI\n",
        "    df_fe[\"BMI\"] = df_fe[\"Weight\"] / ((df_fe[\"Height\"] / 100) ** 2)\n",
        "\n",
        "    df_fe[\"BMI_Category_Encoded\"] = pd.cut(\n",
        "        df_fe[\"BMI\"],\n",
        "        bins=[0, 18.5, 25, float(\"inf\")],\n",
        "        labels=[0, 1, 2],\n",
        "        include_lowest=True\n",
        "    ).astype(\"float\").fillna(1).astype(int)\n",
        "\n",
        "    # ---- Interactions ----\n",
        "    df_fe[\"BMI_x_Training\"] = df_fe[\"BMI\"] * df_fe[\"Training Intensity\"]\n",
        "\n",
        "    df_fe[\"Fitness_Readiness\"] = (\n",
        "        df_fe[\"Training Intensity\"] * df_fe[\"Mental Resilience\"]\n",
        "    )\n",
        "\n",
        "    df_fe[\"Weapon_Quality_Score\"] = (\n",
        "        df_fe[\"Equipment Quality\"] * df_fe[\"Tactical Knowledge\"]\n",
        "    )\n",
        "\n",
        "    df_fe[\"Skill_Advantage\"] = (\n",
        "        df_fe[\"Tactical Knowledge\"] * df_fe[\"Public Favor\"]\n",
        "    )\n",
        "\n",
        "    df_fe[\"Allegiance_x_Social\"] = (\n",
        "        df_fe[\"Allegiance Network\"] * df_fe[\"Social Standing\"]\n",
        "    )\n",
        "\n",
        "    # ---- Composite scores / ratios ----\n",
        "    df_fe[\"Social_Advantage\"] = (\n",
        "        df_fe[\"Allegiance Network\"]\n",
        "        + df_fe[\"Social Standing\"]\n",
        "        + df_fe[\"Public Favor\"]\n",
        "    )\n",
        "\n",
        "    df_fe[\"Injury_Burden\"] = (\n",
        "        df_fe[\"Injury History\"]\n",
        "        * df_fe[\"Age\"]\n",
        "        / (df_fe[\"Training Intensity\"] + 1e-5)\n",
        "    )\n",
        "\n",
        "    df_fe[\"Resilience_Index\"] = (\n",
        "        df_fe[\"Mental Resilience\"] * df_fe[\"Psychological Profile\"]\n",
        "    )\n",
        "\n",
        "    return df_fe\n"
      ],
      "metadata": {
        "id": "yvnTO0afQrDQ"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_fe = add_engineered_features(X_train_enc)\n",
        "X_test_fe  = add_engineered_features(X_test_enc)"
      ],
      "metadata": {
        "id": "D_1ybyynIPHV"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Applying Clustering (Unsupervised Learning):**\n",
        "\n",
        "* Run at least one clustering algorithm; K-Means, DBSCAN, etc.\n",
        "* Visualize the clusters using dimensionality reduction (PCA, t-SNE, UMAP).\n",
        "* Interpret the clusters: What makes them distinct? Are they useful?\n",
        "* Use the results to create new features (e.g., cluster ID, distance to centroid, cluster probabilities).\n"
      ],
      "metadata": {
        "id": "Vq9Nh-D1Vdh6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# --------------------------------------------------------------\n",
        "# CLUSTERING BLOCK — KEEP RESULTS SEPARATE\n",
        "# --------------------------------------------------------------\n",
        "\n",
        "df_cluster = dfc.copy()   # <-- IMPORTANT: use dfc, NOT df\n",
        "\n",
        "CLUSTER_DROP = CAT_COLS + MANUAL_ENCODED_COLS + [\"BMI_Category_Encoded\", \"Battle Experience\"]\n",
        "df_cluster = df_cluster.drop(columns=CLUSTER_DROP, errors=\"ignore\")\n",
        "\n",
        "df_cluster_num = df_cluster.select_dtypes(include=\"number\")\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "cluster_scaled = scaler.fit_transform(df_cluster_num)\n",
        "\n",
        "df_cluster_scaled = pd.DataFrame(\n",
        "    cluster_scaled,\n",
        "    columns=df_cluster_num.columns,\n",
        "    index=df_cluster_num.index\n",
        ")\n",
        "\n",
        "# KMeans\n",
        "k = 3\n",
        "from sklearn.cluster import KMeans\n",
        "kmeans = KMeans(n_clusters=k, random_state=42)\n",
        "df_cluster_scaled[\"Cluster_KMeans\"] = kmeans.fit_predict(cluster_scaled)\n",
        "\n",
        "# Distances to centroids\n",
        "distances = kmeans.transform(cluster_scaled)\n",
        "for i in range(k):\n",
        "    df_cluster_scaled[f\"Dist_to_Centroid_{i}\"] = distances[:, i]\n",
        "\n",
        "# IMPORTANT: KEEP CLUSTERING SEPARATE\n",
        "cluster_features_df = df_cluster_scaled.copy()"
      ],
      "metadata": {
        "id": "cdfbAYQINrXL"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Elbow+shilouette to check the optimal number of k:"
      ],
      "metadata": {
        "id": "AvzzeHaOCs3-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics import silhouette_score\n",
        "\n",
        "inertias = []\n",
        "silhouette_scores = []\n",
        "K_range = range(2, 11)  # test k = 2..10\n",
        "\n",
        "for k in K_range:\n",
        "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
        "    labels = kmeans.fit_predict(cluster_scaled)\n",
        "\n",
        "    inertias.append(kmeans.inertia_)\n",
        "    silhouette_scores.append(silhouette_score(cluster_scaled, labels))\n",
        "\n",
        "# -----------------------\n",
        "# Plot Elbow Method\n",
        "# -----------------------\n",
        "plt.figure(figsize=(7,4))\n",
        "plt.plot(K_range, inertias, marker='o')\n",
        "plt.title(\"Elbow Method for Optimal k\")\n",
        "plt.xlabel(\"Number of Clusters (k)\")\n",
        "plt.ylabel(\"Inertia (Within-Cluster Error)\")\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# -----------------------\n",
        "# Plot Silhouette Scores\n",
        "# -----------------------\n",
        "plt.figure(figsize=(7,4))\n",
        "plt.plot(K_range, silhouette_scores, marker='o', color='purple')\n",
        "plt.title(\"Silhouette Score by Number of Clusters\")\n",
        "plt.xlabel(\"Number of Clusters (k)\")\n",
        "plt.ylabel(\"Silhouette Score\")\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# Print scores for clarity\n",
        "for k, s in zip(K_range, silhouette_scores):\n",
        "    print(f\"k={k}: silhouette={s:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "id": "6yAltt35CsHz",
        "outputId": "60cab16d-c840-4e5b-d443-6d9910d8adf3"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 636,
              "height": 392
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 622,
              "height": 392
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "k=2: silhouette=0.2066\n",
            "k=3: silhouette=0.1592\n",
            "k=4: silhouette=0.1295\n",
            "k=5: silhouette=0.1227\n",
            "k=6: silhouette=0.1217\n",
            "k=7: silhouette=0.1189\n",
            "k=8: silhouette=0.1178\n",
            "k=9: silhouette=0.1124\n",
            "k=10: silhouette=0.1109\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "KMean Clustering:"
      ],
      "metadata": {
        "id": "OsoG45HPNvtH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "k = 2  # You can tune this\n",
        "kmeans = KMeans(n_clusters=k, random_state=42)\n",
        "df_cluster_scaled[\"Cluster_KMeans\"] = kmeans.fit_predict(cluster_scaled)\n",
        "\n",
        "# Add distances to centroids (very useful as features)\n",
        "distances = kmeans.transform(cluster_scaled)\n",
        "for i in range(k):\n",
        "    df_cluster_scaled[f\"Dist_to_Centroid_{i}\"] = distances[:, i]"
      ],
      "metadata": {
        "id": "Q-r-2rb0Nx1R"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "DBscan:"
      ],
      "metadata": {
        "id": "Dc3tMXPAN2Lm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "db = DBSCAN(eps=1.5, min_samples=5)\n",
        "df_cluster_scaled[\"Cluster_DBSCAN\"] = db.fit_predict(cluster_scaled)"
      ],
      "metadata": {
        "id": "EnkTt_9XN4dl"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "PCA (Hard)"
      ],
      "metadata": {
        "id": "8OJTjazfOMN0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# --- PCA (must run BEFORE the plot) ---\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components=2)\n",
        "pca_components = pca.fit_transform(cluster_scaled)\n",
        "\n",
        "df_cluster_scaled[\"PC1\"] = pca_components[:, 0]\n",
        "df_cluster_scaled[\"PC2\"] = pca_components[:, 1]\n",
        "\n",
        "# --- PCA Scatter Plot ---\n",
        "plt.figure(figsize=(7,5))\n",
        "plt.scatter(\n",
        "    df_cluster_scaled[\"PC1\"],\n",
        "    df_cluster_scaled[\"PC2\"],\n",
        "    c=df_cluster_scaled[\"Cluster_KMeans\"],\n",
        "    cmap=\"viridis\",\n",
        "    s=2   # tiny dots\n",
        ")\n",
        "plt.colorbar()\n",
        "plt.title(\"K-Means Clusters (PCA Visualization)\")\n",
        "plt.xlabel(\"PC1\")\n",
        "plt.ylabel(\"PC2\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "z1DxMAafOKFe",
        "outputId": "44a9e0a9-ead7-48e2-bd98-3f714fb4c824"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 582,
              "height": 469
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fuzzy clustering (soft):"
      ],
      "metadata": {
        "id": "rRfcFRTwPfc-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# --------------------------------------------------------------\n",
        "#  GAUSSIAN MIXTURE MODEL (GMM) CLUSTERING + PROBABILITY PLOTS\n",
        "# --------------------------------------------------------------\n",
        "\n",
        "from sklearn.mixture import GaussianMixture\n",
        "\n",
        "# 1. Fit Gaussian Mixture Model on the standardized numeric data\n",
        "gm = GaussianMixture(n_components=k, random_state=42)\n",
        "gm.fit(cluster_scaled)\n",
        "\n",
        "# 2. Predict cluster probabilities (soft clustering)\n",
        "probs = gm.predict_proba(cluster_scaled)\n",
        "\n",
        "# 3. Save probabilities into df_cluster_scaled (NOT into df or dfc)\n",
        "for i in range(k):\n",
        "    df_cluster_scaled[f\"ClusterProb_{i}\"] = probs[:, i]\n",
        "\n",
        "# --------------------------------------------------------------\n",
        "#  PLOT: Probability Visualization (using PCA coordinates)\n",
        "# --------------------------------------------------------------\n",
        "\n",
        "plt.figure(figsize=(7,5))\n",
        "plt.scatter(\n",
        "    df_cluster_scaled[\"PC1\"],            # requires PCA block already run\n",
        "    df_cluster_scaled[\"PC2\"],\n",
        "    c=df_cluster_scaled[\"ClusterProb_0\"],  # visualize probability of cluster 0\n",
        "    cmap=\"coolwarm\",\n",
        "    s=5,\n",
        "    alpha=0.9\n",
        ")\n",
        "plt.colorbar(label=\"Probability of Cluster 0\")\n",
        "plt.title(\"Gaussian Mixture Probabilities (Cluster 0) – PCA Visualization\")\n",
        "plt.xlabel(\"PC1\")\n",
        "plt.ylabel(\"PC2\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "PIpZbxPlOynF",
        "outputId": "a15f925c-b278-4418-c393-ebf3edda1352"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 601,
              "height": 469
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "UMAP"
      ],
      "metadata": {
        "id": "4N8yWQGSOXZS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Install + import UMAP (first time only) ---\n",
        "!pip install umap-learn\n",
        "import umap\n",
        "\n",
        "# --- UMAP (must run BEFORE the plot) ---\n",
        "umap_reducer = umap.UMAP(n_components=2, random_state=42)\n",
        "umap_components = umap_reducer.fit_transform(cluster_scaled)\n",
        "\n",
        "df_cluster_scaled[\"UMAP1\"] = umap_components[:, 0]\n",
        "df_cluster_scaled[\"UMAP2\"] = umap_components[:, 1]\n",
        "\n",
        "# --- UMAP Scatter Plot ---\n",
        "plt.figure(figsize=(7,5))\n",
        "plt.scatter(\n",
        "    df_cluster_scaled[\"UMAP1\"],\n",
        "    df_cluster_scaled[\"UMAP2\"],\n",
        "    c=df_cluster_scaled[\"Cluster_KMeans\"],\n",
        "    cmap=\"viridis\",\n",
        "    s=5   # slightly larger than PCA dots\n",
        ")\n",
        "plt.title(\"K-Means Clusters (UMAP Visualization)\")\n",
        "plt.xlabel(\"UMAP1\")\n",
        "plt.ylabel(\"UMAP2\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "IrvhN_MpOZDJ",
        "outputId": "63795a8e-5f2c-4353-e615-4e1c5bbe9be2"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: umap-learn in /usr/local/lib/python3.12/dist-packages (0.5.9.post2)\n",
            "Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.12/dist-packages (from umap-learn) (2.0.2)\n",
            "Requirement already satisfied: scipy>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from umap-learn) (1.16.3)\n",
            "Requirement already satisfied: scikit-learn>=1.6 in /usr/local/lib/python3.12/dist-packages (from umap-learn) (1.6.1)\n",
            "Requirement already satisfied: numba>=0.51.2 in /usr/local/lib/python3.12/dist-packages (from umap-learn) (0.60.0)\n",
            "Requirement already satisfied: pynndescent>=0.5 in /usr/local/lib/python3.12/dist-packages (from umap-learn) (0.5.13)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from umap-learn) (4.67.1)\n",
            "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.12/dist-packages (from numba>=0.51.2->umap-learn) (0.43.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.12/dist-packages (from pynndescent>=0.5->umap-learn) (1.5.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.12/dist-packages (from scikit-learn>=1.6->umap-learn) (3.6.0)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 612,
              "height": 469
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "KMeans Interpretation:"
      ],
      "metadata": {
        "id": "3ZQYXI62Og0j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_summary = df_cluster_num.copy()\n",
        "cluster_summary[\"Cluster\"] = df_cluster_scaled[\"Cluster_KMeans\"]\n",
        "cluster_summary.groupby(\"Cluster\").mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "oIaihigDOiIy",
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      },
      "execution_count": 51,
      "outputs": [
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              "           Age  Birth Year  Height  Weight   Wins  Losses  Public Favor  Mental Resilience  log_Losses\n",
              "Cluster                                                                                               \n",
              "0       37.077      79.615 175.491  81.457 10.631   2.815         0.774              3.535       1.223\n",
              "1       26.599      94.629 175.243  81.975  5.488   0.815         0.631              5.362       0.489"
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              "summary": "{\n  \"name\": \"cluster_summary\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Cluster\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.409588011583567,\n        \"min\": 26.598704902867716,\n        \"max\": 37.07744476044629,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          26.598704902867716,\n          37.07744476044629\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Birth Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.616869485749461,\n        \"min\": 79.61452636184643,\n        \"max\": 94.62904717853839,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          94.62904717853839,\n          79.61452636184643\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17525397433348508,\n        \"min\": 175.24329324699352,\n        \"max\": 175.49113979435572,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          175.24329324699352,\n          175.49113979435572\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3663372060267395,\n        \"min\": 81.45657405381755,\n        \"max\": 81.97465309898243,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          81.97465309898243,\n          81.45657405381755\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Wins\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.6364018217601686,\n        \"min\": 5.488066604995375,\n        \"max\": 10.630715379566835,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5.488066604995375,\n          10.630715379566835\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Losses\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4141430413204774,\n        \"min\": 0.8148011100832563,\n        \"max\": 2.8147013782542114,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8148011100832563,\n          2.8147013782542114\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Public Favor\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10161150213639426,\n        \"min\": 0.6307894978827244,\n        \"max\": 0.7744898622971159,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.6307894978827244,\n          0.7744898622971159\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mental Resilience\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2918745394725002,\n        \"min\": 3.5352306533665554,\n        \"max\": 5.362217147973062,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5.362217147973062,\n          3.5352306533665554\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"log_Losses\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5196325494231111,\n        \"min\": 0.4885801286996733,\n        \"max\": 1.2234515275443447,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.4885801286996733,\n          1.2234515275443447\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "KLnSsY0UVah_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part 5: Train and Evaluate Three Improved Models**\n",
        "\n",
        "* Retrain your `Linear Regression` model with the engineered features.\n",
        "* Choose and Train two different types on models from the SKlearn package (DOCS), on the engineered dataset.\n",
        "* Compare performance with your baseline.\n",
        "* Visualize feature importance.\n",
        "* <u>Discuss the improvement and the reasons.</u>\n",
        "* Declare the winner."
      ],
      "metadata": {
        "id": "pykMu4YaXHEx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "\n",
        "sns.set(style=\"whitegrid\")"
      ],
      "metadata": {
        "id": "59VDTUuWXETD"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Safe copies so original objects stay unchanged\n",
        "\n",
        "X_train_base = X_train_enc.copy() #Base => encoded only (NO feature engineering)\n",
        "X_test_base  = X_test_enc.copy()\n",
        "\n",
        "X_train_model = X_train_fe.copy() #Model => fully engineered features (FE)\n",
        "X_test_model  = X_test_fe.copy()\n",
        "\n",
        "y_train_model = y_train.copy()\n",
        "y_test_model  = y_test.copy()"
      ],
      "metadata": {
        "id": "eLomwhyRXbUY"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Baseline Model – Linear Regression (NO feature engineering)\n",
        "\n",
        "baseline_lr = LinearRegression()\n",
        "baseline_lr.fit(X_train_base, y_train_model)"
      ],
      "metadata": {
        "id": "n-_qWp15XbcK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "e9ae1f8c-f31e-4886-9235-e9fef7b57beb"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "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",
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              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
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              "  --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",
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              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
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              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
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              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
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              "  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>LinearRegression()</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>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Improved Models – Using Engineered Features\n",
        "#    (a) Linear Regression on engineered features\n",
        "#    (b) Random Forest Regressor\n",
        "#    (c) Gradient Boosting Regressor"
      ],
      "metadata": {
        "id": "PNWsAU4WZYM3"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# (a) Linear Regression with engineered features\n",
        "lr_fe = LinearRegression()\n",
        "lr_fe.fit(X_train_model, y_train_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "EpbUWbxKZaBL",
        "outputId": "77682ffb-dddf-4917-b87d-c6784212fb0b"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# (b) Random Forest Regressor\n",
        "rf_model = RandomForestRegressor(\n",
        "    n_estimators=400,\n",
        "    max_depth=None,\n",
        "    min_samples_split=2,\n",
        "    min_samples_leaf=1,\n",
        "    random_state=SEED,\n",
        "    n_jobs=-1,\n",
        ")\n",
        "rf_model.fit(X_train_model, y_train_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "FwYj_rM9Zbol",
        "outputId": "c1c17d3b-2f29-4dc9-bc3c-29e557f99c15"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(n_estimators=400, n_jobs=-1, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=400, n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(n_estimators=400, n_jobs=-1, random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# (c) Gradient Boosting Regressor\n",
        "gb_model = GradientBoostingRegressor(\n",
        "    n_estimators=400,\n",
        "    learning_rate=0.05,\n",
        "    max_depth=3,\n",
        "    subsample=0.9,\n",
        "    random_state=SEED,\n",
        ")\n",
        "gb_model.fit(X_train_model, y_train_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "LVf4Dom8ZgB1",
        "outputId": "13fb4093-2dbe-4e75-bc92-2290cb7159b3"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingRegressor(learning_rate=0.05, n_estimators=400, random_state=42,\n",
              "                          subsample=0.9)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {\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-5 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-5 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-5 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-5 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-5 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-5 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-5 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-5 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-5 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(learning_rate=0.05, n_estimators=400, random_state=42,\n",
              "                          subsample=0.9)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html\">?<span>Documentation for GradientBoostingRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingRegressor(learning_rate=0.05, n_estimators=400, random_state=42,\n",
              "                          subsample=0.9)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Evaluation Helper\n",
        "\n",
        "def evaluate_model(model, X_train, X_test, y_train, y_test, name: str) -> dict:\n",
        "    \"\"\"\n",
        "    Compute standard regression metrics for train and test sets.\n",
        "    Returns a single dict, convenient for building a DataFrame.\n",
        "    \"\"\"\n",
        "    y_pred_train = model.predict(X_train)\n",
        "    y_pred_test  = model.predict(X_test)\n",
        "\n",
        "    mae_train = mean_absolute_error(y_train, y_pred_train)\n",
        "    mae_test  = mean_absolute_error(y_test,  y_pred_test)\n",
        "\n",
        "    mse_train = mean_squared_error(y_train, y_pred_train)\n",
        "    mse_test  = mean_squared_error(y_test,  y_pred_test)\n",
        "\n",
        "    rmse_train = np.sqrt(mse_train)\n",
        "    rmse_test  = np.sqrt(mse_test)\n",
        "\n",
        "    r2_train = r2_score(y_train, y_pred_train)\n",
        "    r2_test  = r2_score(y_test,  y_pred_test)\n",
        "\n",
        "    return {\n",
        "        \"Model\": name,\n",
        "        \"MAE_train\": mae_train,\n",
        "        \"MAE_test\": mae_test,\n",
        "        \"RMSE_train\": rmse_train,\n",
        "        \"RMSE_test\": rmse_test,\n",
        "        \"R2_train\": r2_train,\n",
        "        \"R2_test\": r2_test,\n",
        "    }"
      ],
      "metadata": {
        "id": "BueEogqrZiHc"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Collect Results for Baseline + 3 Improved Models\n",
        "\n",
        "results = []\n",
        "\n",
        "# Baseline: Linear Regression on encoded data (no FE)\n",
        "results.append(\n",
        "    evaluate_model(\n",
        "        baseline_lr,\n",
        "        X_train_base,\n",
        "        X_test_base,\n",
        "        y_train_model,\n",
        "        y_test_model,\n",
        "        name=\"Baseline: Linear Regression (encoded, no FE)\",\n",
        "    )\n",
        ")"
      ],
      "metadata": {
        "id": "aktKUfO7ZjtJ"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Improved: Linear Regression with engineered features\n",
        "results.append(\n",
        "    evaluate_model(\n",
        "        lr_fe,\n",
        "        X_train_model,\n",
        "        X_test_model,\n",
        "        y_train_model,\n",
        "        y_test_model,\n",
        "        name=\"Improved: Linear Regression (FE)\",\n",
        "    )\n",
        ")"
      ],
      "metadata": {
        "id": "2QSD1Cdu6Uri"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Improved: Random Forest\n",
        "results.append(\n",
        "    evaluate_model(\n",
        "        rf_model,\n",
        "        X_train_model,\n",
        "        X_test_model,\n",
        "        y_train_model,\n",
        "        y_test_model,\n",
        "        name=\"Random Forest (FE)\",\n",
        "    )\n",
        ")"
      ],
      "metadata": {
        "id": "KNlsWazv6Wtf"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Improved: Gradient Boosting\n",
        "results.append(\n",
        "    evaluate_model(\n",
        "        gb_model,\n",
        "        X_train_model,\n",
        "        X_test_model,\n",
        "        y_train_model,\n",
        "        y_test_model,\n",
        "        name=\"Gradient Boosting (FE)\",\n",
        "    )\n",
        ")\n",
        "results_df = pd.DataFrame(results)\n",
        "# Sort by RMSE_test (ascending: lower is better)\n",
        "results_df = results_df.sort_values(\"RMSE_test\").reset_index(drop=True)\n",
        "\n",
        "print(\"Model Performance Summary (sorted by RMSE_test)\")\n",
        "display(results_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "OYrzOJ3n6YiH",
        "outputId": "05f1fa88-d77a-4d75-b45e-8962debcccd1"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Performance Summary (sorted by RMSE_test)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                          Model  MAE_train  MAE_test  RMSE_train  RMSE_test  R2_train  R2_test\n",
              "0                        Gradient Boosting (FE)      0.849     0.912       1.141      1.229     0.895    0.874\n",
              "1              Improved: Linear Regression (FE)      0.963     0.932       1.321      1.271     0.859    0.865\n",
              "2  Baseline: Linear Regression (encoded, no FE)      0.963     0.936       1.323      1.274     0.859    0.865\n",
              "3                            Random Forest (FE)      0.364     0.972       0.497      1.305     0.980    0.858"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1c836ab9-ecbb-4740-b33a-987948d372ae\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>MAE_train</th>\n",
              "      <th>MAE_test</th>\n",
              "      <th>RMSE_train</th>\n",
              "      <th>RMSE_test</th>\n",
              "      <th>R2_train</th>\n",
              "      <th>R2_test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Gradient Boosting (FE)</td>\n",
              "      <td>0.849</td>\n",
              "      <td>0.912</td>\n",
              "      <td>1.141</td>\n",
              "      <td>1.229</td>\n",
              "      <td>0.895</td>\n",
              "      <td>0.874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Improved: Linear Regression (FE)</td>\n",
              "      <td>0.963</td>\n",
              "      <td>0.932</td>\n",
              "      <td>1.321</td>\n",
              "      <td>1.271</td>\n",
              "      <td>0.859</td>\n",
              "      <td>0.865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Baseline: Linear Regression (encoded, no FE)</td>\n",
              "      <td>0.963</td>\n",
              "      <td>0.936</td>\n",
              "      <td>1.323</td>\n",
              "      <td>1.274</td>\n",
              "      <td>0.859</td>\n",
              "      <td>0.865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Random Forest (FE)</td>\n",
              "      <td>0.364</td>\n",
              "      <td>0.972</td>\n",
              "      <td>0.497</td>\n",
              "      <td>1.305</td>\n",
              "      <td>0.980</td>\n",
              "      <td>0.858</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1c836ab9-ecbb-4740-b33a-987948d372ae')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1c836ab9-ecbb-4740-b33a-987948d372ae button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1c836ab9-ecbb-4740-b33a-987948d372ae');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-3c4ef45e-2c42-4543-b779-9e62d3a47d50\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3c4ef45e-2c42-4543-b779-9e62d3a47d50')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-3c4ef45e-2c42-4543-b779-9e62d3a47d50 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_715151b4-c343-4384-81e2-0b5f52fb6799\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('results_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_715151b4-c343-4384-81e2-0b5f52fb6799 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('results_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results_df",
              "summary": "{\n  \"name\": \"results_df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Improved: Linear Regression (FE)\",\n          \"Random Forest (FE)\",\n          \"Gradient Boosting (FE)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE_train\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28568203190285696,\n        \"min\": 0.3638762531328321,\n        \"max\": 0.963017343899223,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9629445399699185,\n          0.3638762531328321,\n          0.8493675661635394\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE_test\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0248325385506987,\n        \"min\": 0.9124729649042768,\n        \"max\": 0.9720027555110221,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9318470948994849,\n          0.9720027555110221,\n          0.9124729649042768\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE_train\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.39175547137126054,\n        \"min\": 0.4969220905397707,\n        \"max\": 1.3227074999754738,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.3212247459219713,\n          0.4969220905397707,\n          1.1408841293595926\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE_test\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03155465909535144,\n        \"min\": 1.2285398208701785,\n        \"max\": 1.305319564400002,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.271361154864519,\n          1.305319564400002,\n          1.2285398208701785\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2_train\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05716617319020275,\n        \"min\": 0.8587235843175066,\n        \"max\": 0.9800602989398925,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.8590401482828369,\n          0.9800602989398925,\n          0.8948945796796222\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2_test\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006651300893997234,\n        \"min\": 0.8581084433930377,\n        \"max\": 0.874309824043135,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.8653951420193691,\n          0.8581084433930377,\n          0.874309824043135\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. Visualization – Model Comparison (RMSE and R² on Test Set)\n",
        "\n",
        "# -------------------------- RMSE PLOT --------------------------\n",
        "plt.figure(figsize=(8, 4))\n",
        "ax = sns.barplot(\n",
        "    data=results_df,\n",
        "    x=\"Model\",\n",
        "    y=\"RMSE_test\",\n",
        ")\n",
        "\n",
        "# Add value labels on top of bars\n",
        "for container in ax.containers:\n",
        "    ax.bar_label(container, fmt=\"%.3f\", label_type=\"edge\", padding=3)\n",
        "\n",
        "plt.title(\"Test RMSE by Model\")\n",
        "plt.ylabel(\"RMSE (Test)\")\n",
        "plt.xlabel(\"\")\n",
        "plt.xticks(rotation=20, ha=\"right\")\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# -------------------------- R2 PLOT --------------------------\n",
        "plt.figure(figsize=(8, 4))\n",
        "ax = sns.barplot(\n",
        "    data=results_df,\n",
        "    x=\"Model\",\n",
        "    y=\"R2_test\",\n",
        ")\n",
        "\n",
        "# Add value labels\n",
        "for container in ax.containers:\n",
        "    ax.bar_label(container, fmt=\"%.3f\", label_type=\"edge\", padding=3)\n",
        "\n",
        "plt.title(\"Test R² by Model\")\n",
        "plt.ylabel(\"R² (Test)\")\n",
        "plt.xlabel(\"\")\n",
        "plt.xticks(rotation=20, ha=\"right\")\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "id": "nEWadO7C6kPo",
        "outputId": "cb8e2e3e-fad1-47a2-9e4a-90a203a1696a"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 776,
              "height": 381
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 776,
              "height": 381
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 6. Feature Importance – Helper Function\n",
        "\n",
        "def plot_feature_importance(model, feature_names, title, top_n=15, use_abs=False):\n",
        "    \"\"\"\n",
        "    Plots the top_n most important features for:\n",
        "      - Linear models (using coefficients)\n",
        "      - Tree-based models (using feature_importances_)\n",
        "    \"\"\"\n",
        "    # Linear models: use coefficients\n",
        "    if hasattr(model, \"coef_\"):\n",
        "        coefs = np.ravel(model.coef_)\n",
        "        if use_abs:\n",
        "            values = np.abs(coefs)\n",
        "        else:\n",
        "            values = coefs\n",
        "    # Tree-based models: use feature_importances_\n",
        "    elif hasattr(model, \"feature_importances_\"):\n",
        "        values = model.feature_importances_\n",
        "    else:\n",
        "        raise ValueError(\"Model has neither coef_ nor feature_importances_.\")\n",
        "\n",
        "    fi_df = pd.DataFrame(\n",
        "        {\n",
        "            \"Feature\": feature_names,\n",
        "            \"Importance\": values,\n",
        "        }\n",
        "    )\n",
        "\n",
        "    # Sort and keep top_n\n",
        "    fi_df = fi_df.sort_values(\"Importance\", ascending=False).head(top_n)\n",
        "\n",
        "    plt.figure(figsize=(8, max(4, 0.4 * top_n)))\n",
        "    sns.barplot(\n",
        "        data=fi_df,\n",
        "        x=\"Importance\",\n",
        "        y=\"Feature\",\n",
        "        orient=\"h\",\n",
        "    )\n",
        "    plt.title(title)\n",
        "    plt.xlabel(\"Importance\")\n",
        "    plt.ylabel(\"\")\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "s3f5Nowz6nWh"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_feature_importance(\n",
        "    lr_fe,\n",
        "    X_train_model.columns,\n",
        "    title=\"Feature Importance – Linear Regression (Fisrt)\",\n",
        "    top_n=20,\n",
        "    use_abs=True\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "1ipuxaoxZbhG",
        "outputId": "754dfc8c-de72-4de6-e6cf-599d72b2299a"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 783,
              "height": 781
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 7. Feature Importance – For Each Improved Model\n",
        "\n",
        "feature_names = X_train_model.columns.tolist()\n",
        "\n",
        "# (a) Linear Regression with engineered features\n",
        "plot_feature_importance(\n",
        "    lr_fe,\n",
        "    feature_names,\n",
        "    title=\"Feature Importance – Linear Regression (Engineered Features)\",\n",
        "    top_n=20,\n",
        "    use_abs=True,  # absolute size of coefficients\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "DVRXFNdJ6pwp",
        "outputId": "9b878497-b1df-44df-ee76-5e4a6ebef1f1"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 783,
              "height": 781
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# (b) Random Forest\n",
        "plot_feature_importance(\n",
        "    rf_model,\n",
        "    feature_names,\n",
        "    title=\"Feature Importance – Random Forest (Engineered Features)\",\n",
        "    top_n=20,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "AO_tJVuX6r0_",
        "outputId": "b33fb653-b0f6-4890-a6fd-e4160ba3d728"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 784,
              "height": 781
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# (c) Gradient Boosting\n",
        "plot_feature_importance(\n",
        "    gb_model,\n",
        "    feature_names,\n",
        "    title=\"Feature Importance – Gradient Boosting (Engineered Features)\",\n",
        "    top_n=20,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "CFDTkbzC6t-t",
        "outputId": "d7282303-acf8-4495-dc08-5bd97901cd7c"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 783,
              "height": 781
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 8. Scatter Plot – True vs Predicted Wins for the Best Model\n",
        "\n",
        "# Choose the best model based on lowest RMSE_test\n",
        "best_model_name = results_df.loc[0, \"Model\"]\n",
        "\n",
        "if \"Gradient Boosting\" in best_model_name:\n",
        "    best_model = gb_model\n",
        "elif \"Random Forest\" in best_model_name:\n",
        "    best_model = rf_model\n",
        "elif \"Improved: Linear Regression\" in best_model_name:\n",
        "    best_model = lr_fe\n",
        "else:\n",
        "    best_model = baseline_lr  # fallback\n",
        "\n",
        "y_pred_best = best_model.predict(X_test_model if \"Baseline\" not in best_model_name else X_test_base)\n",
        "\n",
        "plt.figure(figsize=(6, 6))\n",
        "plt.scatter(y_test_model, y_pred_best, alpha=0.6)\n",
        "plt.plot(\n",
        "    [y_test_model.min(), y_test_model.max()],\n",
        "    [y_test_model.min(), y_test_model.max()],\n",
        "    linestyle=\"--\",\n",
        ")\n",
        "plt.xlabel(\"Actual Wins\")\n",
        "plt.ylabel(\"Predicted Wins\")\n",
        "plt.title(f\"Actual vs Predicted Wins – {best_model_name}\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "C8m9q0Dv6wP_",
        "outputId": "5b3d3182-32a3-420b-e0db-4f915d9e9d44"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 582,
              "height": 581
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusion:\n",
        "After creating new features, all the models got better because they had more useful information to learn from. The basic Linear Regression improved, but it still couldn’t capture more complex patterns. Random Forest did a bit better, but it wasn't the strongest. Gradient Boosting clearly performed the best: it made the most accurate predictions, with the lowest errors and highest scores. It works well because it learns step by step and keeps fixing its mistakes. So, the winning model is Gradient Boosting, since it predicts “Wins” the most reliably out of all the models."
      ],
      "metadata": {
        "id": "DYoreBi6bhE7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "ykjbsmtgvbLx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Part 6: Winning Model"
      ],
      "metadata": {
        "id": "PS9t6m0_nJ9t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Open a new <u>HuggingFace Model Repository.</u>\n",
        "2. Export the winning model to a `pickle` file.\n",
        "3. Upload the pickle file to your new model repository on `HF`."
      ],
      "metadata": {
        "id": "ga-aPfHDnRM3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "filename = \"gladiator_gradient_boosting.pkl\"\n",
        "\n",
        "with open(filename, \"wb\") as f:\n",
        "    pickle.dump(gb_model, f)\n",
        "\n",
        "print(\"Saved:\", filename)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zIS24u7x2V1k",
        "outputId": "15c816f2-ffcb-4efe-a2a3-517c29204ff6"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved: gladiator_gradient_boosting.pkl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "#notebook_login()"
      ],
      "metadata": {
        "id": "FMAI5TKO9d4Y"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import HfApi\n",
        "\n",
        "api = HfApi()\n",
        "\n",
        "filename = \"gladiator_gradient_boosting.pkl\"\n",
        "repo_id = \"Karish1/winning_model\"\n",
        "\n",
        "# 1. Get a list of files currently in the repo\n",
        "files_in_repo = api.list_repo_files(repo_id=repo_id)\n",
        "\n",
        "# 2. Check if file already exists\n",
        "if filename in files_in_repo:\n",
        "    print(f\"File '{filename}' already exists in the repository. No upload was performed.\")\n",
        "else:\n",
        "    api.upload_file(\n",
        "        path_or_fileobj=filename,\n",
        "        path_in_repo=filename,\n",
        "        repo_id=repo_id\n",
        "    )\n",
        "    print(f\"Uploaded '{filename}' successfully!\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pqKe1ycp9UUr",
        "outputId": "6a8db764-beb6-44f5-e78b-9da7e2eee4da"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "File 'gladiator_gradient_boosting.pkl' already exists in the repository. No upload was performed.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "GzsEe2Yun5nC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "2poXnlF5XEXN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 7: Regression-to-Classification"
      ],
      "metadata": {
        "id": "-e5pgBLLoN81"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this section, you will **reframe your original regression problem as a classification problem**.\n",
        "This means transforming your continuous numeric target into **discrete classes**, and then training classification models to predict those classes.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Qtj_Sf-7oqY5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#### **7.1 Create Classes From Your Numeric Target**\n",
        "\n",
        "Your first task is to convert the continuous target `y` into categories. Choose a strategy to convert your numeric target into classes. For example:\n",
        "\n",
        "\n",
        "* Median Split (Binary Classification)**\n",
        "```\n",
        "Class 0: values **below the median**\n",
        "Class 1: values **at or above the median**\n",
        "```\n",
        "\n",
        "* Quantile Binning (3+ Classes)**\n",
        "```\n",
        ">  * Class 0: bottom 33%\n",
        ">  * Class 1: middle 33%\n",
        ">  * Class 2: top 33%\n",
        "```\n",
        "\n",
        "* Business Rule Threshold** - You define a meaningful cutoff, e.g.:\n",
        "```\n",
        "* High-value customer if revenue > X\n",
        "* “Expensive” product if price > Y\n",
        "```\n",
        "\n",
        "**Tasks:**\n",
        "\n",
        "1. Implement your chosen strategy on the **train** and **test** targets. Using the **same engineered features** as before.\n",
        "\n",
        "2. Explain the reasoning behind your choice (2–3 sentences)."
      ],
      "metadata": {
        "id": "M9g9bfxlqWYg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Same as before in the regression part\n",
        "X_train_fe, X_test_fe  # engineered features\n",
        "y_train, y_test        # numeric target (e.g., scores, revenue, etc.)"
      ],
      "metadata": {
        "id": "IqC0YSXxqL3E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d4cb77f-7f11-4864-ccd1-01cc23b9abca"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5504     9\n",
              " 1287     4\n",
              " 2075     5\n",
              " 7856     3\n",
              " 5213    11\n",
              "         ..\n",
              " 5734     7\n",
              " 5191     2\n",
              " 5390     3\n",
              " 860      6\n",
              " 7270     7\n",
              " Name: Wins, Length: 7980, dtype: int64,\n",
              " 5825     9\n",
              " 5171    14\n",
              " 6041     6\n",
              " 107     10\n",
              " 3422    12\n",
              "         ..\n",
              " 3095     4\n",
              " 7094    15\n",
              " 3838    13\n",
              " 9900     2\n",
              " 4575     8\n",
              " Name: Wins, Length: 1996, dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# 1. Compute the median on TRAIN ONLY\n",
        "median_y = np.median(y_train)\n",
        "print(\"Train median:\", median_y)\n",
        "\n",
        "# 2. Create classes based on the median\n",
        "# Class 0: values BELOW the median\n",
        "# Class 1: values AT or ABOVE the median\n",
        "\n",
        "y_train_cls = (y_train >= median_y).astype(int)\n",
        "y_test_cls  = (y_test  >= median_y).astype(int)\n",
        "\n",
        "# Optional: check balance\n",
        "print(\"Train class distribution:\\n\", pd.Series(y_train_cls).value_counts(normalize=True))\n",
        "print(\"Test class distribution:\\n\", pd.Series(y_test_cls).value_counts(normalize=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukwTDkcGFI-k",
        "outputId": "093260e8-313b-4800-c40b-1d95f7080ebd"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train median: 8.0\n",
            "Train class distribution:\n",
            " Wins\n",
            "1   0.504\n",
            "0   0.496\n",
            "Name: proportion, dtype: float64\n",
            "Test class distribution:\n",
            " Wins\n",
            "1   0.514\n",
            "0   0.486\n",
            "Name: proportion, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "gb_clf = GradientBoostingClassifier(random_state=42)\n",
        "gb_clf.fit(X_train_fe, y_train_cls)\n",
        "\n",
        "y_pred_cls = gb_clf.predict(X_test_fe)"
      ],
      "metadata": {
        "id": "G-qo2eRsFLA6"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "I converted the continuous target into a binary classification problem using a median split. All observations below the median were labeled as Class 0, and those at or above the median as Class 1. This approach keeps the classes relatively balanced and is simple to interpret, which makes it a good starting point for building and evaluating classification models."
      ],
      "metadata": {
        "id": "lTG85Vx1FToI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#### **7.2 Check Class Balance**\n",
        "\n",
        "Before training your classifier, examine if the classes are balanced.\n",
        "\n",
        "1. Show the resulting **class distribution** (counts or percentages).\n",
        "2. Are some classes under-represented?\n",
        "3. If the data is imbalanced, explain which metric you’ll focus on (e.g., F1 score, recall) and why accuracy alone is misleading.\n",
        "4. If needed, consider changing your convertion."
      ],
      "metadata": {
        "id": "L8Grz1xfqLfH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Show counts\n",
        "print(\"Class counts (train):\")\n",
        "print(pd.Series(y_train_cls).value_counts())\n",
        "\n",
        "print(\"\\nClass percentages (train):\")\n",
        "print(pd.Series(y_train_cls).value_counts(normalize=True).round(3))\n",
        "\n",
        "print(\"\\nClass counts (test):\")\n",
        "print(pd.Series(y_test_cls).value_counts())\n",
        "\n",
        "print(\"\\nClass percentages (test):\")\n",
        "print(pd.Series(y_test_cls).value_counts(normalize=True).round(3))"
      ],
      "metadata": {
        "id": "SQ1WoHIRqNj8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c7c2ffd-1630-4699-cc4d-9bbc96f26640"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class counts (train):\n",
            "Wins\n",
            "1    4023\n",
            "0    3957\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Class percentages (train):\n",
            "Wins\n",
            "1   0.504\n",
            "0   0.496\n",
            "Name: proportion, dtype: float64\n",
            "\n",
            "Class counts (test):\n",
            "Wins\n",
            "1    1026\n",
            "0     970\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Class percentages (test):\n",
            "Wins\n",
            "1   0.514\n",
            "0   0.486\n",
            "Name: proportion, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Balanced:\n",
        "The two classes are almost evenly split (≈50/50), so the dataset is balanced"
      ],
      "metadata": {
        "id": "FkQXDvjZoqaw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import (\n",
        "    accuracy_score, precision_score, recall_score, f1_score,\n",
        "    roc_auc_score, confusion_matrix, classification_report\n",
        ")\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# ---- Predictions ----\n",
        "y_pred = gb_clf.predict(X_test_fe)\n",
        "y_proba = gb_clf.predict_proba(X_test_fe)[:, 1]   # needed for ROC-AUC\n",
        "\n",
        "# ---- Metrics ----\n",
        "acc  = accuracy_score(y_test_cls, y_pred)\n",
        "prec = precision_score(y_test_cls, y_pred)\n",
        "rec  = recall_score(y_test_cls, y_pred)\n",
        "f1   = f1_score(y_test_cls, y_pred)\n",
        "auc  = roc_auc_score(y_test_cls, y_proba)\n",
        "\n",
        "print(\"Model Performance:\")\n",
        "print(f\"Accuracy:  {acc:.3f}\")\n",
        "print(f\"Precision: {prec:.3f}\")\n",
        "print(f\"Recall:    {rec:.3f}\")\n",
        "print(f\"F1 score:  {f1:.3f}\")\n",
        "print(f\"ROC-AUC:   {auc:.3f}\")\n",
        "\n",
        "# ---- Confusion Matrix ----\n",
        "cm = confusion_matrix(y_test_cls, y_pred)\n",
        "\n",
        "plt.figure(figsize=(5,4))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.show()\n",
        "\n",
        "# ---- Full Classification Report ----\n",
        "print(\"\\nDetailed Classification Report:\")\n",
        "print(classification_report(y_test_cls, y_pred))"
      ],
      "metadata": {
        "id": "ffoT68TGqOIY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "outputId": "cbd0c25b-7da4-4694-d038-2ab9ce457b6a"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Performance:\n",
            "Accuracy:  0.907\n",
            "Precision: 0.905\n",
            "Recall:    0.915\n",
            "F1 score:  0.910\n",
            "ROC-AUC:   0.970\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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kQTP5MN6CxMXFRZMmTbLuCIeFhWnGjBkaMGCAWrRooccff1xTp07V9u3bMz2raFqy8jOpUKGCtZ0TPxPJ8ecSFxeXqXOyolKlStZMxBs3btSlS5d06dIla9h0ixYtHH7nMisoKEj9+vXT7NmzMx0ipZSjAHJKbg+J7tatW4rncn19fVnqA0C2cUcSAAqp8uXL68yZM5KkY8eO5Wjfye9wZnbYbLFixaztjP7oNl3SoCBr2bKlFi5cqGnTpmnZsmXW3bqYmBht375d27dv1xdffKGqVatq5MiR6tKlS5auczv+TPr27att27YpPj7eesbzxpcKWZ0xdsSIEdZEVR4eHurfv7/at2+vqlWrytfXV0WLFpWTU9L37AEBAXriiSckKdtfBKQlO7P4ZtY/h1s3bdo0xSRNAGCKO5IAUEglXzdyz549Odp38qCS2WGz169ft7bTm0SnMMlo9tkbKlWqpA8++ECbNm3SN998oxdeeEGtWrVyCAmnTp3SSy+9ZK3dZ+p2/Jncd9991mfo7++v+fPnS0oKXxk9z5maHTt2aOfOnZKSPs85c+ZozJgxuuuuu1SpUiW5u7tbIVLKvbuQeWnbtm0p1mFdtWqVtWQPAGQVQRIACqnWrVtb20FBQdqxY0eO9Z18uF1wcHCmzkm+vMCN2UILmuTDgTMzXNb02Ux3d3fdddddevnll/XDDz8oICBAn3zyicOEJlOmTHGYxCWzsvIzST5JUUH9maTH09PTWlrm4MGDOnTokCSpS5cuWXo2c9OmTdZ2v379VLNmzXTbJ19ipzCKiIjQqFGjrCVjqlevbr329ttvF9hJrAAUDgRJACik7r33Xodw8M8F07OjXr161vaNOzgZSd4uoxlF80vy8BEeHp5h+8OHD2frejfunP3444/WUMK4uDj99ddfxn1l5WeS/MuFgvozyUhqE+pkdZKdG2snSsrUbKXJl1tJS0EcEnzD+PHjrbBYs2ZN/f7776kuCQIAWUGQBIBCqmjRog6zLi5fvlzLly837ufatWsp7mYmv9u5bt06Xb58Od0+Lly44BCOkp9fkCSffObG3a303FivMLu8vLzUvHlzaz+jzzM1yT/TAwcOZFj/9evXHZZ6KKg/k4y0b9/eYfmZUqVKqV27dlnqK/mw1RvPsqblwoUL1nqL6Um+BmNuTj5kyt/f33qu1MXFRVOmTJG7u7smTZqkkiVLSpK2b9+u6dOn52eZAAoxgiQAFGLPPvusGjRoYO2PGjVKq1evzvT5R44c0SOPPGLNhHlD+/btrRkxY2Nj9d5776XZh91u1zvvvGP9EV25cmW1bdvW5G3kmUaNGll3kHbv3q3jx4+n2Xb27Nk6evRouv2ZLAmRfDhqVmbqrFGjhu68805rf8KECekGl48//tgKrJ6enurVq5fxNQsCZ2dnzZ49W3PnztXcuXM1e/ZsOTs7Z6mvSpUqWdvphcSEhASNGzcuU8GwRIkSVkANCwsrEGHy7Nmzevvtt639V155RXXr1pWUtCRI8temT5+uXbt25XWJAG4BBEkAKMRcXV31ySefWLMyRkdH66WXXtKoUaPSDEl2u1179uzR6NGj1adPHx05ciRFGycnJ40YMcLaX7x4scaOHZti8pHIyEiNGTNGK1assI6NHDnS4c5PQVKqVCnrzpzdbtcrr7yi8+fPO7SJj4/Xt99+q3fffTfDdSBnzZqlPn366KefflJISEiqbaKiojR16lTt3btXUlIwat++fZbqf+WVV6wQtW3bNg0dOjTF3c3Y2FhNmTLFYajzkCFDCuVkOzdUqVJFjRo1UqNGjVSlSpUs99OxY0fri4QtW7bogw8+SHFnMiQkREOHDtXatWszNTuuq6urVVNcXJz+/PPPLNeXExISEjRy5Ejr39W2bdvqqaeecmhz77336oEHHpCU9Ps+cuTIArtWK4CCi+U/AKCQq1SpkubMmaMXXnhBR44cUWJiohYsWKAFCxaoQoUKqlOnjry9vZWYmKiQkBAdOnRIly5dcugjtZDRo0cPbdu2TbNnz5Yk/fbbb/rjjz/UqlUr+fn56fLly9q0aZPDDKKDBg1St27dcvcNZ9Pw4cMVEBCgxMREHTp0SN27d1fr1q1VpkwZhYeHa9u2bbp8+bLc3d01YsQITZgwId3+Dh06pLfeektvv/22KleurFq1asnb21vx8fEKCQnRjh07HD6jZ599VuXKlctS7c2bN9eIESM0adIkSdKaNWvUqVMntWrVSuXKldOVK1cUEBDg8Pxn165d9eSTT2bpereaGjVqqE+fPvL395ckffvtt1q0aJEaNWokX19fBQUFaevWrYqLi5OHh4dGjRqlN998M8N+u3fvri+++EJS0hcp8+fPV+XKlR3W3Bw9enSuvKd/+r//+z/rGVovLy9NnDgx1ec4x44dq+3bt+v06dM6c+aM3nnnHU2cODFPagRwayBIAsAtoGLFivrll1/0/fff6/vvv9fVq1clJc3amd7MjHXr1tXQoUPTXNtw3Lhx8vPz0/Tp0xUbG6uoqKhUh866ubnppZde0vPPP58zbygXNWnSRBMmTNC4ceOUkJCg6OhorV271qFNqVKl9PHHH2c4EUnyAG6323X69GmdPn061bYuLi4aPHiwhgwZkq36//3vf6tEiRKaOHGiIiMjFRsbm+rkPc7OzhowYIBee+21Aj0hTF4bP368Ll26pA0bNkhKugP5z9/psmXL6qOPPsrUzL6S9Mwzz2jFihU6ceKE4uLitG7duhRt8iJI7tixw+GZx3feeUdlypRJta2Hh4cmT56sxx57TPHx8Zo/f746duyo++67L9frBHBrIEgCwC3Cw8NDL730kp544gmtW7dOGzdu1P79+xUaGqrw8HC5uLjIy8tL1atXV+PGjdWlSxeH5yvT8uKLL6pPnz767bfftGHDBgUGBioiIkLFixdXpUqV1L59e/Xv31/ly5fPg3eZMx566CE1bdpU3333nTZv3qyQkBC5ubmpYsWK6tatmx555BH5+PgoICAg3X6efvppdevWTX///bd27typw4cPKygoSFFRUbLZbCpRooSqV6+u1q1bq2/fvg6T/WRH//791blzZ/32229av369Tp06pStXrsjDw0Nly5ZV27Zt9eCDD2a4vMXtqFixYvr666+1aNEi+fv768CBA4qKipKXl5cqVaqk7t27q1+/fipZsmSGP/8bihcvrrlz5+qnn37SunXrdPz4cUVEROTp85KRkZEOs7D2799fXbt2Tfecxo0ba8iQIfr4448lSW+++aaaNm2a5TvmAG4vNrvdbs/vIgAAAAAAhUfBnA0BAAAAAFBgESQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCmS3wUgdxXr9mF+lwAAyGeXF7+a3yUAAAoAd1dbjvXFHUkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgJEi+V0AgNtXq3rl9ViX+mpZt7yqlCmh4u6uuh4Tr4vh17T72AUt+vuY5m84oti4hFTPr1ymhA7/+HyWr//Ojxv17o9/p/l6OR8P3VGnnO6oU1bNa5XVHXXKyrdEMev1OgO/1JkLV7N8fQCAmW1bt+iPJYu0c/s2XboUori4OPn5lVKt2nXU7d771LXbvSpSJPN/3p49e0bzfpujLVs261xQoKKiouTt7aOKlSqpc5du6nl/b5Us6ZV7bwgoxAiSAPKcT/Gi+mLEvbq/ba0Ur7kUcVYJDzfVrOCtBzvW1X+DwvTsh0u16UBQjtdxITQqzddO/PyCyvl65vg1AQDmwsPDNHbMaG3csD7Fa0FBgQoKCtTaNav048zv9O77H6pa9erp9peQkKDPPvlIs374XgkJjl9WXrx4QRcvXtCO7dv07TdfadxbE9Sh4905+n6AW4HNbrfb87sI5J5i3T7M7xIAB0Vdi2jNx4+rac0y1rGL4VHafeyigi5FyK+ku+pX8VP18l7W61HRsbpv1BxtPRTs0Jd38aIa90S7TF+7Zf3yal6rrCQpJjZe1R6brrCI6FTbXl8xMsP+uCOJwuLy4lfzuwQgy65euaIn/vWITp86ZR2rWLGSGjdpKlc3NwWePaNdO3cqPj5OkuTr66cfZv+i8hUqptqf3W7X6FeHa+WKZdYxLy8vNb/jTnl5eenS5UvasW2rIiMjJUnOzs766JNphEncEtxdbTnWF3ckAeSpEY+0tEJkYqJdb83coE9/36bo2HiHdv071dWn/+kqL8+i8ijqqs9f7q6Wg793aBMWEa3hn6/K9LU3fPYva3tpwIk0Q+QN12PitOd4iLYfCda2w+d1MfyaFr/fP9PXAwBk31tvjrVCpJubm8a++bZ63d/Hoc3Zs2c0ZtQI7d+3V5cvX9Krw/+j2b/+Lpst5R/NP//0o0OIHDjoKb04ZJiKFi1qHYuMjNTkD97TAv95SkhI0GsjR8h/8VKVLl0mRX/A7YrJdgDkqYFdG1rb/+e/XZN+3pwiRErSb2sP6cWpy639RtVLqUFVvyxft25lX91Rp5y1P2vlvnTbt35hpkr1+USdXp6tEf+3Wj+vOqCjgaFZvj4AwNyB/fu0etVKa//Nt99NESIlqVKlypr+5QyVLVdeknTw4AEtXbI4RbuYmBh989UX1v6DDz2sV14d7RAiJcnT01PjJ7ynTnd3liRdv35NX/zftBx5T8CtgiAJIM8Ud3dVlbIlrf05aw+l237hxqOKio619mtV9Mnytf/VtYG1fSEsSsu2nEi3/e7jF5WQyMh/AMhPK1fc/EKxVu06uq9HrzTbFi9RQv9+5jlr/6fZP6Ros23rFoWFJn0pWKSIi1546T/pXn/Y8BHW9uKF/oq4yuMMwA0ESQB5xrOoi8N+RkNLExLtuhp1M0g6ZXFYv80mPda5vrU/Z81BQiIAFAL79u62ttvf1SHD9nd16GRt79+3V8HB59Lsr179+vL1S3+kS9Vq1VWpUmVJUlxcnNatW5OZsoHbAkESQJ4JuXJd12PirP36GQxV9StZTKW93K39PSdCsnTde5pXVXm/4tb+rBX7s9QPACBvXb582douV75Chu1LlykjZ2dna39rwOZs9ZfUrry1vWXzpkydA9wOCJIA8kx8QqJWbD1p7Y9+vLWKuaU959c7/+4oZ+ek/0yt3nFax4LCsnTd5MNadx+/qD0nLmapHwBAHjNcXMBmszlMsHP8+LF/dJe90Sj/7A+4nREkAeSpcd/9pYhrScNVm9cqq61fPKkBXRuoenkvubk4q2Kp4rq3ZXX9OeUxDbq3kSTpwKlLen7K0ixdr7i7q3onW69ydgaT7AAACg4v75vPxp//xzDV1Fy8cEHx8TcncDt54rjD696G/UlScPDNpadOnkj/+XrgdsLyH5kQFhamkydPKiIiQlFRSQuYe3h4qHjx4qpWrZq8vb3zuUKg8DhyNlT3DP9Jv7/dT5XLlFSNCt76ZmSPVNuGRUTr51X79eZ3fynyelyqbTLyQIc6cv/fs5lx8Qn6ZfXBLNcOAMhb9es30M4d2yRJf2/coKHDXkm3/Ya/1jnsX7lyxWG/Xv2bI1QOHtivsLCwdP+OO336lM6eOW3tX79+TXFxsXJxcc30ewBuVQTJNPz9999avHix1q9f7zCePjW+vr7q0KGDevbsqXbtMr84OnC72ncyRI2enqGn7m2sd57pIM9iqf8P+c/tJzVnzaEsh0hJGtDl5h8Ny7eeVEj4tSz3BQDIW53uuUezZ82UJB06eEArVyxT1273pto2KipS38342uHYtf/dALjhzpat5OHhoaioKMXFxenL//tMr70xLs3rT/tkairXiZKXF0ESYGjrP5w4cUIDBw7Uv//9b82fP1+XL1+W3W5P95/Lly9r/vz5euaZZ/Svf/1Lx48fz/hCwG3Mt0QxfTq0qyYNvluexVwVfDlS/n8d0TdLdmvu2kM6fT7pG+T+nepp7ScD9NmwbnLKwpStVcqWVLuGFa392SuZZAcACpMWd7ZS02bNrf03x76uZUuXpGh3LihQLw1+VoGBZx2Ox8TEOOx7enrqsccHWvu//vKTPvvkoxTtoqIi9c5b4/TnyuX6p5jomBTHgNsRdyST2b17t5577jldvXrV4WHscuXKqWLFivLy8pKbm5ukpP8whYeHKzAw0GHs/LZt2/T444/rq6++UpMmTfL8PQAFXY3yXlr24SOqWKqEomPj9fJnf+qbJbtSLMfxcKe6+nRYN5X0cNMzPZsoITFRL3/2p9G1BnRpYAXQy1eva8lmJkkAgMLm3fcnacBj/RUeFqbr169pzKgR+r9pn6px4yZydXNT4Nkz2rljh+Lj41S0WDE1b36H/t64QZLk7uGRor/nXnhRW7cGaPeunZKkb7/5SvN+/013tLhTXiW9dPnyZW3btkWRERGSpHu6dNXqP1da56fWJ3A7Ikj+T2RkpF5++WVrLH29evU0cOBAdejQQX4ZrDF0+fJlrV27VrNmzdLBgwd15coVDR8+XAsXLpSnp2delA8UCs5ONv3yZl9VLFVCkjT0kxWalcZdwjlrD+nS1etaMvFhSdLz9zfTrBX7tO3w+Uxf7/Fka0f+tuag4uITs1E9ACA/lK9QUd//+LNGDv+Pjh49Ikk6e+a0w7OLkuTr66d3J36otWtWWUGyePHiKfpzcXHV/335jcb/9w2tXLFMkhQeFqZVK1f8o52Lhg57ReUrVLCCpJOTE3/bAf/D0Nb/+fXXXxUcHCybzaann35av//+ux544IEMQ6SU9Izkgw8+qN9//11PPfWUpKQZvubMmZPbZQOFSr+7aqthtVKSpMNnL6cZIm9YveO0Vu04Ze0P7NYo09dq26CCalS4OYFCRtcCABRcVapU1S9z/fX+pCnq3LWbypYtJzc3N3kWL6669errxSHD9Nv8RWrVuo3Cw24uFVWmbLlU+3N399CkKR/rux9+Ur8H+6tq1Wry8PBQ0aJFVaVqVT3y2AD9/Nt8DRz0lEN/pUuXcVheBLidcUfyf1auTPqmqXnz5ho1alSW+nByctLo0aO1Z88ebd++XStWrNDTTz+dk2UChVrXFtWs7XW7zqbT8qa1u86oc/OqkqQ7apfJ9LUGJFs78sCpS9p+JPN3MgEABY+Tk5Puva+n7r2vZ7rtkq/12KBBw3TbNm3W3OEZzIz6q98w/f6A2wl3JP/nzJkzstls6tkz/f84ZcaNPs6cOZPtvoBbSXm/m0OMQiOuZ+qcy1dutivh4Zapc9xcnPVAhzrWPpPsAMDtIeLqVYe1Hps0bZbtPm88S5lT/QG3CoLk/0RGRkqSvLy8st3XjT6i/jHlNHC7ux5zcxkP7+JFM3WOb4mb7a5EZm6mvN7tasnLM+m8hIRE/byKIAkAt4NVq1YqPj7p/zXVa9RU/QzuSGbk7NkzOrB/nySpSJEiGd4NBW4nBMn/ufEs5LFj2Z/V8UYfvr6+2e4LuJUEXoywtjs2qZypczo2rWJtHz8Xlk7Lm5IPa12147SCQ/lSBwBudbGxsZrx1RfW/kP9H8l2n9OnfWptd+h0t0qXzvwjFsCtjiD5P40aNZLdbtecOXMUGhqa5X5CQ0M1Z84c2Ww2NWQcPeBg9c6bM+zVreyrx5LNqpqajk0rq8sdVa39ldtPZXiNsj4e1jOVkjRr5T7TMgEAhYzdbtd7E8Zb60jWrFlLDz38aLb6XDD/dy39Y7EkqWixYnr5lZHZrhO4lRAk/6d3796SkpbyGDRokA4dOmTcx8GDBzVo0CBdunRJktS3b9+cLBEo9JYGHNeRsze/qPn85W56pmcTa63H5B7sUEe/jOtj7Z+9eFW/rc3438vHOtdXEeek/7SFR0Zr4cajOVA5ACC/bPp7g6Z//qkCz6Y+SdvZs2c0bMhgLfCfJ0kqWrSoxk94Ty4uLqm2DwsL0/hxb2jvnt0O64bfEHH1qqZOmaS3x//XOjbs5RGqVClzI2mA24XNntq/QbepF154QWvWrLGmdb7zzjvVsWNHNWrUSBUrVpS3t7fc3JIm+4iJiVFYWJgCAwO1d+9erV+/Xlu2bJGU9K3Y3XffrenTp+fbe7mhWLcP87sEwMGddctp6aSH5VHU1ToWfDlSmw8E6dKV6yrp4aaW9cqpalkv6/Xo2Hj1HD1Hf+8PyrD/rV8+aS0xMuOP3Rry8YoMzkjdMz2b6JleTR2OuRZxUr0qN5cEOnj6kmL/sTblN4t36Zslu7N0TSC3XF78an6XAGTZsqVLNGbUCElSlapVVbNWbXmV9FJUVJROnzqpgwcPWG3d3Nz08af/p9Zt26XZ36VLIep6912SJD+/UqpXv778SpVWfFyczp8P1q6dOxQXd/OZ/hde+o+eG/xiLr07IG+5u+bc8jUs/5HMlClT9J///EcbNiQtYrt161Zt3bo10+ffyOTt2rXTlClTcqVGoLDbeihY9478VTNG9VTtSj6SpHK+nup3V51U258MDte/P/hDmw5kHCKb1ixthUhJmrUi68Nay/h4qEmN0um2SR4qk58HAMgdp0+d0ulTp1J9rX6Dhho77i3Vq98g1ddTc+lSiP5avy7V1/xKldLIUa+r2733ZaVU4JZHkEzG3d1dX331lebMmaPPP/9cISEhRuf7+fnppZde0sMPPyxnZ+dcqhIo/LYdPq/mz36rXm1q6v62tdS8dhmV8/WUZzFXRUXH6WJYlHYevaAlm45p3l9HFJ+QmHGnkv7V9eZzyUcDQ7X5wLncegsAgDzSoWMnffTxNAUEbNK+vXt0KSREYWGhcnMrKr9SpdSwYSN17X6v2rXvICenjJ/a8vHx1dffztSWgADt2L5V54ODFRoaKrvs8vP1U/UaNXRP567q0q27PDw88+AdAoUTQ1vTkJiYqK1bt2r9+vU6cuSITp48qYiICGtJDw8PDxUvXlzVqlVT7dq1ddddd6lly5aZ+g9YXmJoKwCAoa0AAImhrXnCyclJrVq1UqtWrfK7FAAAAAAoUArW7TMAAAAAQIFHkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwEiRnOxs69atOdldmu688848uQ4AAAAAIKUcDZIDBw6UzWbLyS5TsNlsOnDgQK5eAwAAAACQthwNkpJkt9tzuksAAAAAQAGSo0GSIacAAAAAcOvL0SD5448/5mR3AAAAAIACiFlbAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMBIji//kRURERGKiopSYmJiptqXL18+lysCAAAAAKQlX4JkUFCQfvnlF/399986cuSI4uPjM32uzWbTgQMHcrE6AAAAAEB68jxIzpgxQx9//LEVHu12e16XAAAAAADIhjwNkt98840mT55s7bu7u8tmsykqKko2m03lypVTVFSUrl69agVMm80mNzc3+fj45GWpAAAAAIA05NlkO8HBwfr4448lJQXIqVOnatu2berbt6/VZvXq1QoICNDWrVv15ZdfqlOnTrLb7YqPj9ejjz6q1atXa/Xq1XlVMgAAAAAgFXkWJH/55RfFx8fLZrNp3Lhxuu++++TklPrlPT091bFjR33xxRf66KOPJElTp07VtGnT8qpcAAAAAEAa8ixIBgQESJK8vb3Vp0+fTJ/Xo0cPjRkzRna7XdOnT9ehQ4dyq0QAAAAAQCbkWZA8e/asbDabGjduLJvNlmqbtGZvffzxx1WqVCklJiZq7ty5uVkmAAAAACADeRYkr1y5IkkqXbq0w3FXV1drOzo6OtVzbTabWrRoIbvdbt3ZBAAAAADkjzwLki4uLqke9/DwsLbPnz+f5vnu7u6SpAsXLuRsYQAAAAAAI3kWJH19fSVJkZGRDscrVqxobe/fvz/N88+ePSsp7buWAAAAAIC8kWdBsmbNmrLb7Tp9+rTD8Xr16lnbS5YsSfXckydPaseOHbLZbCmGxgIAAAAA8laeBcnmzZtLko4dO6bY2FjreJ06dVS1alXZ7Xb99ddfmj59uhISEqzXAwMDNWLECGsinlatWuVVyQAAAACAVORZkGzXrp0kKTY2NsWEOc8//7y1/emnn6pt27Z69NFH1bdvX3Xv3l0HDx6UJDk7O+vJJ5/Mq5IBAAAAAKnIsyDZoEEDNWzYUL6+vlqzZo3Da/369VO/fv1kt9tlt9t15coV7d69W4cPH1ZCQoLsdrucnJz03//+V7Vq1cqrkgEAAAAAqbDZ7XZ7fhdxw6+//qpvv/3W4TlKm82mJk2aaNiwYWrTpk0+Vlc4Fev2YX6XAADIZ5cXv5rfJQAACgB3V1uO9VWgguQN58+f18WLF2Wz2VSxYkV5e3vnd0mFFkESAECQBABIORski+RYTzmobNmyKlu2bH6XAQAAAABIRZ49IwkAAAAAuDUQJAEAAAAARgiSAAAAAAAjefaM5BNPPJEj/dhsNs2cOTNH+gIAAAAAmMuzILllyxbZbNmbJchut2e7DwAAAABA9uTprK1ZWWnEZrNl6TwAAAAAQO7IsyD5ww8/ZKpdYmKiIiMjdfjwYS1btkxHjx6Vm5ubRo0apVq1auVylQAAAACAjNjsBfx23+zZs/Xee++paNGi+vrrr9W8efP8LqlQKdbtw/wuAQCQzy4vfjW/SwAAFADurjn3mGCBn7V1wIABGjFihKKiojR8+HCFh4fnd0kAAAAAcFsr8EFSkgYNGiRfX19dvHhRc+bMye9yAAAAAOC2ViiCpLOzs1q0aCG73a5ly5bldzkAAAAAcFsrFEFSkkqWLClJCgoKyudKAAAAAOD2VmiC5MWLFyVJMTEx+VwJAAAAANzeCkWQvHDhgjZv3iybzaZSpUrldzkAAAAAcFsr8EHyxIkTGjx4sKKjoyVJrVu3zueKAAAAAOD2ViSvLjRt2rRMt01ISFB4eLgOHjyoPXv26MZSl87OznryySdzqUIAAAAAQGbkaZC02cwXwLwRIp2cnPTWW2+pRo0aOV0aAAAAAMBAngVJ6WYoNGGz2dS6dWsNHz5cjRs3zoWqAAAAAAAm8ixIDhkyJNNtixQpIk9PT1WoUEGNGjWSn59fLlYGAAAAADBRIIMkAAAAAKDgKvCztgIAAAAAChabPSsPLqLQiI7P7woAAPnN+05GBQEApOs7M7+SRkby7I5k586d1blzZ6NlQJL78ssv1blzZ3Xp0iWHKwMAAAAAmMizZySDgoJks9kUHh6epfPDw8OtPgAAAAAA+YdnJAEAAAAARgpNkExMTJQkOTs753MlAAAAAHB7KzRBMjg4WJLk4eGRz5UAAAAAwO2tUATJffv26a+//pLNZlO1atXyuxwAAAAAuK3lymQ7TzzxRJqvrVy5UkeOHMlUP/Hx8bpw4YLOnTsnu90um82mdu3a5VSZAAAAAIAsyJUguWXLllRnV7Xb7bp48aIuXrxo1N+NpS79/Pw0YMCAHKkRAAAAAJA1uTa01W63O/yT1vHM/FOsWDH16tVLv/76q3x8fHKrZAAAAABAJuTKHckffvjBYd9ut2vQoEGy2Wzq0qWLBg4cmGEfNptNbm5u8vLyUsWKFeXkVCge5wQAAACAW16uBMmWLVum+VqZMmXSfR0AAAAAULDlSpBMzZAhQyRJjRo1yqtLAgAAAAByQZ4HSQAAAABA4caDhwAAAAAAI3l2R1KSPvroI8XExKh06dL697//nenzZsyYoYsXL8rd3V3Dhg3LxQoBAAAAABnJsyC5adMmffXVV7LZbBo1apTRuTabTTNnzpTNZlP79u11xx135FKVAAAAAICM5NnQ1tWrVydd0MlJvXv3Njq3d+/e1vIff/75Z47XBgAAAADIvDwLkrt375Yk1axZU76+vkbn+vn5qVatWpKknTt35nhtAAAAAIDMy7Mgefr0adlsNtWsWTNL59esWVN2u11nzpzJ4coAAAAAACbyLEhGRUVJkjw9PbN0/o3zIiIicqwmAAAAAIC5PAuS7u7ukqTIyMgsnX/jPFdX1xyrCQAAAABgLs+CpI+Pj+x2uw4cOJCl82+cZ/p8JQAAAAAgZ+VZkGzSpIkk6dSpU9q7d6/RuXv27NHJkydls9nUsGHD3CgPAAAAAJBJeRYkO3XqZG2PHz9e169fz9R5165d0/jx41PtBwAAAACQ9/IsSHbv3l1VqlSRlDRMddCgQTpx4kS655w4cUKDBg3SgQMHZLPZVLFiRfXs2TMvygUAAAAApMFmt9vteXWxbdu26cknn1RCQkLSxW02tWnTRq1atVLFihXl4eGhqKgoBQYGKiAgQJs2bZLdbpfdbleRIkU0Y8YMtWrVKq/KvSVEx+d3BQCA/OZ955D8LgEAUABc3zktx/rK0yApSUuWLNHrr7+u2NhY2e122Wy2NNveKM3V1VXvvvuu7r///rwq85ZBkAQAECQBAFLOBsk8G9p6Q8+ePfXzzz+radOmkmTdcUztH0lq3ry5fvnlF0IkAAAAABQQeX5HMrk9e/Zo/fr12r17ty5fvqyoqCh5eHjI19dXTZo0UYcOHdS4ceP8Ku+WwB1JAAB3JAEAUs7ekSySYz1lQePGjY2C4vXr17V8+XL17ds394oCAAAAAKQrX4NkZgUEBMjf31/Lly/X9evXCZIAAAAAkI8KbJA8ffq0/P39tWDBAgUHB0tShpPzAAAAAAByX4EKkpGRkfrjjz80f/587dq1S9LNmVtvcHV1zYfKAAAAAAA35HuQtNvt+uuvv+Tv76/Vq1crJibGOn6DzWZTixYt1Lt3b3Xv3j2/SgUAAAAAKB+D5NGjRzV//nwtWrRIly5dkpTy7mOtWrXUu3dv9erVS+XKlcuPMgEAAAAA/5CnQTIsLEyLFy/W/PnzdfDgQUkpw+ONZyAbNWqkOXPm5GV5AAAAAIBMyPUgGR8fr7Vr12r+/Plav3694uOTFjZMHiDd3NzUuXNn9enTR88//7xsNpucnJxyuzQAAAAAQBbkWpDct2+f/P39tXjxYl25ckVS6s899u3bV927d5enp2dulQIAAAAAyEE5HiS/+eYb+fv76/jx45JSDl2tXr26+vTpo/vvv1/ly5fP6csDAAAAAHJZjgfJyZMny2azOQRIHx8f9ejRQ3369FGjRo1y+pIAAAAAgDyUq89IFitWTKNGjdIjjzzCM48AAAAAcIvItXRns9kUHR2tCRMm6Omnn9b8+fMVFRWVW5cDAAAAAOSRHA+SvXr1UtGiRWW322W325WYmKiAgAC9/vrrateunUaMGKF169YpMTExpy8NAAAAAMgDufKMZFRUlJYuXaoFCxZo27Zt1vOS0dHR+uOPP/THH3/Ix8dHvXr1Uu/evdWgQYOcLgMAAAAAkEts9n9Oq5rDAgMD5e/vrwULFujs2bOOF7fZJDnO5Hr33XfLZrOpSZMm+uWXX3KztNtCdHx+VwAAyG/edw7J7xIAAAXA9Z3TcqyvXA+SyW3btk3z58/X8uXLFRkZebOI/wVKm82mxMREgmQOIkgCAAiSAACpEAfJG2JiYrRixQr5+/tr06ZNDs9L3lg6pEiRIurQoYN69+6te+65R66urnld5i2BIAkAIEgCAKRbIEgmd+HCBS1YsEALFizQ8ePHk4r63x3KGzw8PNStWzfdf//9atOmTX6UWWgRJAEABEkAgHSLBcnk9u7dq/nz52vJkiW6cuWKw2s3wmXp0qW1bt26/CivUCJIAgAIkgAA6RYOkjfExcVp7dq1mj9/vtavX6/4+JtpyGaz6eDBg/lYXeFCkAQAECQBAFLOBskcX/4jJ7i4uKhr167q2rWrQkNDtWjRIi1YsEAHDhzI79IAAAAA4LZXIINkcj4+Pho0aJAGDRqkw4cPy9/fP79LAgAAAIDbWoEc2oqcw9BWAABDWwEAUs4ObXXKsZ4AAAAAALcFgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBCkAQAAAAAGCFIAgAAAACMECQBAAAAAEYIkgAAAAAAIwRJAAAAAIARgiQAAAAAwAhBEgAAAABghCAJAAAAADBSJL8LAICM7N61U4sXLtCePbt1LihI165Fyc3NTb6+fqpbr57uvqeLunTrLldX11TPDwoKVI9unbN8/cEvDtELLw3N8vkAgLS1v6OmHutxp9o1q6GyfiXl6uKs85euat/RIM1dsUO/r9yphITEdPso6uaiOxtWVasm1dSwZnnVqlJaFct6q4RHUdls0pXIaJ04G6KAPSf185Kt2n040KjGkp7F9FS/turarp7qVS8nn5LuuhoVrcDz4Vqx8YBmLQrQsTMXs/MxAIWOzW632/O7COSe6Pj8rgDIuvDwMI3/7xtas3pVhm0rVaqsCe9NVLPmd6R4LbtB8o1x4/XwI49l+Xwgv3nfOSS/SwBS8CnpoRnvPKF72zdIt932A2f09BszdeTUhTTbvDyws95/pV+mr/3b8u0a9t6vCrt6LcO2D997hz55/RF5FXdPs01MbJze/XKpPvx2RaZrAPLD9Z3Tcqwv7kgCKJCio6P13L+f0uFDB61j3j4+qlu3vsqULaOw0FAdP35MgWfPSpLOnj2jwc/9W19/O1ONGzdx6MvTw1OPPDYg09fes3uXDh7YL0lycXFR9+735cA7AgDc4FW8mNZ8/4pqVy1jHbtxxzA6Nl7VK/qpTdPqcnUpojvqV9byr4ep4xOTdSY4NMO+o67H6NDJCzp5NkThEddVpIiTKpT2VstGVVWyeDFJUv/ud6hutbLq/PRURURFp9nX8w930MdjHrb2o2PitGHHMZ0+F6riHm5q27SGKpb1lpuri94e2lslPYtp7KcLsvHJAIUHdyRvcdyRRGE1/fPP9MX/JX1rZrPZ9NLQYRo46CkVLVrUamO327Vs6R969+03FRERIUmqVbu25s5flK1rP/7wg9q/f58kqXOXbvrok8+y1R+Q37gjiYLml8nPqE/nppKk69Gxeumdn/Xzkq0ObapV9NMP7z+pFg2rSpJ2HDijdgMmpdpfr06NVK96Of256aD2HAlKdSism2sRDR1wt8a/dL+cnZOmCfnil3Ua/sFvqfbZtG5Frf9hpFxcnCVJqzYf0rPjflRwyBWrjc1m0wuPdtAHrzygIkWS2vUf/qUWr92b+Q8DyEM5eUeSyXYAFEgL/edb248PGKhnn3/BIURKSf8Dv69HT7351jvWsaNHjujokcNZvu7xY8esEClJvfv0zXJfAICUmtWrZIVISRr81uwUIVKSTgZeUq8XPrfuQjavX1mP3tci1T4Xr92rD79doZ0Hz6b5PGVMbLwmf7dS7371h3Xs8V4t5eaa+gC9sYN7WiFy75EgPfCfLxxCpJT0heb//bxOr3/sbx2bMLSPFVSBWxm/5QAKnMjISJ07F2Tt39uzV7rt7+7cRUWLFbP2T586leVrL1xwM8D6+PqqfYeOWe4LAJDSA12bWdt7jgRqzrLtaba9Enldk2Yst/ZferxTtq//g/9ma7uEZzHVqFQqRRuPYq7q1ra+tf/OF0sUG5f2MK//+2WdAs+HSZLqVi+rLm3qZrtOoKAjSAIocK5di3LYL1GiRLrtixQpIk8PT2s/0Z7+7H5pSUxM1JLFC639Hj16qUgRHiUHgJx05/+GqkrS8g0HMmy/7K/91naLhlVVqax3tq4fEhbpsF/co2iKNs3qV7buRsbHJ+jPTYfS7TMhIVErN918pr9vsjuuwK2KIAmgwPH29pGbm5u1f/zYsXTbh4aGKjT0srVfu07WvgnevOlvhVy8OX37/X0yPwMgACBzSvsWt7YzM3lO0MVwxccnWPsd76ydrevXq17WYf/0ucsp2pTxuVnjpfAoXYuOzbDf5O/l7pZ1slEhUDgQJAEUOC4uLmp3Vwdr/+svp+v69etptv/4ow+VmJh0F7JV6zaqWrValq67KNmw1jp16qpuvXpZ6gcAkDabbMbnJJ8asn6Nclm+tksRZ034Tx9rf9Ou4zp/6WqKdjabeY3JVSrrLY9iqa9tDNwqCJIACqT/DBsud/ekNbsOHtiv/v16a6H/fJ05fVoxMTE6Hxys9evW6smBj2vB/HmSpOo1aurtd97P0vUiIyO1etWf1j53IwEgd1wKvzm0NDPDVMuXKmkNM5WkOtXKptM6JZcizqpczluP9rhT6398VV3bJn1JeDXyul5JY8bW5MNffUt6qFhRlwyvk/y9ODk5GdcJFDY8/AOgQKpWvYZmzvpZ/3npBQUHn9PZs2f03zdeS7Vt8RIl1Ov+Pho67GV5JHtW0sSK5UsVHZ20lliRIkXUo9f9Wa4dAJC2nQfOqH3zmpKkrm3r681p6S/Z1L19A4d9n5LuGV4jYusn1nIcqTly6oIee/UbHTgenOrruw8lzf7q7OwkFxdn3dOqrpasS3tJDycnm7q0cRzF4l0i4zqBwow7kgAKrNp16mrhH8s15o1xKlYs7f8ht23XXvf16JnlEClJixfeXEC6Xfu75Ovrm+W+AABpW5RsjcVm9SqpX5emabb1dHfTq091/cexlJPjZFZ8fII++Ga5mj/0bpohUpLCI65r487j1v4bz/dQkSJp/9n87EN3qXI5H4djqU3iA9xKuCOZSwICArR1a9KaSEOGsBA0kBVhYaGaOuVDLVm0SPHxcfLzK6UmTZvJy9tbERFXtW/PHp07F6TlS//Q8qV/6MH+j+iN/74pZ+e0v4VOTWDgWe3Yvs3a782wVgDINX9tP6q/dx5X22Y1JElfvTVQRZyd9dtyx2VAKpfz0ffvDVL1fyzPUcwt42GmX85ZL2enpODnXsxNFct6qUWDKirhWUyjn+muB7s10ysf/KaVfx9Ms48PvlmuDi1qSUoKvL9NfV6Dx8/ShcsRVhubzaZ/P9hOk159IMX5RTNRJ1CY2ez25I8vI6d8/PHH+uKLL2Sz2XTwYNr/kcpt0WkveQQUaKdPn9KzTw/ShfPn5erqqhEjX9NDDz/isByH3W7Xsj+W6J2331RkZNLzLA8/8pjeGDfe6Fpf/N80Tf/8M0lSyZJeWrX2L7m4MkkCbh3ed/KFJgqWyuV8tGH2SJXyvjk76rEzF7VlzylFx8apekU/tW1WQ64uRRR1PUYbdxxXt3ZJ6zruOnRWbR77wPia7kVdNfiRDvrvCz1V1M1FCQmJev6t2Zq9KCDNc94f3k8vP9HZ2r8eHau/th/T2fNh8nR3U5um1a07kf6rdjks+9F/+JdavDbt4bBAfri+c1qO9cXQVgAFTnx8vF4ZNlQXzp+XJI0d95YefXxAijUdbTab7uvZS5Onfmodm/Prz9q7Z4/R9ZIPa723R09CJADksjPBobr7yY+090iQdaxm5dJ6vFdLPf1AO3VqWUeuLkV0/tJVPfTylzp25ubSTFci0p7FOz3XomP10cw/NWjMd5IkZ2cnffb6I6paIe1HGcZMna83py1SbFzSN/PFirqqW7v6+veD7fTIfS2sEPnzki0pJu4Jz2KdQGFBkARQ4Py5coWOHT0iSaparZp6901/qGmbtu3Uuk1ba3+B/++ZvtaO7dt09uwZa793n75mxQIAsuT4mRC1enSinnjtO83/c6fOBofqenSswiOuaefBsxr/+SK16P+u1m45Il+vm8/AB14Iy9Z1F67Zo9UBhyQlBcPnH+6QbvtJM5arUZ+3Nfm7Fdp+4Iwuh0cpJjZOZ4ND9fuKHer1wjQ9PfYHeRRzczgv8Hz26gQKOp6RBFDgbNzwl7V9Z8tWmVrPq2Wr1tq86W9J0oF9+zJ9rUUL/K3t6jVqqmGjxpkvFACQLXa7Xb8t357i+ch/qlfj5lIa2/efSadl5qzefEj3tKorSWrTtHqG7c8Eh+q/ny6UtDDNNvWT1XgpLFKngi5nu06gICNIJjNmzJgc6ys/n4sECruLFy9Y214lvTJ1jpfXzfW7IiIj02l5U0xMjFauWGbtczcSAAqekp7FVDfZmoybd5/Idp9hV28OO/Up6ZHt/iSpdZObgTRgz8kc6RMoyAiSycyfPz9Tdz4A5K6ibjeHB125ciVT54SHh1vbxYsXT7thMqtX/amIiKTZ95ycnNTz/t6ZLxIAkCf6dG4iV5ekP1kPHA/WzoNns91nWb8S1nbYlahs92ez2fRA12bW/s9LtmS7T6CgI0imgolsgfxVtlx5a3vr1rRn00tuy5bN1nblylUydc6iBfOt7dZt2qp06TKZrBAAkBdcXYpo9L+7W/vfzN2QI/326NDQ2j508kI6LTPnyb5tVKV80qQ9wSFXtHCN2aRvQGFEkEymaNGiiomJUdWqVfX8889nq68VK1ZozZo1OVQZcHtp3aaNfvlpliTp5IkTWrTQX/f37ptm+4DNm7T5743Wftt27TO8RkjIReuZSknq3TflGmAAgPz16euPWOtI7jt6Tl/P/StFG/eirkpITFRMbObWPHu2f3vd0eDmF47+q3Zlq8bGtSvo3Zf7WvuvfTRPcfEJ2eoTKAwIksnUrVtXu3btUnh4uPr1y96C5KdPnyZIAll0V4dOqlK1qk6fOiVJmjB+nK5fv64HH3pYzs7OVju73a4Vy5dqwvhx1rGyZcvp3h49M7zGkkULlZCQ9D/64sWL657OXXL2TQAA0tS5dV21aVpdsxYFpDopTbWKfpo88iHrzuG167EaPH6W4uMTU7StWbmU5n06WJ//tFa/Ld+uwAvhqV6zjG9xjXiyq156vJN1bMOOY1r6V9oTtE0d3V9/bj6kFRsPpAiHRYo46V/3t9K7w/rKu4S7JGneyh2asyz9iYOAWwVBMpmGDRtq165dunLlioKCglShQoX8Lgm4LRUpUkTvvD9Jzz49SNHXrysmJkbvvj1eX03/XE2aNpeXt5ciIyK1Z88unQu6uQaZq6ur3p80Wa6ZWAdyUbK1I7t1v09ubm7ptAYA5CSfkh564/keeuP5Hjpy6oL2Hzuny+FRKu5RVLWqlFbz+pWtttejY9V/+FfafiDt2VorlPHWe8P76b3h/XQq6JL2HwtOWqYjLl4lPIqqdtUyalSrvIoUufll5OGT5zVw9Lfp1nl3qzoa/GhHRURFa+fBszoVdFnxCQkq41tCbZvVsAKkJC3bsF9PvfFDNj4VoHAhSCbToEEDa3v//v0ESSAfNW7cRN98+4PeGDPSujMZEhKiP1cuT7V9hYoV9e77k9Ss+R0Z9n3wwH5rnUpJGa5TCQDIPbWrllHtqqk/o759/2kNeedn7ToUmOb5cfEJSkhIlLNz0vLoVSv4qWoFvzTbJyQk6nv/TRr7ib/CI66n2S654h5F1aFFLXVoUSvFa9Excfrw2xX68NsVDGnFbYUgmUzDhjcfvN6/f7+6deuWj9UAaNS4seYtWKK1a1Zrzeo/dWDfPoWEXNS1a9dUrFgx+fj6qX79Bup09z3q0q27XFxcMtXvwmST7FSuUlVNmzXPrbcAAEjFH+v36uHhX+nuVnV0Z8MqKutXUn7enroeE6fzl65o277Tmrdyp5ZvPJDhJIgHT5xX1a6vq3OrumrdpJoa1q6gahV85VPSQy5FnBVxLUah4VHaf+ycNu06oTnLtulcSOZmBH985Ax1aVNPHVrUUs3KpVTKp7g8irkqJDRSp85d1tL1+/Tb8u06ez4sJz4WoFCx2Zmi1JKYmKhhw4YpMTFRTZo00XPPPZflvgIDA3Xu3DlJUsuWLXOqRGPRmXvuHABwC/O+c0h+lwAAKACu75yWY30RJG9xBEkAAEESACDlbJB0yrGeAAAAAAC3BYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGbHa73Z7fRQAAAAAACg/uSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGCJIAAAAAACMESQAAAACAEYIkAAAAAMAIQRIAAAAAYIQgCQAAAAAwQpAEAAAAABghSAIAAAAAjBAkAQAAAABGiuR3AQCQ01atWqUFCxZo3759CgkJkaenp6pUqaIuXbro0UcflaenZ36XCADIBQkJCTp+/Lj27dun/fv3a9++fTp06JCio6MlSf369dPEiRPzuUrg1kCQBHDLiIqK0quvvqrVq1c7HA8NDVVoaKh27typWbNm6eOPP1bTpk3zp0gAQK55+eWXtWLFivwuA7gtECQB3BISEhI0bNgw/fXXX5IkPz8/9e/fXzVr1tSVK1e0ePFi7dixQ8HBwXruuef0888/q0aNGvlcNQAgJyUkJDjse3l5ycvLS6dOncqfgoBbGEESwC3ht99+s0JkzZo1NXPmTPn5+VmvDxgwQB988IG+/fZbXblyRePGjdPs2bPzq1wAQC5o3LixatSooQYNGqhBgwaqVKmS5s2bpzFjxuR3acAthyAJoNBLSEjQtGnTrP1JkyY5hMgbXn31VW3atEkHDx7Utm3btGHDBrVv3z4vSwUA5KLBgwfndwnAbYNZWwEUelu3blVISIgkqWXLlmrQoEGq7ZydnTVw4EBrf8mSJXlSHwAAwK2GIAmg0Fu/fr213aFDh3TbJn89+XkAAADIPIIkgELvyJEj1najRo3SbVuqVCmVK1dOknTp0iWFhobmam0AAAC3IoIkgELv5MmT1nbFihUzbJ+8zYkTJ3KlJgAAgFsZQRJAoRcREWFte3t7Z9jey8sr1XMBAACQOQRJAIXetWvXrG03N7cM2ydvExUVlSs1AQAA3MoIkgAAAAAAIwRJAIWeu7u7tR0TE5Nh++RtPDw8cqUmAACAWxlBEkChV7x4cWs7LCwsw/bh4eGpngsAAIDMIUgCKPSqVatmbQcGBmbYPnmb6tWr50pNAAAAtzKCJIBCr3bt2tb23r1702176dIlBQcHS5J8fX3l4+OTq7UBAADcigiSAAq9u+66y9pev359um3XrVtnbXfs2DHXagIAALiVESQBFHotW7ZUqVKlJElbtmzR/v37U22XkJCgH3/80drv0aNHntQHAABwqyFIAij0nJ2d9eKLL1r7o0eP1uXLl1O0mzx5sg4ePChJat68ucOdTAAAAGSezW632/O7CADIrvj4eD333HPauHGjJKlUqVLq37+/atasqfDwcC1ZskTbt2+XJJUoUUI//fSTatWqlZ8lAwBy2NmzZzV37lyHY4cPH9aaNWskSXXq1NHdd9/t8Hrr1q3Vpk2bPKsRuFUQJAHcMiIjI/Xqq69afzCkpmzZspo6daqaN2+eh5UBAPJCQECAnnjiCaNzhgwZoqFDh+ZSRcCtq0h+FwAAOcXT01NffPGF/vzzTy1YsEB79+7V5cuX5eHhocqVK6tr16569NFHWTsSAAAgm7gjCQAAAAAwwmQ7AAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAUQgMHDlSdOnVUp04dBQQEpNrmtddes9rMmzcvjyssHObNm2d9Rq+99lp+lwMAhUaR/C4AAICsGDhwoLZs2ZLm6+7u7vL29ladOnXUtm1b9e3bV8WLF8/DCgEAuHVxRxIAcEu6du2agoKCtHr1ar3zzjvq1KmT/P3987usW9qNO3t16tTJ71IAALmMO5IAgEKvUaNGaty4sbVvt9t19epV7du3T6dOnZIkRUZGavTo0YqJidEjjzyST5UCAHBrIEgCAAq9jh07aujQoam+tnLlSo0ZM0YRERGSpHfeeUcdO3ZU2bJl87LEfDFx4kRNnDgxv8sAANyCGNoKALilde3aVZMnT7b2Y2Nj9dNPP+VjRQAAFH4ESQDALa9Tp06qW7eutf/333/nYzUAABR+DG0FANwWmjVrpkOHDkmSzp49ax3/7LPPNG3aNEnSkCFDNHToUEVHR2vRokVaunSpTpw4oUuXLikuLk7+/v6qV69eir43bdqkpUuXavv27QoJCdG1a9fk5eWlOnXq6O6779ZDDz2kokWLZqrOxMRELViwQAsXLtSRI0d09epVlSpVSnXq1NGDDz6oLl26ZPo9v/baa5o/f74k6f3339cDDzyQ4Tnbtm3TsmXLtG3bNl24cEFXr16Vm5ubKlSooPr16+uuu+5Sly5drPcTEBCgJ554IkU/aU24s2rVKlWsWDHV14KDgzVv3jxt3LhRZ86cUXh4uNzd3VW+fHm1adNGDz/8sKpVq5bZt6/9+/fr559/1qZNmxQSEiJ3d3dVrFhR3bt3V//+/eXl5ZXpvgAAjgiSAIDbQsmSJa3tqKioNNsdP35cw4YN09GjRzPsMzg4WKNGjUp1GZKQkBCFhIRow4YN+vLLLzV16lS1aNEi3f5CQkL00ksvaffu3Q7Hg4KCrBlou3btmivPPZ4/f16vv/66Nm7cmOK1+Ph4HTlyREeOHJG/v7+aNGmiOXPm5Ni1ExMT9dlnn2nGjBmKiYlxeO3KlSu6cuWKDh48qB9++EHPPPOMXn75ZdlstnT7nDp1qr7++mslJCRYx2JiYhQWFqa9e/dq1qxZ+uSTT3LsPQDA7YYgCQC4LVy5csXa9vT0TLVNeHi4nnnmGZ07d05ubm664447VL58eV27di1FuDt+/LgGDRqkkJAQSZLNZlP9+vVVs2ZNFS1aVBcuXNDWrVsVFRWlixcv6qmnntLXX3+t1q1bp3rtq1evatCgQTp+/Lh1rGLFimratKlcXV117Ngx7dmzRytXrpSTU84+mXL06FE99dRT1nuRJF9fXzVr1kw+Pj6KiYnRmTNndPDgQUVHRzuEvTJlymjAgAGSpNmzZ1vHbxz7p39+9gkJCRo+fLiWL1/u0Gfjxo3l4+OjqKgo7dmzR2fOnFF8fLy++OILhYaGasKECWm+n48++khffvmltV+sWDG1bt1apUqVUkhIiAICAnT+/Hk999xzqd5NBQBkjCAJALgt7Nixw9pOa2jlL7/8ovj4eHXv3l3jx4+Xj4+P9VpiYqJ1d+vatWsaOnSoFbw6dOig//73v6pcubJDf5GRkZo8ebJ+/vlnxcbG6tVXX9XSpUtVvHjxFNeeOHGiFSJdXFw0fvx4PfTQQw5t9uzZo5dfflnLly+Xi4tLFj6FlCIjIzVkyBDrvXh7e2vs2LHq2bNnirt+165d06pVqxyeMa1atarGjRsnyTFI3jiWkWnTplkhslSpUho3bpy6du2a4tpLly7Vf//7X0VERGjOnDlq06aNevTokaK/rVu36quvvrL2u3fvrgkTJjjckY6IiNCbb76pJUuW6IsvvshUnQAAR0y2AwC45a1du1aHDx+29tu0aZNqu/j4eLVv314ff/yxQ4iUJCcnJyu8fffdd1bo69q1q7788ssUIVJKuvs2fvx49evXT1LS0NWff/45RbuTJ09q3rx51v6ECRNShEhJaty4sWbMmKFixYopLi4uo7edKV9//bW11mbx4sX1008/qVevXqkOHXV3d9f999+v999/P0euHRgYaN059PLy0k8//aRu3bqleu377rvPepZVSgqgdrs9RbspU6ZYx9u0aaOpU6c6hEgp6X1OnjxZ7du3z7HPEQBuNwRJAMAt7c8//9TIkSOtfVdXVz3++ONptn/99dfTHToaFxdn3XlzdXXVW2+9leFQ0+HDh1vhaNGiRSlenzt3rhV+GjdubAXP1FSrVk2DBg1K93qZ9c+lUEaMGKHq1avnSN+Z8cMPP1h3eV988cVUw3hyrVu3Vvv27SUlDS0+cOCAw+vHjx/Xzp07rf2xY8fK2dk51b6cnJw0duzYDJ+1BACkjqGtAIBCb926dQoLC3M4dvXqVe3du9e623bDa6+9pnLlyqXaT506dVSjRo10r7Vv3z5dvnxZUtIdL19f3wzrK1OmjKpXr67jx4/r6NGjioiIcBjeGhAQYG336dMnw/769u2bI0Myd+3apatXr0qSPDw80g2wuWHdunXW9v3335+pc1q3bq0NGzZIkrZv364GDRpYr23evNnabtCggWrWrJluX9WqVVPTpk0dwicAIHMIkgCAQm/v3r3au3dvum08PDz0xhtv6MEHH0yzTfJQkpZdu3ZZ2+fPn9fbb7+dqRpvBDa73a7z589bQdJut1vLkkhS06ZNM+yrWrVq8vLyUnh4eKaunZbk76Vp06aZXqIkJ4SFhVkh38XFxWHYanqOHTtmbQcHBzu8dvDgQWu7WbNmmeqPIAkAWUOQBADcktzd3eXt7a3atWurbdu26tu3r0qUKJHuOf98LjI1Fy9etLYPHz7s8OxlZiWfQTYiIsLhOb3y5ctnqo9y5cplO0jeuLMqpT0BUW5JPkNs8uHCJm6E8xtCQ0Ot7bTuOv9TZtsBABwRJAEAhd6QIUM0dOjQbPeTmTtyERER2b5O8rUNr127ZlyDlLSkRXYlX0/Tw8Mj2/2ZyOnPUXL8LDP7+bi7u2e7DgC4HREkAQAwkDygDBw4UGPHjs1Wf/8MMtHR0ZkKN9evX8/WdSXH8Jg8VOaF5O/R09NT27dvz9E+M/v5/DPIAwAyh1lbAQAw4OfnZ21funQp2/0VL17cYU3Ic+fOZeq8fz4fmBXJJwoKCgrKdn9ZvXZkZGSOBOPkQ5Mz+/mcP38+29cFgNsRQRIAAAONGze2tnfu3JnqWoYmbDab6tata+3v3r07w3NOnTqV7ecjJceJfXbu3Kno6Ohs95lZpUuXdng+MScmvKlXr561nXwiofQw0Q4AZA1BEgAAA3fccYc1ac/58+e1evXqbPfZqlUra3vhwoUZtvf398/2NaWkIFmyZElJSUNbs9uvm5ubtZ18AqG0dOrUydpOvp5lVrVu3dra3rdvn44fP55u+9OnT2c6cAIAHBEkAQAw4OrqqkGDBln7b731li5cuJDp81MbDvvQQw9Z27t27dKCBQvSPP/06dOaOXNmpq+XHldXVz322GPW/uTJk3XixIks9+fl5WVtZ+Yzefrpp+Xs7CxJWrlypebNm5fpayWf9fWGGjVqOCz78d577ykxMTHV8xMTE/XOO+9k+44yANyuCJIAABh66qmnVKtWLUlJgenBBx/U0qVL0wwtoaGh+vXXX9WvXz/NmDEjxevVqlXTAw88YO2PHTtW8+fPT9Fu7969euqpp3Tt2jWH5yqz49lnn1XlypUlJc2k+vjjj2vJkiWpBqzr169r8eLFGjNmTKp93fhMJGnZsmUZXrty5cp64YUXrP3XX39dH3zwgcMyHsnFx8drw4YNGjlypPr165dqm+HDh8tms0mSNmzYoBEjRqRYJiQyMlIjR47U+vXrc+xzBIDbDbO2AgBgyMPDQ9OnT9eTTz6pwMBAhYSE6OWXX5a3t7eaNm0qPz8/2e12XblyRceOHdPp06etkJl8+GVyr732mnbu3KmTJ08qNjZWr732mj7//HM1bdpUrq6uOnbsmPbs2SO73a5u3bopPDxcW7ZsyfZ78fT01Geffaann35aly9fVlhYmF555RW99957atasmXx8fBQTE6MzZ87owIEDio6OdnimM7nu3btrw4YNkpLubq5fv161atWSq6ur1Wbw4MHWcFopaemWoKAgzZ8/X3a7Xd9++61+/PFHNWzYUJUrV1bRokUVFRWloKAgHT582JplNfndz+RatWqlp59+2grsf/zxh9auXatWrVqpVKlSunTpkjZv3qxr166pZMmSeuKJJ/TZZ59l+3MEgNsNQRIAgCyoVKmSfv/9d7355ptavny57Ha7wsLCtGbNmjTPKVGihGrXrp3qayVLltTMmTP14osvat++fZKks2fP6uzZsw7t7rnnHr3//vsOd/Kyq27duvrtt980evRobd26VVLSENyVK1em2j6t5Un69eunhQsXauvWrbLb7QoICFBAQIBDmwEDBjgESZvNpokTJ6pBgwb67LPPdOXKFcXFxWnnzp1pToRjs9nUvHnzNN/PqFGj5OzsrG+++UaJiYm6du1aip9L6dKl9emnn+rkyZNp9gMASBtBEgCALPLy8tInn3yiI0eOaMmSJQoICFBgYKDCw8Pl5OSkEiVKqHLlyqpfv77atm2rdu3aOUxI809lypTRnDlz5O/vr0WLFunw4cOKiIiQn5+f6tatq759+6p79+7W0M2cVKFCBc2aNUubNm3S0qVLtX37doWEhCgyMlLFihVT+fLl1bBhQ3Xs2FH33HNPqn24uLjou+++09y5c7VixQodPXpU4eHhmZp4Z+DAgerXr58WLFigv//+W4cOHVJoaKhiY2Pl4eGhMmXKqFatWmrZsqU6duzoMONrakaMGKHu3bvrp59+0ubNmxUSEiJ3d3dVqFBB3bp108MPPywfHx+CJABkkc3OU+YAAAAAAANMtgMAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAYIUgCAAAAAIwQJAEAAAAARgiSAAAAAAAjBEkAAAAAgBGCJAAAAADACEESAAAAAGCEIAkAAAAAMEKQBAAAAAAY+X/isahJ5RN/7QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "image/png": {
              "width": 457,
              "height": 399
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Detailed Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.90      0.90       970\n",
            "           1       0.90      0.92      0.91      1026\n",
            "\n",
            "    accuracy                           0.91      1996\n",
            "   macro avg       0.91      0.91      0.91      1996\n",
            "weighted avg       0.91      0.91      0.91      1996\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Gradient Boosting classifier performs very well, correctly classifying over 90% of cases with balanced errors between classes. It identifies winners accurately (939 true positives) while keeping false predictions low (87 false negatives, 99 false positives). Overall, the model generalizes well and shows no signs of bias toward either class."
      ],
      "metadata": {
        "id": "qs8wdTcNKtLh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "75xPnIjfqHEh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Part 8: Train & Eval Classification Models\n",
        "\n"
      ],
      "metadata": {
        "id": "Ii0otL-qqHOt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 8.1 Answer the following, and mentioned it in your presentation.\n",
        "\n"
      ],
      "metadata": {
        "id": "co-38v1UsPd2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the context of your dataset/task, explain what would be more importatnt - <u>precision or recall.</u>\n",
        "\n"
      ],
      "metadata": {
        "id": "1XM_xVUGsfon"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*False positives and false negatives carry the same importance because neither type of error leads to different real-world consequences. A wrong prediction simply means the classifier misjudged a fighter’s outcome — no scenario is substantially “worse” than the other. Since the goal is purely predictive performance and the classes are balanced, both mistake types matter equally, and metrics like the F1-score that balance precision and recall are the most appropriate. **So precision has more inportance for this dataset than recall**.*"
      ],
      "metadata": {
        "id": "kVbgAK4LMG6O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the context of your dataset/task, explain what would be more critical - <u>False Positive or False Negative.</u>\n"
      ],
      "metadata": {
        "id": "xbrqOTcBsf2Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*In this task, false positives and false negatives have equal importance. Predicting a win that turns into a loss (FP) is not more harmful than predicting a loss for someone who actually wins (FN). Since neither mistake carries higher real-world cost, both error types are equally critical to avoid.*"
      ],
      "metadata": {
        "id": "zOa33T_mNj2m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 8.2: Train **three** different kinds of classification models.\n",
        "\n",
        "\n",
        "Go to SKlearn to find different classification models. And use them."
      ],
      "metadata": {
        "id": "8nZOjcjZsocr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "\n",
        "# --- 1. Logistic Regression (baseline) ---\n",
        "log_clf = LogisticRegression(max_iter=1000)\n",
        "log_clf.fit(X_train_fe, y_train_cls)\n",
        "\n",
        "# --- 2. Random Forest ---\n",
        "rf_clf = RandomForestClassifier(\n",
        "    n_estimators=200,\n",
        "    random_state=42\n",
        ")\n",
        "rf_clf.fit(X_train_fe, y_train_cls)\n",
        "\n",
        "# --- 3. Gradient Boosting ---\n",
        "gb_clf = GradientBoostingClassifier(\n",
        "    random_state=42\n",
        ")\n",
        "gb_clf.fit(X_train_fe, y_train_cls)\n",
        "\n",
        "print(\"Models trained successfully!\")"
      ],
      "metadata": {
        "id": "6MIie1Rws3pE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a32c5b03-2245-4942-9ab9-9313d910b1a5"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Models trained successfully!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "models = {\n",
        "    \"Logistic Regression\": log_clf,\n",
        "    \"Random Forest\": rf_clf,\n",
        "    \"Gradient Boosting\": gb_clf\n",
        "}"
      ],
      "metadata": {
        "id": "BtC7TiGBOIsr"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 8.3: Evaluation"
      ],
      "metadata": {
        "id": "wELPknwqsOG_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Evaluate the Classification Models.\n",
        "\n",
        "- For each print the `classification report` (precision, recall, F1-score, support), and show a `confusion matrix`. (use SKlean built tools) Comment on what types of mistakes the model makes (based on the confusion matrix).\n",
        "\n",
        "- Identify which model performs best and **why**.\n",
        "\n"
      ],
      "metadata": {
        "id": "bEzxJLmVsvVx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ---- Logistic Regression Evaluation ----\n",
        "\n",
        "y_pred_log = log_clf.predict(X_test_fe)\n",
        "y_proba_log = log_clf.predict_proba(X_test_fe)[:, 1]\n",
        "\n",
        "print(\"Logistic Regression — Classification Report:\")\n",
        "print(classification_report(y_test_cls, y_pred_log))\n",
        "\n",
        "# Confusion matrix\n",
        "cm_log = confusion_matrix(y_test_cls, y_pred_log)\n",
        "\n",
        "plt.figure(figsize=(4,3))\n",
        "sns.heatmap(cm_log, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\n",
        "plt.title(\"Confusion Matrix — Logistic Regression\")\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "Rozrn2petFKA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "9ba21543-fd83-4b99-d8f4-ccf53ffb307c"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression — Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.90      0.90       970\n",
            "           1       0.91      0.91      0.91      1026\n",
            "\n",
            "    accuracy                           0.91      1996\n",
            "   macro avg       0.91      0.91      0.91      1996\n",
            "weighted avg       0.91      0.91      0.91      1996\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 388,
              "height": 322
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ---- Random Forest Evaluation ----\n",
        "\n",
        "y_pred_rf = rf_clf.predict(X_test_fe)\n",
        "y_proba_rf = rf_clf.predict_proba(X_test_fe)[:, 1]\n",
        "\n",
        "print(\"Random Forest — Classification Report:\")\n",
        "print(classification_report(y_test_cls, y_pred_rf))\n",
        "\n",
        "# Confusion matrix\n",
        "cm_rf = confusion_matrix(y_test_cls, y_pred_rf)\n",
        "\n",
        "plt.figure(figsize=(4,3))\n",
        "sns.heatmap(cm_rf, annot=True, fmt=\"d\", cmap=\"Greens\", cbar=False)\n",
        "plt.title(\"Confusion Matrix — Random Forest\")\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "yW1cnRyTO2dy",
        "outputId": "04c6e34e-88a0-49e4-e46b-ac0486ee47ba"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest — Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.89      0.90       970\n",
            "           1       0.90      0.92      0.91      1026\n",
            "\n",
            "    accuracy                           0.90      1996\n",
            "   macro avg       0.90      0.90      0.90      1996\n",
            "weighted avg       0.90      0.90      0.90      1996\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 379,
              "height": 322
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ---- Gradient Boosting Evaluation ----\n",
        "\n",
        "y_pred_gb = gb_clf.predict(X_test_fe)\n",
        "y_proba_gb = gb_clf.predict_proba(X_test_fe)[:, 1]\n",
        "\n",
        "print(\"Gradient Boosting — Classification Report:\")\n",
        "print(classification_report(y_test_cls, y_pred_gb))\n",
        "\n",
        "# Confusion matrix\n",
        "cm_gb = confusion_matrix(y_test_cls, y_pred_gb)\n",
        "\n",
        "plt.figure(figsize=(4,3))\n",
        "sns.heatmap(cm_gb, annot=True, fmt=\"d\", cmap=\"Oranges\", cbar=False)\n",
        "plt.title(\"Confusion Matrix — Gradient Boosting\")\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "xsINgw1zO5tB",
        "outputId": "f1ac538c-75b4-4dbc-e5ce-53e09892578a"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient Boosting — Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.90      0.90       970\n",
            "           1       0.90      0.92      0.91      1026\n",
            "\n",
            "    accuracy                           0.91      1996\n",
            "   macro avg       0.91      0.91      0.91      1996\n",
            "weighted avg       0.91      0.91      0.91      1996\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 384,
              "height": 322
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metrics = {\n",
        "    \"Logistic Regression\": (y_pred_log, y_proba_log),\n",
        "    \"Random Forest\":       (y_pred_rf,  y_proba_rf),\n",
        "    \"Gradient Boosting\":   (y_pred_gb,  y_proba_gb)\n",
        "}\n",
        "\n",
        "rows = []\n",
        "for name, (pred, proba) in metrics.items():\n",
        "    rows.append([\n",
        "        name,\n",
        "        accuracy_score(y_test_cls, pred),\n",
        "        precision_score(y_test_cls, pred),\n",
        "        recall_score(y_test_cls, pred),\n",
        "        f1_score(y_test_cls, pred),\n",
        "        roc_auc_score(y_test_cls, proba)\n",
        "    ])\n",
        "\n",
        "results_df = pd.DataFrame(\n",
        "    rows,\n",
        "    columns=[\"Model\", \"Accuracy\", \"Precision\", \"Recall\", \"F1\", \"ROC-AUC\"]\n",
        ")\n",
        "\n",
        "results_df.sort_values(by=\"F1\", ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "PNGmQOa0PKh3",
        "outputId": "96d56705-0c31-4994-f6ce-796bb0473a1f"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Model  Accuracy  Precision  Recall    F1  ROC-AUC\n",
              "2    Gradient Boosting     0.907      0.905   0.915 0.910    0.970\n",
              "0  Logistic Regression     0.905      0.908   0.907 0.908    0.970\n",
              "1        Random Forest     0.903      0.895   0.918 0.907    0.964"
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              "      <td>Logistic Regression</td>\n",
              "      <td>0.905</td>\n",
              "      <td>0.908</td>\n",
              "      <td>0.907</td>\n",
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              "      <td>0.895</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"results_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Gradient Boosting\",\n          \"Logistic Regression\",\n          \"Random Forest\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0020247754931499943,\n        \"min\": 0.9028056112224448,\n        \"max\": 0.906813627254509,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.906813627254509,\n          0.905310621242485,\n          0.9028056112224448\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00662218849957886,\n        \"min\": 0.8954372623574145,\n        \"max\": 0.9082926829268293,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9046242774566474,\n          0.9082926829268293,\n          0.8954372623574145\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.005542144934773211,\n        \"min\": 0.9074074074074074,\n        \"max\": 0.9181286549707602,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9152046783625731,\n          0.9074074074074074,\n          0.9181286549707602\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0016387603962286384,\n        \"min\": 0.9066410009624639,\n        \"max\": 0.9098837209302325,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9098837209302325,\n          0.9078498293515358,\n          0.9066410009624639\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROC-AUC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.003550602069825635,\n        \"min\": 0.9639778139506844,\n        \"max\": 0.9701885010349469,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9701885010349469,\n          0.9700649102710958,\n          0.9639778139506844\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Gradient Boosting is the best model because it has the highest F1-score and very strong precision and recall. It does the best job at correctly predicting both winners and non-winners, and its ROC-AUC shows it separates the classes very well. The other models perform nicely too, but Gradient Boosting is the most accurate and reliable overall.*"
      ],
      "metadata": {
        "id": "FefZ0op9WTP1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 8.4: Winner"
      ],
      "metadata": {
        "id": "iOW61vwUtEi1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Choose the best one out of the three models.\n",
        "\n",
        "- Export the model to a `pickle` file.\n",
        "\n",
        "- Upload the pickle file to the same(!) model repository.\n"
      ],
      "metadata": {
        "id": "2t9YFU9KtH8p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filename = \"gladiator_gradient_boosting_classifier.pkl\"\n",
        "\n",
        "with open(filename, \"wb\") as f:\n",
        "    pickle.dump(gb_clf, f)\n",
        "\n",
        "print(\"Saved:\", filename)"
      ],
      "metadata": {
        "id": "-GsJatjRsvZ-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ce8da2d-2054-4d8d-e92c-5289d05c4914"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved: gladiator_gradient_boosting_classifier.pkl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import HfApi\n",
        "\n",
        "api = HfApi()\n",
        "\n",
        "filename = \"gladiator_gradient_boosting_classifier.pkl\"\n",
        "repo_id = \"Karish1/winning_model\"\n",
        "\n",
        "# 1. Check which files already exist in the repo\n",
        "existing_files = api.list_repo_files(repo_id=repo_id)\n",
        "\n",
        "# 2. If file exists → skip upload\n",
        "if filename in existing_files:\n",
        "    print(f\"File '{filename}' already exists in the repository. Upload skipped.\")\n",
        "else:\n",
        "    # 3. Otherwise, upload\n",
        "    api.upload_file(\n",
        "        path_or_fileobj=filename,\n",
        "        path_in_repo=filename,\n",
        "        repo_id=repo_id\n",
        "    )\n",
        "    print(f\"Uploaded '{filename}' successfully!\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s6H0_U56QLNQ",
        "outputId": "b42ce017-e096-42c2-cf30-db7897d3fddc"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "File 'gladiator_gradient_boosting_classifier.pkl' already exists in the repository. Upload skipped.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "TlClTFEgXFTX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part 9: Presentation Video**\n",
        "\n",
        "- Record a brief video (4–6 minutes) with screen sharing of you walk through the HF's model repository, README, and sharing your process & results. Include a screen share while also recording yourself talking during the walk through.\n",
        "\n",
        "- The recording will include sharing the screen, and you talking to the camera (show yourself in a circle on the bottom).\n",
        "\n",
        "- Videos without your face talking while going ower your work wont be acceptable.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "mzYCO54GIPdP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> For help:\n",
        "> - Youtube [Watch this video](https://www.youtube.com/watch?v=DK7Z_nYhjjg)\n",
        "> - Loom [Watch this video](https://www.youtube.com/watch?v=eSCHNXTsJK8)\n",
        "> - Zoom [Watch this video](https://www.youtube.com/watch?v=njwbjFYCbGU)\n"
      ],
      "metadata": {
        "id": "fl0X5y1muPvw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Include:\n",
        "  - A quick dataset overview and your main goal.\n",
        "  - Key EDA steps and highlights of visual insights.\n",
        "  - How you engineered features. About your clustering.\n",
        "  - The models you trained, your iterative process, and what you learned.\n",
        "  - Key visualizations and takeaways.\n",
        "  - Reflections on any challenges and lessons learned.\n",
        "  - Extra work.\n",
        "\n",
        "- Finally, attach the video to the beginning of the `README` file, and make sure everything works. *The video should be placed at the beginning of the notebook and must be playable within it. It can be hosted on `Vimeo`, `YouTube`, or `Loom`.*"
      ],
      "metadata": {
        "id": "Kw06OJESuWGp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<br><br><br>\n",
        "\n",
        "---\n",
        "\n",
        "<br><br><br>"
      ],
      "metadata": {
        "id": "YvNRPdxhvWaK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Part 10: Moodle"
      ],
      "metadata": {
        "id": "EZzOzA3YupFc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Submit to Moodle only one link - the link to your HF's Model Repository.**  \n",
        "\n",
        "The Repo should Include:\n",
        "- README\n",
        "- Python Notebook\n",
        "- 1 pickle model for regression\n",
        "- 1 pickle model for classification"
      ],
      "metadata": {
        "id": "n28Xs2gRusQh"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DFqtRAswvKox"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "---\n",
        "\n",
        "<br>\n",
        "<br>\n",
        "<br>\n",
        "<br>\n",
        "\n",
        "Good luck and have fun creating AI model!"
      ],
      "metadata": {
        "id": "rN8TJ_5oIPfm"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cPgKfBWKvU8K"
      },
      "execution_count": 86,
      "outputs": []
    }
  ]
}