├── .gitignore ├── Adaboosting └── Adamushroom.ipynb ├── ChurnProject ├── Churn_cohort.ipynb ├── __init__.py ├── all_utils.py └── model.py ├── Cross Validation └── CV_test.ipynb ├── DT_Project └── DT_Penguin.ipynb ├── Encoding └── Encoder.ipynb ├── KNN └── simpleKNN.ipynb ├── KNN_Project └── KNN_Classifier.ipynb ├── LICENSE ├── Linear_Regression.ipynb ├── LogRegressionProject └── HeartDisease.ipynb ├── Logistic Regression └── simplelogistic.ipynb ├── LogisticProject └── Heartlogistic.ipynb ├── Multiclass_LR └── Multiclass_Logistic.ipynb ├── README.md ├── RFRegression └── RF_Regression.ipynb ├── RandomForest └── RFmodel.ipynb ├── Regularization └── Regularization.ipynb ├── RidgeCV └── Ridge_Lasso_CV.ipynb ├── SVM └── SVM_demo.ipynb ├── SVM_Project ├── SVM_wine.ipynb └── untitled.txt ├── SVR └── SVR_cement_Data.ipynb ├── Test Notebook └── Linear_regression.ipynb ├── data ├── Advertising.csv ├── Ames_Housing_Data.csv ├── Ames_Housing_Feature_Description.txt ├── Ames_NO_Missing_Data.csv ├── Ames_outliers_removed.csv ├── Telco-Customer-Churn.csv ├── cement_slump.csv ├── data_banknote_authentication.csv ├── gene_expression.csv ├── hearing_test.csv ├── heart.csv ├── iris.csv ├── mouse_viral_study.csv ├── mushrooms.csv ├── penguins_size.csv ├── rock_density_xray.csv ├── sonar.all-data.csv └── wine_fraud.csv ├── models ├── final_poly_converter.joblib ├── model.joblib └── poly.joblib ├── outliers └── Ouliers_FE.ipynb ├── regressor.py ├── requirements.txt └── untitled.txt /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /ChurnProject/__init__.py: -------------------------------------------------------------------------------- 1 | print("util package is imported") 2 | __version__ = "0.0.2" -------------------------------------------------------------------------------- /ChurnProject/all_utils.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Sagar-modelling/ML_Live_Class/d4edece87958c661605e5fb85f64e146a8621128/ChurnProject/all_utils.py -------------------------------------------------------------------------------- /ChurnProject/model.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Sagar-modelling/ML_Live_Class/d4edece87958c661605e5fb85f64e146a8621128/ChurnProject/model.py -------------------------------------------------------------------------------- /KNN_Project/KNN_Classifier.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [] 9 | } 10 | ], 11 | "metadata": { 12 | "interpreter": { 13 | "hash": "160e4882c5f944cead9f9ac66379f2a3d928eacd978359051712822c98c3f5ac" 14 | }, 15 | "kernelspec": { 16 | "display_name": "Python 3.7.0 ('mltest')", 17 | "language": "python", 18 | "name": "python3" 19 | }, 20 | "language_info": { 21 | "name": "python", 22 | "version": "3.7.0" 23 | } 24 | }, 25 | "nbformat": 4, 26 | "nbformat_minor": 5 27 | } 28 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 sagar kandpal 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ML_Live_Class 2 | Machine Learning Datasets Implementation 3 | 4 | # Commands Used 5 | ```bash 6 | conda env list 7 | mkdir utils 8 | touch utils/__init__.py #to treat utility as a package 9 | touch utils/model.py 10 | touch utils/all_utils.py (keep all helper functions in all_utils folder) 11 | from utils.model import class 12 | touch requirements.txt 13 | conda create -n sagar python==3.8 -y 14 | conda list 15 | pip list 16 | conda activate sagar 17 | pip freeze > requirements.txt 18 | pip install -r requirements.txt 19 | pip uninstall -y 20 | conda remove --name testenv --all 21 | conda remove -y 22 | git add . && git commit -m "docstring updated" && git push origin main 23 | git remote -v #shows the remote repository URL 24 | git pull #to bring changes from remote repository and merge into local branch 25 | git branch -M # to rename current branch 26 | git checkout "committed id no" (to go to specific version) 27 | pip install notebook #to install jupyter notebook in the vs code 28 | jupyter notebook 29 | cp sample\ notebook/demo.ipynb . 30 | #to see utils as a pckage, just type python in the terminal 31 | import utils 32 | utils.__version__ #check utils package version 33 | conda env export > environment.yml # To share the existing environment with the collabrators 34 | conda env create -f environment.yml 35 | ``` 36 | -------------------------------------------------------------------------------- /Regularization/Regularization.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import numpy as np\n", 10 | "import pandas as pd\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "import seaborn as sns" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 2, 18 | "id": "b47a0266", 19 | "metadata": {}, 20 | "outputs": [ 21 | { 22 | "data": { 23 | "text/html": [ 24 | "
\n", 25 | "\n", 38 | "\n", 39 | " \n", 40 | " \n", 41 | " \n", 42 | " \n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | "
TVradionewspapersales
0230.137.869.222.1
144.539.345.110.4
217.245.969.39.3
3151.541.358.518.5
4180.810.858.412.9
\n", 86 | "
" 87 | ], 88 | "text/plain": [ 89 | " TV radio newspaper sales\n", 90 | "0 230.1 37.8 69.2 22.1\n", 91 | "1 44.5 39.3 45.1 10.4\n", 92 | "2 17.2 45.9 69.3 9.3\n", 93 | "3 151.5 41.3 58.5 18.5\n", 94 | "4 180.8 10.8 58.4 12.9" 95 | ] 96 | }, 97 | "execution_count": 2, 98 | "metadata": {}, 99 | "output_type": "execute_result" 100 | } 101 | ], 102 | "source": [ 103 | "df = pd.read_csv(r\"C:\\Users\\Sagar Kandpal\\Desktop\\ML EXAMPLE\\Modular\\ML_Live_Class\\data\\Advertising.csv\")\n", 104 | "df.head()" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": 3, 110 | "id": "f8547de6", 111 | "metadata": {}, 112 | "outputs": [ 113 | { 114 | "data": { 115 | "text/html": [ 116 | "
\n", 117 | "\n", 130 | "\n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | "
TVradionewspaper
0230.137.869.2
144.539.345.1
217.245.969.3
3151.541.358.5
4180.810.858.4
\n", 172 | "
" 173 | ], 174 | "text/plain": [ 175 | " TV radio newspaper\n", 176 | "0 230.1 37.8 69.2\n", 177 | "1 44.5 39.3 45.1\n", 178 | "2 17.2 45.9 69.3\n", 179 | "3 151.5 41.3 58.5\n", 180 | "4 180.8 10.8 58.4" 181 | ] 182 | }, 183 | "execution_count": 3, 184 | "metadata": {}, 185 | "output_type": "execute_result" 186 | } 187 | ], 188 | "source": [ 189 | "X = df.drop('sales', axis=1)\n", 190 | "X.head()" 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": 4, 196 | "id": "c1ff1642", 197 | "metadata": {}, 198 | "outputs": [ 199 | { 200 | "data": { 201 | "text/plain": [ 202 | "0 22.1\n", 203 | "1 10.4\n", 204 | "2 9.3\n", 205 | "3 18.5\n", 206 | "4 12.9\n", 207 | " ... \n", 208 | "195 7.6\n", 209 | "196 9.7\n", 210 | "197 12.8\n", 211 | "198 25.5\n", 212 | "199 13.4\n", 213 | "Name: sales, Length: 200, dtype: float64" 214 | ] 215 | }, 216 | "execution_count": 4, 217 | "metadata": {}, 218 | "output_type": "execute_result" 219 | } 220 | ], 221 | "source": [ 222 | "y = df['sales']\n", 223 | "y" 224 | ] 225 | }, 226 | { 227 | "cell_type": "code", 228 | "execution_count": 5, 229 | "id": "538b06b7", 230 | "metadata": {}, 231 | "outputs": [], 232 | "source": [ 233 | "from sklearn.preprocessing import PolynomialFeatures\n", 234 | "from sklearn.linear_model import LinearRegression" 235 | ] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": 6, 240 | "id": "42c0ed51", 241 | "metadata": {}, 242 | "outputs": [], 243 | "source": [ 244 | "poly_conv = PolynomialFeatures(degree=3, include_bias=False)" 245 | ] 246 | }, 247 | { 248 | "cell_type": "code", 249 | "execution_count": 7, 250 | "id": "62e90c62", 251 | "metadata": {}, 252 | "outputs": [], 253 | "source": [ 254 | "final_model = LinearRegression()" 255 | ] 256 | }, 257 | { 258 | "cell_type": "code", 259 | "execution_count": 8, 260 | "id": "1d00be61", 261 | "metadata": {}, 262 | "outputs": [ 263 | { 264 | "data": { 265 | "text/plain": [ 266 | "array([[2.30100000e+02, 3.78000000e+01, 6.92000000e+01, ...,\n", 267 | " 9.88757280e+04, 1.81010592e+05, 3.31373888e+05],\n", 268 | " [4.45000000e+01, 3.93000000e+01, 4.51000000e+01, ...,\n", 269 | " 6.96564990e+04, 7.99365930e+04, 9.17338510e+04],\n", 270 | " [1.72000000e+01, 4.59000000e+01, 6.93000000e+01, ...,\n", 271 | " 1.46001933e+05, 2.20434291e+05, 3.32812557e+05],\n", 272 | " ...,\n", 273 | " [1.77000000e+02, 9.30000000e+00, 6.40000000e+00, ...,\n", 274 | " 5.53536000e+02, 3.80928000e+02, 2.62144000e+02],\n", 275 | " [2.83600000e+02, 4.20000000e+01, 6.62000000e+01, ...,\n", 276 | " 1.16776800e+05, 1.84062480e+05, 2.90117528e+05],\n", 277 | " [2.32100000e+02, 8.60000000e+00, 8.70000000e+00, ...,\n", 278 | " 6.43452000e+02, 6.50934000e+02, 6.58503000e+02]])" 279 | ] 280 | }, 281 | "execution_count": 8, 282 | "metadata": {}, 283 | "output_type": "execute_result" 284 | } 285 | ], 286 | "source": [ 287 | "poly_features = poly_conv.fit_transform(X)\n", 288 | "poly_features" 289 | ] 290 | }, 291 | { 292 | "cell_type": "code", 293 | "execution_count": 9, 294 | "id": "2c06f7eb", 295 | "metadata": {}, 296 | "outputs": [ 297 | { 298 | "data": { 299 | "text/plain": [ 300 | "(200, 3)" 301 | ] 302 | }, 303 | "execution_count": 9, 304 | "metadata": {}, 305 | "output_type": "execute_result" 306 | } 307 | ], 308 | "source": [ 309 | "X.shape " 310 | ] 311 | }, 312 | { 313 | "cell_type": "code", 314 | "execution_count": 10, 315 | "id": "5f676257", 316 | "metadata": {}, 317 | "outputs": [ 318 | { 319 | "data": { 320 | "text/plain": [ 321 | "(200, 19)" 322 | ] 323 | }, 324 | "execution_count": 10, 325 | "metadata": {}, 326 | "output_type": "execute_result" 327 | } 328 | ], 329 | "source": [ 330 | "poly_features.shape" 331 | ] 332 | }, 333 | { 334 | "cell_type": "code", 335 | "execution_count": 11, 336 | "id": "b4fd8606", 337 | "metadata": {}, 338 | "outputs": [], 339 | "source": [ 340 | "from sklearn.model_selection import train_test_split\n", 341 | "X_train, X_test, y_train, y_test = train_test_split(\n", 342 | " poly_features, y, test_size=0.33, random_state=101)" 343 | ] 344 | }, 345 | { 346 | "cell_type": "code", 347 | "execution_count": 12, 348 | "id": "1af01d7e", 349 | "metadata": {}, 350 | "outputs": [], 351 | "source": [ 352 | "from sklearn.preprocessing import StandardScaler" 353 | ] 354 | }, 355 | { 356 | "cell_type": "code", 357 | "execution_count": 13, 358 | "id": "4bd31c0a", 359 | "metadata": {}, 360 | "outputs": [], 361 | "source": [ 362 | "scaler = StandardScaler() #it applies z score normalisation(standardisation)" 363 | ] 364 | }, 365 | { 366 | "cell_type": "code", 367 | "execution_count": 14, 368 | "id": "a21dd86d", 369 | "metadata": {}, 370 | "outputs": [ 371 | { 372 | "data": { 373 | "text/plain": [ 374 | "StandardScaler()" 375 | ] 376 | }, 377 | "execution_count": 14, 378 | "metadata": {}, 379 | "output_type": "execute_result" 380 | } 381 | ], 382 | "source": [ 383 | "scaler.fit(X_train)" 384 | ] 385 | }, 386 | { 387 | "cell_type": "code", 388 | "execution_count": 15, 389 | "id": "5e894c1b", 390 | "metadata": {}, 391 | "outputs": [], 392 | "source": [ 393 | "scaled_X_train = scaler.transform(X_train)" 394 | ] 395 | }, 396 | { 397 | "cell_type": "code", 398 | "execution_count": 16, 399 | "id": "c373d573", 400 | "metadata": {}, 401 | "outputs": [], 402 | "source": [ 403 | "scaled_X_test = scaler.transform(X_test)" 404 | ] 405 | }, 406 | { 407 | "cell_type": "code", 408 | "execution_count": 17, 409 | "id": "bceba8fc", 410 | "metadata": {}, 411 | "outputs": [ 412 | { 413 | "data": { 414 | "text/plain": [ 415 | "array([1.87800000e+02, 2.11000000e+01, 9.50000000e+00, 3.52688400e+04,\n", 416 | " 3.96258000e+03, 1.78410000e+03, 4.45210000e+02, 2.00450000e+02,\n", 417 | " 9.02500000e+01, 6.62348815e+06, 7.44172524e+05, 3.35053980e+05,\n", 418 | " 8.36104380e+04, 3.76445100e+04, 1.69489500e+04, 9.39393100e+03,\n", 419 | " 4.22949500e+03, 1.90427500e+03, 8.57375000e+02])" 420 | ] 421 | }, 422 | "execution_count": 17, 423 | "metadata": {}, 424 | "output_type": "execute_result" 425 | } 426 | ], 427 | "source": [ 428 | "X_train[0]" 429 | ] 430 | }, 431 | { 432 | "cell_type": "code", 433 | "execution_count": 18, 434 | "id": "ed443157", 435 | "metadata": {}, 436 | "outputs": [ 437 | { 438 | "data": { 439 | "text/plain": [ 440 | "array([[ 0.455913 , -0.15469995, -0.98899258, ..., -0.60618091,\n", 441 | " -0.56426029, -0.5063835 ],\n", 442 | " [-0.84432792, 1.60997604, 0.21389931, ..., 1.27808026,\n", 443 | " 0.25379368, -0.22928072],\n", 444 | " [-0.2808111 , 0.93019813, 2.36467003, ..., 2.13064753,\n", 445 | " 2.9262847 , 2.91162153],\n", 446 | " ...,\n", 447 | " [ 1.07281562, -1.32199535, 0.32937694, ..., -0.70151704,\n", 448 | " -0.50832904, -0.16601335],\n", 449 | " [ 0.77504147, 0.04442691, -1.2536288 , ..., -0.6570423 ,\n", 450 | " -0.58737703, -0.51185147],\n", 451 | " [ 0.16525695, 0.56627662, 1.09922775, ..., 0.67742414,\n", 452 | " 0.75141387, 0.50797559]])" 453 | ] 454 | }, 455 | "execution_count": 18, 456 | "metadata": {}, 457 | "output_type": "execute_result" 458 | } 459 | ], 460 | "source": [ 461 | "scaled_X_train" 462 | ] 463 | }, 464 | { 465 | "cell_type": "code", 466 | "execution_count": 19, 467 | "id": "75e51afd", 468 | "metadata": {}, 469 | "outputs": [], 470 | "source": [ 471 | "from sklearn.linear_model import Ridge" 472 | ] 473 | }, 474 | { 475 | "cell_type": "code", 476 | "execution_count": 20, 477 | "id": "07d9f4ee", 478 | "metadata": {}, 479 | "outputs": [], 480 | "source": [ 481 | "ridge_model = Ridge(alpha = 5)" 482 | ] 483 | }, 484 | { 485 | "cell_type": "code", 486 | "execution_count": 21, 487 | "id": "24b6e246", 488 | "metadata": {}, 489 | "outputs": [ 490 | { 491 | "name": "stdout", 492 | "output_type": "stream", 493 | "text": [ 494 | "Help on class Ridge in module sklearn.linear_model._ridge:\n", 495 | "\n", 496 | "class Ridge(sklearn.base.MultiOutputMixin, sklearn.base.RegressorMixin, _BaseRidge)\n", 497 | " | Ridge(alpha=1.0, *, fit_intercept=True, normalize='deprecated', copy_X=True, max_iter=None, tol=0.001, solver='auto', positive=False, random_state=None)\n", 498 | " | \n", 499 | " | Linear least squares with l2 regularization.\n", 500 | " | \n", 501 | " | Minimizes the objective function::\n", 502 | " | \n", 503 | " | ||y - Xw||^2_2 + alpha * ||w||^2_2\n", 504 | " | \n", 505 | " | This model solves a regression model where the loss function is\n", 506 | " | the linear least squares function and regularization is given by\n", 507 | " | the l2-norm. Also known as Ridge Regression or Tikhonov regularization.\n", 508 | " | This estimator has built-in support for multi-variate regression\n", 509 | " | (i.e., when y is a 2d-array of shape (n_samples, n_targets)).\n", 510 | " | \n", 511 | " | Read more in the :ref:`User Guide `.\n", 512 | " | \n", 513 | " | Parameters\n", 514 | " | ----------\n", 515 | " | alpha : {float, ndarray of shape (n_targets,)}, default=1.0\n", 516 | " | Regularization strength; must be a positive float. Regularization\n", 517 | " | improves the conditioning of the problem and reduces the variance of\n", 518 | " | the estimates. Larger values specify stronger regularization.\n", 519 | " | Alpha corresponds to ``1 / (2C)`` in other linear models such as\n", 520 | " | :class:`~sklearn.linear_model.LogisticRegression` or\n", 521 | " | :class:`~sklearn.svm.LinearSVC`. If an array is passed, penalties are\n", 522 | " | assumed to be specific to the targets. Hence they must correspond in\n", 523 | " | number.\n", 524 | " | \n", 525 | " | fit_intercept : bool, default=True\n", 526 | " | Whether to fit the intercept for this model. If set\n", 527 | " | to false, no intercept will be used in calculations\n", 528 | " | (i.e. ``X`` and ``y`` are expected to be centered).\n", 529 | " | \n", 530 | " | normalize : bool, default=False\n", 531 | " | This parameter is ignored when ``fit_intercept`` is set to False.\n", 532 | " | If True, the regressors X will be normalized before regression by\n", 533 | " | subtracting the mean and dividing by the l2-norm.\n", 534 | " | If you wish to standardize, please use\n", 535 | " | :class:`~sklearn.preprocessing.StandardScaler` before calling ``fit``\n", 536 | " | on an estimator with ``normalize=False``.\n", 537 | " | \n", 538 | " | .. deprecated:: 1.0\n", 539 | " | ``normalize`` was deprecated in version 1.0 and\n", 540 | " | will be removed in 1.2.\n", 541 | " | \n", 542 | " | copy_X : bool, default=True\n", 543 | " | If True, X will be copied; else, it may be overwritten.\n", 544 | " | \n", 545 | " | max_iter : int, default=None\n", 546 | " | Maximum number of iterations for conjugate gradient solver.\n", 547 | " | For 'sparse_cg' and 'lsqr' solvers, the default value is determined\n", 548 | " | by scipy.sparse.linalg. For 'sag' solver, the default value is 1000.\n", 549 | " | For 'lbfgs' solver, the default value is 15000.\n", 550 | " | \n", 551 | " | tol : float, default=1e-3\n", 552 | " | Precision of the solution.\n", 553 | " | \n", 554 | " | solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga', 'lbfgs'}, default='auto'\n", 555 | " | Solver to use in the computational routines:\n", 556 | " | \n", 557 | " | - 'auto' chooses the solver automatically based on the type of data.\n", 558 | " | \n", 559 | " | - 'svd' uses a Singular Value Decomposition of X to compute the Ridge\n", 560 | " | coefficients. More stable for singular matrices than 'cholesky'.\n", 561 | " | \n", 562 | " | - 'cholesky' uses the standard scipy.linalg.solve function to\n", 563 | " | obtain a closed-form solution.\n", 564 | " | \n", 565 | " | - 'sparse_cg' uses the conjugate gradient solver as found in\n", 566 | " | scipy.sparse.linalg.cg. As an iterative algorithm, this solver is\n", 567 | " | more appropriate than 'cholesky' for large-scale data\n", 568 | " | (possibility to set `tol` and `max_iter`).\n", 569 | " | \n", 570 | " | - 'lsqr' uses the dedicated regularized least-squares routine\n", 571 | " | scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative\n", 572 | " | procedure.\n", 573 | " | \n", 574 | " | - 'sag' uses a Stochastic Average Gradient descent, and 'saga' uses\n", 575 | " | its improved, unbiased version named SAGA. Both methods also use an\n", 576 | " | iterative procedure, and are often faster than other solvers when\n", 577 | " | both n_samples and n_features are large. Note that 'sag' and\n", 578 | " | 'saga' fast convergence is only guaranteed on features with\n", 579 | " | approximately the same scale. You can preprocess the data with a\n", 580 | " | scaler from sklearn.preprocessing.\n", 581 | " | \n", 582 | " | - 'lbfgs' uses L-BFGS-B algorithm implemented in\n", 583 | " | `scipy.optimize.minimize`. It can be used only when `positive`\n", 584 | " | is True.\n", 585 | " | \n", 586 | " | All last six solvers support both dense and sparse data. However, only\n", 587 | " | 'sag', 'sparse_cg', and 'lbfgs' support sparse input when `fit_intercept`\n", 588 | " | is True.\n", 589 | " | \n", 590 | " | .. versionadded:: 0.17\n", 591 | " | Stochastic Average Gradient descent solver.\n", 592 | " | .. versionadded:: 0.19\n", 593 | " | SAGA solver.\n", 594 | " | \n", 595 | " | positive : bool, default=False\n", 596 | " | When set to ``True``, forces the coefficients to be positive.\n", 597 | " | Only 'lbfgs' solver is supported in this case.\n", 598 | " | \n", 599 | " | random_state : int, RandomState instance, default=None\n", 600 | " | Used when ``solver`` == 'sag' or 'saga' to shuffle the data.\n", 601 | " | See :term:`Glossary ` for details.\n", 602 | " | \n", 603 | " | .. versionadded:: 0.17\n", 604 | " | `random_state` to support Stochastic Average Gradient.\n", 605 | " | \n", 606 | " | Attributes\n", 607 | " | ----------\n", 608 | " | coef_ : ndarray of shape (n_features,) or (n_targets, n_features)\n", 609 | " | Weight vector(s).\n", 610 | " | \n", 611 | " | intercept_ : float or ndarray of shape (n_targets,)\n", 612 | " | Independent term in decision function. Set to 0.0 if\n", 613 | " | ``fit_intercept = False``.\n", 614 | " | \n", 615 | " | n_iter_ : None or ndarray of shape (n_targets,)\n", 616 | " | Actual number of iterations for each target. Available only for\n", 617 | " | sag and lsqr solvers. Other solvers will return None.\n", 618 | " | \n", 619 | " | .. versionadded:: 0.17\n", 620 | " | \n", 621 | " | n_features_in_ : int\n", 622 | " | Number of features seen during :term:`fit`.\n", 623 | " | \n", 624 | " | .. versionadded:: 0.24\n", 625 | " | \n", 626 | " | feature_names_in_ : ndarray of shape (`n_features_in_`,)\n", 627 | " | Names of features seen during :term:`fit`. Defined only when `X`\n", 628 | " | has feature names that are all strings.\n", 629 | " | \n", 630 | " | .. versionadded:: 1.0\n", 631 | " | \n", 632 | " | See Also\n", 633 | " | --------\n", 634 | " | RidgeClassifier : Ridge classifier.\n", 635 | " | RidgeCV : Ridge regression with built-in cross validation.\n", 636 | " | :class:`~sklearn.kernel_ridge.KernelRidge` : Kernel ridge regression\n", 637 | " | combines ridge regression with the kernel trick.\n", 638 | " | \n", 639 | " | Examples\n", 640 | " | --------\n", 641 | " | >>> from sklearn.linear_model import Ridge\n", 642 | " | >>> import numpy as np\n", 643 | " | >>> n_samples, n_features = 10, 5\n", 644 | " | >>> rng = np.random.RandomState(0)\n", 645 | " | >>> y = rng.randn(n_samples)\n", 646 | " | >>> X = rng.randn(n_samples, n_features)\n", 647 | " | >>> clf = Ridge(alpha=1.0)\n", 648 | " | >>> clf.fit(X, y)\n", 649 | " | Ridge()\n", 650 | " | \n", 651 | " | Method resolution order:\n", 652 | " | Ridge\n", 653 | " | sklearn.base.MultiOutputMixin\n", 654 | " | sklearn.base.RegressorMixin\n", 655 | " | _BaseRidge\n", 656 | " | sklearn.linear_model._base.LinearModel\n", 657 | " | sklearn.base.BaseEstimator\n", 658 | " | builtins.object\n", 659 | " | \n", 660 | " | Methods defined here:\n", 661 | " | \n", 662 | " | __init__(self, alpha=1.0, *, fit_intercept=True, normalize='deprecated', copy_X=True, max_iter=None, tol=0.001, solver='auto', positive=False, random_state=None)\n", 663 | " | Initialize self. See help(type(self)) for accurate signature.\n", 664 | " | \n", 665 | " | fit(self, X, y, sample_weight=None)\n", 666 | " | Fit Ridge regression model.\n", 667 | " | \n", 668 | " | Parameters\n", 669 | " | ----------\n", 670 | " | X : {ndarray, sparse matrix} of shape (n_samples, n_features)\n", 671 | " | Training data.\n", 672 | " | \n", 673 | " | y : ndarray of shape (n_samples,) or (n_samples, n_targets)\n", 674 | " | Target values.\n", 675 | " | \n", 676 | " | sample_weight : float or ndarray of shape (n_samples,), default=None\n", 677 | " | Individual weights for each sample. If given a float, every sample\n", 678 | " | will have the same weight.\n", 679 | " | \n", 680 | " | Returns\n", 681 | " | -------\n", 682 | " | self : object\n", 683 | " | Fitted estimator.\n", 684 | " | \n", 685 | " | ----------------------------------------------------------------------\n", 686 | " | Data and other attributes defined here:\n", 687 | " | \n", 688 | " | __abstractmethods__ = frozenset()\n", 689 | " | \n", 690 | " | ----------------------------------------------------------------------\n", 691 | " | Data descriptors inherited from sklearn.base.MultiOutputMixin:\n", 692 | " | \n", 693 | " | __dict__\n", 694 | " | dictionary for instance variables (if defined)\n", 695 | " | \n", 696 | " | __weakref__\n", 697 | " | list of weak references to the object (if defined)\n", 698 | " | \n", 699 | " | ----------------------------------------------------------------------\n", 700 | " | Methods inherited from sklearn.base.RegressorMixin:\n", 701 | " | \n", 702 | " | score(self, X, y, sample_weight=None)\n", 703 | " | Return the coefficient of determination of the prediction.\n", 704 | " | \n", 705 | " | The coefficient of determination :math:`R^2` is defined as\n", 706 | " | :math:`(1 - \\frac{u}{v})`, where :math:`u` is the residual\n", 707 | " | sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v`\n", 708 | " | is the total sum of squares ``((y_true - y_true.mean()) ** 2).sum()``.\n", 709 | " | The best possible score is 1.0 and it can be negative (because the\n", 710 | " | model can be arbitrarily worse). A constant model that always predicts\n", 711 | " | the expected value of `y`, disregarding the input features, would get\n", 712 | " | a :math:`R^2` score of 0.0.\n", 713 | " | \n", 714 | " | Parameters\n", 715 | " | ----------\n", 716 | " | X : array-like of shape (n_samples, n_features)\n", 717 | " | Test samples. For some estimators this may be a precomputed\n", 718 | " | kernel matrix or a list of generic objects instead with shape\n", 719 | " | ``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``\n", 720 | " | is the number of samples used in the fitting for the estimator.\n", 721 | " | \n", 722 | " | y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n", 723 | " | True values for `X`.\n", 724 | " | \n", 725 | " | sample_weight : array-like of shape (n_samples,), default=None\n", 726 | " | Sample weights.\n", 727 | " | \n", 728 | " | Returns\n", 729 | " | -------\n", 730 | " | score : float\n", 731 | " | :math:`R^2` of ``self.predict(X)`` wrt. `y`.\n", 732 | " | \n", 733 | " | Notes\n", 734 | " | -----\n", 735 | " | The :math:`R^2` score used when calling ``score`` on a regressor uses\n", 736 | " | ``multioutput='uniform_average'`` from version 0.23 to keep consistent\n", 737 | " | with default value of :func:`~sklearn.metrics.r2_score`.\n", 738 | " | This influences the ``score`` method of all the multioutput\n", 739 | " | regressors (except for\n", 740 | " | :class:`~sklearn.multioutput.MultiOutputRegressor`).\n", 741 | " | \n", 742 | " | ----------------------------------------------------------------------\n", 743 | " | Methods inherited from sklearn.linear_model._base.LinearModel:\n", 744 | " | \n", 745 | " | predict(self, X)\n", 746 | " | Predict using the linear model.\n", 747 | " | \n", 748 | " | Parameters\n", 749 | " | ----------\n", 750 | " | X : array-like or sparse matrix, shape (n_samples, n_features)\n", 751 | " | Samples.\n", 752 | " | \n", 753 | " | Returns\n", 754 | " | -------\n", 755 | " | C : array, shape (n_samples,)\n", 756 | " | Returns predicted values.\n", 757 | " | \n", 758 | " | ----------------------------------------------------------------------\n", 759 | " | Methods inherited from sklearn.base.BaseEstimator:\n", 760 | " | \n", 761 | " | __getstate__(self)\n", 762 | " | \n", 763 | " | __repr__(self, N_CHAR_MAX=700)\n", 764 | " | Return repr(self).\n", 765 | " | \n", 766 | " | __setstate__(self, state)\n", 767 | " | \n", 768 | " | get_params(self, deep=True)\n", 769 | " | Get parameters for this estimator.\n", 770 | " | \n", 771 | " | Parameters\n", 772 | " | ----------\n", 773 | " | deep : bool, default=True\n", 774 | " | If True, will return the parameters for this estimator and\n", 775 | " | contained subobjects that are estimators.\n", 776 | " | \n", 777 | " | Returns\n", 778 | " | -------\n", 779 | " | params : dict\n", 780 | " | Parameter names mapped to their values.\n", 781 | " | \n", 782 | " | set_params(self, **params)\n", 783 | " | Set the parameters of this estimator.\n", 784 | " | \n", 785 | " | The method works on simple estimators as well as on nested objects\n", 786 | " | (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n", 787 | " | parameters of the form ``__`` so that it's\n", 788 | " | possible to update each component of a nested object.\n", 789 | " | \n", 790 | " | Parameters\n", 791 | " | ----------\n", 792 | " | **params : dict\n", 793 | " | Estimator parameters.\n", 794 | " | \n", 795 | " | Returns\n", 796 | " | -------\n", 797 | " | self : estimator instance\n", 798 | " | Estimator instance.\n", 799 | "\n" 800 | ] 801 | } 802 | ], 803 | "source": [ 804 | "help(Ridge)" 805 | ] 806 | }, 807 | { 808 | "cell_type": "code", 809 | "execution_count": 22, 810 | "id": "5d27100f", 811 | "metadata": {}, 812 | "outputs": [ 813 | { 814 | "data": { 815 | "text/plain": [ 816 | "Ridge(alpha=5)" 817 | ] 818 | }, 819 | "execution_count": 22, 820 | "metadata": {}, 821 | "output_type": "execute_result" 822 | } 823 | ], 824 | "source": [ 825 | "ridge_model.fit(X_train, y_train)" 826 | ] 827 | }, 828 | { 829 | "cell_type": "code", 830 | "execution_count": 23, 831 | "id": "622dca00", 832 | "metadata": {}, 833 | "outputs": [], 834 | "source": [ 835 | "test_predictions = ridge_model.predict(X_test)" 836 | ] 837 | }, 838 | { 839 | "cell_type": "code", 840 | "execution_count": 24, 841 | "id": "4fabf92e", 842 | "metadata": {}, 843 | "outputs": [ 844 | { 845 | "data": { 846 | "text/plain": [ 847 | "0.3931122136987665" 848 | ] 849 | }, 850 | "execution_count": 24, 851 | "metadata": {}, 852 | "output_type": "execute_result" 853 | } 854 | ], 855 | "source": [ 856 | "from sklearn.metrics import mean_absolute_error, mean_squared_error\n", 857 | "MAE = mean_absolute_error(y_test, test_predictions)\n", 858 | "MAE" 859 | ] 860 | }, 861 | { 862 | "cell_type": "code", 863 | "execution_count": 25, 864 | "id": "8301ceb4", 865 | "metadata": {}, 866 | "outputs": [ 867 | { 868 | "data": { 869 | "text/plain": [ 870 | "0.5578348973355702" 871 | ] 872 | }, 873 | "execution_count": 25, 874 | "metadata": {}, 875 | "output_type": "execute_result" 876 | } 877 | ], 878 | "source": [ 879 | "RMSE = np.sqrt(mean_squared_error(y_test, test_predictions))\n", 880 | "RMSE" 881 | ] 882 | } 883 | ], 884 | "metadata": { 885 | "interpreter": { 886 | "hash": "160e4882c5f944cead9f9ac66379f2a3d928eacd978359051712822c98c3f5ac" 887 | }, 888 | "kernelspec": { 889 | "display_name": "Python 3.7.0 ('mltest')", 890 | "language": "python", 891 | "name": "python3" 892 | }, 893 | "language_info": { 894 | "codemirror_mode": { 895 | "name": "ipython", 896 | "version": 3 897 | }, 898 | "file_extension": ".py", 899 | "mimetype": "text/x-python", 900 | "name": "python", 901 | "nbconvert_exporter": "python", 902 | "pygments_lexer": "ipython3", 903 | "version": "3.7.0" 904 | } 905 | }, 906 | "nbformat": 4, 907 | "nbformat_minor": 5 908 | } 909 | -------------------------------------------------------------------------------- /SVM_Project/untitled.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Sagar-modelling/ML_Live_Class/d4edece87958c661605e5fb85f64e146a8621128/SVM_Project/untitled.txt -------------------------------------------------------------------------------- /Test Notebook/Linear_regression.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "id": "43d630bd", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import numpy as np\n", 11 | "import pandas as pd\n", 12 | "import matplotlib.pyplot as plt\n", 13 | "import seaborn as sns" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": null, 19 | "id": "aff57b4e", 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [] 23 | } 24 | ], 25 | "metadata": { 26 | "kernelspec": { 27 | "display_name": "Python 3 (ipykernel)", 28 | "language": "python", 29 | "name": "python3" 30 | }, 31 | "language_info": { 32 | "codemirror_mode": { 33 | "name": "ipython", 34 | "version": 3 35 | }, 36 | "file_extension": ".py", 37 | "mimetype": "text/x-python", 38 | "name": "python", 39 | "nbconvert_exporter": "python", 40 | "pygments_lexer": "ipython3", 41 | "version": "3.7.0" 42 | } 43 | }, 44 | "nbformat": 4, 45 | "nbformat_minor": 5 46 | } 47 | -------------------------------------------------------------------------------- /data/Advertising.csv: -------------------------------------------------------------------------------- 1 | TV,radio,newspaper,sales 2 | 230.1,37.8,69.2,22.1 3 | 44.5,39.3,45.1,10.4 4 | 17.2,45.9,69.3,9.3 5 | 151.5,41.3,58.5,18.5 6 | 180.8,10.8,58.4,12.9 7 | 8.7,48.9,75.0,7.2 8 | 57.5,32.8,23.5,11.8 9 | 120.2,19.6,11.6,13.2 10 | 8.6,2.1,1.0,4.8 11 | 199.8,2.6,21.2,10.6 12 | 66.1,5.8,24.2,8.6 13 | 214.7,24.0,4.0,17.4 14 | 23.8,35.1,65.9,9.2 15 | 97.5,7.6,7.2,9.7 16 | 204.1,32.9,46.0,19.0 17 | 195.4,47.7,52.9,22.4 18 | 67.8,36.6,114.0,12.5 19 | 281.4,39.6,55.8,24.4 20 | 69.2,20.5,18.3,11.3 21 | 147.3,23.9,19.1,14.6 22 | 218.4,27.7,53.4,18.0 23 | 237.4,5.1,23.5,12.5 24 | 13.2,15.9,49.6,5.6 25 | 228.3,16.9,26.2,15.5 26 | 62.3,12.6,18.3,9.7 27 | 262.9,3.5,19.5,12.0 28 | 142.9,29.3,12.6,15.0 29 | 240.1,16.7,22.9,15.9 30 | 248.8,27.1,22.9,18.9 31 | 70.6,16.0,40.8,10.5 32 | 292.9,28.3,43.2,21.4 33 | 112.9,17.4,38.6,11.9 34 | 97.2,1.5,30.0,9.6 35 | 265.6,20.0,0.3,17.4 36 | 95.7,1.4,7.4,9.5 37 | 290.7,4.1,8.5,12.8 38 | 266.9,43.8,5.0,25.4 39 | 74.7,49.4,45.7,14.7 40 | 43.1,26.7,35.1,10.1 41 | 228.0,37.7,32.0,21.5 42 | 202.5,22.3,31.6,16.6 43 | 177.0,33.4,38.7,17.1 44 | 293.6,27.7,1.8,20.7 45 | 206.9,8.4,26.4,12.9 46 | 25.1,25.7,43.3,8.5 47 | 175.1,22.5,31.5,14.9 48 | 89.7,9.9,35.7,10.6 49 | 239.9,41.5,18.5,23.2 50 | 227.2,15.8,49.9,14.8 51 | 66.9,11.7,36.8,9.7 52 | 199.8,3.1,34.6,11.4 53 | 100.4,9.6,3.6,10.7 54 | 216.4,41.7,39.6,22.6 55 | 182.6,46.2,58.7,21.2 56 | 262.7,28.8,15.9,20.2 57 | 198.9,49.4,60.0,23.7 58 | 7.3,28.1,41.4,5.5 59 | 136.2,19.2,16.6,13.2 60 | 210.8,49.6,37.7,23.8 61 | 210.7,29.5,9.3,18.4 62 | 53.5,2.0,21.4,8.1 63 | 261.3,42.7,54.7,24.2 64 | 239.3,15.5,27.3,15.7 65 | 102.7,29.6,8.4,14.0 66 | 131.1,42.8,28.9,18.0 67 | 69.0,9.3,0.9,9.3 68 | 31.5,24.6,2.2,9.5 69 | 139.3,14.5,10.2,13.4 70 | 237.4,27.5,11.0,18.9 71 | 216.8,43.9,27.2,22.3 72 | 199.1,30.6,38.7,18.3 73 | 109.8,14.3,31.7,12.4 74 | 26.8,33.0,19.3,8.8 75 | 129.4,5.7,31.3,11.0 76 | 213.4,24.6,13.1,17.0 77 | 16.9,43.7,89.4,8.7 78 | 27.5,1.6,20.7,6.9 79 | 120.5,28.5,14.2,14.2 80 | 5.4,29.9,9.4,5.3 81 | 116.0,7.7,23.1,11.0 82 | 76.4,26.7,22.3,11.8 83 | 239.8,4.1,36.9,12.3 84 | 75.3,20.3,32.5,11.3 85 | 68.4,44.5,35.6,13.6 86 | 213.5,43.0,33.8,21.7 87 | 193.2,18.4,65.7,15.2 88 | 76.3,27.5,16.0,12.0 89 | 110.7,40.6,63.2,16.0 90 | 88.3,25.5,73.4,12.9 91 | 109.8,47.8,51.4,16.7 92 | 134.3,4.9,9.3,11.2 93 | 28.6,1.5,33.0,7.3 94 | 217.7,33.5,59.0,19.4 95 | 250.9,36.5,72.3,22.2 96 | 107.4,14.0,10.9,11.5 97 | 163.3,31.6,52.9,16.9 98 | 197.6,3.5,5.9,11.7 99 | 184.9,21.0,22.0,15.5 100 | 289.7,42.3,51.2,25.4 101 | 135.2,41.7,45.9,17.2 102 | 222.4,4.3,49.8,11.7 103 | 296.4,36.3,100.9,23.8 104 | 280.2,10.1,21.4,14.8 105 | 187.9,17.2,17.9,14.7 106 | 238.2,34.3,5.3,20.7 107 | 137.9,46.4,59.0,19.2 108 | 25.0,11.0,29.7,7.2 109 | 90.4,0.3,23.2,8.7 110 | 13.1,0.4,25.6,5.3 111 | 255.4,26.9,5.5,19.8 112 | 225.8,8.2,56.5,13.4 113 | 241.7,38.0,23.2,21.8 114 | 175.7,15.4,2.4,14.1 115 | 209.6,20.6,10.7,15.9 116 | 78.2,46.8,34.5,14.6 117 | 75.1,35.0,52.7,12.6 118 | 139.2,14.3,25.6,12.2 119 | 76.4,0.8,14.8,9.4 120 | 125.7,36.9,79.2,15.9 121 | 19.4,16.0,22.3,6.6 122 | 141.3,26.8,46.2,15.5 123 | 18.8,21.7,50.4,7.0 124 | 224.0,2.4,15.6,11.6 125 | 123.1,34.6,12.4,15.2 126 | 229.5,32.3,74.2,19.7 127 | 87.2,11.8,25.9,10.6 128 | 7.8,38.9,50.6,6.6 129 | 80.2,0.0,9.2,8.8 130 | 220.3,49.0,3.2,24.7 131 | 59.6,12.0,43.1,9.7 132 | 0.7,39.6,8.7,1.6 133 | 265.2,2.9,43.0,12.7 134 | 8.4,27.2,2.1,5.7 135 | 219.8,33.5,45.1,19.6 136 | 36.9,38.6,65.6,10.8 137 | 48.3,47.0,8.5,11.6 138 | 25.6,39.0,9.3,9.5 139 | 273.7,28.9,59.7,20.8 140 | 43.0,25.9,20.5,9.6 141 | 184.9,43.9,1.7,20.7 142 | 73.4,17.0,12.9,10.9 143 | 193.7,35.4,75.6,19.2 144 | 220.5,33.2,37.9,20.1 145 | 104.6,5.7,34.4,10.4 146 | 96.2,14.8,38.9,11.4 147 | 140.3,1.9,9.0,10.3 148 | 240.1,7.3,8.7,13.2 149 | 243.2,49.0,44.3,25.4 150 | 38.0,40.3,11.9,10.9 151 | 44.7,25.8,20.6,10.1 152 | 280.7,13.9,37.0,16.1 153 | 121.0,8.4,48.7,11.6 154 | 197.6,23.3,14.2,16.6 155 | 171.3,39.7,37.7,19.0 156 | 187.8,21.1,9.5,15.6 157 | 4.1,11.6,5.7,3.2 158 | 93.9,43.5,50.5,15.3 159 | 149.8,1.3,24.3,10.1 160 | 11.7,36.9,45.2,7.3 161 | 131.7,18.4,34.6,12.9 162 | 172.5,18.1,30.7,14.4 163 | 85.7,35.8,49.3,13.3 164 | 188.4,18.1,25.6,14.9 165 | 163.5,36.8,7.4,18.0 166 | 117.2,14.7,5.4,11.9 167 | 234.5,3.4,84.8,11.9 168 | 17.9,37.6,21.6,8.0 169 | 206.8,5.2,19.4,12.2 170 | 215.4,23.6,57.6,17.1 171 | 284.3,10.6,6.4,15.0 172 | 50.0,11.6,18.4,8.4 173 | 164.5,20.9,47.4,14.5 174 | 19.6,20.1,17.0,7.6 175 | 168.4,7.1,12.8,11.7 176 | 222.4,3.4,13.1,11.5 177 | 276.9,48.9,41.8,27.0 178 | 248.4,30.2,20.3,20.2 179 | 170.2,7.8,35.2,11.7 180 | 276.7,2.3,23.7,11.8 181 | 165.6,10.0,17.6,12.6 182 | 156.6,2.6,8.3,10.5 183 | 218.5,5.4,27.4,12.2 184 | 56.2,5.7,29.7,8.7 185 | 287.6,43.0,71.8,26.2 186 | 253.8,21.3,30.0,17.6 187 | 205.0,45.1,19.6,22.6 188 | 139.5,2.1,26.6,10.3 189 | 191.1,28.7,18.2,17.3 190 | 286.0,13.9,3.7,15.9 191 | 18.7,12.1,23.4,6.7 192 | 39.5,41.1,5.8,10.8 193 | 75.5,10.8,6.0,9.9 194 | 17.2,4.1,31.6,5.9 195 | 166.8,42.0,3.6,19.6 196 | 149.7,35.6,6.0,17.3 197 | 38.2,3.7,13.8,7.6 198 | 94.2,4.9,8.1,9.7 199 | 177.0,9.3,6.4,12.8 200 | 283.6,42.0,66.2,25.5 201 | 232.1,8.6,8.7,13.4 202 | -------------------------------------------------------------------------------- /data/Ames_Housing_Feature_Description.txt: -------------------------------------------------------------------------------- 1 | MSSubClass: Identifies the type of dwelling involved in the sale. 2 | 3 | 20 1-STORY 1946 & NEWER ALL STYLES 4 | 30 1-STORY 1945 & OLDER 5 | 40 1-STORY W/FINISHED ATTIC ALL AGES 6 | 45 1-1/2 STORY - UNFINISHED ALL AGES 7 | 50 1-1/2 STORY FINISHED ALL AGES 8 | 60 2-STORY 1946 & NEWER 9 | 70 2-STORY 1945 & OLDER 10 | 75 2-1/2 STORY ALL AGES 11 | 80 SPLIT OR MULTI-LEVEL 12 | 85 SPLIT FOYER 13 | 90 DUPLEX - ALL STYLES AND AGES 14 | 120 1-STORY PUD (Planned Unit Development) - 1946 & NEWER 15 | 150 1-1/2 STORY PUD - ALL AGES 16 | 160 2-STORY PUD - 1946 & NEWER 17 | 180 PUD - MULTILEVEL - INCL SPLIT LEV/FOYER 18 | 190 2 FAMILY CONVERSION - ALL STYLES AND AGES 19 | 20 | MSZoning: Identifies the general zoning classification of the sale. 21 | 22 | A Agriculture 23 | C Commercial 24 | FV Floating Village Residential 25 | I Industrial 26 | RH Residential High Density 27 | RL Residential Low Density 28 | RP Residential Low Density Park 29 | RM Residential Medium Density 30 | 31 | LotFrontage: Linear feet of street connected to property 32 | 33 | LotArea: Lot size in square feet 34 | 35 | Street: Type of road access to property 36 | 37 | Grvl Gravel 38 | Pave Paved 39 | 40 | Alley: Type of alley access to property 41 | 42 | Grvl Gravel 43 | Pave Paved 44 | NA No alley access 45 | 46 | LotShape: General shape of property 47 | 48 | Reg Regular 49 | IR1 Slightly irregular 50 | IR2 Moderately Irregular 51 | IR3 Irregular 52 | 53 | LandContour: Flatness of the property 54 | 55 | Lvl Near Flat/Level 56 | Bnk Banked - Quick and significant rise from street grade to building 57 | HLS Hillside - Significant slope from side to side 58 | Low Depression 59 | 60 | Utilities: Type of utilities available 61 | 62 | AllPub All public Utilities (E,G,W,& S) 63 | NoSewr Electricity, Gas, and Water (Septic Tank) 64 | NoSeWa Electricity and Gas Only 65 | ELO Electricity only 66 | 67 | LotConfig: Lot configuration 68 | 69 | Inside Inside lot 70 | Corner Corner lot 71 | CulDSac Cul-de-sac 72 | FR2 Frontage on 2 sides of property 73 | FR3 Frontage on 3 sides of property 74 | 75 | LandSlope: Slope of property 76 | 77 | Gtl Gentle slope 78 | Mod Moderate Slope 79 | Sev Severe Slope 80 | 81 | Neighborhood: Physical locations within Ames city limits 82 | 83 | Blmngtn Bloomington Heights 84 | Blueste Bluestem 85 | BrDale Briardale 86 | BrkSide Brookside 87 | ClearCr Clear Creek 88 | CollgCr College Creek 89 | Crawfor Crawford 90 | Edwards Edwards 91 | Gilbert Gilbert 92 | IDOTRR Iowa DOT and Rail Road 93 | MeadowV Meadow Village 94 | Mitchel Mitchell 95 | Names North Ames 96 | NoRidge Northridge 97 | NPkVill Northpark Villa 98 | NridgHt Northridge Heights 99 | NWAmes Northwest Ames 100 | OldTown Old Town 101 | SWISU South & West of Iowa State University 102 | Sawyer Sawyer 103 | SawyerW Sawyer West 104 | Somerst Somerset 105 | StoneBr Stone Brook 106 | Timber Timberland 107 | Veenker Veenker 108 | 109 | Condition1: Proximity to various conditions 110 | 111 | Artery Adjacent to arterial street 112 | Feedr Adjacent to feeder street 113 | Norm Normal 114 | RRNn Within 200' of North-South Railroad 115 | RRAn Adjacent to North-South Railroad 116 | PosN Near positive off-site feature--park, greenbelt, etc. 117 | PosA Adjacent to postive off-site feature 118 | RRNe Within 200' of East-West Railroad 119 | RRAe Adjacent to East-West Railroad 120 | 121 | Condition2: Proximity to various conditions (if more than one is present) 122 | 123 | Artery Adjacent to arterial street 124 | Feedr Adjacent to feeder street 125 | Norm Normal 126 | RRNn Within 200' of North-South Railroad 127 | RRAn Adjacent to North-South Railroad 128 | PosN Near positive off-site feature--park, greenbelt, etc. 129 | PosA Adjacent to postive off-site feature 130 | RRNe Within 200' of East-West Railroad 131 | RRAe Adjacent to East-West Railroad 132 | 133 | BldgType: Type of dwelling 134 | 135 | 1Fam Single-family Detached 136 | 2FmCon Two-family Conversion; originally built as one-family dwelling 137 | Duplx Duplex 138 | TwnhsE Townhouse End Unit 139 | TwnhsI Townhouse Inside Unit 140 | 141 | HouseStyle: Style of dwelling 142 | 143 | 1Story One story 144 | 1.5Fin One and one-half story: 2nd level finished 145 | 1.5Unf One and one-half story: 2nd level unfinished 146 | 2Story Two story 147 | 2.5Fin Two and one-half story: 2nd level finished 148 | 2.5Unf Two and one-half story: 2nd level unfinished 149 | SFoyer Split Foyer 150 | SLvl Split Level 151 | 152 | OverallQual: Rates the overall material and finish of the house 153 | 154 | 10 Very Excellent 155 | 9 Excellent 156 | 8 Very Good 157 | 7 Good 158 | 6 Above Average 159 | 5 Average 160 | 4 Below Average 161 | 3 Fair 162 | 2 Poor 163 | 1 Very Poor 164 | 165 | OverallCond: Rates the overall condition of the house 166 | 167 | 10 Very Excellent 168 | 9 Excellent 169 | 8 Very Good 170 | 7 Good 171 | 6 Above Average 172 | 5 Average 173 | 4 Below Average 174 | 3 Fair 175 | 2 Poor 176 | 1 Very Poor 177 | 178 | YearBuilt: Original construction date 179 | 180 | YearRemodAdd: Remodel date (same as construction date if no remodeling or additions) 181 | 182 | RoofStyle: Type of roof 183 | 184 | Flat Flat 185 | Gable Gable 186 | Gambrel Gabrel (Barn) 187 | Hip Hip 188 | Mansard Mansard 189 | Shed Shed 190 | 191 | RoofMatl: Roof material 192 | 193 | ClyTile Clay or Tile 194 | CompShg Standard (Composite) Shingle 195 | Membran Membrane 196 | Metal Metal 197 | Roll Roll 198 | Tar&Grv Gravel & Tar 199 | WdShake Wood Shakes 200 | WdShngl Wood Shingles 201 | 202 | Exterior1st: Exterior covering on house 203 | 204 | AsbShng Asbestos Shingles 205 | AsphShn Asphalt Shingles 206 | BrkComm Brick Common 207 | BrkFace Brick Face 208 | CBlock Cinder Block 209 | CemntBd Cement Board 210 | HdBoard Hard Board 211 | ImStucc Imitation Stucco 212 | MetalSd Metal Siding 213 | Other Other 214 | Plywood Plywood 215 | PreCast PreCast 216 | Stone Stone 217 | Stucco Stucco 218 | VinylSd Vinyl Siding 219 | Wd Sdng Wood Siding 220 | WdShing Wood Shingles 221 | 222 | Exterior2nd: Exterior covering on house (if more than one material) 223 | 224 | AsbShng Asbestos Shingles 225 | AsphShn Asphalt Shingles 226 | BrkComm Brick Common 227 | BrkFace Brick Face 228 | CBlock Cinder Block 229 | CemntBd Cement Board 230 | HdBoard Hard Board 231 | ImStucc Imitation Stucco 232 | MetalSd Metal Siding 233 | Other Other 234 | Plywood Plywood 235 | PreCast PreCast 236 | Stone Stone 237 | Stucco Stucco 238 | VinylSd Vinyl Siding 239 | Wd Sdng Wood Siding 240 | WdShing Wood Shingles 241 | 242 | MasVnrType: Masonry veneer type 243 | 244 | BrkCmn Brick Common 245 | BrkFace Brick Face 246 | CBlock Cinder Block 247 | None None 248 | Stone Stone 249 | 250 | MasVnrArea: Masonry veneer area in square feet 251 | 252 | ExterQual: Evaluates the quality of the material on the exterior 253 | 254 | Ex Excellent 255 | Gd Good 256 | TA Average/Typical 257 | Fa Fair 258 | Po Poor 259 | 260 | ExterCond: Evaluates the present condition of the material on the exterior 261 | 262 | Ex Excellent 263 | Gd Good 264 | TA Average/Typical 265 | Fa Fair 266 | Po Poor 267 | 268 | Foundation: Type of foundation 269 | 270 | BrkTil Brick & Tile 271 | CBlock Cinder Block 272 | PConc Poured Contrete 273 | Slab Slab 274 | Stone Stone 275 | Wood Wood 276 | 277 | BsmtQual: Evaluates the height of the basement 278 | 279 | Ex Excellent (100+ inches) 280 | Gd Good (90-99 inches) 281 | TA Typical (80-89 inches) 282 | Fa Fair (70-79 inches) 283 | Po Poor (<70 inches 284 | NA No Basement 285 | 286 | BsmtCond: Evaluates the general condition of the basement 287 | 288 | Ex Excellent 289 | Gd Good 290 | TA Typical - slight dampness allowed 291 | Fa Fair - dampness or some cracking or settling 292 | Po Poor - Severe cracking, settling, or wetness 293 | NA No Basement 294 | 295 | BsmtExposure: Refers to walkout or garden level walls 296 | 297 | Gd Good Exposure 298 | Av Average Exposure (split levels or foyers typically score average or above) 299 | Mn Mimimum Exposure 300 | No No Exposure 301 | NA No Basement 302 | 303 | BsmtFinType1: Rating of basement finished area 304 | 305 | GLQ Good Living Quarters 306 | ALQ Average Living Quarters 307 | BLQ Below Average Living Quarters 308 | Rec Average Rec Room 309 | LwQ Low Quality 310 | Unf Unfinshed 311 | NA No Basement 312 | 313 | BsmtFinSF1: Type 1 finished square feet 314 | 315 | BsmtFinType2: Rating of basement finished area (if multiple types) 316 | 317 | GLQ Good Living Quarters 318 | ALQ Average Living Quarters 319 | BLQ Below Average Living Quarters 320 | Rec Average Rec Room 321 | LwQ Low Quality 322 | Unf Unfinshed 323 | NA No Basement 324 | 325 | BsmtFinSF2: Type 2 finished square feet 326 | 327 | BsmtUnfSF: Unfinished square feet of basement area 328 | 329 | TotalBsmtSF: Total square feet of basement area 330 | 331 | Heating: Type of heating 332 | 333 | Floor Floor Furnace 334 | GasA Gas forced warm air furnace 335 | GasW Gas hot water or steam heat 336 | Grav Gravity furnace 337 | OthW Hot water or steam heat other than gas 338 | Wall Wall furnace 339 | 340 | HeatingQC: Heating quality and condition 341 | 342 | Ex Excellent 343 | Gd Good 344 | TA Average/Typical 345 | Fa Fair 346 | Po Poor 347 | 348 | CentralAir: Central air conditioning 349 | 350 | N No 351 | Y Yes 352 | 353 | Electrical: Electrical system 354 | 355 | SBrkr Standard Circuit Breakers & Romex 356 | FuseA Fuse Box over 60 AMP and all Romex wiring (Average) 357 | FuseF 60 AMP Fuse Box and mostly Romex wiring (Fair) 358 | FuseP 60 AMP Fuse Box and mostly knob & tube wiring (poor) 359 | Mix Mixed 360 | 361 | 1stFlrSF: First Floor square feet 362 | 363 | 2ndFlrSF: Second floor square feet 364 | 365 | LowQualFinSF: Low quality finished square feet (all floors) 366 | 367 | GrLivArea: Above grade (ground) living area square feet 368 | 369 | BsmtFullBath: Basement full bathrooms 370 | 371 | BsmtHalfBath: Basement half bathrooms 372 | 373 | FullBath: Full bathrooms above grade 374 | 375 | HalfBath: Half baths above grade 376 | 377 | Bedroom: Bedrooms above grade (does NOT include basement bedrooms) 378 | 379 | Kitchen: Kitchens above grade 380 | 381 | KitchenQual: Kitchen quality 382 | 383 | Ex Excellent 384 | Gd Good 385 | TA Typical/Average 386 | Fa Fair 387 | Po Poor 388 | 389 | TotRmsAbvGrd: Total rooms above grade (does not include bathrooms) 390 | 391 | Functional: Home functionality (Assume typical unless deductions are warranted) 392 | 393 | Typ Typical Functionality 394 | Min1 Minor Deductions 1 395 | Min2 Minor Deductions 2 396 | Mod Moderate Deductions 397 | Maj1 Major Deductions 1 398 | Maj2 Major Deductions 2 399 | Sev Severely Damaged 400 | Sal Salvage only 401 | 402 | Fireplaces: Number of fireplaces 403 | 404 | FireplaceQu: Fireplace quality 405 | 406 | Ex Excellent - Exceptional Masonry Fireplace 407 | Gd Good - Masonry Fireplace in main level 408 | TA Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basement 409 | Fa Fair - Prefabricated Fireplace in basement 410 | Po Poor - Ben Franklin Stove 411 | NA No Fireplace 412 | 413 | GarageType: Garage location 414 | 415 | 2Types More than one type of garage 416 | Attchd Attached to home 417 | Basment Basement Garage 418 | BuiltIn Built-In (Garage part of house - typically has room above garage) 419 | CarPort Car Port 420 | Detchd Detached from home 421 | NA No Garage 422 | 423 | GarageYrBlt: Year garage was built 424 | 425 | GarageFinish: Interior finish of the garage 426 | 427 | Fin Finished 428 | RFn Rough Finished 429 | Unf Unfinished 430 | NA No Garage 431 | 432 | GarageCars: Size of garage in car capacity 433 | 434 | GarageArea: Size of garage in square feet 435 | 436 | GarageQual: Garage quality 437 | 438 | Ex Excellent 439 | Gd Good 440 | TA Typical/Average 441 | Fa Fair 442 | Po Poor 443 | NA No Garage 444 | 445 | GarageCond: Garage condition 446 | 447 | Ex Excellent 448 | Gd Good 449 | TA Typical/Average 450 | Fa Fair 451 | Po Poor 452 | NA No Garage 453 | 454 | PavedDrive: Paved driveway 455 | 456 | Y Paved 457 | P Partial Pavement 458 | N Dirt/Gravel 459 | 460 | WoodDeckSF: Wood deck area in square feet 461 | 462 | OpenPorchSF: Open porch area in square feet 463 | 464 | EnclosedPorch: Enclosed porch area in square feet 465 | 466 | 3SsnPorch: Three season porch area in square feet 467 | 468 | ScreenPorch: Screen porch area in square feet 469 | 470 | PoolArea: Pool area in square feet 471 | 472 | PoolQC: Pool quality 473 | 474 | Ex Excellent 475 | Gd Good 476 | TA Average/Typical 477 | Fa Fair 478 | NA No Pool 479 | 480 | Fence: Fence quality 481 | 482 | GdPrv Good Privacy 483 | MnPrv Minimum Privacy 484 | GdWo Good Wood 485 | MnWw Minimum Wood/Wire 486 | NA No Fence 487 | 488 | MiscFeature: Miscellaneous feature not covered in other categories 489 | 490 | Elev Elevator 491 | Gar2 2nd Garage (if not described in garage section) 492 | Othr Other 493 | Shed Shed (over 100 SF) 494 | TenC Tennis Court 495 | NA None 496 | 497 | MiscVal: $Value of miscellaneous feature 498 | 499 | MoSold: Month Sold (MM) 500 | 501 | YrSold: Year Sold (YYYY) 502 | 503 | SaleType: Type of sale 504 | 505 | WD Warranty Deed - Conventional 506 | CWD Warranty Deed - Cash 507 | VWD Warranty Deed - VA Loan 508 | New Home just constructed and sold 509 | COD Court Officer Deed/Estate 510 | Con Contract 15% Down payment regular terms 511 | ConLw Contract Low Down payment and low interest 512 | ConLI Contract Low Interest 513 | ConLD Contract Low Down 514 | Oth Other 515 | 516 | SaleCondition: Condition of sale 517 | 518 | Normal Normal Sale 519 | Abnorml Abnormal Sale - trade, foreclosure, short sale 520 | AdjLand Adjoining Land Purchase 521 | Alloca Allocation - two linked properties with separate deeds, typically condo with a garage unit 522 | Family Sale between family members 523 | Partial Home was not completed when last assessed (associated with New Homes) 524 | -------------------------------------------------------------------------------- /data/cement_slump.csv: -------------------------------------------------------------------------------- 1 | Cement,Slag,Fly ash,Water,SP,Coarse Aggr.,Fine Aggr.,SLUMP(cm),FLOW(cm),Compressive Strength (28-day)(Mpa) 2 | 273,82,105,210,9,904,680,23,62,34.99 3 | 163,149,191,180,12,843,746,0,20,41.14 4 | 162,148,191,179,16,840,743,1,20,41.81 5 | 162,148,190,179,19,838,741,3,21.5,42.08 6 | 154,112,144,220,10,923,658,20,64,26.82 7 | 147,89,115,202,9,860,829,23,55,25.21 8 | 152,139,178,168,18,944,695,0,20,38.86 9 | 145,0,227,240,6,750,853,14.5,58.5,36.59 10 | 152,0,237,204,6,785,892,15.5,51,32.71 11 | 304,0,140,214,6,895,722,19,51,38.46 12 | 145,106,136,208,10,751,883,24.5,61,26.02 13 | 148,109,139,193,7,768,902,23.75,58,28.03 14 | 142,130,167,215,6,735,836,25.5,67,31.37 15 | 354,0,0,234,6,959,691,17,54,33.91 16 | 374,0,0,190,7,1013,730,14.5,42.5,32.44 17 | 159,116,149,175,15,953,720,23.5,54.5,34.05 18 | 153,0,239,200,6,1002,684,12,35,28.29 19 | 295,106,136,206,11,750,766,25,68.5,41.01 20 | 310,0,143,168,10,914,804,20.5,48.2,49.3 21 | 296,97,0,219,9,932,685,15,48.5,29.23 22 | 305,100,0,196,10,959,705,20,49,29.77 23 | 310,0,143,218,10,787,804,13,46,36.19 24 | 148,180,0,183,11,972,757,0,20,18.52 25 | 146,178,0,192,11,961,749,18,46,17.19 26 | 142,130,167,174,11,883,785,0,20,36.72 27 | 140,128,164,183,12,871,775,23.75,53,33.38 28 | 308,111,142,217,10,783,686,25,70,42.08 29 | 295,106,136,208,6,871,650,26.5,70,39.4 30 | 298,107,137,201,6,878,655,16,26,41.27 31 | 314,0,161,207,6,851,757,21.5,64,41.14 32 | 321,0,164,190,5,870,774,24,60,45.82 33 | 349,0,178,230,6,785,721,20,68.5,43.95 34 | 366,0,187,191,7,824,757,24.75,62.7,52.65 35 | 274,89,115,202,9,759,827,26.5,68,35.52 36 | 137,167,214,226,6,708,757,27.5,70,34.45 37 | 275,99,127,184,13,810,790,25.75,64.5,43.54 38 | 252,76,97,194,8,835,821,23,54,33.11 39 | 165,150,0,182,12,1023,729,14.5,20,18.26 40 | 158,0,246,174,7,1035,706,19,43,34.99 41 | 156,0,243,180,11,1022,698,21,57,33.78 42 | 145,177,227,209,11,752,715,2.5,20,35.66 43 | 154,141,181,234,11,797,683,23,65,33.51 44 | 160,146,188,203,11,829,710,13,38,33.51 45 | 291,105,0,205,6,859,797,24,59,27.62 46 | 298,107,0,186,6,879,815,3,20,30.97 47 | 318,126,0,210,6,861,737,17.5,48,31.77 48 | 280,92,118,207,9,883,679,25.5,64,37.39 49 | 287,94,121,188,9,904,696,25,61,43.01 50 | 332,0,170,160,6,900,806,0,20,58.53 51 | 326,0,167,174,6,884,792,21.5,42,52.65 52 | 320,0,163,188,9,866,776,23.5,60,45.69 53 | 342,136,0,225,11,770,747,21,61,32.04 54 | 356,142,0,193,11,801,778,8,30,36.46 55 | 309,0,142,218,10,912,680,24,62,38.59 56 | 322,0,149,186,8,951,709,20.5,61.5,45.42 57 | 159,193,0,208,12,821,818,23,50,19.19 58 | 307,110,0,189,10,904,765,22,40,31.5 59 | 313,124,0,205,11,846,758,22,49,29.63 60 | 143,131,168,217,6,891,672,25,69,26.42 61 | 140,128,164,237,6,869,656,24,65,29.5 62 | 278,0,117,205,9,875,799,19,48,32.71 63 | 288,0,121,177,7,908,829,22.5,48.5,39.93 64 | 299,107,0,210,10,881,745,25,63,28.29 65 | 291,104,0,231,9,857,725,23,69,30.43 66 | 265,86,111,195,6,833,790,27,60,37.39 67 | 159,0,248,175,12,1041,683,21,51,35.39 68 | 160,0,250,168,12,1049,688,18,48,37.66 69 | 166,0,260,183,13,859,827,21,54,40.34 70 | 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3.4359,0.66216,2.1041,1.8922,0 443 | 0.86816,10.2429,-1.4912,-4.0082,0 444 | 3.359,9.8022,-3.8209,-3.7133,0 445 | 3.6702,2.9942,0.85141,0.30688,0 446 | 1.3349,6.1189,0.46497,0.49826,0 447 | 3.1887,-3.4143,2.7742,-0.2026,0 448 | 2.4527,2.9653,0.20021,-0.056479,0 449 | 3.9121,2.9735,0.92852,0.60558,0 450 | 3.9364,10.5885,-3.725,-4.3133,0 451 | 3.9414,-3.2902,3.1674,1.0866,0 452 | 3.6922,-3.9585,4.3439,1.3517,0 453 | 5.681,7.795,-2.6848,-0.92544,0 454 | 0.77124,9.0862,-1.2281,-1.4996,0 455 | 3.5761,9.7753,-3.9795,-3.4638,0 456 | 1.602,6.1251,0.52924,0.47886,0 457 | 2.6682,10.216,-3.4414,-4.0069,0 458 | 2.0007,1.8644,2.6491,0.47369,0 459 | 0.64215,3.1287,4.2933,0.64696,0 460 | 4.3848,-3.0729,3.0423,1.2741,0 461 | 0.77445,9.0552,-2.4089,-1.3884,0 462 | 0.96574,8.393,-1.361,-1.4659,0 463 | 3.0948,8.7324,-2.9007,-0.96682,0 464 | 4.9362,7.6046,-2.3429,-0.85302,0 465 | -1.9458,11.2217,1.9079,-3.4405,0 466 | 5.7403,-0.44284,0.38015,1.3763,0 467 | -2.6989,12.1984,0.67661,-8.5482,0 468 | 1.1472,3.5985,1.9387,-0.43406,0 469 | 2.9742,8.96,-2.9024,-1.0379,0 470 | 4.5707,7.2094,-3.2794,-1.4944,0 471 | 0.1848,6.5079,2.0133,-0.87242,0 472 | 0.87256,9.2931,-0.7843,-2.1978,0 473 | 0.39559,6.8866,1.0588,-0.67587,0 474 | 3.8384,6.1851,-2.0439,-0.033204,0 475 | 2.8209,7.3108,-0.81857,-1.8784,0 476 | 2.5817,9.7546,-3.1749,-2.9957,0 477 | 3.8213,0.23175,2.0133,2.0564,0 478 | 0.3798,0.7098,0.7572,-0.4444,0 479 | 3.4893,6.69,-1.2042,-0.38751,0 480 | -1.7781,0.8546,7.1303,0.027572,0 481 | 2.0962,2.4769,1.9379,-0.040962,0 482 | 0.94732,-0.57113,7.1903,-0.67587,0 483 | 2.8261,9.4007,-3.3034,-1.0509,0 484 | 0.0071249,8.3661,0.50781,-3.8155,0 485 | 0.96788,7.1907,1.2798,-2.4565,0 486 | 4.7432,2.1086,0.1368,1.6543,0 487 | 3.6575,7.2797,-2.2692,-1.144,0 488 | 3.8832,6.4023,-2.432,-0.98363,0 489 | 3.4776,8.811,-3.1886,-0.92285,0 490 | 1.1315,7.9212,1.093,-2.8444,0 491 | 2.8237,2.8597,0.19678,0.57196,0 492 | 1.9321,6.0423,0.26019,-2.053,0 493 | 3.0632,-3.3315,5.1305,0.8267,0 494 | 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8.1,5.6,1 410 | 4.3,8.3,0 411 | 6.4,3.4,1 412 | 4.8,5.1,1 413 | 6.7,7.1,0 414 | 5.1,6.0,1 415 | 5.9,7.4,0 416 | 5.4,7.6,0 417 | 3.0,5.9,0 418 | 3.1,6.2,0 419 | 6.4,6.6,0 420 | 4.5,5.9,1 421 | 5.7,6.5,0 422 | 6.9,8.4,0 423 | 5.9,4.1,1 424 | 2.5,5.8,0 425 | 4.2,6.2,0 426 | 3.9,7.7,0 427 | 4.2,7.9,0 428 | 4.5,7.7,0 429 | 6.2,4.8,0 430 | 5.0,7.4,0 431 | 6.0,8.4,0 432 | 7.1,4.5,1 433 | 5.8,6.8,0 434 | 8.9,5.5,1 435 | 3.5,6.2,0 436 | 2.7,7.1,0 437 | 2.5,6.6,0 438 | 1.9,7.5,0 439 | 6.0,5.8,1 440 | 7.5,2.2,1 441 | 4.2,6.8,0 442 | 7.3,4.6,0 443 | 4.3,3.2,1 444 | 6.0,2.6,1 445 | 1.9,6.2,0 446 | 6.9,3.4,1 447 | 6.9,7.0,0 448 | 5.3,2.1,1 449 | 8.2,6.7,1 450 | 6.0,5.1,0 451 | 8.2,3.7,1 452 | 8.9,6.5,1 453 | 3.6,8.4,0 454 | 8.1,5.6,1 455 | 1.6,3.5,0 456 | 2.5,5.1,0 457 | 7.3,7.7,0 458 | 3.8,7.3,0 459 | 6.5,4.6,0 460 | 5.8,8.0,0 461 | 6.9,4.7,0 462 | 2.9,5.8,0 463 | 5.9,7.2,0 464 | 6.9,5.4,0 465 | 6.3,5.5,0 466 | 2.6,4.6,0 467 | 4.5,3.4,1 468 | 5.3,8.0,0 469 | 3.8,8.7,0 470 | 6.3,5.2,0 471 | 7.3,4.6,1 472 | 4.5,7.9,0 473 | 6.3,3.7,1 474 | 7.1,2.4,1 475 | 5.8,5.3,0 476 | 8.0,4.9,1 477 | 7.4,4.7,1 478 | 7.4,2.4,1 479 | 7.7,4.2,1 480 | 2.6,6.2,0 481 | 4.1,7.8,0 482 | 7.0,3.6,1 483 | 7.4,7.0,0 484 | 4.7,5.4,1 485 | 7.8,1.9,1 486 | 7.0,4.9,1 487 | 7.3,1.0,1 488 | 6.0,4.1,1 489 | 7.3,3.8,1 490 | 7.0,5.2,0 491 | 8.4,5.8,1 492 | 4.3,7.7,0 493 | 5.0,3.7,1 494 | 9.0,6.2,1 495 | 8.0,5.8,1 496 | 5.3,3.4,1 497 | 4.4,7.6,0 498 | 5.6,7.1,0 499 | 5.5,1.4,1 500 | 6.1,7.3,0 501 | 8.6,4.7,1 502 | 4.9,5.4,1 503 | 5.4,8.4,0 504 | 3.8,7.2,0 505 | 8.6,4.3,1 506 | 6.2,6.1,0 507 | 5.4,3.7,1 508 | 6.6,3.7,1 509 | 3.9,8.5,0 510 | 8.3,4.1,1 511 | 3.3,8.7,0 512 | 8.0,3.8,1 513 | 6.4,5.5,0 514 | 5.7,6.7,0 515 | 6.3,4.7,1 516 | 4.1,7.0,0 517 | 7.2,3.3,1 518 | 4.1,7.7,0 519 | 5.7,6.0,0 520 | 2.4,6.2,0 521 | 4.6,4.5,1 522 | 6.2,5.7,0 523 | 4.0,7.5,0 524 | 5.1,3.7,1 525 | 3.9,8.5,0 526 | 3.1,6.7,0 527 | 4.7,5.6,1 528 | 8.4,6.0,1 529 | 7.2,3.7,1 530 | 4.7,4.1,1 531 | 8.7,3.7,1 532 | 5.2,2.5,1 533 | 3.1,7.7,0 534 | 2.7,6.1,0 535 | 8.1,6.1,1 536 | 5.9,5.3,0 537 | 7.5,1.6,1 538 | 2.6,4.0,0 539 | 8.4,4.4,1 540 | 3.7,5.0,0 541 | 3.8,6.8,0 542 | 6.1,3.3,1 543 | 4.7,4.9,1 544 | 5.7,8.1,0 545 | 7.2,3.1,1 546 | 5.0,7.7,0 547 | 3.3,5.9,0 548 | 6.3,6.6,0 549 | 7.3,3.3,1 550 | 7.3,3.2,1 551 | 3.5,7.6,0 552 | 5.1,5.0,1 553 | 7.6,3.0,1 554 | 6.3,8.8,0 555 | 3.6,6.1,1 556 | 6.1,6.6,0 557 | 8.2,2.7,1 558 | 4.3,8.2,0 559 | 6.5,4.7,0 560 | 7.3,7.3,0 561 | 5.6,8.6,0 562 | 8.3,5.3,1 563 | 5.6,2.6,1 564 | 5.9,2.1,1 565 | 3.8,6.9,0 566 | 6.0,7.4,0 567 | 5.7,4.5,1 568 | 5.3,5.9,1 569 | 5.8,7.8,0 570 | 2.7,6.6,0 571 | 7.2,4.7,1 572 | 5.4,8.3,0 573 | 6.7,4.3,0 574 | 8.9,4.4,1 575 | 3.7,6.3,0 576 | 6.7,2.3,1 577 | 6.3,6.6,0 578 | 5.2,8.3,0 579 | 4.0,6.8,0 580 | 4.0,6.8,0 581 | 6.6,6.3,0 582 | 5.6,6.7,0 583 | 6.4,5.6,0 584 | 7.7,5.0,1 585 | 9.1,5.4,1 586 | 5.8,4.4,0 587 | 4.4,6.8,1 588 | 7.8,3.4,1 589 | 3.8,5.0,1 590 | 4.3,6.9,0 591 | 5.2,3.7,1 592 | 9.3,5.6,1 593 | 8.3,4.6,1 594 | 8.6,5.1,1 595 | 1.5,5.3,0 596 | 2.5,6.0,0 597 | 7.6,4.9,0 598 | 4.9,3.7,1 599 | 5.9,7.3,0 600 | 4.5,4.2,1 601 | 3.1,5.4,0 602 | 5.2,7.7,0 603 | 4.8,8.3,0 604 | 6.1,5.7,0 605 | 5.2,4.1,1 606 | 3.1,6.3,0 607 | 5.0,5.6,1 608 | 3.8,8.4,0 609 | 4.5,4.0,1 610 | 8.4,6.2,1 611 | 6.3,3.5,1 612 | 8.8,5.2,1 613 | 6.4,6.6,0 614 | 7.6,3.3,1 615 | 3.2,6.2,0 616 | 2.8,6.1,0 617 | 9.1,5.9,1 618 | 4.4,6.0,1 619 | 2.4,6.4,0 620 | 6.6,6.1,0 621 | 6.8,4.2,0 622 | 9.2,7.4,1 623 | 5.1,6.1,0 624 | 4.8,4.8,1 625 | 5.9,4.6,1 626 | 8.8,2.4,1 627 | 7.3,2.5,1 628 | 4.7,7.5,0 629 | 2.0,6.4,0 630 | 4.8,5.5,1 631 | 6.0,8.5,0 632 | 5.0,8.0,0 633 | 8.4,4.7,1 634 | 8.3,5.2,1 635 | 6.9,1.9,1 636 | 7.8,4.6,1 637 | 5.5,7.0,0 638 | 7.9,5.0,1 639 | 5.6,7.8,0 640 | 8.2,4.6,1 641 | 3.3,7.1,0 642 | 4.8,3.8,1 643 | 2.3,5.8,0 644 | 4.7,6.3,1 645 | 7.8,3.6,1 646 | 4.5,7.8,0 647 | 3.1,8.0,0 648 | 8.3,5.8,1 649 | 7.0,5.6,0 650 | 4.5,8.1,0 651 | 5.3,3.4,1 652 | 3.8,6.3,1 653 | 6.0,5.6,0 654 | 9.2,4.8,1 655 | 8.1,3.9,1 656 | 7.7,5.1,1 657 | 7.4,3.3,1 658 | 3.4,7.0,0 659 | 7.7,3.1,1 660 | 3.6,7.8,0 661 | 8.8,7.4,1 662 | 3.4,7.9,0 663 | 7.9,3.7,1 664 | 6.7,3.1,1 665 | 5.8,6.0,0 666 | 5.5,7.4,0 667 | 6.6,2.2,1 668 | 7.1,3.8,1 669 | 5.4,6.1,0 670 | 5.1,3.4,1 671 | 4.5,2.5,1 672 | 7.0,3.7,1 673 | 4.4,5.9,1 674 | 4.3,9.0,0 675 | 6.6,3.0,1 676 | 3.7,8.0,0 677 | 7.6,3.7,1 678 | 2.9,6.9,0 679 | 4.3,8.3,0 680 | 5.3,4.2,1 681 | 2.2,4.7,0 682 | 7.2,3.4,1 683 | 6.2,2.9,1 684 | 9.3,4.1,1 685 | 3.8,6.4,0 686 | 4.2,6.4,0 687 | 5.5,2.7,1 688 | 2.7,4.0,0 689 | 5.0,3.7,1 690 | 5.6,4.3,1 691 | 3.2,6.8,0 692 | 7.9,2.8,1 693 | 7.4,3.4,1 694 | 3.0,5.2,0 695 | 4.5,6.2,1 696 | 8.1,4.7,1 697 | 8.1,4.2,1 698 | 6.6,3.6,1 699 | 6.8,2.6,1 700 | 4.2,6.1,1 701 | 2.7,6.0,0 702 | 5.9,6.5,0 703 | 5.6,5.1,1 704 | 6.4,5.2,0 705 | 6.4,2.9,1 706 | 6.2,4.5,0 707 | 6.5,2.9,1 708 | 6.6,5.3,0 709 | 5.7,5.6,0 710 | 7.0,5.2,0 711 | 8.1,4.2,1 712 | 5.4,5.0,1 713 | 3.0,7.4,0 714 | 4.3,7.2,0 715 | 3.2,6.2,0 716 | 8.2,3.5,1 717 | 2.1,4.0,0 718 | 6.9,3.0,1 719 | 3.8,8.3,0 720 | 4.7,7.2,0 721 | 2.4,5.7,0 722 | 2.5,6.0,0 723 | 5.0,4.0,1 724 | 7.4,3.5,1 725 | 5.8,6.3,0 726 | 2.9,7.4,0 727 | 2.7,2.7,0 728 | 7.1,5.1,0 729 | 6.4,3.9,1 730 | 6.2,1.5,1 731 | 5.7,8.6,0 732 | 9.7,5.2,1 733 | 5.1,3.7,1 734 | 6.0,5.8,0 735 | 8.7,5.0,1 736 | 2.4,7.1,0 737 | 8.1,2.8,1 738 | 6.9,4.4,0 739 | 6.5,7.0,0 740 | 3.6,9.2,0 741 | 8.3,4.4,1 742 | 4.9,7.7,0 743 | 3.2,4.6,0 744 | 5.7,6.8,0 745 | 2.0,5.4,0 746 | 8.7,4.6,1 747 | 6.6,4.9,0 748 | 4.4,4.3,1 749 | 8.6,4.9,1 750 | 5.0,5.0,1 751 | 7.3,5.7,0 752 | 9.2,6.4,1 753 | 7.3,6.6,0 754 | 3.0,7.9,0 755 | 4.8,6.9,1 756 | 5.0,6.1,0 757 | 8.6,4.9,1 758 | 5.0,7.6,0 759 | 4.9,7.9,0 760 | 6.3,4.0,1 761 | 3.3,8.0,0 762 | 2.4,5.0,0 763 | 8.5,6.3,1 764 | 5.3,6.6,0 765 | 5.7,3.2,1 766 | 8.2,1.9,1 767 | 5.0,7.5,0 768 | 6.6,4.0,1 769 | 8.4,3.8,1 770 | 7.3,3.8,1 771 | 6.9,3.8,1 772 | 5.1,4.7,1 773 | 4.1,6.7,0 774 | 5.5,3.1,1 775 | 2.5,5.2,0 776 | 6.3,6.9,0 777 | 4.1,6.1,0 778 | 3.1,5.3,0 779 | 8.5,5.3,1 780 | 4.6,8.7,0 781 | 6.5,7.6,0 782 | 6.4,5.6,0 783 | 5.3,3.9,1 784 | 6.8,2.9,1 785 | 5.6,8.2,0 786 | 2.9,4.8,0 787 | 3.9,8.2,0 788 | 8.2,2.8,1 789 | 7.0,4.0,1 790 | 6.5,2.4,1 791 | 5.1,5.8,1 792 | 5.3,3.4,1 793 | 3.6,7.3,0 794 | 5.8,7.7,0 795 | 4.8,6.4,0 796 | 3.4,5.6,0 797 | 4.4,4.6,1 798 | 8.2,4.1,1 799 | 3.1,6.1,0 800 | 9.5,6.3,1 801 | 7.6,4.3,1 802 | 5.5,7.7,0 803 | 6.6,3.4,1 804 | 4.1,4.2,1 805 | 7.0,6.4,0 806 | 3.0,3.9,0 807 | 7.6,2.7,1 808 | 4.6,4.2,1 809 | 5.6,6.7,0 810 | 3.2,7.9,0 811 | 7.2,7.1,0 812 | 4.0,6.9,0 813 | 4.8,2.3,1 814 | 5.7,3.5,1 815 | 2.3,4.2,0 816 | 3.2,5.4,0 817 | 8.2,5.0,1 818 | 5.8,3.3,1 819 | 7.4,5.7,0 820 | 7.2,3.2,1 821 | 4.1,7.3,0 822 | 6.5,1.6,1 823 | 5.8,8.8,0 824 | 8.5,5.6,1 825 | 6.5,6.9,0 826 | 7.5,4.2,1 827 | 5.0,6.6,1 828 | 6.1,1.5,1 829 | 1.8,4.9,0 830 | 3.4,5.7,0 831 | 3.2,7.6,0 832 | 8.1,4.7,1 833 | 2.8,8.1,0 834 | 4.8,6.4,0 835 | 3.3,8.2,0 836 | 5.0,7.3,0 837 | 5.1,7.9,0 838 | 6.8,3.9,0 839 | 7.2,1.8,1 840 | 7.7,3.4,1 841 | 9.4,4.9,1 842 | 7.1,2.1,1 843 | 8.3,5.3,1 844 | 7.5,3.8,1 845 | 4.7,2.0,1 846 | 3.7,7.6,0 847 | 5.0,7.6,0 848 | 7.5,3.5,1 849 | 3.2,6.8,0 850 | 5.5,4.0,1 851 | 3.5,8.2,0 852 | 5.3,5.0,1 853 | 4.2,6.3,1 854 | 5.3,4.3,1 855 | 5.6,3.8,1 856 | 2.5,4.2,0 857 | 6.8,8.5,0 858 | 2.3,4.6,0 859 | 3.2,7.2,1 860 | 7.2,1.7,1 861 | 7.5,3.6,1 862 | 8.7,5.0,1 863 | 4.9,7.9,0 864 | 4.3,7.4,0 865 | 4.2,6.5,0 866 | 5.8,7.0,0 867 | 8.4,2.5,1 868 | 4.2,7.8,0 869 | 6.2,3.5,1 870 | 8.0,6.1,1 871 | 8.4,4.0,1 872 | 6.0,6.0,0 873 | 5.4,6.8,0 874 | 6.4,6.2,0 875 | 8.3,4.4,1 876 | 4.8,8.4,0 877 | 5.6,7.4,0 878 | 2.5,6.1,0 879 | 7.1,3.6,1 880 | 2.5,4.7,0 881 | 5.3,7.3,0 882 | 9.0,4.3,1 883 | 4.8,4.1,1 884 | 2.3,6.7,0 885 | 5.2,6.4,1 886 | 4.7,7.7,0 887 | 8.4,4.1,1 888 | 4.8,7.4,1 889 | 8.1,5.6,1 890 | 4.8,6.4,0 891 | 5.9,3.0,1 892 | 3.5,8.2,0 893 | 7.8,4.1,1 894 | 3.6,6.0,0 895 | 5.5,6.3,0 896 | 4.5,5.4,1 897 | 7.1,3.3,1 898 | 3.6,8.6,0 899 | 5.3,4.7,1 900 | 6.5,4.6,0 901 | 6.5,4.2,0 902 | 5.5,7.4,0 903 | 5.6,7.1,0 904 | 8.2,4.1,1 905 | 7.0,4.8,0 906 | 5.0,3.8,1 907 | 3.2,7.0,0 908 | 4.2,6.8,0 909 | 3.0,8.3,0 910 | 6.4,4.5,0 911 | 4.0,8.4,0 912 | 6.7,3.2,1 913 | 7.3,4.7,1 914 | 7.2,3.4,1 915 | 2.3,7.3,0 916 | 4.7,6.7,0 917 | 2.5,5.3,0 918 | 5.8,4.1,1 919 | 9.0,2.8,1 920 | 2.2,5.1,0 921 | 6.4,5.1,0 922 | 9.1,6.5,1 923 | 6.7,7.3,0 924 | 7.7,6.2,1 925 | 6.1,6.9,0 926 | 5.7,2.4,1 927 | 2.9,3.2,0 928 | 3.3,7.2,0 929 | 4.7,5.7,1 930 | 3.1,7.8,0 931 | 6.3,2.4,1 932 | 5.9,6.6,0 933 | 2.4,3.8,0 934 | 5.9,3.3,1 935 | 4.7,4.6,1 936 | 7.2,3.2,1 937 | 5.3,5.6,1 938 | 5.0,3.6,1 939 | 3.6,6.6,0 940 | 5.0,4.0,1 941 | 2.5,5.7,0 942 | 9.1,3.2,1 943 | 5.8,7.5,0 944 | 2.5,5.4,0 945 | 4.3,6.4,0 946 | 6.8,6.2,0 947 | 2.8,5.4,0 948 | 5.0,8.4,0 949 | 8.3,5.9,1 950 | 6.1,4.7,1 951 | 2.5,6.0,0 952 | 4.6,6.7,1 953 | 5.1,5.1,1 954 | 6.3,7.0,0 955 | 8.6,4.4,1 956 | 2.7,6.2,0 957 | 5.5,7.3,0 958 | 4.2,5.2,1 959 | 7.8,3.5,1 960 | 5.2,7.2,0 961 | 8.3,5.7,1 962 | 4.1,7.9,0 963 | 2.6,5.0,0 964 | 5.1,7.2,0 965 | 9.0,5.4,1 966 | 9.0,5.1,1 967 | 3.1,7.0,0 968 | 6.9,4.7,1 969 | 4.1,8.2,0 970 | 3.9,6.7,0 971 | 3.1,7.4,0 972 | 9.1,3.9,1 973 | 5.0,3.3,1 974 | 4.7,8.1,0 975 | 3.1,7.0,0 976 | 9.1,5.8,1 977 | 7.5,2.3,1 978 | 6.9,3.3,1 979 | 6.9,3.2,1 980 | 5.6,3.2,1 981 | 4.3,6.7,0 982 | 4.1,7.6,0 983 | 7.9,2.4,1 984 | 6.3,3.2,1 985 | 3.3,9.4,0 986 | 7.0,2.8,1 987 | 7.1,3.7,1 988 | 5.8,3.8,1 989 | 4.3,8.6,0 990 | 4.1,8.3,0 991 | 5.2,5.5,1 992 | 8.8,6.9,1 993 | 7.0,5.0,0 994 | 5.4,5.0,1 995 | 7.0,3.5,1 996 | 6.6,2.3,1 997 | 4.6,7.0,0 998 | 6.0,6.8,0 999 | 2.5,7.3,0 1000 | 3.5,7.2,0 1001 | 3.0,6.5,0 1002 | 8.2,6.3,1 1003 | 5.2,7.5,0 1004 | 4.2,6.8,0 1005 | 6.7,7.0,0 1006 | 6.3,7.0,0 1007 | 7.0,3.1,1 1008 | 6.4,3.1,1 1009 | 4.1,8.5,0 1010 | 6.2,3.5,1 1011 | 6.4,6.6,0 1012 | 3.1,4.6,0 1013 | 7.3,1.8,1 1014 | 6.0,7.1,0 1015 | 2.2,6.4,0 1016 | 7.5,3.6,1 1017 | 4.4,4.2,1 1018 | 5.1,7.6,0 1019 | 5.1,5.6,1 1020 | 7.0,6.1,0 1021 | 5.3,4.6,1 1022 | 3.4,6.9,0 1023 | 2.7,5.2,0 1024 | 5.3,6.2,0 1025 | 6.5,3.3,1 1026 | 7.9,3.1,1 1027 | 5.8,5.1,0 1028 | 3.6,7.5,0 1029 | 3.7,9.4,0 1030 | 4.0,8.9,0 1031 | 3.5,6.3,0 1032 | 7.0,2.1,1 1033 | 7.6,4.1,1 1034 | 3.5,4.7,0 1035 | 6.1,5.7,0 1036 | 4.8,4.5,1 1037 | 4.2,5.2,1 1038 | 5.6,7.9,0 1039 | 5.9,2.9,1 1040 | 4.5,7.6,0 1041 | 9.3,4.1,1 1042 | 5.8,1.2,1 1043 | 6.3,4.2,1 1044 | 7.0,4.9,1 1045 | 5.1,7.9,0 1046 | 7.8,4.1,1 1047 | 4.5,7.8,0 1048 | 4.9,5.4,1 1049 | 6.9,5.4,0 1050 | 4.1,4.7,1 1051 | 3.1,6.3,0 1052 | 8.2,5.2,1 1053 | 5.4,4.2,1 1054 | 5.6,4.9,1 1055 | 2.1,5.6,0 1056 | 5.5,1.2,1 1057 | 7.0,1.2,1 1058 | 2.0,5.2,0 1059 | 5.4,3.2,1 1060 | 6.6,6.9,0 1061 | 6.3,4.1,1 1062 | 5.2,3.4,1 1063 | 3.8,6.0,1 1064 | 5.9,7.5,0 1065 | 4.0,6.9,0 1066 | 2.4,5.9,0 1067 | 5.7,6.1,1 1068 | 4.3,5.4,1 1069 | 7.2,3.7,1 1070 | 8.1,6.3,1 1071 | 3.9,6.0,1 1072 | 6.6,5.0,0 1073 | 6.7,4.7,1 1074 | 4.9,7.4,0 1075 | 2.7,7.3,0 1076 | 7.5,5.3,0 1077 | 5.5,2.9,1 1078 | 2.9,7.1,0 1079 | 4.0,8.2,0 1080 | 7.2,2.0,1 1081 | 6.8,5.8,0 1082 | 6.3,7.2,0 1083 | 4.2,5.0,1 1084 | 2.8,6.9,0 1085 | 5.7,8.3,0 1086 | 6.3,5.9,0 1087 | 5.7,5.2,1 1088 | 9.5,4.7,1 1089 | 7.8,3.7,1 1090 | 2.6,8.1,0 1091 | 2.7,5.7,0 1092 | 6.3,5.6,0 1093 | 6.5,6.1,0 1094 | 5.1,4.3,1 1095 | 4.9,8.1,0 1096 | 6.6,4.1,1 1097 | 6.6,4.7,1 1098 | 2.5,6.1,0 1099 | 8.2,4.1,1 1100 | 5.4,2.0,1 1101 | 3.6,6.6,0 1102 | 7.5,5.3,0 1103 | 5.5,4.3,1 1104 | 7.0,2.9,1 1105 | 5.9,9.2,0 1106 | 8.7,5.8,1 1107 | 6.5,2.6,1 1108 | 9.5,5.8,1 1109 | 8.9,5.5,1 1110 | 2.9,6.0,0 1111 | 4.0,6.2,1 1112 | 6.3,3.7,1 1113 | 6.0,6.7,0 1114 | 4.9,3.5,1 1115 | 3.5,5.6,0 1116 | 7.7,3.9,1 1117 | 5.4,7.7,0 1118 | 4.6,3.3,1 1119 | 8.1,5.7,1 1120 | 8.1,3.3,1 1121 | 2.9,5.6,0 1122 | 4.6,4.3,1 1123 | 4.5,7.0,0 1124 | 8.8,5.2,1 1125 | 5.8,6.5,0 1126 | 5.3,7.1,0 1127 | 3.2,6.4,0 1128 | 6.0,6.4,0 1129 | 6.2,1.9,1 1130 | 5.9,6.1,0 1131 | 5.0,6.6,1 1132 | 5.4,3.7,1 1133 | 6.7,3.1,0 1134 | 7.5,3.7,1 1135 | 3.8,7.8,0 1136 | 4.1,3.9,1 1137 | 5.2,5.1,1 1138 | 2.4,6.0,0 1139 | 6.5,5.7,0 1140 | 5.5,7.0,0 1141 | 6.9,4.3,1 1142 | 2.4,4.9,0 1143 | 1.8,2.9,0 1144 | 8.2,4.3,1 1145 | 4.2,7.2,0 1146 | 4.0,7.3,0 1147 | 4.4,6.5,1 1148 | 6.0,7.1,0 1149 | 2.4,6.6,0 1150 | 5.1,2.2,1 1151 | 5.1,5.8,1 1152 | 4.9,3.7,1 1153 | 6.4,3.5,1 1154 | 5.2,6.7,0 1155 | 7.2,4.0,0 1156 | 4.7,5.1,1 1157 | 4.2,8.5,0 1158 | 3.0,7.7,0 1159 | 6.2,3.5,1 1160 | 4.9,8.2,0 1161 | 4.7,7.0,0 1162 | 6.0,5.6,0 1163 | 3.1,5.5,0 1164 | 5.0,3.0,1 1165 | 3.3,7.5,0 1166 | 2.8,5.5,0 1167 | 6.9,4.4,1 1168 | 7.4,3.8,1 1169 | 7.0,3.9,1 1170 | 5.4,3.8,1 1171 | 4.5,7.7,0 1172 | 8.1,6.2,1 1173 | 7.0,5.2,0 1174 | 3.8,5.8,0 1175 | 5.8,2.4,1 1176 | 4.1,8.0,0 1177 | 2.0,5.2,0 1178 | 4.8,5.6,1 1179 | 8.5,5.2,1 1180 | 6.4,3.5,0 1181 | 8.4,5.6,1 1182 | 6.4,5.9,0 1183 | 3.8,8.4,0 1184 | 2.6,7.7,0 1185 | 8.8,5.3,1 1186 | 8.5,4.7,1 1187 | 6.5,7.1,0 1188 | 8.6,5.1,1 1189 | 3.9,8.2,0 1190 | 4.2,7.7,0 1191 | 8.3,5.1,1 1192 | 5.3,2.8,1 1193 | 5.7,5.2,0 1194 | 5.7,6.4,0 1195 | 5.9,4.0,1 1196 | 2.7,6.2,0 1197 | 5.6,7.2,0 1198 | 6.1,8.4,0 1199 | 4.6,7.3,0 1200 | 6.4,6.3,0 1201 | 6.5,2.3,1 1202 | 5.3,6.8,0 1203 | 8.6,5.8,1 1204 | 3.4,5.8,0 1205 | 3.8,7.3,0 1206 | 4.4,4.4,1 1207 | 7.2,4.3,1 1208 | 2.2,6.1,0 1209 | 8.2,3.5,1 1210 | 2.6,7.0,0 1211 | 7.5,4.9,0 1212 | 4.4,7.6,0 1213 | 4.2,6.3,0 1214 | 8.3,4.6,1 1215 | 5.1,7.2,0 1216 | 7.1,3.6,1 1217 | 5.6,7.7,0 1218 | 8.6,3.1,1 1219 | 6.9,3.8,1 1220 | 7.9,5.4,1 1221 | 3.8,6.4,0 1222 | 5.3,4.8,1 1223 | 4.6,5.3,1 1224 | 5.8,6.7,0 1225 | 5.2,2.9,1 1226 | 7.0,2.3,1 1227 | 4.1,5.4,1 1228 | 8.6,5.4,1 1229 | 5.3,7.5,0 1230 | 6.2,5.0,0 1231 | 3.2,7.5,0 1232 | 8.7,3.3,1 1233 | 6.7,4.0,0 1234 | 3.3,5.0,0 1235 | 6.6,6.5,0 1236 | 5.8,8.2,0 1237 | 6.0,3.6,1 1238 | 6.5,4.0,0 1239 | 5.8,3.3,1 1240 | 6.8,6.2,0 1241 | 7.2,5.7,0 1242 | 6.6,1.5,1 1243 | 8.2,6.3,1 1244 | 3.9,6.3,0 1245 | 7.9,5.4,1 1246 | 8.6,3.4,1 1247 | 5.0,5.3,1 1248 | 6.9,6.8,0 1249 | 2.9,5.3,0 1250 | 5.6,6.4,1 1251 | 7.2,4.0,1 1252 | 8.7,4.6,1 1253 | 8.4,5.6,1 1254 | 4.3,5.3,1 1255 | 8.2,5.8,1 1256 | 5.1,8.0,0 1257 | 2.5,4.9,0 1258 | 7.0,3.1,1 1259 | 5.9,6.8,0 1260 | 2.9,4.7,0 1261 | 3.7,7.3,0 1262 | 4.6,4.2,1 1263 | 3.2,5.5,0 1264 | 7.3,2.8,1 1265 | 6.9,6.2,0 1266 | 5.5,2.2,1 1267 | 6.3,3.6,1 1268 | 3.9,7.5,0 1269 | 7.8,2.2,1 1270 | 7.1,3.6,1 1271 | 7.3,4.8,1 1272 | 9.2,4.3,1 1273 | 8.0,5.1,1 1274 | 6.0,5.7,0 1275 | 6.4,4.7,0 1276 | 5.2,3.9,1 1277 | 3.3,8.2,0 1278 | 6.0,8.1,0 1279 | 5.9,8.0,0 1280 | 5.7,4.1,1 1281 | 5.4,3.5,1 1282 | 5.8,7.3,0 1283 | 5.3,2.2,1 1284 | 7.0,3.5,1 1285 | 7.8,5.5,1 1286 | 8.5,4.9,1 1287 | 6.1,3.0,1 1288 | 3.1,5.8,0 1289 | 5.0,4.3,1 1290 | 6.9,5.3,0 1291 | 4.1,7.8,0 1292 | 6.9,1.8,1 1293 | 6.1,6.6,0 1294 | 1.9,4.8,0 1295 | 3.5,7.5,0 1296 | 5.5,7.2,0 1297 | 5.1,3.7,1 1298 | 7.9,3.8,1 1299 | 8.2,5.3,1 1300 | 6.6,8.8,0 1301 | 5.3,5.1,0 1302 | 4.3,5.7,0 1303 | 6.2,7.7,0 1304 | 5.6,6.4,0 1305 | 8.4,7.2,1 1306 | 5.8,6.7,0 1307 | 3.5,6.9,0 1308 | 6.6,5.3,0 1309 | 5.6,6.8,0 1310 | 3.7,5.8,1 1311 | 8.0,5.5,1 1312 | 4.9,6.6,0 1313 | 6.0,3.2,1 1314 | 2.5,8.1,0 1315 | 3.0,6.1,0 1316 | 5.9,3.8,1 1317 | 2.3,6.2,0 1318 | 6.5,3.9,1 1319 | 6.1,4.4,0 1320 | 7.0,3.0,1 1321 | 6.3,5.1,1 1322 | 7.7,5.0,0 1323 | 2.8,5.2,0 1324 | 4.1,5.7,1 1325 | 5.8,4.7,1 1326 | 9.0,3.9,1 1327 | 5.2,2.5,1 1328 | 4.3,7.1,0 1329 | 8.4,5.2,1 1330 | 5.4,7.8,0 1331 | 3.1,6.0,0 1332 | 7.3,5.5,0 1333 | 7.6,3.9,1 1334 | 3.8,6.5,0 1335 | 8.8,5.7,1 1336 | 4.6,6.4,1 1337 | 6.7,3.6,1 1338 | 8.7,5.1,1 1339 | 7.1,3.9,1 1340 | 6.7,3.4,1 1341 | 2.5,5.7,0 1342 | 7.8,2.9,1 1343 | 5.6,7.6,0 1344 | 8.2,3.9,1 1345 | 7.0,5.9,0 1346 | 8.4,5.5,1 1347 | 4.0,6.2,0 1348 | 4.1,7.0,0 1349 | 5.2,5.7,1 1350 | 7.4,4.6,1 1351 | 7.7,4.5,1 1352 | 5.9,7.3,0 1353 | 4.7,7.0,0 1354 | 4.2,4.2,1 1355 | 8.3,4.7,1 1356 | 4.8,9.1,0 1357 | 6.1,4.8,1 1358 | 6.1,8.4,0 1359 | 8.8,4.8,1 1360 | 3.1,6.2,0 1361 | 6.0,5.5,0 1362 | 6.4,6.4,0 1363 | 5.7,2.6,1 1364 | 6.4,2.9,1 1365 | 8.6,6.4,1 1366 | 4.9,5.5,1 1367 | 4.5,8.5,0 1368 | 5.3,4.6,1 1369 | 4.4,6.7,0 1370 | 5.7,3.3,1 1371 | 4.3,5.3,1 1372 | 5.4,6.0,0 1373 | 5.3,3.3,1 1374 | 6.7,2.6,1 1375 | 2.9,5.6,0 1376 | 6.0,8.1,0 1377 | 4.2,7.5,0 1378 | 7.7,3.8,1 1379 | 6.6,6.9,0 1380 | 3.9,6.4,0 1381 | 5.5,3.0,1 1382 | 5.6,3.3,1 1383 | 5.4,3.8,1 1384 | 4.4,8.2,0 1385 | 3.6,8.3,0 1386 | 7.6,4.6,1 1387 | 8.5,6.0,1 1388 | 4.6,8.0,0 1389 | 7.9,3.5,1 1390 | 8.7,5.1,1 1391 | 2.2,5.5,0 1392 | 8.3,6.3,1 1393 | 5.2,9.8,0 1394 | 7.5,3.8,1 1395 | 3.4,5.9,0 1396 | 6.2,7.5,0 1397 | 2.1,5.2,0 1398 | 6.2,2.7,1 1399 | 4.5,4.7,1 1400 | 6.3,2.5,1 1401 | 8.7,2.9,1 1402 | 8.3,3.8,1 1403 | 4.2,4.4,1 1404 | 8.2,4.0,1 1405 | 2.3,6.7,0 1406 | 5.8,5.8,0 1407 | 5.4,7.8,0 1408 | 2.4,3.7,0 1409 | 2.1,5.2,0 1410 | 7.4,3.0,1 1411 | 2.3,6.9,0 1412 | 6.4,3.5,1 1413 | 5.2,6.2,0 1414 | 5.8,8.2,0 1415 | 3.2,5.7,0 1416 | 5.1,4.2,1 1417 | 7.7,3.0,1 1418 | 2.5,4.5,0 1419 | 6.0,6.1,0 1420 | 5.1,4.0,1 1421 | 4.8,4.1,1 1422 | 6.0,8.4,0 1423 | 8.5,3.6,1 1424 | 5.4,2.7,1 1425 | 8.6,5.5,1 1426 | 2.9,3.4,0 1427 | 5.7,7.7,0 1428 | 6.4,4.0,1 1429 | 2.2,5.3,0 1430 | 8.1,2.7,1 1431 | 5.4,7.0,0 1432 | 6.1,8.0,0 1433 | 6.7,4.4,0 1434 | 8.3,4.0,1 1435 | 7.2,2.3,1 1436 | 2.5,7.3,0 1437 | 6.5,3.1,1 1438 | 2.1,6.6,0 1439 | 3.7,4.4,1 1440 | 2.8,3.9,0 1441 | 5.6,3.1,1 1442 | 1.9,5.8,0 1443 | 4.9,8.1,0 1444 | 8.2,1.4,1 1445 | 6.4,6.1,0 1446 | 4.4,5.6,1 1447 | 6.8,3.6,1 1448 | 7.4,3.4,1 1449 | 6.0,2.3,1 1450 | 2.7,6.7,0 1451 | 5.9,5.5,1 1452 | 4.3,5.9,1 1453 | 8.4,5.2,1 1454 | 8.6,5.4,1 1455 | 1.2,5.9,0 1456 | 7.4,2.9,1 1457 | 6.2,8.3,0 1458 | 4.2,7.1,0 1459 | 2.4,4.1,0 1460 | 2.9,6.5,0 1461 | 5.1,4.7,1 1462 | 5.9,4.7,0 1463 | 3.5,7.3,0 1464 | 9.0,4.6,1 1465 | 3.5,6.6,0 1466 | 5.7,3.2,1 1467 | 5.2,4.9,1 1468 | 2.8,5.1,0 1469 | 2.8,5.2,0 1470 | 6.6,4.6,0 1471 | 7.9,2.4,1 1472 | 6.7,6.5,0 1473 | 2.5,6.0,0 1474 | 4.8,7.3,0 1475 | 4.1,4.9,1 1476 | 8.4,4.3,1 1477 | 5.9,4.5,1 1478 | 5.5,7.9,0 1479 | 7.6,4.3,1 1480 | 5.3,2.9,1 1481 | 3.4,6.8,0 1482 | 2.0,4.9,0 1483 | 5.0,8.0,0 1484 | 6.4,6.6,0 1485 | 4.8,4.2,1 1486 | 4.2,7.5,0 1487 | 4.8,2.9,1 1488 | 4.5,8.0,0 1489 | 5.6,5.6,0 1490 | 4.7,5.4,1 1491 | 5.6,3.6,1 1492 | 8.8,6.5,1 1493 | 6.9,5.8,0 1494 | 4.3,7.3,0 1495 | 5.8,2.3,1 1496 | 8.7,6.5,1 1497 | 3.2,7.5,0 1498 | 7.2,3.6,1 1499 | 7.3,3.6,1 1500 | 5.3,3.6,1 1501 | 3.6,7.2,0 1502 | 6.9,4.3,0 1503 | 6.4,7.1,0 1504 | 6.1,7.8,0 1505 | 4.1,6.9,0 1506 | 5.6,3.7,1 1507 | 8.6,5.2,1 1508 | 7.5,3.3,1 1509 | 4.7,6.5,1 1510 | 8.5,5.8,1 1511 | 7.3,2.5,1 1512 | 2.4,6.3,0 1513 | 2.5,6.0,0 1514 | 7.3,2.0,1 1515 | 4.7,4.0,1 1516 | 4.6,8.6,0 1517 | 7.4,4.6,1 1518 | 5.2,2.6,1 1519 | 8.9,4.6,1 1520 | 3.8,6.6,0 1521 | 6.4,2.9,1 1522 | 5.7,7.0,1 1523 | 2.7,5.9,0 1524 | 6.4,5.0,1 1525 | 5.2,4.3,1 1526 | 4.6,5.0,1 1527 | 5.4,3.7,1 1528 | 6.6,3.0,1 1529 | 7.8,3.6,1 1530 | 4.5,4.8,1 1531 | 7.0,2.1,1 1532 | 2.4,5.9,0 1533 | 3.7,6.4,0 1534 | 9.1,4.8,1 1535 | 4.8,4.7,1 1536 | 5.9,5.7,0 1537 | 5.8,6.8,0 1538 | 3.2,9.3,0 1539 | 5.1,8.5,0 1540 | 6.1,6.7,0 1541 | 5.6,6.2,0 1542 | 6.0,4.8,0 1543 | 5.4,6.7,0 1544 | 6.8,3.6,1 1545 | 3.0,6.6,0 1546 | 1.0,4.6,0 1547 | 5.9,4.7,0 1548 | 5.2,6.9,0 1549 | 8.2,4.2,1 1550 | 6.9,7.2,0 1551 | 7.9,2.9,1 1552 | 8.1,6.2,1 1553 | 3.7,7.7,0 1554 | 6.8,5.1,0 1555 | 6.7,7.6,0 1556 | 4.6,5.2,1 1557 | 2.7,4.5,0 1558 | 6.6,5.5,0 1559 | 5.1,7.1,0 1560 | 9.7,6.0,1 1561 | 6.7,5.5,0 1562 | 8.9,5.0,1 1563 | 5.0,7.1,0 1564 | 9.4,5.5,1 1565 | 8.4,3.7,1 1566 | 6.4,2.0,1 1567 | 6.5,3.9,1 1568 | 8.5,6.2,1 1569 | 8.1,5.9,1 1570 | 7.9,5.6,1 1571 | 6.1,2.8,1 1572 | 6.4,6.9,0 1573 | 6.3,6.4,0 1574 | 5.4,8.1,0 1575 | 3.5,6.0,0 1576 | 5.0,8.0,0 1577 | 5.7,7.1,0 1578 | 5.6,2.2,1 1579 | 3.8,7.6,0 1580 | 2.8,7.8,0 1581 | 3.2,4.6,0 1582 | 8.4,5.5,1 1583 | 6.5,4.2,0 1584 | 3.5,5.0,1 1585 | 4.7,7.6,0 1586 | 2.7,5.1,0 1587 | 7.9,3.2,1 1588 | 2.7,6.3,0 1589 | 4.6,4.5,1 1590 | 5.0,5.2,1 1591 | 6.4,3.6,1 1592 | 4.4,5.5,1 1593 | 7.8,3.4,1 1594 | 5.6,5.8,0 1595 | 8.3,4.0,1 1596 | 6.4,6.6,0 1597 | 2.9,7.4,0 1598 | 6.0,3.4,1 1599 | 3.3,6.3,0 1600 | 5.0,4.8,1 1601 | 4.4,6.6,1 1602 | 3.4,7.9,0 1603 | 7.0,6.1,0 1604 | 5.8,7.8,0 1605 | 4.3,5.5,1 1606 | 2.5,4.9,0 1607 | 2.5,6.6,0 1608 | 7.7,2.5,1 1609 | 8.8,3.5,1 1610 | 4.7,8.7,0 1611 | 4.6,6.0,1 1612 | 2.7,3.9,0 1613 | 5.8,6.9,0 1614 | 4.2,6.0,0 1615 | 4.7,7.4,0 1616 | 5.5,5.8,0 1617 | 5.5,2.7,1 1618 | 6.6,2.9,1 1619 | 5.8,8.4,0 1620 | 5.0,3.6,1 1621 | 3.9,6.1,0 1622 | 3.6,8.6,0 1623 | 5.1,5.4,1 1624 | 4.1,7.2,0 1625 | 6.0,2.9,1 1626 | 6.4,4.7,0 1627 | 8.6,4.9,1 1628 | 4.7,7.9,0 1629 | 6.7,7.7,0 1630 | 6.2,7.3,0 1631 | 6.7,3.9,1 1632 | 2.6,7.6,0 1633 | 4.7,5.8,1 1634 | 8.6,4.3,1 1635 | 4.6,8.1,0 1636 | 3.7,6.9,0 1637 | 8.3,5.1,1 1638 | 4.4,4.6,1 1639 | 8.2,4.8,1 1640 | 4.2,5.8,1 1641 | 8.5,5.2,1 1642 | 4.8,4.6,1 1643 | 8.5,6.2,1 1644 | 3.5,8.2,0 1645 | 6.3,8.3,0 1646 | 4.8,6.2,1 1647 | 3.0,6.3,0 1648 | 5.8,3.2,1 1649 | 9.6,5.2,1 1650 | 7.4,4.2,1 1651 | 4.5,5.2,1 1652 | 3.8,7.1,0 1653 | 6.0,3.4,1 1654 | 7.0,5.0,0 1655 | 6.6,7.0,0 1656 | 4.7,5.7,1 1657 | 5.1,3.8,1 1658 | 6.8,2.9,1 1659 | 8.1,5.5,1 1660 | 4.3,5.1,1 1661 | 6.4,2.5,1 1662 | 4.9,5.3,1 1663 | 8.1,4.1,1 1664 | 4.6,7.4,0 1665 | 3.1,6.4,0 1666 | 5.2,3.6,1 1667 | 5.9,7.5,0 1668 | 6.6,4.2,1 1669 | 4.4,7.0,0 1670 | 6.5,4.0,0 1671 | 4.7,5.6,1 1672 | 5.6,4.3,1 1673 | 7.0,6.5,0 1674 | 7.0,4.3,0 1675 | 4.9,3.8,1 1676 | 3.7,5.6,1 1677 | 8.4,6.2,1 1678 | 7.7,3.0,1 1679 | 5.9,3.1,1 1680 | 5.3,3.7,1 1681 | 6.7,3.3,1 1682 | 5.3,4.4,1 1683 | 6.2,3.6,1 1684 | 7.7,2.9,1 1685 | 8.9,4.2,1 1686 | 5.7,4.8,1 1687 | 7.1,6.6,0 1688 | 5.1,8.8,0 1689 | 6.0,3.7,0 1690 | 5.4,2.3,1 1691 | 6.5,3.3,1 1692 | 7.7,3.6,1 1693 | 8.1,6.0,1 1694 | 5.1,5.6,0 1695 | 6.4,3.8,1 1696 | 4.6,5.1,1 1697 | 6.1,3.5,1 1698 | 4.6,5.9,1 1699 | 8.9,4.6,1 1700 | 2.1,3.6,0 1701 | 3.7,7.2,0 1702 | 7.5,2.2,1 1703 | 7.7,4.3,1 1704 | 6.2,3.2,1 1705 | 6.8,3.3,1 1706 | 7.2,3.8,1 1707 | 5.8,6.4,0 1708 | 4.3,8.2,0 1709 | 5.3,3.0,1 1710 | 6.3,3.9,1 1711 | 5.5,3.4,1 1712 | 6.7,5.3,0 1713 | 1.7,6.2,0 1714 | 7.4,3.2,1 1715 | 5.1,8.7,0 1716 | 4.7,7.1,0 1717 | 8.0,5.6,1 1718 | 4.3,3.7,1 1719 | 7.9,2.1,1 1720 | 4.7,6.7,1 1721 | 5.0,4.6,1 1722 | 5.1,3.9,1 1723 | 6.5,5.6,0 1724 | 7.2,4.6,1 1725 | 6.0,4.6,1 1726 | 4.6,3.7,1 1727 | 2.8,5.8,0 1728 | 5.2,7.6,0 1729 | 5.4,7.7,0 1730 | 3.3,4.8,1 1731 | 3.2,4.6,0 1732 | 6.2,2.4,1 1733 | 7.8,4.1,1 1734 | 5.1,6.7,1 1735 | 6.8,3.6,1 1736 | 7.6,3.5,1 1737 | 5.8,6.1,0 1738 | 3.9,5.6,1 1739 | 8.5,3.7,1 1740 | 4.0,7.7,0 1741 | 2.3,5.2,0 1742 | 5.0,6.4,1 1743 | 4.4,8.9,0 1744 | 5.1,6.9,0 1745 | 6.5,1.6,1 1746 | 7.5,3.6,1 1747 | 4.7,4.3,1 1748 | 7.1,5.1,0 1749 | 5.3,7.5,0 1750 | 6.7,3.1,1 1751 | 3.8,5.1,0 1752 | 6.3,3.6,0 1753 | 5.3,3.8,1 1754 | 5.5,5.8,1 1755 | 4.6,4.7,1 1756 | 5.7,6.8,0 1757 | 5.0,3.2,1 1758 | 2.5,6.2,0 1759 | 4.0,5.3,0 1760 | 4.2,5.0,1 1761 | 3.5,7.0,0 1762 | 5.6,5.5,0 1763 | 4.8,5.2,1 1764 | 7.7,4.7,1 1765 | 3.3,5.0,0 1766 | 7.1,3.8,0 1767 | 5.1,5.3,1 1768 | 6.6,5.0,1 1769 | 8.3,3.6,1 1770 | 6.9,4.8,1 1771 | 6.0,2.4,1 1772 | 4.8,4.2,1 1773 | 5.5,8.0,0 1774 | 2.3,6.3,0 1775 | 6.5,3.4,0 1776 | 5.4,8.2,0 1777 | 6.3,4.3,1 1778 | 9.0,4.7,1 1779 | 5.3,7.9,0 1780 | 2.4,5.5,0 1781 | 2.7,4.2,0 1782 | 6.4,2.9,1 1783 | 2.8,4.7,0 1784 | 2.6,4.6,0 1785 | 6.8,6.3,0 1786 | 5.6,7.9,0 1787 | 5.6,7.0,0 1788 | 8.0,3.4,1 1789 | 2.8,6.3,0 1790 | 3.1,6.6,0 1791 | 4.9,7.0,0 1792 | 4.2,8.2,0 1793 | 8.1,4.9,1 1794 | 5.1,4.2,1 1795 | 6.9,5.9,0 1796 | 5.3,8.2,0 1797 | 2.9,7.3,0 1798 | 6.1,3.3,1 1799 | 6.5,8.0,0 1800 | 2.5,5.5,0 1801 | 2.6,6.0,0 1802 | 8.1,3.4,1 1803 | 7.7,4.1,1 1804 | 5.1,4.5,1 1805 | 4.3,9.6,0 1806 | 7.7,5.4,1 1807 | 2.9,4.7,0 1808 | 4.6,7.9,0 1809 | 8.2,2.3,1 1810 | 2.6,5.2,0 1811 | 8.4,4.1,1 1812 | 8.3,4.7,1 1813 | 6.2,4.0,1 1814 | 6.3,2.4,1 1815 | 5.2,4.0,1 1816 | 2.9,6.5,0 1817 | 7.3,2.5,1 1818 | 4.0,8.8,0 1819 | 8.9,4.2,1 1820 | 3.6,8.2,0 1821 | 4.7,7.4,0 1822 | 4.6,7.0,1 1823 | 7.1,2.6,1 1824 | 5.8,3.8,1 1825 | 5.8,7.8,0 1826 | 6.2,5.7,0 1827 | 8.2,3.6,1 1828 | 3.1,5.5,0 1829 | 6.8,2.7,1 1830 | 3.8,8.0,0 1831 | 2.1,5.0,0 1832 | 4.1,6.0,1 1833 | 9.3,5.9,1 1834 | 3.7,7.6,0 1835 | 5.5,5.7,0 1836 | 8.1,5.2,1 1837 | 3.2,7.9,0 1838 | 6.0,7.5,0 1839 | 7.4,3.9,0 1840 | 3.3,7.7,0 1841 | 5.2,7.7,0 1842 | 4.0,7.3,0 1843 | 4.0,7.8,0 1844 | 5.4,6.7,0 1845 | 6.0,6.7,0 1846 | 6.1,5.3,0 1847 | 4.3,4.8,1 1848 | 5.3,8.1,0 1849 | 5.0,6.5,0 1850 | 4.9,3.4,1 1851 | 4.0,6.6,0 1852 | 2.7,5.2,0 1853 | 6.6,4.0,1 1854 | 3.3,8.5,0 1855 | 9.2,5.2,1 1856 | 5.5,4.8,1 1857 | 2.6,6.1,0 1858 | 6.2,3.7,1 1859 | 3.7,8.1,0 1860 | 5.0,5.9,1 1861 | 4.0,4.7,1 1862 | 3.0,8.0,0 1863 | 4.7,8.7,0 1864 | 8.7,5.4,1 1865 | 6.5,3.2,1 1866 | 6.8,3.2,1 1867 | 7.0,2.8,1 1868 | 6.2,7.4,0 1869 | 4.3,5.6,1 1870 | 6.3,6.4,0 1871 | 5.3,4.7,1 1872 | 5.6,3.1,1 1873 | 8.5,4.6,1 1874 | 4.3,7.2,0 1875 | 7.8,2.7,1 1876 | 6.4,1.9,1 1877 | 2.5,5.2,0 1878 | 2.8,6.1,0 1879 | 6.6,3.7,1 1880 | 3.1,4.5,0 1881 | 8.0,6.8,1 1882 | 6.1,4.0,0 1883 | 1.6,5.3,0 1884 | 8.5,6.5,1 1885 | 2.9,5.0,0 1886 | 4.3,8.6,0 1887 | 5.3,8.5,0 1888 | 4.5,5.7,1 1889 | 6.4,4.1,0 1890 | 6.6,2.8,1 1891 | 2.8,7.6,0 1892 | 4.3,6.1,1 1893 | 7.8,2.2,1 1894 | 6.0,4.0,1 1895 | 4.9,5.2,1 1896 | 3.5,5.4,0 1897 | 6.0,3.6,0 1898 | 8.9,5.4,1 1899 | 4.0,6.4,0 1900 | 4.8,6.3,0 1901 | 8.1,6.7,1 1902 | 4.0,6.9,0 1903 | 3.1,5.9,0 1904 | 2.8,4.4,0 1905 | 5.6,6.9,0 1906 | 4.9,7.4,0 1907 | 6.2,6.0,0 1908 | 3.5,6.9,0 1909 | 2.9,3.9,0 1910 | 8.1,3.2,1 1911 | 6.6,5.2,0 1912 | 7.9,4.0,1 1913 | 6.4,7.6,0 1914 | 2.5,6.0,0 1915 | 6.1,3.5,1 1916 | 4.8,6.2,0 1917 | 6.5,3.4,0 1918 | 2.6,6.4,0 1919 | 3.6,8.7,0 1920 | 3.6,7.4,0 1921 | 5.0,2.4,1 1922 | 6.4,4.7,1 1923 | 5.9,4.7,0 1924 | 3.7,7.3,0 1925 | 2.9,6.6,0 1926 | 8.8,4.4,1 1927 | 5.7,4.2,1 1928 | 5.7,4.8,0 1929 | 5.7,9.5,0 1930 | 2.6,5.5,0 1931 | 4.6,6.0,1 1932 | 3.2,6.8,0 1933 | 8.7,3.9,1 1934 | 5.5,7.3,0 1935 | 4.4,4.4,1 1936 | 7.6,5.4,0 1937 | 7.2,3.1,1 1938 | 6.8,3.8,1 1939 | 3.4,6.4,0 1940 | 4.4,7.3,0 1941 | 3.1,6.5,0 1942 | 3.4,6.7,0 1943 | 8.3,3.3,1 1944 | 4.9,3.9,1 1945 | 5.9,5.5,0 1946 | 5.1,7.8,0 1947 | 5.6,9.7,0 1948 | 3.9,8.2,0 1949 | 8.3,2.3,1 1950 | 7.8,3.1,1 1951 | 6.9,5.9,0 1952 | 7.7,4.1,1 1953 | 5.7,2.9,1 1954 | 4.2,5.3,1 1955 | 8.7,6.0,1 1956 | 1.9,6.5,0 1957 | 6.1,5.6,0 1958 | 8.4,5.1,1 1959 | 8.5,5.8,1 1960 | 6.9,2.4,1 1961 | 6.3,3.3,1 1962 | 3.6,8.4,0 1963 | 9.2,5.1,1 1964 | 3.4,7.3,0 1965 | 8.3,4.6,1 1966 | 3.4,5.1,1 1967 | 5.9,6.1,0 1968 | 6.2,2.0,1 1969 | 8.0,5.9,1 1970 | 5.5,2.9,1 1971 | 5.7,4.4,1 1972 | 6.1,7.0,0 1973 | 4.6,3.6,1 1974 | 5.4,2.9,1 1975 | 8.1,4.6,1 1976 | 6.3,3.4,1 1977 | 4.9,5.8,0 1978 | 5.7,4.4,1 1979 | 5.2,4.6,1 1980 | 4.0,7.5,0 1981 | 5.5,3.2,1 1982 | 8.0,4.6,1 1983 | 5.2,7.2,0 1984 | 2.2,5.0,0 1985 | 8.2,6.4,1 1986 | 5.9,3.8,1 1987 | 2.9,5.1,0 1988 | 3.9,6.9,0 1989 | 2.6,4.7,0 1990 | 5.4,8.1,0 1991 | 5.3,3.2,1 1992 | 3.2,7.6,0 1993 | 5.7,5.3,0 1994 | 4.7,8.8,0 1995 | 5.2,4.4,1 1996 | 4.9,5.0,1 1997 | 4.1,6.6,0 1998 | 3.3,6.2,0 1999 | 3.2,8.9,0 2000 | 8.0,4.3,1 2001 | 9.3,6.0,1 2002 | 4.7,5.7,1 2003 | 8.1,3.7,1 2004 | 6.2,3.1,1 2005 | 6.6,4.6,1 2006 | 4.5,8.3,0 2007 | 5.1,4.3,1 2008 | 2.8,6.0,0 2009 | 4.0,6.6,0 2010 | 4.6,4.8,1 2011 | 3.1,5.0,0 2012 | 6.0,2.8,1 2013 | 8.5,4.4,1 2014 | 3.0,7.8,0 2015 | 7.0,3.3,1 2016 | 3.3,6.7,0 2017 | 5.2,4.1,1 2018 | 1.8,6.5,0 2019 | 5.3,3.8,1 2020 | 7.0,4.0,1 2021 | 7.4,3.3,1 2022 | 8.7,5.5,1 2023 | 6.6,4.5,0 2024 | 5.7,4.0,1 2025 | 5.6,3.0,1 2026 | 4.1,6.9,0 2027 | 8.0,3.9,1 2028 | 3.7,6.2,0 2029 | 3.0,7.7,0 2030 | 6.6,7.5,0 2031 | 3.2,6.6,0 2032 | 2.0,6.0,0 2033 | 5.2,7.1,0 2034 | 4.3,7.6,0 2035 | 8.1,5.2,1 2036 | 6.5,4.6,1 2037 | 7.9,3.0,1 2038 | 3.1,6.7,0 2039 | 2.6,5.4,0 2040 | 8.2,2.8,1 2041 | 4.7,6.8,1 2042 | 6.8,5.9,0 2043 | 7.4,7.0,0 2044 | 7.8,3.1,1 2045 | 6.0,6.7,0 2046 | 8.0,4.3,1 2047 | 6.3,5.7,0 2048 | 6.7,3.6,1 2049 | 6.5,4.9,0 2050 | 5.2,6.6,0 2051 | 8.6,4.9,1 2052 | 8.0,4.8,0 2053 | 6.7,6.0,1 2054 | 4.8,4.0,1 2055 | 4.3,7.1,0 2056 | 8.5,3.0,1 2057 | 7.1,4.3,0 2058 | 6.3,6.0,0 2059 | 5.1,3.7,1 2060 | 6.8,4.9,1 2061 | 5.6,2.9,1 2062 | 8.3,3.3,1 2063 | 6.0,4.2,1 2064 | 4.2,5.7,1 2065 | 5.4,6.4,0 2066 | 5.3,4.3,1 2067 | 3.3,6.1,0 2068 | 3.8,7.6,0 2069 | 4.0,6.6,0 2070 | 6.1,7.5,0 2071 | 4.0,7.3,0 2072 | 8.4,4.4,1 2073 | 6.7,6.9,0 2074 | 5.3,3.3,1 2075 | 6.3,3.2,1 2076 | 4.5,5.0,1 2077 | 6.5,3.8,1 2078 | 2.6,4.9,0 2079 | 3.2,7.1,0 2080 | 4.2,4.1,1 2081 | 9.3,5.3,1 2082 | 5.4,3.6,1 2083 | 4.1,7.9,0 2084 | 5.1,4.1,1 2085 | 8.7,5.5,1 2086 | 2.4,6.6,0 2087 | 8.1,4.8,1 2088 | 8.2,6.0,1 2089 | 3.5,5.1,0 2090 | 4.9,4.4,1 2091 | 4.6,8.3,0 2092 | 2.8,6.5,0 2093 | 8.3,4.9,1 2094 | 7.8,3.3,1 2095 | 4.9,5.7,1 2096 | 9.0,4.3,1 2097 | 6.9,6.0,0 2098 | 7.2,4.4,0 2099 | 6.0,3.4,1 2100 | 4.2,8.5,0 2101 | 2.6,6.7,0 2102 | 4.7,2.9,1 2103 | 6.7,3.2,1 2104 | 7.3,1.0,1 2105 | 2.8,5.7,0 2106 | 5.5,3.8,1 2107 | 2.5,6.9,0 2108 | 7.8,5.2,1 2109 | 6.4,3.6,1 2110 | 6.1,6.1,0 2111 | 5.9,3.8,1 2112 | 5.3,6.8,0 2113 | 6.9,4.6,1 2114 | 8.6,3.4,1 2115 | 3.9,6.5,1 2116 | 2.7,5.6,0 2117 | 9.0,4.7,1 2118 | 5.9,3.0,1 2119 | 7.4,3.7,1 2120 | 7.4,2.3,1 2121 | 3.8,5.5,0 2122 | 5.8,7.6,0 2123 | 5.1,6.3,1 2124 | 7.4,4.8,1 2125 | 5.2,2.9,1 2126 | 4.9,6.0,1 2127 | 5.7,3.6,1 2128 | 6.1,7.1,0 2129 | 7.4,2.4,1 2130 | 5.3,2.9,1 2131 | 4.5,8.5,0 2132 | 4.2,8.0,0 2133 | 5.8,3.6,1 2134 | 5.7,6.1,0 2135 | 5.0,3.7,1 2136 | 4.8,4.3,1 2137 | 4.9,4.9,1 2138 | 6.3,6.2,0 2139 | 5.7,6.8,0 2140 | 5.6,2.8,1 2141 | 6.5,2.9,1 2142 | 5.1,7.6,0 2143 | 7.1,5.4,0 2144 | 5.6,6.6,0 2145 | 6.3,5.7,0 2146 | 5.0,7.1,0 2147 | 6.9,3.5,1 2148 | 8.9,3.6,1 2149 | 2.6,5.8,0 2150 | 7.2,3.5,1 2151 | 2.5,4.8,0 2152 | 7.9,4.7,1 2153 | 4.4,8.2,0 2154 | 6.2,6.5,0 2155 | 3.1,3.9,0 2156 | 3.6,8.4,0 2157 | 5.3,3.8,1 2158 | 4.7,5.6,1 2159 | 6.6,6.0,0 2160 | 4.5,6.9,0 2161 | 5.9,5.6,1 2162 | 6.6,6.0,0 2163 | 6.2,4.7,0 2164 | 5.6,5.2,1 2165 | 6.5,7.6,0 2166 | 6.1,4.8,0 2167 | 3.4,6.1,0 2168 | 3.7,7.9,0 2169 | 6.1,6.3,0 2170 | 3.4,6.7,0 2171 | 6.9,6.6,0 2172 | 6.6,3.0,1 2173 | 2.9,6.1,0 2174 | 6.8,3.1,1 2175 | 4.3,6.1,1 2176 | 4.9,4.9,1 2177 | 8.6,4.1,1 2178 | 8.1,3.7,1 2179 | 8.6,4.2,1 2180 | 5.5,3.0,1 2181 | 7.8,5.3,1 2182 | 5.3,3.9,1 2183 | 4.8,4.9,1 2184 | 3.1,5.6,0 2185 | 4.9,8.1,1 2186 | 5.4,8.9,0 2187 | 7.1,2.3,1 2188 | 2.7,4.4,0 2189 | 6.3,5.3,0 2190 | 7.5,2.6,1 2191 | 4.4,7.2,0 2192 | 7.6,3.4,1 2193 | 7.0,7.7,0 2194 | 6.9,5.1,0 2195 | 9.0,4.5,1 2196 | 6.1,4.0,1 2197 | 6.2,1.4,1 2198 | 5.2,8.0,0 2199 | 6.9,6.5,0 2200 | 5.9,4.4,1 2201 | 2.9,6.6,0 2202 | 8.0,2.3,1 2203 | 5.0,2.7,1 2204 | 8.0,3.1,1 2205 | 8.6,3.4,1 2206 | 5.1,2.7,1 2207 | 5.7,7.7,0 2208 | 4.2,5.6,1 2209 | 8.6,3.7,1 2210 | 8.8,3.2,1 2211 | 6.8,2.0,1 2212 | 5.5,4.5,1 2213 | 3.6,6.4,0 2214 | 4.5,7.1,1 2215 | 2.4,6.7,0 2216 | 4.4,8.2,0 2217 | 3.7,7.8,0 2218 | 2.9,5.8,0 2219 | 7.4,3.3,1 2220 | 6.6,6.9,0 2221 | 5.1,2.2,1 2222 | 6.1,4.6,0 2223 | 6.8,7.5,0 2224 | 5.4,8.4,0 2225 | 3.9,7.4,0 2226 | 6.3,5.3,0 2227 | 5.5,8.0,0 2228 | 6.5,6.2,0 2229 | 8.2,3.2,1 2230 | 3.1,5.6,0 2231 | 7.5,4.0,1 2232 | 4.1,8.8,0 2233 | 6.2,3.6,1 2234 | 1.9,5.4,0 2235 | 3.4,6.9,0 2236 | 5.3,5.6,0 2237 | 6.5,3.9,0 2238 | 7.3,5.7,0 2239 | 2.9,7.9,0 2240 | 3.6,7.0,0 2241 | 7.4,3.1,1 2242 | 4.8,3.1,1 2243 | 5.6,4.2,1 2244 | 3.2,6.2,0 2245 | 5.4,5.8,1 2246 | 5.1,8.5,0 2247 | 2.6,5.6,0 2248 | 8.6,4.1,1 2249 | 5.0,7.4,0 2250 | 2.5,6.1,0 2251 | 6.3,1.7,1 2252 | 5.7,4.3,0 2253 | 8.7,5.2,1 2254 | 4.6,7.7,0 2255 | 6.0,4.4,0 2256 | 5.2,8.5,0 2257 | 5.0,5.2,1 2258 | 6.8,7.6,0 2259 | 4.6,6.0,1 2260 | 7.2,6.4,0 2261 | 3.9,6.8,0 2262 | 4.8,2.8,1 2263 | 5.2,3.7,1 2264 | 6.5,3.3,1 2265 | 4.7,7.4,0 2266 | 6.2,2.7,1 2267 | 7.9,3.3,1 2268 | 6.7,4.4,0 2269 | 5.7,3.5,1 2270 | 6.8,6.8,0 2271 | 2.0,4.4,0 2272 | 6.1,2.8,1 2273 | 6.9,5.4,0 2274 | 5.4,5.1,1 2275 | 7.5,3.3,1 2276 | 6.4,2.9,1 2277 | 6.0,4.1,0 2278 | 4.6,8.7,0 2279 | 6.2,4.3,1 2280 | 6.0,1.6,1 2281 | 5.0,7.4,0 2282 | 6.0,6.4,0 2283 | 3.7,6.6,0 2284 | 6.9,3.7,1 2285 | 6.8,5.7,0 2286 | 5.6,6.7,0 2287 | 3.2,4.9,0 2288 | 5.2,4.2,1 2289 | 5.7,6.4,0 2290 | 2.8,5.0,0 2291 | 6.3,2.8,1 2292 | 2.5,6.6,0 2293 | 7.4,6.0,1 2294 | 6.7,2.6,1 2295 | 6.3,6.3,0 2296 | 5.7,2.5,1 2297 | 5.8,6.2,0 2298 | 9.5,5.8,1 2299 | 8.7,5.0,1 2300 | 5.2,3.6,1 2301 | 5.2,7.3,0 2302 | 5.6,7.6,0 2303 | 6.4,7.2,0 2304 | 8.0,3.4,1 2305 | 5.7,3.2,1 2306 | 6.0,6.9,0 2307 | 5.4,7.5,0 2308 | 5.8,3.0,1 2309 | 6.1,7.3,0 2310 | 5.5,8.2,0 2311 | 5.8,5.8,0 2312 | 5.7,4.0,1 2313 | 3.6,4.7,1 2314 | 5.9,3.6,1 2315 | 5.6,3.0,1 2316 | 7.9,4.1,1 2317 | 8.0,4.8,1 2318 | 9.2,4.9,1 2319 | 6.2,7.2,0 2320 | 2.5,5.2,0 2321 | 5.1,8.4,0 2322 | 7.2,3.9,1 2323 | 6.8,4.6,1 2324 | 5.6,3.8,1 2325 | 4.9,2.8,1 2326 | 3.2,5.4,0 2327 | 6.4,4.5,0 2328 | 7.3,3.9,1 2329 | 2.3,5.1,0 2330 | 5.6,3.4,1 2331 | 4.4,6.2,1 2332 | 5.6,3.9,1 2333 | 5.9,6.3,0 2334 | 5.1,7.7,0 2335 | 4.9,6.8,0 2336 | 5.6,7.7,0 2337 | 8.8,7.0,1 2338 | 6.2,3.0,1 2339 | 4.5,4.9,1 2340 | 9.9,5.9,1 2341 | 4.3,9.8,0 2342 | 2.6,6.5,0 2343 | 7.1,3.8,0 2344 | 3.7,9.0,0 2345 | 8.3,5.4,1 2346 | 7.3,3.3,1 2347 | 3.7,6.4,0 2348 | 6.2,5.5,0 2349 | 6.1,4.0,1 2350 | 6.4,5.9,0 2351 | 5.1,4.2,1 2352 | 7.9,3.1,1 2353 | 9.1,5.3,1 2354 | 8.2,3.2,1 2355 | 7.9,6.3,1 2356 | 7.5,4.6,0 2357 | 6.2,4.6,1 2358 | 4.8,3.6,1 2359 | 5.1,7.9,0 2360 | 6.1,6.8,0 2361 | 3.9,5.5,1 2362 | 5.3,5.9,0 2363 | 4.2,5.9,1 2364 | 3.5,6.6,0 2365 | 6.0,4.5,1 2366 | 7.6,4.6,1 2367 | 3.6,5.9,0 2368 | 5.7,3.6,1 2369 | 5.2,7.7,0 2370 | 6.2,5.9,0 2371 | 4.2,7.1,0 2372 | 5.2,6.9,0 2373 | 7.2,3.1,1 2374 | 2.8,5.5,0 2375 | 2.9,7.9,0 2376 | 2.4,7.4,0 2377 | 6.0,6.0,0 2378 | 5.1,7.5,0 2379 | 6.7,7.4,0 2380 | 4.7,7.0,0 2381 | 3.3,4.6,0 2382 | 3.4,6.8,0 2383 | 6.0,7.7,0 2384 | 2.1,5.8,0 2385 | 7.8,3.3,1 2386 | 4.6,4.7,1 2387 | 3.3,6.1,0 2388 | 5.9,3.1,1 2389 | 5.4,7.0,1 2390 | 8.6,3.7,1 2391 | 2.2,6.2,0 2392 | 4.9,5.5,1 2393 | 3.0,5.4,0 2394 | 8.2,4.3,1 2395 | 6.9,5.6,0 2396 | 3.4,6.8,0 2397 | 3.3,6.4,0 2398 | 5.0,4.1,1 2399 | 7.1,3.1,1 2400 | 7.6,4.5,1 2401 | 6.0,2.9,1 2402 | 5.7,5.5,0 2403 | 3.2,6.0,0 2404 | 4.2,8.0,0 2405 | 3.9,7.5,0 2406 | 4.7,8.0,0 2407 | 4.2,5.8,1 2408 | 3.2,4.8,0 2409 | 5.1,7.9,0 2410 | 9.3,6.7,1 2411 | 7.7,3.3,1 2412 | 5.1,3.3,1 2413 | 4.4,4.6,1 2414 | 2.2,4.9,0 2415 | 4.5,6.3,0 2416 | 3.6,7.9,0 2417 | 8.9,5.6,1 2418 | 4.7,6.0,1 2419 | 8.2,4.1,1 2420 | 9.0,5.7,1 2421 | 8.8,1.7,1 2422 | 5.7,8.4,0 2423 | 6.6,5.8,0 2424 | 6.5,5.7,0 2425 | 8.5,4.7,1 2426 | 5.2,9.0,0 2427 | 5.6,7.0,0 2428 | 8.1,5.1,1 2429 | 2.3,6.4,0 2430 | 7.3,2.9,1 2431 | 4.7,4.3,1 2432 | 3.2,7.6,0 2433 | 3.3,6.5,0 2434 | 6.3,4.5,0 2435 | 4.6,8.1,0 2436 | 5.2,4.5,1 2437 | 4.9,6.5,0 2438 | 7.9,3.8,1 2439 | 5.7,3.7,1 2440 | 2.5,6.2,0 2441 | 3.5,6.3,1 2442 | 7.0,3.7,1 2443 | 8.6,3.9,1 2444 | 5.6,1.4,1 2445 | 5.0,6.1,1 2446 | 3.6,6.0,0 2447 | 3.0,4.3,0 2448 | 5.1,3.0,1 2449 | 7.8,3.1,1 2450 | 5.2,6.0,1 2451 | 3.0,6.3,0 2452 | 4.6,5.4,1 2453 | 4.0,6.3,0 2454 | 4.0,7.1,0 2455 | 6.7,5.8,0 2456 | 5.3,8.4,0 2457 | 8.2,6.6,1 2458 | 2.8,7.0,0 2459 | 8.3,4.6,1 2460 | 7.5,4.6,1 2461 | 3.1,6.5,0 2462 | 3.2,6.6,0 2463 | 6.4,3.2,1 2464 | 3.5,7.5,0 2465 | 3.1,8.2,0 2466 | 2.3,5.0,0 2467 | 7.4,1.5,1 2468 | 3.9,6.9,0 2469 | 6.5,5.5,0 2470 | 8.8,5.4,1 2471 | 3.9,8.2,0 2472 | 4.3,6.3,0 2473 | 8.7,6.1,1 2474 | 6.7,6.4,0 2475 | 7.2,5.9,0 2476 | 5.8,7.1,0 2477 | 7.9,3.6,1 2478 | 7.1,2.9,1 2479 | 8.2,2.9,1 2480 | 5.5,5.9,0 2481 | 2.6,4.5,0 2482 | 7.9,4.3,1 2483 | 5.2,3.1,1 2484 | 5.4,9.1,0 2485 | 6.8,2.1,1 2486 | 2.0,4.6,0 2487 | 4.4,4.4,1 2488 | 3.8,7.0,0 2489 | 9.1,6.0,1 2490 | 6.1,3.9,1 2491 | 4.8,6.3,1 2492 | 6.3,4.7,0 2493 | 6.7,4.2,1 2494 | 5.7,2.4,1 2495 | 8.2,7.2,1 2496 | 3.3,8.1,0 2497 | 4.6,5.1,1 2498 | 2.6,6.3,0 2499 | 6.5,3.5,1 2500 | 5.0,3.5,1 2501 | 4.4,6.8,0 2502 | 4.0,8.5,0 2503 | 5.6,6.6,0 2504 | 4.2,5.8,1 2505 | 4.9,4.8,1 2506 | 4.4,8.8,0 2507 | 4.7,7.3,0 2508 | 6.9,6.5,0 2509 | 5.3,5.7,0 2510 | 4.6,5.3,1 2511 | 9.0,6.2,1 2512 | 6.6,6.0,0 2513 | 5.6,3.4,1 2514 | 3.9,7.7,0 2515 | 3.9,4.5,1 2516 | 7.5,3.3,0 2517 | 7.3,3.9,1 2518 | 7.4,6.5,0 2519 | 3.4,7.1,0 2520 | 3.5,6.8,0 2521 | 2.3,5.9,0 2522 | 8.2,3.7,1 2523 | 7.7,4.7,1 2524 | 8.3,5.1,1 2525 | 4.9,5.8,1 2526 | 7.9,3.3,1 2527 | 7.2,2.6,1 2528 | 5.4,3.2,1 2529 | 4.0,10.0,0 2530 | 6.7,2.9,1 2531 | 3.1,8.9,0 2532 | 6.2,7.8,0 2533 | 6.9,3.5,1 2534 | 4.8,3.7,1 2535 | 6.6,6.6,0 2536 | 5.8,7.3,0 2537 | 2.9,5.6,0 2538 | 3.5,7.3,0 2539 | 10.0,5.6,1 2540 | 9.8,4.9,1 2541 | 4.8,3.6,1 2542 | 5.7,3.7,1 2543 | 6.8,2.5,1 2544 | 6.5,5.5,0 2545 | 8.3,5.2,1 2546 | 2.4,5.7,0 2547 | 4.6,5.9,1 2548 | 4.3,7.0,0 2549 | 7.8,3.6,1 2550 | 8.2,4.0,1 2551 | 7.2,4.1,1 2552 | 6.7,4.8,1 2553 | 3.3,6.7,0 2554 | 8.9,5.4,1 2555 | 3.9,7.2,0 2556 | 5.2,5.2,1 2557 | 6.7,3.9,1 2558 | 4.3,8.2,0 2559 | 3.6,6.1,0 2560 | 7.6,4.0,1 2561 | 3.6,8.4,0 2562 | 3.3,4.2,1 2563 | 6.4,4.8,0 2564 | 8.0,4.4,1 2565 | 4.8,6.9,0 2566 | 3.2,5.3,0 2567 | 5.2,7.7,0 2568 | 3.4,7.0,0 2569 | 4.3,7.1,0 2570 | 5.4,8.1,0 2571 | 5.1,6.5,0 2572 | 2.8,8.0,0 2573 | 2.9,7.1,0 2574 | 5.2,8.0,0 2575 | 5.3,6.5,0 2576 | 8.1,2.5,1 2577 | 4.4,5.1,1 2578 | 4.5,7.5,0 2579 | 5.0,3.5,1 2580 | 3.5,6.1,0 2581 | 9.0,4.5,1 2582 | 6.1,5.0,0 2583 | 3.8,6.0,0 2584 | 8.4,4.6,1 2585 | 5.6,3.5,1 2586 | 4.5,7.1,0 2587 | 4.3,3.4,1 2588 | 4.2,7.4,0 2589 | 5.9,5.3,0 2590 | 5.0,8.9,0 2591 | 7.9,4.2,1 2592 | 9.1,4.6,1 2593 | 8.2,5.2,1 2594 | 4.4,7.7,0 2595 | 6.6,8.1,0 2596 | 5.5,7.0,0 2597 | 6.7,4.7,0 2598 | 3.4,5.8,0 2599 | 4.4,6.6,0 2600 | 4.1,8.1,0 2601 | 2.0,5.3,0 2602 | 3.8,6.9,0 2603 | 6.3,4.7,0 2604 | 5.0,6.4,1 2605 | 6.3,2.3,1 2606 | 7.9,3.9,1 2607 | 2.6,6.3,0 2608 | 4.1,7.7,0 2609 | 4.9,6.6,0 2610 | 4.8,4.2,1 2611 | 6.5,3.1,1 2612 | 7.0,3.8,1 2613 | 6.0,3.2,1 2614 | 2.3,6.5,0 2615 | 5.5,8.0,0 2616 | 6.9,5.6,0 2617 | 3.9,8.1,0 2618 | 6.5,3.2,1 2619 | 5.7,4.9,1 2620 | 4.1,7.5,0 2621 | 3.2,7.5,0 2622 | 3.2,6.2,0 2623 | 6.6,4.1,1 2624 | 4.3,2.8,1 2625 | 3.5,6.5,0 2626 | 6.1,6.7,0 2627 | 8.2,2.4,1 2628 | 6.8,3.1,1 2629 | 6.6,5.3,0 2630 | 4.4,7.3,0 2631 | 6.9,3.1,0 2632 | 8.7,5.7,1 2633 | 4.8,7.1,0 2634 | 8.2,3.8,1 2635 | 4.1,8.3,0 2636 | 6.0,4.1,1 2637 | 5.0,4.7,1 2638 | 5.4,6.7,0 2639 | 4.5,5.1,1 2640 | 8.4,5.4,1 2641 | 5.8,7.3,0 2642 | 4.3,7.2,0 2643 | 2.3,5.8,0 2644 | 8.1,3.2,1 2645 | 6.5,5.8,0 2646 | 4.4,3.8,1 2647 | 5.4,7.1,0 2648 | 8.8,5.6,1 2649 | 2.7,5.3,0 2650 | 5.0,6.6,0 2651 | 8.3,6.2,1 2652 | 5.6,5.5,1 2653 | 5.1,5.3,1 2654 | 5.7,2.3,1 2655 | 7.5,5.8,0 2656 | 7.9,7.2,1 2657 | 5.2,3.0,1 2658 | 4.2,8.4,0 2659 | 8.1,7.2,1 2660 | 6.9,5.2,0 2661 | 3.0,7.9,0 2662 | 2.8,8.4,0 2663 | 5.2,8.1,0 2664 | 5.2,9.1,0 2665 | 7.6,5.3,0 2666 | 6.5,7.9,0 2667 | 7.4,3.8,1 2668 | 8.9,3.2,1 2669 | 3.8,6.2,0 2670 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4.7,4.9,1 2788 | 8.2,5.8,1 2789 | 4.4,3.9,1 2790 | 8.6,6.9,1 2791 | 2.9,7.4,0 2792 | 4.3,4.1,1 2793 | 7.6,4.6,1 2794 | 5.0,5.2,1 2795 | 4.9,7.6,0 2796 | 4.5,6.0,1 2797 | 7.0,2.0,1 2798 | 5.2,7.7,0 2799 | 6.2,4.6,0 2800 | 5.6,1.9,1 2801 | 4.8,8.4,0 2802 | 4.5,4.5,1 2803 | 2.3,6.4,0 2804 | 9.4,5.9,1 2805 | 8.3,2.6,1 2806 | 4.5,6.8,1 2807 | 4.4,3.5,1 2808 | 3.6,7.0,0 2809 | 8.1,4.2,1 2810 | 5.3,3.7,1 2811 | 5.9,3.1,1 2812 | 5.5,6.9,0 2813 | 6.2,8.0,0 2814 | 3.1,5.5,0 2815 | 6.1,6.5,0 2816 | 5.9,8.0,0 2817 | 2.8,5.4,0 2818 | 2.9,3.7,0 2819 | 5.8,7.0,0 2820 | 2.7,7.2,0 2821 | 5.9,5.1,1 2822 | 3.4,7.5,0 2823 | 8.3,4.4,1 2824 | 7.6,5.2,1 2825 | 2.8,5.3,0 2826 | 2.3,5.5,0 2827 | 1.9,5.9,0 2828 | 6.7,4.3,1 2829 | 2.6,3.8,0 2830 | 5.1,3.0,1 2831 | 4.4,5.5,1 2832 | 4.8,4.7,1 2833 | 2.8,5.1,0 2834 | 8.9,5.4,1 2835 | 9.3,6.3,1 2836 | 4.8,5.1,1 2837 | 2.6,8.2,0 2838 | 4.0,7.8,0 2839 | 8.4,4.2,1 2840 | 5.3,4.6,1 2841 | 8.9,4.4,1 2842 | 8.2,4.3,1 2843 | 6.9,3.3,1 2844 | 4.8,4.4,1 2845 | 5.4,6.4,1 2846 | 3.1,6.7,0 2847 | 7.6,2.4,1 2848 | 3.0,6.9,0 2849 | 5.9,7.8,0 2850 | 4.8,4.6,1 2851 | 7.5,3.4,1 2852 | 7.8,5.7,1 2853 | 8.3,4.3,1 2854 | 5.3,4.2,1 2855 | 5.9,3.0,1 2856 | 7.0,3.9,0 2857 | 2.9,8.5,0 2858 | 5.3,7.0,0 2859 | 9.0,5.8,1 2860 | 3.9,7.9,0 2861 | 2.6,2.5,0 2862 | 4.5,4.7,1 2863 | 3.0,7.6,0 2864 | 6.6,6.9,0 2865 | 5.3,7.2,0 2866 | 8.5,4.5,1 2867 | 3.7,7.8,0 2868 | 6.3,7.0,0 2869 | 8.6,4.6,1 2870 | 8.4,4.8,1 2871 | 3.2,7.7,0 2872 | 8.6,3.3,1 2873 | 2.1,4.1,0 2874 | 8.1,2.8,1 2875 | 3.3,6.4,0 2876 | 4.8,5.7,1 2877 | 8.2,3.9,1 2878 | 5.0,3.4,1 2879 | 9.2,5.3,1 2880 | 5.8,3.2,1 2881 | 4.8,7.2,0 2882 | 4.1,3.2,1 2883 | 8.1,3.2,1 2884 | 5.7,6.9,0 2885 | 3.8,6.9,0 2886 | 4.5,4.7,1 2887 | 7.2,5.9,0 2888 | 5.6,3.5,1 2889 | 6.6,4.0,1 2890 | 6.7,2.9,1 2891 | 4.9,5.3,1 2892 | 6.4,6.5,0 2893 | 6.8,2.7,1 2894 | 5.3,8.1,0 2895 | 4.4,5.3,1 2896 | 5.6,6.5,0 2897 | 8.0,4.5,1 2898 | 4.4,5.1,1 2899 | 3.0,6.0,0 2900 | 3.3,4.4,0 2901 | 7.9,5.9,1 2902 | 6.1,2.7,1 2903 | 2.7,7.1,0 2904 | 2.6,6.2,0 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5.5,4.6,1 2964 | 6.0,6.6,0 2965 | 8.6,4.7,1 2966 | 6.7,6.6,0 2967 | 5.6,3.8,1 2968 | 3.8,7.4,0 2969 | 8.3,4.5,1 2970 | 5.9,2.8,1 2971 | 8.3,4.7,1 2972 | 2.9,8.0,0 2973 | 5.3,7.9,0 2974 | 9.3,5.5,1 2975 | 4.2,7.3,0 2976 | 7.0,4.6,1 2977 | 5.8,4.8,0 2978 | 5.7,6.8,0 2979 | 2.9,6.1,0 2980 | 7.6,4.1,1 2981 | 3.8,7.3,0 2982 | 2.6,6.4,0 2983 | 8.3,3.2,1 2984 | 8.1,4.9,1 2985 | 5.0,5.5,1 2986 | 7.2,3.5,1 2987 | 4.2,6.4,0 2988 | 4.2,4.3,1 2989 | 6.4,5.5,0 2990 | 9.0,4.7,1 2991 | 6.5,2.8,1 2992 | 1.6,4.7,0 2993 | 6.8,7.3,0 2994 | 2.8,7.1,0 2995 | 5.8,8.0,0 2996 | 5.4,2.1,1 2997 | 5.0,6.5,1 2998 | 3.4,6.6,0 2999 | 2.7,6.5,0 3000 | 3.3,5.6,0 3001 | 4.6,8.2,0 3002 | -------------------------------------------------------------------------------- /data/heart.csv: -------------------------------------------------------------------------------- 1 | age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target 2 | 63,1,3,145,233,1,0,150,0,2.3,0,0,1,1 3 | 37,1,2,130,250,0,1,187,0,3.5,0,0,2,1 4 | 41,0,1,130,204,0,0,172,0,1.4,2,0,2,1 5 | 56,1,1,120,236,0,1,178,0,0.8,2,0,2,1 6 | 57,0,0,120,354,0,1,163,1,0.6,2,0,2,1 7 | 57,1,0,140,192,0,1,148,0,0.4,1,0,1,1 8 | 56,0,1,140,294,0,0,153,0,1.3,1,0,2,1 9 | 44,1,1,120,263,0,1,173,0,0,2,0,3,1 10 | 52,1,2,172,199,1,1,162,0,0.5,2,0,3,1 11 | 57,1,2,150,168,0,1,174,0,1.6,2,0,2,1 12 | 54,1,0,140,239,0,1,160,0,1.2,2,0,2,1 13 | 48,0,2,130,275,0,1,139,0,0.2,2,0,2,1 14 | 49,1,1,130,266,0,1,171,0,0.6,2,0,2,1 15 | 64,1,3,110,211,0,0,144,1,1.8,1,0,2,1 16 | 58,0,3,150,283,1,0,162,0,1,2,0,2,1 17 | 50,0,2,120,219,0,1,158,0,1.6,1,0,2,1 18 | 58,0,2,120,340,0,1,172,0,0,2,0,2,1 19 | 66,0,3,150,226,0,1,114,0,2.6,0,0,2,1 20 | 43,1,0,150,247,0,1,171,0,1.5,2,0,2,1 21 | 69,0,3,140,239,0,1,151,0,1.8,2,2,2,1 22 | 59,1,0,135,234,0,1,161,0,0.5,1,0,3,1 23 | 44,1,2,130,233,0,1,179,1,0.4,2,0,2,1 24 | 42,1,0,140,226,0,1,178,0,0,2,0,2,1 25 | 61,1,2,150,243,1,1,137,1,1,1,0,2,1 26 | 40,1,3,140,199,0,1,178,1,1.4,2,0,3,1 27 | 71,0,1,160,302,0,1,162,0,0.4,2,2,2,1 28 | 59,1,2,150,212,1,1,157,0,1.6,2,0,2,1 29 | 51,1,2,110,175,0,1,123,0,0.6,2,0,2,1 30 | 65,0,2,140,417,1,0,157,0,0.8,2,1,2,1 31 | 53,1,2,130,197,1,0,152,0,1.2,0,0,2,1 32 | 41,0,1,105,198,0,1,168,0,0,2,1,2,1 33 | 65,1,0,120,177,0,1,140,0,0.4,2,0,3,1 34 | 44,1,1,130,219,0,0,188,0,0,2,0,2,1 35 | 54,1,2,125,273,0,0,152,0,0.5,0,1,2,1 36 | 51,1,3,125,213,0,0,125,1,1.4,2,1,2,1 37 | 46,0,2,142,177,0,0,160,1,1.4,0,0,2,1 38 | 54,0,2,135,304,1,1,170,0,0,2,0,2,1 39 | 54,1,2,150,232,0,0,165,0,1.6,2,0,3,1 40 | 65,0,2,155,269,0,1,148,0,0.8,2,0,2,1 41 | 65,0,2,160,360,0,0,151,0,0.8,2,0,2,1 42 | 51,0,2,140,308,0,0,142,0,1.5,2,1,2,1 43 | 48,1,1,130,245,0,0,180,0,0.2,1,0,2,1 44 | 45,1,0,104,208,0,0,148,1,3,1,0,2,1 45 | 53,0,0,130,264,0,0,143,0,0.4,1,0,2,1 46 | 39,1,2,140,321,0,0,182,0,0,2,0,2,1 47 | 52,1,1,120,325,0,1,172,0,0.2,2,0,2,1 48 | 44,1,2,140,235,0,0,180,0,0,2,0,2,1 49 | 47,1,2,138,257,0,0,156,0,0,2,0,2,1 50 | 53,0,2,128,216,0,0,115,0,0,2,0,0,1 51 | 53,0,0,138,234,0,0,160,0,0,2,0,2,1 52 | 51,0,2,130,256,0,0,149,0,0.5,2,0,2,1 53 | 66,1,0,120,302,0,0,151,0,0.4,1,0,2,1 54 | 62,1,2,130,231,0,1,146,0,1.8,1,3,3,1 55 | 44,0,2,108,141,0,1,175,0,0.6,1,0,2,1 56 | 63,0,2,135,252,0,0,172,0,0,2,0,2,1 57 | 52,1,1,134,201,0,1,158,0,0.8,2,1,2,1 58 | 48,1,0,122,222,0,0,186,0,0,2,0,2,1 59 | 45,1,0,115,260,0,0,185,0,0,2,0,2,1 60 | 34,1,3,118,182,0,0,174,0,0,2,0,2,1 61 | 57,0,0,128,303,0,0,159,0,0,2,1,2,1 62 | 71,0,2,110,265,1,0,130,0,0,2,1,2,1 63 | 54,1,1,108,309,0,1,156,0,0,2,0,3,1 64 | 52,1,3,118,186,0,0,190,0,0,1,0,1,1 65 | 41,1,1,135,203,0,1,132,0,0,1,0,1,1 66 | 58,1,2,140,211,1,0,165,0,0,2,0,2,1 67 | 35,0,0,138,183,0,1,182,0,1.4,2,0,2,1 68 | 51,1,2,100,222,0,1,143,1,1.2,1,0,2,1 69 | 45,0,1,130,234,0,0,175,0,0.6,1,0,2,1 70 | 44,1,1,120,220,0,1,170,0,0,2,0,2,1 71 | 62,0,0,124,209,0,1,163,0,0,2,0,2,1 72 | 54,1,2,120,258,0,0,147,0,0.4,1,0,3,1 73 | 51,1,2,94,227,0,1,154,1,0,2,1,3,1 74 | 29,1,1,130,204,0,0,202,0,0,2,0,2,1 75 | 51,1,0,140,261,0,0,186,1,0,2,0,2,1 76 | 43,0,2,122,213,0,1,165,0,0.2,1,0,2,1 77 | 55,0,1,135,250,0,0,161,0,1.4,1,0,2,1 78 | 51,1,2,125,245,1,0,166,0,2.4,1,0,2,1 79 | 59,1,1,140,221,0,1,164,1,0,2,0,2,1 80 | 52,1,1,128,205,1,1,184,0,0,2,0,2,1 81 | 58,1,2,105,240,0,0,154,1,0.6,1,0,3,1 82 | 41,1,2,112,250,0,1,179,0,0,2,0,2,1 83 | 45,1,1,128,308,0,0,170,0,0,2,0,2,1 84 | 60,0,2,102,318,0,1,160,0,0,2,1,2,1 85 | 52,1,3,152,298,1,1,178,0,1.2,1,0,3,1 86 | 42,0,0,102,265,0,0,122,0,0.6,1,0,2,1 87 | 67,0,2,115,564,0,0,160,0,1.6,1,0,3,1 88 | 68,1,2,118,277,0,1,151,0,1,2,1,3,1 89 | 46,1,1,101,197,1,1,156,0,0,2,0,3,1 90 | 54,0,2,110,214,0,1,158,0,1.6,1,0,2,1 91 | 58,0,0,100,248,0,0,122,0,1,1,0,2,1 92 | 48,1,2,124,255,1,1,175,0,0,2,2,2,1 93 | 57,1,0,132,207,0,1,168,1,0,2,0,3,1 94 | 52,1,2,138,223,0,1,169,0,0,2,4,2,1 95 | 54,0,1,132,288,1,0,159,1,0,2,1,2,1 96 | 45,0,1,112,160,0,1,138,0,0,1,0,2,1 97 | 53,1,0,142,226,0,0,111,1,0,2,0,3,1 98 | 62,0,0,140,394,0,0,157,0,1.2,1,0,2,1 99 | 52,1,0,108,233,1,1,147,0,0.1,2,3,3,1 100 | 43,1,2,130,315,0,1,162,0,1.9,2,1,2,1 101 | 53,1,2,130,246,1,0,173,0,0,2,3,2,1 102 | 42,1,3,148,244,0,0,178,0,0.8,2,2,2,1 103 | 59,1,3,178,270,0,0,145,0,4.2,0,0,3,1 104 | 63,0,1,140,195,0,1,179,0,0,2,2,2,1 105 | 42,1,2,120,240,1,1,194,0,0.8,0,0,3,1 106 | 50,1,2,129,196,0,1,163,0,0,2,0,2,1 107 | 68,0,2,120,211,0,0,115,0,1.5,1,0,2,1 108 | 69,1,3,160,234,1,0,131,0,0.1,1,1,2,1 109 | 45,0,0,138,236,0,0,152,1,0.2,1,0,2,1 110 | 50,0,1,120,244,0,1,162,0,1.1,2,0,2,1 111 | 50,0,0,110,254,0,0,159,0,0,2,0,2,1 112 | 64,0,0,180,325,0,1,154,1,0,2,0,2,1 113 | 57,1,2,150,126,1,1,173,0,0.2,2,1,3,1 114 | 64,0,2,140,313,0,1,133,0,0.2,2,0,3,1 115 | 43,1,0,110,211,0,1,161,0,0,2,0,3,1 116 | 55,1,1,130,262,0,1,155,0,0,2,0,2,1 117 | 37,0,2,120,215,0,1,170,0,0,2,0,2,1 118 | 41,1,2,130,214,0,0,168,0,2,1,0,2,1 119 | 56,1,3,120,193,0,0,162,0,1.9,1,0,3,1 120 | 46,0,1,105,204,0,1,172,0,0,2,0,2,1 121 | 46,0,0,138,243,0,0,152,1,0,1,0,2,1 122 | 64,0,0,130,303,0,1,122,0,2,1,2,2,1 123 | 59,1,0,138,271,0,0,182,0,0,2,0,2,1 124 | 41,0,2,112,268,0,0,172,1,0,2,0,2,1 125 | 54,0,2,108,267,0,0,167,0,0,2,0,2,1 126 | 39,0,2,94,199,0,1,179,0,0,2,0,2,1 127 | 34,0,1,118,210,0,1,192,0,0.7,2,0,2,1 128 | 47,1,0,112,204,0,1,143,0,0.1,2,0,2,1 129 | 67,0,2,152,277,0,1,172,0,0,2,1,2,1 130 | 52,0,2,136,196,0,0,169,0,0.1,1,0,2,1 131 | 74,0,1,120,269,0,0,121,1,0.2,2,1,2,1 132 | 54,0,2,160,201,0,1,163,0,0,2,1,2,1 133 | 49,0,1,134,271,0,1,162,0,0,1,0,2,1 134 | 42,1,1,120,295,0,1,162,0,0,2,0,2,1 135 | 41,1,1,110,235,0,1,153,0,0,2,0,2,1 136 | 41,0,1,126,306,0,1,163,0,0,2,0,2,1 137 | 49,0,0,130,269,0,1,163,0,0,2,0,2,1 138 | 60,0,2,120,178,1,1,96,0,0,2,0,2,1 139 | 62,1,1,128,208,1,0,140,0,0,2,0,2,1 140 | 57,1,0,110,201,0,1,126,1,1.5,1,0,1,1 141 | 64,1,0,128,263,0,1,105,1,0.2,1,1,3,1 142 | 51,0,2,120,295,0,0,157,0,0.6,2,0,2,1 143 | 43,1,0,115,303,0,1,181,0,1.2,1,0,2,1 144 | 42,0,2,120,209,0,1,173,0,0,1,0,2,1 145 | 67,0,0,106,223,0,1,142,0,0.3,2,2,2,1 146 | 76,0,2,140,197,0,2,116,0,1.1,1,0,2,1 147 | 70,1,1,156,245,0,0,143,0,0,2,0,2,1 148 | 44,0,2,118,242,0,1,149,0,0.3,1,1,2,1 149 | 60,0,3,150,240,0,1,171,0,0.9,2,0,2,1 150 | 44,1,2,120,226,0,1,169,0,0,2,0,2,1 151 | 42,1,2,130,180,0,1,150,0,0,2,0,2,1 152 | 66,1,0,160,228,0,0,138,0,2.3,2,0,1,1 153 | 71,0,0,112,149,0,1,125,0,1.6,1,0,2,1 154 | 64,1,3,170,227,0,0,155,0,0.6,1,0,3,1 155 | 66,0,2,146,278,0,0,152,0,0,1,1,2,1 156 | 39,0,2,138,220,0,1,152,0,0,1,0,2,1 157 | 58,0,0,130,197,0,1,131,0,0.6,1,0,2,1 158 | 47,1,2,130,253,0,1,179,0,0,2,0,2,1 159 | 35,1,1,122,192,0,1,174,0,0,2,0,2,1 160 | 58,1,1,125,220,0,1,144,0,0.4,1,4,3,1 161 | 56,1,1,130,221,0,0,163,0,0,2,0,3,1 162 | 56,1,1,120,240,0,1,169,0,0,0,0,2,1 163 | 55,0,1,132,342,0,1,166,0,1.2,2,0,2,1 164 | 41,1,1,120,157,0,1,182,0,0,2,0,2,1 165 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1 166 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1 167 | 67,1,0,160,286,0,0,108,1,1.5,1,3,2,0 168 | 67,1,0,120,229,0,0,129,1,2.6,1,2,3,0 169 | 62,0,0,140,268,0,0,160,0,3.6,0,2,2,0 170 | 63,1,0,130,254,0,0,147,0,1.4,1,1,3,0 171 | 53,1,0,140,203,1,0,155,1,3.1,0,0,3,0 172 | 56,1,2,130,256,1,0,142,1,0.6,1,1,1,0 173 | 48,1,1,110,229,0,1,168,0,1,0,0,3,0 174 | 58,1,1,120,284,0,0,160,0,1.8,1,0,2,0 175 | 58,1,2,132,224,0,0,173,0,3.2,2,2,3,0 176 | 60,1,0,130,206,0,0,132,1,2.4,1,2,3,0 177 | 40,1,0,110,167,0,0,114,1,2,1,0,3,0 178 | 60,1,0,117,230,1,1,160,1,1.4,2,2,3,0 179 | 64,1,2,140,335,0,1,158,0,0,2,0,2,0 180 | 43,1,0,120,177,0,0,120,1,2.5,1,0,3,0 181 | 57,1,0,150,276,0,0,112,1,0.6,1,1,1,0 182 | 55,1,0,132,353,0,1,132,1,1.2,1,1,3,0 183 | 65,0,0,150,225,0,0,114,0,1,1,3,3,0 184 | 61,0,0,130,330,0,0,169,0,0,2,0,2,0 185 | 58,1,2,112,230,0,0,165,0,2.5,1,1,3,0 186 | 50,1,0,150,243,0,0,128,0,2.6,1,0,3,0 187 | 44,1,0,112,290,0,0,153,0,0,2,1,2,0 188 | 60,1,0,130,253,0,1,144,1,1.4,2,1,3,0 189 | 54,1,0,124,266,0,0,109,1,2.2,1,1,3,0 190 | 50,1,2,140,233,0,1,163,0,0.6,1,1,3,0 191 | 41,1,0,110,172,0,0,158,0,0,2,0,3,0 192 | 51,0,0,130,305,0,1,142,1,1.2,1,0,3,0 193 | 58,1,0,128,216,0,0,131,1,2.2,1,3,3,0 194 | 54,1,0,120,188,0,1,113,0,1.4,1,1,3,0 195 | 60,1,0,145,282,0,0,142,1,2.8,1,2,3,0 196 | 60,1,2,140,185,0,0,155,0,3,1,0,2,0 197 | 59,1,0,170,326,0,0,140,1,3.4,0,0,3,0 198 | 46,1,2,150,231,0,1,147,0,3.6,1,0,2,0 199 | 67,1,0,125,254,1,1,163,0,0.2,1,2,3,0 200 | 62,1,0,120,267,0,1,99,1,1.8,1,2,3,0 201 | 65,1,0,110,248,0,0,158,0,0.6,2,2,1,0 202 | 44,1,0,110,197,0,0,177,0,0,2,1,2,0 203 | 60,1,0,125,258,0,0,141,1,2.8,1,1,3,0 204 | 58,1,0,150,270,0,0,111,1,0.8,2,0,3,0 205 | 68,1,2,180,274,1,0,150,1,1.6,1,0,3,0 206 | 62,0,0,160,164,0,0,145,0,6.2,0,3,3,0 207 | 52,1,0,128,255,0,1,161,1,0,2,1,3,0 208 | 59,1,0,110,239,0,0,142,1,1.2,1,1,3,0 209 | 60,0,0,150,258,0,0,157,0,2.6,1,2,3,0 210 | 49,1,2,120,188,0,1,139,0,2,1,3,3,0 211 | 59,1,0,140,177,0,1,162,1,0,2,1,3,0 212 | 57,1,2,128,229,0,0,150,0,0.4,1,1,3,0 213 | 61,1,0,120,260,0,1,140,1,3.6,1,1,3,0 214 | 39,1,0,118,219,0,1,140,0,1.2,1,0,3,0 215 | 61,0,0,145,307,0,0,146,1,1,1,0,3,0 216 | 56,1,0,125,249,1,0,144,1,1.2,1,1,2,0 217 | 43,0,0,132,341,1,0,136,1,3,1,0,3,0 218 | 62,0,2,130,263,0,1,97,0,1.2,1,1,3,0 219 | 63,1,0,130,330,1,0,132,1,1.8,2,3,3,0 220 | 65,1,0,135,254,0,0,127,0,2.8,1,1,3,0 221 | 48,1,0,130,256,1,0,150,1,0,2,2,3,0 222 | 63,0,0,150,407,0,0,154,0,4,1,3,3,0 223 | 55,1,0,140,217,0,1,111,1,5.6,0,0,3,0 224 | 65,1,3,138,282,1,0,174,0,1.4,1,1,2,0 225 | 56,0,0,200,288,1,0,133,1,4,0,2,3,0 226 | 54,1,0,110,239,0,1,126,1,2.8,1,1,3,0 227 | 70,1,0,145,174,0,1,125,1,2.6,0,0,3,0 228 | 62,1,1,120,281,0,0,103,0,1.4,1,1,3,0 229 | 35,1,0,120,198,0,1,130,1,1.6,1,0,3,0 230 | 59,1,3,170,288,0,0,159,0,0.2,1,0,3,0 231 | 64,1,2,125,309,0,1,131,1,1.8,1,0,3,0 232 | 47,1,2,108,243,0,1,152,0,0,2,0,2,0 233 | 57,1,0,165,289,1,0,124,0,1,1,3,3,0 234 | 55,1,0,160,289,0,0,145,1,0.8,1,1,3,0 235 | 64,1,0,120,246,0,0,96,1,2.2,0,1,2,0 236 | 70,1,0,130,322,0,0,109,0,2.4,1,3,2,0 237 | 51,1,0,140,299,0,1,173,1,1.6,2,0,3,0 238 | 58,1,0,125,300,0,0,171,0,0,2,2,3,0 239 | 60,1,0,140,293,0,0,170,0,1.2,1,2,3,0 240 | 77,1,0,125,304,0,0,162,1,0,2,3,2,0 241 | 35,1,0,126,282,0,0,156,1,0,2,0,3,0 242 | 70,1,2,160,269,0,1,112,1,2.9,1,1,3,0 243 | 59,0,0,174,249,0,1,143,1,0,1,0,2,0 244 | 64,1,0,145,212,0,0,132,0,2,1,2,1,0 245 | 57,1,0,152,274,0,1,88,1,1.2,1,1,3,0 246 | 56,1,0,132,184,0,0,105,1,2.1,1,1,1,0 247 | 48,1,0,124,274,0,0,166,0,0.5,1,0,3,0 248 | 56,0,0,134,409,0,0,150,1,1.9,1,2,3,0 249 | 66,1,1,160,246,0,1,120,1,0,1,3,1,0 250 | 54,1,1,192,283,0,0,195,0,0,2,1,3,0 251 | 69,1,2,140,254,0,0,146,0,2,1,3,3,0 252 | 51,1,0,140,298,0,1,122,1,4.2,1,3,3,0 253 | 43,1,0,132,247,1,0,143,1,0.1,1,4,3,0 254 | 62,0,0,138,294,1,1,106,0,1.9,1,3,2,0 255 | 67,1,0,100,299,0,0,125,1,0.9,1,2,2,0 256 | 59,1,3,160,273,0,0,125,0,0,2,0,2,0 257 | 45,1,0,142,309,0,0,147,1,0,1,3,3,0 258 | 58,1,0,128,259,0,0,130,1,3,1,2,3,0 259 | 50,1,0,144,200,0,0,126,1,0.9,1,0,3,0 260 | 62,0,0,150,244,0,1,154,1,1.4,1,0,2,0 261 | 38,1,3,120,231,0,1,182,1,3.8,1,0,3,0 262 | 66,0,0,178,228,1,1,165,1,1,1,2,3,0 263 | 52,1,0,112,230,0,1,160,0,0,2,1,2,0 264 | 53,1,0,123,282,0,1,95,1,2,1,2,3,0 265 | 63,0,0,108,269,0,1,169,1,1.8,1,2,2,0 266 | 54,1,0,110,206,0,0,108,1,0,1,1,2,0 267 | 66,1,0,112,212,0,0,132,1,0.1,2,1,2,0 268 | 55,0,0,180,327,0,2,117,1,3.4,1,0,2,0 269 | 49,1,2,118,149,0,0,126,0,0.8,2,3,2,0 270 | 54,1,0,122,286,0,0,116,1,3.2,1,2,2,0 271 | 56,1,0,130,283,1,0,103,1,1.6,0,0,3,0 272 | 46,1,0,120,249,0,0,144,0,0.8,2,0,3,0 273 | 61,1,3,134,234,0,1,145,0,2.6,1,2,2,0 274 | 67,1,0,120,237,0,1,71,0,1,1,0,2,0 275 | 58,1,0,100,234,0,1,156,0,0.1,2,1,3,0 276 | 47,1,0,110,275,0,0,118,1,1,1,1,2,0 277 | 52,1,0,125,212,0,1,168,0,1,2,2,3,0 278 | 58,1,0,146,218,0,1,105,0,2,1,1,3,0 279 | 57,1,1,124,261,0,1,141,0,0.3,2,0,3,0 280 | 58,0,1,136,319,1,0,152,0,0,2,2,2,0 281 | 61,1,0,138,166,0,0,125,1,3.6,1,1,2,0 282 | 42,1,0,136,315,0,1,125,1,1.8,1,0,1,0 283 | 52,1,0,128,204,1,1,156,1,1,1,0,0,0 284 | 59,1,2,126,218,1,1,134,0,2.2,1,1,1,0 285 | 40,1,0,152,223,0,1,181,0,0,2,0,3,0 286 | 61,1,0,140,207,0,0,138,1,1.9,2,1,3,0 287 | 46,1,0,140,311,0,1,120,1,1.8,1,2,3,0 288 | 59,1,3,134,204,0,1,162,0,0.8,2,2,2,0 289 | 57,1,1,154,232,0,0,164,0,0,2,1,2,0 290 | 57,1,0,110,335,0,1,143,1,3,1,1,3,0 291 | 55,0,0,128,205,0,2,130,1,2,1,1,3,0 292 | 61,1,0,148,203,0,1,161,0,0,2,1,3,0 293 | 58,1,0,114,318,0,2,140,0,4.4,0,3,1,0 294 | 58,0,0,170,225,1,0,146,1,2.8,1,2,1,0 295 | 67,1,2,152,212,0,0,150,0,0.8,1,0,3,0 296 | 44,1,0,120,169,0,1,144,1,2.8,0,0,1,0 297 | 63,1,0,140,187,0,0,144,1,4,2,2,3,0 298 | 63,0,0,124,197,0,1,136,1,0,1,0,2,0 299 | 59,1,0,164,176,1,0,90,0,1,1,2,1,0 300 | 57,0,0,140,241,0,1,123,1,0.2,1,0,3,0 301 | 45,1,3,110,264,0,1,132,0,1.2,1,0,3,0 302 | 68,1,0,144,193,1,1,141,0,3.4,1,2,3,0 303 | 57,1,0,130,131,0,1,115,1,1.2,1,1,3,0 304 | 57,0,1,130,236,0,0,174,0,0,1,1,2,0 305 | -------------------------------------------------------------------------------- /data/iris.csv: -------------------------------------------------------------------------------- 1 | sepal_length,sepal_width,petal_length,petal_width,species 2 | 5.1,3.5,1.4,0.2,setosa 3 | 4.9,3.0,1.4,0.2,setosa 4 | 4.7,3.2,1.3,0.2,setosa 5 | 4.6,3.1,1.5,0.2,setosa 6 | 5.0,3.6,1.4,0.2,setosa 7 | 5.4,3.9,1.7,0.4,setosa 8 | 4.6,3.4,1.4,0.3,setosa 9 | 5.0,3.4,1.5,0.2,setosa 10 | 4.4,2.9,1.4,0.2,setosa 11 | 4.9,3.1,1.5,0.1,setosa 12 | 5.4,3.7,1.5,0.2,setosa 13 | 4.8,3.4,1.6,0.2,setosa 14 | 4.8,3.0,1.4,0.1,setosa 15 | 4.3,3.0,1.1,0.1,setosa 16 | 5.8,4.0,1.2,0.2,setosa 17 | 5.7,4.4,1.5,0.4,setosa 18 | 5.4,3.9,1.3,0.4,setosa 19 | 5.1,3.5,1.4,0.3,setosa 20 | 5.7,3.8,1.7,0.3,setosa 21 | 5.1,3.8,1.5,0.3,setosa 22 | 5.4,3.4,1.7,0.2,setosa 23 | 5.1,3.7,1.5,0.4,setosa 24 | 4.6,3.6,1.0,0.2,setosa 25 | 5.1,3.3,1.7,0.5,setosa 26 | 4.8,3.4,1.9,0.2,setosa 27 | 5.0,3.0,1.6,0.2,setosa 28 | 5.0,3.4,1.6,0.4,setosa 29 | 5.2,3.5,1.5,0.2,setosa 30 | 5.2,3.4,1.4,0.2,setosa 31 | 4.7,3.2,1.6,0.2,setosa 32 | 4.8,3.1,1.6,0.2,setosa 33 | 5.4,3.4,1.5,0.4,setosa 34 | 5.2,4.1,1.5,0.1,setosa 35 | 5.5,4.2,1.4,0.2,setosa 36 | 4.9,3.1,1.5,0.1,setosa 37 | 5.0,3.2,1.2,0.2,setosa 38 | 5.5,3.5,1.3,0.2,setosa 39 | 4.9,3.1,1.5,0.1,setosa 40 | 4.4,3.0,1.3,0.2,setosa 41 | 5.1,3.4,1.5,0.2,setosa 42 | 5.0,3.5,1.3,0.3,setosa 43 | 4.5,2.3,1.3,0.3,setosa 44 | 4.4,3.2,1.3,0.2,setosa 45 | 5.0,3.5,1.6,0.6,setosa 46 | 5.1,3.8,1.9,0.4,setosa 47 | 4.8,3.0,1.4,0.3,setosa 48 | 5.1,3.8,1.6,0.2,setosa 49 | 4.6,3.2,1.4,0.2,setosa 50 | 5.3,3.7,1.5,0.2,setosa 51 | 5.0,3.3,1.4,0.2,setosa 52 | 7.0,3.2,4.7,1.4,versicolor 53 | 6.4,3.2,4.5,1.5,versicolor 54 | 6.9,3.1,4.9,1.5,versicolor 55 | 5.5,2.3,4.0,1.3,versicolor 56 | 6.5,2.8,4.6,1.5,versicolor 57 | 5.7,2.8,4.5,1.3,versicolor 58 | 6.3,3.3,4.7,1.6,versicolor 59 | 4.9,2.4,3.3,1.0,versicolor 60 | 6.6,2.9,4.6,1.3,versicolor 61 | 5.2,2.7,3.9,1.4,versicolor 62 | 5.0,2.0,3.5,1.0,versicolor 63 | 5.9,3.0,4.2,1.5,versicolor 64 | 6.0,2.2,4.0,1.0,versicolor 65 | 6.1,2.9,4.7,1.4,versicolor 66 | 5.6,2.9,3.6,1.3,versicolor 67 | 6.7,3.1,4.4,1.4,versicolor 68 | 5.6,3.0,4.5,1.5,versicolor 69 | 5.8,2.7,4.1,1.0,versicolor 70 | 6.2,2.2,4.5,1.5,versicolor 71 | 5.6,2.5,3.9,1.1,versicolor 72 | 5.9,3.2,4.8,1.8,versicolor 73 | 6.1,2.8,4.0,1.3,versicolor 74 | 6.3,2.5,4.9,1.5,versicolor 75 | 6.1,2.8,4.7,1.2,versicolor 76 | 6.4,2.9,4.3,1.3,versicolor 77 | 6.6,3.0,4.4,1.4,versicolor 78 | 6.8,2.8,4.8,1.4,versicolor 79 | 6.7,3.0,5.0,1.7,versicolor 80 | 6.0,2.9,4.5,1.5,versicolor 81 | 5.7,2.6,3.5,1.0,versicolor 82 | 5.5,2.4,3.8,1.1,versicolor 83 | 5.5,2.4,3.7,1.0,versicolor 84 | 5.8,2.7,3.9,1.2,versicolor 85 | 6.0,2.7,5.1,1.6,versicolor 86 | 5.4,3.0,4.5,1.5,versicolor 87 | 6.0,3.4,4.5,1.6,versicolor 88 | 6.7,3.1,4.7,1.5,versicolor 89 | 6.3,2.3,4.4,1.3,versicolor 90 | 5.6,3.0,4.1,1.3,versicolor 91 | 5.5,2.5,4.0,1.3,versicolor 92 | 5.5,2.6,4.4,1.2,versicolor 93 | 6.1,3.0,4.6,1.4,versicolor 94 | 5.8,2.6,4.0,1.2,versicolor 95 | 5.0,2.3,3.3,1.0,versicolor 96 | 5.6,2.7,4.2,1.3,versicolor 97 | 5.7,3.0,4.2,1.2,versicolor 98 | 5.7,2.9,4.2,1.3,versicolor 99 | 6.2,2.9,4.3,1.3,versicolor 100 | 5.1,2.5,3.0,1.1,versicolor 101 | 5.7,2.8,4.1,1.3,versicolor 102 | 6.3,3.3,6.0,2.5,virginica 103 | 5.8,2.7,5.1,1.9,virginica 104 | 7.1,3.0,5.9,2.1,virginica 105 | 6.3,2.9,5.6,1.8,virginica 106 | 6.5,3.0,5.8,2.2,virginica 107 | 7.6,3.0,6.6,2.1,virginica 108 | 4.9,2.5,4.5,1.7,virginica 109 | 7.3,2.9,6.3,1.8,virginica 110 | 6.7,2.5,5.8,1.8,virginica 111 | 7.2,3.6,6.1,2.5,virginica 112 | 6.5,3.2,5.1,2.0,virginica 113 | 6.4,2.7,5.3,1.9,virginica 114 | 6.8,3.0,5.5,2.1,virginica 115 | 5.7,2.5,5.0,2.0,virginica 116 | 5.8,2.8,5.1,2.4,virginica 117 | 6.4,3.2,5.3,2.3,virginica 118 | 6.5,3.0,5.5,1.8,virginica 119 | 7.7,3.8,6.7,2.2,virginica 120 | 7.7,2.6,6.9,2.3,virginica 121 | 6.0,2.2,5.0,1.5,virginica 122 | 6.9,3.2,5.7,2.3,virginica 123 | 5.6,2.8,4.9,2.0,virginica 124 | 7.7,2.8,6.7,2.0,virginica 125 | 6.3,2.7,4.9,1.8,virginica 126 | 6.7,3.3,5.7,2.1,virginica 127 | 7.2,3.2,6.0,1.8,virginica 128 | 6.2,2.8,4.8,1.8,virginica 129 | 6.1,3.0,4.9,1.8,virginica 130 | 6.4,2.8,5.6,2.1,virginica 131 | 7.2,3.0,5.8,1.6,virginica 132 | 7.4,2.8,6.1,1.9,virginica 133 | 7.9,3.8,6.4,2.0,virginica 134 | 6.4,2.8,5.6,2.2,virginica 135 | 6.3,2.8,5.1,1.5,virginica 136 | 6.1,2.6,5.6,1.4,virginica 137 | 7.7,3.0,6.1,2.3,virginica 138 | 6.3,3.4,5.6,2.4,virginica 139 | 6.4,3.1,5.5,1.8,virginica 140 | 6.0,3.0,4.8,1.8,virginica 141 | 6.9,3.1,5.4,2.1,virginica 142 | 6.7,3.1,5.6,2.4,virginica 143 | 6.9,3.1,5.1,2.3,virginica 144 | 5.8,2.7,5.1,1.9,virginica 145 | 6.8,3.2,5.9,2.3,virginica 146 | 6.7,3.3,5.7,2.5,virginica 147 | 6.7,3.0,5.2,2.3,virginica 148 | 6.3,2.5,5.0,1.9,virginica 149 | 6.5,3.0,5.2,2.0,virginica 150 | 6.2,3.4,5.4,2.3,virginica 151 | 5.9,3.0,5.1,1.8,virginica 152 | -------------------------------------------------------------------------------- /data/mouse_viral_study.csv: -------------------------------------------------------------------------------- 1 | Med_1_mL,Med_2_mL,Virus Present 2 | 6.508231092778478,8.582530517168834,0 3 | 4.126116159459635,3.0734585320324737,1 4 | 6.427870479400083,6.369758103535007,0 5 | 3.6729533598321504,4.905215249940063,1 6 | 1.5803206800377936,2.440562302195768,1 7 | 2.15822377398559,2.7454512398737183,1 8 | 8.172223496843538,7.328994873431797,0 9 | 8.719652335230442,6.228618301234675,0 10 | 6.779079372963939,6.630667118733948,0 11 | 3.0436331904746012,4.642649940107834,1 12 | 1.7138759191312358,3.5188560013538677,1 13 | 2.020123874999962,3.403189454825145,1 14 | 4.055748603658648,3.1242432294061078,1 15 | 3.6301579288759176,3.7489663385034526,1 16 | 3.0536347897071137,4.872665822711166,1 17 | 8.392228630872772,8.110502310745462,0 18 | 6.176983774867312,7.949241165922479,0 19 | 7.2862327617956435,7.243546847397496,0 20 | 7.481510167476451,6.670184345077146,0 21 | 3.2548003278856052,4.460048182663909,1 22 | 6.90258268522116,7.163204418823208,0 23 | 2.983372599819152,1.6151576047528442,1 24 | 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-------------------------------------------------------------------------------- /data/penguins_size.csv: -------------------------------------------------------------------------------- 1 | species,island,culmen_length_mm,culmen_depth_mm,flipper_length_mm,body_mass_g,sex 2 | Adelie,Torgersen,39.1,18.7,181,3750,MALE 3 | Adelie,Torgersen,39.5,17.4,186,3800,FEMALE 4 | Adelie,Torgersen,40.3,18,195,3250,FEMALE 5 | Adelie,Torgersen,NA,NA,NA,NA,NA 6 | Adelie,Torgersen,36.7,19.3,193,3450,FEMALE 7 | Adelie,Torgersen,39.3,20.6,190,3650,MALE 8 | Adelie,Torgersen,38.9,17.8,181,3625,FEMALE 9 | Adelie,Torgersen,39.2,19.6,195,4675,MALE 10 | Adelie,Torgersen,34.1,18.1,193,3475,NA 11 | Adelie,Torgersen,42,20.2,190,4250,NA 12 | Adelie,Torgersen,37.8,17.1,186,3300,NA 13 | Adelie,Torgersen,37.8,17.3,180,3700,NA 14 | Adelie,Torgersen,41.1,17.6,182,3200,FEMALE 15 | Adelie,Torgersen,38.6,21.2,191,3800,MALE 16 | Adelie,Torgersen,34.6,21.1,198,4400,MALE 17 | Adelie,Torgersen,36.6,17.8,185,3700,FEMALE 18 | Adelie,Torgersen,38.7,19,195,3450,FEMALE 19 | Adelie,Torgersen,42.5,20.7,197,4500,MALE 20 | Adelie,Torgersen,34.4,18.4,184,3325,FEMALE 21 | Adelie,Torgersen,46,21.5,194,4200,MALE 22 | Adelie,Biscoe,37.8,18.3,174,3400,FEMALE 23 | Adelie,Biscoe,37.7,18.7,180,3600,MALE 24 | Adelie,Biscoe,35.9,19.2,189,3800,FEMALE 25 | Adelie,Biscoe,38.2,18.1,185,3950,MALE 26 | Adelie,Biscoe,38.8,17.2,180,3800,MALE 27 | Adelie,Biscoe,35.3,18.9,187,3800,FEMALE 28 | Adelie,Biscoe,40.6,18.6,183,3550,MALE 29 | Adelie,Biscoe,40.5,17.9,187,3200,FEMALE 30 | Adelie,Biscoe,37.9,18.6,172,3150,FEMALE 31 | Adelie,Biscoe,40.5,18.9,180,3950,MALE 32 | Adelie,Dream,39.5,16.7,178,3250,FEMALE 33 | Adelie,Dream,37.2,18.1,178,3900,MALE 34 | Adelie,Dream,39.5,17.8,188,3300,FEMALE 35 | Adelie,Dream,40.9,18.9,184,3900,MALE 36 | Adelie,Dream,36.4,17,195,3325,FEMALE 37 | Adelie,Dream,39.2,21.1,196,4150,MALE 38 | Adelie,Dream,38.8,20,190,3950,MALE 39 | Adelie,Dream,42.2,18.5,180,3550,FEMALE 40 | Adelie,Dream,37.6,19.3,181,3300,FEMALE 41 | Adelie,Dream,39.8,19.1,184,4650,MALE 42 | Adelie,Dream,36.5,18,182,3150,FEMALE 43 | Adelie,Dream,40.8,18.4,195,3900,MALE 44 | Adelie,Dream,36,18.5,186,3100,FEMALE 45 | Adelie,Dream,44.1,19.7,196,4400,MALE 46 | Adelie,Dream,37,16.9,185,3000,FEMALE 47 | Adelie,Dream,39.6,18.8,190,4600,MALE 48 | Adelie,Dream,41.1,19,182,3425,MALE 49 | Adelie,Dream,37.5,18.9,179,2975,NA 50 | Adelie,Dream,36,17.9,190,3450,FEMALE 51 | Adelie,Dream,42.3,21.2,191,4150,MALE 52 | Adelie,Biscoe,39.6,17.7,186,3500,FEMALE 53 | Adelie,Biscoe,40.1,18.9,188,4300,MALE 54 | Adelie,Biscoe,35,17.9,190,3450,FEMALE 55 | Adelie,Biscoe,42,19.5,200,4050,MALE 56 | Adelie,Biscoe,34.5,18.1,187,2900,FEMALE 57 | Adelie,Biscoe,41.4,18.6,191,3700,MALE 58 | Adelie,Biscoe,39,17.5,186,3550,FEMALE 59 | Adelie,Biscoe,40.6,18.8,193,3800,MALE 60 | Adelie,Biscoe,36.5,16.6,181,2850,FEMALE 61 | Adelie,Biscoe,37.6,19.1,194,3750,MALE 62 | Adelie,Biscoe,35.7,16.9,185,3150,FEMALE 63 | Adelie,Biscoe,41.3,21.1,195,4400,MALE 64 | Adelie,Biscoe,37.6,17,185,3600,FEMALE 65 | Adelie,Biscoe,41.1,18.2,192,4050,MALE 66 | Adelie,Biscoe,36.4,17.1,184,2850,FEMALE 67 | Adelie,Biscoe,41.6,18,192,3950,MALE 68 | Adelie,Biscoe,35.5,16.2,195,3350,FEMALE 69 | Adelie,Biscoe,41.1,19.1,188,4100,MALE 70 | Adelie,Torgersen,35.9,16.6,190,3050,FEMALE 71 | Adelie,Torgersen,41.8,19.4,198,4450,MALE 72 | Adelie,Torgersen,33.5,19,190,3600,FEMALE 73 | Adelie,Torgersen,39.7,18.4,190,3900,MALE 74 | Adelie,Torgersen,39.6,17.2,196,3550,FEMALE 75 | Adelie,Torgersen,45.8,18.9,197,4150,MALE 76 | Adelie,Torgersen,35.5,17.5,190,3700,FEMALE 77 | Adelie,Torgersen,42.8,18.5,195,4250,MALE 78 | Adelie,Torgersen,40.9,16.8,191,3700,FEMALE 79 | Adelie,Torgersen,37.2,19.4,184,3900,MALE 80 | Adelie,Torgersen,36.2,16.1,187,3550,FEMALE 81 | Adelie,Torgersen,42.1,19.1,195,4000,MALE 82 | Adelie,Torgersen,34.6,17.2,189,3200,FEMALE 83 | Adelie,Torgersen,42.9,17.6,196,4700,MALE 84 | Adelie,Torgersen,36.7,18.8,187,3800,FEMALE 85 | Adelie,Torgersen,35.1,19.4,193,4200,MALE 86 | Adelie,Dream,37.3,17.8,191,3350,FEMALE 87 | Adelie,Dream,41.3,20.3,194,3550,MALE 88 | Adelie,Dream,36.3,19.5,190,3800,MALE 89 | Adelie,Dream,36.9,18.6,189,3500,FEMALE 90 | Adelie,Dream,38.3,19.2,189,3950,MALE 91 | Adelie,Dream,38.9,18.8,190,3600,FEMALE 92 | Adelie,Dream,35.7,18,202,3550,FEMALE 93 | Adelie,Dream,41.1,18.1,205,4300,MALE 94 | Adelie,Dream,34,17.1,185,3400,FEMALE 95 | Adelie,Dream,39.6,18.1,186,4450,MALE 96 | Adelie,Dream,36.2,17.3,187,3300,FEMALE 97 | Adelie,Dream,40.8,18.9,208,4300,MALE 98 | Adelie,Dream,38.1,18.6,190,3700,FEMALE 99 | Adelie,Dream,40.3,18.5,196,4350,MALE 100 | Adelie,Dream,33.1,16.1,178,2900,FEMALE 101 | Adelie,Dream,43.2,18.5,192,4100,MALE 102 | Adelie,Biscoe,35,17.9,192,3725,FEMALE 103 | Adelie,Biscoe,41,20,203,4725,MALE 104 | Adelie,Biscoe,37.7,16,183,3075,FEMALE 105 | Adelie,Biscoe,37.8,20,190,4250,MALE 106 | Adelie,Biscoe,37.9,18.6,193,2925,FEMALE 107 | Adelie,Biscoe,39.7,18.9,184,3550,MALE 108 | Adelie,Biscoe,38.6,17.2,199,3750,FEMALE 109 | Adelie,Biscoe,38.2,20,190,3900,MALE 110 | Adelie,Biscoe,38.1,17,181,3175,FEMALE 111 | Adelie,Biscoe,43.2,19,197,4775,MALE 112 | Adelie,Biscoe,38.1,16.5,198,3825,FEMALE 113 | Adelie,Biscoe,45.6,20.3,191,4600,MALE 114 | Adelie,Biscoe,39.7,17.7,193,3200,FEMALE 115 | Adelie,Biscoe,42.2,19.5,197,4275,MALE 116 | Adelie,Biscoe,39.6,20.7,191,3900,FEMALE 117 | Adelie,Biscoe,42.7,18.3,196,4075,MALE 118 | Adelie,Torgersen,38.6,17,188,2900,FEMALE 119 | Adelie,Torgersen,37.3,20.5,199,3775,MALE 120 | Adelie,Torgersen,35.7,17,189,3350,FEMALE 121 | Adelie,Torgersen,41.1,18.6,189,3325,MALE 122 | Adelie,Torgersen,36.2,17.2,187,3150,FEMALE 123 | Adelie,Torgersen,37.7,19.8,198,3500,MALE 124 | Adelie,Torgersen,40.2,17,176,3450,FEMALE 125 | Adelie,Torgersen,41.4,18.5,202,3875,MALE 126 | Adelie,Torgersen,35.2,15.9,186,3050,FEMALE 127 | Adelie,Torgersen,40.6,19,199,4000,MALE 128 | Adelie,Torgersen,38.8,17.6,191,3275,FEMALE 129 | Adelie,Torgersen,41.5,18.3,195,4300,MALE 130 | Adelie,Torgersen,39,17.1,191,3050,FEMALE 131 | Adelie,Torgersen,44.1,18,210,4000,MALE 132 | Adelie,Torgersen,38.5,17.9,190,3325,FEMALE 133 | Adelie,Torgersen,43.1,19.2,197,3500,MALE 134 | Adelie,Dream,36.8,18.5,193,3500,FEMALE 135 | Adelie,Dream,37.5,18.5,199,4475,MALE 136 | Adelie,Dream,38.1,17.6,187,3425,FEMALE 137 | Adelie,Dream,41.1,17.5,190,3900,MALE 138 | Adelie,Dream,35.6,17.5,191,3175,FEMALE 139 | Adelie,Dream,40.2,20.1,200,3975,MALE 140 | Adelie,Dream,37,16.5,185,3400,FEMALE 141 | Adelie,Dream,39.7,17.9,193,4250,MALE 142 | Adelie,Dream,40.2,17.1,193,3400,FEMALE 143 | Adelie,Dream,40.6,17.2,187,3475,MALE 144 | Adelie,Dream,32.1,15.5,188,3050,FEMALE 145 | Adelie,Dream,40.7,17,190,3725,MALE 146 | Adelie,Dream,37.3,16.8,192,3000,FEMALE 147 | Adelie,Dream,39,18.7,185,3650,MALE 148 | Adelie,Dream,39.2,18.6,190,4250,MALE 149 | Adelie,Dream,36.6,18.4,184,3475,FEMALE 150 | Adelie,Dream,36,17.8,195,3450,FEMALE 151 | Adelie,Dream,37.8,18.1,193,3750,MALE 152 | Adelie,Dream,36,17.1,187,3700,FEMALE 153 | Adelie,Dream,41.5,18.5,201,4000,MALE 154 | Chinstrap,Dream,46.5,17.9,192,3500,FEMALE 155 | Chinstrap,Dream,50,19.5,196,3900,MALE 156 | Chinstrap,Dream,51.3,19.2,193,3650,MALE 157 | Chinstrap,Dream,45.4,18.7,188,3525,FEMALE 158 | Chinstrap,Dream,52.7,19.8,197,3725,MALE 159 | Chinstrap,Dream,45.2,17.8,198,3950,FEMALE 160 | Chinstrap,Dream,46.1,18.2,178,3250,FEMALE 161 | Chinstrap,Dream,51.3,18.2,197,3750,MALE 162 | Chinstrap,Dream,46,18.9,195,4150,FEMALE 163 | Chinstrap,Dream,51.3,19.9,198,3700,MALE 164 | Chinstrap,Dream,46.6,17.8,193,3800,FEMALE 165 | Chinstrap,Dream,51.7,20.3,194,3775,MALE 166 | Chinstrap,Dream,47,17.3,185,3700,FEMALE 167 | Chinstrap,Dream,52,18.1,201,4050,MALE 168 | Chinstrap,Dream,45.9,17.1,190,3575,FEMALE 169 | Chinstrap,Dream,50.5,19.6,201,4050,MALE 170 | Chinstrap,Dream,50.3,20,197,3300,MALE 171 | Chinstrap,Dream,58,17.8,181,3700,FEMALE 172 | Chinstrap,Dream,46.4,18.6,190,3450,FEMALE 173 | Chinstrap,Dream,49.2,18.2,195,4400,MALE 174 | Chinstrap,Dream,42.4,17.3,181,3600,FEMALE 175 | Chinstrap,Dream,48.5,17.5,191,3400,MALE 176 | Chinstrap,Dream,43.2,16.6,187,2900,FEMALE 177 | Chinstrap,Dream,50.6,19.4,193,3800,MALE 178 | Chinstrap,Dream,46.7,17.9,195,3300,FEMALE 179 | Chinstrap,Dream,52,19,197,4150,MALE 180 | Chinstrap,Dream,50.5,18.4,200,3400,FEMALE 181 | Chinstrap,Dream,49.5,19,200,3800,MALE 182 | Chinstrap,Dream,46.4,17.8,191,3700,FEMALE 183 | Chinstrap,Dream,52.8,20,205,4550,MALE 184 | Chinstrap,Dream,40.9,16.6,187,3200,FEMALE 185 | Chinstrap,Dream,54.2,20.8,201,4300,MALE 186 | Chinstrap,Dream,42.5,16.7,187,3350,FEMALE 187 | Chinstrap,Dream,51,18.8,203,4100,MALE 188 | Chinstrap,Dream,49.7,18.6,195,3600,MALE 189 | Chinstrap,Dream,47.5,16.8,199,3900,FEMALE 190 | Chinstrap,Dream,47.6,18.3,195,3850,FEMALE 191 | Chinstrap,Dream,52,20.7,210,4800,MALE 192 | Chinstrap,Dream,46.9,16.6,192,2700,FEMALE 193 | Chinstrap,Dream,53.5,19.9,205,4500,MALE 194 | Chinstrap,Dream,49,19.5,210,3950,MALE 195 | Chinstrap,Dream,46.2,17.5,187,3650,FEMALE 196 | Chinstrap,Dream,50.9,19.1,196,3550,MALE 197 | Chinstrap,Dream,45.5,17,196,3500,FEMALE 198 | Chinstrap,Dream,50.9,17.9,196,3675,FEMALE 199 | Chinstrap,Dream,50.8,18.5,201,4450,MALE 200 | Chinstrap,Dream,50.1,17.9,190,3400,FEMALE 201 | Chinstrap,Dream,49,19.6,212,4300,MALE 202 | Chinstrap,Dream,51.5,18.7,187,3250,MALE 203 | Chinstrap,Dream,49.8,17.3,198,3675,FEMALE 204 | Chinstrap,Dream,48.1,16.4,199,3325,FEMALE 205 | Chinstrap,Dream,51.4,19,201,3950,MALE 206 | Chinstrap,Dream,45.7,17.3,193,3600,FEMALE 207 | Chinstrap,Dream,50.7,19.7,203,4050,MALE 208 | Chinstrap,Dream,42.5,17.3,187,3350,FEMALE 209 | Chinstrap,Dream,52.2,18.8,197,3450,MALE 210 | Chinstrap,Dream,45.2,16.6,191,3250,FEMALE 211 | Chinstrap,Dream,49.3,19.9,203,4050,MALE 212 | Chinstrap,Dream,50.2,18.8,202,3800,MALE 213 | Chinstrap,Dream,45.6,19.4,194,3525,FEMALE 214 | Chinstrap,Dream,51.9,19.5,206,3950,MALE 215 | Chinstrap,Dream,46.8,16.5,189,3650,FEMALE 216 | Chinstrap,Dream,45.7,17,195,3650,FEMALE 217 | Chinstrap,Dream,55.8,19.8,207,4000,MALE 218 | Chinstrap,Dream,43.5,18.1,202,3400,FEMALE 219 | Chinstrap,Dream,49.6,18.2,193,3775,MALE 220 | Chinstrap,Dream,50.8,19,210,4100,MALE 221 | Chinstrap,Dream,50.2,18.7,198,3775,FEMALE 222 | Gentoo,Biscoe,46.1,13.2,211,4500,FEMALE 223 | Gentoo,Biscoe,50,16.3,230,5700,MALE 224 | Gentoo,Biscoe,48.7,14.1,210,4450,FEMALE 225 | Gentoo,Biscoe,50,15.2,218,5700,MALE 226 | Gentoo,Biscoe,47.6,14.5,215,5400,MALE 227 | Gentoo,Biscoe,46.5,13.5,210,4550,FEMALE 228 | Gentoo,Biscoe,45.4,14.6,211,4800,FEMALE 229 | Gentoo,Biscoe,46.7,15.3,219,5200,MALE 230 | Gentoo,Biscoe,43.3,13.4,209,4400,FEMALE 231 | Gentoo,Biscoe,46.8,15.4,215,5150,MALE 232 | Gentoo,Biscoe,40.9,13.7,214,4650,FEMALE 233 | Gentoo,Biscoe,49,16.1,216,5550,MALE 234 | Gentoo,Biscoe,45.5,13.7,214,4650,FEMALE 235 | Gentoo,Biscoe,48.4,14.6,213,5850,MALE 236 | Gentoo,Biscoe,45.8,14.6,210,4200,FEMALE 237 | Gentoo,Biscoe,49.3,15.7,217,5850,MALE 238 | Gentoo,Biscoe,42,13.5,210,4150,FEMALE 239 | Gentoo,Biscoe,49.2,15.2,221,6300,MALE 240 | Gentoo,Biscoe,46.2,14.5,209,4800,FEMALE 241 | Gentoo,Biscoe,48.7,15.1,222,5350,MALE 242 | Gentoo,Biscoe,50.2,14.3,218,5700,MALE 243 | Gentoo,Biscoe,45.1,14.5,215,5000,FEMALE 244 | Gentoo,Biscoe,46.5,14.5,213,4400,FEMALE 245 | Gentoo,Biscoe,46.3,15.8,215,5050,MALE 246 | Gentoo,Biscoe,42.9,13.1,215,5000,FEMALE 247 | Gentoo,Biscoe,46.1,15.1,215,5100,MALE 248 | Gentoo,Biscoe,44.5,14.3,216,4100,NA 249 | Gentoo,Biscoe,47.8,15,215,5650,MALE 250 | Gentoo,Biscoe,48.2,14.3,210,4600,FEMALE 251 | Gentoo,Biscoe,50,15.3,220,5550,MALE 252 | Gentoo,Biscoe,47.3,15.3,222,5250,MALE 253 | Gentoo,Biscoe,42.8,14.2,209,4700,FEMALE 254 | Gentoo,Biscoe,45.1,14.5,207,5050,FEMALE 255 | Gentoo,Biscoe,59.6,17,230,6050,MALE 256 | Gentoo,Biscoe,49.1,14.8,220,5150,FEMALE 257 | Gentoo,Biscoe,48.4,16.3,220,5400,MALE 258 | Gentoo,Biscoe,42.6,13.7,213,4950,FEMALE 259 | Gentoo,Biscoe,44.4,17.3,219,5250,MALE 260 | Gentoo,Biscoe,44,13.6,208,4350,FEMALE 261 | Gentoo,Biscoe,48.7,15.7,208,5350,MALE 262 | Gentoo,Biscoe,42.7,13.7,208,3950,FEMALE 263 | Gentoo,Biscoe,49.6,16,225,5700,MALE 264 | Gentoo,Biscoe,45.3,13.7,210,4300,FEMALE 265 | Gentoo,Biscoe,49.6,15,216,4750,MALE 266 | Gentoo,Biscoe,50.5,15.9,222,5550,MALE 267 | Gentoo,Biscoe,43.6,13.9,217,4900,FEMALE 268 | Gentoo,Biscoe,45.5,13.9,210,4200,FEMALE 269 | Gentoo,Biscoe,50.5,15.9,225,5400,MALE 270 | Gentoo,Biscoe,44.9,13.3,213,5100,FEMALE 271 | Gentoo,Biscoe,45.2,15.8,215,5300,MALE 272 | Gentoo,Biscoe,46.6,14.2,210,4850,FEMALE 273 | Gentoo,Biscoe,48.5,14.1,220,5300,MALE 274 | Gentoo,Biscoe,45.1,14.4,210,4400,FEMALE 275 | Gentoo,Biscoe,50.1,15,225,5000,MALE 276 | Gentoo,Biscoe,46.5,14.4,217,4900,FEMALE 277 | Gentoo,Biscoe,45,15.4,220,5050,MALE 278 | Gentoo,Biscoe,43.8,13.9,208,4300,FEMALE 279 | Gentoo,Biscoe,45.5,15,220,5000,MALE 280 | Gentoo,Biscoe,43.2,14.5,208,4450,FEMALE 281 | Gentoo,Biscoe,50.4,15.3,224,5550,MALE 282 | Gentoo,Biscoe,45.3,13.8,208,4200,FEMALE 283 | Gentoo,Biscoe,46.2,14.9,221,5300,MALE 284 | Gentoo,Biscoe,45.7,13.9,214,4400,FEMALE 285 | Gentoo,Biscoe,54.3,15.7,231,5650,MALE 286 | Gentoo,Biscoe,45.8,14.2,219,4700,FEMALE 287 | Gentoo,Biscoe,49.8,16.8,230,5700,MALE 288 | Gentoo,Biscoe,46.2,14.4,214,4650,NA 289 | Gentoo,Biscoe,49.5,16.2,229,5800,MALE 290 | Gentoo,Biscoe,43.5,14.2,220,4700,FEMALE 291 | Gentoo,Biscoe,50.7,15,223,5550,MALE 292 | Gentoo,Biscoe,47.7,15,216,4750,FEMALE 293 | Gentoo,Biscoe,46.4,15.6,221,5000,MALE 294 | Gentoo,Biscoe,48.2,15.6,221,5100,MALE 295 | Gentoo,Biscoe,46.5,14.8,217,5200,FEMALE 296 | Gentoo,Biscoe,46.4,15,216,4700,FEMALE 297 | Gentoo,Biscoe,48.6,16,230,5800,MALE 298 | Gentoo,Biscoe,47.5,14.2,209,4600,FEMALE 299 | Gentoo,Biscoe,51.1,16.3,220,6000,MALE 300 | Gentoo,Biscoe,45.2,13.8,215,4750,FEMALE 301 | Gentoo,Biscoe,45.2,16.4,223,5950,MALE 302 | Gentoo,Biscoe,49.1,14.5,212,4625,FEMALE 303 | Gentoo,Biscoe,52.5,15.6,221,5450,MALE 304 | Gentoo,Biscoe,47.4,14.6,212,4725,FEMALE 305 | Gentoo,Biscoe,50,15.9,224,5350,MALE 306 | Gentoo,Biscoe,44.9,13.8,212,4750,FEMALE 307 | Gentoo,Biscoe,50.8,17.3,228,5600,MALE 308 | Gentoo,Biscoe,43.4,14.4,218,4600,FEMALE 309 | Gentoo,Biscoe,51.3,14.2,218,5300,MALE 310 | Gentoo,Biscoe,47.5,14,212,4875,FEMALE 311 | Gentoo,Biscoe,52.1,17,230,5550,MALE 312 | Gentoo,Biscoe,47.5,15,218,4950,FEMALE 313 | Gentoo,Biscoe,52.2,17.1,228,5400,MALE 314 | Gentoo,Biscoe,45.5,14.5,212,4750,FEMALE 315 | Gentoo,Biscoe,49.5,16.1,224,5650,MALE 316 | Gentoo,Biscoe,44.5,14.7,214,4850,FEMALE 317 | Gentoo,Biscoe,50.8,15.7,226,5200,MALE 318 | Gentoo,Biscoe,49.4,15.8,216,4925,MALE 319 | Gentoo,Biscoe,46.9,14.6,222,4875,FEMALE 320 | Gentoo,Biscoe,48.4,14.4,203,4625,FEMALE 321 | Gentoo,Biscoe,51.1,16.5,225,5250,MALE 322 | Gentoo,Biscoe,48.5,15,219,4850,FEMALE 323 | Gentoo,Biscoe,55.9,17,228,5600,MALE 324 | Gentoo,Biscoe,47.2,15.5,215,4975,FEMALE 325 | Gentoo,Biscoe,49.1,15,228,5500,MALE 326 | Gentoo,Biscoe,47.3,13.8,216,4725,NA 327 | Gentoo,Biscoe,46.8,16.1,215,5500,MALE 328 | Gentoo,Biscoe,41.7,14.7,210,4700,FEMALE 329 | Gentoo,Biscoe,53.4,15.8,219,5500,MALE 330 | Gentoo,Biscoe,43.3,14,208,4575,FEMALE 331 | Gentoo,Biscoe,48.1,15.1,209,5500,MALE 332 | Gentoo,Biscoe,50.5,15.2,216,5000,FEMALE 333 | Gentoo,Biscoe,49.8,15.9,229,5950,MALE 334 | Gentoo,Biscoe,43.5,15.2,213,4650,FEMALE 335 | Gentoo,Biscoe,51.5,16.3,230,5500,MALE 336 | Gentoo,Biscoe,46.2,14.1,217,4375,FEMALE 337 | Gentoo,Biscoe,55.1,16,230,5850,MALE 338 | Gentoo,Biscoe,44.5,15.7,217,4875,. 339 | Gentoo,Biscoe,48.8,16.2,222,6000,MALE 340 | Gentoo,Biscoe,47.2,13.7,214,4925,FEMALE 341 | Gentoo,Biscoe,NA,NA,NA,NA,NA 342 | Gentoo,Biscoe,46.8,14.3,215,4850,FEMALE 343 | Gentoo,Biscoe,50.4,15.7,222,5750,MALE 344 | Gentoo,Biscoe,45.2,14.8,212,5200,FEMALE 345 | Gentoo,Biscoe,49.9,16.1,213,5400,MALE 346 | -------------------------------------------------------------------------------- /data/rock_density_xray.csv: -------------------------------------------------------------------------------- 1 | Rebound Signal Strength nHz,Rock Density kg/m3 2 | 72.94512436383434,2.456547879136858 3 | 14.229877200496464,2.601719324133362 4 | 36.59733360554553,1.967003889617306 5 | 9.5788993917866,2.3004385833666268 6 | 21.765897078708488,2.452374393648803 7 | 89.6479725563843,2.499599699939095 8 | 69.49273292902218,2.3850001380096595 9 | 20.150520360804293,2.500672619986891 10 | 82.25633703331846,2.70087981032737 11 | 64.42480216587825,2.2207806716706493 12 | 38.42706533526453,1.9753625646359527 13 | 9.891579940324768,2.6991236146815467 14 | 31.74109839501984,2.231671133809982 15 | 90.49606760311602,2.4317037539201403 16 | 13.193425553761086,2.733012253907157 17 | 9.556656733258617,2.54549069351582 18 | 26.005505770956287,2.3208372567334887 19 | 24.81782724551989,2.605705133655041 20 | 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15.162434319582216,2.644989661809002 301 | 38.020266713914545,2.0442866153346504 302 | -------------------------------------------------------------------------------- /models/final_poly_converter.joblib: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Sagar-modelling/ML_Live_Class/d4edece87958c661605e5fb85f64e146a8621128/models/final_poly_converter.joblib -------------------------------------------------------------------------------- /models/model.joblib: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Sagar-modelling/ML_Live_Class/d4edece87958c661605e5fb85f64e146a8621128/models/model.joblib -------------------------------------------------------------------------------- /models/poly.joblib: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Sagar-modelling/ML_Live_Class/d4edece87958c661605e5fb85f64e146a8621128/models/poly.joblib -------------------------------------------------------------------------------- /regressor.py: -------------------------------------------------------------------------------- 1 | """ 2 | Author: Sagar Kandpal 3 | sagar@ineuron.ai 4 | iNeuron.ai 5 | 6 | """ -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | matplotlib 2 | numpy 3 | pandas 4 | seaborn 5 | joblib 6 | notebook 7 | seaborn 8 | sklearn -------------------------------------------------------------------------------- /untitled.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Sagar-modelling/ML_Live_Class/d4edece87958c661605e5fb85f64e146a8621128/untitled.txt 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