├── Data └── empty ├── PPT ├── empty ├── PS.pptx ├── PS_intro.pptx └── Mathematical Derivations for Math Lovers (Optional).pptx └── codes ├── empty ├── Spam Detection Project using Naive Bayes Algorithm.ipynb └── Distributions in Python with Sleep analysis Project.ipynb /Data/empty: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /PPT/empty: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /codes/empty: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /PPT/PS.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AISPUBLISHING/Course-Mastering-Stat-proba/HEAD/PPT/PS.pptx -------------------------------------------------------------------------------- /PPT/PS_intro.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AISPUBLISHING/Course-Mastering-Stat-proba/HEAD/PPT/PS_intro.pptx -------------------------------------------------------------------------------- /PPT/Mathematical Derivations for Math Lovers (Optional).pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AISPUBLISHING/Course-Mastering-Stat-proba/HEAD/PPT/Mathematical Derivations for Math Lovers (Optional).pptx -------------------------------------------------------------------------------- /codes/Spam Detection Project using Naive Bayes Algorithm.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "db956ebd", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "spam = [\n", 11 | " \"To use your credit, click the new WAP link in the next years txt message or click here\", \n", 12 | " \"Thanks for your subscription to New Ringtone UK your new mobile will be charged £5/month Please confirm annoncement by replying\", \n", 13 | " \"As a valued customer, I am pleased to advise you that following recent delivery waiting review of your Mob No. you are awarded with. Call us to review.\", \n", 14 | " \"Please call our new customer service representative on\", \n", 15 | " \"We are trying to contact you. Last weekends customer draw shows that you won a £1000 prize GUARANTEED. Calling years\", \n", 16 | "]" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 2, 22 | "id": "e4785122", 23 | "metadata": {}, 24 | "outputs": [], 25 | "source": [ 26 | "spam_test = [\"Customer service annoncement. You have a New Years delivery waiting for you. click\"]" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 3, 32 | "id": "ab52c5e8", 33 | "metadata": {}, 34 | "outputs": [], 35 | "source": [ 36 | "non = [\n", 37 | " \"I don't think he goes to usf, he lives around here though\", \n", 38 | " \"New car and house for my parents. i have only new job in hand\", \n", 39 | " \"Great escape. I fancy the bridge but needs her lager. See you tomorrow\", \n", 40 | " \"Tired. I haven't slept well the past few nights.\",\n", 41 | " \"Too late. I said i have the website. I didn't i have or dont have the slippers\", \n", 42 | " \"I might come by tonight then if my class lets out early\", \n", 43 | " \"Jos ask if u wana meet up?\", \n", 44 | " \"That would be great. We'll be at the Guild. We can try meeting with the customer on Bristol road or somewhere\"\n", 45 | " ]" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 4, 51 | "id": "6f5f8809", 52 | "metadata": {}, 53 | "outputs": [], 54 | "source": [ 55 | "spam_test_2 = [\"That would be great. We'll be at the Guild. We can try meeting with the customer on Bristol road or somewhere\"]" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": null, 61 | "id": "4f1147b4", 62 | "metadata": {}, 63 | "outputs": [], 64 | "source": [ 65 | "# !pip install gensim" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 5, 71 | "id": "df3f9c8b", 72 | "metadata": {}, 73 | "outputs": [ 74 | { 75 | "name": "stderr", 76 | "output_type": "stream", 77 | "text": [ 78 | "C:\\Users\\lenovo\\anaconda3\\envs\\saad\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n", 79 | " warnings.warn(msg)\n" 80 | ] 81 | } 82 | ], 83 | "source": [ 84 | "from gensim.parsing.preprocessing import remove_stopwords\n", 85 | "from gensim.parsing.porter import PorterStemmer\n", 86 | "from gensim.utils import tokenize" 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": 6, 92 | "id": "445e69a1", 93 | "metadata": {}, 94 | "outputs": [ 95 | { 96 | "name": "stdout", 97 | "output_type": "stream", 98 | "text": [ 99 | "Thanks for your subscription to New Ringtone UK your new mobile will be charged £5/month Please confirm annoncement by replying\n" 100 | ] 101 | } 102 | ], 103 | "source": [ 104 | "test_sentence = spam[1]\n", 105 | "print(test_sentence)" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 8, 111 | "id": "b050f58c", 112 | "metadata": {}, 113 | "outputs": [ 114 | { 115 | "name": "stdout", 116 | "output_type": "stream", 117 | "text": [ 118 | "Thanks subscription New Ringtone UK new mobile charged £5/month Please confirm annoncement replying\n" 119 | ] 120 | } 121 | ], 122 | "source": [ 123 | "stops = remove_stopwords(test_sentence)\n", 124 | "print(stops)" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": 9, 130 | "id": "0e37ec0e", 131 | "metadata": {}, 132 | "outputs": [ 133 | { 134 | "name": "stdout", 135 | "output_type": "stream", 136 | "text": [ 137 | "thanks subscription new ringtone uk new mobile charged £5/month please confirm annoncement repli\n" 138 | ] 139 | } 140 | ], 141 | "source": [ 142 | "p=PorterStemmer()\n", 143 | "stemmed = p.stem(stops)\n", 144 | "print(stemmed)" 145 | ] 146 | }, 147 | { 148 | "cell_type": "code", 149 | "execution_count": 10, 150 | "id": "b04750b4", 151 | "metadata": {}, 152 | "outputs": [ 153 | { 154 | "name": "stdout", 155 | "output_type": "stream", 156 | "text": [ 157 | "['thanks', 'subscription', 'new', 'ringtone', 'uk', 'new', 'mobile', 'charged', 'month', 'please', 'confirm', 'annoncement', 'repli']\n" 158 | ] 159 | } 160 | ], 161 | "source": [ 162 | "tokens = tokenize(stemmed)\n", 163 | "print(list(tokens))" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": 11, 169 | "id": "2072364f", 170 | "metadata": {}, 171 | "outputs": [], 172 | "source": [ 173 | "def tokenize_sentence(sentence): \n", 174 | " p = PorterStemmer()\n", 175 | " removed_stops = remove_stopwords(sentence)\n", 176 | " stemmed = p.stem(removed_stops)\n", 177 | " tokens = tokenize(stemmed)\n", 178 | " return list(tokens)" 179 | ] 180 | }, 181 | { 182 | "cell_type": "code", 183 | "execution_count": 12, 184 | "id": "87129bf1", 185 | "metadata": {}, 186 | "outputs": [], 187 | "source": [ 188 | "dictionary = set()\n", 189 | "spams_tokenized = []\n", 190 | "nons_tokenized = []\n", 191 | "\n", 192 | "for sentence in spam:\n", 193 | " sentence_tokens = tokenize_sentence(sentence)\n", 194 | " spams_tokenized.append(sentence_tokens)\n", 195 | " dictionary = dictionary.union(sentence_tokens)\n", 196 | " \n", 197 | "for sentence in non: \n", 198 | " sentence_tokens = tokenize_sentence(sentence)\n", 199 | " nons_tokenized.append(sentence_tokens)\n", 200 | " dictionary = dictionary.union(sentence_tokens)" 201 | ] 202 | }, 203 | { 204 | "cell_type": "code", 205 | "execution_count": 13, 206 | "id": "e6bd192b", 207 | "metadata": {}, 208 | "outputs": [ 209 | { 210 | "name": "stdout", 211 | "output_type": "stream", 212 | "text": [ 213 | "[['to', 'use', 'credit', 'click', 'new', 'wap', 'link', 'years', 'txt', 'message', 'click'], ['thanks', 'subscription', 'new', 'ringtone', 'uk', 'new', 'mobile', 'charged', 'month', 'please', 'confirm', 'annoncement', 'repli'], ['as', 'valued', 'customer', 'i', 'pleased', 'advise', 'following', 'recent', 'delivery', 'waiting', 'review', 'mob', 'no', 'awarded', 'with', 'call', 'review'], ['please', 'new', 'customer', 'service', 'repres'], ['we', 'trying', 'contact', 'you', 'last', 'weekends', 'customer', 'draw', 'shows', 'won', 'prize', 'guaranteed', 'calling', 'year']]\n" 214 | ] 215 | } 216 | ], 217 | "source": [ 218 | "print(spams_tokenized)" 219 | ] 220 | }, 221 | { 222 | "cell_type": "code", 223 | "execution_count": 15, 224 | "id": "43d8384a", 225 | "metadata": {}, 226 | "outputs": [ 227 | { 228 | "name": "stdout", 229 | "output_type": "stream", 230 | "text": [ 231 | "[['i', 'don', 't', 'think', 'goes', 'usf', 'l'], ['new', 'car', 'house', 'parents', 'new', 'job', 'hand'], ['great', 'escape', 'i', 'fancy', 'bridge', 'needs', 'lager', 'see', 'tomorrow'], ['tired', 'i', 'haven', 't', 'slept', 'past', 'nights'], ['too', 'late', 'i', 'said', 'website', 'i', 'didn', 't', 'dont', 'slipp'], ['i', 'come', 'tonight', 'class', 'lets', 'earli'], ['jos', 'ask', 'u', 'wana', 'meet', 'up'], ['that', 'great', 'we', 'll', 'guild', 'we', 'try', 'meeting', 'customer', 'bristol', 'road']]\n" 232 | ] 233 | } 234 | ], 235 | "source": [ 236 | "print(nons_tokenized)" 237 | ] 238 | }, 239 | { 240 | "cell_type": "code", 241 | "execution_count": 16, 242 | "id": "51904cff", 243 | "metadata": {}, 244 | "outputs": [ 245 | { 246 | "name": "stdout", 247 | "output_type": "stream", 248 | "text": [ 249 | "{'escape', 'tired', 'great', 'll', 'wap', 'txt', 'following', 'charged', 'parents', 'link', 'slept', 'to', 'house', 'nights', 'ringtone', 'no', 'with', 'you', 'use', 'credit', 'ask', 'new', 'thanks', 't', 'hand', 'valued', 'awarded', 'website', 'jos', 'waiting', 'come', 'needs', 'i', 'usf', 'lager', 'see', 'mobile', 'dont', 'pleased', 'contact', 'guaranteed', 'lets', 'draw', 'too', 'up', 'uk', 'meet', 'customer', 'late', 'earli', 'weekends', 'annoncement', 'as', 'we', 'tomorrow', 'slipp', 'year', 'bridge', 'won', 'repres', 'said', 'car', 'last', 'tonight', 'mob', 'calling', 'past', 'u', 'prize', 'fancy', 'recent', 'think', 'try', 'trying', 'delivery', 'that', 'meeting', 'haven', 'month', 'years', 'advise', 'repli', 'review', 'service', 'call', 'don', 'shows', 'bristol', 'goes', 'confirm', 'click', 'please', 'road', 'message', 'job', 'wana', 'l', 'class', 'guild', 'subscription', 'didn'}\n" 250 | ] 251 | } 252 | ], 253 | "source": [ 254 | "print(dictionary)" 255 | ] 256 | }, 257 | { 258 | "cell_type": "code", 259 | "execution_count": 17, 260 | "id": "cb80dab2", 261 | "metadata": {}, 262 | "outputs": [ 263 | { 264 | "name": "stdout", 265 | "output_type": "stream", 266 | "text": [ 267 | "101\n" 268 | ] 269 | } 270 | ], 271 | "source": [ 272 | "total_word_counts = len(dictionary)\n", 273 | "total_spam_messages = len(spams_tokenized)\n", 274 | "total_all_messages = len(spams_tokenized) + len(nons_tokenized)\n", 275 | "print(total_word_counts)" 276 | ] 277 | }, 278 | { 279 | "cell_type": "code", 280 | "execution_count": 18, 281 | "id": "aa417e3f", 282 | "metadata": {}, 283 | "outputs": [ 284 | { 285 | "name": "stdout", 286 | "output_type": "stream", 287 | "text": [ 288 | "0.38461538461538464\n" 289 | ] 290 | } 291 | ], 292 | "source": [ 293 | "p_spam = total_spam_messages/total_all_messages\n", 294 | "print(p_spam)" 295 | ] 296 | }, 297 | { 298 | "cell_type": "code", 299 | "execution_count": 19, 300 | "id": "3b89b3fc", 301 | "metadata": {}, 302 | "outputs": [], 303 | "source": [ 304 | "def count_word_in_messages(word, messages): \n", 305 | " total_count = 0\n", 306 | " for msg in messages: \n", 307 | " if word in msg: # notice this ensured uniqueness automatically \n", 308 | " total_count += 1 \n", 309 | " \n", 310 | " return total_count " 311 | ] 312 | }, 313 | { 314 | "cell_type": "code", 315 | "execution_count": 20, 316 | "id": "1e2bc0e1", 317 | "metadata": {}, 318 | "outputs": [ 319 | { 320 | "name": "stdout", 321 | "output_type": "stream", 322 | "text": [ 323 | "['that', 'great', 'we', 'll', 'guild', 'we', 'try', 'meeting', 'customer', 'bristol', 'road']\n", 324 | "----------------\n", 325 | "Runnig for word: that\n", 326 | "P( w | spam) = 0.0\n", 327 | "P( w ) = 0.07692307692307693\n", 328 | "P( spam ) = 0.38461538461538464\n", 329 | "P( spam | w ) = 0.0\n", 330 | "\n", 331 | "----------------\n", 332 | "Runnig for word: great\n", 333 | "P( w | spam) = 0.0\n", 334 | "P( w ) = 0.15384615384615385\n", 335 | "P( spam ) = 0.38461538461538464\n", 336 | "P( spam | w ) = 0.0\n", 337 | "\n", 338 | "----------------\n", 339 | "Runnig for word: we\n", 340 | "P( w | spam) = 0.2\n", 341 | "P( w ) = 0.15384615384615385\n", 342 | "P( spam ) = 0.38461538461538464\n", 343 | "P( spam | w ) = 0.5\n", 344 | "\n", 345 | "----------------\n", 346 | "Runnig for word: ll\n", 347 | "P( w | spam) = 0.0\n", 348 | "P( w ) = 0.07692307692307693\n", 349 | "P( spam ) = 0.38461538461538464\n", 350 | "P( spam | w ) = 0.0\n", 351 | "\n", 352 | "----------------\n", 353 | "Runnig for word: guild\n", 354 | "P( w | spam) = 0.0\n", 355 | "P( w ) = 0.07692307692307693\n", 356 | "P( spam ) = 0.38461538461538464\n", 357 | "P( spam | w ) = 0.0\n", 358 | "\n", 359 | "----------------\n", 360 | "Runnig for word: we\n", 361 | "P( w | spam) = 0.2\n", 362 | "P( w ) = 0.15384615384615385\n", 363 | "P( spam ) = 0.38461538461538464\n", 364 | "P( spam | w ) = 0.5\n", 365 | "\n", 366 | "----------------\n", 367 | "Runnig for word: try\n", 368 | "P( w | spam) = 0.0\n", 369 | "P( w ) = 0.07692307692307693\n", 370 | "P( spam ) = 0.38461538461538464\n", 371 | "P( spam | w ) = 0.0\n", 372 | "\n", 373 | "----------------\n", 374 | "Runnig for word: meeting\n", 375 | "P( w | spam) = 0.0\n", 376 | "P( w ) = 0.07692307692307693\n", 377 | "P( spam ) = 0.38461538461538464\n", 378 | "P( spam | w ) = 0.0\n", 379 | "\n", 380 | "----------------\n", 381 | "Runnig for word: customer\n", 382 | "P( w | spam) = 0.6\n", 383 | "P( w ) = 0.3076923076923077\n", 384 | "P( spam ) = 0.38461538461538464\n", 385 | "P( spam | w ) = 0.75\n", 386 | "\n", 387 | "----------------\n", 388 | "Runnig for word: bristol\n", 389 | "P( w | spam) = 0.0\n", 390 | "P( w ) = 0.07692307692307693\n", 391 | "P( spam ) = 0.38461538461538464\n", 392 | "P( spam | w ) = 0.0\n", 393 | "\n", 394 | "----------------\n", 395 | "Runnig for word: road\n", 396 | "P( w | spam) = 0.0\n", 397 | "P( w ) = 0.07692307692307693\n", 398 | "P( spam ) = 0.38461538461538464\n", 399 | "P( spam | w ) = 0.0\n", 400 | "\n", 401 | "P( spam | all_words ) = 0.0\n" 402 | ] 403 | } 404 | ], 405 | "source": [ 406 | "final_prob = 1 # can't start from 0 \n", 407 | "\n", 408 | "\n", 409 | "for test_sentence in spam_test_2: \n", 410 | " test_sentence = tokenize_sentence(test_sentence)\n", 411 | " print(test_sentence)\n", 412 | " \n", 413 | " # let's run this for each word separately \n", 414 | " for word in test_sentence: \n", 415 | " print(\"----------------\")\n", 416 | " print(\"Runnig for word:\", word)\n", 417 | " \n", 418 | " # Find P( w | spam)\n", 419 | " spam_count = count_word_in_messages(word, spams_tokenized)\n", 420 | " p_w_spam = spam_count / total_spam_messages \n", 421 | " print(\"P( w | spam) = \", p_w_spam)\n", 422 | " \n", 423 | " # Find P( w )\n", 424 | " w_count = count_word_in_messages(word, spams_tokenized)\n", 425 | " w_count += count_word_in_messages(word, nons_tokenized)\n", 426 | " p_w = w_count / total_all_messages\n", 427 | " print(\"P( w ) = \", p_w)\n", 428 | " \n", 429 | " \n", 430 | " # Find P( spam | w )\n", 431 | " p_spam_w = (p_w_spam * p_spam) / p_w\n", 432 | " print(\"P( spam ) = \", p_spam)\n", 433 | " print(\"P( spam | w ) = \", p_spam_w)\n", 434 | " print(\"\")\n", 435 | " final_prob *= p_spam_w\n", 436 | " \n", 437 | " \n", 438 | " print(\"P( spam | all_words ) = \", final_prob)" 439 | ] 440 | }, 441 | { 442 | "cell_type": "code", 443 | "execution_count": null, 444 | "id": "13bc303f", 445 | "metadata": {}, 446 | "outputs": [], 447 | "source": [] 448 | }, 449 | { 450 | "cell_type": "code", 451 | "execution_count": null, 452 | "id": "974e434d", 453 | "metadata": {}, 454 | "outputs": [], 455 | "source": [] 456 | }, 457 | { 458 | "cell_type": "code", 459 | "execution_count": null, 460 | "id": "276dce54", 461 | "metadata": {}, 462 | "outputs": [], 463 | "source": [] 464 | }, 465 | { 466 | "cell_type": "code", 467 | "execution_count": null, 468 | "id": "8ebe786e", 469 | "metadata": {}, 470 | "outputs": [], 471 | "source": [] 472 | }, 473 | { 474 | "cell_type": "code", 475 | "execution_count": null, 476 | "id": "4dc394df", 477 | "metadata": {}, 478 | "outputs": [], 479 | "source": [] 480 | }, 481 | { 482 | "cell_type": "code", 483 | "execution_count": null, 484 | "id": "ca1c7c6c", 485 | "metadata": {}, 486 | "outputs": [], 487 | "source": [] 488 | }, 489 | { 490 | "cell_type": "code", 491 | "execution_count": null, 492 | "id": "0dd15659", 493 | "metadata": {}, 494 | "outputs": [], 495 | "source": [] 496 | } 497 | ], 498 | "metadata": { 499 | "kernelspec": { 500 | "display_name": "Python 3 (ipykernel)", 501 | "language": "python", 502 | "name": "python3" 503 | }, 504 | "language_info": { 505 | "codemirror_mode": { 506 | "name": "ipython", 507 | "version": 3 508 | }, 509 | "file_extension": ".py", 510 | "mimetype": "text/x-python", 511 | "name": "python", 512 | "nbconvert_exporter": "python", 513 | "pygments_lexer": "ipython3", 514 | "version": "3.8.11" 515 | } 516 | }, 517 | "nbformat": 4, 518 | "nbformat_minor": 5 519 | } 520 | -------------------------------------------------------------------------------- /codes/Distributions in Python with Sleep analysis Project.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "1587ffcb", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import matplotlib.pyplot as plt\n", 11 | "%matplotlib inline\n", 12 | "\n", 13 | "import seaborn as sns\n", 14 | "sns.set(color_codes = True)" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 2, 20 | "id": "4d2f974f", 21 | "metadata": {}, 22 | "outputs": [], 23 | "source": [ 24 | "from scipy.stats import bernoulli" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 4, 30 | "id": "a9a0893f", 31 | "metadata": {}, 32 | "outputs": [ 33 | { 34 | "data": { 35 | "text/plain": [ 36 | "[Text(0.5, 0, 'Bernoulli Distribution'), Text(0, 0.5, 'Frequency')]" 37 | ] 38 | }, 39 | "execution_count": 4, 40 | "metadata": {}, 41 | "output_type": "execute_result" 42 | }, 43 | { 44 | "data": { 45 | "image/png": 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mSp2DiIhcrN1dQH/6058clysrKyUPQ0RErtNuAeTl5Tkuz58/X/IwRETkOu0WgBCizctEROT5nB4N9HZ/vJ2IiNxbuweB7XY7ampqIISAzWZzXL4mKChI6nxERCSRdgvg3LlzGDVqlONNf+TIkY77eBooEZFna7cAzpw546ocRETkYk4fAyAiorsLC4CISKZYAEREMsUCICKSKRYAEZFMSVoAb731FiZPnozJkydj3bp1AICcnBykpKQgPj4e69evl3LxRETUDskKICcnB0ePHsXOnTuxa9cufPfdd8jMzER6ejo2bdqEvXv3Ii8vD9nZ2VJFICKidkhWADqdDsuXL4eXlxc0Gg169+6NwsJCxMTEIDo6Gmq1GikpKTf9zgAREbmGZAXQt29fDBs2DABQWFiIffv2QaFQQKfTOabR6/UwGAxSRSAionY49YMwP8X58+excOFCLFu2DCqVCoWFhY77hBC3PchcaKhfJydsodP5SzLfzlRe2Qh/Px9otd7QhWi7Os4tecJrCXhGTk/ICLhvTvHjOgO4b8YbuTKnpAVw8uRJLFmyBOnp6Zg8eTJOnDgBo9HouN9oNEKv19/WPCsq6mG3d+7Q1DqdP4zGuk6dpyRUKtTVm9DYaIbRZuvqNG3ylNfSE3J6QkbAvXM2mq2oqzcBgNtmvJ4Ur6VSqbjlB2fJdgGVlpbi6aefxt/+9jdMnjwZADB06FAUFBSgqKgINpsNmZmZGDdunFQRiIioHZJtAbz77rswm81Yu3at47a0tDSsXbsWixcvhtlsRlxcHBITE6WKQERE7ZCsAFasWIEVK1a0eV9GRoZUiyUiIifxm8BERDLFAiAikikWABGRTLEAiIhkigVARCRTLAAiIpliARARyRQLgIhIplgAREQyxQIgIpIpFgARkUyxAIiIZIoFQEQkUywAIiKZYgEQEckUC4CISKZYAEREMsUCICKSKRYAEZFMsQCIiGSKBUBEJFMsACIimWIBEBHJFAuAiEimWABERDLFAiAikilJC6C+vh7Jycm4fPkyACAnJwcpKSmIj4/H+vXrpVw0ERF1QLIC+PbbbzFz5kwUFhYCAEwmE9LT07Fp0ybs3bsXeXl5yM7OlmrxRETUAckKYPv27Vi5ciX0ej0A4PTp04iJiUF0dDTUajVSUlKQlZUl1eKJiKgDaqlm/PLLL7e6Xl5eDp1O57iu1+thMBikWjwREXVAsgK4kd1uh0KhcFwXQrS67qzQUL/OjOWg0/lLMt/OVF7ZCH8/H2i13tCFaLs6zi15wmsJeEZOT8gIuG9O8eM6A7hvxhu5MqfLCiAiIgJGo9Fx3Wg0OnYP3Y6KinrY7aIzo0Gn84fRWNep85SESoW6ehMaG80w2mxdnaZNnvJaekJOT8gIuHfORrMVdfUmAHDbjNeT4rVUKhW3/ODsstNAhw4dioKCAhQVFcFmsyEzMxPjxo1z1eKJiOgGLtsC8Pb2xtq1a7F48WKYzWbExcUhMTHRVYsnIqIbSF4Ahw8fdlyOjY1FRkaG1IskIiIn8JvAREQyxQIgIpIpFgARkUyxAIiIZIoFQEQkUywAIiKZYgEQEckUC4CISKZYAEREMsUCICKSKRYAEZFMsQCIiGSKBUBEJFMsACIimWIBEBHJFAuAiEimWABERDLFAiCiu5YQAja7gLnZBqvN3tVx3I7LfhOYiEgqNrsdxYZ6nC2uxqXyOpRXNcFQ1YT6JgsAYOuBcwAAP18Ngvy8EBboi56R/uh1TyB6RgZA6yPPt0J5Pmsi8nhWmx25FyrweV4Zvi+shKnZBgAICfBGeLAW9/fXwddbDUNlI/pEB8NisaKmvhnV9WaUVTbimx+uAgCUCgX6RQdiWF8dhvcNgy7ItyuflkuxAIjIoxQb6nA0txTHvzegrtGCgG5eGDU4AgN6BKFfdBCC/Lwd0zaYrfgy34C4+3tAYbO1mk+jyYqCslqcKarCNz9cxceHzuPjQ+fRt3sgxgyJxC8G6OHrfXe/Rd7dz87D2IVAZa0JhqomGKub0NBkQUOTFY1mCwAFlColyirqcbXGBF2gDwL9vBEe7At9sC9USh7OobtXbUMzvviuDEdzy3DZWA+1SoFhfXUY87MI/KxXyB39/9f6qDH43hAMvjcEqXG9UV7dhC/zDcjJK8PmfWew7eA5/Ly/Dg8OicSAmGAoFQoJnlnXYgF0oWaLDReu1OJscRXOFlfjYmktLNbWB6q81MqWTyEKwG4XMDXbcKaoutU0apUSESFa3Bvpj77dA9G3exDCg32huAv/w5J8WG12fPvDVXyeW4bcixWw2QV6RgbgN/H9MGJgOPx8NZ26PH2QLybH3otJo2JwsbQWn+eW4fj3BnzxnQFhgT54cEgkxgyJRGigT6cutyuxAFzI3GzDD1dqcLa4GmeLq1BQWgurTUChAHqE+2P8sCjcE6ZFeLAWuiBfBHTTQKNWOR4vVCpknyzG8H462Kx2VNWbUVbRiJKrDSgxNuDUOSOOni4FAPhrNejbPQgDegRhYEww7gnrxkIgtyeEQGFZHXJyy3A834D6JgsC/bwQ/4tojB4SiaiwbpJnUCgU6H1PIHrfE4iZv+yDkz+uV7uOFmD30QIM6hmCsfdFYnhfHTRqz97yZgFIyNRsxQ8l197wq1FQWgubXUCpUCAmwh8PPxCN/tFB6Ns96LbOQlCrlAjUeiEkwAe97wl03G4XAqUVjfjhcjV+uFyDs5eq8fU5IwAgQKvBgJhgDIwJxoCYYOiDuIVA7qPEWI/j+eU4kW9AeVUT1Colft4vDGOGRGLQvcFdtotTo1Zh1KAIjBoUgavVTTiaW4rPc0vx/3Z/h24+aowaFIFfDNSjT1QglErPW59YAJ2otrEZF0pq8MPlGpy7VI3CsjrY7AIqpQL3RvgjYUQP9O8RhD5RgZIcXFIqFIgK64aosG6IGxYFALha3YT84iqcKarC90VVOJFfDqDlTImBPYIdpRAScPds1pL7s9ntKLhSh9yLFfj6vBElxgYoFMCAHsFIGtkDDwzQo5tP5+7i+anCgnwxbWwvTBnTE/lFVfjs9BVkf3sFh76+DH+tBkP7hOHnfXUYdG8wvDSqjmfoBrqkAPbs2YO3334bVqsVv/3tbzF79uyuiPGT1DdZcLm8HiVXG1BYWosfSmpgqGoCAKiUCvS8JwCJI3tgQI9g9I4KgI9X13RtWJAvxgb5Yux990AIgbLKRpwpqkJ+URW+/fEUOgAIC/RBTIQ/7o3wR0yEP2LC/eGv9eqSzHT3MVtsKCqra/mA9ONWcaPZCoUC6BMViNkT++GBAXoEdnP//3NKpQKDe4ZgcM8QNJmtyL1YgVPnr+Lk2XIcPV0KtUqBXpEB6NejZeu+R7i/2z4vl78rGQwGrF+/Hjt27ICXlxfS0tIwcuRI9OnTx9VRbsluF2g0W9FgsqC6zoyKWhMqas2oqDHhak0TSq42oKa+2TG9v1aDPlGBGDfsHvSJCsS9Ef6t9t27C4VCgcjQbogM7YYJP+8OuxC4XF6P/KIqXLhSi+KyOpw8a3RM7+erQXiIL/RBWoQH+7Ycl/DzQmC3lr9uvpq78swIun1CCDSYrKisNaGytmWdqaw1oayyESXGBhirmyB+nDY82Bc/76/DkF6hGBgT3OkHc13J11uNEQPDMWJgOKw2O84UV+H7giqcvVSNvceKYRdFAIBAPy901/lBF+QLXZAPdIEt61Ognxf8fDVQq7pmF5fLCyAnJwejRo1CUFAQACAhIQFZWVn4/e9/79Tj72Q/29XqJhzPL4fVaofFZofNbofVJmC1tVxXKpWoa2hGo8mCJrMNpmZrm/Px89Ug2N8bY34WgfCQbogM1SI8RIsArcYl+9OFUgGtT8t/ls7Y36iEAvdGBuDeyADHbU1mq+Og8tWaJlytbkJFrQkXrtTc9HiVUgFvLzW8NUp4e6ngrVHDz1cDpQLQqJVQKgGlQgmVUgHlj3+Oy4qWf8v/PIubL7X5kiraeMR1010/x2uE460HuHZRq/VCQ2PzDRMKtOXGm8Utrt306JsX++P82l7Ojcvy9fVCY1PzLSbs+PEtk7Udov2srZ+T3SbQbLXBaheOdchqtcPUbEOT2QqTxYZGk+WmZatUCoQG+GBInzBEhvgiMqwbYsL9XfqGr1YpofXRtPxfE9Kuo15KFe7rHYb7eocBaNnquVxejysVjbhytR5llU0oLK3F94WVNz3WW6OC1keNAD9vKNGy/mjUSnipVVCrFPDv5oUJw6PgfQe7ltp7r3B5AZSXl0On0zmu6/V6nD592unHBwff/lkAoaF+6N9b1/GEHmDy2N6SL6P7PUGSL4PIVbpHBnY8kUTuiei6ZTvD5dsddru91adlIQTPRiEi6gIuL4CIiAgYjf/Zz2w0GqHX610dg4hI9lxeAKNHj8axY8dQWVmJpqYmHDhwAOPGjXN1DCIi2XP5MYDw8HA899xzmDt3LiwWC2bMmIH77rvP1TGIiGRPIdo7JYGIiO5anj2QBRER3TEWABGRTLEAiIhkigVARCRTsiyAK1euYPbs2UhMTMSTTz6JhoaGm6YpLy/H/PnzMXXqVDzyyCM4duyYS7Lt2bMHkyZNQnx8PLZu3XrT/fn5+Zg+fToSEhLwwgsvwGpte9gKqXWU89NPP8XUqVMxZcoUPPXUU6ipuXkoia7OeM2RI0fw0EMPuTBZax3lvHjxIubMmYMpU6Zg/vz5bvlafvfdd0hNTcWUKVOwcOFC1NbWujzjNfX19UhOTsbly5dvus9d1p/2Mrp03REytGDBApGZmSmEEOKtt94S69atu2maP/zhD2LLli1CCCEuXLggRo8eLaxWq6S5ysrKxIQJE0RVVZVoaGgQKSkp4vz5862mmTx5sjh16pQQQojnn39ebN26VdJMd5Kzrq5OjBkzRpSVlQkhhHj99dfFSy+95FYZrzEajSIxMVFMmDDBpfmu6Sin3W4X8fHxIjs7WwghxKuvvtrm/9euzCiEEDNnzhRHjhwRQgixZs0a8dprr7k04zXffPONSE5OFoMHDxaXLl266X53WH/ay+jqdUd2WwAWiwVffvklEhISAADTp09HVlbWTdNNnDgRycnJAICYmBiYzWY0NjZKmu36gfK0Wq1joLxrSkpKYDKZMGzYsHazS62jnBaLBStXrkR4eDgAoH///igtLXWrjNesWLHC6YEIpdBRzu+++w5ardbxZclFixa5fPh0Z15Lu93u2JJuamqCj0/X/L7E9u3bsXLlyjZHF3CX9ae9jK5ed2RXAFVVVfDz84Na3fIdOJ1OB4PBcNN0CQkJCAxsGcjp3XffxcCBA+Hv7y9ptrYGyrs+24333yq71DrKGRwcjIkTJwIATCYT3nnnHTz88MNulREAPvjgAwwaNAhDhw51abbrdZSzuLgYYWFhSE9PxyOPPIKVK1dCq9W6VUYAWL58OVasWIEHH3wQOTk5SEtLc2nGa15++WU88MADbd7nLutPexldve7c1QWwb98+jBs3rtXfH/7wh5sGn2tvMLrNmzfjk08+wbp166SO2+FAee4ykJ6zOerq6rBgwQIMGDAAjzzyiCsjdpjx3LlzOHDgAJ566imX5rpRRzmtVitOnDiBmTNnYufOnYiOjsbatWvdKqPJZMILL7yAzZs34+jRo5g1axb++Mc/ujSjM9xl/XGGq9adu7oAkpKS8O9//7vV33vvvYe6ujrYbDYA7Q9Gt27dOvzjH//A1q1bERkZKXnejgbKu/H+q1evdslAes4M6FdeXo5Zs2ahf//+ePnll10dscOMWVlZMBqNSE1NxYIFCxx53S2nTqdDTEwMhgwZAgBITk6+reHTXZHx3Llz8Pb2dgzp8utf/xonTpxwaUZnuMv60xFXrjt3dQG0RaPR4IEHHsDevXsBALt27WpzMLrNmzfj+PHj+OijjxAREeGSbB0NlBcVFQVvb2+cPHkSALB79+4uGUivo5w2mw2LFi1CUlISXnjhhS75lNVRxiVLlmD//v3YvXs33nnnHej1emzbts3tcg4fPhyVlZU4c+YMAODw4cMYPHiwW2WMiYlBWVkZLl68CAA4dOiQo7DcibusP+1x+boj2eFlN3b58mXxm9/8RiQlJYl58+aJ6upqIYQQ27ZtE6+//rqw2+3igQceEOPHjxdTpkxx/F07Mi+ljIwMMXnyZBEfHy/eeecdIYQQTzzxhDh9+rQQQoj8/HyRmpoqEhISxNKlS4XZbJY80+3mPHDggOjfv3+r1y49Pd2tMl7v0qVLXXYWkBAd5/zmm29EamqqmDRpkpg3b564evWq22U8cuSISElJEcnJyeK3v/2tKC4udnnG602YMMFxho07rj+3yujqdYeDwRERyZTsdgEREVELFgARkUyxAIiIZIoFQEQkUywAIiKZYgGQy1y+fBkDBw7E1KlTMXXqVKSkpOBXv/qV47zsrvTmm29i1apVAICHHnoIubm5yM3NxZIlS26atq3nkZaW5vhuCQC88cYb2LVrV7vLfOutt/Dpp5+2ed/1j+/fvz8qKytv6/mcPn0af/7znwHgls+DyOU/Ck/y5uPjg927dzuu7927F88//zwOHDjQhanaNmTIEGzYsKHN+258HiUlJXjsscegUqmQkJCAZ555psP5Hz9+HH369GnzPmce354ffvjBMc5Ne8+D5I0FQF2qurq61QBdhw8fxttvvw2LxQIfHx/88Y9/xPDhw/Hmm2/im2++QXl5Ofr374+YmBiUlJTAaDSipKQE4eHhePXVV6HX63H+/HmsWrUK1dXVUCgUmDdvHqZNm4bjx4/jpZdeQmZmJgDcdP1GHd1/vaioKCxZsgTvvvsuEhISsHz5cvTt2xfz58/Hhg0bcPDgQWg0GgQHB2PNmjU4ePAg8vLysG7dOqhUKhw6dAjV1dW4dOkSxo8fj4qKCsfjAeD1119Hbm4u7HY7nn32WUyYMAE7duzA/v378d///d8A4Lj+l7/8BRs2bEBdXR2ef/55TJs2zfE86urq8OKLL+LMmTNQKBQYO3Ysli5dCrVajSFDhmDBggX4/PPPUV5ejieeeKJLhscg12EBkEuZTCZMnToVAFBbWwuj0YiNGzcCAAoLC7F+/Xp88MEHCA4Oxvnz5/H44487tg5KSkqQmZkJtVqNN998E1999RV27doFPz8/LFq0CB9//DGeeuopPPnkk1i2bBni4+NhMBjwq1/9CjExMZI/twEDBuDcuXOtbistLcX777+PY8eOwcvLC++99x5Onz6N2bNnIysrC7Nnz8bEiRNx6NAhmEwm/POf/wTQMrrm9bp3745Vq1bh3LlzmDNnDvbt23fLHJGRkY6hLtasWYPjx4877lu9ejWCgoKwZ88eWCwWPPnkk3jvvfewYMECNDc3Izg4GB9//DHy8vIwc+ZMpKamwtvbuxNfJXInLAByqRt3neTk5ODpp59GRkaG45PnY4895rhfoVCguLgYADBs2DDHMN4AMGLECPj5+QEABg0ahJqaGhQWFsJsNiM+Ph4AEB4ejvj4eHz22WcYOXKkpM9NoVDcNA5+eHi4Y0THayPSxsbGtvn4+++//5bznjlzJgCgX79+6N27N06dOnVHGf/973/jo48+gkKhgJeXF9LS0vD+++9jwYIFAIBf/vKXAIDBgwejubkZjY2NLIC7GAuAutTo0aPRo0cPx+6N2NhYvP766477S0tLodfrcfDgwZvGwb/+zVahUEAIAZvNdtMAWkIIWK1WxzTXWCyWTn0uubm56NevX6vblEoltmzZgtzcXBw7dgyvvPIKxo4di2XLlt30+PbG+Vcq/3O+ht1uh1qtvqPnc+OQyHa7vdXPIl57s782DUeKubvxLCDqUgUFBSgpKcHAgQMRGxuLzz//HBcuXAAAZGdnY8qUKTCZTE7Pr1evXlCr1Y7dRgaDAfv378fo0aMREhKCK1euoKKiAkIIx+6WznoemzZtwrx581rdfubMGSQnJ6N3795YuHAhHnvsMeTm5gIAVCqV079Ju3PnTgAtvxBWXFyMoUOHIiQkBOfPn4fZbIbFYsH+/fsd099q3g8++CC2bNkCIQSam5uxfft2jB49+k6fNnk4bgGQS11/DABo+QS6atUq9OzZEwCwatUqLF26FEIIqNVqvP322+jWrZvT89doNNi0aRNWr16NN998EzabDU8//TRGjRoFAEhLS0Nqaip0Oh3Gjx/veDP+Kc9DqVTC29sbS5cuxfjx41tNN2DAACQlJSE1NRVarRY+Pj5YsWIFgJbTTV977TWnPrlfunQJ06ZNg0KhwGuvvYagoCCMGTMGv/jFL5CUlASdToeRI0fi7NmzAFp2l23cuBG///3vMWfOHMd8VqxYgdWrVyMlJQUWiwVjx47FokWL7ug1IM/H0UCJiGSKu4CIiGSKBUBEJFMsACIimWIBEBHJFAuAiEimWABERDLFAiAikikWABGRTP1//ZqP1P9o/ZoAAAAASUVORK5CYII=\n", 46 | "text/plain": [ 47 | "
" 48 | ] 49 | }, 50 | "metadata": {}, 51 | "output_type": "display_data" 52 | } 53 | ], 54 | "source": [ 55 | "data_bern = bernoulli.rvs(size=10000, p=0.6)\n", 56 | "ax = sns.distplot(data_bern, bins=100, kde=True)\n", 57 | "ax.set(xlabel=\"Bernoulli Distribution\", ylabel='Frequency')" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 5, 63 | "id": "75d18e0b", 64 | "metadata": {}, 65 | "outputs": [], 66 | "source": [ 67 | "from scipy.stats import binom" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 6, 73 | "id": "ef5e8ae5", 74 | "metadata": {}, 75 | "outputs": [ 76 | { 77 | "name": "stderr", 78 | "output_type": "stream", 79 | "text": [ 80 | "C:\\Users\\lenovo\\anaconda3\\envs\\saad\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", 81 | " warnings.warn(msg, FutureWarning)\n" 82 | ] 83 | }, 84 | { 85 | "data": { 86 | "image/png": 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\n", 87 | "text/plain": [ 88 | "
" 89 | ] 90 | }, 91 | "metadata": {}, 92 | "output_type": "display_data" 93 | } 94 | ], 95 | "source": [ 96 | "data_binom = binom.rvs(n=10, p=0.6, size=1000)\n", 97 | "ax = sns.distplot(data_binom,\n", 98 | " bins=100,\n", 99 | " kde=False,\n", 100 | " )\n", 101 | "_ = ax.set(xlabel='Binomial Distribution ', ylabel='Frequency')" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 8, 107 | "id": "dda8b83f", 108 | "metadata": {}, 109 | "outputs": [], 110 | "source": [ 111 | "from scipy.stats import uniform" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": 10, 117 | "id": "a72fb1db", 118 | "metadata": {}, 119 | "outputs": [ 120 | { 121 | "data": { 122 | "text/plain": [ 123 | "[Text(0.5, 0, 'Uniform Distribution'), Text(0, 0.5, 'Frequency')]" 124 | ] 125 | }, 126 | "execution_count": 10, 127 | "metadata": {}, 128 | "output_type": "execute_result" 129 | }, 130 | { 131 | "data": { 132 | "image/png": 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\n", 133 | "text/plain": [ 134 | "
" 135 | ] 136 | }, 137 | "metadata": {}, 138 | "output_type": "display_data" 139 | } 140 | ], 141 | "source": [ 142 | "n = 10000\n", 143 | "n *=100\n", 144 | "start =10\n", 145 | "width=20\n", 146 | "\n", 147 | "data_uniform = uniform.rvs(size=n,loc=start,scale=width)\n", 148 | "ax=sns.distplot(data_uniform,bins=100,kde=False)\n", 149 | "ax.set(xlabel='Uniform Distribution', ylabel='Frequency')" 150 | ] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": 11, 155 | "id": "947b6ca3", 156 | "metadata": {}, 157 | "outputs": [], 158 | "source": [ 159 | "import numpy as np\n", 160 | "import pandas as pd\n", 161 | "from IPython.core.pylabtools import figsize\n", 162 | "\n", 163 | "figsize(16,6)" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": 12, 169 | "id": "c6c1e192", 170 | "metadata": {}, 171 | "outputs": [], 172 | "source": [ 173 | "sleep_data=pd.read_csv('data/sleep_data.csv')" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 13, 179 | "id": "3f9c3010", 180 | "metadata": {}, 181 | "outputs": [ 182 | { 183 | "data": { 184 | "text/html": [ 185 | "
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Unnamed: 0indicatortime_offset
02017-12-01 21:00:000-60
12017-12-01 21:01:000-59
22017-12-01 21:02:000-58
32017-12-01 21:03:000-57
42017-12-01 21:04:000-56
............
113352018-02-01 23:55:001115
113362018-02-01 23:56:001116
113372018-02-01 23:57:001117
113382018-02-01 23:58:001118
113392018-02-01 23:59:001119
\n", 277 | "

11340 rows × 3 columns

\n", 278 | "
" 279 | ], 280 | "text/plain": [ 281 | " Unnamed: 0 indicator time_offset\n", 282 | "0 2017-12-01 21:00:00 0 -60\n", 283 | "1 2017-12-01 21:01:00 0 -59\n", 284 | "2 2017-12-01 21:02:00 0 -58\n", 285 | "3 2017-12-01 21:03:00 0 -57\n", 286 | "4 2017-12-01 21:04:00 0 -56\n", 287 | "... ... ... ...\n", 288 | "11335 2018-02-01 23:55:00 1 115\n", 289 | "11336 2018-02-01 23:56:00 1 116\n", 290 | "11337 2018-02-01 23:57:00 1 117\n", 291 | "11338 2018-02-01 23:58:00 1 118\n", 292 | "11339 2018-02-01 23:59:00 1 119\n", 293 | "\n", 294 | "[11340 rows x 3 columns]" 295 | ] 296 | }, 297 | "execution_count": 13, 298 | "metadata": {}, 299 | "output_type": "execute_result" 300 | } 301 | ], 302 | "source": [ 303 | "sleep_data" 304 | ] 305 | }, 306 | { 307 | "cell_type": "code", 308 | "execution_count": 15, 309 | "id": "f2c6dfc1", 310 | "metadata": {}, 311 | "outputs": [ 312 | { 313 | "data": { 314 | "image/png": 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\n", 315 | "text/plain": [ 316 | "
" 317 | ] 318 | }, 319 | "metadata": {}, 320 | "output_type": "display_data" 321 | } 322 | ], 323 | "source": [ 324 | "sleep_labels=['9:00','9:30','10:00','10:30','11:00','11:30','12:00']\n", 325 | "plt.scatter(sleep_data['time_offset'],sleep_data['indicator'],s=60,alpha=0.01,facecolor='b',edgecolor='b')\n", 326 | "\n", 327 | "plt.yticks([0,1],['Awake','Asleep']);plt.xlabel('PM time')\n", 328 | "plt.title('Falling sleep data',size=18)\n", 329 | "plt.xticks([-60,-30,0,30,60,90,120],sleep_labels);" 330 | ] 331 | }, 332 | { 333 | "cell_type": "code", 334 | "execution_count": 16, 335 | "id": "e2950e07", 336 | "metadata": {}, 337 | "outputs": [], 338 | "source": [ 339 | "sleep_data.sort_values('time_offset', inplace=True)\n", 340 | "time = np.array(sleep_data.loc[:,'time_offset'])\n", 341 | "sleep_obs = np.array(sleep_data.loc[:,'indicator'])" 342 | ] 343 | }, 344 | { 345 | "cell_type": "code", 346 | "execution_count": 17, 347 | "id": "90d38ab7", 348 | "metadata": {}, 349 | "outputs": [ 350 | { 351 | "data": { 352 | "text/plain": [ 353 | "array([0, 0, 0, ..., 1, 1, 1], dtype=int64)" 354 | ] 355 | }, 356 | "execution_count": 17, 357 | "metadata": {}, 358 | "output_type": "execute_result" 359 | } 360 | ], 361 | "source": [ 362 | "sleep_obs" 363 | ] 364 | }, 365 | { 366 | "cell_type": "code", 367 | "execution_count": 18, 368 | "id": "53e28f37", 369 | "metadata": {}, 370 | "outputs": [ 371 | { 372 | "data": { 373 | "text/plain": [ 374 | "array([-60, -60, -60, ..., 119, 119, 119], dtype=int64)" 375 | ] 376 | }, 377 | "execution_count": 18, 378 | "metadata": {}, 379 | "output_type": "execute_result" 380 | } 381 | ], 382 | "source": [ 383 | "time" 384 | ] 385 | }, 386 | { 387 | "cell_type": "code", 388 | "execution_count": 19, 389 | "id": "44b58c78", 390 | "metadata": {}, 391 | "outputs": [ 392 | { 393 | "data": { 394 | "text/html": [ 395 | "
\n", 396 | "\n", 409 | "\n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | "
Unnamed: 0indicatortime_offset
02017-12-01 21:00:000-60
43202017-12-25 21:00:000-60
12602017-12-08 21:00:000-60
100802018-01-26 21:00:000-60
68402018-01-08 21:00:000-60
............
89992018-01-19 23:59:001119
21592017-12-12 23:59:001119
91792018-01-20 23:59:001119
55792017-12-31 23:59:001119
113392018-02-01 23:59:001119
\n", 487 | "

11340 rows × 3 columns

\n", 488 | "
" 489 | ], 490 | "text/plain": [ 491 | " Unnamed: 0 indicator time_offset\n", 492 | "0 2017-12-01 21:00:00 0 -60\n", 493 | "4320 2017-12-25 21:00:00 0 -60\n", 494 | "1260 2017-12-08 21:00:00 0 -60\n", 495 | "10080 2018-01-26 21:00:00 0 -60\n", 496 | "6840 2018-01-08 21:00:00 0 -60\n", 497 | "... ... ... ...\n", 498 | "8999 2018-01-19 23:59:00 1 119\n", 499 | "2159 2017-12-12 23:59:00 1 119\n", 500 | "9179 2018-01-20 23:59:00 1 119\n", 501 | "5579 2017-12-31 23:59:00 1 119\n", 502 | "11339 2018-02-01 23:59:00 1 119\n", 503 | "\n", 504 | "[11340 rows x 3 columns]" 505 | ] 506 | }, 507 | "execution_count": 19, 508 | "metadata": {}, 509 | "output_type": "execute_result" 510 | } 511 | ], 512 | "source": [ 513 | "sleep_data" 514 | ] 515 | }, 516 | { 517 | "cell_type": "code", 518 | "execution_count": 20, 519 | "id": "b8184785", 520 | "metadata": {}, 521 | "outputs": [ 522 | { 523 | "data": { 524 | "text/plain": [ 525 | "array([0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0], dtype=int64)" 526 | ] 527 | }, 528 | "execution_count": 20, 529 | "metadata": {}, 530 | "output_type": "execute_result" 531 | } 532 | ], 533 | "source": [ 534 | "sleep_obs[3260:3275]" 535 | ] 536 | }, 537 | { 538 | "cell_type": "code", 539 | "execution_count": 21, 540 | "id": "6e966ca1", 541 | "metadata": {}, 542 | "outputs": [], 543 | "source": [ 544 | "time_offset=15" 545 | ] 546 | }, 547 | { 548 | "cell_type": "code", 549 | "execution_count": 22, 550 | "id": "f2017f31", 551 | "metadata": {}, 552 | "outputs": [], 553 | "source": [ 554 | "awake_vals = sleep_data.loc[(sleep_data['time_offset']==time_offset)\n", 555 | " &(sleep_data['indicator']==0)]" 556 | ] 557 | }, 558 | { 559 | "cell_type": "code", 560 | "execution_count": 23, 561 | "id": "35b7f108", 562 | "metadata": {}, 563 | "outputs": [], 564 | "source": [ 565 | "sleep_vals = sleep_data.loc[(sleep_data['time_offset'] == time_offset) \n", 566 | " & (sleep_data['indicator'] == 1)]" 567 | ] 568 | }, 569 | { 570 | "cell_type": "code", 571 | "execution_count": 24, 572 | "id": "6895bd42", 573 | "metadata": {}, 574 | "outputs": [ 575 | { 576 | "name": "stdout", 577 | "output_type": "stream", 578 | "text": [ 579 | "31 32\n" 580 | ] 581 | } 582 | ], 583 | "source": [ 584 | "print(len(sleep_vals), len(awake_vals))" 585 | ] 586 | }, 587 | { 588 | "cell_type": "code", 589 | "execution_count": 25, 590 | "id": "6eca6a13", 591 | "metadata": {}, 592 | "outputs": [], 593 | "source": [ 594 | "p_sleep_at_offset=len(sleep_vals)/(len(sleep_vals)+len(awake_vals))" 595 | ] 596 | }, 597 | { 598 | "cell_type": "code", 599 | "execution_count": 26, 600 | "id": "8c86bdcb", 601 | "metadata": {}, 602 | "outputs": [ 603 | { 604 | "data": { 605 | "text/plain": [ 606 | "0.49206349206349204" 607 | ] 608 | }, 609 | "execution_count": 26, 610 | "metadata": {}, 611 | "output_type": "execute_result" 612 | } 613 | ], 614 | "source": [ 615 | "p_sleep_at_offset" 616 | ] 617 | }, 618 | { 619 | "cell_type": "code", 620 | "execution_count": 27, 621 | "id": "12b162f3", 622 | "metadata": {}, 623 | "outputs": [], 624 | "source": [ 625 | "alpha = 0.977400\n", 626 | "beta = -0.067270\n", 627 | "\n", 628 | "def calculate_prior(time, alpha, beta):\n", 629 | " p = 1.0 / (1.0 + np.exp(np.dot(beta, time) + alpha))\n", 630 | " return p\n", 631 | "\n", 632 | " \n", 633 | "time_est = np.linspace(time.min()- 5, time.max() + 5, 1000)[:, None]\n", 634 | "\n", 635 | "\n", 636 | "sleep_est = calculate_prior(time_est, alpha, beta)" 637 | ] 638 | }, 639 | { 640 | "cell_type": "code", 641 | "execution_count": 28, 642 | "id": "560613cb", 643 | "metadata": {}, 644 | "outputs": [ 645 | { 646 | "data": { 647 | "image/png": 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\n", 648 | "text/plain": [ 649 | "
" 650 | ] 651 | }, 652 | "metadata": {}, 653 | "output_type": "display_data" 654 | } 655 | ], 656 | "source": [ 657 | "plt.plot(time_est,sleep_est,color='green',lw=1,label=\"Model\")\n", 658 | "plt.scatter(time,sleep_obs,edgecolor='navy',s=10,alpha=0.1,label='Observations')\n", 659 | "plt.title('Prior Probability Distribution for Sleep as Function of Time');\n", 660 | "plt.legend()\n", 661 | "plt.ylabel('Probability')\n", 662 | "plt.xlabel('PM Time');\n", 663 | "plt.xticks([-60, -30, 0, 30, 60, 90, 120], sleep_labels);" 664 | ] 665 | }, 666 | { 667 | "cell_type": "code", 668 | "execution_count": 30, 669 | "id": "189abf70", 670 | "metadata": {}, 671 | "outputs": [ 672 | { 673 | "name": "stdout", 674 | "output_type": "stream", 675 | "text": [ 676 | "9:30 PM probability of being asleep: 4.76%.\n", 677 | "10:00 PM probability of being asleep: 27.34%.\n", 678 | "10:30 PM probability of being asleep: 50.79%.\n" 679 | ] 680 | } 681 | ], 682 | "source": [ 683 | "print('9:30 PM probability of being asleep: {:.2f}%.'.\n", 684 | " format(100 * calculate_prior(-30, alpha, beta)))\n", 685 | "print('10:00 PM probability of being asleep: {:.2f}%.'.\n", 686 | " format(100 * calculate_prior(0, alpha, beta)))\n", 687 | "print('10:30 PM probability of being asleep: {:.2f}%.'.\n", 688 | " format(100 * calculate_prior(15, alpha, beta)))" 689 | ] 690 | }, 691 | { 692 | "cell_type": "code", 693 | "execution_count": null, 694 | "id": "c85d5d5d", 695 | "metadata": {}, 696 | "outputs": [], 697 | "source": [] 698 | }, 699 | { 700 | "cell_type": "code", 701 | "execution_count": null, 702 | "id": "8f35b47b", 703 | "metadata": {}, 704 | "outputs": [], 705 | "source": [] 706 | }, 707 | { 708 | "cell_type": "code", 709 | "execution_count": null, 710 | "id": "a8231cfa", 711 | "metadata": {}, 712 | "outputs": [], 713 | "source": [] 714 | } 715 | ], 716 | "metadata": { 717 | "kernelspec": { 718 | "display_name": "Python 3 (ipykernel)", 719 | "language": "python", 720 | "name": "python3" 721 | }, 722 | "language_info": { 723 | "codemirror_mode": { 724 | "name": "ipython", 725 | "version": 3 726 | }, 727 | "file_extension": ".py", 728 | "mimetype": "text/x-python", 729 | "name": "python", 730 | "nbconvert_exporter": "python", 731 | "pygments_lexer": "ipython3", 732 | "version": "3.8.11" 733 | } 734 | }, 735 | "nbformat": 4, 736 | "nbformat_minor": 5 737 | } 738 | --------------------------------------------------------------------------------