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tp: add lda

Van Nghia Vo 2 years ago
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763729d97f
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      tp/lda/README.md
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  4. 561
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      tp/lda/features.txt
  5. 1164
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      tp/lda/har.ipynb
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      tp/lda/slides.pdf
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+###<a href="https://www.insa-toulouse.fr/" ><img src="https://www.math.univ-toulouse.fr/~besse/Wikistat/Images/Logo_INSAvilletoulouse-RVB.png" style="float:left; max-width: 80px; display: inline" alt="INSA"/> |  [*Mathématiques Appliquées*](http://www.math.insa-toulouse.fr/fr/index.html), [`Science des Données`](http://www.math.insa-toulouse.fr/fr/enseignement.html)
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+
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+# Tutoriels de Science des Données en [Python](https://www.python.org/) et [R](href="https://cran.r-project.org/)
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+# [Exploration Statistique](\http://wikistat.fr)
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+
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+## *Human activity recognition (HAR)*
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+- [`Explo-Python-HAR`](https://github.com/wikistat/Exploration/blob/master/HumanActivityrecognition/Explo-Python-Har-brutes.ipynb) Visualisation et ACP de signaux issus d'un smartphone  (accéléromètre et gyroscope). Le but à terme est de pouvoir identifier l'activité du porteur. Analyse des signaux buts.
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+- [`Explo-Python-HAR`](https://github.com/wikistat/Exploration/blob/master/HumanActivityrecognition/Explo-Python-Har-metier.ipynb) Visualisation et ACP de signaux issus d'un smartphone  (accéléromètre et gyroscope). Ce sont des transformations (*features*) des signaux qui sont explorés puis modélisés: min max, variance, corrélations, entropie, Fourier... Analyse en composantes principales, analyse factorielle discriminante puis une première modélisation par régression logistique.
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+Les données initiales sont composées de 10300 enregistrements de 9 signaux provenant de l'accéléromètre d'un smartphone. Des séquences de 2s sont enregistrées et échantillonnées pendant 6 activités différentes.
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+La modélisation avancée de ces données est étudiée dans des calepins des autres saisons.
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+1 tBodyAcc-mean()-X
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+2 tBodyAcc-mean()-Y
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+3 tBodyAcc-mean()-Z
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+4 tBodyAcc-std()-X
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+10 tBodyAcc-max()-X
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+16 tBodyAcc-sma()
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+404 fBodyAccJerk-bandsEnergy()-1,16
405
+405 fBodyAccJerk-bandsEnergy()-17,32
406
+406 fBodyAccJerk-bandsEnergy()-33,48
407
+407 fBodyAccJerk-bandsEnergy()-49,64
408
+408 fBodyAccJerk-bandsEnergy()-1,24
409
+409 fBodyAccJerk-bandsEnergy()-25,48
410
+410 fBodyAccJerk-bandsEnergy()-1,8
411
+411 fBodyAccJerk-bandsEnergy()-9,16
412
+412 fBodyAccJerk-bandsEnergy()-17,24
413
+413 fBodyAccJerk-bandsEnergy()-25,32
414
+414 fBodyAccJerk-bandsEnergy()-33,40
415
+415 fBodyAccJerk-bandsEnergy()-41,48
416
+416 fBodyAccJerk-bandsEnergy()-49,56
417
+417 fBodyAccJerk-bandsEnergy()-57,64
418
+418 fBodyAccJerk-bandsEnergy()-1,16
419
+419 fBodyAccJerk-bandsEnergy()-17,32
420
+420 fBodyAccJerk-bandsEnergy()-33,48
421
+421 fBodyAccJerk-bandsEnergy()-49,64
422
+422 fBodyAccJerk-bandsEnergy()-1,24
423
+423 fBodyAccJerk-bandsEnergy()-25,48
424
+424 fBodyGyro-mean()-X
425
+425 fBodyGyro-mean()-Y
426
+426 fBodyGyro-mean()-Z
427
+427 fBodyGyro-std()-X
428
+428 fBodyGyro-std()-Y
429
+429 fBodyGyro-std()-Z
430
+430 fBodyGyro-mad()-X
431
+431 fBodyGyro-mad()-Y
432
+432 fBodyGyro-mad()-Z
433
+433 fBodyGyro-max()-X
434
+434 fBodyGyro-max()-Y
435
+435 fBodyGyro-max()-Z
436
+436 fBodyGyro-min()-X
437
+437 fBodyGyro-min()-Y
438
+438 fBodyGyro-min()-Z
439
+439 fBodyGyro-sma()
440
+440 fBodyGyro-energy()-X
441
+441 fBodyGyro-energy()-Y
442
+442 fBodyGyro-energy()-Z
443
+443 fBodyGyro-iqr()-X
444
+444 fBodyGyro-iqr()-Y
445
+445 fBodyGyro-iqr()-Z
446
+446 fBodyGyro-entropy()-X
447
+447 fBodyGyro-entropy()-Y
448
+448 fBodyGyro-entropy()-Z
449
+449 fBodyGyro-maxInds-X
450
+450 fBodyGyro-maxInds-Y
451
+451 fBodyGyro-maxInds-Z
452
+452 fBodyGyro-meanFreq()-X
453
+453 fBodyGyro-meanFreq()-Y
454
+454 fBodyGyro-meanFreq()-Z
455
+455 fBodyGyro-skewness()-X
456
+456 fBodyGyro-kurtosis()-X
457
+457 fBodyGyro-skewness()-Y
458
+458 fBodyGyro-kurtosis()-Y
459
+459 fBodyGyro-skewness()-Z
460
+460 fBodyGyro-kurtosis()-Z
461
+461 fBodyGyro-bandsEnergy()-1,8
462
+462 fBodyGyro-bandsEnergy()-9,16
463
+463 fBodyGyro-bandsEnergy()-17,24
464
+464 fBodyGyro-bandsEnergy()-25,32
465
+465 fBodyGyro-bandsEnergy()-33,40
466
+466 fBodyGyro-bandsEnergy()-41,48
467
+467 fBodyGyro-bandsEnergy()-49,56
468
+468 fBodyGyro-bandsEnergy()-57,64
469
+469 fBodyGyro-bandsEnergy()-1,16
470
+470 fBodyGyro-bandsEnergy()-17,32
471
+471 fBodyGyro-bandsEnergy()-33,48
472
+472 fBodyGyro-bandsEnergy()-49,64
473
+473 fBodyGyro-bandsEnergy()-1,24
474
+474 fBodyGyro-bandsEnergy()-25,48
475
+475 fBodyGyro-bandsEnergy()-1,8
476
+476 fBodyGyro-bandsEnergy()-9,16
477
+477 fBodyGyro-bandsEnergy()-17,24
478
+478 fBodyGyro-bandsEnergy()-25,32
479
+479 fBodyGyro-bandsEnergy()-33,40
480
+480 fBodyGyro-bandsEnergy()-41,48
481
+481 fBodyGyro-bandsEnergy()-49,56
482
+482 fBodyGyro-bandsEnergy()-57,64
483
+483 fBodyGyro-bandsEnergy()-1,16
484
+484 fBodyGyro-bandsEnergy()-17,32
485
+485 fBodyGyro-bandsEnergy()-33,48
486
+486 fBodyGyro-bandsEnergy()-49,64
487
+487 fBodyGyro-bandsEnergy()-1,24
488
+488 fBodyGyro-bandsEnergy()-25,48
489
+489 fBodyGyro-bandsEnergy()-1,8
490
+490 fBodyGyro-bandsEnergy()-9,16
491
+491 fBodyGyro-bandsEnergy()-17,24
492
+492 fBodyGyro-bandsEnergy()-25,32
493
+493 fBodyGyro-bandsEnergy()-33,40
494
+494 fBodyGyro-bandsEnergy()-41,48
495
+495 fBodyGyro-bandsEnergy()-49,56
496
+496 fBodyGyro-bandsEnergy()-57,64
497
+497 fBodyGyro-bandsEnergy()-1,16
498
+498 fBodyGyro-bandsEnergy()-17,32
499
+499 fBodyGyro-bandsEnergy()-33,48
500
+500 fBodyGyro-bandsEnergy()-49,64
501
+501 fBodyGyro-bandsEnergy()-1,24
502
+502 fBodyGyro-bandsEnergy()-25,48
503
+503 fBodyAccMag-mean()
504
+504 fBodyAccMag-std()
505
+505 fBodyAccMag-mad()
506
+506 fBodyAccMag-max()
507
+507 fBodyAccMag-min()
508
+508 fBodyAccMag-sma()
509
+509 fBodyAccMag-energy()
510
+510 fBodyAccMag-iqr()
511
+511 fBodyAccMag-entropy()
512
+512 fBodyAccMag-maxInds
513
+513 fBodyAccMag-meanFreq()
514
+514 fBodyAccMag-skewness()
515
+515 fBodyAccMag-kurtosis()
516
+516 fBodyBodyAccJerkMag-mean()
517
+517 fBodyBodyAccJerkMag-std()
518
+518 fBodyBodyAccJerkMag-mad()
519
+519 fBodyBodyAccJerkMag-max()
520
+520 fBodyBodyAccJerkMag-min()
521
+521 fBodyBodyAccJerkMag-sma()
522
+522 fBodyBodyAccJerkMag-energy()
523
+523 fBodyBodyAccJerkMag-iqr()
524
+524 fBodyBodyAccJerkMag-entropy()
525
+525 fBodyBodyAccJerkMag-maxInds
526
+526 fBodyBodyAccJerkMag-meanFreq()
527
+527 fBodyBodyAccJerkMag-skewness()
528
+528 fBodyBodyAccJerkMag-kurtosis()
529
+529 fBodyBodyGyroMag-mean()
530
+530 fBodyBodyGyroMag-std()
531
+531 fBodyBodyGyroMag-mad()
532
+532 fBodyBodyGyroMag-max()
533
+533 fBodyBodyGyroMag-min()
534
+534 fBodyBodyGyroMag-sma()
535
+535 fBodyBodyGyroMag-energy()
536
+536 fBodyBodyGyroMag-iqr()
537
+537 fBodyBodyGyroMag-entropy()
538
+538 fBodyBodyGyroMag-maxInds
539
+539 fBodyBodyGyroMag-meanFreq()
540
+540 fBodyBodyGyroMag-skewness()
541
+541 fBodyBodyGyroMag-kurtosis()
542
+542 fBodyBodyGyroJerkMag-mean()
543
+543 fBodyBodyGyroJerkMag-std()
544
+544 fBodyBodyGyroJerkMag-mad()
545
+545 fBodyBodyGyroJerkMag-max()
546
+546 fBodyBodyGyroJerkMag-min()
547
+547 fBodyBodyGyroJerkMag-sma()
548
+548 fBodyBodyGyroJerkMag-energy()
549
+549 fBodyBodyGyroJerkMag-iqr()
550
+550 fBodyBodyGyroJerkMag-entropy()
551
+551 fBodyBodyGyroJerkMag-maxInds
552
+552 fBodyBodyGyroJerkMag-meanFreq()
553
+553 fBodyBodyGyroJerkMag-skewness()
554
+554 fBodyBodyGyroJerkMag-kurtosis()
555
+555 angle(tBodyAccMean,gravity)
556
+556 angle(tBodyAccJerkMean),gravityMean)
557
+557 angle(tBodyGyroMean,gravityMean)
558
+558 angle(tBodyGyroJerkMean,gravityMean)
559
+559 angle(X,gravityMean)
560
+560 angle(Y,gravityMean)
561
+561 angle(Z,gravityMean)

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tp/lda/slides.pdf View File


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tp/lda/y_test.txt
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tp/lda/y_train.txt
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