{"id":30936,"date":"2019-10-31T21:38:20","date_gmt":"2019-10-31T18:38:20","guid":{"rendered":"https:\/\/prohoster.info\/blog\/mashinnoe-obuchenie-bez-python-anaconda-i-prochih-presmykayushhihsya\/"},"modified":"2019-10-31T21:38:20","modified_gmt":"2019-10-31T18:38:20","slug":"mashinnoe-obuchenie-bez-python-anaconda-i-prochih-presmykayushhihsya","status":"publish","type":"post","link":"https:\/\/prohoster.info\/cs\/blog\/news\/mashinnoe-obuchenie-bez-python-anaconda-i-prochih-presmykayushhihsya","title":{"rendered":"Strojov\u00e9 u\u010den\u00ed bez Pythonu, Anacondy a dal\u0161\u00edch plaz\u016f","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Ne, samoz\u0159ejm\u011b to nemysl\u00edm v\u00e1\u017en\u011b. Mus\u00ed b\u00fdt n\u011bjak\u00e1 hranice, do jak\u00e9 m\u00edry je mo\u017en\u00e9 zjednodu\u0161it t\u00e9ma. Ale pro prvn\u00ed f\u00e1ze, pochopen\u00ed z\u00e1kladn\u00edch koncept\u016f a rychl\u00e9 \u201evno\u0159en\u00ed\u201c do t\u00e9matu, to m\u016f\u017ee b\u00fdt p\u0159ijateln\u00e9. A jak spr\u00e1vn\u011b pojmenovat tento materi\u00e1l (mo\u017enosti: \u201eStrojov\u00e9 u\u010den\u00ed pro za\u010d\u00e1te\u010dn\u00edky\u201c, \u201eAnal\u00fdza dat od z\u00e1klad\u016f\u201c, \u201eAlgoritmy pro mal\u00e9 d\u011bti\u201c), probereme na konci. <\/p>\n

K v\u011bci. Napsal jsem n\u011bkolik praktick\u00fdch program\u016f v MS Excel pro vizualizaci a p\u0159ehledn\u00e9 zobrazen\u00ed proces\u016f, kter\u00e9 prob\u00edhaj\u00ed v r\u016fzn\u00fdch metod\u00e1ch strojov\u00e9ho u\u010den\u00ed p\u0159i anal\u00fdze dat. Vid\u011bt znamen\u00e1 v\u011b\u0159it, jak se \u0159\u00edk\u00e1 v kultu\u0159e, kter\u00e1 v\u011bt\u0161inu t\u011bchto metod vyvinula (mimochodem, zdaleka ne v\u0161echny. Mocn\u00e1 \u201emetoda podp\u016frn\u00fdch vektor\u016f\u201c, neboli SVM, support vector machine \u2013 je vyn\u00e1lezem na\u0161eho krajana Vladimira Vapnika, Moskevsk\u00fd institut \u0159\u00edzen\u00ed. Rok 1963, mimochodem! Te\u010f pracuje a u\u010d\u00ed v USA).<\/p>\n

T\u0159i soubory na uk\u00e1zku
\n<\/a><\/noindex><\/p>\n

1. Klasifikace metodou k-pr\u016fm\u011br\u016f<\/h2>\n

\n\u00dakoly tohoto typu pat\u0159\u00ed k \u201eu\u010den\u00ed bez u\u010ditele\u201c, kdy pot\u0159ebujeme rozd\u011blit vstupn\u00ed data do p\u0159edem zn\u00e1m\u00e9ho po\u010dtu kategori\u00ed, ale p\u0159itom nem\u00e1me \u017e\u00e1dn\u00e9 \u201espr\u00e1vn\u00e9 odpov\u011bdi\u201c, ty mus\u00edme vyz\u00edskat ze samotn\u00fdch dat. Z\u00e1kladn\u00ed klasick\u00e1 \u00faloha nalezen\u00ed poddruh\u016f kv\u011bt\u016f kosatce (Ronald Fisher, rok 1936!), kter\u00e1 se pova\u017euje za prvn\u00ed vla\u0161tovku t\u00e9to oblasti znalost\u00ed \u2013 je pr\u00e1v\u011b tohoto druhu.<\/p>\n

Metoda je dost jednoduch\u00e1. M\u00e1me sadu objekt\u016f, prezentovan\u00fdch jako vektory (sady N \u010d\u00edsel). U kosatc\u016f to jsou sady 4 \u010d\u00edsel, charakterizuj\u00edc\u00ed kv\u011bt: d\u00e9lka a \u0161\u00ed\u0159ka vn\u011bj\u0161\u00ed a vnit\u0159n\u00ed \u010d\u00e1sti okv\u011btn\u00edho pl\u00e1tku, odpov\u00eddaj\u00edc\u00edm zp\u016fsobem (Kosatce Fishera \u2014 Wikipedie<\/a><\/noindex>). Jako vzd\u00e1lenost, nebo m\u00edru bl\u00edzkosti mezi objekty, se vol\u00ed b\u011b\u017en\u00e1 eukleidovsk\u00e1 metrika.<\/p>\n

D\u00e1le se n\u00e1hodn\u011b (nebo i nijak n\u00e1hodn\u011b, viz d\u00e1le) vyb\u00edraj\u00ed centra klastr\u016f a vypo\u010d\u00edt\u00e1vaj\u00ed se vzd\u00e1lenosti od ka\u017ed\u00e9ho objektu k centr\u016fm klastr\u016f. Ka\u017ed\u00fd objekt je v tomto kroku iterace ozna\u010den jako pat\u0159\u00edc\u00ed k nejbl\u00ed\u017ee um\u00edst\u011bn\u00e9mu centru. Pot\u00e9 se centrum ka\u017ed\u00e9ho klastra posune na aritmetick\u00fd pr\u016fm\u011br sou\u0159adnic jeho \u010dlen\u016f (podobn\u011b jako v fyzice se tak\u00e9 naz\u00fdv\u00e1 \u201est\u0159ed hmotnosti\u201c), a proces se opakuje.<\/p>\n

Proces se dostate\u010dn\u011b rychle konverguje. Na obr\u00e1zc\u00edch to v dvourozm\u011brn\u00e9m prostoru vypad\u00e1 takto:<\/p>\n

1. Po\u010d\u00e1te\u010dn\u00ed n\u00e1hodn\u00e9 rozlo\u017een\u00ed bod\u016f na rovin\u011b a po\u010det cluster\u016f<\/p>\n

\"Strojov\u00e9
\n
\n2. Ur\u010den\u00ed center cluster\u016f a p\u0159i\u0159azen\u00ed bod\u016f k jejich cluster\u016fm<\/p>\n

\"Strojov\u00e9
\n
\n3. P\u0159esunov\u00e1n\u00ed sou\u0159adnic center cluster\u016f, p\u0159epo\u010det p\u0159\u00edslu\u0161nosti bod\u016f, dokud se centra nestabilizuj\u00ed. Je vid\u011bt trajektorie pohybu centra clustera k kone\u010dn\u00e9 poloze.<\/p>\n

\"Strojov\u00e9
\n
\nV jak\u00e9mkoli okam\u017eiku lze zadat nov\u00e1 centra cluster\u016f (ani\u017e bychom generovali nov\u00e9 rozlo\u017een\u00ed bod\u016f!) a vid\u011bt, \u017ee proces d\u011blen\u00ed nen\u00ed v\u017edy jednozna\u010dn\u00fd. Matematicky to znamen\u00e1, \u017ee u optimalizovan\u00e9 funkce (sou\u010det \u010dtverc\u016f vzd\u00e1lenost\u00ed od bod\u016f k centr\u016fm jejich cluster\u016f) nach\u00e1z\u00edme nikoli glob\u00e1ln\u00ed, ale lok\u00e1ln\u00ed minimum. Tuto probl\u00e9m lze p\u0159ekonat bu\u010f v\u00fdb\u011brem po\u010d\u00e1te\u010dn\u00ed center cluster\u016f bez n\u00e1hody, nebo proch\u00e1zen\u00edm mo\u017en\u00fdch center (n\u011bkdy je v\u00fdhodn\u00e9 um\u00edstit je p\u0159esn\u011b na n\u011bkter\u00fd z bod\u016f, pak m\u00e1me alespo\u0148 z\u00e1ruku, \u017ee nedostaneme pr\u00e1zdn\u00e9 clustery). V ka\u017ed\u00e9m p\u0159\u00edpad\u011b m\u00e1 kone\u010dn\u00e1 mno\u017eina v\u017edy p\u0159esnou doln\u00ed mez. <\/p>\n

S t\u00edmto souborem si m\u016f\u017eete pohr\u00e1t na tomto odkazu<\/a><\/noindex> (nezapome\u0148te zapnout podporu maker. Soubory byly zkontrolov\u00e1ny na viry)<\/p>\n

Popis metody na Wikipedii \u2014 metoda k-pr\u016fm\u011brech<\/a><\/noindex><\/p>\n

2. Aproximace polynomy a rozd\u011blen\u00ed dat. P\u0159eu\u010den\u00ed<\/h2>\n

\nVynikaj\u00edc\u00ed v\u011bdec a populariz\u00e1tor v\u011bdy o datech K.V. Voroncov stru\u010dn\u011b hovo\u0159\u00ed o metod\u00e1ch strojov\u00e9ho u\u010den\u00ed jako o \"v\u011bd\u011b o proch\u00e1zen\u00ed k\u0159ivek skrze body\". V tomto p\u0159\u00edkladu budeme hledat vzor v datech metodou nejmen\u0161\u00edch \u010dtverc\u016f. <\/p>\n

Jsou uk\u00e1z\u00e1ny techniky rozd\u011blen\u00ed p\u016fvodn\u00edch dat na \"tr\u00e9ninkov\u00e9\" a \"kontroln\u00ed\", stejn\u011b jako jev zn\u00e1m\u00fd jako p\u0159e\u00fa\u010den\u00ed. P\u0159i spr\u00e1vn\u00e9 aproximaci budeme m\u00edt n\u011bjakou chybu na tr\u00e9ninkov\u00fdch datech a o n\u011bco v\u011bt\u0161\u00ed na kontroln\u00edch. P\u0159i nespr\u00e1vn\u00e9 - p\u0159esnou p\u0159izp\u016fsoben\u00ed tr\u00e9ninkov\u00fdm dat\u016fm a obrovskou chybu na kontroln\u00edch.<\/p>\n

(Zn\u00e1m\u00fd fakt, \u017ee skrze N bod\u016f lze proch\u00e1zet jedinou k\u0159ivku N-1. stupn\u011b, a tento zp\u016fsob v obecn\u00e9m p\u0159\u00edpad\u011b ned\u00e1v\u00e1 po\u017eadovan\u00fd v\u00fdsledek. Interpoluj\u00edc\u00ed polynom Lagrange na Wikipedii<\/a><\/noindex>)<\/p>\n

1. Zad\u00e1me po\u010d\u00e1te\u010dn\u00ed rozlo\u017een\u00ed<\/p>\n

\"Strojov\u00e9
\n
\n2. D\u011bl\u00edme body na \"tr\u00e9ninkov\u00e9\" a \"kontroln\u00ed\" v pom\u011bru 70 ku 30.<\/p>\n

\"Strojov\u00e9
\n
\n3. Prov\u00e1d\u00edme aproximuj\u00edc\u00ed k\u0159ivku podle tr\u00e9ninkov\u00fdch bod\u016f, vid\u00edme chybu, kterou d\u00e1v\u00e1 na kontroln\u00edch datech.<\/p>\n

\"Strojov\u00e9
\n
\n4. Prov\u00e1d\u00edme p\u0159esnou k\u0159ivku p\u0159es tr\u00e9ninkov\u00e9 body a vid\u00edme ohromuj\u00edc\u00ed chybu na kontroln\u00edch datech (a nulovou na tr\u00e9ninkov\u00fdch, ale co z toho?).<\/p>\n

\"Strojov\u00e9
\n
\nZde je samoz\u0159ejm\u011b uvedena nejjednodu\u0161\u0161\u00ed varianta s jedin\u00fdm rozd\u011blen\u00edm na \"tr\u00e9ninkov\u00e9\" a \"kontroln\u00ed\" podmno\u017einy, v obecn\u00e9m p\u0159\u00edpad\u011b se to d\u011bl\u00e1 mnohokr\u00e1t pro nejlep\u0161\u00ed p\u0159izp\u016fsoben\u00ed koeficient\u016f.<\/p>\n

Soubor je k dispozici zde, prov\u011b\u0159en\u00fd antivirem.<\/a><\/noindex> Aktivujte makra pro spr\u00e1vnou funkci.<\/p>\n

3. Gradientn\u00ed sestup a dynamika zm\u011bny chyby.<\/h2>\n

\nZde bude \u010dty\u0159rozm\u011brn\u00fd p\u0159\u00edpad a line\u00e1rn\u00ed regrese. Koeficienty line\u00e1rn\u00ed regrese budou ur\u010dov\u00e1ny po kroc\u00edch metodou gradientn\u00edho sestupu, ze za\u010d\u00e1tku budou v\u0161echny koeficienty nulov\u00e9. Na samostatn\u00e9m grafu je vid\u011bt dynamika sni\u017eov\u00e1n\u00ed chyby p\u0159i st\u00e1le p\u0159esn\u011bj\u0161\u00edm p\u0159izp\u016fsoben\u00ed koeficient\u016f. Existuje mo\u017enost pod\u00edvat se na v\u0161echny \u010dty\u0159i dvourozm\u011brn\u00e9 projekce.<\/p>\n

Pokud nastav\u00edte p\u0159\u00edli\u0161 velk\u00fd krok gradientn\u00edho sestupu, je vid\u011bt, \u017ee poka\u017ed\u00e9 p\u0159eskakujeme minimum a k v\u00fdsledku dojdeme za v\u011bt\u0161\u00ed po\u010det krok\u016f, i kdy\u017e nakonec stejn\u011b dojdeme (pokud p\u0159\u00edli\u0161 nezv\u00fd\u0161\u00edme krok sestupu \u2013 pak algoritmus \"vyjde z kontrolu\"). A graf z\u00e1vislosti chyby na kroku iterace nebude plynul\u00fd, ale \"trhan\u00fd\".<\/p>\n

1. Generujeme data, nastavujeme krok gradientn\u00edho sestupu.<\/p>\n

\"Strojov\u00e9
\n
\n2. P\u0159i spr\u00e1vn\u00e9m v\u00fdb\u011bru kroku gradientn\u00edho sestupu hladce a dostate\u010dn\u011b rychle dos\u00e1hneme minima.<\/p>\n

\"Strojov\u00e9
\n
\n3. P\u0159i nespr\u00e1vn\u00e9m v\u00fdb\u011bru kroku gradientn\u00edho sestupu p\u0159eskakujeme maximum, graf chyby je \"trhan\u00fd\", konvergence zab\u00edr\u00e1 v\u00edce krok\u016f.<\/p>\n

\"Strojov\u00e9
\na<\/p>\n

\"Strojov\u00e9
\n
\n4. P\u0159i zcela nespr\u00e1vn\u00e9m v\u00fdb\u011bru kroku gradientn\u00edho sestupu se vzdalujeme od minima.<\/p>\n

\"Strojov\u00e9
\n
\n(Abychom reprodukovali proces p\u0159i uveden\u00fdch hodnot\u00e1ch kroku gradientn\u00edho sestupu na obr\u00e1zc\u00edch, za\u0161krtn\u011bte \"reference data\").<\/p>\n

Soubor \u2013 na tomto odkazu, je pot\u0159eba aktivovat makra, viry nejsou.<\/a><\/noindex><\/p>\n

Jak tvrd\u00ed uzn\u00e1van\u00e9 spole\u010denstv\u00ed, je takov\u00e9 zjednodu\u0161en\u00ed a metoda p\u0159ed\u00e1v\u00e1n\u00ed materi\u00e1lu akceptovateln\u00e1? M\u011blo by se to p\u0159elo\u017eit do angli\u010dtiny? <\/b>
\n
Zdroj: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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