Python’da \u00e7ok boyutlu \u00f6l\u00e7eklendirmeyi ger\u00e7ekle\u015ftirmenin en kolay yolu, sklearn.manifold<\/strong> alt mod\u00fcl\u00fcn\u00fcn MDS()<\/strong> i\u015flevini kullanmakt\u0131r.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekte bu fonksiyonun pratikte nas\u0131l kullan\u0131laca\u011f\u0131 g\u00f6sterilmektedir.<\/span><\/p>\n \u00c7e\u015fitli basketbol oyuncular\u0131 hakk\u0131nda bilgi i\u00e7eren a\u015fa\u011f\u0131daki pandalar DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n Sklearn.manifold<\/strong> mod\u00fcl\u00fcn\u00fcn MDS()<\/strong> fonksiyonu ile \u00e7ok boyutlu \u00f6l\u00e7eklendirme ger\u00e7ekle\u015ftirmek i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz:<\/span><\/p>\n Orijinal DataFrame’in her sat\u0131r\u0131 bir (x, y) koordinat\u0131na indirgenmi\u015ftir.<\/span><\/p>\n Bu koordinatlar\u0131 2 boyutlu alanda g\u00f6rselle\u015ftirmek i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz:<\/span> <\/p>\n Orijinal DataFrame’de, orijinal d\u00f6rt s\u00fctunda (say\u0131lar, asistler, bloklar ve ribaundlar) benzer de\u011ferlere sahip olan oyuncular, olay \u00f6rg\u00fcs\u00fcnde birbirine yak\u0131nd\u0131r.<\/span><\/p>\n \u00d6rne\u011fin F<\/strong> ve G<\/strong> oyuncular\u0131 birbirine kapal\u0131d\u0131r. \u0130\u015fte orijinal DataFrame’deki de\u011ferleri:<\/span><\/p>\n Say\u0131, asist, blok ve ribaund de\u011ferleri olduk\u00e7a benzer, bu da 2 boyutlu senaryoda neden birbirlerine bu kadar yak\u0131n olduklar\u0131n\u0131 a\u00e7\u0131kl\u0131yor.<\/span><\/p>\n Bunun tersine, olay \u00f6rg\u00fcs\u00fcnde birbirlerinden \u00e7ok uzakta olan B<\/strong> ve K<\/strong> oyuncular\u0131n\u0131 d\u00fc\u015f\u00fcn\u00fcn.<\/span><\/p>\n Orijinal DataFrame\u2019deki de\u011ferlerine bakarsak olduk\u00e7a farkl\u0131 olduklar\u0131n\u0131 g\u00f6rebiliriz:<\/span><\/p>\n Dolay\u0131s\u0131yla 2 boyutlu \u00e7izim, her oyuncunun DataFframe’deki t\u00fcm de\u011fi\u015fkenler aras\u0131nda ne kadar benzer oldu\u011funu g\u00f6rselle\u015ftirmenin iyi bir yoludur.<\/span><\/p>\n Benzer istatistiklere sahip oyuncular birbirine yak\u0131n grupland\u0131r\u0131l\u0131rken, \u00e7ok farkl\u0131 istatistiklere sahip oyuncular olay \u00f6rg\u00fcs\u00fcnde birbirlerinden daha uzakta bulunur.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde Python’da di\u011fer genel g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Python’da veriler nas\u0131l normalle\u015ftirilir?<\/a> \u0130statistikte, \u00e7ok boyutlu \u00f6l\u00e7eklendirme , soyut bir Kartezyen uzayda (genellikle 2B uzay) bir veri k\u00fcmesindeki g\u00f6zlemlerin benzerli\u011fini g\u00f6rselle\u015ftirmenin bir yoludur. Python’da \u00e7ok boyutlu \u00f6l\u00e7eklendirmeyi ger\u00e7ekle\u015ftirmenin en kolay yolu, sklearn.manifold alt mod\u00fcl\u00fcn\u00fcn MDS() i\u015flevini kullanmakt\u0131r. A\u015fa\u011f\u0131daki \u00f6rnekte bu fonksiyonun pratikte nas\u0131l kullan\u0131laca\u011f\u0131 g\u00f6sterilmektedir. \u00d6rnek: Python’da \u00c7ok Boyutlu \u00d6l\u00e7ekleme \u00c7e\u015fitli basketbol oyuncular\u0131 hakk\u0131nda bilgi i\u00e7eren a\u015fa\u011f\u0131daki pandalar […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-3606","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n \u00d6rnek: Python’da \u00c7ok Boyutlu \u00d6l\u00e7ekleme<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#create DataFrane\n<\/span>df = pd. DataFrame<\/span> ({' player<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K '],\n ' points<\/span> ': [4, 4, 6, 7, 8, 14, 16, 19, 25, 25, 28],\n ' assists<\/span> ': [3, 2, 2, 5, 4, 8, 7, 6, 8, 10, 11],\n ' blocks<\/span> ': [7, 3, 6, 7, 5, 8, 8, 4, 2, 2, 1],\n ' rebounds<\/span> ': [4, 5, 5, 6, 5, 8, 10, 4, 3, 2, 2]})\n\n#set player column as index column\n<\/span>df = df. set_index<\/span> (' player<\/span> ')\n\n#view Dataframe\n<\/span>print<\/span> (df)\n\n points assists blocks rebounds\nplayer \nA 4 3 7 4\nB 4 2 3 5\nC 6 2 6 5\nD 7 5 7 6\nE 8 4 5 5\nF 14 8 8 8\nG 16 7 8 10\nH 19 6 4 4\nI 25 8 2 3\nD 25 10 2 2\nK 28 11 1 2\n<\/strong><\/pre>\n from<\/span> sklearn. manifold<\/span> import<\/span> MDS\n\n#perform multi-dimensional scaling\n<\/span>mds = MDS(random_state= 0<\/span> )\nscaled_df = mds. fit_transform<\/span> (df)\n\n#view results of multi-dimensional scaling\n<\/span>print<\/span> (scaled_df)\n\n[[ 7.43654469 8.10247222]\n [4.13193821 10.27360901]\n [5.20534681 7.46919526]\n [6.22323046 4.45148627]\n [3.74110999 5.25591459]\n [3.69073384 -2.88017811]\n [3.89092087 -5.19100988]\n [ -3.68593169 -3.0821144 ]\n [ -9.13631889 -6.81016012]\n [ -8.97898385 -8.50414387]\n [-12.51859044 -9.08507097]]<\/span><\/span><\/strong><\/pre>\n import<\/span> matplotlib.pyplot as<\/span> plt\n\n#create scatterplot\n<\/span>plt. scatter<\/span> (scaled_df[:,0], scaled_df[:,1])\n\n#add axis labels\n<\/span>plt. xlabel<\/span> (' Coordinate 1<\/span> ')\nplt. ylabel<\/span> (' Coordinate 2<\/span> ')\n\n#add lables to each point\n<\/span>for<\/span> i, txt in<\/span> enumerate( df.index<\/span> ):\n plt. annotate<\/span> (txt, (scaled_df[:,0][i]+.3, scaled_df[:,1][i]))\n\n#display scatterplot\n<\/span>plt. show<\/span> ()\n<\/strong><\/pre>\n![\"Python'da](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/mds2.jpg\")
<\/p>\n #select rows with index labels 'F' and 'G'\ndf. loc<\/span> [[' F<\/span> ',' G<\/span> ']]\n\n points assists blocks rebounds\nplayer\t\t\t\t\nF 14 8 8 8\nG 16 7 8 10\n<\/span><\/span><\/strong><\/pre>\n #select rows with index labels 'B' and 'K'\ndf. loc<\/span> [[' B<\/span> ',' K<\/span> ']]<\/span><\/span>\n\n points assists blocks rebounds\nplayer\t\t\t\t\nB 4 2 3 5\nK 28 11 1 2<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Python’da Ayk\u0131r\u0131 De\u011ferler Nas\u0131l Kald\u0131r\u0131l\u0131r<\/a>
Python’da normallik nas\u0131l test edilir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"