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# this tempestado one day vaiacabar

11eimilia11 Feb 21st, 2019 66 Never
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1. import pandas as pd
2. import cv2
3. import numpy as np
4. from Lista02 import FuncoesML as fun
5. from sklearn.model_selection import KFold
6. from sklearn.neighbors import KNeighborsClassifier
7. from sklearn.tree import DecisionTreeClassifier
8. from sklearn.naive_bayes import GaussianNB
9. from sklearn.svm import SVC
10. import time
11. from sklearn.linear_model import LogisticRegression
12. from sklearn.neural_network import MLPClassifier
13. from sklearn.ensemble import RandomForestClassifier
14. from sklearn.metrics import recall_score
15. from sklearn.metrics import precision_score
16. from sklearn.metrics import f1_score
17.
18. #lendo a imagem
20. squares = []
22.
23. circles = []
25.
26. triangles = []
28.
29. ellipses = []
31.
32. trapezia = []
34.
35. rectangles = []
37.
38. rhombuses = []
40.
41. lines = []
43.
44. hexagons = []
46.
48.
49. # Selecionando aleatoriamente 72 imagens de cada classe
50.
51. squares_selec, squares_naoselec = fun.seleciona_imagens(squares,72)
52. circles_selec, circles_naoselec = fun.seleciona_imagens(circles,72)
53. triangles_selec, triangles_naoselec = fun.seleciona_imagens(triangles,72)
54. ellipses_selec, ellipses_naoselec = fun.seleciona_imagens(ellipses,72)
55. trapezia_selec, trapezia_naoselec = fun.seleciona_imagens(trapezia,72)
56. rectangles_selec, rectangles_naoselec = fun.seleciona_imagens(rectangles,72)
57. rhombuses_selec, rhombuses_naoselec = fun.seleciona_imagens(rhombuses,72)
58. lines_selec, lines_naoselec = fun.seleciona_imagens(lines,72)
59. hexagons_selec, hexagons_naoselec = fun.seleciona_imagens(hexagons,72)
60.
61. #Salvando em pastas diferentes as imagens para seleção de características e as de teste
62.
63. fun.save_images(squares_selec,'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomSquares/')
64. fun.save_images(squares_naoselec,'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Squares/')
65. fun.save_images(circles_selec,'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomCircles/')
66. fun.save_images(circles_naoselec,'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Circles/')
67. fun.save_images(triangles_selec,'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomTriangles/')
68. fun.save_images(triangles_naoselec,'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Triangles/')
69. fun.save_images(ellipses_selec, 'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomEllipses/')
70. fun.save_images(ellipses_naoselec,'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Ellipses/')
71. fun.save_images(trapezia_selec,'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomTrapezia/')
72. fun.save_images(trapezia_naoselec,'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Trapezia/')
73. fun.save_images(rectangles_selec,'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomRectangles/')
74. fun.save_images(rectangles_naoselec, 'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Rectangles/')
75. fun.save_images(rhombuses_selec,'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomRhombuses/')
76. fun.save_images(rhombuses_naoselec, 'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Rhombuses/')
77. fun.save_images(lines_selec, 'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomLines/')
78. fun.save_images(lines_naoselec, 'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Lines/')
79. fun.save_images(hexagons_selec, 'C:/Users/Auricelia/Desktop/DataSetsML/Random/RandomHexagons/')
80. fun.save_images(hexagons_naoselec, 'C:/Users/Auricelia/Desktop/DataSetsML/Not_Random/Hexagons/')
81.
82.
83.
84. # PRE PROCESSING
85.
86. #criando cópias de cada uma das pastas para redimensionar as imagens
88.
89. squares16_s = squares_selec.copy()
90. squares16_n = squares_naoselec.copy()
91.
92. squares32_s = squares_selec.copy()
93. squares32_n = squares_naoselec.copy()
94.
95. squares64_s = squares_selec.copy()
96. squares64_n = squares_naoselec.copy()
97.
98. squares128_s = squares_selec.copy()
99. squares128_n = squares_naoselec.copy()
100.
101. squares16_s = fun.resizeImages(squares16_s,16,16)
102. squares16_n = fun.resizeImages(squares16_n,16,16)
103.
104. squares32_s = fun.resizeImages(squares32_s,32,32)
105. squares32_n = fun.resizeImages(squares32_n,32,32)
106.
107. squares64_s = fun.resizeImages(squares64_s,64,64)
108. squares64_n = fun.resizeImages(squares64_n,64,64)
109.
110. squares128_s = fun.resizeImages(squares128_s,128,128)
111. squares128_n = fun.resizeImages(squares128_n,128,128)
112.
113. #círculos
114. circles16_s = circles_selec.copy()
115. circles16_n = circles_naoselec.copy()
116.
117. circles32_s = circles_selec.copy()
118. circles32_n = circles_naoselec.copy()
119.
120. circles64_s = circles_selec.copy()
121. circles64_n = circles_naoselec.copy()
122.
123. circles128_s = circles_selec.copy()
124. circles128_n = circles_naoselec.copy()
125.
126. circles16_s = fun.resizeImages(circles16_s,16,16)
127. circles16_n = fun.resizeImages(circles16_n,16,16)
128.
129. circles32_s = fun.resizeImages(circles32_s,32,32)
130. circles32_n = fun.resizeImages(circles32_n,32,32)
131.
132. circles64_s = fun.resizeImages(circles64_s,64,64)
133. circles64_n = fun.resizeImages(circles64_n,64,64)
134. circles128_s = fun.resizeImages(circles128_s,128,128)
135. circles128_n = fun.resizeImages(circles128_n,128,128)
136.
137. #elipses
138. ellipsis16_s = ellipses_selec.copy()
139. ellipsis16_n = ellipses_naoselec.copy()
140.
141. ellipsis32_s = ellipses_selec.copy()
142. ellipsis32_n = ellipses_naoselec.copy()
143.
144. ellipsis64_s = ellipses_selec.copy()
145. ellipsis64_n = ellipses_naoselec.copy()
146.
147. ellipsis128_s = ellipses_selec.copy()
148. ellipsis128_n = ellipses_naoselec.copy()
149.
150. ellipsis16_s = fun.resizeImages(ellipsis16_s,16,16)
151. ellipsis16_n = fun.resizeImages(ellipsis16_n,16,16)
152.
153. ellipsis32_s = fun.resizeImages(ellipsis32_s,32,32)
154. ellipsis32_n = fun.resizeImages(ellipsis32_n,32,32)
155.
156. ellipsis64_s = fun.resizeImages(ellipsis64_s,64,64)
157. ellipsis64_n = fun.resizeImages(ellipsis64_n,64,64)
158.
159. ellipsis128_s = fun.resizeImages(ellipsis128_s,128,128)
160. ellipsis128_n = fun.resizeImages(ellipsis128_n,128,128)
161.
162. #hexágonos
163. hexagons16_s = hexagons_selec.copy()
164. hexagons16_n = hexagons_naoselec.copy()
165. hexagons32_s = hexagons_selec.copy()
166. hexagons32_n = hexagons_naoselec.copy()
167. hexagons64_s = hexagons_selec.copy()
168. hexagons64_n = hexagons_naoselec.copy()
169. hexagons128_s = hexagons_selec.copy()
170. hexagons128_n = hexagons_naoselec.copy()
171.
172. hexagons16_s = fun.resizeImages(hexagons16_s,16,16)
173. hexagons16_n = fun.resizeImages(hexagons16_n,16,16)
174. hexagons32_s = fun.resizeImages(hexagons32_s,32,32)
175. hexagons32_n = fun.resizeImages(hexagons32_n,32,32)
176. hexagons64_s = fun.resizeImages(hexagons64_s,64,64)
177. hexagons64_n = fun.resizeImages(hexagons64_n,64,64)
178. hexagons128_s = fun.resizeImages(hexagons128_s,128,128)
179. hexagons128_n = fun.resizeImages(hexagons128_n,128,128)
180.
181. #linhas
182. lines16_s = lines_selec.copy()
183. lines16_n = lines_naoselec.copy()
184. lines32_s = lines_selec.copy()
185. lines32_n = lines_naoselec.copy()
186. lines64_s = lines_selec.copy()
187. lines64_n = lines_naoselec.copy()
188. lines128_s = lines_selec.copy()
189. lines128_n = lines_naoselec.copy()
190.
191. lines16_s = fun.resizeImages(lines16_s,16,16)
192. lines16_n = fun.resizeImages(lines16_n,16,16)
193. lines32_s = fun.resizeImages(lines32_s,32,32)
194. lines32_n = fun.resizeImages(lines32_n,32,32)
195. lines64_s = fun.resizeImages(lines64_s,64,64)
196. lines64_n = fun.resizeImages(lines64_n,64,64)
197. lines128_s = fun.resizeImages(lines128_s,128,128)
198. lines128_n = fun.resizeImages(lines128_n,128,128)
199.
200. #retângulos
201. rectangles16_s = rectangles_selec.copy()
202. rectangles16_n = rectangles_naoselec.copy()
203. rectangles32_s = rectangles_selec.copy()
204. rectangles32_n = rectangles_naoselec.copy()
205. rectangles64_s = rectangles_selec.copy()
206. rectangles64_n = rectangles_naoselec.copy()
207. rectangles128_s = rectangles_selec.copy()
208. rectangles128_n = rectangles_naoselec.copy()
209.
210. rectangles16_s = fun.resizeImages(rectangles16_s,16,16)
211. rectangles16_n = fun.resizeImages(rectangles16_n,16,16)
212. rectangles32_s = fun.resizeImages(rectangles32_s,32,32)
213. rectangles32_n = fun.resizeImages(rectangles32_n,32,32)
214. rectangles64_s = fun.resizeImages(rectangles64_s,64,64)
215. rectangles64_n = fun.resizeImages(rectangles64_n,64,64)
216. rectangles128_s = fun.resizeImages(rectangles128_s,128,128)
217. rectangles128_n = fun.resizeImages(rectangles128_n,128,128)
218.
219. #losangos
220. rhombuses16_s = rhombuses_selec.copy()
221. rhombuses16_n = rhombuses_naoselec.copy()
222. rhombuses32_s = rhombuses_selec.copy()
223. rhombuses32_n = rhombuses_naoselec.copy()
224. rhombuses64_s = rhombuses_selec.copy()
225. rhombuses64_n = rhombuses_naoselec.copy()
226. rhombuses128_s = rhombuses_selec.copy()
227. rhombuses128_n = rhombuses_naoselec.copy()
228.
229. rhombuses16_s = fun.resizeImages(rhombuses16_s,16,16)
230. rhombuses16_n = fun.resizeImages(rhombuses16_n,16,16)
231. rhombuses32_s = fun.resizeImages(rhombuses32_s,32,32)
232. rhombuses32_n = fun.resizeImages(rhombuses32_n,32,32)
233. rhombuses64_s = fun.resizeImages(rhombuses64_s,64,64)
234. rhombuses64_n = fun.resizeImages(rhombuses64_n,64,64)
235. rhombuses128_s = fun.resizeImages(rhombuses128_s,128,128)
236. rhombuses128_n = fun.resizeImages(rhombuses128_n,128,128)
237.
238. #trapézios
239. trapezia16_s = trapezia_selec.copy()
240. trapezia16_n = trapezia_naoselec.copy()
241. trapezia32_s = trapezia_selec.copy()
242. trapezia32_n = trapezia_naoselec.copy()
243. trapezia64_s = trapezia_selec.copy()
244. trapezia64_n = trapezia_naoselec.copy()
245. trapezia128_s = trapezia_selec.copy()
246. trapezia128_n = trapezia_naoselec.copy()
247.
248. trapezia16_s = fun.resizeImages(trapezia16_s,16,16)
249. trapezia16_n = fun.resizeImages(trapezia16_n,16,16)
250. trapezia32_s = fun.resizeImages(trapezia32_s,32,32)
251. trapezia32_n = fun.resizeImages(trapezia32_n,32,32)
252. trapezia64_s = fun.resizeImages(trapezia64_s,64,64)
253. trapezia64_n = fun.resizeImages(trapezia64_n,64,64)
254. trapezia128_s = fun.resizeImages(trapezia128_s,128,128)
255. trapezia128_n = fun.resizeImages(trapezia128_n,128,128)
256.
257. #triângulos
258. triangles16_s = triangles_selec.copy()
259. triangles16_n = triangles_naoselec.copy()
260. triangles32_s = triangles_selec.copy()
261. triangles32_n = triangles_naoselec.copy()
262. triangles64_s = triangles_selec.copy()
263. triangles64_n = triangles_naoselec.copy()
264. triangles128_s = triangles_selec.copy()
265. triangles128_n = triangles_naoselec.copy()
266.
267. triangles16_s = fun.resizeImages(triangles16_s,16,16)
268. triangles16_n = fun.resizeImages(triangles16_n,16,16)
269. triangles32_s = fun.resizeImages(triangles32_s,32,32)
270. triangles32_n = fun.resizeImages(triangles32_n,32,32)
271. triangles64_s = fun.resizeImages(triangles64_s,64,64)
272. triangles64_n = fun.resizeImages(triangles64_n,64,64)
273. triangles128_s = fun.resizeImages(triangles128_s,128,128)
274. triangles128_n = fun.resizeImages(triangles128_n,128,128)
275.
276. #convertendo para níveis de cinza
277.
278. squares16_s = fun.grayConversion(squares16_s)
279. squares16_n = fun.grayConversion(squares16_n)
280. squares32_s = fun.grayConversion(squares32_s)
281. squares32_n = fun.grayConversion(squares32_n)
282. squares64_s = fun.grayConversion(squares64_s)
283. squares64_n = fun.grayConversion(squares64_n)
284. squares128_s = fun.grayConversion(squares128_s)
285. squares128_n = fun.grayConversion(squares128_n)
286.
287.
288. circles16_s = fun.grayConversion(circles16_s)
289. circles16_n = fun.grayConversion(circles16_n)
290. circles32_s = fun.grayConversion(circles32_s)
291. circles32_n = fun.grayConversion(circles32_n)
292. circles64_s = fun.grayConversion(circles64_s)
293. circles64_n = fun.grayConversion(circles64_n)
294. circles128_s = fun.grayConversion(circles128_s)
295. circles128_n = fun.grayConversion(circles128_n)
296.
297. triangles16_s = fun.grayConversion(triangles16_s)
298. triangles16_n = fun.grayConversion(triangles16_n)
299. triangles32_s = fun.grayConversion(triangles32_s)
300. triangles32_n = fun.grayConversion(triangles32_n)
301. triangles64_s = fun.grayConversion(triangles64_s)
302. triangles64_n = fun.grayConversion(triangles64_n)
303. triangles128_s = fun.grayConversion(triangles128_s)
304. triangles128_n = fun.grayConversion(triangles128_n)
305.
306.
307. trapezia16_s = fun.grayConversion(trapezia16_s)
308. trapezia16_n = fun.grayConversion(trapezia16_n)
309. trapezia32_s = fun.grayConversion(trapezia32_s)
310. trapezia32_n = fun.grayConversion(trapezia32_n)
311. trapezia64_s = fun.grayConversion(trapezia64_s)
312. trapezia64_n = fun.grayConversion(trapezia64_n)
313. trapezia128_s = fun.grayConversion(trapezia128_s)
314. trapezia128_n = fun.grayConversion(trapezia128_n)
315.
316. rhombuses16_s = fun.grayConversion(rhombuses16_s)
317. rhombuses16_n = fun.grayConversion(rhombuses16_n)
318. rhombuses32_s = fun.grayConversion(rhombuses32_s)
319. rhombuses32_n = fun.grayConversion(rhombuses32_n)
320. rhombuses64_s = fun.grayConversion(rhombuses64_s)
321. rhombuses64_n = fun.grayConversion(rhombuses64_n)
322. rhombuses128_s = fun.grayConversion(rhombuses128_s)
323. rhombuses128_n = fun.grayConversion(rhombuses128_n)
324.
325. rectangles16_s = fun.grayConversion(rectangles16_s)
326. rectangles16_n = fun.grayConversion(rectangles16_n)
327. rectangles32_s = fun.grayConversion(rectangles32_s)
328. rectangles32_n = fun.grayConversion(rectangles32_n)
329. rectangles64_s = fun.grayConversion(rectangles64_s)
330. rectangles64_n = fun.grayConversion(rectangles64_n)
331. rectangles128_s = fun.grayConversion(rectangles128_s)
332. rectangles128_n = fun.grayConversion(rectangles128_n)
333.
334. lines16_s = fun.grayConversion(lines16_s)
335. lines16_n = fun.grayConversion(lines16_n)
336. lines32_s = fun.grayConversion(lines32_s)
337. lines32_n = fun.grayConversion(lines32_n)
338. lines64_s = fun.grayConversion(lines64_s)
339. lines64_n = fun.grayConversion(lines64_n)
340. lines128_s = fun.grayConversion(lines128_s)
341. lines128_n = fun.grayConversion(lines128_n)
342.
343. hexagons16_s = fun.grayConversion(hexagons16_s)
344. hexagons16_n = fun.grayConversion(hexagons16_n)
345. hexagons32_s = fun.grayConversion(hexagons32_s)
346. hexagons32_n = fun.grayConversion(hexagons32_n)
347. hexagons64_s = fun.grayConversion(hexagons64_s)
348. hexagons64_n = fun.grayConversion(hexagons64_n)
349. hexagons128_s = fun.grayConversion(hexagons128_s)
350. hexagons128_n = fun.grayConversion(hexagons128_n)
351.
352. ellipsis16_s = fun.grayConversion(ellipsis16_s)
353. ellipsis16_n = fun.grayConversion(ellipsis16_n)
354. ellipsis32_s = fun.grayConversion(ellipsis32_s)
355. ellipsis32_n = fun.grayConversion(ellipsis32_n)
356. ellipsis64_s = fun.grayConversion(ellipsis64_s)
357. ellipsis64_n = fun.grayConversion(ellipsis64_n)
358. ellipsis128_s = fun.grayConversion(ellipsis128_s)
359. ellipsis128_n = fun.grayConversion(ellipsis128_n)
360.
361. #aplicando o filtro gaussiano
362.
363. squares16_s = fun.blurConversion(squares16_s,5,0)
364. squares16_n = fun.blurConversion(squares16_n,5,0)
365. squares32_s = fun.blurConversion(squares32_s,5,0)
366. squares32_n = fun.blurConversion(squares32_n,5,0)
367. squares64_s = fun.blurConversion(squares64_s,5,0)
368. squares64_n = fun.blurConversion(squares64_n,5,0)
369. squares128_s = fun.blurConversion(squares128_s,5,0)
370. squares128_n = fun.blurConversion(squares128_n,5,0)
371.
372. circles16_s = fun.blurConversion(circles16_s, 5, 0)
373. circles16_n = fun.blurConversion(circles16_n, 5, 0)
374. circles32_s = fun.blurConversion(circles32_s,5 ,0)
375. circles32_n = fun.blurConversion(circles32_n,5 ,0)
376. circles64_s = fun.blurConversion(circles64_s,5,0)
377. circles64_n = fun.blurConversion(circles64_n,5,0)
378. circles128_s = fun.blurConversion(circles128_s,5,0)
379. circles128_n = fun.blurConversion(circles128_n,5,0)
380.
381. triangles16_s = fun.blurConversion(triangles16_s,5,0)
382. triangles16_n = fun.blurConversion(triangles16_n,5,0)
383. triangles32_s = fun.blurConversion(triangles32_s,5,0)
384. triangles32_n = fun.blurConversion(triangles32_n,5,0)
385. triangles64_s = fun.blurConversion(triangles64_s,5,0)
386. triangles64_n = fun.blurConversion(triangles64_n,5,0)
387. triangles128_s = fun.blurConversion(triangles128_s,5,0)
388. triangles128_n = fun.blurConversion(triangles128_n,5,0)
389.
390. trapezia16_s = fun.blurConversion(trapezia16_s,5,0)
391. trapezia16_n = fun.blurConversion(trapezia16_n,5,0)
392. trapezia32_s = fun.blurConversion(trapezia32_s,5,0)
393. trapezia64_s = fun.blurConversion(trapezia64_s,5,0)
394. trapezia64_n = fun.blurConversion(trapezia64_n,5,0)
395. trapezia128_s = fun.blurConversion(trapezia128_s,5,0)
396. trapezia128_n = fun.blurConversion(trapezia128_n,5,0)
397.
398. rhombuses16_s = fun.blurConversion(rhombuses16_s,5,0)
399. rhombuses16_n = fun.blurConversion(rhombuses16_n,5,0)
400. rhombuses32_s = fun.blurConversion(rhombuses32_s,5,0)
401. rhombuses32_n = fun.blurConversion(rhombuses32_n,5,0)
402. rhombuses64_s = fun.blurConversion(rhombuses64_s,5,0)
403. rhombuses64_n = fun.blurConversion(rhombuses64_n,5,0)
404. rhombuses128_s = fun.blurConversion(rhombuses128_s,5,0)
405. rhombuses128_n = fun.blurConversion(rhombuses128_n,5,0)
406.
407. rectangles16_s = fun.blurConversion(rectangles16_s,5,0)
408. rectangles16_n = fun.blurConversion(rectangles16_n,5,0)
409. rectangles32_s = fun.blurConversion(rectangles32_s,5,0)
410. rectangles32_n = fun.blurConversion(rectangles32_n,5,0)
411. rectangles64_s = fun.blurConversion(rectangles64_s,5,0)
412. rectangles64_n = fun.blurConversion(rectangles64_n,5,0)
413. rectangles128_s = fun.blurConversion(rectangles128_s,5,0)
414. rectangles128_n = fun.blurConversion(rectangles128_n,5,0)
415.
416. lines16_s = fun.blurConversion(lines16_s,5,0)
417. lines16_n = fun.blurConversion(lines16_n,5,0)
418. lines32_s = fun.blurConversion(lines32_s,5,0)
419. lines32_n = fun.blurConversion(lines32_n,5,0)
420. lines64_s = fun.blurConversion(lines64_s,5,0)
421. lines64_n = fun.blurConversion(lines64_n,5,0)
422. lines128_s = fun.blurConversion(lines128_s,5,0)
423. lines128_n = fun.blurConversion(lines128_n,5,0)
424.
425. hexagons16_s = fun.blurConversion(hexagons16_s,5,0)
426. hexagons16_n = fun.blurConversion(hexagons16_n,5,0)
427. hexagons32_s = fun.blurConversion(hexagons32_s,5,0)
428. hexagons32_n = fun.blurConversion(hexagons32_n,5,0)
429. hexagons64_s = fun.blurConversion(hexagons64_s,5,0)
430. hexagons64_n = fun.blurConversion(hexagons64_n,5,0)
431. hexagons128_s = fun.blurConversion(hexagons128_s,5,0)
432. hexagons128_n = fun.blurConversion(hexagons128_n,5,0)
433.
434. ellipsis16_s = fun.blurConversion(ellipsis16_s,5,0)
435. ellipsis16_n = fun.blurConversion(ellipsis16_n,5,0)
436. ellipsis32_s = fun.blurConversion(ellipsis32_s,5,0)
437. ellipsis32_n = fun.blurConversion(ellipsis32_n,5,0)
438. ellipsis64_s = fun.blurConversion(ellipsis64_s,5,0)
439. ellipsis64_n = fun.blurConversion(ellipsis64_n,5,0)
440. ellipsis128_s = fun.blurConversion(ellipsis128_s,5,0)
441. ellipsis128_n = fun.blurConversion(ellipsis128_n,5,0)
442.
443.
444. #convertendo para binária
445. squares16_s = fun.binaryConversion(squares16_s,255,31)
446. squares16_n = fun.binaryConversion(squares16_n,255,31)
447. squares32_s = fun.binaryConversion(squares32_s,255,31)
448. squares32_n = fun.binaryConversion(squares32_n,255,31)
449. squares64_s = fun.binaryConversion(squares64_s,255,31)
450. squares64_n = fun.binaryConversion(squares64_n,255,31)
451. squares128_s = fun.binaryConversion(squares128_s,255,31)
452. squares128_n = fun.binaryConversion(squares128_n,255,31)
453.
454. circles16_s = fun.binaryConversion(circles16_s, 255, 31)
455. circles16_n = fun.binaryConversion(circles16_n, 255, 31)
456. circles32_s = fun.binaryConversion(circles32_s,255,31)
457. circles32_n = fun.binaryConversion(circles32_n,255,31)
458. circles64_s = fun.binaryConversion(circles64_s,255,31)
459. circles64_n = fun.binaryConversion(circles64_n,255,31)
460. circles128_s = fun.binaryConversion(circles128_s,255,31)
461. circles128_n = fun.binaryConversion(circles128_n,255,31)
462.
463. triangles16_s = fun.binaryConversion(triangles16_s,255,31)
464. triangles16_n = fun.binaryConversion(triangles16_n,255,31)
465. triangles32_s = fun.binaryConversion(triangles32_s,255,31)
466. triangles32_n = fun.binaryConversion(triangles32_n,255,31)
467. triangles64_s = fun.binaryConversion(triangles64_s,255,31)
468. triangles64_n = fun.binaryConversion(triangles64_n,255,31)
469. triangles128_s = fun.binaryConversion(triangles128_s,255,31)
470. triangles128_n = fun.binaryConversion(triangles128_n,255,31)
471.
472. trapezia16_s = fun.binaryConversion(trapezia16_s,255,31)
473. trapezia16_n = fun.binaryConversion(trapezia16_n,255,31)
474. trapezia32_s = fun.binaryConversion(trapezia32_s,255,31)
475. trapezia32_n = fun.binaryConversion(trapezia32_n,255,31)
476. trapezia64_s = fun.binaryConversion(trapezia64_s,255,31)
477. trapezia64_n = fun.binaryConversion(trapezia64_n,255,31)
478. trapezia128_s = fun.binaryConversion(trapezia128_s,255,31)
479. trapezia128_n = fun.binaryConversion(trapezia128_n,255,31)
480.
481. rhombuses16_s = fun.binaryConversion(rhombuses16_s,255,31)
482. rhombuses16_n = fun.binaryConversion(rhombuses16_n,255,31)
483. rhombuses32_s = fun.binaryConversion(rhombuses32_s,255,31)
484. rhombuses32_n = fun.binaryConversion(rhombuses32_n,255,31)
485. rhombuses64_s = fun.binaryConversion(rhombuses64_s,255,31)
486. rhombuses64_n = fun.binaryConversion(rhombuses64_n,255,31)
487. rhombuses128_s = fun.binaryConversion(rhombuses128_s,255,31)
488. rhombuses128_n = fun.binaryConversion(rhombuses128_n,255,31)
489.
490. rectangles16_s = fun.binaryConversion(rectangles16_s,255,31)
491. rectangles16_n = fun.binaryConversion(rectangles16_n,255,31)
492. rectangles32_s = fun.binaryConversion(rectangles32_s,255,31)
493. rectangles32_n = fun.binaryConversion(rectangles32_n,255,31)
494. rectangles64_s = fun.binaryConversion(rectangles64_s,255,31)
495. rectangles64_n = fun.binaryConversion(rectangles64_n,255,31)
496. rectangles128_s = fun.binaryConversion(rectangles128_s,255,31)
497. rectangles128_n = fun.binaryConversion(rectangles128_n,255,31)
498.
499. lines16_s = fun.binaryConversion(lines16_s,255,31)
500. lines16_n = fun.binaryConversion(lines16_n,255,31)
501. lines32_s = fun.binaryConversion(lines32_s,255,31)
502. lines32_n = fun.binaryConversion(lines32_n,255,31)
503. lines64_s = fun.binaryConversion(lines64_s,255,31)
504. lines64_n = fun.binaryConversion(lines64_n,255,31)
505. lines128_s = fun.binaryConversion(lines128_s,255,31)
506. lines128_n = fun.binaryConversion(lines128_n,255,31)
507.
508. hexagons16_s = fun.binaryConversion(hexagons16_s,255,31)
509. hexagons16_n = fun.binaryConversion(hexagons16_n,255,31)
510. hexagons32_s = fun.binaryConversion(hexagons32_s,255,31)
511. hexagons32_n = fun.binaryConversion(hexagons32_n,255,31)
512. hexagons64_s = fun.binaryConversion(hexagons64_s,255,31)
513. hexagons64_n = fun.binaryConversion(hexagons64_n,255,31)
514. hexagons128_s = fun.binaryConversion(hexagons128_s,255,31)
515. hexagons128_n = fun.binaryConversion(hexagons128_n,255,31)
516.
517. ellipsis16_s = fun.binaryConversion(ellipsis16_s,255,31)
518. ellipsis16_n = fun.binaryConversion(ellipsis16_n,255,31)
519. ellipsis32_s = fun.binaryConversion(ellipsis32_s,255,31)
520. ellipsis32_n = fun.binaryConversion(ellipsis32_n,255,31)
521. ellipsis64_s = fun.binaryConversion(ellipsis64_s,255,31)
522. ellipsis64_n = fun.binaryConversion(ellipsis64_n,255,31)
523. ellipsis128_s = fun.binaryConversion(ellipsis128_s,255,31)
524. ellipsis128_n = fun.binaryConversion(ellipsis128_n,255,31)
525.
526. #invertendo as cores
527.
528. squares16_s = fun.invertConversion(squares16_s)
529. squares16_n = fun.invertConversion(squares16_n)
530. squares32_s = fun.invertConversion(squares32_s)
531. squares32_n = fun.invertConversion(squares32_n)
532. squares64_s = fun.invertConversion(squares64_s)
533. squares64_n = fun.invertConversion(squares64_n)
534. squares128_s = fun.invertConversion(squares128_s)
535. squares128_n = fun.invertConversion(squares128_n)
536.
537. circles16_s = fun.invertConversion(circles16_s)
538. circles16_n = fun.invertConversion(circles16_n)
539. circles32_s = fun.invertConversion(circles32_s)
540. circles32_n = fun.invertConversion(circles32_n)
541. circles64_s = fun.invertConversion(circles64_s)
542. circles64_n = fun.invertConversion(circles64_n)
543. circles128_s = fun.invertConversion(circles128_s)
544. circles128_n = fun.invertConversion(circles128_n)
545.
546. triangles16_s = fun.invertConversion(triangles16_s)
547. triangles16_n = fun.invertConversion(triangles16_n)
548. triangles32_s = fun.invertConversion(triangles32_s)
549. triangles32_n = fun.invertConversion(triangles32_n)
550. triangles64_s = fun.invertConversion(triangles64_s)
551. triangles64_n = fun.invertConversion(triangles64_n)
552. triangles128_s = fun.invertConversion(triangles128_s)
553. triangles128_n = fun.invertConversion(triangles128_n)
554.
555. trapezia16_s = fun.invertConversion(trapezia16_s)
556. trapezia16_n = fun.invertConversion(trapezia16_n)
557. trapezia32_s = fun.invertConversion(trapezia32_s)
558. trapezia32_n = fun.invertConversion(trapezia32_n)
559. trapezia64_s = fun.invertConversion(trapezia64_s)
560. trapezia64_n = fun.invertConversion(trapezia64_n)
561. trapezia128_s = fun.invertConversion(trapezia128_s)
562. trapezia128_n = fun.invertConversion(trapezia128_n)
563.
564. rhombuses16_s = fun.invertConversion(rhombuses16_s)
565. rhombuses16_n = fun.invertConversion(rhombuses16_n)
566. rhombuses32_s = fun.invertConversion(rhombuses32_s)
567. rhombuses32_n = fun.invertConversion(rhombuses32_n)
568. rhombuses64_s = fun.invertConversion(rhombuses64_s)
569. rhombuses64_n = fun.invertConversion(rhombuses64_n)
570. rhombuses128_s = fun.invertConversion(rhombuses128_s)
571. rhombuses128_n = fun.invertConversion(rhombuses128_n)
572.
573. rectangles16_s = fun.invertConversion(rectangles16_s)
574. rectangles16_n = fun.invertConversion(rectangles16_n)
575. rectangles32_s = fun.invertConversion(rectangles32_s)
576. rectangles32_n = fun.invertConversion(rectangles32_n)
577. rectangles64_s = fun.invertConversion(rectangles64_s)
578. rectangles64_n = fun.invertConversion(rectangles64_n)
579. rectangles128_s = fun.invertConversion(rectangles128_s)
580. rectangles128_n = fun.invertConversion(rectangles128_n)
581.
582. lines16_s = fun.invertConversion(lines16_s)
583. lines16_n = fun.invertConversion(lines16_n)
584. lines32_s = fun.invertConversion(lines32_s)
585. lines32_n = fun.invertConversion(lines32_n)
586. lines64_s = fun.invertConversion(lines64_s)
587. lines64_n = fun.invertConversion(lines64_n)
588. lines128_s = fun.invertConversion(lines128_s)
589. lines128_n = fun.invertConversion(lines128_n)
590.
591. hexagons16_s = fun.invertConversion(hexagons16_s)
592. hexagons16_n = fun.invertConversion(hexagons16_n)
593. hexagons32_s = fun.invertConversion(hexagons32_s)
594. hexagons32_n = fun.invertConversion(hexagons32_n)
595. hexagons64_s = fun.invertConversion(hexagons64_s)
596. hexagons64_n = fun.invertConversion(hexagons64_n)
597. hexagons128_s = fun.invertConversion(hexagons128_s)
598. hexagons128_n = fun.invertConversion(hexagons128_n)
599.
600. ellipsis16_s = fun.invertConversion(ellipsis16_s)
601. ellipsis16_n = fun.invertConversion(ellipsis16_n)
602. ellipsis32_s = fun.invertConversion(ellipsis32_s)
603. ellipsis32_n = fun.invertConversion(ellipsis32_n)
604. ellipsis64_s = fun.invertConversion(ellipsis64_s)
605. ellipsis64_n = fun.invertConversion(ellipsis64_n)
606. ellipsis128_s = fun.invertConversion(ellipsis128_s)
607. ellipsis128_n = fun.invertConversion(ellipsis128_n)
608. print('terminou pre processing')
609.
610. # extraindo caracteristicas das imagens
611.
612. squares128_vector_s = fun.extratorCaracteristicas(squares128_s)
613. squares128_vector_n = fun.extratorCaracteristicas(squares128_n)
614. circles128_vector_s = fun.extratorCaracteristicas(circles128_s)
615. circles128_vector_n = fun.extratorCaracteristicas(circles128_n)
616. triangles128_vector_s = fun.extratorCaracteristicas(triangles128_s)
617. triangles128_vector_n = fun.extratorCaracteristicas(triangles128_n)
618. trapezia128_vector_s = fun.extratorCaracteristicas(trapezia128_s)
619. trapezia128_vector_n = fun.extratorCaracteristicas(trapezia128_n)
620. rhombuses128_vector_s = fun.extratorCaracteristicas(rhombuses128_s)
621. rhombuses128_vector_n = fun.extratorCaracteristicas(rhombuses128_n)
622. rectangles128_vector_s = fun.extratorCaracteristicas(rectangles128_s)
623. rectangles128_vector_n = fun.extratorCaracteristicas(rectangles128_n)
624. lines128_vector_s = fun.extratorCaracteristicas(lines128_s)
625. lines128_vector_n = fun.extratorCaracteristicas(lines128_n)
626. hexagons128_vector_s = fun.extratorCaracteristicas(hexagons128_s)
627. hexagons128_vector_n = fun.extratorCaracteristicas(hexagons128_n)
628. ellipsis128_vector_s = fun.extratorCaracteristicas(ellipsis128_s)
629. ellipsis128_vector_n = fun.extratorCaracteristicas(ellipsis128_n)
630.
631. squares64_vector_s = fun.extratorCaracteristicas(squares64_s)
632. squares64_vector_n = fun.extratorCaracteristicas(squares64_n)
633. circles64_vector_s = fun.extratorCaracteristicas(circles64_s)
634. circles64_vector_n = fun.extratorCaracteristicas(circles64_n)
635. triangles64_vector_s = fun.extratorCaracteristicas(triangles64_s)
636. triangles64_vector_n = fun.extratorCaracteristicas(triangles64_n)
637. trapezia64_vector_s = fun.extratorCaracteristicas(trapezia64_s)
638. trapezia64_vector_n = fun.extratorCaracteristicas(trapezia64_n)
639. rhombuses64_vector_s = fun.extratorCaracteristicas(rhombuses64_s)
640. rhombuses64_vector_n = fun.extratorCaracteristicas(rhombuses64_n)
641. rectangles64_vector_s = fun.extratorCaracteristicas(rectangles64_s)
642. rectangles64_vector_n = fun.extratorCaracteristicas(rectangles64_n)
643. lines64_vector_s = fun.extratorCaracteristicas(lines64_s)
644. lines64_vector_n = fun.extratorCaracteristicas(lines64_n)
645. hexagons64_vector_s = fun.extratorCaracteristicas(hexagons64_s)
646. hexagons64_vector_n = fun.extratorCaracteristicas(hexagons64_n)
647. ellipsis64_vector_s = fun.extratorCaracteristicas(ellipsis64_s)
648. ellipsis64_vector_n = fun.extratorCaracteristicas(ellipsis64_n)
649.
650. squares32_vector_s = fun.extratorCaracteristicas(squares32_s)
651. squares32_vector_n = fun.extratorCaracteristicas(squares32_n)
652. circles32_vector_s = fun.extratorCaracteristicas(circles32_s)
653. circles32_vector_n = fun.extratorCaracteristicas(circles32_n)
654. triangles32_vector_s = fun.extratorCaracteristicas(triangles32_s)
655. triangles32_vector_n = fun.extratorCaracteristicas(triangles32_n)
656. trapezia32_vector_s = fun.extratorCaracteristicas(trapezia32_s)
657. trapezia32_vector_n = fun.extratorCaracteristicas(trapezia32_n)
658. rhombuses32_vector_s = fun.extratorCaracteristicas(rhombuses32_s)
659. rhombuses32_vector_n = fun.extratorCaracteristicas(rhombuses32_n)
660. rectangles32_vector_s = fun.extratorCaracteristicas(rectangles32_s)
661. rectangles32_vector_n = fun.extratorCaracteristicas(rectangles32_n)
662. lines32_vector_s = fun.extratorCaracteristicas(lines32_s)
663. lines32_vector_n = fun.extratorCaracteristicas(lines32_n)
664. hexagons32_vector_s = fun.extratorCaracteristicas(hexagons32_s)
665. hexagons32_vector_n = fun.extratorCaracteristicas(hexagons32_n)
666. ellipsis32_vector_s = fun.extratorCaracteristicas(ellipsis32_s)
667. ellipsis32_vector_n = fun.extratorCaracteristicas(ellipsis32_n)
668.
669.
670. squares16_vector_s = fun.extratorCaracteristicas(squares16_s)
671. squares16_vector_n = fun.extratorCaracteristicas(squares16_n)
672. circles16_vector_s = fun.extratorCaracteristicas(circles16_s)
673. circles16_vector_n = fun.extratorCaracteristicas(circles16_n)
674. triangles16_vector_s = fun.extratorCaracteristicas(triangles16_s)
675. triangles16_vector_n = fun.extratorCaracteristicas(triangles16_n)
676. trapezia16_vector_s = fun.extratorCaracteristicas(trapezia16_s)
677. trapezia16_vector_n = fun.extratorCaracteristicas(trapezia16_n)
678. rhombuses16_vector_s = fun.extratorCaracteristicas(rhombuses16_s)
679. rhombuses16_vector_n = fun.extratorCaracteristicas(rhombuses16_n)
680. rectangles16_vector_s = fun.extratorCaracteristicas(rectangles16_s)
681. rectangles16_vector_n = fun.extratorCaracteristicas(rectangles16_n)
682. lines16_vector_s = fun.extratorCaracteristicas(lines16_s)
683. lines16_vector_n = fun.extratorCaracteristicas(lines16_n)
684. hexagons16_vector_s = fun.extratorCaracteristicas(hexagons16_s)
685. hexagons16_vector_n = fun.extratorCaracteristicas(hexagons16_n)
686. ellipsis16_vector_s = fun.extratorCaracteristicas(ellipsis16_s)
687. ellipsis16_vector_n = fun.extratorCaracteristicas(ellipsis16_n)
688.
689.
690. print('terminou extracao carac')
691.
692. # transformando os vetores em dataframes
693.
694.
695. squares128_vector_s = pd.DataFrame(squares128_vector_s)
696. squares128_vector_n = pd.DataFrame(squares128_vector_n)
697. circles128_vector_s = pd.DataFrame(circles128_vector_s)
698. circles128_vector_n = pd.DataFrame(circles128_vector_n)
699. triangles128_vector_s = pd.DataFrame(triangles128_vector_s)
700. triangles128_vector_n = pd.DataFrame(triangles128_vector_n)
701. trapezia128_vector_s = pd.DataFrame(trapezia128_vector_s)
702. trapezia128_vector_n = pd.DataFrame(trapezia128_vector_n)
703. rhombuses128_vector_s = pd.DataFrame(rhombuses128_vector_s)
704. rhombuses128_vector_n = pd.DataFrame(rhombuses128_vector_n)
705. rectangles128_vector_s = pd.DataFrame(rectangles128_vector_s)
706. rectangles128_vector_n = pd.DataFrame(rectangles128_vector_n)
707. lines128_vector_s = pd.DataFrame(lines128_vector_s)
708. lines128_vector_n = pd.DataFrame(lines128_vector_n)
709. hexagons128_vector_s = pd.DataFrame(hexagons128_vector_s)
710. hexagons128_vector_n = pd.DataFrame(hexagons128_vector_n)
711. ellipsis128_vector_s = pd.DataFrame(ellipsis128_vector_s)
712. ellipsis128_vector_n = pd.DataFrame(ellipsis128_vector_n)
713.
714. squares32_vector_s = pd.DataFrame(squares32_vector_s)
715. squares32_vector_n = pd.DataFrame(squares32_vector_n)
716. circles32_vector_s = pd.DataFrame(circles32_vector_s)
717. circles32_vector_n = pd.DataFrame(circles32_vector_n)
718. triangles32_vector_s = pd.DataFrame(triangles32_vector_s)
719. triangles32_vector_n = pd.DataFrame(triangles32_vector_n)
720. trapezia32_vector_s = pd.DataFrame(trapezia32_vector_s)
721. trapezia32_vector_n = pd.DataFrame(trapezia32_vector_n)
722. rhombuses32_vector_s = pd.DataFrame(rhombuses32_vector_s)
723. rhombuses32_vector_n = pd.DataFrame(rhombuses32_vector_n)
724. rectangles32_vector_s = pd.DataFrame(rectangles32_vector_s)
725. rectangles32_vector_n = pd.DataFrame(rectangles32_vector_n)
726. hexagons32_vector_s = pd.DataFrame(hexagons32_vector_s)
727. hexagons32_vector_n = pd.DataFrame(hexagons32_vector_n)
728. ellipsis32_vector_s = pd.DataFrame(ellipsis32_vector_s)
729. ellipsis32_vector_n = pd.DataFrame(ellipsis32_vector_n)
730. lines32_vector_s = pd.DataFrame(lines32_vector_s)
731. lines32_vector_n = pd.DataFrame(lines32_vector_n)
732.
733. squares64_vector_s = pd.DataFrame(squares64_vector_s)
734. squares64_vector_n = pd.DataFrame(squares64_vector_n)
735. circles64_vector_s = pd.DataFrame(circles64_vector_s)
736. circles64_vector_n = pd.DataFrame(circles64_vector_n)
737. triangles64_vector_s = pd.DataFrame(triangles64_vector_s)
738. triangles64_vector_n = pd.DataFrame(triangles64_vector_n)
739. trapezia64_vector_s = pd.DataFrame(trapezia64_vector_s)
740. trapezia64_vector_n = pd.DataFrame(trapezia64_vector_n)
741. rhombuses64_vector_s = pd.DataFrame(rhombuses64_vector_s)
742. rhombuses64_vector_n = pd.DataFrame(rhombuses64_vector_n)
743. rectangles64_vector_s = pd.DataFrame(rectangles64_vector_s)
744. rectangles64_vector_n = pd.DataFrame(rectangles64_vector_n)
745. lines64_vector_s = pd.DataFrame(lines64_vector_s)
746. lines64_vector_n = pd.DataFrame(lines64_vector_n)
747. hexagons64_vector_s = pd.DataFrame(hexagons64_vector_s)
748. hexagons64_vector_n = pd.DataFrame(hexagons64_vector_n)
749. ellipsis64_vector_s = pd.DataFrame(ellipsis64_vector_s)
750. ellipsis64_vector_n = pd.DataFrame(ellipsis64_vector_n)
751.
752.
753. circles16_vector_s = pd.DataFrame(circles16_vector_s)
754. circles16_vector_n = pd.DataFrame(circles16_vector_n)
755. squares16_vector_s = pd.DataFrame(squares16_vector_s)
756. squares16_vector_n = pd.DataFrame(squares16_vector_n)
757. triangles16_vector_s = pd.DataFrame(triangles16_vector_s)
758. triangles16_vector_n = pd.DataFrame(triangles16_vector_n)
759. trapezia16_vector_s = pd.DataFrame(trapezia16_vector_s)
760. trapezia16_vector_n = pd.DataFrame(trapezia16_vector_n)
761. rhombuses16_vector_s = pd.DataFrame(rhombuses16_vector_s)
762. rhombuses16_vector_n = pd.DataFrame(rhombuses16_vector_n)
763. rectangles16_vector_s = pd.DataFrame(rectangles16_vector_s)
764. rectangles16_vector_n = pd.DataFrame(rectangles16_vector_n)
765. lines16_vector_s = pd.DataFrame(lines16_vector_s)
766. lines16_vector_n = pd.DataFrame(lines16_vector_n)
767. hexagons16_vector_s = pd.DataFrame(hexagons16_vector_s)
768. hexagons16_vector_n = pd.DataFrame(hexagons16_vector_n)
769. ellipsis16_vector_s = pd.DataFrame(ellipsis16_vector_s)
770. ellipsis16_vector_n = pd.DataFrame(ellipsis16_vector_n)
771.
772.
773. print('terminou transformar em dataframe')
774.
775. #incluindo a classe nos dataframes
776.
777. squares128_vector_s['Classe'] = 'square'
778. squares128_vector_n['Classe'] = 'square'
779. circles128_vector_s['Classe'] = 'circle'
780. circles128_vector_n['Classe'] = 'circle'
781. triangles128_vector_s['Classe'] = 'triangle'
782. triangles128_vector_n['Classe'] = 'triangle'
783. trapezia128_vector_s['Classe'] = 'trapezia'
784. trapezia128_vector_n['Classe'] = 'trapezia'
785. rhombuses128_vector_s['Classe'] = 'rhombuse'
786. rhombuses128_vector_n['Classe'] = 'rhombuse'
787. rectangles128_vector_s['Classe'] = 'rectangle'
788. rectangles128_vector_n['Classe'] = 'rectangle'
789. lines128_vector_s['Classe'] = 'line'
790. lines128_vector_n['Classe'] = 'line'
791. hexagons128_vector_s['Classe'] = 'hexagon'
792. hexagons128_vector_n['Classe'] = 'hexagon'
793. ellipsis128_vector_s['Classe'] = 'ellipse'
794. ellipsis128_vector_n['Classe'] = 'ellipse'
795.
796. squares32_vector_s['Classe'] = 'square'
797. squares32_vector_n['Classe'] = 'square'
798. circles32_vector_s['Classe'] = 'circle'
799. circles32_vector_n['Classe'] = 'circle'
800. triangles32_vector_s['Classe'] = 'triangle'
801. triangles32_vector_n['Classe'] = 'triangle'
802. trapezia32_vector_s['Classe'] = 'trapezia'
803. trapezia32_vector_n['Classe'] = 'trapezia'
804. rhombuses32_vector_s['Classe'] = 'rhombuse'
805. rhombuses32_vector_n['Classe'] = 'rhombuse'
806. rectangles32_vector_s['Classe'] = 'rectangle'
807. rectangles32_vector_n['Classe'] = 'rectangle'
808. lines32_vector_s['Classe'] = 'line'
809. lines32_vector_n['Classe'] = 'line'
810. hexagons32_vector_s['Classe'] = 'hexagon'
811. hexagons32_vector_n['Classe'] = 'hexagon'
812. ellipsis32_vector_s['Classe'] = 'ellipse'
813. ellipsis32_vector_n['Classe'] = 'ellipse'
814.
815. squares64_vector_s['Classe'] = 'square'
816. squares64_vector_n['Classe'] = 'square'
817. circles64_vector_s['Classe'] = 'circle'
818. circles64_vector_n['Classe'] = 'circle'
819. triangles64_vector_s['Classe'] = 'triangle'
820. triangles64_vector_n['Classe'] = 'triangle'
821. trapezia64_vector_s['Classe'] = 'trapezia'
822. trapezia64_vector_n['Classe'] = 'trapezia'
823. rhombuses64_vector_s['Classe'] = 'rhombuse'
824. rhombuses64_vector_n['Classe'] = 'rhombuse'
825. rectangles64_vector_s['Classe'] = 'rectangle'
826. rectangles64_vector_n['Classe'] = 'rectangle'
827. lines64_vector_s['Classe'] = 'line'
828. lines64_vector_n['Classe'] = 'line'
829. hexagons64_vector_s['Classe'] = 'hexagon'
830. hexagons64_vector_n['Classe'] = 'hexagon'
831. ellipsis64_vector_s['Classe'] = 'ellipse'
832. ellipsis64_vector_n['Classe'] = 'ellipse'
833.
834.
835. squares16_vector_s['Classe'] = 'square'
836. squares16_vector_n['Classe'] = 'square'
837. circles16_vector_s['Classe'] = 'circle'
838. circles16_vector_n['Classe'] = 'circle'
839. triangles16_vector_s['Classe'] = 'triangle'
840. triangles16_vector_n['Classe'] = 'triangle'
841. trapezia16_vector_s['Classe'] = 'trapezia'
842. trapezia16_vector_n['Classe'] = 'trapezia'
843. rhombuses16_vector_s['Classe'] = 'rhombuse'
844. rhombuses16_vector_n['Classe'] = 'rhombuse'
845. rectangles16_vector_s['Classe'] = 'rectangle'
846. rectangles16_vector_n['Classe'] = 'rectangle'
847. lines16_vector_s['Classe'] = 'line'
848. lines16_vector_n['Classe'] = 'line'
849. hexagons16_vector_s['Classe'] = 'hexagon'
850. hexagons16_vector_n['Classe'] = 'hexagon'
851. ellipsis16_vector_s['Classe'] = 'ellipse'
852. ellipsis16_vector_n['Classe'] = 'ellipse'
853.
854.
855.
856. dfs64_s = [squares64_vector_s,circles64_vector_s,triangles64_vector_s,trapezia64_vector_s,rhombuses64_vector_s,
857.          rectangles64_vector_s,lines64_vector_s,hexagons64_vector_s,ellipsis64_vector_s]
858.
859. dfs64_n = [squares64_vector_n,circles64_vector_n,triangles64_vector_n,trapezia64_vector_n,rhombuses64_vector_n,
860.          rectangles64_vector_n,lines64_vector_n,hexagons64_vector_n,ellipsis64_vector_n]
861.
862. dfs128_s = [squares128_vector_s,circles128_vector_s,triangles128_vector_s,trapezia128_vector_s,rhombuses128_vector_s,
863.           rectangles128_vector_s,lines128_vector_s,hexagons128_vector_s,ellipsis128_vector_s]
864.
865. dfs128_n = [squares128_vector_n,circles128_vector_n,triangles128_vector_n,trapezia128_vector_n,rhombuses128_vector_n,
866.           rectangles128_vector_n,lines128_vector_n,hexagons128_vector_n,ellipsis128_vector_n]
867.
868. dfs32_s = [squares32_vector_s,circles32_vector_s,triangles32_vector_s,trapezia32_vector_s,rhombuses32_vector_s,
869.          rectangles32_vector_s,lines32_vector_s,hexagons32_vector_s,ellipsis32_vector_s]
870.
871. dfs32_n = [squares32_vector_n,circles32_vector_n,triangles32_vector_n,trapezia32_vector_n,rhombuses32_vector_n,
872.          rectangles32_vector_n,lines32_vector_n,hexagons32_vector_n,ellipsis32_vector_n]
873.
874. dfs16_s = [squares16_vector_s,circles16_vector_s,triangles16_vector_s,trapezia16_vector_s,rhombuses16_vector_s,
875.        rectangles16_vector_s,lines16_vector_s,hexagons16_vector_s,ellipsis16_vector_s]
876. dfs16_n = [squares16_vector_n,circles16_vector_n,triangles16_vector_n,trapezia16_vector_n,rhombuses16_vector_n,
877.        rectangles16_vector_n,lines16_vector_n,hexagons16_vector_n,ellipsis16_vector_n]
878.
879.
880.
881. # USANDO AS IMAGENS 128x128
882.
883. dataFrame128_s = pd.concat(dfs128_s, ignore_index=True)
884. dataFrame128_2_s = dataFrame128_s.copy()
885. del dataFrame128_s['Classe']
886. dataFrame128_s.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Images_128_s_NOCLASS.csv')
887. dataFrame128_s = fun.normalizar(dataFrame128_s)
888. dataFrame128_s.fillna(0)
889. dataFrame128_s['Classe'] = dataFrame128_2_s['Classe']
890. dataFrame128_s.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Images_128_s.csv')
891.
892. dataFrame128_n = pd.concat(dfs128_n, ignore_index=True)
893. dataFrame128_n.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Images_128_n.csv')
894. dataFrame128_2_n = dataFrame128_n.copy()
895. del dataFrame128_n['Classe']
896. dataFrame128_n = fun.normalizar(dataFrame128_n)
897. dataFrame128_n.fillna(0)
898. dataFrame128_n['Classe'] = dataFrame128_2_n['Classe']
899.
900. dataFrame64_s = pd.concat(dfs64_s, ignore_index=True)
901. print(dataFrame64_s)
902. dataFrame64_s = dataFrame64_s.fillna(0)
903. print(dataFrame64_s)
904. dataFrame64_2_s = dataFrame64_s.copy()
905. del dataFrame64_s['Classe']
906. dataFrame64_s.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Images_64_sNOCLASS.csv')
907. dataFrame64_s = fun.normalizar(dataFrame64_s)
908. dataFrame64_s['Classe'] = dataFrame64_2_s['Classe']
909. dataFrame64_s.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Images_64_s.csv')
910.
911. dataFrame64_n = pd.concat(dfs64_n, ignore_index=True)
912. print(dataFrame64_n)
913. dataFrame64_n.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Images_64_n.csv')
914. dataFrame64_n = dataFrame64_n.fillna(0)
915. print(dataFrame64_n)
916. dataFrame64_2_n = dataFrame64_n.copy()
917. del dataFrame64_n['Classe']
918. dataFrame64_n = fun.normalizar(dataFrame64_n)
919. dataFrame64_n['Classe'] = dataFrame64_2_n['Classe']
920.
921. dataFrame32_s = pd.concat(dfs32_s, ignore_index=True)
922. dataFrame32_s = dataFrame32_s.fillna(0)
923. dataFrame32_2_s = dataFrame32_s.copy()
924. del dataFrame32_s['Classe']
925. dataFrame32_s.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Images_32_sNOCLASS.csv')
926. dataFrame32_s = fun.normalizar(dataFrame32_s)
927. dataFrame32_s['Classe'] = dataFrame32_2_s['Classe']
928. dataFrame32_s.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Images_32_s.csv')
929.
930. dataFrame32_n = pd.concat(dfs32_n, ignore_index=True)
931. dataFrame32_n.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Images_32_n.csv')
932. dataFrame32_n = dataFrame32_n.fillna(0)
933. dataFrame32_2_n = dataFrame32_n.copy()
934. del dataFrame32_n['Classe']
935. dataFrame32_n = fun.normalizar(dataFrame32_n)
936. dataFrame32_n['Classe'] = dataFrame32_2_n['Classe']
937.
938. dataFrame16_s = pd.concat(dfs16_s, ignore_index=True)
939. dataFrame16_s = dataFrame16_s.fillna(0)
940. dataFrame16_2_s = dataFrame16_s.copy()
941. del dataFrame16_s['Classe']
942. dataFrame16_s.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Images_16_sNOCLASS.csv')
943. dataFrame16_s = fun.normalizar(dataFrame16_s)
944. dataFrame16_s['Classe'] = dataFrame16_2_s['Classe']
945. dataFrame16_s.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Images_16_s.csv')
946.
947. dataFrame16_n = pd.concat(dfs16_n, ignore_index=True)
948. dataFrame16_n.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Images_16_n.csv')
949. dataFrame16_n = dataFrame16_n.fillna(0)
950. dataFrame16_2_n = dataFrame16_n.copy()
951. del dataFrame16_n['Classe']
952. dataFrame16_n = fun.normalizar(dataFrame16_n)
953. dataFrame16_n['Classe'] = dataFrame16_2_n['Classe']
954.
955. # Criando o objeto do tipo k-folds com 10 folds
956. # kfold = KFold(10, True, 1)
957.
958.
959. # Criando o k-fold com 5 folds para execução do algoritmo genético
960. kfold = KFold(5, True, 1)
961.
962. #Inicializando o Classificador do algoritmo genético
963.
964. # Random Forest Classifier
965. RandomForest = RandomForestClassifier()
966. RandomForest_acerto = []
967. # RandomForest_tempo = []
968. # RandomForest_precision = []
969. # RandomForest_recall = []
970. # RandomForest_fscore = []
971.
972. #criando a população com 20 cromossomos de tamanho 38
973. cromossomos = fun.create_population(20, 38)
974.
975. for cromo in cromossomos:
976.     positions = fun.positions_chromossome(cromo)
977.     imagens, df_classe = fun.decode_chromossome(cromo)
978.     imagens = np.array(imagens)
979.     caracteristicas = fun.carac_imagens(positions, imagens)
980.
981.     for x in range(0, 10):
982.
983.         tempo1 = time.time()
984.         cols = list(df_classe.columns)
985.         cols.remove('Classe')
986.         df_images_noclass = df_classe[cols]
987.         df_images_class = df_classe['Classe']
988.         c = kfold.split(df_classe)
989.
990.         for train_index, test_index in c:
991.
992.             noclass_train, noclass_test = df_images_noclass.iloc[train_index], df_images_noclass.iloc[test_index]
993.             class_train, class_test = df_images_class.iloc[train_index], df_images_class.iloc[test_index]
994.
995.             RandomForest_inicio = time.time()
996.             RandomForest.fit(noclass_train, class_train)
997.             RandomForest_acerto.append(RandomForest.score(noclass_test, class_test))
998.
999.
1000.         df_classe = df_classe.sample(frac=1)
1001.         print("Terminou a ", x)
1002.         tempo2 = time.time()
1003.         print("Tempo da rodada ", x, (tempo2 - tempo1) / 60)
1004.
1005.     tempofinal = time.time()
1006.
1007. print('acuracia media ',np.mean(RandomForest_acerto))
1008. print('acuracia' )
1009. print(RandomForest_acerto)
1010. # Instanciando os algoritmos e seus vetores de tempo e acurácia
1011. '''
1012. #instanciando DataFrame com dados de saida
1015.
1016.
1017. # Random Forest Classifier
1018. RandomForest = RandomForestClassifier()
1019. RandomForest_acerto = []
1020. RandomForest_tempo = []
1021. RandomForest_precision = []
1022. RandomForest_recall = []
1023. RandomForest_fscore = []
1024.
1025. # SVM com função de kernel linear
1026. SVMachine_L = SVC(kernel='linear')
1027. SVMachine_L_acerto = []
1028. SVMachine_L_tempo = []
1029. SVMachine_L_precision = []
1030. SVMachine_L_recall = []
1031. SVMachine_L_fscore = []
1032.
1033. #SVM com função de kernel RBF
1034. SVMachine_RBF = SVC(kernel='rbf', gamma='scale')
1035. SVMachine_RBF_acerto = []
1036. SVMachine_RBF_tempo = []
1037. SVMachine_RBF_precision = []
1038. SVMachine_RBF_recall = []
1039. SVMachine_RBF_fscore = []
1040.
1041. # KNN com k = 3, 5, 7
1042. KNN_3 = KNeighborsClassifier(n_neighbors=3, metric='euclidean')
1043. KNN_3_acerto = []
1044. KNN_3_tempo = []
1045. KNN_3_precision = []
1046. KNN_3_recall = []
1047. KNN_3_fscore = []
1048.
1049. KNN_5 = KNeighborsClassifier(n_neighbors=5, metric='euclidean')
1050. KNN_5_acerto = []
1051. KNN_5_tempo = []
1052. KNN_5_precision = []
1053. KNN_5_recall = []
1054. KNN_5_fscore = []
1055.
1056. KNN_7 =  KNeighborsClassifier(n_neighbors=7, metric='euclidean')
1057. KNN_7_acerto = []
1058. KNN_7_tempo = []
1059. KNN_7_precision = []
1060. KNN_7_recall = []
1061. KNN_7_fscore = []
1062.
1063. # KNN Ponderado com k = 3, 5, 7
1064. KNNP_3 = KNeighborsClassifier(n_neighbors=3, weights='distance',metric='euclidean')
1065. KNNP_3_acerto = []
1066. KNNP_3_tempo = []
1067. KNNP_3_precision = []
1068. KNNP_3_recall = []
1069. KNNP_3_fscore = []
1070.
1071. KNNP_5 = KNeighborsClassifier(n_neighbors=5, weights='distance', metric='euclidean')
1072. KNNP_5_acerto = []
1073. KNNP_5_tempo = []
1074. KNNP_5_precision = []
1075. KNNP_5_recall = []
1076. KNNP_5_fscore = []
1077.
1078. KNNP_7 = KNeighborsClassifier(n_neighbors=7, weights='distance', metric='euclidean')
1079. KNNP_7_acerto = []
1080. KNNP_7_tempo = []
1081. KNNP_7_precision = []
1082. KNNP_7_recall = []
1083. KNNP_7_fscore = []
1084.
1085. # Naïve Bayes
1086. NaiveBayes = GaussianNB()
1087. NaiveBayes_acerto = []
1088. NaiveBayes_tempo = []
1089. NaiveBayes_precision = []
1090. NaiveBayes_recall = []
1091. NaiveBayes_fscore = []
1092.
1093. # Árvore de decisão
1094. DecisionTree = DecisionTreeClassifier()
1095. DecisionTree_acerto = []
1096. DecisionTree_tempo = []
1097. DecisionTree_precision = []
1098. DecisionTree_recall = []
1099. DecisionTree_fscore = []
1100.
1101. # MultiLayer Perceptron
1102. MLP = MLPClassifier()
1103. MLP_acerto = []
1104. MLP_tempo = []
1105. MLPP_precision = []
1106. MLP_recall = []
1107. MLP_fscore = []
1108.
1109. # Regressão Logística
1110. RegrLogistica = LogisticRegression(solver='lbfgs')
1111. RegrLogistica_acerto = []
1112. RegrLogistica_tempo = []
1113. RegreLogistica_precision = []
1114. RegrLogistica_recall = []
1115. RegrLogistica_fscore = []
1116.
1117. # ____________________ USANDO IMAGENS 128x128
1118.
1119. print('comecou o K fold')
1120.
1121. tempoinicial = time.time()
1122.
1123. for x in range(0, 10):
1124.
1125.    tempo1 = time.time()
1126.    cols = list(dataFrame128.columns)
1127.    cols.remove('Classe')
1128.    df_images_noclass = dataFrame128[cols]
1129.    df_images_class = dataFrame128['Classe']
1130.    c = kfold.split(dataFrame128)
1131.
1132.    for train_index, test_index in c:
1133.
1134.        noclass_train, noclass_test = df_images_noclass.iloc[train_index], df_images_noclass.iloc[test_index]
1135.        class_train, class_test = df_images_class.iloc[train_index], df_images_class.iloc[test_index]
1136.
1137.        KNN3_inicio = time.time()
1138.        KNN_3.fit(noclass_train, class_train)
1139.        KNN_3_acerto.append(KNN_3.score(noclass_test, class_test))
1140.        KNN_3_recall. append(recall_score(class_test, KNN_3.predict(noclass_test),average='weighted'))
1141.        KNN_3_precision.append(precision_score(class_test,KNN_3.predict(noclass_test),average='weighted'))
1142.        KNN_3_fscore.append(f1_score(class_test,KNN_3.predict(noclass_test),average='weighted'))
1143.        KNN3_fim = time.time()
1144.        KNN_3_tempo.append(KNN3_fim - KNN3_inicio)
1145.
1146.        KNN5_inicio = time.time()
1147.        KNN_5.fit(noclass_train, class_train)
1148.        KNN_5_acerto.append(KNN_5.score(noclass_test, class_test))
1149.        KNN_5_recall.append(recall_score(class_test,KNN_5.predict(noclass_test),average='weighted'))
1150.        KNN_5_precision.append(precision_score(class_test,KNN_5.predict(noclass_test),average='weighted'))
1151.        KNN_5_fscore.append(f1_score(class_test,KNN_5.predict(noclass_test),average='weighted'))
1152.        KNN5_fim = time.time()
1153.        KNN_5_tempo.append(KNN5_fim - KNN5_inicio)
1154.
1155.
1156.
1157.        KNN7_inicio = time.time()
1158.        KNN_7.fit(noclass_train, class_train)
1159.        KNN_7_acerto.append(KNN_3.score(noclass_test, class_test))
1160.        KNN_7_precision.append(precision_score(class_test,KNN_7.predict(noclass_test),average='weighted'))
1161.        KNN_7_recall.append(recall_score(class_test,KNN_7.predict(noclass_test),average='weighted'))
1162.        KNN_7_fscore.append(f1_score(class_test,KNN_7.predict(noclass_test),average='weighted'))
1163.        KNN7_fim = time.time()
1164.        KNN_7_tempo.append(KNN3_fim - KNN3_inicio)
1165.
1166.        KNNP3_inicio = time.time()
1167.        KNNP_3.fit(noclass_train, class_train)
1168.        KNNP_3_acerto.append(KNNP_3.score(noclass_test, class_test))
1169.        KNNP_3_precision.append(precision_score(class_test,KNNP_3.predict(noclass_test),average='weighted'))
1170.        KNNP_3_recall.append(recall_score(class_test,KNNP_3.predict(noclass_test),average='weighted'))
1171.        KNNP_3_fscore.append(f1_score(class_test,KNNP_3.predict(noclass_test),average='weighted'))
1172.        KNNP3_fim = time.time()
1173.        KNNP_3_tempo.append(KNNP3_fim - KNNP3_inicio)
1174.
1175.        KNNP5_inicio = time.time()
1176.        KNNP_5.fit(noclass_train, class_train)
1177.        KNNP_5_acerto.append(KNNP_5.score(noclass_test, class_test))
1178.        KNNP_5_precision.append(precision_score(class_test,KNNP_5.predict(noclass_test),average='weighted'))
1179.        KNNP_5_recall.append(recall_score(class_test,KNNP_5.predict(noclass_test),average='weighted'))
1180.        KNNP_5_fscore.append(f1_score(class_test,KNNP_5.predict(noclass_test),average='weighted'))
1181.        KNNP5_fim = time.time()
1182.        KNNP_5_tempo.append(KNNP5_fim - KNNP5_inicio)
1183.
1184.        KNNP7_inicio = time.time()
1185.        KNNP_7.fit(noclass_train, class_train)
1186.        KNNP_7_acerto.append(KNNP_7.score(noclass_test, class_test))
1187.        KNNP_7_precision.append(precision_score(class_test,KNNP_7.predict(noclass_test),average='weighted'))
1188.        KNNP_7_recall.append(recall_score(class_test,KNNP_7.predict(noclass_test),average='weighted'))
1189.        KNNP_7_fscore.append(f1_score(class_test,KNNP_7.predict(noclass_test),average='weighted'))
1190.        KNNP7_fim = time.time()
1191.        KNNP_7_tempo.append(KNNP7_fim - KNNP7_inicio)
1192.
1193.        NaiveBayes_inicio = time.time()
1194.        NaiveBayes.fit(noclass_train, class_train)
1195.        NaiveBayes_acerto.append(NaiveBayes.score(noclass_test, class_test))
1196.        NaiveBayes_precision.append(precision_score(class_test,NaiveBayes.predict(noclass_test),average='weighted'))
1197.        NaiveBayes_recall.append(recall_score(class_test,NaiveBayes.predict(noclass_test),average='weighted'))
1198.        NaiveBayes_fscore.append(f1_score(class_test,NaiveBayes.predict(noclass_test),average='weighted'))
1199.        NaiveBayes_fim = time.time()
1200.        NaiveBayes_tempo.append(NaiveBayes_fim - NaiveBayes_inicio)
1201.
1202.        DecisionTree_inicio = time.time()
1203.        DecisionTree.fit(noclass_train, class_train)
1204.        DecisionTree_acerto.append(DecisionTree.score(noclass_test, class_test))
1205.        DecisionTree_precision.append(precision_score(class_test,DecisionTree.predict(noclass_test),average='weighted'))
1206.        DecisionTree_recall.append(recall_score(class_test,DecisionTree.predict(noclass_test),average='weighted'))
1207.        DecisionTree_fscore.append(f1_score(class_test,DecisionTree.predict(noclass_test),average='weighted'))
1208.        DecisionTree_fim = time.time()
1209.        DecisionTree_tempo.append(DecisionTree_fim - DecisionTree_inicio)
1210.
1211.        SVMachine_L_inicio = time.time()
1212.        SVMachine_L.fit(noclass_train, class_train)
1213.        SVMachine_L_acerto.append(SVMachine_L.score(noclass_test, class_test))
1214.        SVMachine_L_precision.append(precision_score(class_test,SVMachine_L.predict(noclass_test),average='weighted'))
1215.        SVMachine_L_recall.append(recall_score(class_test,SVMachine_L.predict(noclass_test),average='weighted'))
1216.        SVMachine_L_fscore.append(f1_score(class_test,SVMachine_L.predict(noclass_test),average='weighted'))
1217.        SVMachine_L_fim = time.time()
1218.        SVMachine_L_tempo.append(SVMachine_L_fim - SVMachine_L_inicio)
1219.
1220.        SVMachine_RBF_inicio = time.time()
1221.        SVMachine_RBF.fit(noclass_train, class_train)
1222.        SVMachine_RBF_acerto.append(SVMachine_RBF.score(noclass_test, class_test))
1223.        SVMachine_RBF_recall.append(recall_score(class_test,SVMachine_RBF.predict(noclass_test),average='weighted'))
1224.        SVMachine_RBF_precision.append(precision_score(class_test,SVMachine_RBF.predict(noclass_test),average='weighted'))
1225.        SVMachine_RBF_fscore.append(f1_score(class_test,SVMachine_RBF.predict(noclass_test),average='weighted'))
1226.        SVMachine_RBF_fim = time.time()
1227.        SVMachine_RBF_tempo.append(SVMachine_RBF_fim - SVMachine_RBF_inicio)
1228.
1229.        RegrLogistica_inicio = time.time()
1230.        RegrLogistica.fit(noclass_train, class_train)
1231.        RegrLogistica_acerto.append(RegrLogistica.score(noclass_test, class_test))
1232.        RegreLogistica_precision.append(precision_score(class_test, (RegrLogistica.predict(noclass_test)),average='weighted'))
1233.        RegrLogistica_recall.append(recall_score(class_test,RegrLogistica.predict(noclass_test),average='weighted'))
1234.        RegrLogistica_fscore.append(f1_score(class_test,RegrLogistica.predict(noclass_test),average='weighted'))
1235.        RegrLogistica_fim = time.time()
1236.        RegrLogistica_tempo.append(RegrLogistica_fim - RegrLogistica_inicio)
1237.
1238.        MLP_inicio = time.time()
1239.        MLP.fit(noclass_train, class_train)
1240.        MLP_acerto.append(MLP.score(noclass_test, class_test))
1241.        MLPP_precision.append(precision_score(class_test,MLP.predict(noclass_test),average='weighted'))
1242.        MLP_recall.append(recall_score(class_test,MLP.predict(noclass_test),average='weighted'))
1243.        MLP_fscore.append(f1_score(class_test,MLP.predict(noclass_test),average='weighted'))
1244.        MLP_fim = time.time()
1245.        MLP_tempo.append(MLP_fim - MLP_inicio)
1246.
1247.        RandomForest_inicio = time.time()
1248.        RandomForest.fit(noclass_train, class_train)
1249.        RandomForest_acerto.append(RandomForest.score(noclass_test, class_test))
1250.        RandomForest_recall.append(recall_score(class_test,RandomForest.predict(noclass_test),average='weighted'))
1251.        RandomForest_precision.append(precision_score(class_test,RandomForest.predict(noclass_test),average='weighted'))
1252.        RandomForest_fscore.append(f1_score(class_test,RandomForest.predict(noclass_test),average='weighted'))
1253.        RandomForest_fim = time.time()
1254.        RandomForest_tempo.append(RandomForest_fim - RandomForest_inicio)
1255.
1256.
1257.    dataFrame128 = dataFrame128.sample(frac=1)
1258.    print("Terminou a ", x)
1259.    tempo2 = time.time()
1260.    print("Tempo da rodada ", x, (tempo2 - tempo1) / 60)
1261.
1262. tempofinal = time.time()
1263.
1264. fun.tendencia_central('KNN k = 3', KNN_3_acerto, KNN_3_tempo)
1265. fun.tendencia_central('KNN k = 5', KNN_5_acerto, KNN_5_tempo)
1266. fun.tendencia_central('KNN k = 7', KNN_7_acerto, KNN_7_tempo)
1267. fun.tendencia_central('KNN Ponderado k = 3', KNNP_3_acerto, KNNP_3_tempo)
1268. fun.tendencia_central('KNN Ponderado k = 5', KNNP_5_acerto, KNNP_5_tempo)
1269. fun.tendencia_central('KNN Ponderado k = 7', KNNP_7_acerto, KNNP_7_tempo)
1270. fun.tendencia_central('Naïve Bayes', NaiveBayes_acerto, NaiveBayes_tempo)
1271. fun.tendencia_central('Árvore de decisão', DecisionTree_acerto, DecisionTree_tempo)
1272. fun.tendencia_central('MLP', MLP_acerto, MLP_tempo)
1273. fun.tendencia_central('Regressão Logística', RegrLogistica_acerto, RegrLogistica_tempo)
1274. fun.tendencia_central('SVM linear', SVMachine_L_acerto, SVMachine_L_tempo)
1275. fun.tendencia_central('SVM RBF', SVMachine_RBF_acerto, SVMachine_RBF_tempo)
1276. fun.tendencia_central('Random Forest', RandomForest_acerto,RandomForest_tempo)
1277.
1278. Acuracia128 = [KNN_3_acerto,KNN_5_acerto,KNN_7_acerto,KNNP_3_acerto,KNNP_5_acerto,KNNP_7_acerto,
1279.               NaiveBayes_acerto,DecisionTree_acerto,MLP_acerto,RegrLogistica_acerto,SVMachine_L_acerto,
1280.               SVMachine_RBF_acerto,RandomForest_acerto]
1281. Precision128 = [KNN_3_precision,KNN_5_precision,KNN_7_precision,KNNP_3_precision,KNNP_5_precision,KNNP_7_precision,
1282.         NaiveBayes_precision,DecisionTree_precision,MLPP_precision,RegreLogistica_precision,SVMachine_L_precision,
1283.         SVMachine_RBF_precision,RandomForest_precision]
1284. Recall128 = [KNN_3_recall,KNN_5_recall,KNN_7_recall,KNNP_3_recall,KNNP_5_recall,KNNP_7_recall,NaiveBayes_recall,
1285.          DecisionTree_recall,MLP_recall,RegrLogistica_recall,SVMachine_L_recall,SVMachine_RBF_recall,RandomForest_recall]
1286.
1287. Fscore128 = [KNN_3_fscore,KNN_5_fscore,KNN_7_fscore,KNNP_3_fscore,KNN_5_fscore,KNN_7_fscore,NaiveBayes_fscore,DecisionTree_fscore,
1288.          MLP_fscore,RegrLogistica_fscore,SVMachine_L_fscore, SVMachine_RBF_fscore,RandomForest_fscore]
1289.
1290. Acuracia128 = pd.DataFrame(Acuracia128)
1291. Precision128 = pd.DataFrame(Precision128)
1292. Recall128 = pd.DataFrame(Recall128)
1293. Fscore128 = pd.DataFrame(Fscore128)
1294.
1295. Acuracia128.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Acuracia128.csv')
1296. Precision128.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Precision128.csv')
1297. Recall128.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Recall128.csv')
1298. Fscore128.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Fscore128.csv')
1299.
1300. mediasacuracias = {
1301.
1302.
1303.    "KNN Ponderado k = 3": np.mean(KNNP_3_acerto),
1304.    "KNN Ponderado k = 5": np.mean(KNNP_5_acerto),
1305.    "KNN Ponderado k = 7": np.mean(KNNP_7_acerto),
1306.    "Naive Bayes": np.mean(NaiveBayes_acerto),
1307.    "KNN k = 3": np.mean(KNN_3_acerto),
1308.    "KNN k = 5": np.mean(KNN_5_acerto),
1309.    "KNN k = 7": np.mean(KNN_7_acerto),
1310.    "Decision Tree": np.mean(DecisionTree_acerto),
1311.    "SVM Linear": np.mean(SVMachine_L_acerto),
1312.    "SVM RBF": np.mean(SVMachine_RBF_acerto),
1313.    "Regressao Logistica": np.mean(RegrLogistica_acerto),
1314.    "MLP": np.mean(MLP_acerto),
1315.    "Random Forest": np.mean(RandomForest_acerto)
1316. }
1317.
1318.
1319.
1320. mediasacuracias = sorted(mediasacuracias.items(),
1321.                         key=lambda x: x[1])
1322. print(mediasacuracias)
1323. print("Tempo total: ", (tempofinal - tempoinicial) / 60)
1324.
1325.
1326. ## USANDO A BASE COM IMAGENS DE 64x64
1327.
1328.
1329.
1330. print('comecou o K fold')
1331.
1332. tempoinicial = time.time()
1333.
1334. for x in range(0, 10):
1335.
1336.    tempo1 = time.time()
1337.    cols = list(dataFrame64.columns)
1338.    cols.remove('Classe')
1339.    df_images_noclass = dataFrame64[cols]
1340.    df_images_class = dataFrame64['Classe']
1341.    c = kfold.split(dataFrame64)
1342.
1343.    for train_index, test_index in c:
1344.
1345.        noclass_train, noclass_test = df_images_noclass.iloc[train_index], df_images_noclass.iloc[test_index]
1346.        class_train, class_test = df_images_class.iloc[train_index], df_images_class.iloc[test_index]
1347.
1348.        KNN3_inicio = time.time()
1349.        KNN_3.fit(noclass_train, class_train)
1350.        KNN_3_acerto.append(KNN_3.score(noclass_test, class_test))
1351.        KNN_3_recall. append(recall_score(class_test, KNN_3.predict(noclass_test),average='weighted'))
1352.        KNN_3_precision.append(precision_score(class_test,KNN_3.predict(noclass_test),average='weighted'))
1353.        KNN_3_fscore.append(f1_score(class_test,KNN_3.predict(noclass_test),average='weighted'))
1354.        KNN3_fim = time.time()
1355.        KNN_3_tempo.append(KNN3_fim - KNN3_inicio)
1356.
1357.        KNN5_inicio = time.time()
1358.        KNN_5.fit(noclass_train, class_train)
1359.        KNN_5_acerto.append(KNN_5.score(noclass_test, class_test))
1360.        KNN_5_recall.append(recall_score(class_test,KNN_5.predict(noclass_test),average='weighted'))
1361.        KNN_5_precision.append(precision_score(class_test,KNN_5.predict(noclass_test),average='weighted'))
1362.        KNN_5_fscore.append(f1_score(class_test,KNN_5.predict(noclass_test),average='weighted'))
1363.        KNN5_fim = time.time()
1364.        KNN_5_tempo.append(KNN5_fim - KNN5_inicio)
1365.
1366.
1367.
1368.        KNN7_inicio = time.time()
1369.        KNN_7.fit(noclass_train, class_train)
1370.        KNN_7_acerto.append(KNN_3.score(noclass_test, class_test))
1371.        KNN_7_precision.append(precision_score(class_test,KNN_7.predict(noclass_test),average='weighted'))
1372.        KNN_7_recall.append(recall_score(class_test,KNN_7.predict(noclass_test),average='weighted'))
1373.        KNN_7_fscore.append(f1_score(class_test,KNN_7.predict(noclass_test),average='weighted'))
1374.        KNN7_fim = time.time()
1375.        KNN_7_tempo.append(KNN3_fim - KNN3_inicio)
1376.
1377.        KNNP3_inicio = time.time()
1378.        KNNP_3.fit(noclass_train, class_train)
1379.        KNNP_3_acerto.append(KNNP_3.score(noclass_test, class_test))
1380.        KNNP_3_precision.append(precision_score(class_test,KNNP_3.predict(noclass_test),average='weighted'))
1381.        KNNP_3_recall.append(recall_score(class_test,KNNP_3.predict(noclass_test),average='weighted'))
1382.        KNNP_3_fscore.append(f1_score(class_test,KNNP_3.predict(noclass_test),average='weighted'))
1383.        KNNP3_fim = time.time()
1384.        KNNP_3_tempo.append(KNNP3_fim - KNNP3_inicio)
1385.
1386.        KNNP5_inicio = time.time()
1387.        KNNP_5.fit(noclass_train, class_train)
1388.        KNNP_5_acerto.append(KNNP_5.score(noclass_test, class_test))
1389.        KNNP_5_precision.append(precision_score(class_test,KNNP_5.predict(noclass_test),average='weighted'))
1390.        KNNP_5_recall.append(recall_score(class_test,KNNP_5.predict(noclass_test),average='weighted'))
1391.        KNNP_5_fscore.append(f1_score(class_test,KNNP_5.predict(noclass_test),average='weighted'))
1392.        KNNP5_fim = time.time()
1393.        KNNP_5_tempo.append(KNNP5_fim - KNNP5_inicio)
1394.
1395.        KNNP7_inicio = time.time()
1396.        KNNP_7.fit(noclass_train, class_train)
1397.        KNNP_7_acerto.append(KNNP_7.score(noclass_test, class_test))
1398.        KNNP_7_precision.append(precision_score(class_test,KNNP_7.predict(noclass_test),average='weighted'))
1399.        KNNP_7_recall.append(recall_score(class_test,KNNP_7.predict(noclass_test),average='weighted'))
1400.        KNNP_7_fscore.append(f1_score(class_test,KNNP_7.predict(noclass_test),average='weighted'))
1401.        KNNP7_fim = time.time()
1402.        KNNP_7_tempo.append(KNNP7_fim - KNNP7_inicio)
1403.
1404.        NaiveBayes_inicio = time.time()
1405.        NaiveBayes.fit(noclass_train, class_train)
1406.        NaiveBayes_acerto.append(NaiveBayes.score(noclass_test, class_test))
1407.        NaiveBayes_precision.append(precision_score(class_test,NaiveBayes.predict(noclass_test),average='weighted'))
1408.        NaiveBayes_recall.append(recall_score(class_test,NaiveBayes.predict(noclass_test),average='weighted'))
1409.        NaiveBayes_fscore.append(f1_score(class_test,NaiveBayes.predict(noclass_test),average='weighted'))
1410.        NaiveBayes_fim = time.time()
1411.        NaiveBayes_tempo.append(NaiveBayes_fim - NaiveBayes_inicio)
1412.
1413.        DecisionTree_inicio = time.time()
1414.        DecisionTree.fit(noclass_train, class_train)
1415.        DecisionTree_acerto.append(DecisionTree.score(noclass_test, class_test))
1416.        DecisionTree_precision.append(precision_score(class_test,DecisionTree.predict(noclass_test),average='weighted'))
1417.        DecisionTree_recall.append(recall_score(class_test,DecisionTree.predict(noclass_test),average='weighted'))
1418.        DecisionTree_fscore.append(f1_score(class_test,DecisionTree.predict(noclass_test),average='weighted'))
1419.        DecisionTree_fim = time.time()
1420.        DecisionTree_tempo.append(DecisionTree_fim - DecisionTree_inicio)
1421.
1422.        SVMachine_L_inicio = time.time()
1423.        SVMachine_L.fit(noclass_train, class_train)
1424.        SVMachine_L_acerto.append(SVMachine_L.score(noclass_test, class_test))
1425.        SVMachine_L_precision.append(precision_score(class_test,SVMachine_L.predict(noclass_test),average='weighted'))
1426.        SVMachine_L_recall.append(recall_score(class_test,SVMachine_L.predict(noclass_test),average='weighted'))
1427.        SVMachine_L_fscore.append(f1_score(class_test,SVMachine_L.predict(noclass_test),average='weighted'))
1428.        SVMachine_L_fim = time.time()
1429.        SVMachine_L_tempo.append(SVMachine_L_fim - SVMachine_L_inicio)
1430.
1431.        SVMachine_RBF_inicio = time.time()
1432.        SVMachine_RBF.fit(noclass_train, class_train)
1433.        SVMachine_RBF_acerto.append(SVMachine_RBF.score(noclass_test, class_test))
1434.        SVMachine_RBF_recall.append(recall_score(class_test,SVMachine_RBF.predict(noclass_test),average='weighted'))
1435.        SVMachine_RBF_precision.append(precision_score(class_test,SVMachine_RBF.predict(noclass_test),average='weighted'))
1436.        SVMachine_RBF_fscore.append(f1_score(class_test,SVMachine_RBF.predict(noclass_test),average='weighted'))
1437.        SVMachine_RBF_fim = time.time()
1438.        SVMachine_RBF_tempo.append(SVMachine_RBF_fim - SVMachine_RBF_inicio)
1439.
1440.        RegrLogistica_inicio = time.time()
1441.        RegrLogistica.fit(noclass_train, class_train)
1442.        RegrLogistica_acerto.append(RegrLogistica.score(noclass_test, class_test))
1443.        RegreLogistica_precision.append(precision_score(class_test, (RegrLogistica.predict(noclass_test)),average='weighted'))
1444.        RegrLogistica_recall.append(recall_score(class_test,RegrLogistica.predict(noclass_test),average='weighted'))
1445.        RegrLogistica_fscore.append(f1_score(class_test,RegrLogistica.predict(noclass_test),average='weighted'))
1446.        RegrLogistica_fim = time.time()
1447.        RegrLogistica_tempo.append(RegrLogistica_fim - RegrLogistica_inicio)
1448.
1449.        MLP_inicio = time.time()
1450.        MLP.fit(noclass_train, class_train)
1451.        MLP_acerto.append(MLP.score(noclass_test, class_test))
1452.        MLPP_precision.append(precision_score(class_test,MLP.predict(noclass_test),average='weighted'))
1453.        MLP_recall.append(recall_score(class_test,MLP.predict(noclass_test),average='weighted'))
1454.        MLP_fscore.append(f1_score(class_test,MLP.predict(noclass_test),average='weighted'))
1455.        MLP_fim = time.time()
1456.        MLP_tempo.append(MLP_fim - MLP_inicio)
1457.
1458.        RandomForest_inicio = time.time()
1459.        RandomForest.fit(noclass_train, class_train)
1460.        RandomForest_acerto.append(RandomForest.score(noclass_test, class_test))
1461.        RandomForest_recall.append(recall_score(class_test,RandomForest.predict(noclass_test),average='weighted'))
1462.        RandomForest_precision.append(precision_score(class_test,RandomForest.predict(noclass_test),average='weighted'))
1463.        RandomForest_fscore.append(f1_score(class_test,RandomForest.predict(noclass_test),average='weighted'))
1464.        RandomForest_fim = time.time()
1465.        RandomForest_tempo.append(RandomForest_fim - RandomForest_inicio)
1466.
1467.
1468.    dataFrame64 = dataFrame64.sample(frac=1)
1469.    print("Terminou a ", x)
1470.    tempo2 = time.time()
1471.    print("Tempo da rodada ", x, (tempo2 - tempo1) / 60)
1472.
1473. tempofinal = time.time()
1474.
1475. fun.tendencia_central('KNN k = 3', KNN_3_acerto, KNN_3_tempo)
1476. fun.tendencia_central('KNN k = 5', KNN_5_acerto, KNN_5_tempo)
1477. fun.tendencia_central('KNN k = 7', KNN_7_acerto, KNN_7_tempo)
1478. fun.tendencia_central('KNN Ponderado k = 3', KNNP_3_acerto, KNNP_3_tempo)
1479. fun.tendencia_central('KNN Ponderado k = 5', KNNP_5_acerto, KNNP_5_tempo)
1480. fun.tendencia_central('KNN Ponderado k = 7', KNNP_7_acerto, KNNP_7_tempo)
1481. fun.tendencia_central('Naïve Bayes', NaiveBayes_acerto, NaiveBayes_tempo)
1482. fun.tendencia_central('Árvore de decisão', DecisionTree_acerto, DecisionTree_tempo)
1483. fun.tendencia_central('MLP', MLP_acerto, MLP_tempo)
1484. fun.tendencia_central('Regressão Logística', RegrLogistica_acerto, RegrLogistica_tempo)
1485. fun.tendencia_central('SVM linear', SVMachine_L_acerto, SVMachine_L_tempo)
1486. fun.tendencia_central('SVM RBF', SVMachine_RBF_acerto, SVMachine_RBF_tempo)
1487. fun.tendencia_central('Random Forest', RandomForest_acerto,RandomForest_tempo)
1488.
1489. mediasacuracias = {
1490.
1491.
1492.    "KNN Ponderado k = 3": np.mean(KNNP_3_acerto),
1493.    "KNN Ponderado k = 5": np.mean(KNNP_5_acerto),
1494.    "KNN Ponderado k = 7": np.mean(KNNP_7_acerto),
1495.    "Naive Bayes": np.mean(NaiveBayes_acerto),
1496.    "KNN k = 3": np.mean(KNN_3_acerto),
1497.    "KNN k = 5": np.mean(KNN_5_acerto),
1498.    "KNN k = 7": np.mean(KNN_7_acerto),
1499.    "Decision Tree": np.mean(DecisionTree_acerto),
1500.    "SVM Linear": np.mean(SVMachine_L_acerto),
1501.    "SVM RBF": np.mean(SVMachine_RBF_acerto),
1502.    "Regressao Logistica": np.mean(RegrLogistica_acerto),
1503.    "MLP": np.mean(MLP_acerto),
1504.    "Random Forest": np.mean(RandomForest_acerto)
1505. }
1506.
1507. mediasacuracias = sorted(mediasacuracias.items(),
1508.                         key=lambda x: x[1])
1509. print(mediasacuracias)
1510. print("Tempo total: ", (tempofinal - tempoinicial) / 60)
1511. Acuracia64 = [KNN_3_acerto,KNN_5_acerto,KNN_7_acerto,KNNP_3_acerto,KNNP_5_acerto,KNNP_7_acerto,
1512.               NaiveBayes_acerto,DecisionTree_acerto,MLP_acerto,RegrLogistica_acerto,SVMachine_L_acerto,
1513.               SVMachine_RBF_acerto,RandomForest_acerto]
1514. Precision64 = [KNN_3_precision,KNN_5_precision,KNN_7_precision,KNNP_3_precision,KNNP_5_precision,KNNP_7_precision,
1515.         NaiveBayes_precision,DecisionTree_precision,MLPP_precision,RegreLogistica_precision,SVMachine_L_precision,
1516.         SVMachine_RBF_precision,RandomForest_precision]
1517. Recall64 = [KNN_3_recall,KNN_5_recall,KNN_7_recall,KNNP_3_recall,KNNP_5_recall,KNNP_7_recall,NaiveBayes_recall,
1518.          DecisionTree_recall,MLP_recall,RegrLogistica_recall,SVMachine_L_recall,SVMachine_RBF_recall,RandomForest_recall]
1519.
1520. Fscore64 = [KNN_3_fscore,KNN_5_fscore,KNN_7_fscore,KNNP_3_fscore,KNN_5_fscore,KNN_7_fscore,NaiveBayes_fscore,DecisionTree_fscore,
1521.          MLP_fscore,RegrLogistica_fscore,SVMachine_L_fscore, SVMachine_RBF_fscore,RandomForest_fscore]
1522.
1523. Acuracia64 = pd.DataFrame(Acuracia64)
1524. Precision64 = pd.DataFrame(Precision64)
1525. Recall64 = pd.DataFrame(Recall64)
1526. Fscore64 = pd.DataFrame(Fscore64)
1527.
1528. Acuracia64.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Acuracia64.csv')
1529. Precision64.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Precision64.csv')
1530. Recall64.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Recall64.csv')
1531. Fscore64.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Fscore64.csv')
1532.
1533.
1534.
1535.
1536. # USANDO BASE DE 32x32
1537.
1538.
1539.
1540. print('comecou o K fold')
1541.
1542. tempoinicial = time.time()
1543.
1544. for x in range(0, 10):
1545.
1546.    tempo1 = time.time()
1547.    cols = list(dataFrame32.columns)
1548.    cols.remove('Classe')
1549.    df_images_noclass = dataFrame32[cols]
1550.    df_images_class = dataFrame32['Classe']
1551.    c = kfold.split(dataFrame32)
1552.
1553.    for train_index, test_index in c:
1554.
1555.        noclass_train, noclass_test = df_images_noclass.iloc[train_index], df_images_noclass.iloc[test_index]
1556.        class_train, class_test = df_images_class.iloc[train_index], df_images_class.iloc[test_index]
1557.
1558.        KNN3_inicio = time.time()
1559.        KNN_3.fit(noclass_train, class_train)
1560.        KNN_3_acerto.append(KNN_3.score(noclass_test, class_test))
1561.        KNN_3_recall. append(recall_score(class_test, KNN_3.predict(noclass_test),average='weighted'))
1562.        KNN_3_precision.append(precision_score(class_test,KNN_3.predict(noclass_test),average='weighted'))
1563.        KNN_3_fscore.append(f1_score(class_test,KNN_3.predict(noclass_test),average='weighted'))
1564.        KNN3_fim = time.time()
1565.        KNN_3_tempo.append(KNN3_fim - KNN3_inicio)
1566.
1567.        KNN5_inicio = time.time()
1568.        KNN_5.fit(noclass_train, class_train)
1569.        KNN_5_acerto.append(KNN_5.score(noclass_test, class_test))
1570.        KNN_5_recall.append(recall_score(class_test,KNN_5.predict(noclass_test),average='weighted'))
1571.        KNN_5_precision.append(precision_score(class_test,KNN_5.predict(noclass_test),average='weighted'))
1572.        KNN_5_fscore.append(f1_score(class_test,KNN_5.predict(noclass_test),average='weighted'))
1573.        KNN5_fim = time.time()
1574.        KNN_5_tempo.append(KNN5_fim - KNN5_inicio)
1575.
1576.
1577.
1578.        KNN7_inicio = time.time()
1579.        KNN_7.fit(noclass_train, class_train)
1580.        KNN_7_acerto.append(KNN_3.score(noclass_test, class_test))
1581.        KNN_7_precision.append(precision_score(class_test,KNN_7.predict(noclass_test),average='weighted'))
1582.        KNN_7_recall.append(recall_score(class_test,KNN_7.predict(noclass_test),average='weighted'))
1583.        KNN_7_fscore.append(f1_score(class_test,KNN_7.predict(noclass_test),average='weighted'))
1584.        KNN7_fim = time.time()
1585.        KNN_7_tempo.append(KNN3_fim - KNN3_inicio)
1586.
1587.        KNNP3_inicio = time.time()
1588.        KNNP_3.fit(noclass_train, class_train)
1589.        KNNP_3_acerto.append(KNNP_3.score(noclass_test, class_test))
1590.        KNNP_3_precision.append(precision_score(class_test,KNNP_3.predict(noclass_test),average='weighted'))
1591.        KNNP_3_recall.append(recall_score(class_test,KNNP_3.predict(noclass_test),average='weighted'))
1592.        KNNP_3_fscore.append(f1_score(class_test,KNNP_3.predict(noclass_test),average='weighted'))
1593.        KNNP3_fim = time.time()
1594.        KNNP_3_tempo.append(KNNP3_fim - KNNP3_inicio)
1595.
1596.        KNNP5_inicio = time.time()
1597.        KNNP_5.fit(noclass_train, class_train)
1598.        KNNP_5_acerto.append(KNNP_5.score(noclass_test, class_test))
1599.        KNNP_5_precision.append(precision_score(class_test,KNNP_5.predict(noclass_test),average='weighted'))
1600.        KNNP_5_recall.append(recall_score(class_test,KNNP_5.predict(noclass_test),average='weighted'))
1601.        KNNP_5_fscore.append(f1_score(class_test,KNNP_5.predict(noclass_test),average='weighted'))
1602.        KNNP5_fim = time.time()
1603.        KNNP_5_tempo.append(KNNP5_fim - KNNP5_inicio)
1604.
1605.        KNNP7_inicio = time.time()
1606.        KNNP_7.fit(noclass_train, class_train)
1607.        KNNP_7_acerto.append(KNNP_7.score(noclass_test, class_test))
1608.        KNNP_7_precision.append(precision_score(class_test,KNNP_7.predict(noclass_test),average='weighted'))
1609.        KNNP_7_recall.append(recall_score(class_test,KNNP_7.predict(noclass_test),average='weighted'))
1610.        KNNP_7_fscore.append(f1_score(class_test,KNNP_7.predict(noclass_test),average='weighted'))
1611.        KNNP7_fim = time.time()
1612.        KNNP_7_tempo.append(KNNP7_fim - KNNP7_inicio)
1613.
1614.        NaiveBayes_inicio = time.time()
1615.        NaiveBayes.fit(noclass_train, class_train)
1616.        NaiveBayes_acerto.append(NaiveBayes.score(noclass_test, class_test))
1617.        NaiveBayes_precision.append(precision_score(class_test,NaiveBayes.predict(noclass_test),average='weighted'))
1618.        NaiveBayes_recall.append(recall_score(class_test,NaiveBayes.predict(noclass_test),average='weighted'))
1619.        NaiveBayes_fscore.append(f1_score(class_test,NaiveBayes.predict(noclass_test),average='weighted'))
1620.        NaiveBayes_fim = time.time()
1621.        NaiveBayes_tempo.append(NaiveBayes_fim - NaiveBayes_inicio)
1622.
1623.        DecisionTree_inicio = time.time()
1624.        DecisionTree.fit(noclass_train, class_train)
1625.        DecisionTree_acerto.append(DecisionTree.score(noclass_test, class_test))
1626.        DecisionTree_precision.append(precision_score(class_test,DecisionTree.predict(noclass_test),average='weighted'))
1627.        DecisionTree_recall.append(recall_score(class_test,DecisionTree.predict(noclass_test),average='weighted'))
1628.        DecisionTree_fscore.append(f1_score(class_test,DecisionTree.predict(noclass_test),average='weighted'))
1629.        DecisionTree_fim = time.time()
1630.        DecisionTree_tempo.append(DecisionTree_fim - DecisionTree_inicio)
1631.
1632.        SVMachine_L_inicio = time.time()
1633.        SVMachine_L.fit(noclass_train, class_train)
1634.        SVMachine_L_acerto.append(SVMachine_L.score(noclass_test, class_test))
1635.        SVMachine_L_precision.append(precision_score(class_test,SVMachine_L.predict(noclass_test),average='weighted'))
1636.        SVMachine_L_recall.append(recall_score(class_test,SVMachine_L.predict(noclass_test),average='weighted'))
1637.        SVMachine_L_fscore.append(f1_score(class_test,SVMachine_L.predict(noclass_test),average='weighted'))
1638.        SVMachine_L_fim = time.time()
1639.        SVMachine_L_tempo.append(SVMachine_L_fim - SVMachine_L_inicio)
1640.
1641.        SVMachine_RBF_inicio = time.time()
1642.        SVMachine_RBF.fit(noclass_train, class_train)
1643.        SVMachine_RBF_acerto.append(SVMachine_RBF.score(noclass_test, class_test))
1644.        SVMachine_RBF_recall.append(recall_score(class_test,SVMachine_RBF.predict(noclass_test),average='weighted'))
1645.        SVMachine_RBF_precision.append(precision_score(class_test,SVMachine_RBF.predict(noclass_test),average='weighted'))
1646.        SVMachine_RBF_fscore.append(f1_score(class_test,SVMachine_RBF.predict(noclass_test),average='weighted'))
1647.        SVMachine_RBF_fim = time.time()
1648.        SVMachine_RBF_tempo.append(SVMachine_RBF_fim - SVMachine_RBF_inicio)
1649.
1650.        RegrLogistica_inicio = time.time()
1651.        RegrLogistica.fit(noclass_train, class_train)
1652.        RegrLogistica_acerto.append(RegrLogistica.score(noclass_test, class_test))
1653.        RegreLogistica_precision.append(precision_score(class_test, (RegrLogistica.predict(noclass_test)),average='weighted'))
1654.        RegrLogistica_recall.append(recall_score(class_test,RegrLogistica.predict(noclass_test),average='weighted'))
1655.        RegrLogistica_fscore.append(f1_score(class_test,RegrLogistica.predict(noclass_test),average='weighted'))
1656.        RegrLogistica_fim = time.time()
1657.        RegrLogistica_tempo.append(RegrLogistica_fim - RegrLogistica_inicio)
1658.
1659.        MLP_inicio = time.time()
1660.        MLP.fit(noclass_train, class_train)
1661.        MLP_acerto.append(MLP.score(noclass_test, class_test))
1662.        MLPP_precision.append(precision_score(class_test,MLP.predict(noclass_test),average='weighted'))
1663.        MLP_recall.append(recall_score(class_test,MLP.predict(noclass_test),average='weighted'))
1664.        MLP_fscore.append(f1_score(class_test,MLP.predict(noclass_test),average='weighted'))
1665.        MLP_fim = time.time()
1666.        MLP_tempo.append(MLP_fim - MLP_inicio)
1667.
1668.        RandomForest_inicio = time.time()
1669.        RandomForest.fit(noclass_train, class_train)
1670.        RandomForest_acerto.append(RandomForest.score(noclass_test, class_test))
1671.        RandomForest_recall.append(recall_score(class_test,RandomForest.predict(noclass_test),average='weighted'))
1672.        RandomForest_precision.append(precision_score(class_test,RandomForest.predict(noclass_test),average='weighted'))
1673.        RandomForest_fscore.append(f1_score(class_test,RandomForest.predict(noclass_test),average='weighted'))
1674.        RandomForest_fim = time.time()
1675.        RandomForest_tempo.append(RandomForest_fim - RandomForest_inicio)
1676.
1677.
1678.    dataFrame32 = dataFrame32.sample(frac=1)
1679.    print("Terminou a ", x)
1680.    tempo2 = time.time()
1681.    print("Tempo da rodada ", x, (tempo2 - tempo1) / 60)
1682.
1683. tempofinal = time.time()
1684.
1685. fun.tendencia_central('KNN k = 3', KNN_3_acerto, KNN_3_tempo)
1686. fun.tendencia_central('KNN k = 5', KNN_5_acerto, KNN_5_tempo)
1687. fun.tendencia_central('KNN k = 7', KNN_7_acerto, KNN_7_tempo)
1688. fun.tendencia_central('KNN Ponderado k = 3', KNNP_3_acerto, KNNP_3_tempo)
1689. fun.tendencia_central('KNN Ponderado k = 5', KNNP_5_acerto, KNNP_5_tempo)
1690. fun.tendencia_central('KNN Ponderado k = 7', KNNP_7_acerto, KNNP_7_tempo)
1691. fun.tendencia_central('Naïve Bayes', NaiveBayes_acerto, NaiveBayes_tempo)
1692. fun.tendencia_central('Árvore de decisão', DecisionTree_acerto, DecisionTree_tempo)
1693. fun.tendencia_central('MLP', MLP_acerto, MLP_tempo)
1694. fun.tendencia_central('Regressão Logística', RegrLogistica_acerto, RegrLogistica_tempo)
1695. fun.tendencia_central('SVM linear', SVMachine_L_acerto, SVMachine_L_tempo)
1696. fun.tendencia_central('SVM RBF', SVMachine_RBF_acerto, SVMachine_RBF_tempo)
1697. fun.tendencia_central('Random Forest', RandomForest_acerto,RandomForest_tempo)
1698.
1699. mediasacuracias = {
1700.
1701.
1702.    "KNN Ponderado k = 3": np.mean(KNNP_3_acerto),
1703.    "KNN Ponderado k = 5": np.mean(KNNP_5_acerto),
1704.    "KNN Ponderado k = 7": np.mean(KNNP_7_acerto),
1705.    "Naive Bayes": np.mean(NaiveBayes_acerto),
1706.    "KNN k = 3": np.mean(KNN_3_acerto),
1707.    "KNN k = 5": np.mean(KNN_5_acerto),
1708.    "KNN k = 7": np.mean(KNN_7_acerto),
1709.    "Decision Tree": np.mean(DecisionTree_acerto),
1710.    "SVM Linear": np.mean(SVMachine_L_acerto),
1711.    "SVM RBF": np.mean(SVMachine_RBF_acerto),
1712.    "Regressao Logistica": np.mean(RegrLogistica_acerto),
1713.    "MLP": np.mean(MLP_acerto),
1714.    "Random Forest": np.mean(RandomForest_acerto)
1715. }
1716.
1717. mediasacuracias = sorted(mediasacuracias.items(),
1718.                         key=lambda x: x[1])
1719. print(mediasacuracias)
1720. print("Tempo total: ", (tempofinal - tempoinicial) / 60)
1721. Acuracia32 = [KNN_3_acerto,KNN_5_acerto,KNN_7_acerto,KNNP_3_acerto,KNNP_5_acerto,KNNP_7_acerto,
1722.               NaiveBayes_acerto,DecisionTree_acerto,MLP_acerto,RegrLogistica_acerto,SVMachine_L_acerto,
1723.               SVMachine_RBF_acerto,RandomForest_acerto]
1724. Precision32 = [KNN_3_precision,KNN_5_precision,KNN_7_precision,KNNP_3_precision,KNNP_5_precision,KNNP_7_precision,
1725.         NaiveBayes_precision,DecisionTree_precision,MLPP_precision,RegreLogistica_precision,SVMachine_L_precision,
1726.         SVMachine_RBF_precision,RandomForest_precision]
1727. Recall32 = [KNN_3_recall,KNN_5_recall,KNN_7_recall,KNNP_3_recall,KNNP_5_recall,KNNP_7_recall,NaiveBayes_recall,
1728.          DecisionTree_recall,MLP_recall,RegrLogistica_recall,SVMachine_L_recall,SVMachine_RBF_recall,RandomForest_recall]
1729.
1730. Fscore32 = [KNN_3_fscore,KNN_5_fscore,KNN_7_fscore,KNNP_3_fscore,KNN_5_fscore,KNN_7_fscore,NaiveBayes_fscore,DecisionTree_fscore,
1731.          MLP_fscore,RegrLogistica_fscore,SVMachine_L_fscore, SVMachine_RBF_fscore,RandomForest_fscore]
1732.
1733. Acuracia32 = pd.DataFrame(Acuracia32)
1734. Precision32 = pd.DataFrame(Precision32)
1735. Recall32 = pd.DataFrame(Recall32)
1736. Fscore32 = pd.DataFrame(Fscore32)
1737.
1738. Acuracia32.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Acuracia32.csv')
1739. Precision32.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Precision32.csv')
1740. Recall32.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Recall32.csv')
1741. Fscore32.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Fscore32.csv')
1742.
1743.
1744. '''
1745.
1746. '''
1747.
1748. # USANDO A BASE COM IMAGENS 16x16
1749.
1750. print('comecou o K fold')
1751.
1752. tempoinicial = time.time()
1753.
1754. for x in range(0, 10):
1755.
1756.    tempo1 = time.time()
1757.    cols = list(dataFrame16.columns)
1758.    cols.remove('Classe')
1759.    df_images_noclass = dataFrame16[cols]
1760.    df_images_class = dataFrame16['Classe']
1761.    c = kfold.split(dataFrame16)
1762.
1763.    for train_index, test_index in c:
1764.
1765.        noclass_train, noclass_test = df_images_noclass.iloc[train_index], df_images_noclass.iloc[test_index]
1766.        class_train, class_test = df_images_class.iloc[train_index], df_images_class.iloc[test_index]
1767.
1768.        KNN3_inicio = time.time()
1769.        KNN_3.fit(noclass_train, class_train)
1770.        KNN_3_acerto.append(KNN_3.score(noclass_test, class_test))
1771.        KNN_3_recall. append(recall_score(class_test, KNN_3.predict(noclass_test),average='weighted'))
1772.        KNN_3_precision.append(precision_score(class_test,KNN_3.predict(noclass_test),average='weighted'))
1773.        KNN_3_fscore.append(f1_score(class_test,KNN_3.predict(noclass_test),average='weighted'))
1774.        KNN3_fim = time.time()
1775.        KNN_3_tempo.append(KNN3_fim - KNN3_inicio)
1776.
1777.        KNN5_inicio = time.time()
1778.        KNN_5.fit(noclass_train, class_train)
1779.        KNN_5_acerto.append(KNN_5.score(noclass_test, class_test))
1780.        KNN_5_recall.append(recall_score(class_test,KNN_5.predict(noclass_test),average='weighted'))
1781.        KNN_5_precision.append(precision_score(class_test,KNN_5.predict(noclass_test),average='weighted'))
1782.        KNN_5_fscore.append(f1_score(class_test,KNN_5.predict(noclass_test),average='weighted'))
1783.        KNN5_fim = time.time()
1784.        KNN_5_tempo.append(KNN5_fim - KNN5_inicio)
1785.
1786.
1787.        KNN7_inicio = time.time()
1788.        KNN_7.fit(noclass_train, class_train)
1789.        KNN_7_acerto.append(KNN_3.score(noclass_test, class_test))
1790.        KNN_7_precision.append(precision_score(class_test,KNN_7.predict(noclass_test),average='weighted'))
1791.        KNN_7_recall.append(recall_score(class_test,KNN_7.predict(noclass_test),average='weighted'))
1792.        KNN_7_fscore.append(f1_score(class_test,KNN_7.predict(noclass_test),average='weighted'))
1793.        KNN7_fim = time.time()
1794.        KNN_7_tempo.append(KNN3_fim - KNN3_inicio)
1795.
1796.        KNNP3_inicio = time.time()
1797.        KNNP_3.fit(noclass_train, class_train)
1798.        KNNP_3_acerto.append(KNNP_3.score(noclass_test, class_test))
1799.        KNNP_3_precision.append(precision_score(class_test,KNNP_3.predict(noclass_test),average='weighted'))
1800.        KNNP_3_recall.append(recall_score(class_test,KNNP_3.predict(noclass_test),average='weighted'))
1801.        KNNP_3_fscore.append(f1_score(class_test,KNNP_3.predict(noclass_test),average='weighted'))
1802.        KNNP3_fim = time.time()
1803.        KNNP_3_tempo.append(KNNP3_fim - KNNP3_inicio)
1804.
1805.        KNNP5_inicio = time.time()
1806.        KNNP_5.fit(noclass_train, class_train)
1807.        KNNP_5_acerto.append(KNNP_5.score(noclass_test, class_test))
1808.        KNNP_5_precision.append(precision_score(class_test,KNNP_5.predict(noclass_test),average='weighted'))
1809.        KNNP_5_recall.append(recall_score(class_test,KNNP_5.predict(noclass_test),average='weighted'))
1810.        KNNP_5_fscore.append(f1_score(class_test,KNNP_5.predict(noclass_test),average='weighted'))
1811.        KNNP5_fim = time.time()
1812.        KNNP_5_tempo.append(KNNP5_fim - KNNP5_inicio)
1813.
1814.        KNNP7_inicio = time.time()
1815.        KNNP_7.fit(noclass_train, class_train)
1816.        KNNP_7_acerto.append(KNNP_7.score(noclass_test, class_test))
1817.        KNNP_7_precision.append(precision_score(class_test,KNNP_7.predict(noclass_test),average='weighted'))
1818.        KNNP_7_recall.append(recall_score(class_test,KNNP_7.predict(noclass_test),average='weighted'))
1819.        KNNP_7_fscore.append(f1_score(class_test,KNNP_7.predict(noclass_test),average='weighted'))
1820.        KNNP7_fim = time.time()
1821.        KNNP_7_tempo.append(KNNP7_fim - KNNP7_inicio)
1822.
1823.        NaiveBayes_inicio = time.time()
1824.        NaiveBayes.fit(noclass_train, class_train)
1825.        NaiveBayes_acerto.append(NaiveBayes.score(noclass_test, class_test))
1826.        NaiveBayes_precision.append(precision_score(class_test,NaiveBayes.predict(noclass_test),average='weighted'))
1827.        NaiveBayes_recall.append(recall_score(class_test,NaiveBayes.predict(noclass_test),average='weighted'))
1828.        NaiveBayes_fscore.append(f1_score(class_test,NaiveBayes.predict(noclass_test),average='weighted'))
1829.        NaiveBayes_fim = time.time()
1830.        NaiveBayes_tempo.append(NaiveBayes_fim - NaiveBayes_inicio)
1831.
1832.        DecisionTree_inicio = time.time()
1833.        DecisionTree.fit(noclass_train, class_train)
1834.        DecisionTree_acerto.append(DecisionTree.score(noclass_test, class_test))
1835.        DecisionTree_precision.append(precision_score(class_test,DecisionTree.predict(noclass_test),average='weighted'))
1836.        DecisionTree_recall.append(recall_score(class_test,DecisionTree.predict(noclass_test),average='weighted'))
1837.        DecisionTree_fscore.append(f1_score(class_test,DecisionTree.predict(noclass_test),average='weighted'))
1838.        DecisionTree_fim = time.time()
1839.        DecisionTree_tempo.append(DecisionTree_fim - DecisionTree_inicio)
1840.
1841.        SVMachine_L_inicio = time.time()
1842.        SVMachine_L.fit(noclass_train, class_train)
1843.        SVMachine_L_acerto.append(SVMachine_L.score(noclass_test, class_test))
1844.        SVMachine_L_precision.append(precision_score(class_test,SVMachine_L.predict(noclass_test),average='weighted'))
1845.        SVMachine_L_recall.append(recall_score(class_test,SVMachine_L.predict(noclass_test),average='weighted'))
1846.        SVMachine_L_fscore.append(f1_score(class_test,SVMachine_L.predict(noclass_test),average='weighted'))
1847.        SVMachine_L_fim = time.time()
1848.        SVMachine_L_tempo.append(SVMachine_L_fim - SVMachine_L_inicio)
1849.
1850.        SVMachine_RBF_inicio = time.time()
1851.        SVMachine_RBF.fit(noclass_train, class_train)
1852.        SVMachine_RBF_acerto.append(SVMachine_RBF.score(noclass_test, class_test))
1853.        SVMachine_RBF_recall.append(recall_score(class_test,SVMachine_RBF.predict(noclass_test),average='weighted'))
1854.        SVMachine_RBF_precision.append(precision_score(class_test,SVMachine_RBF.predict(noclass_test),average='weighted'))
1855.        SVMachine_RBF_fscore.append(f1_score(class_test,SVMachine_RBF.predict(noclass_test),average='weighted'))
1856.        SVMachine_RBF_fim = time.time()
1857.        SVMachine_RBF_tempo.append(SVMachine_RBF_fim - SVMachine_RBF_inicio)
1858.
1859.        RegrLogistica_inicio = time.time()
1860.        RegrLogistica.fit(noclass_train, class_train)
1861.        RegrLogistica_acerto.append(RegrLogistica.score(noclass_test, class_test))
1862.        RegreLogistica_precision.append(precision_score(class_test, (RegrLogistica.predict(noclass_test)),average='weighted'))
1863.        RegrLogistica_recall.append(recall_score(class_test,RegrLogistica.predict(noclass_test),average='weighted'))
1864.        RegrLogistica_fscore.append(f1_score(class_test,RegrLogistica.predict(noclass_test),average='weighted'))
1865.        RegrLogistica_fim = time.time()
1866.        RegrLogistica_tempo.append(RegrLogistica_fim - RegrLogistica_inicio)
1867.
1868.        MLP_inicio = time.time()
1869.        MLP.fit(noclass_train, class_train)
1870.        MLP_acerto.append(MLP.score(noclass_test, class_test))
1871.        MLPP_precision.append(precision_score(class_test,MLP.predict(noclass_test),average='weighted'))
1872.        MLP_recall.append(recall_score(class_test,MLP.predict(noclass_test),average='weighted'))
1873.        MLP_fscore.append(f1_score(class_test,MLP.predict(noclass_test),average='weighted'))
1874.        MLP_fim = time.time()
1875.        MLP_tempo.append(MLP_fim - MLP_inicio)
1876.
1877.        RandomForest_inicio = time.time()
1878.        RandomForest.fit(noclass_train, class_train)
1879.        RandomForest_acerto.append(RandomForest.score(noclass_test, class_test))
1880.        RandomForest_recall.append(recall_score(class_test,RandomForest.predict(noclass_test),average='weighted'))
1881.        RandomForest_precision.append(precision_score(class_test,RandomForest.predict(noclass_test),average='weighted'))
1882.        RandomForest_fscore.append(f1_score(class_test,RandomForest.predict(noclass_test),average='weighted'))
1883.        RandomForest_fim = time.time()
1884.        RandomForest_tempo.append(RandomForest_fim - RandomForest_inicio)
1885.
1886.
1887.    dataFrame16 = dataFrame16.sample(frac=1)
1888.    print("Terminou a ", x)
1889.    tempo2 = time.time()
1890.    print("Tempo da rodada ", x, (tempo2 - tempo1) / 60)
1891.
1892. tempofinal = time.time()
1893.
1894. fun.tendencia_central('KNN k = 3', KNN_3_acerto, KNN_3_tempo)
1895. fun.tendencia_central('KNN k = 5', KNN_5_acerto, KNN_5_tempo)
1896. fun.tendencia_central('KNN k = 7', KNN_7_acerto, KNN_7_tempo)
1897. fun.tendencia_central('KNN Ponderado k = 3', KNNP_3_acerto, KNNP_3_tempo)
1898. fun.tendencia_central('KNN Ponderado k = 5', KNNP_5_acerto, KNNP_5_tempo)
1899. fun.tendencia_central('KNN Ponderado k = 7', KNNP_7_acerto, KNNP_7_tempo)
1900. fun.tendencia_central('Naïve Bayes', NaiveBayes_acerto, NaiveBayes_tempo)
1901. fun.tendencia_central('Árvore de decisão', DecisionTree_acerto, DecisionTree_tempo)
1902. fun.tendencia_central('MLP', MLP_acerto, MLP_tempo)
1903. fun.tendencia_central('Regressão Logística', RegrLogistica_acerto, RegrLogistica_tempo)
1904. fun.tendencia_central('SVM linear', SVMachine_L_acerto, SVMachine_L_tempo)
1905. fun.tendencia_central('SVM RBF', SVMachine_RBF_acerto, SVMachine_RBF_tempo)
1906. fun.tendencia_central('Random Forest', RandomForest_acerto,RandomForest_tempo)
1907.
1908. mediasacuracias = {
1909.
1910.
1911.    "KNN Ponderado k = 3": np.mean(KNNP_3_acerto),
1912.    "KNN Ponderado k = 5": np.mean(KNNP_5_acerto),
1913.    "KNN Ponderado k = 7": np.mean(KNNP_7_acerto),
1914.    "Naive Bayes": np.mean(NaiveBayes_acerto),
1915.    "KNN k = 3": np.mean(KNN_3_acerto),
1916.    "KNN k = 5": np.mean(KNN_5_acerto),
1917.    "KNN k = 7": np.mean(KNN_7_acerto),
1918.    "Decision Tree": np.mean(DecisionTree_acerto),
1919.    "SVM Linear": np.mean(SVMachine_L_acerto),
1920.    "SVM RBF": np.mean(SVMachine_RBF_acerto),
1921.    "Regressao Logistica": np.mean(RegrLogistica_acerto),
1922.    "MLP": np.mean(MLP_acerto),
1923.    "Random Forest": np.mean(RandomForest_acerto)
1924. }
1925.
1926. mediasacuracias = sorted(mediasacuracias.items(),
1927.                         key=lambda x: x[1])
1928. print(mediasacuracias)
1929. print("Tempo total: ", (tempofinal - tempoinicial) / 60)
1930. Acuracia16 = [KNN_3_acerto,KNN_5_acerto,KNN_7_acerto,KNNP_3_acerto,KNNP_5_acerto,KNNP_7_acerto,
1931.               NaiveBayes_acerto,DecisionTree_acerto,MLP_acerto,RegrLogistica_acerto,SVMachine_L_acerto,
1932.               SVMachine_RBF_acerto,RandomForest_acerto]
1933. Precision16 = [KNN_3_precision,KNN_5_precision,KNN_7_precision,KNNP_3_precision,KNNP_5_precision,KNNP_7_precision,
1934.         NaiveBayes_precision,DecisionTree_precision,MLPP_precision,RegreLogistica_precision,SVMachine_L_precision,
1935.         SVMachine_RBF_precision,RandomForest_precision]
1936. Recall16 = [KNN_3_recall,KNN_5_recall,KNN_7_recall,KNNP_3_recall,KNNP_5_recall,KNNP_7_recall,NaiveBayes_recall,
1937.          DecisionTree_recall,MLP_recall,RegrLogistica_recall,SVMachine_L_recall,SVMachine_RBF_recall,RandomForest_recall]
1938.
1939. Fscore16 = [KNN_3_fscore,KNN_5_fscore,KNN_7_fscore,KNNP_3_fscore,KNN_5_fscore,KNN_7_fscore,NaiveBayes_fscore,DecisionTree_fscore,
1940.          MLP_fscore,RegrLogistica_fscore,SVMachine_L_fscore, SVMachine_RBF_fscore,RandomForest_fscore]
1941.
1942. Acuracia16 = pd.DataFrame(Acuracia16)
1943. Precision16 = pd.DataFrame(Precision16)
1944. Recall16 = pd.DataFrame(Recall16)
1945. Fscore16 = pd.DataFrame(Fscore16)
1946.
1947. Acuracia16.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Acuracia16.csv')
1948. Precision16.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Precision16.csv')
1949. Recall16.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Recall16.csv')
1950. Fscore16.to_csv('C:/Users/Auricelia/Desktop/DataSetsML/Fscore16.csv')
1951. '''
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