Spaces:
Sleeping
Sleeping
Upload 3 files
Browse files- heart.csv +1191 -0
- main.py +284 -0
- requirements.txt +7 -0
heart.csv
ADDED
@@ -0,0 +1,1191 @@
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1 |
+
age,sex,chest pain type,resting bp s,cholesterol,fasting blood sugar,resting ecg,max heart rate,exercise angina,oldpeak,ST slope,target
|
2 |
+
40,1,2,140,289,0,0,172,0,0.0,1,0
|
3 |
+
49,0,3,160,180,0,0,156,0,1.0,2,1
|
4 |
+
37,1,2,130,283,0,1,98,0,0.0,1,0
|
5 |
+
48,0,4,138,214,0,0,108,1,1.5,2,1
|
6 |
+
54,1,3,150,195,0,0,122,0,0.0,1,0
|
7 |
+
39,1,3,120,339,0,0,170,0,0.0,1,0
|
8 |
+
45,0,2,130,237,0,0,170,0,0.0,1,0
|
9 |
+
54,1,2,110,208,0,0,142,0,0.0,1,0
|
10 |
+
37,1,4,140,207,0,0,130,1,1.5,2,1
|
11 |
+
48,0,2,120,284,0,0,120,0,0.0,1,0
|
12 |
+
37,0,3,130,211,0,0,142,0,0.0,1,0
|
13 |
+
58,1,2,136,164,0,1,99,1,2.0,2,1
|
14 |
+
39,1,2,120,204,0,0,145,0,0.0,1,0
|
15 |
+
49,1,4,140,234,0,0,140,1,1.0,2,1
|
16 |
+
42,0,3,115,211,0,1,137,0,0.0,1,0
|
17 |
+
54,0,2,120,273,0,0,150,0,1.5,2,0
|
18 |
+
38,1,4,110,196,0,0,166,0,0.0,2,1
|
19 |
+
43,0,2,120,201,0,0,165,0,0.0,1,0
|
20 |
+
60,1,4,100,248,0,0,125,0,1.0,2,1
|
21 |
+
36,1,2,120,267,0,0,160,0,3.0,2,1
|
22 |
+
43,0,1,100,223,0,0,142,0,0.0,1,0
|
23 |
+
44,1,2,120,184,0,0,142,0,1.0,2,0
|
24 |
+
49,0,2,124,201,0,0,164,0,0.0,1,0
|
25 |
+
44,1,2,150,288,0,0,150,1,3.0,2,1
|
26 |
+
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184 |
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185 |
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187 |
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188 |
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190 |
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192 |
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193 |
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194 |
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195 |
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196 |
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197 |
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198 |
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199 |
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200 |
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201 |
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202 |
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203 |
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207 |
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210 |
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211 |
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212 |
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213 |
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214 |
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215 |
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216 |
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217 |
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47,1,4,150,226,0,0,98,1,1.5,2,1
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218 |
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30,0,1,170,237,0,1,170,0,0.0,1,0
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219 |
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39,1,4,110,280,0,0,150,0,0.0,2,1
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220 |
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54,1,3,120,217,0,0,137,0,0.0,1,0
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221 |
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55,1,2,140,196,0,0,150,0,0.0,1,0
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222 |
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29,1,2,140,263,0,0,170,0,0.0,1,0
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223 |
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46,1,4,130,222,0,0,112,0,0.0,2,1
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224 |
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51,0,4,160,303,0,0,150,1,1.0,2,1
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225 |
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48,0,3,120,195,0,0,125,0,0.0,1,0
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226 |
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33,1,3,120,298,0,0,185,0,0.0,1,0
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227 |
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55,1,2,120,256,1,0,137,0,0.0,1,0
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228 |
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50,1,4,145,264,0,0,150,0,0.0,2,1
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229 |
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53,1,3,120,195,0,0,140,0,0.0,1,0
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230 |
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38,1,4,92,117,0,0,134,1,2.5,2,1
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231 |
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41,1,2,120,295,0,0,170,0,0.0,1,0
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232 |
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37,0,4,130,173,0,1,184,0,0.0,1,0
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233 |
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37,1,4,130,315,0,0,158,0,0.0,1,0
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234 |
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40,1,3,130,281,0,0,167,0,0.0,1,0
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235 |
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38,0,2,120,275,0,0,129,0,0.0,1,0
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236 |
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41,1,4,112,250,0,0,142,0,0.0,1,0
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237 |
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54,0,2,140,309,0,1,140,0,0.0,1,0
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238 |
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39,1,2,120,200,0,0,160,1,1.0,2,0
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239 |
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41,1,4,120,336,0,0,118,1,3.0,2,1
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240 |
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55,1,1,140,295,0,0,136,0,0.0,2,1
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241 |
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48,1,4,160,355,0,0,99,1,2.0,2,1
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242 |
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48,1,4,160,193,0,0,102,1,3.0,2,1
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243 |
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55,1,2,145,326,0,0,155,0,0.0,1,0
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244 |
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54,1,4,200,198,0,0,142,1,2.0,2,1
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245 |
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55,1,2,160,292,1,0,143,1,2.0,2,1
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246 |
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43,0,2,120,266,0,0,118,0,0.0,1,0
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620 |
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622 |
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630 |
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635 |
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636 |
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639 |
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640 |
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660 |
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662 |
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663 |
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58,1,3,140,211,1,2,165,0,0,1,0
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665 |
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666 |
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44,1,4,110,197,0,2,177,0,0,1,1
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667 |
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66,1,2,160,246,0,0,120,1,0,2,1
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668 |
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65,0,4,150,225,0,2,114,0,1,2,1
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669 |
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42,1,4,136,315,0,0,125,1,1.8,2,1
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670 |
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52,1,2,128,205,1,0,184,0,0,1,0
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671 |
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672 |
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673 |
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674 |
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675 |
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676 |
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677 |
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678 |
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62,1,2,120,281,0,2,103,0,1.4,2,1
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679 |
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57,1,3,150,126,1,0,173,0,0.2,1,0
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680 |
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51,0,4,130,305,0,0,142,1,1.2,2,1
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681 |
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682 |
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683 |
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63,1,1,145,233,1,2,150,0,2.3,3,0
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684 |
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57,1,4,150,276,0,2,112,1,0.6,2,1
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685 |
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51,1,4,140,261,0,2,186,1,0,1,0
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686 |
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687 |
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688 |
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689 |
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61,1,4,120,260,0,0,140,1,3.6,2,1
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690 |
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57,0,4,120,354,0,0,163,1,0.6,1,0
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691 |
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70,1,2,156,245,0,2,143,0,0,1,0
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692 |
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76,0,3,140,197,0,1,116,0,1.1,2,0
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693 |
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67,0,4,106,223,0,0,142,0,0.3,1,0
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694 |
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45,1,4,142,309,0,2,147,1,0,2,1
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695 |
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45,1,4,104,208,0,2,148,1,3,2,0
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696 |
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39,0,3,94,199,0,0,179,0,0,1,0
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697 |
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42,0,3,120,209,0,0,173,0,0,2,0
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698 |
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56,1,2,120,236,0,0,178,0,0.8,1,0
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699 |
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58,1,4,146,218,0,0,105,0,2,2,1
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700 |
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35,1,4,120,198,0,0,130,1,1.6,2,1
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701 |
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58,1,4,150,270,0,2,111,1,0.8,1,1
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702 |
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41,1,3,130,214,0,2,168,0,2,2,0
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703 |
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57,1,4,110,201,0,0,126,1,1.5,2,0
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704 |
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42,1,1,148,244,0,2,178,0,0.8,1,0
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705 |
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62,1,2,128,208,1,2,140,0,0,1,0
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706 |
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59,1,1,178,270,0,2,145,0,4.2,3,0
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707 |
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41,0,2,126,306,0,0,163,0,0,1,0
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708 |
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50,1,4,150,243,0,2,128,0,2.6,2,1
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709 |
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59,1,2,140,221,0,0,164,1,0,1,0
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710 |
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61,0,4,130,330,0,2,169,0,0,1,1
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711 |
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54,1,4,124,266,0,2,109,1,2.2,2,1
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712 |
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54,1,4,110,206,0,2,108,1,0,2,1
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713 |
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52,1,4,125,212,0,0,168,0,1,1,1
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714 |
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47,1,4,110,275,0,2,118,1,1,2,1
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715 |
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66,1,4,120,302,0,2,151,0,0.4,2,0
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716 |
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58,1,4,100,234,0,0,156,0,0.1,1,1
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717 |
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64,0,3,140,313,0,0,133,0,0.2,1,0
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718 |
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50,0,2,120,244,0,0,162,0,1.1,1,0
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719 |
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44,0,3,108,141,0,0,175,0,0.6,2,0
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720 |
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67,1,4,120,237,0,0,71,0,1,2,1
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721 |
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49,0,4,130,269,0,0,163,0,0,1,0
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722 |
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57,1,4,165,289,1,2,124,0,1,2,1
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723 |
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63,1,4,130,254,0,2,147,0,1.4,2,1
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724 |
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48,1,4,124,274,0,2,166,0,0.5,2,1
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725 |
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51,1,3,100,222,0,0,143,1,1.2,2,0
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726 |
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60,0,4,150,258,0,2,157,0,2.6,2,1
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727 |
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59,1,4,140,177,0,0,162,1,0,1,1
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728 |
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45,0,2,112,160,0,0,138,0,0,2,0
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729 |
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55,0,4,180,327,0,1,117,1,3.4,2,1
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730 |
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41,1,2,110,235,0,0,153,0,0,1,0
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731 |
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60,0,4,158,305,0,2,161,0,0,1,1
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732 |
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54,0,3,135,304,1,0,170,0,0,1,0
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733 |
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42,1,2,120,295,0,0,162,0,0,1,0
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734 |
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49,0,2,134,271,0,0,162,0,0,2,0
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735 |
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46,1,4,120,249,0,2,144,0,0.8,1,1
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736 |
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56,0,4,200,288,1,2,133,1,4,3,1
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737 |
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66,0,1,150,226,0,0,114,0,2.6,3,0
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738 |
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56,1,4,130,283,1,2,103,1,1.6,3,1
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739 |
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49,1,3,120,188,0,0,139,0,2,2,1
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740 |
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54,1,4,122,286,0,2,116,1,3.2,2,1
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741 |
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57,1,4,152,274,0,0,88,1,1.2,2,1
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742 |
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65,0,3,160,360,0,2,151,0,0.8,1,0
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743 |
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54,1,3,125,273,0,2,152,0,0.5,3,0
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744 |
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54,0,3,160,201,0,0,163,0,0,1,0
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745 |
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62,1,4,120,267,0,0,99,1,1.8,2,1
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746 |
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52,0,3,136,196,0,2,169,0,0.1,2,0
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747 |
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52,1,2,134,201,0,0,158,0,0.8,1,0
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748 |
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60,1,4,117,230,1,0,160,1,1.4,1,1
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749 |
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63,0,4,108,269,0,0,169,1,1.8,2,1
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750 |
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66,1,4,112,212,0,2,132,1,0.1,1,1
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751 |
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42,1,4,140,226,0,0,178,0,0,1,0
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752 |
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64,1,4,120,246,0,2,96,1,2.2,3,1
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753 |
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54,1,3,150,232,0,2,165,0,1.6,1,0
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754 |
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46,0,3,142,177,0,2,160,1,1.4,3,0
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755 |
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67,0,3,152,277,0,0,172,0,0,1,0
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756 |
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56,1,4,125,249,1,2,144,1,1.2,2,1
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757 |
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34,0,2,118,210,0,0,192,0,0.7,1,0
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758 |
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57,1,4,132,207,0,0,168,1,0,1,0
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759 |
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64,1,4,145,212,0,2,132,0,2,2,1
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760 |
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59,1,4,138,271,0,2,182,0,0,1,0
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761 |
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50,1,3,140,233,0,0,163,0,0.6,2,1
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762 |
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51,1,1,125,213,0,2,125,1,1.4,1,0
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763 |
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54,1,2,192,283,0,2,195,0,0,1,1
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764 |
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53,1,4,123,282,0,0,95,1,2,2,1
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765 |
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52,1,4,112,230,0,0,160,0,0,1,1
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766 |
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40,1,4,110,167,0,2,114,1,2,2,1
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767 |
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58,1,3,132,224,0,2,173,0,3.2,1,1
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768 |
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41,0,3,112,268,0,2,172,1,0,1,0
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769 |
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41,1,3,112,250,0,0,179,0,0,1,0
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770 |
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50,0,3,120,219,0,0,158,0,1.6,2,0
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771 |
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54,0,3,108,267,0,2,167,0,0,1,0
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772 |
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64,0,4,130,303,0,0,122,0,2,2,0
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773 |
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51,0,3,130,256,0,2,149,0,0.5,1,0
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774 |
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46,0,2,105,204,0,0,172,0,0,1,0
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775 |
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55,1,4,140,217,0,0,111,1,5.6,3,1
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776 |
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45,1,2,128,308,0,2,170,0,0,1,0
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777 |
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56,1,1,120,193,0,2,162,0,1.9,2,0
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778 |
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66,0,4,178,228,1,0,165,1,1,2,1
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779 |
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38,1,1,120,231,0,0,182,1,3.8,2,1
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780 |
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62,0,4,150,244,0,0,154,1,1.4,2,1
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781 |
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55,1,2,130,262,0,0,155,0,0,1,0
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782 |
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58,1,4,128,259,0,2,130,1,3,2,1
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783 |
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43,1,4,110,211,0,0,161,0,0,1,0
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784 |
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64,0,4,180,325,0,0,154,1,0,1,0
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785 |
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50,0,4,110,254,0,2,159,0,0,1,0
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786 |
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53,1,3,130,197,1,2,152,0,1.2,3,0
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787 |
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45,0,4,138,236,0,2,152,1,0.2,2,0
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788 |
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65,1,1,138,282,1,2,174,0,1.4,2,1
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789 |
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69,1,1,160,234,1,2,131,0,0.1,2,0
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790 |
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69,1,3,140,254,0,2,146,0,2,2,1
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791 |
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67,1,4,100,299,0,2,125,1,0.9,2,1
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792 |
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68,0,3,120,211,0,2,115,0,1.5,2,0
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793 |
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34,1,1,118,182,0,2,174,0,0,1,0
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794 |
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62,0,4,138,294,1,0,106,0,1.9,2,1
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795 |
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51,1,4,140,298,0,0,122,1,4.2,2,1
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796 |
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46,1,3,150,231,0,0,147,0,3.6,2,1
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797 |
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67,1,4,125,254,1,0,163,0,0.2,2,1
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798 |
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50,1,3,129,196,0,0,163,0,0,1,0
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799 |
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42,1,3,120,240,1,0,194,0,0.8,3,0
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800 |
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56,0,4,134,409,0,2,150,1,1.9,2,1
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801 |
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41,1,4,110,172,0,2,158,0,0,1,1
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802 |
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42,0,4,102,265,0,2,122,0,0.6,2,0
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803 |
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53,1,3,130,246,1,2,173,0,0,1,0
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804 |
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43,1,3,130,315,0,0,162,0,1.9,1,0
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805 |
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56,1,4,132,184,0,2,105,1,2.1,2,1
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806 |
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52,1,4,108,233,1,0,147,0,0.1,1,0
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807 |
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62,0,4,140,394,0,2,157,0,1.2,2,0
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808 |
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70,1,3,160,269,0,0,112,1,2.9,2,1
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809 |
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54,1,4,140,239,0,0,160,0,1.2,1,0
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810 |
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70,1,4,145,174,0,0,125,1,2.6,3,1
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811 |
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54,1,2,108,309,0,0,156,0,0,1,0
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812 |
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35,1,4,126,282,0,2,156,1,0,1,1
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813 |
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48,1,3,124,255,1,0,175,0,0,1,0
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814 |
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55,0,2,135,250,0,2,161,0,1.4,2,0
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815 |
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58,0,4,100,248,0,2,122,0,1,2,0
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816 |
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54,0,3,110,214,0,0,158,0,1.6,2,0
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817 |
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69,0,1,140,239,0,0,151,0,1.8,1,0
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818 |
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77,1,4,125,304,0,2,162,1,0,1,1
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819 |
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68,1,3,118,277,0,0,151,0,1,1,0
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820 |
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58,1,4,125,300,0,2,171,0,0,1,1
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821 |
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60,1,4,125,258,0,2,141,1,2.8,2,1
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822 |
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51,1,4,140,299,0,0,173,1,1.6,1,1
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823 |
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55,1,4,160,289,0,2,145,1,0.8,2,1
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824 |
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52,1,1,152,298,1,0,178,0,1.2,2,0
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825 |
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60,0,3,102,318,0,0,160,0,0,1,0
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826 |
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58,1,3,105,240,0,2,154,1,0.6,2,0
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827 |
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64,1,3,125,309,0,0,131,1,1.8,2,1
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828 |
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37,1,3,130,250,0,0,187,0,3.5,3,0
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829 |
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59,1,1,170,288,0,2,159,0,0.2,2,1
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830 |
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51,1,3,125,245,1,2,166,0,2.4,2,0
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831 |
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43,0,3,122,213,0,0,165,0,0.2,2,0
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832 |
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58,1,4,128,216,0,2,131,1,2.2,2,1
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833 |
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29,1,2,130,204,0,2,202,0,0,1,0
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834 |
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41,0,2,130,204,0,2,172,0,1.4,1,0
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835 |
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63,0,3,135,252,0,2,172,0,0,1,0
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836 |
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51,1,3,94,227,0,0,154,1,0,1,0
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837 |
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54,1,3,120,258,0,2,147,0,0.4,2,0
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838 |
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44,1,2,120,220,0,0,170,0,0,1,0
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839 |
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54,1,4,110,239,0,0,126,1,2.8,2,1
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840 |
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65,1,4,135,254,0,2,127,0,2.8,2,1
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841 |
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57,1,3,150,168,0,0,174,0,1.6,1,0
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842 |
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63,1,4,130,330,1,2,132,1,1.8,1,1
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843 |
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35,0,4,138,183,0,0,182,0,1.4,1,0
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844 |
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41,1,2,135,203,0,0,132,0,0,2,0
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845 |
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62,0,3,130,263,0,0,97,0,1.2,2,1
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846 |
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43,0,4,132,341,1,2,136,1,3,2,1
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847 |
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58,0,1,150,283,1,2,162,0,1,1,0
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848 |
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52,1,1,118,186,0,2,190,0,0,2,0
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849 |
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61,0,4,145,307,0,2,146,1,1,2,1
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850 |
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39,1,4,118,219,0,0,140,0,1.2,2,1
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851 |
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45,1,4,115,260,0,2,185,0,0,1,0
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852 |
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52,1,4,128,255,0,0,161,1,0,1,1
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853 |
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62,1,3,130,231,0,0,146,0,1.8,2,0
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854 |
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62,0,4,160,164,0,2,145,0,6.2,3,1
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855 |
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53,0,4,138,234,0,2,160,0,0,1,0
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856 |
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43,1,4,120,177,0,2,120,1,2.5,2,1
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857 |
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47,1,3,138,257,0,2,156,0,0,1,0
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858 |
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52,1,2,120,325,0,0,172,0,0.2,1,0
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859 |
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68,1,3,180,274,1,2,150,1,1.6,2,1
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860 |
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39,1,3,140,321,0,2,182,0,0,1,0
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861 |
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53,0,4,130,264,0,2,143,0,0.4,2,0
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862 |
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62,0,4,140,268,0,2,160,0,3.6,3,1
|
863 |
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51,0,3,140,308,0,2,142,0,1.5,1,0
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864 |
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60,1,4,130,253,0,0,144,1,1.4,1,1
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865 |
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65,1,4,110,248,0,2,158,0,0.6,1,1
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866 |
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65,0,3,155,269,0,0,148,0,0.8,1,0
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867 |
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60,1,3,140,185,0,2,155,0,3,2,1
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868 |
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60,1,4,145,282,0,2,142,1,2.8,2,1
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869 |
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54,1,4,120,188,0,0,113,0,1.4,2,1
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870 |
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44,1,2,130,219,0,2,188,0,0,1,0
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871 |
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44,1,4,112,290,0,2,153,0,0,1,1
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872 |
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51,1,3,110,175,0,0,123,0,0.6,1,0
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873 |
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59,1,3,150,212,1,0,157,0,1.6,1,0
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874 |
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71,0,2,160,302,0,0,162,0,0.4,1,0
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875 |
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61,1,3,150,243,1,0,137,1,1,2,0
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876 |
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55,1,4,132,353,0,0,132,1,1.2,2,1
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877 |
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64,1,3,140,335,0,0,158,0,0,1,1
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878 |
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43,1,4,150,247,0,0,171,0,1.5,1,0
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879 |
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58,0,3,120,340,0,0,172,0,0,1,0
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880 |
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60,1,4,130,206,0,2,132,1,2.4,2,1
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881 |
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58,1,2,120,284,0,2,160,0,1.8,2,1
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882 |
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49,1,2,130,266,0,0,171,0,0.6,1,0
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883 |
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48,1,2,110,229,0,0,168,0,1,3,1
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884 |
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52,1,3,172,199,1,0,162,0,0.5,1,0
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885 |
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44,1,2,120,263,0,0,173,0,0,1,0
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886 |
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56,0,2,140,294,0,2,153,0,1.3,2,0
|
887 |
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57,1,4,140,192,0,0,148,0,0.4,2,0
|
888 |
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67,1,4,160,286,0,2,108,1,1.5,2,1
|
889 |
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63,1,1,145,233,1,2,150,0,2.3,3,0
|
890 |
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67,1,4,160,286,0,2,108,1,1.5,2,1
|
891 |
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67,1,4,120,229,0,2,129,1,2.6,2,1
|
892 |
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37,1,3,130,250,0,0,187,0,3.5,3,0
|
893 |
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41,0,2,130,204,0,2,172,0,1.4,1,0
|
894 |
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56,1,2,120,236,0,0,178,0,0.8,1,0
|
895 |
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62,0,4,140,268,0,2,160,0,3.6,3,1
|
896 |
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57,0,4,120,354,0,0,163,1,0.6,1,0
|
897 |
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63,1,4,130,254,0,2,147,0,1.4,2,1
|
898 |
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53,1,4,140,203,1,2,155,1,3.1,3,1
|
899 |
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57,1,4,140,192,0,0,148,0,0.4,2,0
|
900 |
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56,0,2,140,294,0,2,153,0,1.3,2,0
|
901 |
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56,1,3,130,256,1,2,142,1,0.6,2,1
|
902 |
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44,1,2,120,263,0,0,173,0,0,1,0
|
903 |
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52,1,3,172,199,1,0,162,0,0.5,1,0
|
904 |
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57,1,3,150,168,0,0,174,0,1.6,1,0
|
905 |
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48,1,2,110,229,0,0,168,0,1,3,1
|
906 |
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54,1,4,140,239,0,0,160,0,1.2,1,0
|
907 |
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48,0,3,130,275,0,0,139,0,0.2,1,0
|
908 |
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49,1,2,130,266,0,0,171,0,0.6,1,0
|
909 |
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64,1,1,110,211,0,2,144,1,1.8,2,0
|
910 |
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58,0,1,150,283,1,2,162,0,1,1,0
|
911 |
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58,1,2,120,284,0,2,160,0,1.8,2,1
|
912 |
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58,1,3,132,224,0,2,173,0,3.2,1,1
|
913 |
+
60,1,4,130,206,0,2,132,1,2.4,2,1
|
914 |
+
50,0,3,120,219,0,0,158,0,1.6,2,0
|
915 |
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58,0,3,120,340,0,0,172,0,0,1,0
|
916 |
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66,0,1,150,226,0,0,114,0,2.6,3,0
|
917 |
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43,1,4,150,247,0,0,171,0,1.5,1,0
|
918 |
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40,1,4,110,167,0,2,114,1,2,2,1
|
919 |
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69,0,1,140,239,0,0,151,0,1.8,1,0
|
920 |
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60,1,4,117,230,1,0,160,1,1.4,1,1
|
921 |
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64,1,3,140,335,0,0,158,0,0,1,1
|
922 |
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59,1,4,135,234,0,0,161,0,0.5,2,0
|
923 |
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44,1,3,130,233,0,0,179,1,0.4,1,0
|
924 |
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42,1,4,140,226,0,0,178,0,0,1,0
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925 |
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43,1,4,120,177,0,2,120,1,2.5,2,1
|
926 |
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57,1,4,150,276,0,2,112,1,0.6,2,1
|
927 |
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55,1,4,132,353,0,0,132,1,1.2,2,1
|
928 |
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61,1,3,150,243,1,0,137,1,1,2,0
|
929 |
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65,0,4,150,225,0,2,114,0,1,2,1
|
930 |
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40,1,1,140,199,0,0,178,1,1.4,1,0
|
931 |
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71,0,2,160,302,0,0,162,0,0.4,1,0
|
932 |
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59,1,3,150,212,1,0,157,0,1.6,1,0
|
933 |
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61,0,4,130,330,0,2,169,0,0,1,1
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934 |
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58,1,3,112,230,0,2,165,0,2.5,2,1
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935 |
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51,1,3,110,175,0,0,123,0,0.6,1,0
|
936 |
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50,1,4,150,243,0,2,128,0,2.6,2,1
|
937 |
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65,0,3,140,417,1,2,157,0,0.8,1,0
|
938 |
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53,1,3,130,197,1,2,152,0,1.2,3,0
|
939 |
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41,0,2,105,198,0,0,168,0,0,1,0
|
940 |
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65,1,4,120,177,0,0,140,0,0.4,1,0
|
941 |
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44,1,4,112,290,0,2,153,0,0,1,1
|
942 |
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44,1,2,130,219,0,2,188,0,0,1,0
|
943 |
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60,1,4,130,253,0,0,144,1,1.4,1,1
|
944 |
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54,1,4,124,266,0,2,109,1,2.2,2,1
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945 |
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50,1,3,140,233,0,0,163,0,0.6,2,1
|
946 |
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41,1,4,110,172,0,2,158,0,0,1,1
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947 |
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54,1,3,125,273,0,2,152,0,0.5,3,0
|
948 |
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51,1,1,125,213,0,2,125,1,1.4,1,0
|
949 |
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51,0,4,130,305,0,0,142,1,1.2,2,1
|
950 |
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46,0,3,142,177,0,2,160,1,1.4,3,0
|
951 |
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58,1,4,128,216,0,2,131,1,2.2,2,1
|
952 |
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54,0,3,135,304,1,0,170,0,0,1,0
|
953 |
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54,1,4,120,188,0,0,113,0,1.4,2,1
|
954 |
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60,1,4,145,282,0,2,142,1,2.8,2,1
|
955 |
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60,1,3,140,185,0,2,155,0,3,2,1
|
956 |
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54,1,3,150,232,0,2,165,0,1.6,1,0
|
957 |
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59,1,4,170,326,0,2,140,1,3.4,3,1
|
958 |
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46,1,3,150,231,0,0,147,0,3.6,2,1
|
959 |
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65,0,3,155,269,0,0,148,0,0.8,1,0
|
960 |
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67,1,4,125,254,1,0,163,0,0.2,2,1
|
961 |
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62,1,4,120,267,0,0,99,1,1.8,2,1
|
962 |
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65,1,4,110,248,0,2,158,0,0.6,1,1
|
963 |
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44,1,4,110,197,0,2,177,0,0,1,1
|
964 |
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65,0,3,160,360,0,2,151,0,0.8,1,0
|
965 |
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60,1,4,125,258,0,2,141,1,2.8,2,1
|
966 |
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51,0,3,140,308,0,2,142,0,1.5,1,0
|
967 |
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48,1,2,130,245,0,2,180,0,0.2,2,0
|
968 |
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58,1,4,150,270,0,2,111,1,0.8,1,1
|
969 |
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45,1,4,104,208,0,2,148,1,3,2,0
|
970 |
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53,0,4,130,264,0,2,143,0,0.4,2,0
|
971 |
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39,1,3,140,321,0,2,182,0,0,1,0
|
972 |
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68,1,3,180,274,1,2,150,1,1.6,2,1
|
973 |
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52,1,2,120,325,0,0,172,0,0.2,1,0
|
974 |
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44,1,3,140,235,0,2,180,0,0,1,0
|
975 |
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47,1,3,138,257,0,2,156,0,0,1,0
|
976 |
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53,0,3,128,216,0,2,115,0,0,1,0
|
977 |
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53,0,4,138,234,0,2,160,0,0,1,0
|
978 |
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51,0,3,130,256,0,2,149,0,0.5,1,0
|
979 |
+
66,1,4,120,302,0,2,151,0,0.4,2,0
|
980 |
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62,0,4,160,164,0,2,145,0,6.2,3,1
|
981 |
+
62,1,3,130,231,0,0,146,0,1.8,2,0
|
982 |
+
44,0,3,108,141,0,0,175,0,0.6,2,0
|
983 |
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63,0,3,135,252,0,2,172,0,0,1,0
|
984 |
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52,1,4,128,255,0,0,161,1,0,1,1
|
985 |
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59,1,4,110,239,0,2,142,1,1.2,2,1
|
986 |
+
60,0,4,150,258,0,2,157,0,2.6,2,1
|
987 |
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52,1,2,134,201,0,0,158,0,0.8,1,0
|
988 |
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48,1,4,122,222,0,2,186,0,0,1,0
|
989 |
+
45,1,4,115,260,0,2,185,0,0,1,0
|
990 |
+
34,1,1,118,182,0,2,174,0,0,1,0
|
991 |
+
57,0,4,128,303,0,2,159,0,0,1,0
|
992 |
+
71,0,3,110,265,1,2,130,0,0,1,0
|
993 |
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49,1,3,120,188,0,0,139,0,2,2,1
|
994 |
+
54,1,2,108,309,0,0,156,0,0,1,0
|
995 |
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59,1,4,140,177,0,0,162,1,0,1,1
|
996 |
+
57,1,3,128,229,0,2,150,0,0.4,2,1
|
997 |
+
61,1,4,120,260,0,0,140,1,3.6,2,1
|
998 |
+
39,1,4,118,219,0,0,140,0,1.2,2,1
|
999 |
+
61,0,4,145,307,0,2,146,1,1,2,1
|
1000 |
+
56,1,4,125,249,1,2,144,1,1.2,2,1
|
1001 |
+
52,1,1,118,186,0,2,190,0,0,2,0
|
1002 |
+
43,0,4,132,341,1,2,136,1,3,2,1
|
1003 |
+
62,0,3,130,263,0,0,97,0,1.2,2,1
|
1004 |
+
41,1,2,135,203,0,0,132,0,0,2,0
|
1005 |
+
58,1,3,140,211,1,2,165,0,0,1,0
|
1006 |
+
35,0,4,138,183,0,0,182,0,1.4,1,0
|
1007 |
+
63,1,4,130,330,1,2,132,1,1.8,1,1
|
1008 |
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65,1,4,135,254,0,2,127,0,2.8,2,1
|
1009 |
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48,1,4,130,256,1,2,150,1,0,1,1
|
1010 |
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63,0,4,150,407,0,2,154,0,4,2,1
|
1011 |
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51,1,3,100,222,0,0,143,1,1.2,2,0
|
1012 |
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55,1,4,140,217,0,0,111,1,5.6,3,1
|
1013 |
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65,1,1,138,282,1,2,174,0,1.4,2,1
|
1014 |
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45,0,2,130,234,0,2,175,0,0.6,2,0
|
1015 |
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56,0,4,200,288,1,2,133,1,4,3,1
|
1016 |
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54,1,4,110,239,0,0,126,1,2.8,2,1
|
1017 |
+
44,1,2,120,220,0,0,170,0,0,1,0
|
1018 |
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62,0,4,124,209,0,0,163,0,0,1,0
|
1019 |
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54,1,3,120,258,0,2,147,0,0.4,2,0
|
1020 |
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51,1,3,94,227,0,0,154,1,0,1,0
|
1021 |
+
29,1,2,130,204,0,2,202,0,0,1,0
|
1022 |
+
51,1,4,140,261,0,2,186,1,0,1,0
|
1023 |
+
43,0,3,122,213,0,0,165,0,0.2,2,0
|
1024 |
+
55,0,2,135,250,0,2,161,0,1.4,2,0
|
1025 |
+
70,1,4,145,174,0,0,125,1,2.6,3,1
|
1026 |
+
62,1,2,120,281,0,2,103,0,1.4,2,1
|
1027 |
+
35,1,4,120,198,0,0,130,1,1.6,2,1
|
1028 |
+
51,1,3,125,245,1,2,166,0,2.4,2,0
|
1029 |
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59,1,2,140,221,0,0,164,1,0,1,0
|
1030 |
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59,1,1,170,288,0,2,159,0,0.2,2,1
|
1031 |
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52,1,2,128,205,1,0,184,0,0,1,0
|
1032 |
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64,1,3,125,309,0,0,131,1,1.8,2,1
|
1033 |
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58,1,3,105,240,0,2,154,1,0.6,2,0
|
1034 |
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47,1,3,108,243,0,0,152,0,0,1,1
|
1035 |
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57,1,4,165,289,1,2,124,0,1,2,1
|
1036 |
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41,1,3,112,250,0,0,179,0,0,1,0
|
1037 |
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45,1,2,128,308,0,2,170,0,0,1,0
|
1038 |
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60,0,3,102,318,0,0,160,0,0,1,0
|
1039 |
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52,1,1,152,298,1,0,178,0,1.2,2,0
|
1040 |
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42,0,4,102,265,0,2,122,0,0.6,2,0
|
1041 |
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67,0,3,115,564,0,2,160,0,1.6,2,0
|
1042 |
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55,1,4,160,289,0,2,145,1,0.8,2,1
|
1043 |
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64,1,4,120,246,0,2,96,1,2.2,3,1
|
1044 |
+
70,1,4,130,322,0,2,109,0,2.4,2,1
|
1045 |
+
51,1,4,140,299,0,0,173,1,1.6,1,1
|
1046 |
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58,1,4,125,300,0,2,171,0,0,1,1
|
1047 |
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60,1,4,140,293,0,2,170,0,1.2,2,1
|
1048 |
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68,1,3,118,277,0,0,151,0,1,1,0
|
1049 |
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46,1,2,101,197,1,0,156,0,0,1,0
|
1050 |
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77,1,4,125,304,0,2,162,1,0,1,1
|
1051 |
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54,0,3,110,214,0,0,158,0,1.6,2,0
|
1052 |
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58,0,4,100,248,0,2,122,0,1,2,0
|
1053 |
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48,1,3,124,255,1,0,175,0,0,1,0
|
1054 |
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57,1,4,132,207,0,0,168,1,0,1,0
|
1055 |
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52,1,3,138,223,0,0,169,0,0,1,0
|
1056 |
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54,0,2,132,288,1,2,159,1,0,1,0
|
1057 |
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35,1,4,126,282,0,2,156,1,0,1,1
|
1058 |
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45,0,2,112,160,0,0,138,0,0,2,0
|
1059 |
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70,1,3,160,269,0,0,112,1,2.9,2,1
|
1060 |
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53,1,4,142,226,0,2,111,1,0,1,0
|
1061 |
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59,0,4,174,249,0,0,143,1,0,2,1
|
1062 |
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62,0,4,140,394,0,2,157,0,1.2,2,0
|
1063 |
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64,1,4,145,212,0,2,132,0,2,2,1
|
1064 |
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57,1,4,152,274,0,0,88,1,1.2,2,1
|
1065 |
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52,1,4,108,233,1,0,147,0,0.1,1,0
|
1066 |
+
56,1,4,132,184,0,2,105,1,2.1,2,1
|
1067 |
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43,1,3,130,315,0,0,162,0,1.9,1,0
|
1068 |
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53,1,3,130,246,1,2,173,0,0,1,0
|
1069 |
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48,1,4,124,274,0,2,166,0,0.5,2,1
|
1070 |
+
56,0,4,134,409,0,2,150,1,1.9,2,1
|
1071 |
+
42,1,1,148,244,0,2,178,0,0.8,1,0
|
1072 |
+
59,1,1,178,270,0,2,145,0,4.2,3,0
|
1073 |
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60,0,4,158,305,0,2,161,0,0,1,1
|
1074 |
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63,0,2,140,195,0,0,179,0,0,1,0
|
1075 |
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42,1,3,120,240,1,0,194,0,0.8,3,0
|
1076 |
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66,1,2,160,246,0,0,120,1,0,2,1
|
1077 |
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54,1,2,192,283,0,2,195,0,0,1,1
|
1078 |
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69,1,3,140,254,0,2,146,0,2,2,1
|
1079 |
+
50,1,3,129,196,0,0,163,0,0,1,0
|
1080 |
+
51,1,4,140,298,0,0,122,1,4.2,2,1
|
1081 |
+
43,1,4,132,247,1,2,143,1,0.1,2,1
|
1082 |
+
62,0,4,138,294,1,0,106,0,1.9,2,1
|
1083 |
+
68,0,3,120,211,0,2,115,0,1.5,2,0
|
1084 |
+
67,1,4,100,299,0,2,125,1,0.9,2,1
|
1085 |
+
69,1,1,160,234,1,2,131,0,0.1,2,0
|
1086 |
+
45,0,4,138,236,0,2,152,1,0.2,2,0
|
1087 |
+
50,0,2,120,244,0,0,162,0,1.1,1,0
|
1088 |
+
59,1,1,160,273,0,2,125,0,0,1,1
|
1089 |
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50,0,4,110,254,0,2,159,0,0,1,0
|
1090 |
+
64,0,4,180,325,0,0,154,1,0,1,0
|
1091 |
+
57,1,3,150,126,1,0,173,0,0.2,1,0
|
1092 |
+
64,0,3,140,313,0,0,133,0,0.2,1,0
|
1093 |
+
43,1,4,110,211,0,0,161,0,0,1,0
|
1094 |
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45,1,4,142,309,0,2,147,1,0,2,1
|
1095 |
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58,1,4,128,259,0,2,130,1,3,2,1
|
1096 |
+
50,1,4,144,200,0,2,126,1,0.9,2,1
|
1097 |
+
55,1,2,130,262,0,0,155,0,0,1,0
|
1098 |
+
62,0,4,150,244,0,0,154,1,1.4,2,1
|
1099 |
+
37,0,3,120,215,0,0,170,0,0,1,0
|
1100 |
+
38,1,1,120,231,0,0,182,1,3.8,2,1
|
1101 |
+
41,1,3,130,214,0,2,168,0,2,2,0
|
1102 |
+
66,0,4,178,228,1,0,165,1,1,2,1
|
1103 |
+
52,1,4,112,230,0,0,160,0,0,1,1
|
1104 |
+
56,1,1,120,193,0,2,162,0,1.9,2,0
|
1105 |
+
46,0,2,105,204,0,0,172,0,0,1,0
|
1106 |
+
46,0,4,138,243,0,2,152,1,0,2,0
|
1107 |
+
64,0,4,130,303,0,0,122,0,2,2,0
|
1108 |
+
59,1,4,138,271,0,2,182,0,0,1,0
|
1109 |
+
41,0,3,112,268,0,2,172,1,0,1,0
|
1110 |
+
54,0,3,108,267,0,2,167,0,0,1,0
|
1111 |
+
39,0,3,94,199,0,0,179,0,0,1,0
|
1112 |
+
53,1,4,123,282,0,0,95,1,2,2,1
|
1113 |
+
63,0,4,108,269,0,0,169,1,1.8,2,1
|
1114 |
+
34,0,2,118,210,0,0,192,0,0.7,1,0
|
1115 |
+
47,1,4,112,204,0,0,143,0,0.1,1,0
|
1116 |
+
67,0,3,152,277,0,0,172,0,0,1,0
|
1117 |
+
54,1,4,110,206,0,2,108,1,0,2,1
|
1118 |
+
66,1,4,112,212,0,2,132,1,0.1,1,1
|
1119 |
+
52,0,3,136,196,0,2,169,0,0.1,2,0
|
1120 |
+
55,0,4,180,327,0,1,117,1,3.4,2,1
|
1121 |
+
49,1,3,118,149,0,2,126,0,0.8,1,1
|
1122 |
+
74,0,2,120,269,0,2,121,1,0.2,1,0
|
1123 |
+
54,0,3,160,201,0,0,163,0,0,1,0
|
1124 |
+
54,1,4,122,286,0,2,116,1,3.2,2,1
|
1125 |
+
56,1,4,130,283,1,2,103,1,1.6,3,1
|
1126 |
+
46,1,4,120,249,0,2,144,0,0.8,1,1
|
1127 |
+
49,0,2,134,271,0,0,162,0,0,2,0
|
1128 |
+
42,1,2,120,295,0,0,162,0,0,1,0
|
1129 |
+
41,1,2,110,235,0,0,153,0,0,1,0
|
1130 |
+
41,0,2,126,306,0,0,163,0,0,1,0
|
1131 |
+
49,0,4,130,269,0,0,163,0,0,1,0
|
1132 |
+
61,1,1,134,234,0,0,145,0,2.6,2,1
|
1133 |
+
60,0,3,120,178,1,0,96,0,0,1,0
|
1134 |
+
67,1,4,120,237,0,0,71,0,1,2,1
|
1135 |
+
58,1,4,100,234,0,0,156,0,0.1,1,1
|
1136 |
+
47,1,4,110,275,0,2,118,1,1,2,1
|
1137 |
+
52,1,4,125,212,0,0,168,0,1,1,1
|
1138 |
+
62,1,2,128,208,1,2,140,0,0,1,0
|
1139 |
+
57,1,4,110,201,0,0,126,1,1.5,2,0
|
1140 |
+
58,1,4,146,218,0,0,105,0,2,2,1
|
1141 |
+
64,1,4,128,263,0,0,105,1,0.2,2,0
|
1142 |
+
51,0,3,120,295,0,2,157,0,0.6,1,0
|
1143 |
+
43,1,4,115,303,0,0,181,0,1.2,2,0
|
1144 |
+
42,0,3,120,209,0,0,173,0,0,2,0
|
1145 |
+
67,0,4,106,223,0,0,142,0,0.3,1,0
|
1146 |
+
76,0,3,140,197,0,1,116,0,1.1,2,0
|
1147 |
+
70,1,2,156,245,0,2,143,0,0,1,0
|
1148 |
+
57,1,2,124,261,0,0,141,0,0.3,1,1
|
1149 |
+
44,0,3,118,242,0,0,149,0,0.3,2,0
|
1150 |
+
58,0,2,136,319,1,2,152,0,0,1,1
|
1151 |
+
60,0,1,150,240,0,0,171,0,0.9,1,0
|
1152 |
+
44,1,3,120,226,0,0,169,0,0,1,0
|
1153 |
+
61,1,4,138,166,0,2,125,1,3.6,2,1
|
1154 |
+
42,1,4,136,315,0,0,125,1,1.8,2,1
|
1155 |
+
52,1,4,128,204,1,0,156,1,1,2,1
|
1156 |
+
59,1,3,126,218,1,0,134,0,2.2,2,1
|
1157 |
+
40,1,4,152,223,0,0,181,0,0,1,1
|
1158 |
+
42,1,3,130,180,0,0,150,0,0,1,0
|
1159 |
+
61,1,4,140,207,0,2,138,1,1.9,1,1
|
1160 |
+
66,1,4,160,228,0,2,138,0,2.3,1,0
|
1161 |
+
46,1,4,140,311,0,0,120,1,1.8,2,1
|
1162 |
+
71,0,4,112,149,0,0,125,0,1.6,2,0
|
1163 |
+
59,1,1,134,204,0,0,162,0,0.8,1,1
|
1164 |
+
64,1,1,170,227,0,2,155,0,0.6,2,0
|
1165 |
+
66,0,3,146,278,0,2,152,0,0,2,0
|
1166 |
+
39,0,3,138,220,0,0,152,0,0,2,0
|
1167 |
+
57,1,2,154,232,0,2,164,0,0,1,1
|
1168 |
+
58,0,4,130,197,0,0,131,0,0.6,2,0
|
1169 |
+
57,1,4,110,335,0,0,143,1,3,2,1
|
1170 |
+
47,1,3,130,253,0,0,179,0,0,1,0
|
1171 |
+
55,0,4,128,205,0,1,130,1,2,2,1
|
1172 |
+
35,1,2,122,192,0,0,174,0,0,1,0
|
1173 |
+
61,1,4,148,203,0,0,161,0,0,1,1
|
1174 |
+
58,1,4,114,318,0,1,140,0,4.4,3,1
|
1175 |
+
58,0,4,170,225,1,2,146,1,2.8,2,1
|
1176 |
+
58,1,2,125,220,0,0,144,0,0.4,2,0
|
1177 |
+
56,1,2,130,221,0,2,163,0,0,1,0
|
1178 |
+
56,1,2,120,240,0,0,169,0,0,3,0
|
1179 |
+
67,1,3,152,212,0,2,150,0,0.8,2,1
|
1180 |
+
55,0,2,132,342,0,0,166,0,1.2,1,0
|
1181 |
+
44,1,4,120,169,0,0,144,1,2.8,3,1
|
1182 |
+
63,1,4,140,187,0,2,144,1,4,1,1
|
1183 |
+
63,0,4,124,197,0,0,136,1,0,2,1
|
1184 |
+
41,1,2,120,157,0,0,182,0,0,1,0
|
1185 |
+
59,1,4,164,176,1,2,90,0,1,2,1
|
1186 |
+
57,0,4,140,241,0,0,123,1,0.2,2,1
|
1187 |
+
45,1,1,110,264,0,0,132,0,1.2,2,1
|
1188 |
+
68,1,4,144,193,1,0,141,0,3.4,2,1
|
1189 |
+
57,1,4,130,131,0,0,115,1,1.2,2,1
|
1190 |
+
57,0,2,130,236,0,2,174,0,0,2,1
|
1191 |
+
38,1,3,138,175,0,0,173,0,0,1,0
|
main.py
ADDED
@@ -0,0 +1,284 @@
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|
|
|
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|
|
|
|
|
|
|
1 |
+
import streamlit as st
|
2 |
+
import pandas as pd
|
3 |
+
import numpy as np
|
4 |
+
import matplotlib.pyplot as plt
|
5 |
+
from sklearn.model_selection import train_test_split
|
6 |
+
from sklearn.ensemble import GradientBoostingRegressor
|
7 |
+
from sklearn.ensemble import RandomForestRegressor, VotingRegressor
|
8 |
+
from sklearn.tree import DecisionTreeRegressor
|
9 |
+
from sklearn.linear_model import LinearRegression
|
10 |
+
from sklearn.neighbors import KNeighborsRegressor
|
11 |
+
from sklearn.svm import SVR
|
12 |
+
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
|
13 |
+
from sklearn.neural_network import MLPRegressor
|
14 |
+
from lightgbm import LGBMRegressor
|
15 |
+
from xgboost import XGBRegressor
|
16 |
+
import math
|
17 |
+
|
18 |
+
st.title('Heart Disease Prediction Application')
|
19 |
+
st.write('''
|
20 |
+
Please fill in the attributes below, then hit the Predict button
|
21 |
+
to get your results.
|
22 |
+
''')
|
23 |
+
|
24 |
+
st.header('Input Attributes')
|
25 |
+
age = st.slider('Your Age (Years)', min_value=0.0, max_value=100.0, value=50.0, step=1.0)
|
26 |
+
st.write(''' ''')
|
27 |
+
gen = st.radio("Your Gender", ('Male', 'Female'))
|
28 |
+
st.write(''' ''')
|
29 |
+
cp = st.radio("Chest Pain", ('Typical Angina', 'Atypical Angina', 'Non-Anginal Pain', 'Asymptomatic'))
|
30 |
+
st.write(''' ''')
|
31 |
+
resting_bp = st.slider('Resting Blood Pressure (In mm Hg)', min_value=0.0, max_value=200.0, value=100.0, step=1.0)
|
32 |
+
st.write(''' ''')
|
33 |
+
serum = st.slider('Serum Cholesterol (In mm mg/dl)', min_value=0.0, max_value=400.0, value=200.0, step=1.0)
|
34 |
+
st.write(''' ''')
|
35 |
+
bs = st.radio("Is Your Fasting Blood Sugar > 120 mg/dl?", ('Yes', 'No'))
|
36 |
+
st.write(''' ''')
|
37 |
+
re = st.radio("Resting Electrocardiogram Results", ('Normal', 'ST-T Wave Abnormality (T Wave Inversions and/or ST Elevation or Depression of > 0.05 mV)', 'Showing Probable or Definite Left Ventricular Hypertrophy by Estes Criteria'))
|
38 |
+
st.write(''' ''')
|
39 |
+
max_heart = st.slider('Maximum Heart Rate', min_value=0.0, max_value=300.0, value=150.0, step=1.0)
|
40 |
+
st.write(''' ''')
|
41 |
+
|
42 |
+
ex = st.radio("Exercise Induced Angina", ('Yes', 'No'))
|
43 |
+
st.write(''' ''')
|
44 |
+
oldpeak = st.slider('ST Depression Induced by Exercise Relative to Rest', min_value=-5.0, max_value=5.0, value=0.0, step=0.01)
|
45 |
+
st.write(''' ''')
|
46 |
+
sp = st.radio("The Slope of the Peak Exercise ST Segment", ('Upsloping', 'Flat', 'Downsloping'))
|
47 |
+
st.write(''' ''')
|
48 |
+
|
49 |
+
selected_models = st.multiselect("Choose Regressor Models", ('Random Forest',
|
50 |
+
'Linear Regression',
|
51 |
+
'K-Nearest Neighbors',
|
52 |
+
'Decision Tree',
|
53 |
+
'Support Vector Machine',
|
54 |
+
'Gradient Boosting Regression',
|
55 |
+
'XGBoost Regression',
|
56 |
+
'LightGBM Regression'))
|
57 |
+
st.write(''' ''')
|
58 |
+
|
59 |
+
# Initialize an empty list to store the selected models
|
60 |
+
models_to_run = []
|
61 |
+
|
62 |
+
# Check which models were selected and add them to the models_to_run list
|
63 |
+
if 'Random Forest' in selected_models:
|
64 |
+
models_to_run.append(RandomForestRegressor())
|
65 |
+
|
66 |
+
if 'Linear Regression' in selected_models:
|
67 |
+
models_to_run.append(LinearRegression())
|
68 |
+
|
69 |
+
if 'K-Nearest Neighbors' in selected_models:
|
70 |
+
models_to_run.append(KNeighborsRegressor())
|
71 |
+
|
72 |
+
if 'Decision Tree' in selected_models:
|
73 |
+
models_to_run.append(DecisionTreeRegressor())
|
74 |
+
|
75 |
+
if 'Support Vector Machine' in selected_models:
|
76 |
+
models_to_run.append(SVR())
|
77 |
+
|
78 |
+
if 'Gradient Boosting Regression' in selected_models:
|
79 |
+
models_to_run.append(GradientBoostingRegressor())
|
80 |
+
|
81 |
+
if 'XGBoost Regression' in selected_models:
|
82 |
+
models_to_run.append(XGBRegressor())
|
83 |
+
|
84 |
+
if 'LightGBM Regression' in selected_models:
|
85 |
+
models_to_run.append(LGBMRegressor())
|
86 |
+
|
87 |
+
if 'Neural Network (MLP) Regression' in selected_models:
|
88 |
+
models_to_run.append(MLPRegressor())
|
89 |
+
|
90 |
+
# gender conversion
|
91 |
+
if gen == "Male":
|
92 |
+
gender = 1
|
93 |
+
else:
|
94 |
+
gender = 0
|
95 |
+
|
96 |
+
# Chest Pain
|
97 |
+
if cp == "Typical Angina":
|
98 |
+
chest = 1
|
99 |
+
elif cp == "Atypical Angina":
|
100 |
+
chest = 2
|
101 |
+
elif cp == "Non-Anginal Pain":
|
102 |
+
chest = 3
|
103 |
+
else:
|
104 |
+
chest = 4
|
105 |
+
|
106 |
+
# blood_sugar conversion
|
107 |
+
if bs == "Yes":
|
108 |
+
blood_sugar = 1
|
109 |
+
else:
|
110 |
+
blood_sugar = 0
|
111 |
+
|
112 |
+
# electro conversion
|
113 |
+
if re == "Normal":
|
114 |
+
electro = 0
|
115 |
+
elif re == "ST-T Wave Abnormality (T Wave Inversions and/or ST Elevation or Depression of > 0.05 mV)":
|
116 |
+
electro = 1
|
117 |
+
else:
|
118 |
+
electro = 2
|
119 |
+
|
120 |
+
# exercise conversion
|
121 |
+
if ex == "Yes":
|
122 |
+
exercise = 1
|
123 |
+
else:
|
124 |
+
exercise = 0
|
125 |
+
|
126 |
+
# slope conversion
|
127 |
+
if sp == "Upsloping":
|
128 |
+
slope = 1
|
129 |
+
elif sp == "Flat":
|
130 |
+
slope = 2
|
131 |
+
else:
|
132 |
+
slope = 3
|
133 |
+
|
134 |
+
user_input = np.array([age, gender, chest, blood_sugar, resting_bp, serum, electro, max_heart,
|
135 |
+
exercise, oldpeak, slope]).reshape(1, -1)
|
136 |
+
|
137 |
+
# import dataset
|
138 |
+
def get_dataset():
|
139 |
+
data = pd.read_csv('heart.csv')
|
140 |
+
|
141 |
+
return data
|
142 |
+
|
143 |
+
def generate_model_labels(model_names):
|
144 |
+
model_labels = []
|
145 |
+
for name in model_names:
|
146 |
+
words = name.split()
|
147 |
+
if len(words) > 1:
|
148 |
+
# Multiple words, use initials
|
149 |
+
label = "".join(word[0] for word in words)
|
150 |
+
else:
|
151 |
+
# Single word, take the first 3 letters
|
152 |
+
label = name[:3]
|
153 |
+
model_labels.append(label)
|
154 |
+
return model_labels
|
155 |
+
|
156 |
+
if st.button('Submit'):
|
157 |
+
df = get_dataset()
|
158 |
+
|
159 |
+
# fix column names
|
160 |
+
df.columns = (["age", "sex", "chest pain type", "resting bp s", "cholesterol",
|
161 |
+
"fasting blood sugar", "resting ecg", "max heart rate", "exercise angina",
|
162 |
+
"oldpeak", "ST slope", "target"])
|
163 |
+
|
164 |
+
# Split the dataset into train and test
|
165 |
+
X = df.drop('target', axis=1)
|
166 |
+
y = df['target']
|
167 |
+
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
|
168 |
+
|
169 |
+
# Create two columns to divide the screen
|
170 |
+
left_column, right_column = st.columns(2)
|
171 |
+
|
172 |
+
# Left column content
|
173 |
+
with left_column:
|
174 |
+
# Create a VotingRegressor with the selected models
|
175 |
+
ensemble = VotingRegressor(
|
176 |
+
estimators=[('rf', RandomForestRegressor()), ('lr', LinearRegression()), ('dt', DecisionTreeRegressor())]
|
177 |
+
)
|
178 |
+
|
179 |
+
# Fit the voting regressor to the training data
|
180 |
+
ensemble.fit(X_train, y_train)
|
181 |
+
|
182 |
+
# Make predictions on the test set
|
183 |
+
ensemble_predictions = ensemble.predict(user_input)
|
184 |
+
|
185 |
+
# Evaluate the model's performance on the test set
|
186 |
+
ensemble_r2 = r2_score(y_test, ensemble.predict(X_test))
|
187 |
+
ensemble_mse = mean_squared_error(y_test, ensemble.predict(X_test))
|
188 |
+
ensemble_mae = mean_absolute_error(y_test, ensemble.predict(X_test))
|
189 |
+
ensemble_rmse = np.sqrt(ensemble_mse)
|
190 |
+
|
191 |
+
st.write(f'According to Ensemble Model, You have a predicted value of: {ensemble_predictions[0]:.2f}')
|
192 |
+
st.write('Ensemble Model R-squared (R2) Score:', ensemble_r2)
|
193 |
+
st.write('Ensemble Model Root Mean Squared Error (RMSE):', ensemble_rmse)
|
194 |
+
st.write('Ensemble Model Mean Squared Error (MSE):', ensemble_mse)
|
195 |
+
st.write('Ensemble Model Mean Absolute Error (MAE):', ensemble_mae)
|
196 |
+
st.write('------------------------------------------------------------------------------------------------------')
|
197 |
+
|
198 |
+
# Add padding between the columns
|
199 |
+
st.empty()
|
200 |
+
|
201 |
+
# Right column content
|
202 |
+
with right_column:
|
203 |
+
for model in models_to_run:
|
204 |
+
# Train the selected model
|
205 |
+
model.fit(X_train, y_train)
|
206 |
+
|
207 |
+
# Make predictions on the test set
|
208 |
+
model_predictions = model.predict(user_input)
|
209 |
+
|
210 |
+
# Evaluate the model's performance on the test set
|
211 |
+
model_r2 = r2_score(y_test, model.predict(X_test))
|
212 |
+
model_mse = mean_squared_error(y_test, model.predict(X_test))
|
213 |
+
model_mae = mean_absolute_error(y_test, model.predict(X_test))
|
214 |
+
model_rmse = np.sqrt(model_mse)
|
215 |
+
|
216 |
+
st.write(f'According to {type(model).__name__} Model, You have a predicted value of: {model_predictions[0]:.2f}')
|
217 |
+
st.write(f'{type(model).__name__} Model R-squared (R2) Score:', model_r2)
|
218 |
+
st.write(f'{type(model).__name__} Model Root Mean Squared Error (RMSE):', model_rmse)
|
219 |
+
st.write(f'{type(model).__name__} Model Mean Squared Error (MSE):', model_mse)
|
220 |
+
st.write(f'{type(model).__name__} Model Mean Absolute Error (MAE):', model_mae)
|
221 |
+
st.write('------------------------------------------------------------------------------------------------------')
|
222 |
+
|
223 |
+
# Initialize lists to store model names and their respective performance metrics
|
224 |
+
model_names = ['Ensemble']
|
225 |
+
r2_scores = [ensemble_r2]
|
226 |
+
rmses = [ensemble_rmse]
|
227 |
+
mses = [ensemble_mse]
|
228 |
+
maes = [ensemble_mae]
|
229 |
+
|
230 |
+
# Loop through the selected models to compute their performance metrics
|
231 |
+
for model in models_to_run:
|
232 |
+
model_names.append(type(model).__name__)
|
233 |
+
model.fit(X_train, y_train)
|
234 |
+
model_predictions = model.predict(X_test)
|
235 |
+
model_r2 = r2_score(y_test, model_predictions)
|
236 |
+
model_mse = mean_squared_error(y_test, model_predictions)
|
237 |
+
model_mae = mean_absolute_error(y_test, model_predictions)
|
238 |
+
model_rmse = np.sqrt(model_mse)
|
239 |
+
r2_scores.append(model_r2)
|
240 |
+
rmses.append(model_rmse)
|
241 |
+
mses.append(model_mse)
|
242 |
+
maes.append(model_mae)
|
243 |
+
|
244 |
+
# Create a DataFrame to store the performance metrics
|
245 |
+
metrics_df = pd.DataFrame({
|
246 |
+
'Model': model_names,
|
247 |
+
'R-squared (R2)': r2_scores,
|
248 |
+
'Root Mean Squared Error (RMSE)': rmses,
|
249 |
+
'Mean Squared Error (MSE)': mses,
|
250 |
+
'Mean Absolute Error (MAE)': maes
|
251 |
+
})
|
252 |
+
|
253 |
+
# Get the model labels
|
254 |
+
model_labels = generate_model_labels(metrics_df['Model'])
|
255 |
+
|
256 |
+
# Plot the comparison graphs
|
257 |
+
plt.figure(figsize=(12, 10))
|
258 |
+
|
259 |
+
# R-squared (R2) score comparison
|
260 |
+
plt.subplot(2, 2, 1)
|
261 |
+
plt.bar(model_labels, metrics_df['R-squared (R2)'], color='skyblue')
|
262 |
+
plt.title('R-squared (R2) Score Comparison')
|
263 |
+
plt.ylim(0, 1)
|
264 |
+
|
265 |
+
# Root Mean Squared Error (RMSE) comparison
|
266 |
+
plt.subplot(2, 2, 2)
|
267 |
+
plt.bar(model_labels, metrics_df['Root Mean Squared Error (RMSE)'], color='orange')
|
268 |
+
plt.title('Root Mean Squared Error (RMSE) Comparison')
|
269 |
+
|
270 |
+
# Mean Squared Error (MSE) comparison
|
271 |
+
plt.subplot(2, 2, 3)
|
272 |
+
plt.bar(model_labels, metrics_df['Mean Squared Error (MSE)'], color='green')
|
273 |
+
plt.title('Mean Squared Error (MSE) Comparison')
|
274 |
+
|
275 |
+
# Mean Absolute Error (MAE) comparison
|
276 |
+
plt.subplot(2, 2, 4)
|
277 |
+
plt.bar(model_labels, metrics_df['Mean Absolute Error (MAE)'], color='purple')
|
278 |
+
plt.title('Mean Absolute Error (MAE) Comparison')
|
279 |
+
|
280 |
+
# Adjust layout to prevent overlapping of titles
|
281 |
+
plt.tight_layout()
|
282 |
+
|
283 |
+
# Display the graphs in Streamlit
|
284 |
+
st.pyplot()
|
requirements.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
lightgbm==4.0.0
|
2 |
+
matplotlib==3.7.2
|
3 |
+
numpy==1.25.1
|
4 |
+
pandas==2.0.3
|
5 |
+
scikit_learn==1.3.0
|
6 |
+
streamlit==1.25.0
|
7 |
+
xgboost==1.7.6
|