Adding all files

samyak24jain · Mar 15, 2017 · 39a4c76 · 39a4c76 · aaljubiri · Mar 5, 2018
commit 39a4c76
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Showing 2 changed files with 249 additions and 0 deletions.
diff --git a/SPECTF_New.csv b/SPECTF_New.csv
@@ -0,0 +1,111 @@
+Attr_1,Attr_2,Attr_3,Attr_4,Attr_5,Attr_6,Attr_7,Attr_8,Attr_9,Attr_10,Attr_11,Attr_12,Attr_13,Attr_14,Attr_15,Attr_16,Attr_17,Attr_18,Attr_19,Attr_20,Attr_21,Attr_22,Attr_23,Attr_24,Attr_25,Attr_26,Attr_27,Attr_28,Attr_29,Attr_30,Attr_31,Attr_32,Attr_33,Attr_34,Attr_35,Attr_36,Attr_37,Attr_38,Attr_39,Attr_40,Attr_41,Attr_42,Attr_43,Attr_44,Class
+57,69,68,75,69,74,73,71,57,61,72,74,73,69,61,58,60,55,71,62,79,70,77,71,65,63,69,55,61,68,75,74,63,64,63,58,69,67,79,77,72,70,61,65,Yes
+76,59,82,76,80,56,74,67,67,58,77,71,56,68,60,64,66,66,66,66,82,79,40,45,49,48,75,70,64,35,71,60,39,28,71,66,73,61,71,49,53,45,29,15,Yes
+65,62,67,68,65,67,71,71,64,56,73,72,68,69,56,57,67,62,74,66,80,76,80,78,53,47,48,36,68,65,74,73,60,60,67,63,74,63,77,79,68,70,59,56,Yes
+70,54,66,66,76,46,74,58,68,52,81,58,67,58,68,32,73,59,76,51,82,57,76,54,58,30,69,41,59,59,67,73,62,55,60,55,65,56,65,44,73,36,51,28,Yes
+64,64,70,75,70,71,74,71,59,60,62,68,70,66,69,72,69,69,61,63,56,60,62,66,69,71,62,63,67,65,62,58,52,51,67,66,61,56,64,65,71,73,57,63,Yes
+65,67,69,76,62,68,65,66,65,64,74,73,60,75,66,63,64,62,73,65,77,74,69,69,66,59,68,59,69,69,76,79,65,63,60,60,69,64,69,74,69,70,62,57,No
+68,70,69,74,65,70,72,73,71,68,73,70,68,74,63,65,65,63,70,67,74,70,69,72,62,63,63,61,66,66,74,80,70,72,69,67,60,57,69,62,66,75,50,50,No
+64,66,68,71,62,64,74,73,63,67,66,74,70,74,59,64,75,73,70,66,79,81,79,78,61,62,76,72,67,67,71,75,65,62,70,69,68,65,75,72,62,64,57,54,No
+58,63,80,71,76,70,70,71,64,63,74,78,77,75,62,61,62,56,71,52,82,71,84,85,71,71,57,47,42,39,70,70,50,70,50,46,58,60,76,73,82,77,65,66,No
+77,79,79,77,74,76,76,81,65,68,66,66,74,73,72,68,67,73,63,62,72,67,76,69,68,64,64,61,69,68,73,75,70,66,64,70,70,70,73,76,79,73,65,63,No
+54,53,73,68,77,65,65,72,60,50,69,64,73,75,74,76,69,72,66,60,74,67,68,69,69,78,56,67,69,61,76,71,67,64,73,70,55,49,70,61,73,70,61,61,Yes
+59,57,67,71,66,68,68,70,56,62,77,61,67,71,75,71,67,64,62,54,64,75,71,72,76,79,75,70,71,77,71,69,56,54,62,64,56,53,71,68,64,63,56,56,No
+68,64,74,80,76,72,78,75,67,64,75,80,78,77,66,64,67,67,70,60,78,82,70,68,63,60,64,60,54,56,70,73,59,65,55,58,50,51,73,70,69,65,42,41,No
+78,73,68,74,68,69,63,74,68,67,73,73,66,71,64,67,66,68,61,66,75,71,60,62,64,66,65,67,66,62,74,75,61,61,63,66,68,65,71,62,69,67,61,59,Yes
+64,72,70,74,63,70,73,74,61,69,65,75,61,71,65,67,73,69,72,71,74,81,69,69,65,65,78,75,67,71,66,74,54,60,64,68,64,68,69,71,65,65,56,58,Yes
+70,64,68,67,76,68,76,69,67,64,69,65,62,65,70,67,74,68,65,65,74,75,64,69,63,63,64,64,56,61,62,68,66,66,62,58,57,48,75,64,79,74,59,58,No
+71,75,78,78,68,67,75,72,67,68,72,75,74,74,67,66,66,67,66,66,78,80,73,75,67,72,67,67,67,65,77,78,61,64,63,66,51,57,77,67,78,76,60,59,No
+56,56,63,66,76,76,68,73,62,54,65,62,70,65,74,72,65,64,64,53,64,56,63,61,73,64,66,60,66,74,76,75,65,61,71,73,60,53,61,73,67,68,59,56,No
+72,66,75,67,61,59,64,63,61,67,75,76,66,48,61,56,69,68,68,68,68,75,69,67,68,71,70,68,48,47,74,79,63,75,62,62,64,67,56,52,69,83,59,73,Yes
+71,76,74,79,71,69,77,75,64,64,75,78,69,70,59,52,71,62,47,50,68,65,67,62,56,48,58,50,65,63,75,75,64,62,60,56,64,63,61,65,64,58,51,38,Yes
+68,70,66,72,63,71,77,82,61,63,61,62,61,65,65,62,72,77,69,73,72,78,74,77,69,69,77,74,64,63,66,70,58,60,65,69,75,77,77,77,69,77,65,64,No
+64,53,74,70,65,63,70,70,57,57,64,64,73,74,65,59,65,63,63,62,72,73,79,76,75,68,60,58,69,66,72,76,64,65,63,65,63,65,75,80,74,67,71,67,No
+74,73,72,79,66,61,76,66,65,64,78,74,62,57,48,36,62,50,67,63,79,70,61,57,52,36,69,49,55,65,74,73,58,60,64,62,73,69,62,67,60,56,53,46,Yes
+69,64,73,72,49,70,66,71,57,56,64,62,76,74,65,62,63,58,63,63,75,76,78,80,75,77,51,62,74,68,77,77,70,68,68,64,59,58,69,66,74,75,62,59,No
+70,75,72,72,67,71,71,78,63,67,73,76,71,74,59,61,67,64,74,71,77,77,70,72,61,61,62,58,63,69,76,75,64,65,66,67,68,70,70,71,64,67,56,54,No
+63,63,69,72,67,62,65,57,68,53,68,71,73,78,64,58,61,54,70,61,72,67,72,68,57,55,61,53,66,60,76,77,73,66,66,58,75,70,77,67,78,68,64,58,Yes
+63,58,66,55,56,58,69,74,44,48,63,60,76,67,73,73,58,66,68,63,72,74,70,72,77,75,70,71,71,67,75,73,60,59,71,70,65,62,70,69,71,70,58,61,Yes
+71,61,74,74,76,74,69,56,68,78,71,78,58,64,70,72,71,68,72,71,79,78,67,68,63,60,67,67,76,74,67,79,67,71,71,64,70,74,83,76,74,73,54,54,No
+67,57,73,78,63,68,72,73,61,59,59,76,71,72,69,66,70,68,65,77,79,68,71,75,62,66,70,78,68,69,70,76,65,65,66,67,62,72,69,72,70,68,60,59,No
+62,67,68,70,65,70,73,77,69,70,69,73,71,74,71,71,76,75,66,67,73,73,70,74,63,67,58,68,66,69,78,79,69,70,71,73,72,71,73,77,72,76,64,66,No
+70,72,65,64,71,69,75,76,65,68,69,79,65,74,62,67,73,73,53,51,72,69,74,68,49,44,52,48,65,68,64,72,57,58,66,69,64,69,76,73,66,69,50,53,Yes
+70,64,52,58,75,89,70,72,26,30,46,55,54,59,40,40,39,37,35,17,59,52,66,72,23,46,8,31,17,20,49,72,61,70,31,13,40,23,31,30,57,67,41,57,Yes
+67,65,77,74,67,66,67,70,65,64,75,78,66,74,62,60,65,65,73,72,75,76,74,81,66,65,65,63,63,67,76,80,63,64,63,64,73,72,76,75,72,74,65,64,No
+76,72,73,69,67,73,74,72,60,65,73,66,66,73,68,67,69,70,60,58,66,76,69,75,65,64,63,60,74,71,77,79,61,68,71,70,62,63,73,76,62,69,52,59,No
+61,63,58,62,56,60,67,75,61,57,64,71,56,59,66,62,73,76,71,75,83,84,69,71,69,69,69,71,49,43,59,64,56,61,64,72,70,73,69,70,65,68,65,62,Yes
+74,81,80,78,70,69,74,77,69,71,73,76,68,68,62,61,68,68,67,70,74,80,68,74,57,62,57,65,61,65,71,76,63,65,67,67,70,74,63,72,68,70,61,64,No
+75,71,54,51,53,50,68,69,46,55,11,12,43,48,61,60,73,77,46,45,68,59,65,73,54,60,55,66,54,41,53,52,58,63,72,65,33,23,64,54,36,46,45,52,Yes
+68,64,65,68,63,64,77,73,75,72,80,77,70,71,61,61,73,68,63,62,76,73,69,69,48,59,62,44,66,59,75,74,64,64,63,61,70,69,74,67,51,48,45,45,Yes
+70,72,70,70,65,69,70,65,69,72,77,78,68,71,63,70,69,71,68,74,75,74,69,65,71,68,63,64,66,68,73,75,67,67,69,65,66,71,69,65,78,75,66,63,No
+76,75,68,78,71,72,72,75,61,65,67,70,67,75,60,58,63,67,59,63,67,72,74,73,56,56,52,52,67,68,73,78,65,68,61,67,69,74,77,75,74,70,63,61,No
+69,66,62,75,67,71,72,76,69,70,66,69,71,80,66,64,71,77,65,61,72,67,71,69,65,57,69,65,68,65,76,73,63,64,69,70,72,72,69,68,70,73,63,59,Yes
+80,76,75,75,69,68,74,75,77,77,76,78,74,70,66,65,67,75,74,73,74,77,68,67,61,58,60,67,61,63,75,75,66,62,59,61,77,74,69,67,65,66,61,58,No
+72,62,69,67,78,82,74,65,69,63,70,70,72,74,70,71,72,75,66,65,73,78,74,79,74,69,69,70,71,69,72,70,62,65,65,71,63,60,69,73,67,71,56,58,Yes
+71,75,76,74,71,68,67,68,69,75,75,74,59,58,71,69,70,74,74,72,71,78,69,72,69,72,63,61,59,70,72,75,61,66,70,71,59,64,64,60,72,61,55,63,Yes
+60,51,75,60,65,45,64,55,55,61,66,74,61,50,62,41,70,63,60,62,76,69,70,54,51,47,77,80,69,48,74,59,72,57,76,68,69,63,62,53,57,31,46,30,Yes
+74,73,72,75,63,62,67,67,73,74,75,79,70,71,64,67,65,69,79,78,81,80,71,73,60,62,69,67,69,69,75,75,66,67,67,66,71,73,66,69,62,65,55,56,No
+75,75,70,77,67,75,75,75,67,66,74,73,68,72,64,70,76,70,67,63,74,75,72,68,69,68,75,69,71,74,75,76,63,70,71,69,66,63,70,73,66,68,58,59,No
+62,67,64,70,59,58,67,74,60,66,68,68,73,71,60,63,64,74,64,65,74,77,69,73,59,58,58,67,65,69,78,76,61,62,64,67,72,74,71,71,71,69,66,61,No
+61,60,60,62,64,72,68,67,74,68,76,70,74,71,76,74,74,70,75,66,69,62,65,60,66,65,68,59,64,59,72,65,55,56,66,66,66,60,60,58,60,67,49,52,Yes
+78,76,71,72,65,71,75,74,70,64,65,76,65,73,59,57,65,65,73,73,81,80,68,66,59,44,62,63,62,59,71,74,59,60,64,61,77,76,62,67,44,42,44,30,Yes
+70,66,61,66,61,58,69,69,72,68,62,71,71,71,63,59,74,75,70,69,83,77,73,70,41,37,39,40,58,46,75,73,65,66,67,69,70,66,70,64,60,55,49,41,Yes
+73,74,65,66,69,69,67,81,65,62,69,65,67,68,70,67,72,68,66,66,78,61,67,70,67,63,68,66,70,70,69,70,66,66,66,69,72,70,74,76,75,72,67,63,No
+79,78,66,63,69,62,78,70,72,71,73,78,75,65,68,62,76,71,68,68,72,71,52,48,23,26,66,59,66,66,72,74,56,58,67,63,66,69,70,74,34,33,11,12,Yes
+66,81,75,72,69,67,74,81,67,73,76,75,69,64,58,57,74,74,56,62,74,78,55,50,37,38,65,73,61,61,73,73,61,63,67,66,60,71,46,58,35,37,24,20,Yes
+76,77,69,70,64,69,76,80,50,56,70,74,61,55,54,59,65,65,53,72,76,73,51,47,43,46,66,80,73,73,71,65,60,53,69,70,54,62,64,66,51,42,28,22,Yes
+66,64,64,66,67,72,72,77,65,66,64,64,57,61,70,66,74,78,73,72,77,75,60,58,58,61,63,60,59,60,65,65,58,58,69,73,76,75,73,75,72,72,62,62,No
+72,63,68,62,72,63,79,61,57,49,76,75,55,57,43,37,54,52,57,56,78,78,57,55,35,37,57,57,41,35,67,70,75,72,53,46,63,62,53,43,38,35,32,26,Yes
+32,41,76,34,65,53,30,54,16,51,61,43,74,70,62,21,34,42,61,37,58,66,54,49,17,19,11,42,53,30,71,9,61,16,31,43,67,61,29,31,17,8,18,11,Yes
+69,78,74,76,70,67,69,73,68,75,75,76,71,77,58,61,66,70,67,72,76,72,56,62,56,61,57,62,67,73,76,74,58,63,64,68,66,76,70,72,64,68,60,56,No
+40,73,61,74,61,67,56,75,64,71,75,76,63,61,56,67,70,73,69,60,74,63,65,73,57,63,66,69,60,66,72,71,57,64,64,69,67,68,59,66,55,62,51,60,Yes
+65,66,71,72,67,75,76,83,70,74,70,76,70,68,63,71,69,76,72,73,76,80,69,68,57,59,59,56,62,68,75,73,65,61,69,73,66,70,63,65,65,67,53,42,Yes
+80,74,82,77,74,74,73,77,64,61,73,73,73,72,62,66,66,68,63,61,69,75,70,75,59,64,63,69,66,66,70,77,62,62,60,65,67,66,74,71,66,71,59,62,Yes
+67,64,73,75,77,77,74,70,65,62,74,75,65,67,68,70,66,69,67,60,74,75,62,64,66,71,62,61,64,69,73,76,64,66,61,64,65,60,68,75,74,80,67,68,No
+71,71,69,71,65,65,76,73,67,66,69,79,76,76,63,62,74,71,58,66,76,78,78,74,68,66,69,68,68,68,71,74,59,57,65,66,73,71,78,74,68,70,57,55,No
+63,61,75,72,68,71,69,70,64,56,70,75,76,73,70,72,76,73,68,68,79,76,68,76,66,73,58,68,72,75,72,75,65,61,66,67,58,57,72,72,68,71,57,58,No
+70,69,67,66,68,60,76,77,70,67,71,71,79,79,70,64,77,76,63,54,68,65,72,67,59,52,56,50,67,61,74,72,67,59,68,66,73,68,74,68,77,69,65,62,Yes
+59,75,70,76,62,70,65,74,65,67,75,76,70,73,63,61,74,67,78,69,75,73,70,68,67,64,79,68,70,75,76,77,59,63,72,69,64,64,65,72,61,61,51,55,No
+64,58,70,78,66,66,74,72,66,63,72,74,72,65,59,58,64,67,63,61,75,74,67,58,60,53,61,58,62,61,76,72,64,65,62,64,77,71,74,72,74,73,70,59,No
+59,58,69,74,71,73,70,68,57,55,64,68,76,75,67,66,66,63,72,67,77,75,80,75,73,69,65,61,71,75,71,77,65,66,66,66,61,56,74,71,72,69,62,60,No
+72,70,75,80,73,70,76,73,66,56,72,70,73,75,67,65,69,69,73,72,81,77,80,79,67,64,64,66,69,68,70,75,63,59,66,63,74,77,81,78,79,75,65,66,No
+68,59,77,69,77,64,75,71,64,53,67,63,66,74,74,62,67,65,70,61,73,66,78,75,72,67,67,53,72,64,71,77,66,60,73,73,70,58,70,59,77,71,70,63,Yes
+61,68,62,70,76,79,71,71,73,77,75,77,68,73,72,72,71,68,72,75,81,80,72,73,61,67,62,61,66,68,72,71,67,67,69,69,66,71,67,66,74,71,58,58,No
+75,73,72,77,68,67,76,73,67,65,76,78,66,74,67,62,70,69,66,64,77,74,75,73,67,69,73,69,68,68,72,74,61,61,70,67,72,71,79,75,77,75,67,71,No
+70,66,66,68,71,69,64,61,68,67,50,53,73,71,73,63,71,73,80,81,82,82,67,71,52,47,67,64,66,67,66,75,58,62,65,65,71,67,70,71,67,64,52,53,Yes
+69,68,75,74,78,72,75,72,61,57,72,71,75,72,70,68,68,62,62,66,67,67,74,78,64,68,62,62,64,63,75,77,66,67,69,65,62,63,64,59,74,75,63,67,Yes
+73,80,78,78,75,73,78,75,70,66,77,77,67,71,60,63,71,74,77,75,74,76,61,67,60,60,64,63,62,58,72,77,59,58,65,70,68,69,64,64,63,63,56,51,No
+55,66,58,75,71,77,68,73,63,69,77,78,60,69,59,60,69,69,67,70,72,80,67,72,53,60,70,58,46,67,68,84,69,79,70,74,62,71,67,66,63,61,53,60,Yes
+66,67,63,70,69,70,73,72,61,62,68,68,70,71,71,67,71,69,65,65,76,78,71,70,65,64,68,71,70,71,73,74,58,63,68,71,70,72,77,79,79,79,66,66,No
+70,69,60,62,58,60,71,77,69,69,73,68,68,70,69,65,76,75,63,64,67,74,56,60,54,44,68,69,68,68,74,73,61,59,68,67,64,68,64,76,64,61,54,49,Yes
+75,72,75,79,72,68,79,77,69,66,73,77,67,73,57,58,69,69,67,65,77,68,69,65,58,54,68,60,67,66,75,78,63,66,68,64,72,69,73,61,52,44,34,37,Yes
+61,76,71,68,77,69,77,69,64,75,71,81,75,72,71,69,70,73,61,71,69,79,64,65,62,66,61,65,71,68,67,71,59,64,66,65,60,68,74,71,69,68,63,59,No
+76,73,74,76,60,69,76,76,68,69,78,79,57,62,69,69,67,66,73,69,80,81,58,68,75,69,73,70,58,65,79,76,74,71,66,64,65,62,78,68,75,68,62,60,No
+59,62,72,74,66,66,74,76,63,67,72,76,71,74,66,64,70,69,63,66,70,72,70,76,65,69,61,66,64,65,67,72,57,63,65,71,67,69,77,78,77,76,70,70,No
+69,71,70,78,61,63,67,65,59,59,66,69,71,75,65,58,60,55,62,59,67,66,74,74,64,60,57,54,70,73,69,76,62,64,61,61,66,65,72,73,68,68,59,63,Yes
+62,66,66,68,73,76,68,71,62,62,63,68,74,75,63,68,71,68,58,58,65,66,65,76,64,70,63,60,70,72,71,77,67,69,72,71,63,63,70,72,75,79,62,59,No
+59,68,69,67,69,59,78,73,66,65,77,73,74,66,66,55,71,66,69,68,75,73,80,79,69,65,69,66,68,65,75,71,59,61,65,64,73,71,81,75,74,65,69,66,No
+70,65,65,62,68,67,77,74,62,61,66,61,69,74,64,62,71,71,74,70,77,78,69,70,67,65,67,70,49,48,73,71,65,73,73,73,75,71,73,72,73,70,65,64,Yes
+59,52,70,67,73,66,72,61,58,52,72,71,70,77,66,65,67,55,61,57,68,66,72,74,63,64,56,54,67,54,76,74,65,67,66,56,62,56,72,62,74,74,64,67,Yes
+62,56,66,57,74,75,68,59,65,59,74,66,71,65,67,69,66,66,71,72,80,74,71,63,62,72,62,66,66,61,73,68,59,59,63,62,73,73,76,67,77,71,62,58,Yes
+74,69,75,70,70,74,77,77,65,67,73,72,69,73,68,66,69,68,67,63,78,74,71,63,66,61,68,63,74,69,74,75,65,61,66,67,63,61,71,68,66,65,54,57,No
+67,66,65,77,66,70,72,72,72,67,76,72,73,76,74,71,74,73,69,61,78,70,76,73,70,70,62,51,70,68,79,77,75,68,72,71,69,63,65,61,73,73,64,67,Yes
+71,62,70,64,67,64,79,65,70,69,72,71,68,65,61,61,73,71,75,74,80,74,54,47,53,37,77,68,72,59,72,68,60,60,73,70,66,65,64,55,61,41,51,46,Yes
+64,60,71,69,71,65,66,64,68,59,63,67,68,64,73,59,73,60,72,56,77,68,69,69,66,59,57,40,55,53,69,63,71,63,66,55,66,58,65,65,75,64,61,56,Yes
+64,70,71,69,72,70,75,78,61,66,69,68,68,70,71,70,75,76,73,72,80,78,79,81,74,70,72,79,73,75,77,73,65,64,72,72,59,62,71,74,68,67,58,57,No
+72,74,67,69,69,70,74,81,66,70,73,78,69,80,65,65,70,77,69,70,73,79,64,68,56,58,67,64,68,68,69,74,62,67,66,70,73,77,74,77,71,72,63,65,No
+62,71,78,84,64,68,72,74,53,57,71,70,71,72,54,53,63,67,54,57,71,71,70,74,54,57,54,57,62,64,75,72,62,65,60,64,64,67,72,77,62,62,61,65,No
+77,61,62,68,62,58,72,68,77,71,76,77,72,75,62,57,77,74,61,58,72,76,69,68,56,53,57,54,69,70,73,79,65,70,66,68,67,66,71,67,60,60,53,57,Yes
+65,68,70,78,65,72,72,74,64,69,71,73,72,68,62,62,65,62,72,75,79,78,72,76,66,67,62,61,68,76,72,78,65,64,67,63,64,67,67,77,66,66,59,57,No
+76,69,78,73,68,67,75,70,77,70,79,73,79,75,74,71,76,68,81,79,77,78,75,76,66,76,65,67,67,57,68,75,50,62,62,59,47,49,75,65,74,70,51,48,Yes
+68,65,72,72,47,74,76,74,67,66,71,69,69,67,63,64,68,68,70,74,77,77,73,60,49,48,42,69,70,69,76,79,63,66,64,69,71,73,73,75,68,56,58,44,No
+72,61,64,66,64,59,68,66,76,66,77,75,71,72,72,62,72,68,77,78,77,81,67,67,69,68,65,68,69,64,72,73,56,56,69,64,67,71,69,68,65,73,56,52,No
+65,69,66,76,58,67,65,72,66,64,77,75,64,62,63,59,71,61,67,45,74,45,66,46,64,48,62,27,65,67,72,72,62,66,71,64,71,70,72,69,70,65,63,61,Yes
+67,51,73,65,69,56,72,63,64,56,68,67,70,62,70,58,68,62,65,59,77,69,68,59,64,55,65,60,70,60,72,65,58,51,66,59,71,62,74,60,76,65,62,56,Yes
+73,76,68,74,56,59,73,76,54,48,75,78,47,53,25,19,60,56,56,54,80,79,47,53,19,14,58,50,67,71,63,54,49,48,66,65,62,58,57,72,31,30,15,11,Yes
+59,65,53,60,72,74,67,69,64,67,64,65,71,70,68,71,72,70,70,70,75,78,72,71,71,71,67,64,67,71,71,74,62,66,72,73,57,57,64,71,70,69,53,49,No
+76,65,60,40,32,34,65,50,53,37,66,53,33,31,30,30,69,75,64,45,68,65,57,43,23,23,53,77,48,32,55,32,48,23,62,51,65,59,43,39,35,30,24,21,Yes
+70,71,71,74,68,66,72,70,66,69,74,72,70,69,64,66,73,72,75,73,81,81,76,75,71,70,74,82,69,72,76,74,60,56,66,66,64,64,73,72,66,63,54,58,No
+68,76,79,78,63,73,68,78,64,71,73,77,67,71,58,57,61,63,52,64,64,74,53,72,36,44,52,54,49,56,73,81,65,80,53,60,63,70,58,64,52,57,49,50,Yes
+64,63,62,59,60,61,70,74,68,63,66,68,64,66,67,66,72,68,63,59,75,72,67,62,63,55,66,56,65,64,70,71,61,63,67,63,69,69,77,65,74,62,60,54,Yes
+66,54,69,66,69,69,75,72,63,62,68,66,68,70,71,68,70,69,66,68,73,72,65,73,67,63,60,57,70,68,75,75,65,67,69,65,65,64,67,69,71,68,59,59,No
diff --git a/fcm_final.py b/fcm_final.py
@@ -0,0 +1,138 @@
+import pandas as pd
+import numpy as np
+import random
+import operator
+import math
+
+
+df_full = pd.read_csv("SPECTF_New.csv")
+columns = list(df_full.columns)
+features = columns[:len(columns)-1]
+class_labels = list(df_full[columns[-1]])
+df = df_full[features]
+
+# Number of Attributes
+num_attr = len(df.columns) - 1
+
+# Number of Clusters
+k = 2
+
+# Maximum number of iterations
+MAX_ITER = 100
+
+# Number of data points
+n = len(df)
+
+# Fuzzy parameter
+m = 2.00
+
+def accuracy(cluster_labels, class_labels):
+    county = [0,0]
+    countn = [0,0]
+    tp = [0, 0]
+    tn = [0, 0]
+    fp = [0, 0]
+    fn = [0, 0]
+
+    for i in range(len(df)):
+        # Yes = 1, No = 0
+        if cluster_labels[i] == 1 and class_labels[i] == 'Yes':
+            tp[0] = tp[0] + 1
+        if cluster_labels[i] == 0 and class_labels[i] == 'No':
+            tn[0] = tn[0] + 1
+        if cluster_labels[i] == 1 and class_labels[i] == 'No':
+            fp[0] = fp[0] + 1
+        if cluster_labels[i] == 0 and class_labels[i] == 'Yes':
+            fn[0] = fn[0] + 1
+
+    for i in range(len(df)):
+        # Yes = 0, No = 1
+        if cluster_labels[i] == 0 and class_labels[i] == 'Yes':
+            tp[1] = tp[1] + 1
+        if cluster_labels[i] == 1 and class_labels[i] == 'No':
+            tn[1] = tn[1] + 1
+        if cluster_labels[i] == 0 and class_labels[i] == 'No':
+            fp[1] = fp[1] + 1
+        if cluster_labels[i] == 1 and class_labels[i] == 'Yes':
+            fn[1] = fn[1] + 1
+
+    a0 = float((tp[0] + tn[0]))/(tp[0] + tn[0] + fn[0] + fp[0])
+    a1 = float((tp[1] + tn[1]))/(tp[1] + tn[1] + fn[1] + fp[1])
+    p0 = float(tp[0])/(tp[0] + fp[0])
+    p1 = float(tp[1])/(tp[1] + fp[1])
+    r0 = float(tp[0])/(tp[0] + fn[0])
+    r1 = float(tp[1])/(tp[1] + fn[1])
+
+    accuracy = [a0*100,a1*100]
+    precision = [p0*100,p1*100]
+    recall = [r0*100,r1*100]
+
+    return accuracy, precision, recall
+
+
+def initializeMembershipMatrix():
+    membership_mat = list()
+    for i in range(n):
+        random_num_list = [random.random() for i in range(k)]
+        summation = sum(random_num_list)
+        temp_list = [x/summation for x in random_num_list]
+        membership_mat.append(temp_list)
+    return membership_mat
+
+
+def calculateClusterCenter(membership_mat):
+    cluster_mem_val = zip(*membership_mat)
+    cluster_centers = list()
+    for j in range(k):
+        x = list(cluster_mem_val[j])
+        xraised = [e ** m for e in x]
+        denominator = sum(xraised)
+        temp_num = list()
+        for i in range(n):
+            data_point = list(df.iloc[i])
+            prod = [xraised[i] * val for val in data_point]
+            temp_num.append(prod)
+        numerator = map(sum, zip(*temp_num))
+        center = [z/denominator for z in numerator]
+        cluster_centers.append(center)
+    return cluster_centers
+
+
+def updateMembershipValue(membership_mat, cluster_centers):
+    p = float(2/(m-1))
+    for i in range(n):
+        x = list(df.iloc[i])
+        distances = [np.linalg.norm(map(operator.sub, x, cluster_centers[j])) for j in range(k)]
+        for j in range(k):
+            den = sum([math.pow(float(distances[j]/distances[c]), p) for c in range(k)])
+            membership_mat[i][j] = float(1/den)       
+    return membership_mat
+
+
+def getClusters(membership_mat):
+    cluster_labels = list()
+    for i in range(n):
+        max_val, idx = max((val, idx) for (idx, val) in enumerate(membership_mat[i]))
+        cluster_labels.append(idx)
+    return cluster_labels
+
+
+def fuzzyCMeansClustering():
+    # Membership Matrix
+    membership_mat = initializeMembershipMatrix()
+    curr = 0
+    while curr <= MAX_ITER:
+        cluster_centers = calculateClusterCenter(membership_mat)
+        membership_mat = updateMembershipValue(membership_mat, cluster_centers)
+        cluster_labels = getClusters(membership_mat)
+        curr += 1
+    print(membership_mat)
+    return cluster_labels, cluster_centers
+
+
+labels, centers = fuzzyCMeansClustering()
+a,p,r = accuracy(labels, class_labels)
+
+print("Accuracy = " + str(a))
+print("Precision = " + str(p))
+print("Recall = " + str(r))