removed duplicated letter_recog sample

diff --git a/samples/c/letter-recognition.data b/samples/c/letter-recognition.data
deleted file mode 100644 (file)
index 4dfa6f0..0000000
--- a/samples/c/letter-recognition.data
+++ /dev/null
@@ -1,20000 +0,0 @@
-T,2,8,3,5,1,8,13,0,6,6,10,8,0,8,0,8
-I,5,12,3,7,2,10,5,5,4,13,3,9,2,8,4,10
-D,4,11,6,8,6,10,6,2,6,10,3,7,3,7,3,9
-N,7,11,6,6,3,5,9,4,6,4,4,10,6,10,2,8
-G,2,1,3,1,1,8,6,6,6,6,5,9,1,7,5,10
-S,4,11,5,8,3,8,8,6,9,5,6,6,0,8,9,7
-B,4,2,5,4,4,8,7,6,6,7,6,6,2,8,7,10
-A,1,1,3,2,1,8,2,2,2,8,2,8,1,6,2,7
-J,2,2,4,4,2,10,6,2,6,12,4,8,1,6,1,7
-M,11,15,13,9,7,13,2,6,2,12,1,9,8,1,1,8
-X,3,9,5,7,4,8,7,3,8,5,6,8,2,8,6,7
-O,6,13,4,7,4,6,7,6,3,10,7,9,5,9,5,8
-G,4,9,6,7,6,7,8,6,2,6,5,11,4,8,7,8
-M,6,9,8,6,9,7,8,6,5,7,5,8,8,9,8,6
-R,5,9,5,7,6,6,11,7,3,7,3,9,2,7,5,11
-F,6,9,5,4,3,10,6,3,5,10,5,7,3,9,6,9
-O,3,4,4,3,2,8,7,7,5,7,6,8,2,8,3,8
-C,7,10,5,5,2,6,8,6,8,11,7,11,2,8,5,9
-T,6,11,6,8,5,6,11,5,6,11,9,4,3,12,2,4
-J,2,2,3,3,1,10,6,3,6,12,4,9,0,7,1,7
-J,1,3,2,2,1,8,8,2,5,14,5,8,0,7,0,7
-H,4,5,5,4,4,7,7,6,6,7,6,8,3,8,3,8
-S,3,2,3,3,2,8,8,7,5,7,5,7,2,8,9,8
-O,6,11,7,8,5,7,6,9,6,7,5,9,4,8,5,5
-J,3,6,4,4,2,6,6,4,4,14,8,12,1,6,1,6
-C,6,11,7,8,3,7,8,7,11,4,7,14,1,7,4,8
-M,7,11,11,8,9,3,8,4,5,10,11,10,10,9,5,7
-W,12,14,12,8,5,9,10,4,3,5,10,7,10,12,2,6
-H,6,9,8,7,6,8,6,6,7,7,7,9,6,8,4,8
-G,3,6,4,4,2,6,6,5,5,6,6,9,2,8,4,8
-L,2,3,3,4,1,0,1,5,6,0,0,6,0,8,0,8
-L,1,3,3,1,1,6,4,1,7,8,3,10,0,7,2,9
-X,8,12,8,6,4,3,10,4,7,12,11,9,3,7,3,4
-B,5,9,7,7,10,9,8,4,4,6,8,6,6,11,8,7
-M,6,9,9,7,6,5,6,3,5,10,9,9,8,5,2,7
-G,4,7,6,5,3,6,6,6,8,6,5,9,3,10,4,8
-O,4,7,5,5,3,7,7,8,6,7,6,8,3,8,3,8
-P,3,6,4,4,2,4,14,8,1,11,6,3,0,10,4,8
-G,4,9,5,6,6,8,5,4,3,7,5,11,6,8,5,11
-E,3,4,3,6,2,3,8,6,10,7,6,15,0,8,7,8
-X,5,11,8,8,4,8,8,1,8,10,5,7,3,8,4,8
-E,3,7,4,5,4,7,7,5,8,8,8,9,3,9,6,9
-X,4,6,7,4,3,9,7,2,8,11,3,7,3,8,4,9
-G,4,5,5,8,3,7,6,8,8,6,6,10,1,8,6,11
-V,7,9,6,5,2,8,10,4,5,8,10,5,4,12,3,8
-X,4,10,5,7,5,8,7,3,8,5,6,6,3,8,6,8
-W,5,9,6,7,8,7,9,5,3,7,9,8,6,8,3,8
-G,1,3,2,1,1,7,7,5,6,7,5,10,1,9,3,9
-R,8,10,8,6,6,7,7,3,5,8,4,8,6,6,7,7
-S,3,9,4,7,2,7,5,6,10,5,6,10,0,9,9,8
-Y,7,11,9,8,8,9,5,6,4,7,8,8,3,9,8,3
-L,3,6,3,4,1,1,0,6,6,0,1,5,0,8,0,8
-Y,2,4,4,3,2,6,10,1,7,8,11,8,1,11,2,7
-J,4,5,6,6,5,8,10,4,4,7,7,9,3,7,7,5
-S,4,9,5,6,4,7,8,7,7,8,4,7,2,7,9,8
-Q,6,9,7,11,7,8,5,7,5,9,6,9,3,8,6,8
-Q,3,5,5,4,4,9,3,4,3,8,4,9,3,6,4,8
-D,2,6,3,4,2,6,7,10,8,6,6,6,3,8,3,8
-C,6,10,4,5,2,6,9,6,6,11,7,7,2,9,5,9
-L,5,10,6,8,5,9,3,2,6,9,2,9,2,5,3,9
-N,3,3,3,5,2,7,7,13,2,5,6,8,5,8,0,8
-C,2,3,3,1,1,5,9,4,6,11,9,11,1,9,2,8
-E,6,9,4,4,2,7,7,4,7,10,6,10,1,9,7,9
-X,4,9,5,6,5,7,6,3,5,6,6,9,2,8,8,8
-H,3,3,4,1,2,8,7,5,6,7,6,8,5,8,3,7
-L,2,3,2,4,1,0,1,5,6,0,0,6,0,8,0,8
-H,3,5,5,4,3,7,8,3,6,10,6,8,3,8,3,8
-E,2,3,3,2,2,7,7,5,7,7,6,8,2,8,5,10
-Y,5,10,6,7,6,9,6,6,4,7,8,7,6,9,8,3
-H,8,12,8,6,4,9,8,4,5,8,4,5,6,9,5,9
-Q,5,10,5,5,4,9,6,5,6,10,6,7,4,8,9,9
-Q,3,8,5,7,3,8,7,9,6,6,6,9,3,8,5,9
-Q,4,6,6,4,5,8,5,6,4,6,7,10,4,7,5,9
-J,2,8,3,6,1,13,3,8,4,13,3,11,1,6,0,8
-J,4,7,5,5,2,8,9,1,7,14,5,6,0,8,1,8
-D,4,5,5,4,3,7,7,7,7,7,6,5,2,8,3,7
-R,2,6,4,4,3,6,7,5,5,6,5,7,3,7,5,8
-M,4,5,7,4,4,6,6,3,4,9,9,10,7,5,2,8
-A,3,7,5,5,3,12,2,3,2,10,2,9,2,6,3,8
-N,3,5,4,4,2,7,8,5,4,7,7,7,6,9,2,5
-M,6,10,9,7,8,3,7,4,5,10,11,10,7,9,3,7
-P,8,14,7,8,4,5,10,6,3,12,5,4,4,10,4,8
-C,6,12,5,7,4,8,5,4,3,9,9,11,4,9,8,10
-P,6,10,8,8,7,8,5,7,5,7,6,6,3,9,8,9
-N,2,3,3,1,1,5,9,3,3,10,8,8,4,8,0,8
-W,3,4,4,3,2,9,10,3,2,5,9,7,6,11,0,8
-V,3,8,5,6,6,8,7,4,1,8,7,8,5,10,4,7
-V,6,9,9,8,10,8,7,5,5,7,6,8,8,9,9,3
-M,6,7,9,5,7,4,7,3,5,10,10,11,8,6,3,7
-E,4,8,5,6,4,7,7,4,8,11,8,9,2,9,5,7
-N,6,11,8,8,9,5,8,3,4,8,8,9,7,9,5,4
-Y,8,10,8,7,4,3,10,3,7,11,12,6,1,11,3,5
-W,4,8,5,6,3,6,8,4,1,7,8,8,8,9,0,8
-O,6,7,8,6,6,6,6,5,6,8,5,8,3,6,5,6
-N,4,4,4,6,2,7,7,14,2,5,6,8,6,8,0,8
-H,4,8,5,6,5,7,10,8,5,8,5,6,3,6,7,11
-O,4,7,5,5,3,8,7,8,5,10,6,8,3,8,3,8
-N,4,8,5,6,4,7,7,9,4,6,4,6,3,7,3,8
-H,4,9,5,6,2,7,6,15,1,7,7,8,3,8,0,8
-U,7,11,8,9,4,3,9,6,7,11,11,10,3,9,2,6
-Y,2,7,3,5,1,6,10,0,3,7,11,8,1,11,0,8
-S,6,8,7,6,4,6,9,4,8,11,7,6,2,8,5,5
-S,5,9,6,7,5,7,7,7,5,7,6,7,2,8,9,8
-T,2,3,2,2,1,7,11,2,6,7,10,8,1,11,1,7
-C,2,3,3,2,1,4,7,4,6,10,9,13,1,9,2,7
-V,4,10,7,7,2,9,8,5,3,5,14,8,3,9,0,8
-T,5,8,5,6,3,6,12,4,7,12,9,4,2,12,2,4
-K,7,12,6,6,3,7,8,2,7,9,7,8,5,8,3,7
-D,3,8,4,6,4,7,8,7,6,7,7,5,3,8,3,7
-W,2,1,3,1,1,7,8,4,0,7,8,8,6,10,0,8
-I,2,9,3,7,2,8,7,0,7,13,6,9,0,8,1,8
-E,3,8,3,6,2,3,7,6,10,7,6,14,0,8,7,8
-F,6,9,8,7,4,6,12,6,6,13,7,3,2,9,2,5
-I,3,9,4,7,3,7,7,0,7,13,6,8,0,8,1,8
-H,5,10,7,8,8,7,7,6,6,7,6,9,3,8,4,8
-U,4,7,4,5,2,7,5,13,5,7,14,7,3,9,0,8
-C,2,3,3,2,1,6,8,7,7,9,7,12,1,10,3,9
-F,5,9,6,7,7,7,6,6,4,7,7,8,5,10,9,12
-A,3,8,5,6,3,9,2,2,3,8,2,8,2,6,3,7
-Z,3,7,5,5,3,7,8,2,9,11,7,7,1,8,6,7
-X,3,7,5,5,2,10,5,2,8,10,1,7,3,7,3,10
-N,3,3,5,2,2,5,10,3,4,10,8,7,5,8,1,8
-R,6,9,5,4,3,10,6,5,5,10,2,8,6,6,4,9
-B,3,3,3,4,3,7,7,5,5,7,6,6,5,8,5,10
-B,5,9,7,7,7,8,8,3,6,10,5,6,3,7,6,8
-U,5,6,5,4,2,5,8,6,8,9,8,8,3,9,2,5
-J,4,4,6,5,4,8,9,4,5,7,6,9,3,7,8,8
-F,3,7,4,5,2,1,11,4,4,11,11,8,0,8,2,7
-M,6,8,9,6,6,11,6,2,5,9,3,6,9,7,2,9
-H,1,1,2,1,1,7,7,12,1,7,6,8,3,8,0,8
-A,2,1,4,2,1,8,1,2,2,7,2,8,2,5,2,7
-O,5,10,4,5,3,6,6,6,2,9,6,9,5,9,4,8
-E,1,0,1,1,1,4,7,5,8,7,6,13,0,8,6,10
-U,3,3,3,1,1,5,8,5,7,10,9,8,3,10,2,6
-A,3,7,5,5,3,10,4,1,2,8,3,9,2,4,2,7
-X,4,8,5,6,3,5,8,1,8,10,9,9,2,9,3,6
-S,5,8,7,6,4,8,8,4,9,11,3,8,2,6,4,9
-J,1,3,2,2,1,10,7,1,5,11,4,8,0,7,0,7
-K,5,11,8,8,5,6,7,2,7,10,7,10,4,8,4,8
-I,2,6,2,4,1,9,5,0,6,13,5,9,0,8,1,8
-P,7,11,10,8,6,6,11,3,6,13,6,3,0,10,3,8
-T,2,1,2,1,0,8,15,1,4,6,10,8,0,8,0,8
-C,1,1,2,2,1,6,8,6,6,9,7,12,1,9,3,10
-H,4,9,4,7,4,7,8,13,1,7,5,8,3,8,0,8
-A,2,4,4,3,2,10,2,2,2,9,2,9,2,7,3,9
-L,6,11,9,8,11,7,7,3,6,6,7,11,6,11,9,7
-T,3,6,4,4,3,7,12,4,6,7,11,8,2,12,1,7
-A,2,8,4,6,2,12,2,4,3,11,2,10,3,6,3,9
-V,3,9,5,7,3,8,9,4,1,6,12,8,2,10,0,8
-U,5,7,6,5,5,8,8,8,5,6,7,9,4,8,4,5
-I,2,9,2,6,3,7,7,0,7,7,6,8,0,8,2,8
-Z,8,14,8,8,5,8,5,2,9,12,6,9,3,7,6,7
-V,4,7,6,5,6,8,8,4,2,8,7,8,7,11,5,8
-M,5,9,7,6,7,9,7,5,5,6,7,6,10,7,4,6
-L,4,8,6,6,4,6,5,2,8,7,2,8,1,6,3,7
-O,3,3,5,5,2,9,6,9,7,7,5,10,3,8,4,8
-B,4,2,5,4,4,7,7,5,6,7,6,6,2,8,7,10
-A,8,15,7,8,4,8,2,3,2,8,5,12,5,4,5,5
-K,6,9,9,6,7,9,6,1,7,10,3,8,3,7,3,9
-H,3,7,4,5,4,7,7,5,6,7,6,8,3,8,3,8
-T,7,9,7,7,5,6,10,1,8,11,9,5,2,9,3,4
-W,6,5,7,4,4,4,11,2,2,9,9,8,7,11,1,7
-F,3,8,3,5,1,1,12,4,5,12,11,8,0,8,2,6
-A,3,3,5,4,1,7,6,3,0,7,0,8,2,7,1,8
-E,3,8,3,6,3,3,6,5,9,5,4,13,0,8,6,9
-M,4,9,5,7,4,7,7,12,1,7,9,8,8,6,0,8
-B,4,9,5,7,6,7,8,9,6,7,6,6,2,8,8,10
-M,3,7,5,5,6,7,8,6,4,6,6,8,5,8,7,9
-Y,6,9,6,7,4,5,8,0,8,8,9,5,2,11,4,4
-V,4,9,6,7,3,6,12,3,4,7,12,9,3,10,1,8
-S,2,8,3,6,3,8,8,7,6,7,4,6,2,7,8,8
-C,2,1,2,2,1,6,8,7,7,8,7,12,1,9,4,10
-H,4,9,6,6,8,7,7,6,3,7,6,7,8,7,9,9
-U,5,8,5,6,4,8,6,11,4,8,13,7,3,9,0,8
-P,6,10,6,6,4,7,10,5,2,11,5,4,4,11,5,7
-Z,3,8,4,6,2,7,7,4,13,10,6,8,0,8,8,8
-E,3,4,5,2,2,7,7,2,8,11,7,9,2,8,4,8
-N,5,9,7,6,4,4,10,3,4,9,10,9,5,8,1,8
-P,6,10,5,6,3,5,9,6,3,10,5,6,5,9,4,7
-H,4,5,5,4,4,7,7,6,6,7,6,7,3,8,3,6
-T,6,11,6,8,5,6,11,3,7,11,9,5,2,12,2,4
-B,5,5,5,8,4,7,9,10,8,7,5,7,2,8,9,10
-U,5,10,6,7,2,7,4,14,6,7,15,8,3,9,0,8
-S,3,4,4,6,2,8,8,6,9,5,5,5,0,8,9,7
-F,5,6,5,8,2,1,14,5,3,12,9,5,0,8,3,6
-J,3,9,4,7,4,9,6,1,6,11,4,7,0,6,1,6
-R,3,6,4,4,4,6,7,5,5,7,6,7,3,7,4,9
-E,3,2,3,4,3,7,8,5,7,7,5,9,2,8,5,10
-P,6,9,6,4,4,8,9,5,3,10,5,5,5,10,5,6
-I,2,2,1,4,1,7,7,1,7,7,6,8,0,8,3,8
-K,7,10,9,8,9,7,7,5,5,7,6,7,7,6,9,13
-D,4,10,6,7,4,8,6,8,7,4,5,3,3,8,4,8
-B,4,7,6,5,7,9,6,4,4,6,6,7,7,8,6,7
-H,5,10,7,8,6,7,9,8,5,8,5,6,3,7,7,10
-M,7,10,10,7,7,9,6,2,5,9,5,7,8,6,2,8
-P,6,11,7,8,3,4,13,9,2,10,6,4,1,10,4,8
-O,4,8,6,6,4,8,5,8,4,7,5,8,3,8,3,8
-S,2,8,3,6,3,8,7,7,5,7,6,7,2,9,8,8
-K,4,7,6,5,4,3,8,3,7,11,11,12,3,8,3,5
-V,7,10,7,8,3,3,11,4,4,10,12,8,3,10,1,8
-F,7,11,10,8,5,6,11,2,7,14,7,4,1,10,2,7
-Y,3,4,5,6,1,6,10,3,2,9,13,8,1,11,0,8
-D,4,6,6,4,3,11,6,3,8,11,3,6,3,8,3,9
-Z,1,0,1,1,0,7,7,2,10,8,6,8,0,8,6,8
-Q,3,5,3,6,3,7,8,6,3,8,8,9,3,8,5,7
-S,8,14,8,8,4,9,4,4,4,13,5,8,3,10,3,8
-B,5,7,7,5,6,8,6,5,6,9,6,7,3,9,6,10
-K,8,13,9,7,5,6,7,3,6,10,8,10,6,10,4,7
-V,3,10,5,7,2,5,8,5,3,9,14,8,3,10,0,8
-B,4,7,4,5,3,6,7,9,7,7,6,7,2,8,9,10
-Y,2,2,4,4,2,6,10,1,7,7,11,9,1,11,1,8
-O,3,4,4,3,2,7,7,7,5,7,6,8,2,8,3,8
-Z,6,10,8,7,5,8,6,3,10,12,4,9,1,7,6,9
-L,2,3,3,2,1,6,4,1,8,7,2,10,0,7,2,8
-J,1,3,2,2,1,10,6,2,6,12,4,9,0,7,1,7
-Z,1,3,3,2,1,7,8,1,9,11,7,7,1,8,5,7
-A,3,5,5,7,2,7,7,3,1,6,0,8,3,7,1,8
-L,3,5,4,5,3,8,7,4,5,7,6,8,3,8,7,10
-J,2,5,3,3,1,10,7,1,7,12,3,7,0,7,1,7
-Q,5,6,6,8,5,8,7,6,3,7,7,12,3,8,6,8
-N,1,3,2,1,1,7,8,5,3,7,7,7,4,8,1,6
-Z,2,6,3,4,1,7,7,3,13,9,6,8,0,8,8,8
-E,4,9,5,7,3,3,7,6,11,7,6,15,0,8,7,7
-J,4,7,3,10,3,9,7,3,3,11,4,5,3,8,6,9
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-D,4,10,6,8,4,8,7,7,7,10,5,4,3,7,5,9
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-N,4,11,5,8,5,7,7,13,1,6,6,8,6,9,1,6
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-Z,2,4,4,3,2,7,8,2,9,12,7,7,1,8,5,6
-N,5,11,7,8,6,7,7,8,5,7,5,6,3,7,3,8
-N,4,7,5,5,4,6,7,8,5,7,5,7,3,7,3,8
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-N,10,13,9,7,4,4,9,4,7,3,3,11,5,8,2,7
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-C,3,2,4,4,2,6,8,7,7,8,7,13,1,9,4,10
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-P,4,8,6,6,5,7,5,6,5,7,6,8,3,8,4,8
-R,12,15,9,8,5,9,6,6,5,10,2,8,7,6,6,11
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-Z,2,1,3,3,2,7,8,5,9,6,6,9,1,9,7,8
-A,7,13,6,8,4,8,2,3,2,8,4,12,5,5,4,7
-S,4,9,6,7,4,10,6,5,6,10,2,7,2,7,4,11
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-I,2,9,3,7,2,7,7,0,7,13,6,8,0,8,1,8
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-O,5,11,7,8,5,8,5,9,5,6,6,4,5,7,5,9
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-U,2,3,3,2,1,7,8,6,6,7,9,8,3,10,1,8
-T,5,8,7,6,7,7,8,4,6,7,6,9,5,8,5,7
-U,4,7,5,5,3,7,9,6,7,5,10,9,3,9,1,8
-A,4,9,7,7,5,6,5,2,3,4,1,6,5,7,5,4
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-X,3,6,4,4,1,7,7,4,4,7,6,8,3,8,4,8
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-Q,3,5,4,6,4,9,8,7,2,4,7,11,3,9,6,10
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-U,4,5,4,4,2,4,8,5,7,10,9,9,3,9,2,6
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-A,2,5,3,3,2,11,3,2,2,9,2,9,2,6,2,8
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-A,3,5,5,4,2,10,2,2,2,8,2,9,3,5,2,8
-P,1,0,1,0,0,5,11,6,1,9,6,5,0,9,3,8
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-W,5,11,8,8,4,8,8,5,2,7,8,8,9,9,0,8
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-T,4,8,5,6,4,6,11,3,7,8,11,8,2,12,1,7
-U,3,7,5,5,4,8,7,7,5,6,7,9,5,8,3,7
-U,3,8,4,6,3,8,6,12,4,7,11,8,3,9,0,8
-I,1,6,2,4,1,7,7,0,7,14,6,8,0,8,1,8
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-A,4,9,6,7,4,12,2,3,2,10,2,9,2,7,3,8
-I,3,10,4,7,2,7,7,0,8,14,6,8,0,8,1,8
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-H,4,9,4,6,2,7,8,15,1,7,5,8,3,8,0,8
-C,2,1,2,1,0,6,7,6,9,7,6,14,0,8,4,10
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-F,1,3,3,2,1,5,11,3,4,12,7,5,1,9,1,7
-A,3,2,5,4,2,10,2,2,2,9,1,8,2,6,3,8
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-X,9,15,10,9,6,8,8,2,7,11,6,6,5,12,4,8
-Y,3,4,5,6,6,7,8,4,1,6,8,9,4,11,7,7
-V,3,4,5,6,1,9,8,4,3,6,14,8,3,10,0,8
-M,3,8,4,6,3,7,6,11,1,7,9,8,8,6,0,8
-X,3,8,5,6,3,7,7,4,9,6,6,8,3,8,6,8
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-P,3,5,6,4,3,7,10,4,4,12,4,3,1,10,3,8
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-P,6,9,9,7,6,6,13,7,2,12,5,2,1,11,4,8
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-W,5,7,5,5,5,5,9,3,3,9,7,7,6,11,3,6
-B,2,3,4,2,2,9,7,2,5,10,5,6,2,8,4,9
-C,6,12,4,6,2,5,10,5,8,11,8,9,2,8,5,8
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-V,5,10,7,9,8,7,7,6,4,7,6,8,7,10,8,11
-Y,4,4,6,6,1,7,10,3,2,7,13,8,2,11,0,8
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-L,6,14,6,8,4,8,4,3,4,12,8,11,4,9,6,10
-B,6,10,9,7,7,8,7,5,6,9,5,6,3,7,7,10
-R,6,11,6,6,5,7,8,3,6,8,3,8,6,6,6,7
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-Q,6,8,6,9,6,8,7,7,4,8,7,10,3,8,6,8
-X,4,6,5,5,5,7,9,2,6,8,6,8,3,6,6,7
-Z,4,6,6,9,4,12,3,3,6,10,2,8,1,7,5,11
-L,4,8,5,6,2,3,1,7,9,0,1,3,0,7,1,5
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-W,4,5,6,3,4,6,11,2,2,7,9,8,8,11,1,8
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-A,2,3,3,1,1,8,2,2,1,7,2,8,2,7,2,7
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-M,5,7,7,6,8,5,7,5,4,6,5,8,11,7,5,9
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-U,5,11,7,8,5,4,9,6,7,9,11,10,3,9,1,8
-B,7,11,7,6,5,9,7,3,5,10,5,7,6,7,7,8
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-O,2,3,3,2,1,7,7,7,4,7,6,8,2,8,2,8
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-Y,2,3,4,4,1,8,10,2,2,6,13,8,2,11,0,8
-I,3,10,4,8,3,7,9,0,7,13,6,7,0,9,2,7
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-J,5,11,7,8,8,9,7,4,4,8,4,6,4,7,6,4
-C,5,10,5,7,3,3,8,5,7,11,10,13,1,9,3,8
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-O,2,5,3,3,2,7,7,8,4,7,6,8,2,8,3,8
-O,3,7,4,5,3,7,8,7,4,8,7,10,3,8,3,9
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-W,4,2,6,4,4,8,11,2,2,6,9,8,9,13,1,7
-D,4,6,5,4,3,8,8,7,8,9,5,3,3,8,4,6
-T,3,8,4,6,2,8,14,0,5,6,10,8,0,8,0,8
-N,1,0,2,1,1,7,7,11,1,5,6,8,4,8,0,8
-H,4,8,6,10,7,9,10,3,1,8,7,6,3,10,8,7
-D,3,7,5,5,4,8,7,3,5,10,5,6,3,8,3,8
-R,5,9,7,7,6,10,6,3,5,10,3,7,4,6,4,10
-B,7,10,6,6,4,8,5,4,5,10,6,9,5,7,7,10
-A,5,8,8,7,7,7,7,2,5,7,9,10,5,11,3,7
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-O,9,13,6,7,4,5,8,6,4,10,8,9,5,10,5,8
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-L,6,15,6,8,4,7,5,3,5,11,7,11,3,8,6,9
-Q,4,6,5,7,5,8,5,7,3,6,5,9,3,8,5,9
-B,5,7,7,5,6,7,9,4,6,10,6,6,3,7,6,8
-R,5,8,7,7,7,7,8,3,3,7,5,7,6,8,6,6
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-U,4,5,4,8,2,7,4,15,6,7,12,8,3,9,0,8
-E,6,9,8,8,8,7,9,5,6,6,8,11,8,9,9,8
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-L,5,11,6,8,5,3,4,3,8,2,0,8,0,7,1,5
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-T,5,7,7,6,6,5,8,3,8,7,7,9,3,7,7,5
-O,2,2,3,3,2,7,7,7,4,7,6,8,2,8,3,8
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-D,2,5,3,3,3,7,7,6,6,7,6,5,2,8,2,7
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-C,2,5,3,4,2,6,8,7,7,9,8,13,1,9,4,10
-M,4,10,5,8,6,7,5,11,1,7,9,8,9,5,2,8
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-L,5,11,7,8,4,7,3,2,8,7,2,8,1,6,3,7
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-Z,5,7,7,5,6,9,5,5,4,7,5,7,3,7,10,5
-R,4,6,5,4,5,8,7,6,3,8,5,7,4,7,7,10
-V,5,10,5,7,4,4,11,1,2,8,10,7,3,10,1,8
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-K,9,14,9,8,6,3,8,4,6,10,11,12,5,8,4,6
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-L,5,7,6,5,4,6,6,7,7,5,6,10,2,8,4,10
-Z,4,7,6,5,4,9,6,2,8,11,4,10,2,7,6,10
-F,4,7,6,5,4,5,11,3,5,13,7,5,1,10,2,7
-Q,5,5,7,4,5,8,5,5,4,7,4,10,5,6,6,8
-C,8,12,6,6,5,7,7,4,4,9,8,10,4,9,8,10
-G,3,7,4,5,3,6,5,5,6,6,6,9,2,9,3,7
-V,4,5,6,3,2,4,12,3,3,10,11,7,2,10,1,8
-I,6,9,8,7,4,6,7,2,7,7,6,11,0,8,4,8
-C,4,5,6,8,2,6,8,7,10,5,7,13,1,7,4,9
-D,4,8,5,6,4,7,6,7,8,6,5,5,3,8,3,7
-X,5,11,6,8,2,7,7,5,4,7,6,8,3,8,4,8
-E,5,11,5,8,3,3,7,6,11,7,6,14,0,8,8,7
-E,1,1,2,2,1,4,7,5,8,7,6,13,0,8,7,9
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-V,5,9,7,7,3,9,12,3,4,4,11,9,3,10,1,8
-D,2,6,4,4,3,9,6,4,6,10,4,6,3,8,3,8
-L,4,10,4,8,1,0,1,5,6,0,0,6,0,8,0,8
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-U,3,5,5,5,4,8,7,4,4,6,7,7,4,8,1,6
-I,2,6,3,4,1,7,7,0,7,13,6,8,0,8,1,8
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-M,5,7,8,5,7,8,7,2,4,9,6,7,7,5,2,7
-I,4,10,6,8,4,7,7,0,8,13,6,8,0,8,1,8
-W,4,5,5,4,3,4,10,3,2,9,9,7,6,11,1,6
-I,4,5,5,6,5,7,10,4,5,8,7,8,4,7,6,6
-H,8,10,8,5,5,8,7,3,5,10,5,7,6,9,4,8
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-T,4,4,4,3,2,5,11,2,8,12,9,4,1,10,2,4
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-S,3,9,4,7,4,7,7,7,5,7,7,9,2,9,8,8
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-A,4,7,7,5,4,6,5,2,3,4,2,6,4,6,4,5
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-H,7,11,9,8,8,8,7,2,6,10,5,8,4,9,4,8
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-X,4,10,6,8,4,7,8,3,9,6,5,6,4,10,8,6
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-T,5,10,5,7,3,5,13,3,7,12,9,3,1,11,2,4
-C,3,6,5,4,3,7,7,7,6,8,6,11,3,9,4,8
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-X,7,9,6,5,3,6,8,2,9,10,9,9,4,8,4,6
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-L,4,8,5,6,3,6,4,3,7,6,1,8,1,6,3,7
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-E,4,8,6,6,5,5,8,5,7,11,9,9,3,8,5,5
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-U,4,7,5,5,2,7,5,12,5,7,15,8,3,9,0,8
-V,3,11,5,8,4,7,11,2,3,6,11,9,2,10,1,8
-U,5,9,5,6,4,8,5,12,4,7,12,8,3,9,0,8
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-S,6,10,7,8,4,8,7,5,9,11,4,7,2,7,5,9
-C,4,7,5,5,3,8,7,8,6,6,7,10,3,8,3,9
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-T,3,3,4,2,1,5,11,2,7,11,9,5,1,10,2,5
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-X,4,9,6,6,4,3,9,2,8,11,12,10,3,8,4,4
-R,4,9,6,6,5,9,7,5,5,9,4,7,3,7,4,11
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-Z,2,1,2,1,1,7,7,5,8,6,6,8,1,8,6,8
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-G,4,5,5,4,3,6,6,6,6,6,6,10,2,9,4,8
-W,3,6,5,4,6,10,7,4,2,7,6,8,5,10,1,5
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-B,4,8,6,6,8,8,8,4,3,6,7,7,6,11,8,8
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-V,4,9,6,7,1,6,8,5,3,8,14,8,3,9,0,8
-T,7,8,7,6,5,7,10,3,7,10,9,5,4,11,5,5
-A,6,10,8,8,8,7,7,8,4,7,6,8,4,8,11,2
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-O,4,6,4,4,3,7,7,7,5,10,7,9,3,8,3,8
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-T,2,3,3,2,1,9,12,3,6,6,11,9,2,11,1,8
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-X,10,15,11,8,6,7,8,2,8,11,8,8,5,12,4,7
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-V,2,1,4,1,1,9,12,2,2,5,10,9,2,10,1,8
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-T,2,6,4,4,2,5,13,0,5,9,10,7,0,8,0,8
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-E,3,3,4,5,2,3,7,6,10,7,6,14,0,8,7,7
-M,4,8,5,6,3,7,7,12,1,7,9,8,8,6,0,8
-L,1,3,2,2,1,7,4,2,7,7,2,9,0,7,2,8
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-R,5,10,6,8,7,6,7,5,6,6,4,8,3,6,5,8
-S,4,9,5,6,5,8,7,5,8,5,5,8,0,8,8,7
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-O,5,8,6,6,4,7,9,8,5,6,7,9,3,8,3,7
-R,3,7,5,5,3,10,7,3,6,10,3,7,3,7,3,10
-O,1,0,1,1,0,7,7,7,5,7,6,8,2,8,3,8
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-N,5,10,7,7,5,7,8,6,6,6,6,6,6,8,1,6
-S,5,5,6,7,3,8,8,6,9,5,6,7,0,8,9,7
-A,6,11,5,6,3,8,4,3,3,7,4,11,5,6,4,7
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-V,5,10,8,7,5,7,12,2,3,5,10,9,4,12,2,7
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-R,6,10,8,7,6,8,9,4,6,8,4,8,3,6,6,11
-Y,3,5,5,7,7,9,6,4,1,6,7,8,6,10,8,9
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-C,2,1,3,3,1,6,8,7,7,8,8,13,1,10,4,10
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-H,4,8,6,10,7,8,4,4,2,7,4,6,5,7,8,8
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-S,5,6,6,5,6,9,8,4,5,7,6,8,5,10,8,11
-Q,2,2,3,2,2,8,7,6,3,6,6,10,2,9,3,10
-F,4,7,6,5,3,5,11,4,5,13,7,4,2,10,2,7
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-A,4,9,6,6,2,6,3,3,3,6,2,7,3,6,3,7
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-U,9,11,10,8,7,4,8,5,9,9,7,9,8,10,6,1
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-S,3,2,3,3,2,8,7,6,5,7,6,8,2,8,9,8
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-X,6,11,9,8,4,4,9,3,9,11,12,10,3,9,4,5
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-I,5,7,6,5,3,7,6,2,7,7,6,8,0,9,4,8
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-T,6,10,6,7,4,4,13,4,6,12,10,4,2,12,2,4
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-C,3,10,4,8,3,5,7,6,7,7,6,13,1,8,4,10
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-N,4,5,5,8,3,7,7,14,2,4,6,8,6,8,0,8
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-L,4,10,5,8,3,5,3,6,8,1,2,4,1,6,1,5
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-R,4,9,4,4,3,9,7,3,5,9,3,8,5,7,5,8
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-K,5,9,7,6,5,4,7,2,7,10,10,12,3,8,3,6
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-Q,5,6,6,8,3,9,9,8,7,5,8,10,3,8,5,10
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-U,3,7,5,6,5,8,7,3,3,6,6,8,4,8,2,8
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-A,4,9,4,4,2,10,2,4,2,11,5,12,4,4,5,10
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-X,3,1,4,2,2,8,7,4,9,6,6,8,3,8,6,8
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-D,5,9,6,7,6,8,8,7,6,8,7,3,5,8,4,9
-T,6,7,6,5,4,6,12,5,6,10,9,4,3,13,3,4
-V,4,10,6,8,3,5,11,3,5,10,12,9,3,10,1,8
-T,2,8,4,5,1,8,15,1,6,6,11,8,0,8,0,8
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-M,6,8,9,7,10,8,7,4,5,7,6,7,12,8,6,4
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-A,2,4,4,2,2,10,2,2,2,9,2,8,2,6,2,8
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-N,3,4,5,3,2,7,9,3,4,10,6,6,5,9,0,7
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-G,5,5,6,7,3,8,6,8,9,6,5,11,1,8,6,11
-G,3,3,4,5,2,7,8,8,7,6,7,8,2,7,6,11
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-B,4,5,4,3,4,7,7,6,6,7,6,6,2,8,6,10
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-C,4,5,5,4,2,4,8,5,7,11,10,12,1,9,3,7
-N,5,10,5,8,3,7,7,15,2,4,6,8,6,8,0,8
-S,3,6,4,4,3,8,6,6,5,7,7,10,2,10,8,8
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-Y,6,11,6,8,2,3,12,5,6,13,12,6,2,11,2,6
-X,4,9,7,7,3,7,7,1,9,10,6,8,3,8,4,7
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-O,2,5,3,4,2,8,7,7,4,9,6,8,2,8,2,8
-Z,4,4,6,6,3,12,4,4,4,11,2,9,2,7,4,11
-C,2,4,3,3,1,6,8,7,7,9,7,12,1,10,4,10
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-C,3,7,4,5,1,6,8,6,9,5,6,14,1,7,4,9
-M,5,5,6,4,5,8,6,6,5,7,7,8,9,6,3,7
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-A,5,11,5,6,4,10,3,4,2,10,5,11,5,3,5,10
-U,7,10,8,8,5,3,8,5,7,10,10,10,3,9,2,6
-C,5,10,3,5,2,5,9,6,6,11,8,8,2,9,5,8
-S,4,7,6,6,7,8,7,5,4,7,7,7,5,9,10,12
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-Z,7,10,9,7,6,7,8,2,9,12,6,8,1,8,6,8
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-U,3,6,3,4,1,7,5,13,5,7,13,8,3,9,0,8
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-C,7,10,8,8,4,2,9,5,9,11,11,13,1,7,3,6
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-O,4,7,5,5,3,8,8,8,5,7,7,6,3,8,4,8
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-R,7,11,9,8,6,9,7,5,7,8,4,6,3,7,6,11
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-O,4,9,6,7,7,8,8,5,1,7,7,9,8,9,4,9
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-V,6,11,6,6,3,7,7,4,4,7,7,6,6,12,2,8
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-V,3,2,5,3,2,9,12,3,3,5,11,9,2,10,1,9
-A,3,9,5,6,3,6,3,2,3,5,2,8,2,6,3,6
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-D,4,8,6,6,4,9,6,5,7,10,3,5,3,8,3,8
-U,4,5,4,7,2,7,5,15,5,7,13,8,3,9,0,8
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-C,6,10,6,7,4,6,8,7,8,13,8,10,2,11,3,7
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-U,5,9,6,7,4,8,5,13,5,8,12,8,3,9,0,8
-P,2,3,2,1,1,6,10,5,4,9,7,4,1,9,3,7
-X,4,5,5,7,2,7,7,4,4,7,6,8,3,8,4,8
-Q,3,5,4,6,3,7,8,5,2,7,9,11,3,9,5,7
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-G,7,8,9,7,9,7,8,5,3,7,7,8,10,10,10,7
-B,3,6,4,4,4,6,8,7,4,7,5,6,2,8,6,6
-T,3,3,4,2,1,5,11,3,7,11,9,4,1,10,2,5
-Z,2,4,4,3,2,8,6,2,9,12,5,9,1,8,5,9
-C,4,8,5,6,2,6,9,7,10,5,7,12,1,6,4,8
-V,2,7,4,5,1,9,8,4,2,5,13,8,3,10,0,8
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-B,4,11,5,8,4,6,7,9,7,7,6,7,2,8,9,10
-N,5,9,7,7,6,6,7,7,6,7,5,8,6,7,3,7
-W,10,10,9,8,9,5,11,4,3,8,7,7,10,12,4,5
-N,2,3,2,1,1,7,8,6,4,7,6,7,4,8,1,7
-A,4,4,6,6,2,6,7,3,0,6,0,8,2,7,1,7
-R,4,9,6,7,6,8,7,7,3,8,5,7,4,7,7,10
-U,4,4,4,3,2,4,8,5,7,11,10,9,3,9,2,6
-R,2,3,3,2,2,7,7,5,4,9,5,7,2,7,3,10
-E,2,2,3,4,3,7,7,6,7,7,6,9,2,8,6,10
-M,5,10,7,5,4,12,2,2,2,10,3,9,5,4,1,9
-I,1,7,0,4,0,7,7,4,4,7,6,8,0,8,0,8
-A,5,7,7,5,7,7,8,8,4,7,5,8,4,8,9,4
-W,4,7,6,5,3,6,8,5,1,7,8,8,8,9,0,8
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-O,4,5,5,7,3,7,8,9,8,7,8,8,3,8,4,8
-I,1,5,1,4,1,7,7,1,7,7,6,8,0,8,2,8
-K,1,0,1,0,0,5,7,7,1,7,6,11,2,8,2,11
-M,5,8,8,6,5,5,7,3,4,10,9,10,8,6,3,8
-N,10,13,9,7,4,7,10,5,5,3,5,11,6,11,3,6
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-H,2,1,3,3,2,8,7,6,6,7,6,8,3,8,3,7
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-N,5,9,6,7,3,7,7,15,2,4,6,8,6,8,0,8
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-I,4,11,3,6,2,10,7,6,3,13,4,8,2,8,4,9
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-J,5,10,6,8,5,8,6,4,5,8,6,7,3,7,4,6
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-N,5,8,7,6,5,7,6,10,5,6,4,5,4,7,4,9
-A,4,5,6,4,4,8,8,3,4,6,7,7,5,8,4,5
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-D,4,11,5,8,3,6,7,11,9,7,6,6,3,8,4,8
-A,3,10,5,8,4,12,3,2,2,9,2,8,2,6,2,8
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-P,5,7,7,5,4,6,11,7,2,11,5,3,1,10,3,8
-G,7,13,6,7,4,10,6,5,6,11,4,8,5,7,5,9
-F,5,8,7,6,7,7,8,6,4,8,6,8,4,10,8,11
-F,7,11,6,6,3,7,8,2,8,11,6,6,2,10,5,6
-K,9,13,8,7,4,7,7,3,7,9,9,9,6,11,3,7
-W,5,7,5,5,5,6,10,4,2,9,7,7,6,11,2,6
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-I,1,11,0,8,0,7,7,4,4,7,6,8,0,8,0,8
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-E,6,10,9,8,6,6,8,4,9,11,9,9,2,9,5,6
-W,4,4,6,3,3,7,11,3,2,6,9,8,8,11,1,8
-F,1,0,1,0,0,3,11,4,2,11,8,6,0,8,2,8
-U,9,15,7,8,4,8,6,4,6,3,9,5,6,6,2,6
-X,3,3,5,2,2,9,7,2,8,11,4,7,2,7,3,8
-Q,4,9,5,11,5,9,8,8,2,4,7,11,4,9,6,9
-I,1,8,0,6,0,7,7,4,4,7,6,8,0,8,0,8
-P,4,5,4,7,2,4,12,9,2,10,6,4,1,10,4,8
-R,9,11,7,6,4,8,7,5,5,10,4,9,6,5,6,11
-I,1,8,0,6,1,7,7,5,3,7,6,8,0,8,0,8
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-Z,8,14,8,8,5,11,4,5,8,12,3,10,4,4,8,12
-K,2,1,3,2,2,5,7,4,7,7,6,11,3,8,4,10
-C,6,12,4,6,2,7,7,7,6,11,6,8,2,9,5,9
-I,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8
-S,5,8,6,6,3,9,6,4,8,11,4,8,2,8,5,11
-W,7,11,10,8,15,10,5,5,2,7,6,7,12,10,3,4
-K,2,6,4,4,4,7,7,3,5,6,5,8,3,8,3,9
-R,3,5,5,4,3,9,7,4,5,9,4,7,3,7,4,10
-K,4,7,5,5,5,5,6,4,6,6,6,11,3,8,5,10
-C,6,10,7,8,3,4,9,6,8,12,10,11,2,9,3,6
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-U,9,11,10,8,7,5,7,5,9,9,6,9,8,9,6,1
-P,4,6,6,9,9,7,8,6,0,8,6,7,6,10,6,11
-Y,3,4,4,3,1,4,11,2,7,11,10,5,1,11,2,5
-H,3,4,5,6,4,7,4,5,2,6,4,6,4,6,7,7
-Z,3,10,4,8,4,7,8,3,12,9,6,8,0,8,7,7
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-X,5,9,5,5,2,11,6,3,7,9,3,7,3,9,4,10
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-E,3,2,3,4,3,7,7,6,7,7,6,9,2,8,6,9
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-A,2,3,3,2,1,10,2,2,1,8,2,9,2,5,1,9
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-W,4,10,7,8,6,7,8,4,1,7,9,8,7,11,0,8
-Y,3,5,6,7,1,6,11,2,3,9,12,8,1,10,0,8
-I,3,8,4,6,2,7,7,0,9,14,6,8,0,8,1,8
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-G,3,2,4,4,3,6,6,6,6,6,6,10,2,9,4,9
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-Y,3,4,5,5,1,8,12,2,3,6,12,8,1,10,0,8
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-N,11,14,9,8,4,10,12,6,4,4,6,10,5,11,2,5
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-C,3,5,4,3,2,6,8,7,8,8,8,13,1,9,4,10
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-O,6,10,8,8,5,8,10,9,6,8,8,5,5,8,5,9
-C,6,11,7,8,9,7,5,4,4,8,7,11,6,10,5,8
-G,4,10,5,7,4,5,6,6,5,9,8,11,2,9,4,10
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-Y,3,5,4,3,2,4,11,2,7,11,10,6,1,11,2,5
-N,5,7,7,5,3,7,7,3,4,10,7,8,5,8,0,7
-W,3,4,5,3,3,9,11,3,2,5,9,7,6,10,0,8
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-A,6,9,5,4,2,11,3,3,1,9,3,10,4,5,4,10
-C,4,9,6,7,4,7,8,8,6,6,6,9,4,7,4,8
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-A,1,1,2,1,0,7,4,2,0,7,2,8,2,7,1,8
-U,4,8,5,6,3,4,9,5,6,9,8,9,3,9,2,6
-W,5,5,7,7,4,8,8,5,2,7,8,8,9,9,0,8
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-M,4,9,5,6,3,7,7,12,1,7,9,8,8,6,0,8
-X,2,4,4,3,2,10,6,2,8,10,3,7,2,8,3,9
-O,5,9,6,7,4,8,7,8,5,7,6,8,3,8,3,8
-W,3,4,4,3,3,4,10,4,2,9,8,7,5,11,1,6
-E,2,5,4,3,2,6,8,2,7,11,7,9,2,8,4,8
-E,2,3,2,2,1,7,7,5,7,7,5,8,2,8,5,10
-F,2,4,3,3,2,5,10,4,5,10,9,5,2,9,3,6
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-V,8,13,7,7,4,5,10,4,5,9,9,5,4,10,2,8
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-X,3,3,5,2,2,5,9,2,7,10,9,8,2,8,3,6
-W,5,7,7,6,9,7,6,4,5,7,5,8,9,9,7,8
-X,2,3,3,2,2,7,7,3,9,6,6,8,2,8,6,8
-D,5,10,5,6,4,6,8,4,6,9,6,6,5,9,6,5
-G,5,11,6,8,5,6,6,6,6,10,6,13,3,8,4,9
-T,8,12,8,7,4,7,8,2,7,12,7,7,3,9,5,6
-B,2,3,3,1,2,7,7,4,5,7,6,6,1,8,5,9
-N,6,7,8,5,4,4,11,3,4,10,10,9,5,8,1,8
-Q,5,13,5,8,4,8,6,5,7,11,5,7,3,8,9,9
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-B,3,3,3,4,3,7,7,9,6,7,6,7,2,8,9,10
-Q,2,3,3,3,2,9,10,5,2,5,7,10,3,9,5,10
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-Y,9,14,8,8,5,4,9,4,3,11,10,6,4,11,4,5
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-C,4,9,5,6,2,6,8,7,11,5,6,13,1,7,4,9
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-R,4,11,4,8,3,6,9,10,5,7,6,8,3,8,6,11
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-C,2,4,3,2,1,4,9,4,7,12,10,10,1,9,2,7
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-U,5,10,5,7,2,8,5,13,5,6,15,8,3,9,0,8
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-P,6,9,8,7,4,9,9,3,6,14,4,3,1,10,3,9
-E,7,9,5,5,2,7,7,5,7,10,6,9,1,9,7,9
-H,3,6,4,4,2,7,7,14,1,7,6,8,3,8,0,8
-L,5,9,5,7,1,0,0,7,6,0,1,4,0,8,0,8
-X,4,8,6,6,4,9,6,1,8,10,3,7,3,9,4,9
-D,2,3,3,2,2,7,7,6,6,7,6,5,2,8,3,7
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-X,7,11,6,6,3,5,8,3,9,10,9,10,4,7,4,5
-O,4,7,4,5,3,7,7,7,4,9,7,10,3,8,3,9
-T,6,11,5,6,2,5,10,3,7,13,7,5,2,8,4,4
-W,10,10,10,5,4,8,10,4,2,5,10,7,10,12,2,6
-G,1,0,2,0,1,8,7,5,5,6,6,9,1,8,5,10
-Z,3,10,4,8,5,6,8,5,9,7,7,9,2,9,7,8
-L,1,3,2,2,1,7,4,2,7,7,2,9,0,7,2,8
-I,5,8,6,6,4,7,6,2,7,7,6,8,0,9,4,8
-Q,6,7,6,9,7,8,5,6,3,9,5,11,5,7,7,5
-M,8,10,8,6,5,4,9,5,4,4,3,12,9,10,2,8
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-O,9,14,6,8,5,5,7,7,4,10,7,10,5,10,5,8
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-J,2,7,3,5,1,14,2,6,5,14,2,11,0,7,0,8
-C,5,10,6,7,3,5,9,7,8,6,8,14,1,7,4,9
-U,3,7,3,5,3,7,7,11,4,7,11,8,3,9,0,8
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-Q,4,7,6,7,3,9,6,8,7,7,5,10,3,8,4,8
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-Q,4,5,5,7,3,8,7,8,6,6,7,9,3,8,5,9
-I,1,7,2,5,1,7,7,0,7,13,6,8,0,8,1,8
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-I,3,6,4,4,2,6,9,0,6,13,7,7,0,8,1,7
-I,1,6,3,4,3,10,6,1,4,8,5,5,3,8,5,6
-R,5,11,5,8,6,6,8,9,5,7,6,8,3,8,5,12
-K,5,9,5,4,3,6,7,2,5,10,6,9,5,8,3,7
-A,1,0,2,0,0,8,3,2,0,7,2,8,2,6,1,8
-Z,3,5,5,8,4,12,4,2,5,9,3,7,1,8,5,10
-V,6,10,5,8,3,4,11,2,4,9,11,7,3,9,1,8
-Z,2,5,4,4,2,7,7,2,10,11,6,8,1,8,6,7
-H,3,6,4,4,4,7,7,5,6,7,6,9,3,8,3,8
-C,3,6,4,4,1,5,7,7,10,6,6,13,1,8,4,8
-M,3,6,5,4,5,10,5,2,2,8,4,8,5,7,2,6
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-R,5,9,7,7,8,5,8,4,5,6,5,9,7,6,10,7
-N,6,9,5,4,2,6,9,4,5,4,4,10,5,10,2,8
-T,4,5,5,4,2,5,12,3,8,11,9,4,1,11,2,4
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-O,5,10,7,8,3,8,7,8,8,7,6,9,3,8,4,8
-A,2,1,3,1,1,7,4,2,0,7,2,8,2,7,1,7
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-K,4,8,6,6,4,6,7,2,6,10,7,10,3,8,3,8
-D,3,4,4,3,3,6,7,6,6,7,6,5,2,8,3,7
-O,6,11,6,8,7,8,6,8,4,9,4,8,3,8,3,8
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-P,4,10,5,8,4,4,12,6,5,12,9,3,1,10,4,6
-I,6,7,8,8,7,8,8,4,5,6,6,7,3,8,10,10
-B,4,8,6,6,5,7,7,6,6,9,6,6,3,8,7,8
-V,2,1,4,2,1,5,12,3,3,9,11,8,2,11,1,8
-K,2,3,3,1,2,6,7,4,7,7,6,11,3,8,4,9
-D,3,2,4,3,2,8,7,7,7,7,6,4,2,8,3,7
-R,3,6,5,4,5,7,8,3,4,6,6,9,6,9,7,6
-U,2,0,2,0,0,7,5,10,4,7,13,8,3,10,0,8
-R,4,5,6,4,3,9,7,2,6,10,3,6,2,7,3,10
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-A,4,9,6,6,5,10,3,1,2,7,3,9,5,5,3,7
-B,2,4,3,2,2,8,8,3,5,10,5,6,2,8,5,9
-I,0,1,0,1,0,7,7,4,4,7,6,8,0,8,0,8
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-H,4,9,5,6,2,7,9,15,2,7,3,8,3,8,0,8
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-X,5,8,7,6,5,8,5,3,5,7,7,9,4,8,10,9
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-M,5,10,8,7,7,9,6,2,4,8,5,7,7,6,2,8
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-E,3,5,6,3,3,5,8,2,9,11,7,9,2,8,4,8
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-M,2,1,3,3,3,8,6,6,4,7,7,8,6,5,2,8
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-A,4,9,7,7,4,9,3,2,3,6,1,7,2,6,2,7
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-G,3,5,4,3,2,6,6,6,5,8,7,11,2,8,4,10
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-E,2,6,3,4,2,3,8,5,9,7,6,14,0,8,6,9
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-Z,4,8,6,6,4,7,8,2,9,11,6,8,1,8,6,8
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-L,2,5,4,4,2,7,4,1,8,8,2,10,0,7,2,8
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-R,6,11,9,8,7,7,8,5,7,7,5,6,3,6,6,9
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-M,3,3,4,2,2,9,6,7,3,6,7,6,6,6,1,5
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-T,8,12,7,6,4,5,11,2,7,12,7,5,2,7,4,4
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-S,3,6,5,5,5,9,8,5,5,7,6,8,5,8,9,11
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-S,6,9,5,4,2,11,2,2,5,10,2,9,2,8,3,11
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-A,2,1,3,1,1,7,4,2,0,7,2,8,3,6,1,8
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-Q,6,9,8,8,7,5,4,4,4,4,3,7,4,8,6,5
-V,4,9,6,7,3,6,12,3,4,8,12,8,3,10,1,9
-S,7,11,5,6,3,7,3,2,5,7,2,7,3,7,5,9
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-F,4,7,5,8,6,7,9,5,4,8,6,7,4,9,8,7
-H,8,14,7,8,4,8,8,4,6,8,5,6,6,8,5,8
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-Z,3,5,4,4,3,7,7,5,9,6,6,8,1,8,7,8
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-H,5,10,6,7,3,7,6,15,1,7,8,8,3,8,0,8
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-I,3,8,4,6,2,6,9,0,7,13,7,7,0,8,1,7
-S,3,5,4,3,3,8,8,7,5,7,6,8,2,8,9,8
-E,5,4,5,6,3,3,7,6,11,7,6,15,0,8,7,7
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-R,2,1,2,2,2,6,7,4,4,6,5,6,2,7,3,8
-L,4,7,5,5,2,8,3,2,8,9,2,9,1,6,3,9
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-E,4,9,6,7,5,6,8,3,7,11,8,8,3,8,5,6
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-K,6,10,8,8,5,5,8,2,7,10,8,11,3,8,3,7
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-A,2,7,4,5,3,11,3,2,2,9,2,9,1,6,2,8
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-I,1,6,2,4,1,7,7,1,8,7,6,8,0,8,3,8
-B,3,5,4,4,3,8,7,6,6,6,6,5,2,8,7,8
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-Y,5,9,7,7,6,9,5,7,5,7,9,8,3,9,8,4
-Z,2,1,2,1,1,8,7,5,9,6,6,8,1,8,6,8
-G,7,13,6,7,3,9,6,5,6,11,4,8,5,7,5,8
-I,3,9,5,6,6,10,6,2,5,8,5,5,3,8,5,7
-Y,3,8,4,5,1,8,10,2,3,5,12,8,1,10,0,8
-B,2,5,4,3,3,8,8,3,5,10,5,6,3,7,5,9
-O,1,0,1,0,0,8,7,6,4,7,6,8,2,8,2,8
-B,6,9,8,6,7,10,6,3,6,10,3,7,4,7,5,12
-G,3,4,4,6,2,7,6,8,7,6,6,10,1,7,6,11
-A,3,8,5,5,2,6,4,3,1,6,1,8,2,7,2,7
-H,3,7,5,5,5,6,7,6,6,7,6,10,3,8,3,9
-Z,3,9,4,7,2,7,7,4,14,10,6,8,0,8,8,8
-I,1,3,1,2,1,7,7,1,7,7,6,8,0,8,2,8
-Y,6,9,8,7,8,9,7,6,4,6,8,8,3,9,8,4
-Q,4,6,4,7,4,7,11,4,3,6,10,11,3,10,6,8
-H,6,11,8,8,9,6,7,6,7,7,7,10,6,8,4,9
-M,11,12,11,6,5,7,10,5,4,4,5,11,11,13,2,7
-Y,1,0,2,0,0,7,10,3,1,7,12,8,1,11,0,8
-S,3,5,4,3,3,8,7,7,5,7,7,8,2,8,9,8
-O,1,3,2,1,1,7,7,5,3,9,6,8,2,8,2,8
-L,4,8,6,6,7,8,7,3,5,6,7,9,6,10,7,5
-P,4,8,5,6,4,4,11,4,6,11,9,5,0,10,3,7
-A,4,8,6,6,4,8,3,2,2,6,2,7,2,6,2,7
-U,3,8,5,7,5,7,7,4,3,6,6,8,6,8,1,8
-R,8,14,8,8,6,8,8,3,5,9,4,8,6,6,7,7
-A,3,6,5,4,3,8,6,2,4,8,2,5,2,6,2,7
-U,3,6,4,4,3,8,8,8,5,6,7,9,3,8,4,6
-N,5,10,7,8,5,7,8,5,5,7,7,7,6,7,3,7
-U,2,2,3,3,1,7,8,7,7,8,10,7,3,9,1,8
-U,3,3,3,1,1,6,8,5,7,8,7,8,3,10,2,5
-B,4,4,5,7,4,6,6,9,7,6,6,7,2,8,9,10
-O,6,10,7,8,6,7,8,7,4,10,7,7,5,9,4,9
-H,7,9,10,7,6,9,6,3,6,10,5,7,3,8,3,8
-J,1,1,1,1,0,12,4,6,4,13,4,10,0,7,0,8
-J,2,9,3,7,1,14,3,6,5,14,1,9,0,7,0,8
-I,2,5,0,7,0,7,7,4,4,7,6,8,0,8,0,8
-G,3,3,4,5,2,7,8,8,7,6,7,7,2,7,6,11
-P,3,5,4,4,2,6,10,6,4,9,7,2,1,10,4,6
-H,4,7,6,5,4,7,7,3,6,10,5,9,3,8,3,9
-G,5,11,7,8,6,7,6,7,8,6,5,8,2,10,8,12
-U,6,8,8,6,6,8,8,8,5,5,7,10,3,8,4,6
-U,6,11,6,6,4,6,5,6,5,6,9,9,5,9,3,10
-W,8,12,8,6,4,2,9,2,2,10,11,9,8,10,0,7
-Q,3,5,4,6,3,8,7,7,2,5,7,10,3,8,6,9
-E,2,6,2,4,2,3,6,6,10,7,7,14,0,8,7,8
-P,6,9,8,6,5,9,8,5,6,12,4,4,5,9,5,7
-M,3,3,4,2,2,8,6,6,3,6,7,8,6,6,1,7
-J,3,8,4,6,2,10,6,1,6,13,3,7,0,7,0,8
-T,5,10,6,8,7,8,7,4,7,7,6,9,5,7,5,7
-L,1,4,3,3,1,6,4,1,7,8,2,10,0,7,2,8
-S,4,11,5,8,4,8,7,8,7,8,5,7,2,8,9,8
-N,4,7,6,5,4,7,9,6,6,7,6,6,6,8,1,6
-P,3,6,4,4,2,5,11,9,3,9,6,5,1,10,4,8
-D,3,3,3,4,2,5,7,10,8,7,6,6,3,8,4,8
-X,6,10,9,7,4,8,6,1,9,10,4,8,3,8,4,8
-M,3,3,5,2,2,5,6,4,3,10,10,10,6,5,2,7
-A,4,11,6,8,5,7,4,2,1,7,1,8,2,7,2,7
-T,7,10,7,8,4,6,12,4,8,12,9,3,2,12,3,4
-L,4,8,5,6,3,7,3,2,8,7,2,8,1,6,2,7
-W,6,12,7,6,5,8,8,3,3,6,9,7,10,10,3,6
-P,4,6,5,9,8,8,9,4,0,8,7,6,5,10,5,7
-Q,4,9,5,8,3,7,6,8,6,6,7,7,3,8,6,9
-T,5,7,5,5,3,6,11,3,6,11,9,5,3,11,3,4
-L,3,6,4,4,2,4,5,2,8,3,2,7,0,7,1,6
-E,3,4,5,3,2,7,7,2,8,11,6,9,2,8,4,9
-W,4,7,6,5,3,8,8,4,1,7,8,8,8,9,0,8
-C,10,14,8,8,6,7,8,4,4,9,8,10,4,9,9,11
-K,7,9,8,5,4,5,8,4,6,10,11,11,6,12,4,7
-M,3,6,4,4,2,8,6,11,1,6,9,8,7,6,0,8
-T,5,9,7,7,6,7,6,7,7,6,7,9,4,11,6,8
-E,3,6,5,6,5,6,8,4,3,7,7,10,4,10,7,12
-T,6,8,6,6,3,4,12,2,9,12,9,5,0,10,2,4
-P,2,4,3,3,2,5,10,4,4,10,8,5,0,10,3,7
-V,4,9,6,7,7,8,6,4,2,8,7,8,8,9,4,7
-J,1,1,2,1,0,11,6,2,6,12,3,8,0,7,0,8
-E,3,4,4,3,3,7,7,5,8,7,6,8,2,8,6,9
-C,4,6,5,4,5,7,7,4,3,6,8,10,5,11,3,8
-R,5,8,7,6,7,8,8,7,3,7,4,6,5,7,7,8
-T,3,10,5,7,3,9,12,3,7,5,12,8,2,12,1,8
-U,4,5,5,4,3,6,8,5,7,7,10,10,3,9,1,8
-W,6,7,8,6,10,7,7,5,5,7,5,8,10,8,8,8
-T,4,10,5,7,3,8,14,1,5,6,10,9,0,8,0,8
-X,4,10,6,8,4,7,7,4,9,6,6,8,3,8,7,7
-R,3,5,5,4,5,7,8,3,3,7,5,8,6,8,4,8
-Y,1,1,3,2,1,6,10,1,6,8,11,8,1,11,2,8
-K,5,9,5,4,2,7,7,3,6,9,8,9,6,11,3,7
-M,8,9,11,8,12,7,9,4,4,7,5,8,12,6,6,7
-Z,5,10,6,8,3,7,7,4,15,9,6,8,0,8,8,8
-V,5,8,5,6,2,2,12,5,4,12,12,7,3,10,1,8
-G,4,4,5,6,3,7,5,7,8,6,5,8,2,8,6,11
-L,3,7,5,5,2,6,3,3,8,6,2,8,1,6,3,7
-A,3,11,5,8,4,12,3,3,3,10,2,9,2,7,3,9
-Q,6,11,6,6,3,7,6,4,8,9,5,9,3,7,9,10
-X,9,14,8,8,4,9,6,3,8,9,4,7,5,8,5,9
-G,2,4,3,3,2,7,7,5,5,6,6,9,2,8,4,9
-A,2,2,4,4,2,8,2,1,2,7,2,8,2,7,3,7
-A,3,7,4,5,2,7,4,2,0,6,2,8,2,7,1,7
-O,3,6,4,4,3,8,8,7,4,7,7,8,3,8,2,8
-I,1,5,1,4,1,8,7,1,7,7,6,7,0,8,3,7
-L,3,5,5,4,2,7,4,1,8,8,2,10,0,7,2,8
-X,3,7,5,5,3,8,8,3,8,6,6,7,3,8,6,8
-M,5,10,6,8,4,7,7,12,2,7,9,8,9,6,0,8
-Z,2,5,3,4,2,7,7,5,9,6,6,8,1,8,7,8
-J,3,7,5,5,4,10,5,2,6,8,5,5,3,7,5,7
-V,5,11,8,8,10,8,5,5,2,8,8,8,6,9,4,8
-V,5,9,4,4,2,9,10,4,4,6,10,6,4,11,3,7
-L,3,7,4,5,2,4,4,3,10,2,1,7,0,7,2,5
-H,5,7,8,5,6,5,9,3,6,10,8,8,3,8,3,6
-C,5,4,6,6,2,6,7,7,11,8,6,13,1,9,4,8
-X,3,5,5,3,2,5,9,2,8,11,10,9,2,9,3,6
-Y,5,7,5,5,2,3,10,2,7,11,11,6,1,11,2,5
-D,4,8,6,6,4,7,8,7,7,10,6,5,3,8,4,7
-U,9,11,9,8,6,3,8,5,8,10,9,10,3,9,2,6
-I,5,12,5,6,3,6,11,3,5,13,6,4,1,8,5,8
-Q,1,1,2,1,1,8,7,6,3,6,6,8,2,8,3,8
-J,2,5,3,3,1,10,6,2,6,12,4,9,1,6,1,7
-G,4,9,6,6,6,7,7,6,3,7,6,9,5,7,7,7
-U,4,4,5,3,2,5,8,5,7,10,9,8,3,9,2,5
-X,7,11,11,8,7,4,8,2,9,10,11,9,5,8,5,4
-P,4,6,5,4,4,8,5,6,5,7,6,7,2,9,7,9
-T,2,3,3,2,1,5,12,3,7,11,9,5,1,10,2,5
-T,5,11,6,8,8,6,8,4,5,6,6,9,6,7,6,7
-Y,4,10,6,7,1,9,11,3,2,5,13,8,1,11,0,8
-G,5,10,6,7,4,6,7,7,7,10,7,11,2,10,4,9
-K,7,14,8,8,5,9,6,3,5,11,3,7,5,7,4,8
-S,4,8,5,6,6,9,9,5,4,8,4,6,4,9,9,7
-N,4,10,6,8,5,8,8,6,5,7,6,6,6,9,2,6
-M,4,3,4,5,3,7,7,12,1,7,9,8,8,6,0,8
-G,2,3,3,2,1,7,7,5,5,10,7,10,2,9,4,10
-L,2,3,2,2,1,5,4,5,6,2,2,5,1,7,1,6
-X,3,9,4,6,1,7,7,4,4,7,6,8,3,8,4,8
-L,3,4,3,6,1,0,1,5,6,0,0,7,0,8,0,8
-V,6,11,6,6,3,7,9,3,4,8,9,6,5,9,3,6
-P,4,9,6,6,4,7,9,3,4,12,5,4,2,9,3,8
-K,3,1,5,3,3,6,7,4,8,7,6,11,3,8,5,9
-W,4,10,6,8,7,8,7,6,2,7,8,8,9,7,4,9
-Q,2,0,2,1,1,8,7,6,3,6,6,9,2,8,3,8
-Q,2,2,3,3,2,8,8,6,1,5,6,9,2,9,5,10
-Q,4,8,6,7,5,8,8,7,5,6,7,9,3,8,4,9
-S,3,6,5,4,5,6,7,3,2,8,6,6,2,7,10,1
-H,3,8,5,6,4,8,8,7,7,7,5,7,3,8,3,7
-W,2,3,4,2,2,7,10,2,2,7,9,8,6,11,0,8
-O,10,14,7,8,4,6,8,5,4,7,4,6,5,9,5,7
-Y,6,9,5,5,2,5,9,3,3,10,9,5,3,10,3,4
-L,1,0,1,0,0,2,2,5,4,1,2,6,0,8,0,8
-T,6,8,6,6,3,4,12,3,7,12,10,5,1,11,1,5
-G,2,3,2,2,1,6,8,5,4,9,8,9,2,8,4,9
-E,5,10,4,5,3,7,8,4,3,10,5,9,4,9,7,10
-J,6,7,8,8,7,8,8,5,7,7,5,8,4,10,10,8
-Q,4,3,5,5,2,8,9,7,6,6,8,9,3,8,5,9
-N,7,14,9,8,5,3,9,4,4,13,12,10,6,9,0,8
-T,4,5,4,4,2,6,11,2,7,11,9,4,1,11,2,4
-M,5,8,7,6,7,8,6,6,5,7,7,10,11,6,2,9
-O,4,5,5,4,3,7,7,8,5,7,6,8,2,8,3,8
-A,6,10,7,6,3,12,0,4,1,11,4,12,4,4,3,11
-T,2,9,4,7,2,7,13,0,5,7,10,8,0,8,0,8
-N,3,5,6,3,2,7,9,2,5,10,6,6,5,8,1,7
-F,4,10,4,7,3,1,12,4,4,11,10,8,0,8,2,6
-P,2,5,3,7,5,8,6,5,1,7,6,7,6,8,5,9
-U,3,7,5,5,3,4,8,6,6,7,9,10,3,9,1,8
-H,5,7,8,5,5,9,7,3,6,10,4,7,5,8,4,9
-O,6,9,4,4,2,8,7,5,5,8,4,7,4,9,5,8
-S,2,8,3,6,2,8,8,6,9,5,6,7,0,8,9,8
-J,5,9,6,7,3,6,8,2,6,15,6,8,2,8,2,8
-O,4,5,5,4,4,8,4,4,4,9,4,10,3,6,6,6
-B,4,8,6,6,5,7,8,6,6,10,6,6,3,8,7,8
-F,3,8,5,6,3,6,11,4,6,11,9,4,2,10,3,5
-E,3,9,3,6,2,3,6,6,10,7,7,14,0,8,7,8
-F,3,6,5,4,3,5,10,4,6,10,9,5,2,9,3,5
-A,2,3,3,1,1,6,2,2,1,5,2,8,1,6,1,7
-Q,3,5,4,6,4,8,9,4,2,5,8,11,2,10,5,8
-C,6,9,6,7,4,4,7,5,7,10,9,14,4,9,5,5
-S,4,9,6,6,7,9,4,4,4,9,6,9,4,7,10,9
-F,7,10,9,8,7,9,7,2,6,12,4,6,5,9,4,9
-C,5,10,7,9,8,5,6,4,4,7,6,11,5,11,8,10
-V,4,7,6,5,6,8,6,4,2,7,8,8,7,9,4,6
-T,4,4,5,3,2,5,12,2,8,11,9,4,0,10,2,4
-N,5,9,5,4,2,9,11,5,3,5,6,9,5,11,2,6
-E,1,0,1,0,0,5,8,5,7,7,6,12,0,8,6,10
-L,3,8,3,6,2,0,2,4,6,1,0,8,0,8,0,8
-A,3,9,5,6,2,6,5,3,1,6,1,8,2,7,2,7
-K,5,11,5,8,5,3,8,7,3,6,4,11,3,8,2,11
-M,6,9,10,7,12,7,5,3,2,7,5,8,15,7,4,6
-R,2,3,3,2,2,7,7,5,5,7,5,6,2,7,4,8
-S,6,12,6,7,3,6,8,3,6,13,7,7,2,9,3,7
-Y,3,9,5,6,3,7,9,1,6,6,11,8,2,11,2,7
-V,7,10,5,5,2,6,11,5,4,11,9,4,4,11,3,10
-S,2,0,2,1,1,8,7,4,6,5,6,8,0,8,7,8
-M,5,6,8,4,5,9,6,2,4,9,5,7,8,6,2,8
-O,9,15,6,8,5,5,7,7,4,10,7,10,5,9,5,8
-L,3,7,3,5,1,0,1,6,6,0,0,6,0,8,0,8
-D,6,9,8,8,8,7,6,5,7,7,5,9,6,5,10,3
-P,2,1,3,2,1,4,10,3,5,10,8,5,0,9,3,7
-W,3,8,5,6,5,11,11,2,2,5,8,7,7,12,1,7
-O,4,3,5,4,2,7,6,8,8,6,5,7,3,8,4,8
-E,4,9,5,6,3,5,9,2,10,10,8,9,2,8,5,5
-J,2,11,3,8,2,15,4,4,5,13,1,8,0,7,0,8
-T,5,8,7,7,7,7,9,4,8,7,7,8,3,10,8,6
-D,2,2,3,3,2,7,7,7,6,6,6,4,2,8,3,7
-C,7,10,8,8,4,4,8,6,9,12,9,13,2,9,3,7
-T,6,9,6,7,5,6,11,3,7,11,9,5,2,12,2,4
-S,2,3,4,2,1,8,7,2,6,10,6,8,1,9,5,8
-A,4,9,6,6,2,9,5,3,1,8,1,8,2,7,2,8

diff --git a/samples/c/letter_recog.cpp b/samples/c/letter_recog.cpp
deleted file mode 100644 (file)
index 555203c..0000000
--- a/samples/c/letter_recog.cpp
+++ /dev/null
@@ -1,532 +0,0 @@
-#include "opencv2/core/core_c.h"
-#include "opencv2/ml/ml.hpp"
-
-#include <stdio.h>
-
-/*
-The sample demonstrates how to train Random Trees classifier
-(or Boosting classifier, or MLP - see main()) using the provided dataset.
-
-We use the sample database letter-recognition.data
-from UCI Repository, here is the link:
-
-Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).
-UCI Repository of machine learning databases
-[http://www.ics.uci.edu/~mlearn/MLRepository.html].
-Irvine, CA: University of California, Department of Information and Computer Science.
-
-The dataset consists of 20000 feature vectors along with the
-responses - capital latin letters A..Z.
-The first 16000 (10000 for boosting)) samples are used for training
-and the remaining 4000 (10000 for boosting) - to test the classifier.
-*/
-
-// This function reads data and responses from the file <filename>
-static int
-read_num_class_data( const char* filename, int var_count,
-                     CvMat** data, CvMat** responses )
-{
-    const int M = 1024;
-    FILE* f = fopen( filename, "rt" );
-    CvMemStorage* storage;
-    CvSeq* seq;
-    char buf[M+2];
-    float* el_ptr;
-    CvSeqReader reader;
-    int i, j;
-
-    if( !f )
-        return 0;
-
-    el_ptr = new float[var_count+1];
-    storage = cvCreateMemStorage();
-    seq = cvCreateSeq( 0, sizeof(*seq), (var_count+1)*sizeof(float), storage );
-
-    for(;;)
-    {
-        char* ptr;
-        if( !fgets( buf, M, f ) || !strchr( buf, ',' ) )
-            break;
-        el_ptr[0] = buf[0];
-        ptr = buf+2;
-        for( i = 1; i <= var_count; i++ )
-        {
-            int n = 0;
-            sscanf( ptr, "%f%n", el_ptr + i, &n );
-            ptr += n + 1;
-        }
-        if( i <= var_count )
-            break;
-        cvSeqPush( seq, el_ptr );
-    }
-    fclose(f);
-
-    *data = cvCreateMat( seq->total, var_count, CV_32F );
-    *responses = cvCreateMat( seq->total, 1, CV_32F );
-
-    cvStartReadSeq( seq, &reader );
-
-    for( i = 0; i < seq->total; i++ )
-    {
-        const float* sdata = (float*)reader.ptr + 1;
-        float* ddata = data[0]->data.fl + var_count*i;
-        float* dr = responses[0]->data.fl + i;
-
-        for( j = 0; j < var_count; j++ )
-            ddata[j] = sdata[j];
-        *dr = sdata[-1];
-        CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
-    }
-
-    cvReleaseMemStorage( &storage );
-    delete el_ptr;
-    return 1;
-}
-
-static
-int build_rtrees_classifier( char* data_filename,
-    char* filename_to_save, char* filename_to_load )
-{
-    CvMat* data = 0;
-    CvMat* responses = 0;
-    CvMat* var_type = 0;
-    CvMat* sample_idx = 0;
-
-    int ok = read_num_class_data( data_filename, 16, &data, &responses );
-    int nsamples_all = 0, ntrain_samples = 0;
-    int i = 0;
-    double train_hr = 0, test_hr = 0;
-    CvRTrees forest;
-    CvMat* var_importance = 0;
-
-    if( !ok )
-    {
-        printf( "Could not read the database %s\n", data_filename );
-        return -1;
-    }
-
-    printf( "The database %s is loaded.\n", data_filename );
-    nsamples_all = data->rows;
-    ntrain_samples = (int)(nsamples_all*0.8);
-
-    // Create or load Random Trees classifier
-    if( filename_to_load )
-    {
-        // load classifier from the specified file
-        forest.load( filename_to_load );
-        ntrain_samples = 0;
-        if( forest.get_tree_count() == 0 )
-        {
-            printf( "Could not read the classifier %s\n", filename_to_load );
-            return -1;
-        }
-        printf( "The classifier %s is loaded.\n", data_filename );
-    }
-    else
-    {
-        // create classifier by using <data> and <responses>
-        printf( "Training the classifier ...\n");
-
-        // 1. create type mask
-        var_type = cvCreateMat( data->cols + 1, 1, CV_8U );
-        cvSet( var_type, cvScalarAll(CV_VAR_ORDERED) );
-        cvSetReal1D( var_type, data->cols, CV_VAR_CATEGORICAL );
-
-        // 2. create sample_idx
-        sample_idx = cvCreateMat( 1, nsamples_all, CV_8UC1 );
-        {
-            CvMat mat;
-            cvGetCols( sample_idx, &mat, 0, ntrain_samples );
-            cvSet( &mat, cvRealScalar(1) );
-
-            cvGetCols( sample_idx, &mat, ntrain_samples, nsamples_all );
-            cvSetZero( &mat );
-        }
-
-        // 3. train classifier
-        forest.train( data, CV_ROW_SAMPLE, responses, 0, sample_idx, var_type, 0,
-            CvRTParams(10,10,0,false,15,0,true,4,100,0.01f,CV_TERMCRIT_ITER));
-        printf( "\n");
-    }
-
-    // compute prediction error on train and test data
-    for( i = 0; i < nsamples_all; i++ )
-    {
-        double r;
-        CvMat sample;
-        cvGetRow( data, &sample, i );
-
-        r = forest.predict( &sample );
-        r = fabs((double)r - responses->data.fl[i]) <= FLT_EPSILON ? 1 : 0;
-
-        if( i < ntrain_samples )
-            train_hr += r;
-        else
-            test_hr += r;
-    }
-
-    test_hr /= (double)(nsamples_all-ntrain_samples);
-    train_hr /= (double)ntrain_samples;
-    printf( "Recognition rate: train = %.1f%%, test = %.1f%%\n",
-            train_hr*100., test_hr*100. );
-
-    printf( "Number of trees: %d\n", forest.get_tree_count() );
-
-    // Print variable importance
-    var_importance = (CvMat*)forest.get_var_importance();
-    if( var_importance )
-    {
-        double rt_imp_sum = cvSum( var_importance ).val[0];
-        printf("var#\timportance (in %%):\n");
-        for( i = 0; i < var_importance->cols; i++ )
-            printf( "%-2d\t%-4.1f\n", i,
-            100.f*var_importance->data.fl[i]/rt_imp_sum);
-    }
-
-    //Print some proximitites
-    printf( "Proximities between some samples corresponding to the letter 'T':\n" );
-    {
-        CvMat sample1, sample2;
-        const int pairs[][2] = {{0,103}, {0,106}, {106,103}, {-1,-1}};
-
-        for( i = 0; pairs[i][0] >= 0; i++ )
-        {
-            cvGetRow( data, &sample1, pairs[i][0] );
-            cvGetRow( data, &sample2, pairs[i][1] );
-            printf( "proximity(%d,%d) = %.1f%%\n", pairs[i][0], pairs[i][1],
-                forest.get_proximity( &sample1, &sample2 )*100. );
-        }
-    }
-
-    // Save Random Trees classifier to file if needed
-    if( filename_to_save )
-        forest.save( filename_to_save );
-
-    cvReleaseMat( &sample_idx );
-    cvReleaseMat( &var_type );
-    cvReleaseMat( &data );
-    cvReleaseMat( &responses );
-
-    return 0;
-}
-
-
-static
-int build_boost_classifier( char* data_filename,
-    char* filename_to_save, char* filename_to_load )
-{
-    const int class_count = 26;
-    CvMat* data = 0;
-    CvMat* responses = 0;
-    CvMat* var_type = 0;
-    CvMat* temp_sample = 0;
-    CvMat* weak_responses = 0;
-
-    int ok = read_num_class_data( data_filename, 16, &data, &responses );
-    int nsamples_all = 0, ntrain_samples = 0;
-    int var_count;
-    int i, j, k;
-    double train_hr = 0, test_hr = 0;
-    CvBoost boost;
-
-    if( !ok )
-    {
-        printf( "Could not read the database %s\n", data_filename );
-        return -1;
-    }
-
-    printf( "The database %s is loaded.\n", data_filename );
-    nsamples_all = data->rows;
-    ntrain_samples = (int)(nsamples_all*0.5);
-    var_count = data->cols;
-
-    // Create or load Boosted Tree classifier
-    if( filename_to_load )
-    {
-        // load classifier from the specified file
-        boost.load( filename_to_load );
-        ntrain_samples = 0;
-        if( !boost.get_weak_predictors() )
-        {
-            printf( "Could not read the classifier %s\n", filename_to_load );
-            return -1;
-        }
-        printf( "The classifier %s is loaded.\n", data_filename );
-    }
-    else
-    {
-        // !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-        //
-        // As currently boosted tree classifier in MLL can only be trained
-        // for 2-class problems, we transform the training database by
-        // "unrolling" each training sample as many times as the number of
-        // classes (26) that we have.
-        //
-        // !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-
-        CvMat* new_data = cvCreateMat( ntrain_samples*class_count, var_count + 1, CV_32F );
-        CvMat* new_responses = cvCreateMat( ntrain_samples*class_count, 1, CV_32S );
-
-        // 1. unroll the database type mask
-        printf( "Unrolling the database...\n");
-        for( i = 0; i < ntrain_samples; i++ )
-        {
-            float* data_row = (float*)(data->data.ptr + data->step*i);
-            for( j = 0; j < class_count; j++ )
-            {
-                float* new_data_row = (float*)(new_data->data.ptr +
-                                new_data->step*(i*class_count+j));
-                for( k = 0; k < var_count; k++ )
-                    new_data_row[k] = data_row[k];
-                new_data_row[var_count] = (float)j;
-                new_responses->data.i[i*class_count + j] = responses->data.fl[i] == j+'A';
-            }
-        }
-
-        // 2. create type mask
-        var_type = cvCreateMat( var_count + 2, 1, CV_8U );
-        cvSet( var_type, cvScalarAll(CV_VAR_ORDERED) );
-        // the last indicator variable, as well
-        // as the new (binary) response are categorical
-        cvSetReal1D( var_type, var_count, CV_VAR_CATEGORICAL );
-        cvSetReal1D( var_type, var_count+1, CV_VAR_CATEGORICAL );
-
-        // 3. train classifier
-        printf( "Training the classifier (may take a few minutes)...\n");
-        boost.train( new_data, CV_ROW_SAMPLE, new_responses, 0, 0, var_type, 0,
-            CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0 ));
-        cvReleaseMat( &new_data );
-        cvReleaseMat( &new_responses );
-        printf("\n");
-    }
-
-    temp_sample = cvCreateMat( 1, var_count + 1, CV_32F );
-    weak_responses = cvCreateMat( 1, boost.get_weak_predictors()->total, CV_32F ); 
-
-    // compute prediction error on train and test data
-    for( i = 0; i < nsamples_all; i++ )
-    {
-        int best_class = 0;
-        double max_sum = -DBL_MAX;
-        double r;
-        CvMat sample;
-        cvGetRow( data, &sample, i );
-        for( k = 0; k < var_count; k++ )
-            temp_sample->data.fl[k] = sample.data.fl[k];
-
-        for( j = 0; j < class_count; j++ )
-        {
-            temp_sample->data.fl[var_count] = (float)j;
-            boost.predict( temp_sample, 0, weak_responses );
-            double sum = cvSum( weak_responses ).val[0];
-            if( max_sum < sum )
-            {
-                max_sum = sum;
-                best_class = j + 'A';
-            }
-        }
-
-        r = fabs(best_class - responses->data.fl[i]) < FLT_EPSILON ? 1 : 0;
-
-        if( i < ntrain_samples )
-            train_hr += r;
-        else
-            test_hr += r;
-    }
-
-    test_hr /= (double)(nsamples_all-ntrain_samples);
-    train_hr /= (double)ntrain_samples;
-    printf( "Recognition rate: train = %.1f%%, test = %.1f%%\n",
-            train_hr*100., test_hr*100. );
-
-    printf( "Number of trees: %d\n", boost.get_weak_predictors()->total );
-
-    // Save classifier to file if needed
-    if( filename_to_save )
-        boost.save( filename_to_save );
-
-    cvReleaseMat( &temp_sample );
-    cvReleaseMat( &weak_responses );
-    cvReleaseMat( &var_type );
-    cvReleaseMat( &data );
-    cvReleaseMat( &responses );
-
-    return 0;
-}
-
-
-static
-int build_mlp_classifier( char* data_filename,
-    char* filename_to_save, char* filename_to_load )
-{
-    const int class_count = 26;
-    CvMat* data = 0;
-    CvMat train_data;
-    CvMat* responses = 0;
-    CvMat* mlp_response = 0;
-
-    int ok = read_num_class_data( data_filename, 16, &data, &responses );
-    int nsamples_all = 0, ntrain_samples = 0;
-    int i, j;
-    double train_hr = 0, test_hr = 0;
-    CvANN_MLP mlp;
-
-    if( !ok )
-    {
-        printf( "Could not read the database %s\n", data_filename );
-        return -1;
-    }
-
-    printf( "The database %s is loaded.\n", data_filename );
-    nsamples_all = data->rows;
-    ntrain_samples = (int)(nsamples_all*0.8);
-
-    // Create or load MLP classifier
-    if( filename_to_load )
-    {
-        // load classifier from the specified file
-        mlp.load( filename_to_load );
-        ntrain_samples = 0;
-        if( !mlp.get_layer_count() )
-        {
-            printf( "Could not read the classifier %s\n", filename_to_load );
-            return -1;
-        }
-        printf( "The classifier %s is loaded.\n", data_filename );
-    }
-    else
-    {
-        // !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-        //
-        // MLP does not support categorical variables by explicitly.
-        // So, instead of the output class label, we will use
-        // a binary vector of <class_count> components for training and,
-        // therefore, MLP will give us a vector of "probabilities" at the
-        // prediction stage
-        //
-        // !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
-
-        CvMat* new_responses = cvCreateMat( ntrain_samples, class_count, CV_32F );
-
-        // 1. unroll the responses
-        printf( "Unrolling the responses...\n");
-        for( i = 0; i < ntrain_samples; i++ )
-        {
-            int cls_label = cvRound(responses->data.fl[i]) - 'A';
-            float* bit_vec = (float*)(new_responses->data.ptr + i*new_responses->step);
-            for( j = 0; j < class_count; j++ )
-                bit_vec[j] = 0.f;
-            bit_vec[cls_label] = 1.f;
-        }
-        cvGetRows( data, &train_data, 0, ntrain_samples );
-
-        // 2. train classifier
-        int layer_sz[] = { data->cols, 100, 100, class_count };
-        CvMat layer_sizes =
-            cvMat( 1, (int)(sizeof(layer_sz)/sizeof(layer_sz[0])), CV_32S, layer_sz );
-        mlp.create( &layer_sizes );
-        printf( "Training the classifier (may take a few minutes)...\n");
-        mlp.train( &train_data, new_responses, 0, 0,
-            CvANN_MLP_TrainParams(cvTermCriteria(CV_TERMCRIT_ITER,300,0.01),
-#if 1
-            CvANN_MLP_TrainParams::BACKPROP,0.001));
-#else
-            CvANN_MLP_TrainParams::RPROP,0.05));
-#endif
-        cvReleaseMat( &new_responses );
-        printf("\n");
-    }
-
-    mlp_response = cvCreateMat( 1, class_count, CV_32F );
-
-    // compute prediction error on train and test data
-    for( i = 0; i < nsamples_all; i++ )
-    {
-        int best_class;
-        CvMat sample;
-        cvGetRow( data, &sample, i );
-        CvPoint max_loc = {0,0};
-        mlp.predict( &sample, mlp_response );
-        cvMinMaxLoc( mlp_response, 0, 0, 0, &max_loc, 0 );
-        best_class = max_loc.x + 'A';
-
-        int r = fabs((double)best_class - responses->data.fl[i]) < FLT_EPSILON ? 1 : 0;
-
-        if( i < ntrain_samples )
-            train_hr += r;
-        else
-            test_hr += r;
-    }
-
-    test_hr /= (double)(nsamples_all-ntrain_samples);
-    train_hr /= (double)ntrain_samples;
-    printf( "Recognition rate: train = %.1f%%, test = %.1f%%\n",
-            train_hr*100., test_hr*100. );
-
-    // Save classifier to file if needed
-    if( filename_to_save )
-        mlp.save( filename_to_save );
-
-    cvReleaseMat( &mlp_response );
-    cvReleaseMat( &data );
-    cvReleaseMat( &responses );
-
-    return 0;
-}
-
-
-int main( int argc, char *argv[] )
-{
-    char* filename_to_save = 0;
-    char* filename_to_load = 0;
-    char default_data_filename[] = "./letter-recognition.data";
-    char* data_filename = default_data_filename;
-    int method = 0;
-
-    int i;
-    for( i = 1; i < argc; i++ )
-    {
-        if( strcmp(argv[i],"-data") == 0 ) // flag "-data letter_recognition.xml"
-        {
-            i++;
-            data_filename = argv[i];
-        }
-        else if( strcmp(argv[i],"-save") == 0 ) // flag "-save filename.xml"
-        {
-            i++;
-            filename_to_save = argv[i];
-        }
-        else if( strcmp(argv[i],"-load") == 0) // flag "-load filename.xml"
-        {
-            i++;
-            filename_to_load = argv[i];
-        }
-        else if( strcmp(argv[i],"-boost") == 0)
-        {
-            method = 1;
-        }
-        else if( strcmp(argv[i],"-mlp") == 0 )
-        {
-            method = 2;
-        }
-        else
-            break;
-    }
-
-    if( i < argc ||
-        (method == 0 ?
-        build_rtrees_classifier( data_filename, filename_to_save, filename_to_load ) :
-        method == 1 ?
-        build_boost_classifier( data_filename, filename_to_save, filename_to_load ) :
-        method == 2 ?
-        build_mlp_classifier( data_filename, filename_to_save, filename_to_load ) :
-        -1) < 0)
-    {
-        printf("This is letter recognition sample.\n"
-                "The usage: letter_recog [-data <path to letter-recognition.data>] \\\n"
-                "  [-save <output XML file for the classifier>] \\\n"
-                "  [-load <XML file with the pre-trained classifier>] \\\n"
-                "  [-boost|-mlp] # to use boost/mlp classifier instead of default Random Trees\n" );
-    }
-    return 0;
-}