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dataset.py
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array_dataset = [1004, 1005, 1006, 1007, 1015, 1016, 100, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 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1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 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1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52 , 1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005,1004, 1005, 1006, 1007, 1015, 1016, 100, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006]
ckks_normal_array = [ 1019, 52, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005, 1006, 1007, 1015, 1016, 1017, 1018, 1019, 52,1004, 1005]
ckks_cube_array = [19, 52, 40, 56, 68, 17, 15, 52,47, 53, 16, 42, 30, 33, 17, 40, 80, 52,41, 15]
print(len(array_dataset))