******************************************************* *************************************************** ********************************************** ****************************************** T H E M O N K' S P R O B L E M S ****************************************** ********************************************** *************************************************** ******************************************************* The MONK's problems are a collection of three binary classification problems over a six-attribute discrete domain. Each training/test data is of the form : -> where is an ASCII-string, represents the value of attribute # n, and is either 0 or 1, depending on the class this example belongs to. The attributes may take the following values: attribute#1 : {1, 2, 3} attribute#2 : {1, 2, 3} attribute#3 : {1, 2} attribute#4 : {1, 2, 3} attribute#5 : {1, 2, 3, 4} attribute#6 : {1, 2} Thus, the six attributes span a space of 432=3x3x2x3x4x2 examples. /*********************************************************************\ *********************************************************************** \*********************************************************************/ The "true" concepts underlying each MONK's problem are given by: MONK-1: (attribute_1 = attribute_2) or (attribute_5 = 1) MONK-2: (attribute_n = 1) for EXACTLY TWO choices of n (in {1,2,...,6}) MONK-3: (attribute_5 = 3 and attribute_4 = 1) or (attribute_5 != 4 and attribute_2 != 3) (with "!=" denoting inequality). MONK-3 has 5% additional noise (misclassifications) in the training set. /*********************************************************************\ *********************************************************************** \*********************************************************************/ The MONK's problem were the basis of a first international comparison of learning algorithms. The result of this comparison is summarized in "The MONK's Problems - A Performance Comparison of Different Learning algorithms" by S.B. Thrun, J. Bala, E. Bloedorn, I. Bratko, B. Cestnik, J. Cheng, K. De Jong, S. Dzeroski, S.E. Fahlman, D. Fisher, R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J. Kreuziger, R.S. Michalski, T. Mitchell, P. Pachowicz, Y. Reich H. Vafaie, W. Van de Welde, W. Wenzel, J. Wnek, and J. Zhang has been published as Technical Report CS-CMU-91-197, Carnegie Mellon University in Dec. 1991. Here is the abstract: This report summarizes a comparison of different learning techniques which was performed at the 2nd European Summer School on Machine Learning, held in Belgium during summer 1991. A variety of symbolic and non-symbolic learning techniques -- namely AQ17-DCI, AQ17-HCI, AQ17-FCLS, AQ14-NT, AQ15-GA, Assistant Professional, mFOIL, ID5R, IDL, ID5R-hat, TDIDT, ID3, AQR, CN2, CLASS WEB, ECOBWEB, PRISM, Backpropagation, and Cascade Correlation -- are compared on three classification problems, the MONK's problems. The MONK's problems are derived from a domain in which each training example is represented by six discrete-valued attributes. Each problem involves learning a binary function defined over this domain, from a sample of training examples of this function. Experiments were performed with and without noise in the training examples. One significant characteristic of this comparison is that it was performed by a collection of researchers, each of whom was an advocate of the technique they tested (often they were the creators of the various methods). In this sense, the results are less biased than in comparisons performed by a single person advocating a specific learning method, and more accurately reflect the generalization behavior of the learning techniques as applied by knowledgeable users. /*********************************************************************\ *********************************************************************** \*********************************************************************/ The report is available via anonymous ftp by typing the following commands on your local UNIX-machine. For the first 5 files, you need to have a Postscript printer (most Laser Printer do). unix> ftp -i archive.cis.ohio-state.edu Name: anonymous Password: neuron ftp> cd pub/neuroprose ftp> binary ftp> get thrun.comparison.ps.Z ftp> get thrun.comparison.dat.Z ftp> bye unix> zcat thrun.comparison.ps.Z|lpr unix> zcat thrun.comparison.dat.Z|lpr /*********************************************************************\ *********************************************************************** \*********************************************************************/ Questions concerning the MONK's problems or the report may be addressed to: Sebastian Thrun School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA E-mail: thrun@cs.cmu.edu I would be glad to receive more classification results - please feel free to send me the results obtained with your favorite algorithm! I will collect all results and make them available upon request. --- Sebastian /*********************************************************************\ *********************************************************************** \*********************************************************************/ /* Training Data Set #1 */ data_5: 1 1 1 1 3 1 -> 1 data_6: 1 1 1 1 3 2 -> 1 data_19: 1 1 1 3 2 1 -> 1 data_22: 1 1 1 3 3 2 -> 1 data_27: 1 1 2 1 2 1 -> 1 data_28: 1 1 2 1 2 2 -> 1 data_37: 1 1 2 2 3 1 -> 1 data_39: 1 1 2 2 4 1 -> 1 data_42: 1 1 2 3 1 2 -> 1 data_50: 1 2 1 1 1 2 -> 1 data_51: 1 2 1 1 2 1 -> 0 data_53: 1 2 1 1 3 1 -> 0 data_56: 1 2 1 1 4 2 -> 0 data_57: 1 2 1 2 1 1 -> 1 data_61: 1 2 1 2 3 1 -> 0 data_62: 1 2 1 2 3 2 -> 0 data_64: 1 2 1 2 4 2 -> 0 data_67: 1 2 1 3 2 1 -> 0 data_72: 1 2 1 3 4 2 -> 0 data_76: 1 2 2 1 2 2 -> 0 data_86: 1 2 2 2 3 2 -> 0 data_87: 1 2 2 2 4 1 -> 0 data_88: 1 2 2 2 4 2 -> 0 data_92: 1 2 2 3 2 2 -> 0 data_93: 1 2 2 3 3 1 -> 0 data_94: 1 2 2 3 3 2 -> 0 data_99: 1 3 1 1 2 1 -> 0 data_103: 1 3 1 1 4 1 -> 0 data_107: 1 3 1 2 2 1 -> 0 data_111: 1 3 1 2 4 1 -> 0 data_114: 1 3 1 3 1 2 -> 1 data_116: 1 3 1 3 2 2 -> 0 data_117: 1 3 1 3 3 1 -> 0 data_119: 1 3 1 3 4 1 -> 0 data_120: 1 3 1 3 4 2 -> 0 data_124: 1 3 2 1 2 2 -> 0 data_130: 1 3 2 2 1 2 -> 1 data_132: 1 3 2 2 2 2 -> 0 data_134: 1 3 2 2 3 2 -> 0 data_135: 1 3 2 2 4 1 -> 0 data_136: 1 3 2 2 4 2 -> 0 data_137: 1 3 2 3 1 1 -> 1 data_139: 1 3 2 3 2 1 -> 0 data_143: 1 3 2 3 4 1 -> 0 data_144: 1 3 2 3 4 2 -> 0 data_149: 2 1 1 1 3 1 -> 0 data_150: 2 1 1 1 3 2 -> 0 data_153: 2 1 1 2 1 1 -> 1 data_154: 2 1 1 2 1 2 -> 1 data_156: 2 1 1 2 2 2 -> 0 data_157: 2 1 1 2 3 1 -> 0 data_159: 2 1 1 2 4 1 -> 0 data_160: 2 1 1 2 4 2 -> 0 data_167: 2 1 1 3 4 1 -> 0 data_172: 2 1 2 1 2 2 -> 0 data_173: 2 1 2 1 3 1 -> 0 data_176: 2 1 2 1 4 2 -> 0 data_181: 2 1 2 2 3 1 -> 0 data_184: 2 1 2 2 4 2 -> 0 data_188: 2 1 2 3 2 2 -> 0 data_191: 2 1 2 3 4 1 -> 0 data_195: 2 2 1 1 2 1 -> 1 data_196: 2 2 1 1 2 2 -> 1 data_197: 2 2 1 1 3 1 -> 1 data_206: 2 2 1 2 3 2 -> 1 data_209: 2 2 1 3 1 1 -> 1 data_210: 2 2 1 3 1 2 -> 1 data_212: 2 2 1 3 2 2 -> 1 data_214: 2 2 1 3 3 2 -> 1 data_216: 2 2 1 3 4 2 -> 1 data_217: 2 2 2 1 1 1 -> 1 data_222: 2 2 2 1 3 2 -> 1 data_223: 2 2 2 1 4 1 -> 1 data_224: 2 2 2 1 4 2 -> 1 data_227: 2 2 2 2 2 1 -> 1 data_239: 2 2 2 3 4 1 -> 1 data_241: 2 3 1 1 1 1 -> 1 data_249: 2 3 1 2 1 1 -> 1 data_253: 2 3 1 2 3 1 -> 0 data_258: 2 3 1 3 1 2 -> 1 data_261: 2 3 1 3 3 1 -> 0 data_264: 2 3 1 3 4 2 -> 0 data_270: 2 3 2 1 3 2 -> 0 data_273: 2 3 2 2 1 1 -> 1 data_274: 2 3 2 2 1 2 -> 1 data_275: 2 3 2 2 2 1 -> 0 data_286: 2 3 2 3 3 2 -> 0 data_289: 3 1 1 1 1 1 -> 1 data_290: 3 1 1 1 1 2 -> 1 data_297: 3 1 1 2 1 1 -> 1 data_300: 3 1 1 2 2 2 -> 0 data_308: 3 1 1 3 2 2 -> 0 data_313: 3 1 2 1 1 1 -> 1 data_316: 3 1 2 1 2 2 -> 0 data_324: 3 1 2 2 2 2 -> 0 data_326: 3 1 2 2 3 2 -> 0 data_332: 3 1 2 3 2 2 -> 0 data_337: 3 2 1 1 1 1 -> 1 data_344: 3 2 1 1 4 2 -> 0 data_346: 3 2 1 2 1 2 -> 1 data_352: 3 2 1 2 4 2 -> 0 data_361: 3 2 2 1 1 1 -> 1 data_362: 3 2 2 1 1 2 -> 1 data_366: 3 2 2 1 3 2 -> 0 data_377: 3 2 2 3 1 1 -> 1 data_379: 3 2 2 3 2 1 -> 0 data_383: 3 2 2 3 4 1 -> 0 data_385: 3 3 1 1 1 1 -> 1 data_387: 3 3 1 1 2 1 -> 1 data_392: 3 3 1 1 4 2 -> 1 data_398: 3 3 1 2 3 2 -> 1 data_400: 3 3 1 2 4 2 -> 1 data_402: 3 3 1 3 1 2 -> 1 data_403: 3 3 1 3 2 1 -> 1 data_404: 3 3 1 3 2 2 -> 1 data_408: 3 3 1 3 4 2 -> 1 data_409: 3 3 2 1 1 1 -> 1 data_414: 3 3 2 1 3 2 -> 1 data_415: 3 3 2 1 4 1 -> 1 data_416: 3 3 2 1 4 2 -> 1 data_426: 3 3 2 3 1 2 -> 1 data_428: 3 3 2 3 2 2 -> 1 data_430: 3 3 2 3 3 2 -> 1 data_432: 3 3 2 3 4 2 -> 1 /* Testing Data Set #1 */ data_1: 1 1 1 1 1 1 -> 1 data_2: 1 1 1 1 1 2 -> 1 data_3: 1 1 1 1 2 1 -> 1 data_4: 1 1 1 1 2 2 -> 1 data_5: 1 1 1 1 3 1 -> 1 data_6: 1 1 1 1 3 2 -> 1 data_7: 1 1 1 1 4 1 -> 1 data_8: 1 1 1 1 4 2 -> 1 data_9: 1 1 1 2 1 1 -> 1 data_10: 1 1 1 2 1 2 -> 1 data_11: 1 1 1 2 2 1 -> 1 data_12: 1 1 1 2 2 2 -> 1 data_13: 1 1 1 2 3 1 -> 1 data_14: 1 1 1 2 3 2 -> 1 data_15: 1 1 1 2 4 1 -> 1 data_16: 1 1 1 2 4 2 -> 1 data_17: 1 1 1 3 1 1 -> 1 data_18: 1 1 1 3 1 2 -> 1 data_19: 1 1 1 3 2 1 -> 1 data_20: 1 1 1 3 2 2 -> 1 data_21: 1 1 1 3 3 1 -> 1 data_22: 1 1 1 3 3 2 -> 1 data_23: 1 1 1 3 4 1 -> 1 data_24: 1 1 1 3 4 2 -> 1 data_25: 1 1 2 1 1 1 -> 1 data_26: 1 1 2 1 1 2 -> 1 data_27: 1 1 2 1 2 1 -> 1 data_28: 1 1 2 1 2 2 -> 1 data_29: 1 1 2 1 3 1 -> 1 data_30: 1 1 2 1 3 2 -> 1 data_31: 1 1 2 1 4 1 -> 1 data_32: 1 1 2 1 4 2 -> 1 data_33: 1 1 2 2 1 1 -> 1 data_34: 1 1 2 2 1 2 -> 1 data_35: 1 1 2 2 2 1 -> 1 data_36: 1 1 2 2 2 2 -> 1 data_37: 1 1 2 2 3 1 -> 1 data_38: 1 1 2 2 3 2 -> 1 data_39: 1 1 2 2 4 1 -> 1 data_40: 1 1 2 2 4 2 -> 1 data_41: 1 1 2 3 1 1 -> 1 data_42: 1 1 2 3 1 2 -> 1 data_43: 1 1 2 3 2 1 -> 1 data_44: 1 1 2 3 2 2 -> 1 data_45: 1 1 2 3 3 1 -> 1 data_46: 1 1 2 3 3 2 -> 1 data_47: 1 1 2 3 4 1 -> 1 data_48: 1 1 2 3 4 2 -> 1 data_49: 1 2 1 1 1 1 -> 1 data_50: 1 2 1 1 1 2 -> 1 data_51: 1 2 1 1 2 1 -> 0 data_52: 1 2 1 1 2 2 -> 0 data_53: 1 2 1 1 3 1 -> 0 data_54: 1 2 1 1 3 2 -> 0 data_55: 1 2 1 1 4 1 -> 0 data_56: 1 2 1 1 4 2 -> 0 data_57: 1 2 1 2 1 1 -> 1 data_58: 1 2 1 2 1 2 -> 1 data_59: 1 2 1 2 2 1 -> 0 data_60: 1 2 1 2 2 2 -> 0 data_61: 1 2 1 2 3 1 -> 0 data_62: 1 2 1 2 3 2 -> 0 data_63: 1 2 1 2 4 1 -> 0 data_64: 1 2 1 2 4 2 -> 0 data_65: 1 2 1 3 1 1 -> 1 data_66: 1 2 1 3 1 2 -> 1 data_67: 1 2 1 3 2 1 -> 0 data_68: 1 2 1 3 2 2 -> 0 data_69: 1 2 1 3 3 1 -> 0 data_70: 1 2 1 3 3 2 -> 0 data_71: 1 2 1 3 4 1 -> 0 data_72: 1 2 1 3 4 2 -> 0 data_73: 1 2 2 1 1 1 -> 1 data_74: 1 2 2 1 1 2 -> 1 data_75: 1 2 2 1 2 1 -> 0 data_76: 1 2 2 1 2 2 -> 0 data_77: 1 2 2 1 3 1 -> 0 data_78: 1 2 2 1 3 2 -> 0 data_79: 1 2 2 1 4 1 -> 0 data_80: 1 2 2 1 4 2 -> 0 data_81: 1 2 2 2 1 1 -> 1 data_82: 1 2 2 2 1 2 -> 1 data_83: 1 2 2 2 2 1 -> 0 data_84: 1 2 2 2 2 2 -> 0 data_85: 1 2 2 2 3 1 -> 0 data_86: 1 2 2 2 3 2 -> 0 data_87: 1 2 2 2 4 1 -> 0 data_88: 1 2 2 2 4 2 -> 0 data_89: 1 2 2 3 1 1 -> 1 data_90: 1 2 2 3 1 2 -> 1 data_91: 1 2 2 3 2 1 -> 0 data_92: 1 2 2 3 2 2 -> 0 data_93: 1 2 2 3 3 1 -> 0 data_94: 1 2 2 3 3 2 -> 0 data_95: 1 2 2 3 4 1 -> 0 data_96: 1 2 2 3 4 2 -> 0 data_97: 1 3 1 1 1 1 -> 1 data_98: 1 3 1 1 1 2 -> 1 data_99: 1 3 1 1 2 1 -> 0 data_100: 1 3 1 1 2 2 -> 0 data_101: 1 3 1 1 3 1 -> 0 data_102: 1 3 1 1 3 2 -> 0 data_103: 1 3 1 1 4 1 -> 0 data_104: 1 3 1 1 4 2 -> 0 data_105: 1 3 1 2 1 1 -> 1 data_106: 1 3 1 2 1 2 -> 1 data_107: 1 3 1 2 2 1 -> 0 data_108: 1 3 1 2 2 2 -> 0 data_109: 1 3 1 2 3 1 -> 0 data_110: 1 3 1 2 3 2 -> 0 data_111: 1 3 1 2 4 1 -> 0 data_112: 1 3 1 2 4 2 -> 0 data_113: 1 3 1 3 1 1 -> 1 data_114: 1 3 1 3 1 2 -> 1 data_115: 1 3 1 3 2 1 -> 0 data_116: 1 3 1 3 2 2 -> 0 data_117: 1 3 1 3 3 1 -> 0 data_118: 1 3 1 3 3 2 -> 0 data_119: 1 3 1 3 4 1 -> 0 data_120: 1 3 1 3 4 2 -> 0 data_121: 1 3 2 1 1 1 -> 1 data_122: 1 3 2 1 1 2 -> 1 data_123: 1 3 2 1 2 1 -> 0 data_124: 1 3 2 1 2 2 -> 0 data_125: 1 3 2 1 3 1 -> 0 data_126: 1 3 2 1 3 2 -> 0 data_127: 1 3 2 1 4 1 -> 0 data_128: 1 3 2 1 4 2 -> 0 data_129: 1 3 2 2 1 1 -> 1 data_130: 1 3 2 2 1 2 -> 1 data_131: 1 3 2 2 2 1 -> 0 data_132: 1 3 2 2 2 2 -> 0 data_133: 1 3 2 2 3 1 -> 0 data_134: 1 3 2 2 3 2 -> 0 data_135: 1 3 2 2 4 1 -> 0 data_136: 1 3 2 2 4 2 -> 0 data_137: 1 3 2 3 1 1 -> 1 data_138: 1 3 2 3 1 2 -> 1 data_139: 1 3 2 3 2 1 -> 0 data_140: 1 3 2 3 2 2 -> 0 data_141: 1 3 2 3 3 1 -> 0 data_142: 1 3 2 3 3 2 -> 0 data_143: 1 3 2 3 4 1 -> 0 data_144: 1 3 2 3 4 2 -> 0 data_145: 2 1 1 1 1 1 -> 1 data_146: 2 1 1 1 1 2 -> 1 data_147: 2 1 1 1 2 1 -> 0 data_148: 2 1 1 1 2 2 -> 0 data_149: 2 1 1 1 3 1 -> 0 data_150: 2 1 1 1 3 2 -> 0 data_151: 2 1 1 1 4 1 -> 0 data_152: 2 1 1 1 4 2 -> 0 data_153: 2 1 1 2 1 1 -> 1 data_154: 2 1 1 2 1 2 -> 1 data_155: 2 1 1 2 2 1 -> 0 data_156: 2 1 1 2 2 2 -> 0 data_157: 2 1 1 2 3 1 -> 0 data_158: 2 1 1 2 3 2 -> 0 data_159: 2 1 1 2 4 1 -> 0 data_160: 2 1 1 2 4 2 -> 0 data_161: 2 1 1 3 1 1 -> 1 data_162: 2 1 1 3 1 2 -> 1 data_163: 2 1 1 3 2 1 -> 0 data_164: 2 1 1 3 2 2 -> 0 data_165: 2 1 1 3 3 1 -> 0 data_166: 2 1 1 3 3 2 -> 0 data_167: 2 1 1 3 4 1 -> 0 data_168: 2 1 1 3 4 2 -> 0 data_169: 2 1 2 1 1 1 -> 1 data_170: 2 1 2 1 1 2 -> 1 data_171: 2 1 2 1 2 1 -> 0 data_172: 2 1 2 1 2 2 -> 0 data_173: 2 1 2 1 3 1 -> 0 data_174: 2 1 2 1 3 2 -> 0 data_175: 2 1 2 1 4 1 -> 0 data_176: 2 1 2 1 4 2 -> 0 data_177: 2 1 2 2 1 1 -> 1 data_178: 2 1 2 2 1 2 -> 1 data_179: 2 1 2 2 2 1 -> 0 data_180: 2 1 2 2 2 2 -> 0 data_181: 2 1 2 2 3 1 -> 0 data_182: 2 1 2 2 3 2 -> 0 data_183: 2 1 2 2 4 1 -> 0 data_184: 2 1 2 2 4 2 -> 0 data_185: 2 1 2 3 1 1 -> 1 data_186: 2 1 2 3 1 2 -> 1 data_187: 2 1 2 3 2 1 -> 0 data_188: 2 1 2 3 2 2 -> 0 data_189: 2 1 2 3 3 1 -> 0 data_190: 2 1 2 3 3 2 -> 0 data_191: 2 1 2 3 4 1 -> 0 data_192: 2 1 2 3 4 2 -> 0 data_193: 2 2 1 1 1 1 -> 1 data_194: 2 2 1 1 1 2 -> 1 data_195: 2 2 1 1 2 1 -> 1 data_196: 2 2 1 1 2 2 -> 1 data_197: 2 2 1 1 3 1 -> 1 data_198: 2 2 1 1 3 2 -> 1 data_199: 2 2 1 1 4 1 -> 1 data_200: 2 2 1 1 4 2 -> 1 data_201: 2 2 1 2 1 1 -> 1 data_202: 2 2 1 2 1 2 -> 1 data_203: 2 2 1 2 2 1 -> 1 data_204: 2 2 1 2 2 2 -> 1 data_205: 2 2 1 2 3 1 -> 1 data_206: 2 2 1 2 3 2 -> 1 data_207: 2 2 1 2 4 1 -> 1 data_208: 2 2 1 2 4 2 -> 1 data_209: 2 2 1 3 1 1 -> 1 data_210: 2 2 1 3 1 2 -> 1 data_211: 2 2 1 3 2 1 -> 1 data_212: 2 2 1 3 2 2 -> 1 data_213: 2 2 1 3 3 1 -> 1 data_214: 2 2 1 3 3 2 -> 1 data_215: 2 2 1 3 4 1 -> 1 data_216: 2 2 1 3 4 2 -> 1 data_217: 2 2 2 1 1 1 -> 1 data_218: 2 2 2 1 1 2 -> 1 data_219: 2 2 2 1 2 1 -> 1 data_220: 2 2 2 1 2 2 -> 1 data_221: 2 2 2 1 3 1 -> 1 data_222: 2 2 2 1 3 2 -> 1 data_223: 2 2 2 1 4 1 -> 1 data_224: 2 2 2 1 4 2 -> 1 data_225: 2 2 2 2 1 1 -> 1 data_226: 2 2 2 2 1 2 -> 1 data_227: 2 2 2 2 2 1 -> 1 data_228: 2 2 2 2 2 2 -> 1 data_229: 2 2 2 2 3 1 -> 1 data_230: 2 2 2 2 3 2 -> 1 data_231: 2 2 2 2 4 1 -> 1 data_232: 2 2 2 2 4 2 -> 1 data_233: 2 2 2 3 1 1 -> 1 data_234: 2 2 2 3 1 2 -> 1 data_235: 2 2 2 3 2 1 -> 1 data_236: 2 2 2 3 2 2 -> 1 data_237: 2 2 2 3 3 1 -> 1 data_238: 2 2 2 3 3 2 -> 1 data_239: 2 2 2 3 4 1 -> 1 data_240: 2 2 2 3 4 2 -> 1 data_241: 2 3 1 1 1 1 -> 1 data_242: 2 3 1 1 1 2 -> 1 data_243: 2 3 1 1 2 1 -> 0 data_244: 2 3 1 1 2 2 -> 0 data_245: 2 3 1 1 3 1 -> 0 data_246: 2 3 1 1 3 2 -> 0 data_247: 2 3 1 1 4 1 -> 0 data_248: 2 3 1 1 4 2 -> 0 data_249: 2 3 1 2 1 1 -> 1 data_250: 2 3 1 2 1 2 -> 1 data_251: 2 3 1 2 2 1 -> 0 data_252: 2 3 1 2 2 2 -> 0 data_253: 2 3 1 2 3 1 -> 0 data_254: 2 3 1 2 3 2 -> 0 data_255: 2 3 1 2 4 1 -> 0 data_256: 2 3 1 2 4 2 -> 0 data_257: 2 3 1 3 1 1 -> 1 data_258: 2 3 1 3 1 2 -> 1 data_259: 2 3 1 3 2 1 -> 0 data_260: 2 3 1 3 2 2 -> 0 data_261: 2 3 1 3 3 1 -> 0 data_262: 2 3 1 3 3 2 -> 0 data_263: 2 3 1 3 4 1 -> 0 data_264: 2 3 1 3 4 2 -> 0 data_265: 2 3 2 1 1 1 -> 1 data_266: 2 3 2 1 1 2 -> 1 data_267: 2 3 2 1 2 1 -> 0 data_268: 2 3 2 1 2 2 -> 0 data_269: 2 3 2 1 3 1 -> 0 data_270: 2 3 2 1 3 2 -> 0 data_271: 2 3 2 1 4 1 -> 0 data_272: 2 3 2 1 4 2 -> 0 data_273: 2 3 2 2 1 1 -> 1 data_274: 2 3 2 2 1 2 -> 1 data_275: 2 3 2 2 2 1 -> 0 data_276: 2 3 2 2 2 2 -> 0 data_277: 2 3 2 2 3 1 -> 0 data_278: 2 3 2 2 3 2 -> 0 data_279: 2 3 2 2 4 1 -> 0 data_280: 2 3 2 2 4 2 -> 0 data_281: 2 3 2 3 1 1 -> 1 data_282: 2 3 2 3 1 2 -> 1 data_283: 2 3 2 3 2 1 -> 0 data_284: 2 3 2 3 2 2 -> 0 data_285: 2 3 2 3 3 1 -> 0 data_286: 2 3 2 3 3 2 -> 0 data_287: 2 3 2 3 4 1 -> 0 data_288: 2 3 2 3 4 2 -> 0 data_289: 3 1 1 1 1 1 -> 1 data_290: 3 1 1 1 1 2 -> 1 data_291: 3 1 1 1 2 1 -> 0 data_292: 3 1 1 1 2 2 -> 0 data_293: 3 1 1 1 3 1 -> 0 data_294: 3 1 1 1 3 2 -> 0 data_295: 3 1 1 1 4 1 -> 0 data_296: 3 1 1 1 4 2 -> 0 data_297: 3 1 1 2 1 1 -> 1 data_298: 3 1 1 2 1 2 -> 1 data_299: 3 1 1 2 2 1 -> 0 data_300: 3 1 1 2 2 2 -> 0 data_301: 3 1 1 2 3 1 -> 0 data_302: 3 1 1 2 3 2 -> 0 data_303: 3 1 1 2 4 1 -> 0 data_304: 3 1 1 2 4 2 -> 0 data_305: 3 1 1 3 1 1 -> 1 data_306: 3 1 1 3 1 2 -> 1 data_307: 3 1 1 3 2 1 -> 0 data_308: 3 1 1 3 2 2 -> 0 data_309: 3 1 1 3 3 1 -> 0 data_310: 3 1 1 3 3 2 -> 0 data_311: 3 1 1 3 4 1 -> 0 data_312: 3 1 1 3 4 2 -> 0 data_313: 3 1 2 1 1 1 -> 1 data_314: 3 1 2 1 1 2 -> 1 data_315: 3 1 2 1 2 1 -> 0 data_316: 3 1 2 1 2 2 -> 0 data_317: 3 1 2 1 3 1 -> 0 data_318: 3 1 2 1 3 2 -> 0 data_319: 3 1 2 1 4 1 -> 0 data_320: 3 1 2 1 4 2 -> 0 data_321: 3 1 2 2 1 1 -> 1 data_322: 3 1 2 2 1 2 -> 1 data_323: 3 1 2 2 2 1 -> 0 data_324: 3 1 2 2 2 2 -> 0 data_325: 3 1 2 2 3 1 -> 0 data_326: 3 1 2 2 3 2 -> 0 data_327: 3 1 2 2 4 1 -> 0 data_328: 3 1 2 2 4 2 -> 0 data_329: 3 1 2 3 1 1 -> 1 data_330: 3 1 2 3 1 2 -> 1 data_331: 3 1 2 3 2 1 -> 0 data_332: 3 1 2 3 2 2 -> 0 data_333: 3 1 2 3 3 1 -> 0 data_334: 3 1 2 3 3 2 -> 0 data_335: 3 1 2 3 4 1 -> 0 data_336: 3 1 2 3 4 2 -> 0 data_337: 3 2 1 1 1 1 -> 1 data_338: 3 2 1 1 1 2 -> 1 data_339: 3 2 1 1 2 1 -> 0 data_340: 3 2 1 1 2 2 -> 0 data_341: 3 2 1 1 3 1 -> 0 data_342: 3 2 1 1 3 2 -> 0 data_343: 3 2 1 1 4 1 -> 0 data_344: 3 2 1 1 4 2 -> 0 data_345: 3 2 1 2 1 1 -> 1 data_346: 3 2 1 2 1 2 -> 1 data_347: 3 2 1 2 2 1 -> 0 data_348: 3 2 1 2 2 2 -> 0 data_349: 3 2 1 2 3 1 -> 0 data_350: 3 2 1 2 3 2 -> 0 data_351: 3 2 1 2 4 1 -> 0 data_352: 3 2 1 2 4 2 -> 0 data_353: 3 2 1 3 1 1 -> 1 data_354: 3 2 1 3 1 2 -> 1 data_355: 3 2 1 3 2 1 -> 0 data_356: 3 2 1 3 2 2 -> 0 data_357: 3 2 1 3 3 1 -> 0 data_358: 3 2 1 3 3 2 -> 0 data_359: 3 2 1 3 4 1 -> 0 data_360: 3 2 1 3 4 2 -> 0 data_361: 3 2 2 1 1 1 -> 1 data_362: 3 2 2 1 1 2 -> 1 data_363: 3 2 2 1 2 1 -> 0 data_364: 3 2 2 1 2 2 -> 0 data_365: 3 2 2 1 3 1 -> 0 data_366: 3 2 2 1 3 2 -> 0 data_367: 3 2 2 1 4 1 -> 0 data_368: 3 2 2 1 4 2 -> 0 data_369: 3 2 2 2 1 1 -> 1 data_370: 3 2 2 2 1 2 -> 1 data_371: 3 2 2 2 2 1 -> 0 data_372: 3 2 2 2 2 2 -> 0 data_373: 3 2 2 2 3 1 -> 0 data_374: 3 2 2 2 3 2 -> 0 data_375: 3 2 2 2 4 1 -> 0 data_376: 3 2 2 2 4 2 -> 0 data_377: 3 2 2 3 1 1 -> 1 data_378: 3 2 2 3 1 2 -> 1 data_379: 3 2 2 3 2 1 -> 0 data_380: 3 2 2 3 2 2 -> 0 data_381: 3 2 2 3 3 1 -> 0 data_382: 3 2 2 3 3 2 -> 0 data_383: 3 2 2 3 4 1 -> 0 data_384: 3 2 2 3 4 2 -> 0 data_385: 3 3 1 1 1 1 -> 1 data_386: 3 3 1 1 1 2 -> 1 data_387: 3 3 1 1 2 1 -> 1 data_388: 3 3 1 1 2 2 -> 1 data_389: 3 3 1 1 3 1 -> 1 data_390: 3 3 1 1 3 2 -> 1 data_391: 3 3 1 1 4 1 -> 1 data_392: 3 3 1 1 4 2 -> 1 data_393: 3 3 1 2 1 1 -> 1 data_394: 3 3 1 2 1 2 -> 1 data_395: 3 3 1 2 2 1 -> 1 data_396: 3 3 1 2 2 2 -> 1 data_397: 3 3 1 2 3 1 -> 1 data_398: 3 3 1 2 3 2 -> 1 data_399: 3 3 1 2 4 1 -> 1 data_400: 3 3 1 2 4 2 -> 1 data_401: 3 3 1 3 1 1 -> 1 data_402: 3 3 1 3 1 2 -> 1 data_403: 3 3 1 3 2 1 -> 1 data_404: 3 3 1 3 2 2 -> 1 data_405: 3 3 1 3 3 1 -> 1 data_406: 3 3 1 3 3 2 -> 1 data_407: 3 3 1 3 4 1 -> 1 data_408: 3 3 1 3 4 2 -> 1 data_409: 3 3 2 1 1 1 -> 1 data_410: 3 3 2 1 1 2 -> 1 data_411: 3 3 2 1 2 1 -> 1 data_412: 3 3 2 1 2 2 -> 1 data_413: 3 3 2 1 3 1 -> 1 data_414: 3 3 2 1 3 2 -> 1 data_415: 3 3 2 1 4 1 -> 1 data_416: 3 3 2 1 4 2 -> 1 data_417: 3 3 2 2 1 1 -> 1 data_418: 3 3 2 2 1 2 -> 1 data_419: 3 3 2 2 2 1 -> 1 data_420: 3 3 2 2 2 2 -> 1 data_421: 3 3 2 2 3 1 -> 1 data_422: 3 3 2 2 3 2 -> 1 data_423: 3 3 2 2 4 1 -> 1 data_424: 3 3 2 2 4 2 -> 1 data_425: 3 3 2 3 1 1 -> 1 data_426: 3 3 2 3 1 2 -> 1 data_427: 3 3 2 3 2 1 -> 1 data_428: 3 3 2 3 2 2 -> 1 data_429: 3 3 2 3 3 1 -> 1 data_430: 3 3 2 3 3 2 -> 1 data_431: 3 3 2 3 4 1 -> 1 data_432: 3 3 2 3 4 2 -> 1 /* Training Data Set #2 */ data_4: 1 1 1 1 2 2 -> 0 data_7: 1 1 1 1 4 1 -> 0 data_9: 1 1 1 2 1 1 -> 0 data_10: 1 1 1 2 1 2 -> 0 data_11: 1 1 1 2 2 1 -> 0 data_13: 1 1 1 2 3 1 -> 0 data_15: 1 1 1 2 4 1 -> 0 data_19: 1 1 1 3 2 1 -> 0 data_23: 1 1 1 3 4 1 -> 0 data_25: 1 1 2 1 1 1 -> 0 data_26: 1 1 2 1 1 2 -> 0 data_37: 1 1 2 2 3 1 -> 0 data_39: 1 1 2 2 4 1 -> 0 data_40: 1 1 2 2 4 2 -> 1 data_42: 1 1 2 3 1 2 -> 0 data_44: 1 1 2 3 2 2 -> 1 data_50: 1 2 1 1 1 2 -> 0 data_58: 1 2 1 2 1 2 -> 0 data_60: 1 2 1 2 2 2 -> 1 data_61: 1 2 1 2 3 1 -> 0 data_62: 1 2 1 2 3 2 -> 1 data_63: 1 2 1 2 4 1 -> 0 data_65: 1 2 1 3 1 1 -> 0 data_66: 1 2 1 3 1 2 -> 0 data_68: 1 2 1 3 2 2 -> 1 data_69: 1 2 1 3 3 1 -> 0 data_70: 1 2 1 3 3 2 -> 1 data_71: 1 2 1 3 4 1 -> 0 data_72: 1 2 1 3 4 2 -> 1 data_75: 1 2 2 1 2 1 -> 0 data_79: 1 2 2 1 4 1 -> 0 data_85: 1 2 2 2 3 1 -> 1 data_87: 1 2 2 2 4 1 -> 1 data_89: 1 2 2 3 1 1 -> 0 data_90: 1 2 2 3 1 2 -> 1 data_93: 1 2 2 3 3 1 -> 1 data_94: 1 2 2 3 3 2 -> 0 data_95: 1 2 2 3 4 1 -> 1 data_96: 1 2 2 3 4 2 -> 0 data_98: 1 3 1 1 1 2 -> 0 data_100: 1 3 1 1 2 2 -> 0 data_101: 1 3 1 1 3 1 -> 0 data_102: 1 3 1 1 3 2 -> 0 data_107: 1 3 1 2 2 1 -> 0 data_108: 1 3 1 2 2 2 -> 1 data_110: 1 3 1 2 3 2 -> 1 data_111: 1 3 1 2 4 1 -> 0 data_116: 1 3 1 3 2 2 -> 1 data_117: 1 3 1 3 3 1 -> 0 data_120: 1 3 1 3 4 2 -> 1 data_125: 1 3 2 1 3 1 -> 0 data_126: 1 3 2 1 3 2 -> 1 data_127: 1 3 2 1 4 1 -> 0 data_130: 1 3 2 2 1 2 -> 1 data_134: 1 3 2 2 3 2 -> 0 data_136: 1 3 2 2 4 2 -> 0 data_139: 1 3 2 3 2 1 -> 1 data_145: 2 1 1 1 1 1 -> 0 data_148: 2 1 1 1 2 2 -> 0 data_149: 2 1 1 1 3 1 -> 0 data_156: 2 1 1 2 2 2 -> 1 data_162: 2 1 1 3 1 2 -> 0 data_164: 2 1 1 3 2 2 -> 1 data_166: 2 1 1 3 3 2 -> 1 data_167: 2 1 1 3 4 1 -> 0 data_169: 2 1 2 1 1 1 -> 0 data_172: 2 1 2 1 2 2 -> 1 data_175: 2 1 2 1 4 1 -> 0 data_179: 2 1 2 2 2 1 -> 1 data_184: 2 1 2 2 4 2 -> 0 data_185: 2 1 2 3 1 1 -> 0 data_186: 2 1 2 3 1 2 -> 1 data_188: 2 1 2 3 2 2 -> 0 data_190: 2 1 2 3 3 2 -> 0 data_192: 2 1 2 3 4 2 -> 0 data_197: 2 2 1 1 3 1 -> 0 data_200: 2 2 1 1 4 2 -> 1 data_201: 2 2 1 2 1 1 -> 0 data_205: 2 2 1 2 3 1 -> 1 data_213: 2 2 1 3 3 1 -> 1 data_214: 2 2 1 3 3 2 -> 0 data_215: 2 2 1 3 4 1 -> 1 data_217: 2 2 2 1 1 1 -> 0 data_220: 2 2 2 1 2 2 -> 0 data_222: 2 2 2 1 3 2 -> 0 data_223: 2 2 2 1 4 1 -> 1 data_224: 2 2 2 1 4 2 -> 0 data_225: 2 2 2 2 1 1 -> 1 data_228: 2 2 2 2 2 2 -> 0 data_229: 2 2 2 2 3 1 -> 0 data_233: 2 2 2 3 1 1 -> 1 data_235: 2 2 2 3 2 1 -> 0 data_236: 2 2 2 3 2 2 -> 0 data_240: 2 2 2 3 4 2 -> 0 data_241: 2 3 1 1 1 1 -> 0 data_242: 2 3 1 1 1 2 -> 0 data_246: 2 3 1 1 3 2 -> 1 data_249: 2 3 1 2 1 1 -> 0 data_253: 2 3 1 2 3 1 -> 1 data_254: 2 3 1 2 3 2 -> 0 data_256: 2 3 1 2 4 2 -> 0 data_258: 2 3 1 3 1 2 -> 1 data_259: 2 3 1 3 2 1 -> 1 data_263: 2 3 1 3 4 1 -> 1 data_266: 2 3 2 1 1 2 -> 1 data_267: 2 3 2 1 2 1 -> 1 data_269: 2 3 2 1 3 1 -> 1 data_272: 2 3 2 1 4 2 -> 0 data_273: 2 3 2 2 1 1 -> 1 data_275: 2 3 2 2 2 1 -> 0 data_278: 2 3 2 2 3 2 -> 0 data_285: 2 3 2 3 3 1 -> 0 data_286: 2 3 2 3 3 2 -> 0 data_288: 2 3 2 3 4 2 -> 0 data_295: 3 1 1 1 4 1 -> 0 data_298: 3 1 1 2 1 2 -> 0 data_300: 3 1 1 2 2 2 -> 1 data_302: 3 1 1 2 3 2 -> 1 data_303: 3 1 1 2 4 1 -> 0 data_304: 3 1 1 2 4 2 -> 1 data_305: 3 1 1 3 1 1 -> 0 data_306: 3 1 1 3 1 2 -> 0 data_308: 3 1 1 3 2 2 -> 1 data_310: 3 1 1 3 3 2 -> 1 data_313: 3 1 2 1 1 1 -> 0 data_316: 3 1 2 1 2 2 -> 1 data_317: 3 1 2 1 3 1 -> 0 data_318: 3 1 2 1 3 2 -> 1 data_319: 3 1 2 1 4 1 -> 0 data_320: 3 1 2 1 4 2 -> 1 data_323: 3 1 2 2 2 1 -> 1 data_330: 3 1 2 3 1 2 -> 1 data_331: 3 1 2 3 2 1 -> 1 data_332: 3 1 2 3 2 2 -> 0 data_336: 3 1 2 3 4 2 -> 0 data_338: 3 2 1 1 1 2 -> 0 data_340: 3 2 1 1 2 2 -> 1 data_341: 3 2 1 1 3 1 -> 0 data_342: 3 2 1 1 3 2 -> 1 data_346: 3 2 1 2 1 2 -> 1 data_347: 3 2 1 2 2 1 -> 1 data_353: 3 2 1 3 1 1 -> 0 data_355: 3 2 1 3 2 1 -> 1 data_357: 3 2 1 3 3 1 -> 1 data_358: 3 2 1 3 3 2 -> 0 data_361: 3 2 2 1 1 1 -> 0 data_364: 3 2 2 1 2 2 -> 0 data_365: 3 2 2 1 3 1 -> 1 data_366: 3 2 2 1 3 2 -> 0 data_369: 3 2 2 2 1 1 -> 1 data_371: 3 2 2 2 2 1 -> 0 data_372: 3 2 2 2 2 2 -> 0 data_374: 3 2 2 2 3 2 -> 0 data_377: 3 2 2 3 1 1 -> 1 data_382: 3 2 2 3 3 2 -> 0 data_384: 3 2 2 3 4 2 -> 0 data_385: 3 3 1 1 1 1 -> 0 data_387: 3 3 1 1 2 1 -> 0 data_389: 3 3 1 1 3 1 -> 0 data_390: 3 3 1 1 3 2 -> 1 data_398: 3 3 1 2 3 2 -> 0 data_409: 3 3 2 1 1 1 -> 0 data_417: 3 3 2 2 1 1 -> 1 data_419: 3 3 2 2 2 1 -> 0 data_421: 3 3 2 2 3 1 -> 0 data_422: 3 3 2 2 3 2 -> 0 data_425: 3 3 2 3 1 1 -> 1 data_427: 3 3 2 3 2 1 -> 0 data_432: 3 3 2 3 4 2 -> 0 /* Testing Data Set #2 */ data_1: 1 1 1 1 1 1 -> 0 data_2: 1 1 1 1 1 2 -> 0 data_3: 1 1 1 1 2 1 -> 0 data_4: 1 1 1 1 2 2 -> 0 data_5: 1 1 1 1 3 1 -> 0 data_6: 1 1 1 1 3 2 -> 0 data_7: 1 1 1 1 4 1 -> 0 data_8: 1 1 1 1 4 2 -> 0 data_9: 1 1 1 2 1 1 -> 0 data_10: 1 1 1 2 1 2 -> 0 data_11: 1 1 1 2 2 1 -> 0 data_12: 1 1 1 2 2 2 -> 0 data_13: 1 1 1 2 3 1 -> 0 data_14: 1 1 1 2 3 2 -> 0 data_15: 1 1 1 2 4 1 -> 0 data_16: 1 1 1 2 4 2 -> 0 data_17: 1 1 1 3 1 1 -> 0 data_18: 1 1 1 3 1 2 -> 0 data_19: 1 1 1 3 2 1 -> 0 data_20: 1 1 1 3 2 2 -> 0 data_21: 1 1 1 3 3 1 -> 0 data_22: 1 1 1 3 3 2 -> 0 data_23: 1 1 1 3 4 1 -> 0 data_24: 1 1 1 3 4 2 -> 0 data_25: 1 1 2 1 1 1 -> 0 data_26: 1 1 2 1 1 2 -> 0 data_27: 1 1 2 1 2 1 -> 0 data_28: 1 1 2 1 2 2 -> 0 data_29: 1 1 2 1 3 1 -> 0 data_30: 1 1 2 1 3 2 -> 0 data_31: 1 1 2 1 4 1 -> 0 data_32: 1 1 2 1 4 2 -> 0 data_33: 1 1 2 2 1 1 -> 0 data_34: 1 1 2 2 1 2 -> 0 data_35: 1 1 2 2 2 1 -> 0 data_36: 1 1 2 2 2 2 -> 1 data_37: 1 1 2 2 3 1 -> 0 data_38: 1 1 2 2 3 2 -> 1 data_39: 1 1 2 2 4 1 -> 0 data_40: 1 1 2 2 4 2 -> 1 data_41: 1 1 2 3 1 1 -> 0 data_42: 1 1 2 3 1 2 -> 0 data_43: 1 1 2 3 2 1 -> 0 data_44: 1 1 2 3 2 2 -> 1 data_45: 1 1 2 3 3 1 -> 0 data_46: 1 1 2 3 3 2 -> 1 data_47: 1 1 2 3 4 1 -> 0 data_48: 1 1 2 3 4 2 -> 1 data_49: 1 2 1 1 1 1 -> 0 data_50: 1 2 1 1 1 2 -> 0 data_51: 1 2 1 1 2 1 -> 0 data_52: 1 2 1 1 2 2 -> 0 data_53: 1 2 1 1 3 1 -> 0 data_54: 1 2 1 1 3 2 -> 0 data_55: 1 2 1 1 4 1 -> 0 data_56: 1 2 1 1 4 2 -> 0 data_57: 1 2 1 2 1 1 -> 0 data_58: 1 2 1 2 1 2 -> 0 data_59: 1 2 1 2 2 1 -> 0 data_60: 1 2 1 2 2 2 -> 1 data_61: 1 2 1 2 3 1 -> 0 data_62: 1 2 1 2 3 2 -> 1 data_63: 1 2 1 2 4 1 -> 0 data_64: 1 2 1 2 4 2 -> 1 data_65: 1 2 1 3 1 1 -> 0 data_66: 1 2 1 3 1 2 -> 0 data_67: 1 2 1 3 2 1 -> 0 data_68: 1 2 1 3 2 2 -> 1 data_69: 1 2 1 3 3 1 -> 0 data_70: 1 2 1 3 3 2 -> 1 data_71: 1 2 1 3 4 1 -> 0 data_72: 1 2 1 3 4 2 -> 1 data_73: 1 2 2 1 1 1 -> 0 data_74: 1 2 2 1 1 2 -> 0 data_75: 1 2 2 1 2 1 -> 0 data_76: 1 2 2 1 2 2 -> 1 data_77: 1 2 2 1 3 1 -> 0 data_78: 1 2 2 1 3 2 -> 1 data_79: 1 2 2 1 4 1 -> 0 data_80: 1 2 2 1 4 2 -> 1 data_81: 1 2 2 2 1 1 -> 0 data_82: 1 2 2 2 1 2 -> 1 data_83: 1 2 2 2 2 1 -> 1 data_84: 1 2 2 2 2 2 -> 0 data_85: 1 2 2 2 3 1 -> 1 data_86: 1 2 2 2 3 2 -> 0 data_87: 1 2 2 2 4 1 -> 1 data_88: 1 2 2 2 4 2 -> 0 data_89: 1 2 2 3 1 1 -> 0 data_90: 1 2 2 3 1 2 -> 1 data_91: 1 2 2 3 2 1 -> 1 data_92: 1 2 2 3 2 2 -> 0 data_93: 1 2 2 3 3 1 -> 1 data_94: 1 2 2 3 3 2 -> 0 data_95: 1 2 2 3 4 1 -> 1 data_96: 1 2 2 3 4 2 -> 0 data_97: 1 3 1 1 1 1 -> 0 data_98: 1 3 1 1 1 2 -> 0 data_99: 1 3 1 1 2 1 -> 0 data_100: 1 3 1 1 2 2 -> 0 data_101: 1 3 1 1 3 1 -> 0 data_102: 1 3 1 1 3 2 -> 0 data_103: 1 3 1 1 4 1 -> 0 data_104: 1 3 1 1 4 2 -> 0 data_105: 1 3 1 2 1 1 -> 0 data_106: 1 3 1 2 1 2 -> 0 data_107: 1 3 1 2 2 1 -> 0 data_108: 1 3 1 2 2 2 -> 1 data_109: 1 3 1 2 3 1 -> 0 data_110: 1 3 1 2 3 2 -> 1 data_111: 1 3 1 2 4 1 -> 0 data_112: 1 3 1 2 4 2 -> 1 data_113: 1 3 1 3 1 1 -> 0 data_114: 1 3 1 3 1 2 -> 0 data_115: 1 3 1 3 2 1 -> 0 data_116: 1 3 1 3 2 2 -> 1 data_117: 1 3 1 3 3 1 -> 0 data_118: 1 3 1 3 3 2 -> 1 data_119: 1 3 1 3 4 1 -> 0 data_120: 1 3 1 3 4 2 -> 1 data_121: 1 3 2 1 1 1 -> 0 data_122: 1 3 2 1 1 2 -> 0 data_123: 1 3 2 1 2 1 -> 0 data_124: 1 3 2 1 2 2 -> 1 data_125: 1 3 2 1 3 1 -> 0 data_126: 1 3 2 1 3 2 -> 1 data_127: 1 3 2 1 4 1 -> 0 data_128: 1 3 2 1 4 2 -> 1 data_129: 1 3 2 2 1 1 -> 0 data_130: 1 3 2 2 1 2 -> 1 data_131: 1 3 2 2 2 1 -> 1 data_132: 1 3 2 2 2 2 -> 0 data_133: 1 3 2 2 3 1 -> 1 data_134: 1 3 2 2 3 2 -> 0 data_135: 1 3 2 2 4 1 -> 1 data_136: 1 3 2 2 4 2 -> 0 data_137: 1 3 2 3 1 1 -> 0 data_138: 1 3 2 3 1 2 -> 1 data_139: 1 3 2 3 2 1 -> 1 data_140: 1 3 2 3 2 2 -> 0 data_141: 1 3 2 3 3 1 -> 1 data_142: 1 3 2 3 3 2 -> 0 data_143: 1 3 2 3 4 1 -> 1 data_144: 1 3 2 3 4 2 -> 0 data_145: 2 1 1 1 1 1 -> 0 data_146: 2 1 1 1 1 2 -> 0 data_147: 2 1 1 1 2 1 -> 0 data_148: 2 1 1 1 2 2 -> 0 data_149: 2 1 1 1 3 1 -> 0 data_150: 2 1 1 1 3 2 -> 0 data_151: 2 1 1 1 4 1 -> 0 data_152: 2 1 1 1 4 2 -> 0 data_153: 2 1 1 2 1 1 -> 0 data_154: 2 1 1 2 1 2 -> 0 data_155: 2 1 1 2 2 1 -> 0 data_156: 2 1 1 2 2 2 -> 1 data_157: 2 1 1 2 3 1 -> 0 data_158: 2 1 1 2 3 2 -> 1 data_159: 2 1 1 2 4 1 -> 0 data_160: 2 1 1 2 4 2 -> 1 data_161: 2 1 1 3 1 1 -> 0 data_162: 2 1 1 3 1 2 -> 0 data_163: 2 1 1 3 2 1 -> 0 data_164: 2 1 1 3 2 2 -> 1 data_165: 2 1 1 3 3 1 -> 0 data_166: 2 1 1 3 3 2 -> 1 data_167: 2 1 1 3 4 1 -> 0 data_168: 2 1 1 3 4 2 -> 1 data_169: 2 1 2 1 1 1 -> 0 data_170: 2 1 2 1 1 2 -> 0 data_171: 2 1 2 1 2 1 -> 0 data_172: 2 1 2 1 2 2 -> 1 data_173: 2 1 2 1 3 1 -> 0 data_174: 2 1 2 1 3 2 -> 1 data_175: 2 1 2 1 4 1 -> 0 data_176: 2 1 2 1 4 2 -> 1 data_177: 2 1 2 2 1 1 -> 0 data_178: 2 1 2 2 1 2 -> 1 data_179: 2 1 2 2 2 1 -> 1 data_180: 2 1 2 2 2 2 -> 0 data_181: 2 1 2 2 3 1 -> 1 data_182: 2 1 2 2 3 2 -> 0 data_183: 2 1 2 2 4 1 -> 1 data_184: 2 1 2 2 4 2 -> 0 data_185: 2 1 2 3 1 1 -> 0 data_186: 2 1 2 3 1 2 -> 1 data_187: 2 1 2 3 2 1 -> 1 data_188: 2 1 2 3 2 2 -> 0 data_189: 2 1 2 3 3 1 -> 1 data_190: 2 1 2 3 3 2 -> 0 data_191: 2 1 2 3 4 1 -> 1 data_192: 2 1 2 3 4 2 -> 0 data_193: 2 2 1 1 1 1 -> 0 data_194: 2 2 1 1 1 2 -> 0 data_195: 2 2 1 1 2 1 -> 0 data_196: 2 2 1 1 2 2 -> 1 data_197: 2 2 1 1 3 1 -> 0 data_198: 2 2 1 1 3 2 -> 1 data_199: 2 2 1 1 4 1 -> 0 data_200: 2 2 1 1 4 2 -> 1 data_201: 2 2 1 2 1 1 -> 0 data_202: 2 2 1 2 1 2 -> 1 data_203: 2 2 1 2 2 1 -> 1 data_204: 2 2 1 2 2 2 -> 0 data_205: 2 2 1 2 3 1 -> 1 data_206: 2 2 1 2 3 2 -> 0 data_207: 2 2 1 2 4 1 -> 1 data_208: 2 2 1 2 4 2 -> 0 data_209: 2 2 1 3 1 1 -> 0 data_210: 2 2 1 3 1 2 -> 1 data_211: 2 2 1 3 2 1 -> 1 data_212: 2 2 1 3 2 2 -> 0 data_213: 2 2 1 3 3 1 -> 1 data_214: 2 2 1 3 3 2 -> 0 data_215: 2 2 1 3 4 1 -> 1 data_216: 2 2 1 3 4 2 -> 0 data_217: 2 2 2 1 1 1 -> 0 data_218: 2 2 2 1 1 2 -> 1 data_219: 2 2 2 1 2 1 -> 1 data_220: 2 2 2 1 2 2 -> 0 data_221: 2 2 2 1 3 1 -> 1 data_222: 2 2 2 1 3 2 -> 0 data_223: 2 2 2 1 4 1 -> 1 data_224: 2 2 2 1 4 2 -> 0 data_225: 2 2 2 2 1 1 -> 1 data_226: 2 2 2 2 1 2 -> 0 data_227: 2 2 2 2 2 1 -> 0 data_228: 2 2 2 2 2 2 -> 0 data_229: 2 2 2 2 3 1 -> 0 data_230: 2 2 2 2 3 2 -> 0 data_231: 2 2 2 2 4 1 -> 0 data_232: 2 2 2 2 4 2 -> 0 data_233: 2 2 2 3 1 1 -> 1 data_234: 2 2 2 3 1 2 -> 0 data_235: 2 2 2 3 2 1 -> 0 data_236: 2 2 2 3 2 2 -> 0 data_237: 2 2 2 3 3 1 -> 0 data_238: 2 2 2 3 3 2 -> 0 data_239: 2 2 2 3 4 1 -> 0 data_240: 2 2 2 3 4 2 -> 0 data_241: 2 3 1 1 1 1 -> 0 data_242: 2 3 1 1 1 2 -> 0 data_243: 2 3 1 1 2 1 -> 0 data_244: 2 3 1 1 2 2 -> 1 data_245: 2 3 1 1 3 1 -> 0 data_246: 2 3 1 1 3 2 -> 1 data_247: 2 3 1 1 4 1 -> 0 data_248: 2 3 1 1 4 2 -> 1 data_249: 2 3 1 2 1 1 -> 0 data_250: 2 3 1 2 1 2 -> 1 data_251: 2 3 1 2 2 1 -> 1 data_252: 2 3 1 2 2 2 -> 0 data_253: 2 3 1 2 3 1 -> 1 data_254: 2 3 1 2 3 2 -> 0 data_255: 2 3 1 2 4 1 -> 1 data_256: 2 3 1 2 4 2 -> 0 data_257: 2 3 1 3 1 1 -> 0 data_258: 2 3 1 3 1 2 -> 1 data_259: 2 3 1 3 2 1 -> 1 data_260: 2 3 1 3 2 2 -> 0 data_261: 2 3 1 3 3 1 -> 1 data_262: 2 3 1 3 3 2 -> 0 data_263: 2 3 1 3 4 1 -> 1 data_264: 2 3 1 3 4 2 -> 0 data_265: 2 3 2 1 1 1 -> 0 data_266: 2 3 2 1 1 2 -> 1 data_267: 2 3 2 1 2 1 -> 1 data_268: 2 3 2 1 2 2 -> 0 data_269: 2 3 2 1 3 1 -> 1 data_270: 2 3 2 1 3 2 -> 0 data_271: 2 3 2 1 4 1 -> 1 data_272: 2 3 2 1 4 2 -> 0 data_273: 2 3 2 2 1 1 -> 1 data_274: 2 3 2 2 1 2 -> 0 data_275: 2 3 2 2 2 1 -> 0 data_276: 2 3 2 2 2 2 -> 0 data_277: 2 3 2 2 3 1 -> 0 data_278: 2 3 2 2 3 2 -> 0 data_279: 2 3 2 2 4 1 -> 0 data_280: 2 3 2 2 4 2 -> 0 data_281: 2 3 2 3 1 1 -> 1 data_282: 2 3 2 3 1 2 -> 0 data_283: 2 3 2 3 2 1 -> 0 data_284: 2 3 2 3 2 2 -> 0 data_285: 2 3 2 3 3 1 -> 0 data_286: 2 3 2 3 3 2 -> 0 data_287: 2 3 2 3 4 1 -> 0 data_288: 2 3 2 3 4 2 -> 0 data_289: 3 1 1 1 1 1 -> 0 data_290: 3 1 1 1 1 2 -> 0 data_291: 3 1 1 1 2 1 -> 0 data_292: 3 1 1 1 2 2 -> 0 data_293: 3 1 1 1 3 1 -> 0 data_294: 3 1 1 1 3 2 -> 0 data_295: 3 1 1 1 4 1 -> 0 data_296: 3 1 1 1 4 2 -> 0 data_297: 3 1 1 2 1 1 -> 0 data_298: 3 1 1 2 1 2 -> 0 data_299: 3 1 1 2 2 1 -> 0 data_300: 3 1 1 2 2 2 -> 1 data_301: 3 1 1 2 3 1 -> 0 data_302: 3 1 1 2 3 2 -> 1 data_303: 3 1 1 2 4 1 -> 0 data_304: 3 1 1 2 4 2 -> 1 data_305: 3 1 1 3 1 1 -> 0 data_306: 3 1 1 3 1 2 -> 0 data_307: 3 1 1 3 2 1 -> 0 data_308: 3 1 1 3 2 2 -> 1 data_309: 3 1 1 3 3 1 -> 0 data_310: 3 1 1 3 3 2 -> 1 data_311: 3 1 1 3 4 1 -> 0 data_312: 3 1 1 3 4 2 -> 1 data_313: 3 1 2 1 1 1 -> 0 data_314: 3 1 2 1 1 2 -> 0 data_315: 3 1 2 1 2 1 -> 0 data_316: 3 1 2 1 2 2 -> 1 data_317: 3 1 2 1 3 1 -> 0 data_318: 3 1 2 1 3 2 -> 1 data_319: 3 1 2 1 4 1 -> 0 data_320: 3 1 2 1 4 2 -> 1 data_321: 3 1 2 2 1 1 -> 0 data_322: 3 1 2 2 1 2 -> 1 data_323: 3 1 2 2 2 1 -> 1 data_324: 3 1 2 2 2 2 -> 0 data_325: 3 1 2 2 3 1 -> 1 data_326: 3 1 2 2 3 2 -> 0 data_327: 3 1 2 2 4 1 -> 1 data_328: 3 1 2 2 4 2 -> 0 data_329: 3 1 2 3 1 1 -> 0 data_330: 3 1 2 3 1 2 -> 1 data_331: 3 1 2 3 2 1 -> 1 data_332: 3 1 2 3 2 2 -> 0 data_333: 3 1 2 3 3 1 -> 1 data_334: 3 1 2 3 3 2 -> 0 data_335: 3 1 2 3 4 1 -> 1 data_336: 3 1 2 3 4 2 -> 0 data_337: 3 2 1 1 1 1 -> 0 data_338: 3 2 1 1 1 2 -> 0 data_339: 3 2 1 1 2 1 -> 0 data_340: 3 2 1 1 2 2 -> 1 data_341: 3 2 1 1 3 1 -> 0 data_342: 3 2 1 1 3 2 -> 1 data_343: 3 2 1 1 4 1 -> 0 data_344: 3 2 1 1 4 2 -> 1 data_345: 3 2 1 2 1 1 -> 0 data_346: 3 2 1 2 1 2 -> 1 data_347: 3 2 1 2 2 1 -> 1 data_348: 3 2 1 2 2 2 -> 0 data_349: 3 2 1 2 3 1 -> 1 data_350: 3 2 1 2 3 2 -> 0 data_351: 3 2 1 2 4 1 -> 1 data_352: 3 2 1 2 4 2 -> 0 data_353: 3 2 1 3 1 1 -> 0 data_354: 3 2 1 3 1 2 -> 1 data_355: 3 2 1 3 2 1 -> 1 data_356: 3 2 1 3 2 2 -> 0 data_357: 3 2 1 3 3 1 -> 1 data_358: 3 2 1 3 3 2 -> 0 data_359: 3 2 1 3 4 1 -> 1 data_360: 3 2 1 3 4 2 -> 0 data_361: 3 2 2 1 1 1 -> 0 data_362: 3 2 2 1 1 2 -> 1 data_363: 3 2 2 1 2 1 -> 1 data_364: 3 2 2 1 2 2 -> 0 data_365: 3 2 2 1 3 1 -> 1 data_366: 3 2 2 1 3 2 -> 0 data_367: 3 2 2 1 4 1 -> 1 data_368: 3 2 2 1 4 2 -> 0 data_369: 3 2 2 2 1 1 -> 1 data_370: 3 2 2 2 1 2 -> 0 data_371: 3 2 2 2 2 1 -> 0 data_372: 3 2 2 2 2 2 -> 0 data_373: 3 2 2 2 3 1 -> 0 data_374: 3 2 2 2 3 2 -> 0 data_375: 3 2 2 2 4 1 -> 0 data_376: 3 2 2 2 4 2 -> 0 data_377: 3 2 2 3 1 1 -> 1 data_378: 3 2 2 3 1 2 -> 0 data_379: 3 2 2 3 2 1 -> 0 data_380: 3 2 2 3 2 2 -> 0 data_381: 3 2 2 3 3 1 -> 0 data_382: 3 2 2 3 3 2 -> 0 data_383: 3 2 2 3 4 1 -> 0 data_384: 3 2 2 3 4 2 -> 0 data_385: 3 3 1 1 1 1 -> 0 data_386: 3 3 1 1 1 2 -> 0 data_387: 3 3 1 1 2 1 -> 0 data_388: 3 3 1 1 2 2 -> 1 data_389: 3 3 1 1 3 1 -> 0 data_390: 3 3 1 1 3 2 -> 1 data_391: 3 3 1 1 4 1 -> 0 data_392: 3 3 1 1 4 2 -> 1 data_393: 3 3 1 2 1 1 -> 0 data_394: 3 3 1 2 1 2 -> 1 data_395: 3 3 1 2 2 1 -> 1 data_396: 3 3 1 2 2 2 -> 0 data_397: 3 3 1 2 3 1 -> 1 data_398: 3 3 1 2 3 2 -> 0 data_399: 3 3 1 2 4 1 -> 1 data_400: 3 3 1 2 4 2 -> 0 data_401: 3 3 1 3 1 1 -> 0 data_402: 3 3 1 3 1 2 -> 1 data_403: 3 3 1 3 2 1 -> 1 data_404: 3 3 1 3 2 2 -> 0 data_405: 3 3 1 3 3 1 -> 1 data_406: 3 3 1 3 3 2 -> 0 data_407: 3 3 1 3 4 1 -> 1 data_408: 3 3 1 3 4 2 -> 0 data_409: 3 3 2 1 1 1 -> 0 data_410: 3 3 2 1 1 2 -> 1 data_411: 3 3 2 1 2 1 -> 1 data_412: 3 3 2 1 2 2 -> 0 data_413: 3 3 2 1 3 1 -> 1 data_414: 3 3 2 1 3 2 -> 0 data_415: 3 3 2 1 4 1 -> 1 data_416: 3 3 2 1 4 2 -> 0 data_417: 3 3 2 2 1 1 -> 1 data_418: 3 3 2 2 1 2 -> 0 data_419: 3 3 2 2 2 1 -> 0 data_420: 3 3 2 2 2 2 -> 0 data_421: 3 3 2 2 3 1 -> 0 data_422: 3 3 2 2 3 2 -> 0 data_423: 3 3 2 2 4 1 -> 0 data_424: 3 3 2 2 4 2 -> 0 data_425: 3 3 2 3 1 1 -> 1 data_426: 3 3 2 3 1 2 -> 0 data_427: 3 3 2 3 2 1 -> 0 data_428: 3 3 2 3 2 2 -> 0 data_429: 3 3 2 3 3 1 -> 0 data_430: 3 3 2 3 3 2 -> 0 data_431: 3 3 2 3 4 1 -> 0 data_432: 3 3 2 3 4 2 -> 0 /* Training Data Set #3 */ data_2: 1 1 1 1 1 2 -> 1 data_3: 1 1 1 1 2 1 -> 1 data_4: 1 1 1 1 2 2 -> 1 data_5: 1 1 1 1 3 1 -> 0 data_7: 1 1 1 1 4 1 -> 0 data_9: 1 1 1 2 1 1 -> 1 data_12: 1 1 1 2 2 2 -> 1 data_16: 1 1 1 2 4 2 -> 0 data_28: 1 1 2 1 2 2 -> 1 data_32: 1 1 2 1 4 2 -> 0 data_36: 1 1 2 2 2 2 -> 1 data_39: 1 1 2 2 4 1 -> 0 data_40: 1 1 2 2 4 2 -> 0 data_41: 1 1 2 3 1 1 -> 1 data_42: 1 1 2 3 1 2 -> 1 data_45: 1 1 2 3 3 1 -> 1 data_46: 1 1 2 3 3 2 -> 1 data_53: 1 2 1 1 3 1 -> 1 data_59: 1 2 1 2 2 1 -> 1 data_60: 1 2 1 2 2 2 -> 1 data_61: 1 2 1 2 3 1 -> 0 data_65: 1 2 1 3 1 1 -> 1 data_66: 1 2 1 3 1 2 -> 1 data_67: 1 2 1 3 2 1 -> 1 data_68: 1 2 1 3 2 2 -> 1 data_70: 1 2 1 3 3 2 -> 1 data_71: 1 2 1 3 4 1 -> 0 data_77: 1 2 2 1 3 1 -> 1 data_80: 1 2 2 1 4 2 -> 0 data_81: 1 2 2 2 1 1 -> 1 data_83: 1 2 2 2 2 1 -> 1 data_84: 1 2 2 2 2 2 -> 1 data_89: 1 2 2 3 1 1 -> 1 data_91: 1 2 2 3 2 1 -> 1 data_92: 1 2 2 3 2 2 -> 1 data_99: 1 3 1 1 2 1 -> 0 data_103: 1 3 1 1 4 1 -> 0 data_110: 1 3 1 2 3 2 -> 0 data_111: 1 3 1 2 4 1 -> 0 data_113: 1 3 1 3 1 1 -> 0 data_117: 1 3 1 3 3 1 -> 0 data_121: 1 3 2 1 1 1 -> 0 data_122: 1 3 2 1 1 2 -> 0 data_123: 1 3 2 1 2 1 -> 0 data_128: 1 3 2 1 4 2 -> 0 data_134: 1 3 2 2 3 2 -> 0 data_136: 1 3 2 2 4 2 -> 0 data_143: 1 3 2 3 4 1 -> 0 data_145: 2 1 1 1 1 1 -> 1 data_146: 2 1 1 1 1 2 -> 1 data_151: 2 1 1 1 4 1 -> 0 data_152: 2 1 1 1 4 2 -> 0 data_153: 2 1 1 2 1 1 -> 1 data_154: 2 1 1 2 1 2 -> 1 data_164: 2 1 1 3 2 2 -> 1 data_166: 2 1 1 3 3 2 -> 1 data_167: 2 1 1 3 4 1 -> 0 data_172: 2 1 2 1 2 2 -> 1 data_183: 2 1 2 2 4 1 -> 0 data_186: 2 1 2 3 1 2 -> 1 data_198: 2 2 1 1 3 2 -> 1 data_200: 2 2 1 1 4 2 -> 0 data_202: 2 2 1 2 1 2 -> 1 data_203: 2 2 1 2 2 1 -> 0 data_209: 2 2 1 3 1 1 -> 1 data_212: 2 2 1 3 2 2 -> 1 data_213: 2 2 1 3 3 1 -> 0 data_214: 2 2 1 3 3 2 -> 0 data_216: 2 2 1 3 4 2 -> 0 data_220: 2 2 2 1 2 2 -> 1 data_226: 2 2 2 2 1 2 -> 1 data_229: 2 2 2 2 3 1 -> 1 data_230: 2 2 2 2 3 2 -> 1 data_239: 2 2 2 3 4 1 -> 0 data_245: 2 3 1 1 3 1 -> 1 data_249: 2 3 1 2 1 1 -> 0 data_251: 2 3 1 2 2 1 -> 0 data_252: 2 3 1 2 2 2 -> 0 data_254: 2 3 1 2 3 2 -> 0 data_261: 2 3 1 3 3 1 -> 0 data_266: 2 3 2 1 1 2 -> 0 data_268: 2 3 2 1 2 2 -> 0 data_271: 2 3 2 1 4 1 -> 0 data_277: 2 3 2 2 3 1 -> 0 data_280: 2 3 2 2 4 2 -> 0 data_281: 2 3 2 3 1 1 -> 0 data_283: 2 3 2 3 2 1 -> 0 data_288: 2 3 2 3 4 2 -> 0 data_289: 3 1 1 1 1 1 -> 1 data_291: 3 1 1 1 2 1 -> 1 data_293: 3 1 1 1 3 1 -> 1 data_304: 3 1 1 2 4 2 -> 0 data_306: 3 1 1 3 1 2 -> 1 data_312: 3 1 1 3 4 2 -> 0 data_315: 3 1 2 1 2 1 -> 1 data_326: 3 1 2 2 3 2 -> 1 data_328: 3 1 2 2 4 2 -> 0 data_329: 3 1 2 3 1 1 -> 1 data_340: 3 2 1 1 2 2 -> 1 data_343: 3 2 1 1 4 1 -> 0 data_349: 3 2 1 2 3 1 -> 1 data_354: 3 2 1 3 1 2 -> 1 data_364: 3 2 2 1 2 2 -> 1 data_366: 3 2 2 1 3 2 -> 1 data_370: 3 2 2 2 1 2 -> 1 data_377: 3 2 2 3 1 1 -> 1 data_382: 3 2 2 3 3 2 -> 1 data_383: 3 2 2 3 4 1 -> 0 data_390: 3 3 1 1 3 2 -> 1 data_391: 3 3 1 1 4 1 -> 1 data_400: 3 3 1 2 4 2 -> 0 data_401: 3 3 1 3 1 1 -> 0 data_403: 3 3 1 3 2 1 -> 0 data_404: 3 3 1 3 2 2 -> 0 data_407: 3 3 1 3 4 1 -> 0 data_409: 3 3 2 1 1 1 -> 0 data_410: 3 3 2 1 1 2 -> 0 data_420: 3 3 2 2 2 2 -> 0 data_422: 3 3 2 2 3 2 -> 0 data_425: 3 3 2 3 1 1 -> 0 data_430: 3 3 2 3 3 2 -> 0 data_432: 3 3 2 3 4 2 -> 0 /* Testing Data Set #3 */ data_1: 1 1 1 1 1 1 -> 1 data_2: 1 1 1 1 1 2 -> 1 data_3: 1 1 1 1 2 1 -> 1 data_4: 1 1 1 1 2 2 -> 1 data_5: 1 1 1 1 3 1 -> 1 data_6: 1 1 1 1 3 2 -> 1 data_7: 1 1 1 1 4 1 -> 0 data_8: 1 1 1 1 4 2 -> 0 data_9: 1 1 1 2 1 1 -> 1 data_10: 1 1 1 2 1 2 -> 1 data_11: 1 1 1 2 2 1 -> 1 data_12: 1 1 1 2 2 2 -> 1 data_13: 1 1 1 2 3 1 -> 1 data_14: 1 1 1 2 3 2 -> 1 data_15: 1 1 1 2 4 1 -> 0 data_16: 1 1 1 2 4 2 -> 0 data_17: 1 1 1 3 1 1 -> 1 data_18: 1 1 1 3 1 2 -> 1 data_19: 1 1 1 3 2 1 -> 1 data_20: 1 1 1 3 2 2 -> 1 data_21: 1 1 1 3 3 1 -> 1 data_22: 1 1 1 3 3 2 -> 1 data_23: 1 1 1 3 4 1 -> 0 data_24: 1 1 1 3 4 2 -> 0 data_25: 1 1 2 1 1 1 -> 1 data_26: 1 1 2 1 1 2 -> 1 data_27: 1 1 2 1 2 1 -> 1 data_28: 1 1 2 1 2 2 -> 1 data_29: 1 1 2 1 3 1 -> 1 data_30: 1 1 2 1 3 2 -> 1 data_31: 1 1 2 1 4 1 -> 0 data_32: 1 1 2 1 4 2 -> 0 data_33: 1 1 2 2 1 1 -> 1 data_34: 1 1 2 2 1 2 -> 1 data_35: 1 1 2 2 2 1 -> 1 data_36: 1 1 2 2 2 2 -> 1 data_37: 1 1 2 2 3 1 -> 1 data_38: 1 1 2 2 3 2 -> 1 data_39: 1 1 2 2 4 1 -> 0 data_40: 1 1 2 2 4 2 -> 0 data_41: 1 1 2 3 1 1 -> 1 data_42: 1 1 2 3 1 2 -> 1 data_43: 1 1 2 3 2 1 -> 1 data_44: 1 1 2 3 2 2 -> 1 data_45: 1 1 2 3 3 1 -> 1 data_46: 1 1 2 3 3 2 -> 1 data_47: 1 1 2 3 4 1 -> 0 data_48: 1 1 2 3 4 2 -> 0 data_49: 1 2 1 1 1 1 -> 1 data_50: 1 2 1 1 1 2 -> 1 data_51: 1 2 1 1 2 1 -> 1 data_52: 1 2 1 1 2 2 -> 1 data_53: 1 2 1 1 3 1 -> 1 data_54: 1 2 1 1 3 2 -> 1 data_55: 1 2 1 1 4 1 -> 0 data_56: 1 2 1 1 4 2 -> 0 data_57: 1 2 1 2 1 1 -> 1 data_58: 1 2 1 2 1 2 -> 1 data_59: 1 2 1 2 2 1 -> 1 data_60: 1 2 1 2 2 2 -> 1 data_61: 1 2 1 2 3 1 -> 1 data_62: 1 2 1 2 3 2 -> 1 data_63: 1 2 1 2 4 1 -> 0 data_64: 1 2 1 2 4 2 -> 0 data_65: 1 2 1 3 1 1 -> 1 data_66: 1 2 1 3 1 2 -> 1 data_67: 1 2 1 3 2 1 -> 1 data_68: 1 2 1 3 2 2 -> 1 data_69: 1 2 1 3 3 1 -> 1 data_70: 1 2 1 3 3 2 -> 1 data_71: 1 2 1 3 4 1 -> 0 data_72: 1 2 1 3 4 2 -> 0 data_73: 1 2 2 1 1 1 -> 1 data_74: 1 2 2 1 1 2 -> 1 data_75: 1 2 2 1 2 1 -> 1 data_76: 1 2 2 1 2 2 -> 1 data_77: 1 2 2 1 3 1 -> 1 data_78: 1 2 2 1 3 2 -> 1 data_79: 1 2 2 1 4 1 -> 0 data_80: 1 2 2 1 4 2 -> 0 data_81: 1 2 2 2 1 1 -> 1 data_82: 1 2 2 2 1 2 -> 1 data_83: 1 2 2 2 2 1 -> 1 data_84: 1 2 2 2 2 2 -> 1 data_85: 1 2 2 2 3 1 -> 1 data_86: 1 2 2 2 3 2 -> 1 data_87: 1 2 2 2 4 1 -> 0 data_88: 1 2 2 2 4 2 -> 0 data_89: 1 2 2 3 1 1 -> 1 data_90: 1 2 2 3 1 2 -> 1 data_91: 1 2 2 3 2 1 -> 1 data_92: 1 2 2 3 2 2 -> 1 data_93: 1 2 2 3 3 1 -> 1 data_94: 1 2 2 3 3 2 -> 1 data_95: 1 2 2 3 4 1 -> 0 data_96: 1 2 2 3 4 2 -> 0 data_97: 1 3 1 1 1 1 -> 0 data_98: 1 3 1 1 1 2 -> 0 data_99: 1 3 1 1 2 1 -> 0 data_100: 1 3 1 1 2 2 -> 0 data_101: 1 3 1 1 3 1 -> 1 data_102: 1 3 1 1 3 2 -> 1 data_103: 1 3 1 1 4 1 -> 0 data_104: 1 3 1 1 4 2 -> 0 data_105: 1 3 1 2 1 1 -> 0 data_106: 1 3 1 2 1 2 -> 0 data_107: 1 3 1 2 2 1 -> 0 data_108: 1 3 1 2 2 2 -> 0 data_109: 1 3 1 2 3 1 -> 0 data_110: 1 3 1 2 3 2 -> 0 data_111: 1 3 1 2 4 1 -> 0 data_112: 1 3 1 2 4 2 -> 0 data_113: 1 3 1 3 1 1 -> 0 data_114: 1 3 1 3 1 2 -> 0 data_115: 1 3 1 3 2 1 -> 0 data_116: 1 3 1 3 2 2 -> 0 data_117: 1 3 1 3 3 1 -> 0 data_118: 1 3 1 3 3 2 -> 0 data_119: 1 3 1 3 4 1 -> 0 data_120: 1 3 1 3 4 2 -> 0 data_121: 1 3 2 1 1 1 -> 0 data_122: 1 3 2 1 1 2 -> 0 data_123: 1 3 2 1 2 1 -> 0 data_124: 1 3 2 1 2 2 -> 0 data_125: 1 3 2 1 3 1 -> 1 data_126: 1 3 2 1 3 2 -> 1 data_127: 1 3 2 1 4 1 -> 0 data_128: 1 3 2 1 4 2 -> 0 data_129: 1 3 2 2 1 1 -> 0 data_130: 1 3 2 2 1 2 -> 0 data_131: 1 3 2 2 2 1 -> 0 data_132: 1 3 2 2 2 2 -> 0 data_133: 1 3 2 2 3 1 -> 0 data_134: 1 3 2 2 3 2 -> 0 data_135: 1 3 2 2 4 1 -> 0 data_136: 1 3 2 2 4 2 -> 0 data_137: 1 3 2 3 1 1 -> 0 data_138: 1 3 2 3 1 2 -> 0 data_139: 1 3 2 3 2 1 -> 0 data_140: 1 3 2 3 2 2 -> 0 data_141: 1 3 2 3 3 1 -> 0 data_142: 1 3 2 3 3 2 -> 0 data_143: 1 3 2 3 4 1 -> 0 data_144: 1 3 2 3 4 2 -> 0 data_145: 2 1 1 1 1 1 -> 1 data_146: 2 1 1 1 1 2 -> 1 data_147: 2 1 1 1 2 1 -> 1 data_148: 2 1 1 1 2 2 -> 1 data_149: 2 1 1 1 3 1 -> 1 data_150: 2 1 1 1 3 2 -> 1 data_151: 2 1 1 1 4 1 -> 0 data_152: 2 1 1 1 4 2 -> 0 data_153: 2 1 1 2 1 1 -> 1 data_154: 2 1 1 2 1 2 -> 1 data_155: 2 1 1 2 2 1 -> 1 data_156: 2 1 1 2 2 2 -> 1 data_157: 2 1 1 2 3 1 -> 1 data_158: 2 1 1 2 3 2 -> 1 data_159: 2 1 1 2 4 1 -> 0 data_160: 2 1 1 2 4 2 -> 0 data_161: 2 1 1 3 1 1 -> 1 data_162: 2 1 1 3 1 2 -> 1 data_163: 2 1 1 3 2 1 -> 1 data_164: 2 1 1 3 2 2 -> 1 data_165: 2 1 1 3 3 1 -> 1 data_166: 2 1 1 3 3 2 -> 1 data_167: 2 1 1 3 4 1 -> 0 data_168: 2 1 1 3 4 2 -> 0 data_169: 2 1 2 1 1 1 -> 1 data_170: 2 1 2 1 1 2 -> 1 data_171: 2 1 2 1 2 1 -> 1 data_172: 2 1 2 1 2 2 -> 1 data_173: 2 1 2 1 3 1 -> 1 data_174: 2 1 2 1 3 2 -> 1 data_175: 2 1 2 1 4 1 -> 0 data_176: 2 1 2 1 4 2 -> 0 data_177: 2 1 2 2 1 1 -> 1 data_178: 2 1 2 2 1 2 -> 1 data_179: 2 1 2 2 2 1 -> 1 data_180: 2 1 2 2 2 2 -> 1 data_181: 2 1 2 2 3 1 -> 1 data_182: 2 1 2 2 3 2 -> 1 data_183: 2 1 2 2 4 1 -> 0 data_184: 2 1 2 2 4 2 -> 0 data_185: 2 1 2 3 1 1 -> 1 data_186: 2 1 2 3 1 2 -> 1 data_187: 2 1 2 3 2 1 -> 1 data_188: 2 1 2 3 2 2 -> 1 data_189: 2 1 2 3 3 1 -> 1 data_190: 2 1 2 3 3 2 -> 1 data_191: 2 1 2 3 4 1 -> 0 data_192: 2 1 2 3 4 2 -> 0 data_193: 2 2 1 1 1 1 -> 1 data_194: 2 2 1 1 1 2 -> 1 data_195: 2 2 1 1 2 1 -> 1 data_196: 2 2 1 1 2 2 -> 1 data_197: 2 2 1 1 3 1 -> 1 data_198: 2 2 1 1 3 2 -> 1 data_199: 2 2 1 1 4 1 -> 0 data_200: 2 2 1 1 4 2 -> 0 data_201: 2 2 1 2 1 1 -> 1 data_202: 2 2 1 2 1 2 -> 1 data_203: 2 2 1 2 2 1 -> 1 data_204: 2 2 1 2 2 2 -> 1 data_205: 2 2 1 2 3 1 -> 1 data_206: 2 2 1 2 3 2 -> 1 data_207: 2 2 1 2 4 1 -> 0 data_208: 2 2 1 2 4 2 -> 0 data_209: 2 2 1 3 1 1 -> 1 data_210: 2 2 1 3 1 2 -> 1 data_211: 2 2 1 3 2 1 -> 1 data_212: 2 2 1 3 2 2 -> 1 data_213: 2 2 1 3 3 1 -> 1 data_214: 2 2 1 3 3 2 -> 1 data_215: 2 2 1 3 4 1 -> 0 data_216: 2 2 1 3 4 2 -> 0 data_217: 2 2 2 1 1 1 -> 1 data_218: 2 2 2 1 1 2 -> 1 data_219: 2 2 2 1 2 1 -> 1 data_220: 2 2 2 1 2 2 -> 1 data_221: 2 2 2 1 3 1 -> 1 data_222: 2 2 2 1 3 2 -> 1 data_223: 2 2 2 1 4 1 -> 0 data_224: 2 2 2 1 4 2 -> 0 data_225: 2 2 2 2 1 1 -> 1 data_226: 2 2 2 2 1 2 -> 1 data_227: 2 2 2 2 2 1 -> 1 data_228: 2 2 2 2 2 2 -> 1 data_229: 2 2 2 2 3 1 -> 1 data_230: 2 2 2 2 3 2 -> 1 data_231: 2 2 2 2 4 1 -> 0 data_232: 2 2 2 2 4 2 -> 0 data_233: 2 2 2 3 1 1 -> 1 data_234: 2 2 2 3 1 2 -> 1 data_235: 2 2 2 3 2 1 -> 1 data_236: 2 2 2 3 2 2 -> 1 data_237: 2 2 2 3 3 1 -> 1 data_238: 2 2 2 3 3 2 -> 1 data_239: 2 2 2 3 4 1 -> 0 data_240: 2 2 2 3 4 2 -> 0 data_241: 2 3 1 1 1 1 -> 0 data_242: 2 3 1 1 1 2 -> 0 data_243: 2 3 1 1 2 1 -> 0 data_244: 2 3 1 1 2 2 -> 0 data_245: 2 3 1 1 3 1 -> 1 data_246: 2 3 1 1 3 2 -> 1 data_247: 2 3 1 1 4 1 -> 0 data_248: 2 3 1 1 4 2 -> 0 data_249: 2 3 1 2 1 1 -> 0 data_250: 2 3 1 2 1 2 -> 0 data_251: 2 3 1 2 2 1 -> 0 data_252: 2 3 1 2 2 2 -> 0 data_253: 2 3 1 2 3 1 -> 0 data_254: 2 3 1 2 3 2 -> 0 data_255: 2 3 1 2 4 1 -> 0 data_256: 2 3 1 2 4 2 -> 0 data_257: 2 3 1 3 1 1 -> 0 data_258: 2 3 1 3 1 2 -> 0 data_259: 2 3 1 3 2 1 -> 0 data_260: 2 3 1 3 2 2 -> 0 data_261: 2 3 1 3 3 1 -> 0 data_262: 2 3 1 3 3 2 -> 0 data_263: 2 3 1 3 4 1 -> 0 data_264: 2 3 1 3 4 2 -> 0 data_265: 2 3 2 1 1 1 -> 0 data_266: 2 3 2 1 1 2 -> 0 data_267: 2 3 2 1 2 1 -> 0 data_268: 2 3 2 1 2 2 -> 0 data_269: 2 3 2 1 3 1 -> 1 data_270: 2 3 2 1 3 2 -> 1 data_271: 2 3 2 1 4 1 -> 0 data_272: 2 3 2 1 4 2 -> 0 data_273: 2 3 2 2 1 1 -> 0 data_274: 2 3 2 2 1 2 -> 0 data_275: 2 3 2 2 2 1 -> 0 data_276: 2 3 2 2 2 2 -> 0 data_277: 2 3 2 2 3 1 -> 0 data_278: 2 3 2 2 3 2 -> 0 data_279: 2 3 2 2 4 1 -> 0 data_280: 2 3 2 2 4 2 -> 0 data_281: 2 3 2 3 1 1 -> 0 data_282: 2 3 2 3 1 2 -> 0 data_283: 2 3 2 3 2 1 -> 0 data_284: 2 3 2 3 2 2 -> 0 data_285: 2 3 2 3 3 1 -> 0 data_286: 2 3 2 3 3 2 -> 0 data_287: 2 3 2 3 4 1 -> 0 data_288: 2 3 2 3 4 2 -> 0 data_289: 3 1 1 1 1 1 -> 1 data_290: 3 1 1 1 1 2 -> 1 data_291: 3 1 1 1 2 1 -> 1 data_292: 3 1 1 1 2 2 -> 1 data_293: 3 1 1 1 3 1 -> 1 data_294: 3 1 1 1 3 2 -> 1 data_295: 3 1 1 1 4 1 -> 0 data_296: 3 1 1 1 4 2 -> 0 data_297: 3 1 1 2 1 1 -> 1 data_298: 3 1 1 2 1 2 -> 1 data_299: 3 1 1 2 2 1 -> 1 data_300: 3 1 1 2 2 2 -> 1 data_301: 3 1 1 2 3 1 -> 1 data_302: 3 1 1 2 3 2 -> 1 data_303: 3 1 1 2 4 1 -> 0 data_304: 3 1 1 2 4 2 -> 0 data_305: 3 1 1 3 1 1 -> 1 data_306: 3 1 1 3 1 2 -> 1 data_307: 3 1 1 3 2 1 -> 1 data_308: 3 1 1 3 2 2 -> 1 data_309: 3 1 1 3 3 1 -> 1 data_310: 3 1 1 3 3 2 -> 1 data_311: 3 1 1 3 4 1 -> 0 data_312: 3 1 1 3 4 2 -> 0 data_313: 3 1 2 1 1 1 -> 1 data_314: 3 1 2 1 1 2 -> 1 data_315: 3 1 2 1 2 1 -> 1 data_316: 3 1 2 1 2 2 -> 1 data_317: 3 1 2 1 3 1 -> 1 data_318: 3 1 2 1 3 2 -> 1 data_319: 3 1 2 1 4 1 -> 0 data_320: 3 1 2 1 4 2 -> 0 data_321: 3 1 2 2 1 1 -> 1 data_322: 3 1 2 2 1 2 -> 1 data_323: 3 1 2 2 2 1 -> 1 data_324: 3 1 2 2 2 2 -> 1 data_325: 3 1 2 2 3 1 -> 1 data_326: 3 1 2 2 3 2 -> 1 data_327: 3 1 2 2 4 1 -> 0 data_328: 3 1 2 2 4 2 -> 0 data_329: 3 1 2 3 1 1 -> 1 data_330: 3 1 2 3 1 2 -> 1 data_331: 3 1 2 3 2 1 -> 1 data_332: 3 1 2 3 2 2 -> 1 data_333: 3 1 2 3 3 1 -> 1 data_334: 3 1 2 3 3 2 -> 1 data_335: 3 1 2 3 4 1 -> 0 data_336: 3 1 2 3 4 2 -> 0 data_337: 3 2 1 1 1 1 -> 1 data_338: 3 2 1 1 1 2 -> 1 data_339: 3 2 1 1 2 1 -> 1 data_340: 3 2 1 1 2 2 -> 1 data_341: 3 2 1 1 3 1 -> 1 data_342: 3 2 1 1 3 2 -> 1 data_343: 3 2 1 1 4 1 -> 0 data_344: 3 2 1 1 4 2 -> 0 data_345: 3 2 1 2 1 1 -> 1 data_346: 3 2 1 2 1 2 -> 1 data_347: 3 2 1 2 2 1 -> 1 data_348: 3 2 1 2 2 2 -> 1 data_349: 3 2 1 2 3 1 -> 1 data_350: 3 2 1 2 3 2 -> 1 data_351: 3 2 1 2 4 1 -> 0 data_352: 3 2 1 2 4 2 -> 0 data_353: 3 2 1 3 1 1 -> 1 data_354: 3 2 1 3 1 2 -> 1 data_355: 3 2 1 3 2 1 -> 1 data_356: 3 2 1 3 2 2 -> 1 data_357: 3 2 1 3 3 1 -> 1 data_358: 3 2 1 3 3 2 -> 1 data_359: 3 2 1 3 4 1 -> 0 data_360: 3 2 1 3 4 2 -> 0 data_361: 3 2 2 1 1 1 -> 1 data_362: 3 2 2 1 1 2 -> 1 data_363: 3 2 2 1 2 1 -> 1 data_364: 3 2 2 1 2 2 -> 1 data_365: 3 2 2 1 3 1 -> 1 data_366: 3 2 2 1 3 2 -> 1 data_367: 3 2 2 1 4 1 -> 0 data_368: 3 2 2 1 4 2 -> 0 data_369: 3 2 2 2 1 1 -> 1 data_370: 3 2 2 2 1 2 -> 1 data_371: 3 2 2 2 2 1 -> 1 data_372: 3 2 2 2 2 2 -> 1 data_373: 3 2 2 2 3 1 -> 1 data_374: 3 2 2 2 3 2 -> 1 data_375: 3 2 2 2 4 1 -> 0 data_376: 3 2 2 2 4 2 -> 0 data_377: 3 2 2 3 1 1 -> 1 data_378: 3 2 2 3 1 2 -> 1 data_379: 3 2 2 3 2 1 -> 1 data_380: 3 2 2 3 2 2 -> 1 data_381: 3 2 2 3 3 1 -> 1 data_382: 3 2 2 3 3 2 -> 1 data_383: 3 2 2 3 4 1 -> 0 data_384: 3 2 2 3 4 2 -> 0 data_385: 3 3 1 1 1 1 -> 0 data_386: 3 3 1 1 1 2 -> 0 data_387: 3 3 1 1 2 1 -> 0 data_388: 3 3 1 1 2 2 -> 0 data_389: 3 3 1 1 3 1 -> 1 data_390: 3 3 1 1 3 2 -> 1 data_391: 3 3 1 1 4 1 -> 0 data_392: 3 3 1 1 4 2 -> 0 data_393: 3 3 1 2 1 1 -> 0 data_394: 3 3 1 2 1 2 -> 0 data_395: 3 3 1 2 2 1 -> 0 data_396: 3 3 1 2 2 2 -> 0 data_397: 3 3 1 2 3 1 -> 0 data_398: 3 3 1 2 3 2 -> 0 data_399: 3 3 1 2 4 1 -> 0 data_400: 3 3 1 2 4 2 -> 0 data_401: 3 3 1 3 1 1 -> 0 data_402: 3 3 1 3 1 2 -> 0 data_403: 3 3 1 3 2 1 -> 0 data_404: 3 3 1 3 2 2 -> 0 data_405: 3 3 1 3 3 1 -> 0 data_406: 3 3 1 3 3 2 -> 0 data_407: 3 3 1 3 4 1 -> 0 data_408: 3 3 1 3 4 2 -> 0 data_409: 3 3 2 1 1 1 -> 0 data_410: 3 3 2 1 1 2 -> 0 data_411: 3 3 2 1 2 1 -> 0 data_412: 3 3 2 1 2 2 -> 0 data_413: 3 3 2 1 3 1 -> 1 data_414: 3 3 2 1 3 2 -> 1 data_415: 3 3 2 1 4 1 -> 0 data_416: 3 3 2 1 4 2 -> 0 data_417: 3 3 2 2 1 1 -> 0 data_418: 3 3 2 2 1 2 -> 0 data_419: 3 3 2 2 2 1 -> 0 data_420: 3 3 2 2 2 2 -> 0 data_421: 3 3 2 2 3 1 -> 0 data_422: 3 3 2 2 3 2 -> 0 data_423: 3 3 2 2 4 1 -> 0 data_424: 3 3 2 2 4 2 -> 0 data_425: 3 3 2 3 1 1 -> 0 data_426: 3 3 2 3 1 2 -> 0 data_427: 3 3 2 3 2 1 -> 0 data_428: 3 3 2 3 2 2 -> 0 data_429: 3 3 2 3 3 1 -> 0 data_430: 3 3 2 3 3 2 -> 0 data_431: 3 3 2 3 4 1 -> 0 data_432: 3 3 2 3 4 2 -> 0 /*********************************************************************\ *********************************************************************** \*********************************************************************/