18. c6: {x,o,b} 19. d1: {x,o,b} 20. d2: {x,o,b} 21. d3: {x,o,b} 22. d4: {x,o,b} 23. d5: {x,o,b} 24. d6: {x,o,b} 25. e1: {x,o,b} 26. e2: {x,o,b} 27. e3: {x,o,b} 28. e4: {x,o,b} 29. e5: {x,o,b} 30. e6: {x,o,b} 31. f1: {x,o,b} 32. f2: {x,o,b} 33. f3: {x,o,b} 34. f4: {x,o,b} 35. f5: {x,o,b} 36. f6: {x,o,b} 37. g1: {x,o,b} 38. g2: {x,o,b} 39. g3: {x,o,b} 40. g4: {x,o,b} 41. g5: {x,o,b} 42. g6: {x,o,b} 43. Class: {win,loss,draw} """ if __name__ == "__main__": util.check_dict(convert())
to 13 contain static features and columns 14 to 51 dynamic features. 7. Description of attributes: The full names for each attribute are provided in the paper, tables 2 and 3. Raw data: all attributes are numeric. Attributes 5, 9, 11, 13 and 35 are integer-valued. All other attributes are continuous. Training, validation and test data: all data are numeric and continuous on account of being normalized. 8. Missing Attribute Values: There are no missing values. 9. Class Distribution: number of positive instances in the sets for each heuristic (H1 to H5) and the "decline" option H0. H1 H2 H3 H4 H5 H0 Training set: 556 229 373 303 312 1286 Validation set: 260 133 187 146 159 644 Test set: 273 124 188 168 153 624 """ if __name__ == "__main__": util.check_dict(convert())
T20: continuous. T21: continuous. T22: continuous. T23: continuous. T_PK: continuous. T_AV: continuous. T85: continuous. RH85: continuous. U85: continuous. V85: continuous. HT85: continuous. T70: continuous. RH70: continuous. U70: continuous. V70: continuous. HT70: continuous. T50: continuous. RH50: continuous. U50: continuous. V50: continuous. HT50: continuous. KI: continuous. TT: continuous. SLP: continuous. SLP_: continuous. Precp: continuous. """ if __name__ == "__main__": check_dict(convert())