1 | import math
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2 | from math import sqrt
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3 |
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4 | from .NegotiationData import NegotiationData
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5 |
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6 |
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7 | class LearnedData:
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8 | """This class hold the learned data of our agent.
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9 | """
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10 |
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11 | __tSplit: int = 40
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12 | __tPhase: float = 0.2
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13 | __newWeight: float = 0.3
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14 | __newWeightForReject: float = 0.3
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15 | __smoothWidth: int = 3 # from each side of the element
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16 | __smoothWidthForReject: int = 3 # from each side of the element
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17 | __opponentDecrease: float = 0.65
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18 | __defualtAlpha: float = 10.7
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19 |
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20 | def __init__(self):
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21 |
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22 | self.__opponentName: str = None
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23 | # average utility of agreement
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24 | self.__avgUtility: float = 0.0
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25 | # num of negotiations against this opponent
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26 | self.__numEncounters: int = 0
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27 | self.__avgMaxUtilityOpponent: float = 0.0
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28 |
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29 | # our new data structures
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30 | self.__stdUtility: float = 0.0
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31 | self.__negoResults: list = []
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32 | self.__avgOpponentUtility: float = 0.0
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33 | self.__opponentAlpha: float = 0.0
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34 | self.__opponentUtilByTime: list = []
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35 | self.__opponentMaxReject: list = [0.0] * self.__tSplit
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36 |
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37 | def encode(self, paramList: list):
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38 | """ This function get deserialize json
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39 | """
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40 | self.__opponentName = paramList[0]
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41 | self.__avgUtility = paramList[1]
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42 | self.__numEncounters = paramList[2]
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43 | self.__avgMaxUtilityOpponent = paramList[3]
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44 | self.__stdUtility = paramList[4]
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45 | self.__negoResults = paramList[5]
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46 | self.__avgOpponentUtility = paramList[6]
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47 | self.__opponentAlpha = paramList[7]
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48 | self.__opponentUtilByTime = paramList[8]
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49 | self.__opponentMaxReject = paramList[9]
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50 |
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51 | def update(self, negotiationData: NegotiationData):
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52 | """ Update the learned data with a negotiation data of a previous negotiation
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53 | session
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54 | negotiationData NegotiationData class holding the negotiation data
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55 | that is obtain during a negotiation session.
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56 | """
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57 | # Keep track of the average utility that we obtained Double
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58 | newUtil = negotiationData.getAgreementUtil() if (negotiationData.getAgreementUtil() > 0) \
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59 | else self.__avgUtility - 1.1 * pow(self.__stdUtility, 2)
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60 |
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61 | self.__avgUtility = (self.__avgUtility * self.__numEncounters + newUtil) \
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62 | / (self.__numEncounters + 1)
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63 |
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64 | # add utility to UtiList calculate std deviation of results
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65 | self.__negoResults.append(negotiationData.getAgreementUtil())
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66 | self.__stdUtility = 0.0
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67 |
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68 | for util in self.__negoResults:
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69 | self.__stdUtility += pow(util - self.__avgUtility, 2)
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70 | self.__stdUtility = sqrt(self.__stdUtility / (self.__numEncounters + 1))
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71 |
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72 | # Track the average value of the maximum that an opponent has offered us across
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73 | # multiple negotiation sessions Double
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74 | self.__avgMaxUtilityOpponent = (
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75 | self.__avgMaxUtilityOpponent * self.__numEncounters + negotiationData.getMaxReceivedUtil()) \
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76 | / (self.__numEncounters + 1)
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77 |
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78 | self.__avgOpponentUtility = (
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79 | self.__avgOpponentUtility * self.__numEncounters + negotiationData.getOpponentUtil()) \
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80 | / (self.__numEncounters + 1)
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81 |
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82 | # update opponent utility over time
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83 | opponentTimeUtil: list = [0.0] * self.__tSplit if self.__opponentUtilByTime == [] else self.__opponentUtilByTime
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84 | # update opponent reject over time
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85 | opponentMaxReject: list = [0.0] * self.__tSplit if self.__opponentMaxReject == [] else self.__opponentMaxReject
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86 |
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87 | # update values in the array
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88 | newUtilData: list = negotiationData.getOpponentUtilByTime()
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89 | newOpponentMaxReject = negotiationData.getOpponentMaxReject()
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90 |
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91 | if self.__numEncounters == 0:
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92 | self.__opponentUtilByTime = newUtilData
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93 | self.__opponentMaxReject = newOpponentMaxReject
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94 |
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95 | else:
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96 | # find the ratio of decrease in the array, for updating 0 - s in the array
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97 | ratio: float = ((1 - self.__newWeight) * opponentTimeUtil[0] + self.__newWeight * newUtilData[0]) / \
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98 | opponentTimeUtil[0] \
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99 | if opponentTimeUtil[0] > 0.0 else 1
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100 |
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101 | # update the array
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102 | for i in range(self.__tSplit):
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103 | if (newUtilData[i] > 0):
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104 | opponentTimeUtil[i] = (
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105 | (1 - self.__newWeight) * opponentTimeUtil[i] + self.__newWeight * newUtilData[i])
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106 | else:
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107 | opponentTimeUtil[i] *= ratio
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108 |
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109 | self.__opponentUtilByTime = opponentTimeUtil
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110 |
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111 | # find the ratio of decrease in the array, for updating 0 - s in the array
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112 | ratio: float = ((1 - self.__newWeightForReject) * opponentMaxReject[0] + self.__newWeightForReject *
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113 | newOpponentMaxReject[0]) / \
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114 | opponentMaxReject[0] \
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115 | if opponentMaxReject[0] > 0.0 else 1
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116 |
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117 | # update the array
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118 | for i in range(self.__tSplit):
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119 | if (newOpponentMaxReject[i] > 0):
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120 | opponentMaxReject[i] = (
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121 | (1 - self.__newWeightForReject) * opponentMaxReject[i] + self.__newWeightForReject *
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122 | newOpponentMaxReject[i])
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123 | else:
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124 | opponentMaxReject[i] *= ratio
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125 |
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126 | self.__opponentMaxReject = opponentMaxReject
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127 |
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128 | self.__opponentAlpha = self.calcAlpha()
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129 |
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130 | # Keep track of the number of negotiations that we performed
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131 | self.__numEncounters += 1
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132 |
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133 | def calcAlpha(self):
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134 | # smoothing with smooth width of smoothWidth
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135 | alphaArray: list = self.getSmoothThresholdOverTime()
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136 |
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137 | # find the last index with data in alphaArray
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138 |
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139 | maxIndex: int = 0
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140 | while maxIndex < self.__tSplit and alphaArray[maxIndex] > 0.2:
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141 | maxIndex += 1
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142 |
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143 | # find t, time that threshold decrease by 50 %
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144 | maxValue: float = alphaArray[0]
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145 | minValue: float = alphaArray[max(maxIndex - self.__smoothWidth - 1, 0)]
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146 |
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147 | # if there is no clear trend-line, return default value
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148 | if maxValue - minValue < 0.1:
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149 | return self.__defualtAlpha
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150 |
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151 | t: int = 0
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152 | while t < maxIndex and alphaArray[t] > (maxValue - self.__opponentDecrease * (maxValue - minValue)):
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153 | t += 1
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154 |
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155 | calibratedPolynom: list = [572.83, -1186.7, 899.29, -284.68, 32.911]
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156 | alpha: float = calibratedPolynom[0]
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157 |
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158 | tTime: float = self.__tPhase + (1 - self.__tPhase) * (
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159 | maxIndex * (float(t) / self.__tSplit) + (self.__tSplit - maxIndex) * 0.85) / self.__tSplit
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160 | for i in range(1, len(calibratedPolynom)):
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161 | alpha = alpha * tTime + calibratedPolynom[i]
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162 |
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163 | return alpha
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164 |
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165 | def getSmoothThresholdOverTime(self):
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166 | # smoothing with smooth width of smoothWidth
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167 | smoothedTimeUtil: list = [0.0] * self.__tSplit
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168 |
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169 | # ignore zeros in end of the array
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170 | tSplitWithoutZero = self.__tSplit - 1
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171 | while self.__opponentUtilByTime[tSplitWithoutZero] == 0 and tSplitWithoutZero > 0:
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172 | tSplitWithoutZero -= 1
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173 | tSplitWithoutZero += 1
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174 | for i in range(tSplitWithoutZero):
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175 | for j in range(max(i - self.__smoothWidth, 0), min(i + self.__smoothWidth + 1, tSplitWithoutZero)):
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176 | smoothedTimeUtil[i] += self.__opponentUtilByTime[j]
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177 | smoothedTimeUtil[i] /= (min(i + self.__smoothWidth + 1, tSplitWithoutZero) - max(i - self.__smoothWidth, 0))
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178 |
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179 | return smoothedTimeUtil
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180 |
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181 | def getSmoothRejectOverTime(self):
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182 | # smoothing with smooth width of smoothWidth
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183 | smoothedRejectUtil: list = [0.0] * self.__tSplit
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184 |
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185 | # ignore zeros in end of the array
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186 | tSplitWithoutZero = self.__tSplit - 1
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187 | while self.__opponentMaxReject[tSplitWithoutZero] == 0 and tSplitWithoutZero > 0:
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188 | tSplitWithoutZero -= 1
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189 | tSplitWithoutZero += 1
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190 | for i in range(tSplitWithoutZero):
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191 | for j in range(max(i - self.__smoothWidthForReject, 0),
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192 | min(i + self.__smoothWidthForReject + 1, tSplitWithoutZero)):
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193 | smoothedRejectUtil[i] += self.__opponentMaxReject[j]
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194 | smoothedRejectUtil[i] /= (min(i + self.__smoothWidthForReject + 1, tSplitWithoutZero) - max(
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195 | i - self.__smoothWidthForReject, 0))
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196 |
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197 | return smoothedRejectUtil
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198 |
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199 | def getAvgUtility(self):
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200 | return self.__avgUtility
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201 |
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202 | def getStdUtility(self):
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203 | return self.__stdUtility
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204 |
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205 | def getOpponentAlpha(self):
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206 | return self.__opponentAlpha
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207 |
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208 | def getOpUtility(self):
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209 | return self.__avgOpponentUtility
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210 |
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211 | def getAvgMaxUtility(self):
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212 | return self.__avgMaxUtilityOpponent
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213 |
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214 | def getOpponentEncounters(self):
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215 | return self.__numEncounters
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216 |
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217 | def setOpponentName(self, opponentName):
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218 | self.__opponentName = opponentName
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