addtl testing...
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@ -132,6 +132,40 @@ class Temperature(object):
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exploring a whole range of possible solutions. It even seems, at least
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to me, better than the distribution obtained by the original copycat.
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instead of log2, trying ln --> return (-f * math.log(f)):
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wyz: 78 (avg time 7793.7, avg temp 16.6)
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xyy: 202 (avg time 9168.5, avg temp 27.5)
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wxz: 1 (avg time 3154.0, avg temp 33.4)
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dyz: 63 (avg time 7950.3, avg temp 41.7)
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yyz: 217 (avg time 8147.4, avg temp 41.7)
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xyz: 201 (avg time 7579.7, avg temp 62.5)
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xxy: 1 (avg time 7994.0, avg temp 64.8)
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yzz: 8 (avg time 4672.6, avg temp 65.7)
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xd: 9 (avg time 9215.2, avg temp 68.1)
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xyd: 217 (avg time 7677.9, avg temp 73.8)
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dz: 3 (avg time 20379.0, avg temp 77.3)
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(quickly) trying out (1-this_entropy_function):
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xyd: 100 (avg time 2984.3, avg temp 18.2)
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And that's beautiful! One wants an inverse function that punishes
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exploration and creativity, that takes all the fluidity off
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the system.
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But somehow this completely messes up with abc abd iijjkk:
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jijjkk: 66 (avg time 3200.1, avg temp 61.3)
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iijjkk: 114 (avg time 5017.2, avg temp 63.5)
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dijjkk: 23 (avg time 2209.0, avg temp 67.3)
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iijjkl: 748 (avg time 3262.8, avg temp 70.0)
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iijjkd: 49 (avg time 2315.9, avg temp 76.3)
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Which leads me to suspect that someone may have overfitted the
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model for either xyz or iijjkk or some other problem, and one
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improvement there means disaster here.
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Need to play with this more... and WTF is f anyways?
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"""
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if value == 0 or value == 0.5 or self.value() == 0:
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