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https://github.com/peter-tanner/Algorithms-Agents-and-Artificial-Intelligence-project-final.git
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66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
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# > Sometimes people say that we would like to just model
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# > it by 01.
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# > Okay, They're very uncertain.
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# > Would be zero and very certain would be won.
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# > Okay, you can do that.
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# > Okay.
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# > So it depends upon your understanding.
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# > All right?
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# > Yes.
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# Source: CITS3001 - 13 Sep 2022, 11:00 - Lecture
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# Custom float class which clamps at ends
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class Uncertainty(float):
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UNCERTAINTY_MIN = 0.0
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UNCERTAINTY_MAX = 1.0
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UNCERTAINTY_THRESHOLD = 0.2 # Threshold at which a person is "convinced" by their opinion and can be counted as either "voting" or "not voting"
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def __init__(self, value: float):
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float.__init__(value)
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if value.imag != 0.0:
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raise ValueError("Must be real")
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if value > Uncertainty.UNCERTAINTY_MAX or value < Uncertainty.UNCERTAINTY_MIN:
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raise ValueError("Outside of range")
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def clamp(__x):
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return max(min(__x, Uncertainty.UNCERTAINTY_MAX), Uncertainty.UNCERTAINTY_MIN)
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def short_init(__x) -> "Uncertainty":
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return Uncertainty(Uncertainty.clamp(__x))
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def __add__(self, __x) -> "Uncertainty":
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return Uncertainty.short_init(self.real + __x)
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def __sub__(self, __x) -> "Uncertainty":
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return Uncertainty.short_init(self.real - __x)
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def __rsub__(self, __x) -> "Uncertainty":
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return Uncertainty.short_init(__x - self.real)
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def __radd__(self, __x) -> "Uncertainty":
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return self.__add__(__x)
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def __mul__(self, __x) -> "Uncertainty":
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return Uncertainty.short_init(self.real * __x)
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def certainty(self) -> float:
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return Uncertainty.UNCERTAINTY_MAX - self.real + Uncertainty.UNCERTAINTY_MIN
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def clone(self) -> "Uncertainty":
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return Uncertainty(self.real)
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# > All right, So what you need to do is that
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# > after every interaction, if the opinion changes, then you need
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# > to change uncertainty value as well.
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# > All right, So, um, and this is the tricky part
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# Source: CITS3001 - 13 Sep 2022, 11:00 - Lecture
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#
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#
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# > the project we have just
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# > two opinions, and you need to come up with a
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# > way to change the uncertainty.
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# Source: CITS3001 - 13 Sep 2022, 11:00 - Lecture
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