29 lines
917 B
Python
29 lines
917 B
Python
def _weighted(temp, prob, s, u, alpha=1, beta=1):
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weighted = (temp / 100) * s + ((100 - temp) / 100) * u
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return weighted
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def _weighted_inverse(temp, prob):
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iprob = 1 - prob
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return _weighted(temp, prob, iprob, prob)
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# Uses .5 instead of 1-prob
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def _fifty_converge(temp, prob):
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return _weighted(temp, prob, .5, prob)
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# Curves to the average of the (1-p) and .5
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def _soft_curve(temp, prob):
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return min(1, _weighted(temp, prob, (1.5-prob)/2, prob))
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# Curves to the weighted average of the (1-p) and .5
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def _weighted_soft_curve(temp, prob):
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weight = 100
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gamma = .5 # convergance value
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alpha = 1 # gamma weight
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beta = 3 # iprob weight
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curved = min(1,
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(temp / weight) *
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((alpha * gamma + beta * (1 - prob)) /
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(alpha + beta)) +
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((weight - temp) / weight) * prob)
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return curved
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