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Adds meta and parameterized meta formulas, for fun

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This commit is contained in:
LSaldyt
2017-11-14 17:16:02 -07:00
parent 97c9b2eb57
commit 6951fd5de7
2 changed files with 29 additions and 10 deletions
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2
copycat/copycat.py
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@ -71,7 +71,7 @@ class Copycat(object):
self.workspace.resetWithStrings(initial, modified, target) self.workspace.resetWithStrings(initial, modified, target)
answers = {} answers = {}
for formula in ['original', 'best', 'sbest', 'pbest', 'none']: for formula in ['original', 'best', 'sbest', 'pbest', 'meta', 'pmeta', 'none']:
self.temperature.useAdj(formula) self.temperature.useAdj(formula)
answers = {} answers = {}
for i in range(iterations): for i in range(iterations):

37
copycat/temperature.py
View File

@ -24,19 +24,19 @@ def _entropy(temp, prob):
f = (c + 1) * prob f = (c + 1) * prob
return -f * math.log2(f) return -f * math.log2(f)
def _weighted(temp, prob, s, u, alpha=1, beta=1): def _weighted(temp, s, u):
weighted = (temp / 100) * s + ((100 - temp) / 100) * u weighted = (temp / 100) * s + ((100 - temp) / 100) * u
return weighted return weighted
def _weighted_inverse(temp, prob): def _weighted_inverse(temp, prob):
iprob = 1 - prob iprob = 1 - prob
return _weighted(temp, prob, iprob, prob) return _weighted(temp, iprob, prob)
def _fifty_converge(temp, prob): # Uses .5 instead of 1-prob def _fifty_converge(temp, prob): # Uses .5 instead of 1-prob
return _weighted(temp, prob, .5, prob) return _weighted(temp, .5, prob)
def _soft_curve(temp, prob): # Curves to the average of the (1-p) and .5 def _soft_curve(temp, prob): # Curves to the average of the (1-p) and .5
return min(1, _weighted(temp, prob, (1.5-prob)/2, prob)) return min(1, _weighted(temp, (1.5-prob)/2, prob))
def _weighted_soft_curve(temp, prob): # Curves to the weighted average of the (1-p) and .5 def _weighted_soft_curve(temp, prob): # Curves to the weighted average of the (1-p) and .5
weight = 100 weight = 100
@ -49,25 +49,25 @@ def _weighted_soft_curve(temp, prob): # Curves to the weighted average of the (1
def _alt_fifty(temp, prob): def _alt_fifty(temp, prob):
s = .5 s = .5
u = prob ** 2 if prob < .5 else math.sqrt(prob) u = prob ** 2 if prob < .5 else math.sqrt(prob)
return _weighted(temp, prob, s, u) return _weighted(temp, s, u)
def _averaged_alt(temp, prob): def _averaged_alt(temp, prob):
s = (1.5 - prob)/2 s = (1.5 - prob)/2
u = prob ** 2 if prob < .5 else math.sqrt(prob) u = prob ** 2 if prob < .5 else math.sqrt(prob)
return _weighted(temp, prob, s, u) return _weighted(temp, s, u)
def _working_best(temp, prob): def _working_best(temp, prob):
s = .5 # convergence s = .5 # convergence
r = 1.05 # power r = 1.05 # power
u = prob ** r if prob < .5 else prob ** (1/r) u = prob ** r if prob < .5 else prob ** (1/r)
return _weighted(temp, prob, s, u) return _weighted(temp, s, u)
def _soft_best(temp, prob): def _soft_best(temp, prob):
s = .5 # convergence s = .5 # convergence
r = 1.05 # power r = 1.05 # power
u = prob ** r if prob < .5 else prob ** (1/r) u = prob ** r if prob < .5 else prob ** (1/r)
return _weighted(temp, prob, s, u) return _weighted(temp, s, u)
def _parameterized_best(temp, prob): def _parameterized_best(temp, prob):
# (D$66/100)*($E$64*$B68 + $G$64*$F$64)/($E$64 + $G$64)+((100-D$66)/100)*IF($B68 > 0.5, $B68^(1/$H$64), $B68^$H$64) # (D$66/100)*($E$64*$B68 + $G$64*$F$64)/($E$64 + $G$64)+((100-D$66)/100)*IF($B68 > 0.5, $B68^(1/$H$64), $B68^$H$64)
@ -78,7 +78,24 @@ def _parameterized_best(temp, prob):
s = (alpha * prob + beta * s) / (alpha + beta) s = (alpha * prob + beta * s) / (alpha + beta)
r = 1.05 r = 1.05
u = prob ** r if prob < .5 else prob ** (1/r) u = prob ** r if prob < .5 else prob ** (1/r)
return _weighted(temp, prob, s, u) return _weighted(temp, s, u)
def _meta(temp, prob):
r = _weighted(temp, 1, 2) # Make r a function of temperature
s = .5
u = prob ** r if prob < .5 else prob ** (1/r)
return _weighted(temp, s, u)
def _meta_parameterized(temp, prob):
r = _weighted(temp, 1, 2) # Make r a function of temperature
alpha = 5
beta = 1
s = .5
s = (alpha * prob + beta * s) / (alpha + beta)
u = prob ** r if prob < .5 else prob ** (1/r)
return _weighted(temp, s, u)
def _none(temp, prob): def _none(temp, prob):
return prob return prob
@ -99,6 +116,8 @@ class Temperature(object):
'best' : _working_best, 'best' : _working_best,
'sbest' : _soft_best, 'sbest' : _soft_best,
'pbest' : _parameterized_best, 'pbest' : _parameterized_best,
'meta' : _meta,
'pmeta' : _meta_parameterized,
'none' : _none} 'none' : _none}
self.diffs = 0 self.diffs = 0
self.ndiffs = 0 self.ndiffs = 0