alex/copycat
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Adds parameterized and soft "best" formula alts

Browse Source
This commit is contained in:
LSaldyt
2017-11-01 10:58:53 -07:00
parent 176e6cd4e2
commit 9a5aca5b80
2 changed files with 24 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
copycat/copycat.py
View File

@ -71,7 +71,7 @@ class Copycat(object):
self.workspace.resetWithStrings(initial, modified, target)
answers = {}
for formula in ['original', 'best']:
for formula in ['original', 'best', 'sbest', 'pbest']:
self.temperature.useAdj(formula)
answers = {}
for i in range(iterations):

25
copycat/temperature.py
View File

@ -24,7 +24,7 @@ def _entropy(temp, prob):
f = (c + 1) * prob
return -f * math.log2(f)
def _weighted(temp, prob, s, u):
def _weighted(temp, prob, s, u, alpha=1, beta=1):
weighted = (temp / 100) * s + ((100 - temp) / 100) * u
return weighted
@ -56,12 +56,31 @@ def _averaged_alt(temp, prob):
u = prob ** 2 if prob < .5 else math.sqrt(prob)
return _weighted(temp, prob, s, u)
def _working_best(temp, prob):
s = .5 # convergence
r = 2 # power
u = prob ** r if prob < .5 else prob ** (1/r)
return _weighted(temp, prob, s, u)
def _soft_best(temp, prob):
s = .5 # convergence
r = 1.05 # power
u = prob ** r if prob < .5 else prob ** (1/r)
return _weighted(temp, prob, s, u)
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)
# (T/100) * (alpha * p + beta * .5) / (alpha + beta) + ((100 - T)/100) * IF(p > .5, p^(1/2), p^2)
alpha = 5
beta = 1
s = .5
s = (alpha * prob + beta * s) / (alpha + beta)
r = 1.05
u = prob ** r if prob < .5 else prob ** (1/r)
return _weighted(temp, prob, s, u)
class Temperature(object):
def __init__(self):
self.reset()
@ -75,7 +94,9 @@ class Temperature(object):
'weighted_soft' : _weighted_soft_curve,
'alt_fifty' : _alt_fifty,
'average_alt' : _averaged_alt,
'best' : _working_best}
'best' : _working_best,
'sbest' : _soft_best,
'pbest' : _parameterized_best}
def reset(self):
self.actual_value = 100.0