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Merge branch 'feature-adjustment-formula' into develop

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LSaldyt
2017-10-16 13:43:01 -07:00
parent 81f2ef2473 1835367ea9
commit 268e00998a
3 changed files with 65 additions and 24 deletions
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7
copycat/copycat.py
View File

@ -71,6 +71,13 @@ class Copycat(object):
self.temperature.useAdj('original')
#self.temperature.useAdj('entropy')
#self.temperature.useAdj('inverse') # 100 weight
#self.temperature.useAdj('fifty_converge')
#self.temperature.useAdj('soft')
#self.temperature.useAdj('weighted_soft')
#self.temperature.useAdj('alt_fifty')
#self.temperature.useAdj('average_alt')
self.temperature.useAdj('best')
answers = {}
for i in range(iterations):
answer = self.runTrial()

50
copycat/temperature.py
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@ -24,18 +24,58 @@ def _entropy(temp, prob):
f = (c + 1) * prob
return -f * math.log2(f)
def _inverse_prob(temp, prob):
def _weighted(temp, prob, s, u):
weighted = (temp / 100) * s + ((100 - temp) / 100) * u
return weighted
def _weighted_inverse(temp, prob):
iprob = 1 - prob
return (temp / 100) * iprob + ((100 - temp) / 100) * prob
return _weighted(temp, prob, iprob, prob)
def _fifty_converge(temp, prob): # Uses .5 instead of 1-prob
return _weighted(temp, prob, .5, prob)
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))
def _weighted_soft_curve(temp, prob): # Curves to the weighted average of the (1-p) and .5
weight = 100
gamma = .5 # convergance value
alpha = 1 # gamma weight
beta = 3 # iprob weight
curved = min(1, (temp / weight) * ((alpha * gamma + beta * (1 - prob)) / (alpha + beta)) + ((weight - temp) / weight) * prob)
return curved
def _alt_fifty(temp, prob):
s = .5
u = prob ** 2 if prob < .5 else math.sqrt(prob)
return _weighted(temp, prob, s, u)
def _averaged_alt(temp, prob):
s = (1.5 - prob)/2
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 = 1.05 # power
u = prob ** r if prob < .5 else prob ** (1/r)
return _weighted(temp, prob, s, u)
class Temperature(object):
def __init__(self):
self.reset()
self.adjustmentType = 'inverse'
self._adjustmentFormulas = {
'original' : _original,
'entropy' : _entropy,
'inverse' : _inverse_prob}
'original' : _original,
'entropy' : _entropy,
'inverse' : _weighted_inverse,
'fifty_converge' : _fifty_converge,
'soft' : _soft_curve,
'weighted_soft' : _weighted_soft_curve,
'alt_fifty' : _alt_fifty,
'average_alt' : _averaged_alt,
'best' : _working_best}
def reset(self):
self.actual_value = 100.0

32
tests.py
View File

@ -70,16 +70,13 @@ class TestCopycat(unittest.TestCase):
def test_abc_xyz(self):
self.run_testcase('abc', 'abd', 'xyz', 100,
{'dyz': {'avgtemp': 26.143509984937367, 'avgtime': 9866.625, 'count': 8},
'wyz': {'avgtemp': 12.249539212574128,
'avgtime': 9520.666666666666,
'count': 18},
'xyd': {'avgtemp': 38.73402068486291, 'avgtime': 7439.225, 'count': 40},
'xyy': {'avgtemp': 24.614440709519627, 'avgtime': 3522.625, 'count': 8},
'xyz': {'avgtemp': 57.674822842028945, 'avgtime': 8315.2, 'count': 5},
'yyz': {'avgtemp': 26.874886217740315,
'avgtime': 8493.142857142857,
'count': 21}})
{'dyz': {'avgtemp': 16.78130739435325, 'avgtime': 393.0, 'count': 1},
'wyz': {'avgtemp': 26.100450643627426, 'avgtime': 4040.0, 'count': 2},
'xyd': {'avgtemp': 21.310415433987586,
'avgtime': 5592.277777777777,
'count': 90},
'xyz': {'avgtemp': 23.798124933747882, 'avgtime': 3992.0, 'count': 1},
'yyz': {'avgtemp': 27.137975077133788, 'avgtime': 4018.5, 'count': 6}})
def test_ambiguous_case(self):
self.run_testcase('abc', 'abd', 'ijkk', 100,
@ -99,15 +96,12 @@ class TestCopycat(unittest.TestCase):
def test_mrrjjj(self):
self.run_testcase('abc', 'abd', 'mrrjjj', 30,
{'drrjjj': {'avgtemp': 47.3961, 'avgtime': 1538.0, 'count': 1},
'mrrjjd': {'avgtemp': 70.5363, 'avgtime': 681.0, 'count': 1},
'mrrjjjj': {'avgtemp': 19.1294, 'avgtime': 2075.0, 'count': 1},
'mrrjjk': {'avgtemp': 48.0952,
'avgtime': 2203.5714,
'count': 14},
'mrrkkk': {'avgtemp': 43.6931,
'avgtime': 2251.4615,
'count': 13}})
{'mrrjjd': {'avgtemp': 44.46354725386579, 'avgtime': 1262.0, 'count': 1},
'mrrjjjj': {'avgtemp': 17.50702440140412, 'avgtime': 1038.375, 'count': 8},
'mrrjjk': {'avgtemp': 55.189156978290264,
'avgtime': 1170.6363636363637,
'count': 11},
'mrrkkk': {'avgtemp': 43.709349775080746, 'avgtime': 1376.2, 'count': 10}})
'''
Below are examples of improvements that could be made to copycat.