Merge branch 'feature-adjustment-formula' into develop

copycat/copycat.py

@@ -5,6 +5,9 @@ from .temperature import Temperature
 from .workspace import Workspace
 from .gui import GUI
 
+from pprint import pprint
+
+
 class Reporter(object):
     """Do-nothing base class for defining new reporter types"""
     def report_answer(self, answer):
@@ -88,6 +91,7 @@ class Copycat(object):
             self.gui.update(self)
             self.gui.refresh()
         answers = {}
+        self.temperature.useAdj('pbest')
         while True:
             if self.check_reset():
                 answers = {}
@@ -108,26 +112,34 @@ class Copycat(object):
         for answer, d in answers.items():
             d['avgtemp'] = d.pop('sumtemp') / d['count']
             d['avgtime'] = d.pop('sumtime') / d['count']
+        pprint(answers)
+        return answers
 
     def run(self, initial, modified, target, iterations):
         self.temperature.useAdj('best')
         self.gui.app.reset_with_strings(initial, modified, target)
         self.workspace.resetWithStrings(initial, modified, target)
         answers = {}
-        for i in range(iterations):
+        for formula in ['original', 'best', 'sbest', 'pbest']:
-            answer = self.runTrial()
+            self.temperature.useAdj(formula)
-            d = answers.setdefault(answer['answer'], {
+            answers = {}
-                'count': 0,
+            for i in range(iterations):
-                'sumtemp': 0, # TODO: use entropy
+                answer = self.runTrial()
-                'sumtime': 0
+                d = answers.setdefault(answer['answer'], {
-            })
+                    'count': 0,
-            d['count'] += 1
+                    'sumtemp': 0, # TODO: use entropy
-            d['sumtemp'] += answer['temp'] # TODO: use entropy
+                    'sumtime': 0
-            d['sumtime'] += answer['time']
+                })
+                d['count'] += 1
+                d['sumtemp'] += answer['temp'] # TODO: use entropy
+                d['sumtime'] += answer['time']
+
+            for answer, d in answers.items():
+                d['avgtemp'] = d.pop('sumtemp') / d['count']
+                d['avgtime'] = d.pop('sumtime') / d['count']
+            print('The formula {} provided:'.format(formula))
+            pprint(answers)
 
-        for answer, d in answers.items():
-            d['avgtemp'] = d.pop('sumtemp') / d['count']
-            d['avgtime'] = d.pop('sumtime') / d['count']
         return answers
 
     def run_forever(self, initial, modified, target):

copycat/gui/plot.py

@@ -2,6 +2,8 @@ import matplotlib.pyplot as plt; plt.rcdefaults()
 import numpy as np
 import matplotlib.pyplot as plt
 
+plt.style.use('dark_background')
+
 def plot_imbedded(answers, status):
     answers = sorted(answers.items(), key=lambda kv : kv[1]['count'])
     objects = [t[0] for t in answers]

copycat/temperature.py

@@ -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

gui.py

@@ -4,6 +4,10 @@ import logging
 
 from copycat import Copycat, Reporter
 
+import matplotlib.pyplot as plt
+
+plt.style.use('dark_background')
+
 class SimpleReporter(Reporter):
     def report_answer(self, answer):
         print('Answered %s (time %d, final temperature %.1f)' % (