Updates paper draft for final chi2 table

papers/resources/adj.l

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+(defun get-temperature-adjusted-probability (prob &aux low-prob-factor
+						       result)
+; This function is a filter:  it inputs a value (from 0 to 100) and returns
+; a probability (from 0 - 1) based on that value and the temperature.  When
+; the temperature is 0, the result is (/ value 100), but at higher 
+; temperatures, values below 50 get raised and values above 50 get lowered
+; as a function of temperature.
+; I think this whole formula could probably be simplified.
+
+  (setq result
+	(cond ((= prob 0) 0)
+	      ((<= prob .5)
+               (setq low-prob-factor (max 1 (truncate (abs (log prob 10)))))
+               (min (+ prob 
+		       (* (/ (- 10 (sqrt (fake-reciprocal *temperature*))) 
+			     100) 
+			  (- (expt 10 (- (1- low-prob-factor))) prob)))
+		    .5))
+		     
+   	      ((= prob .5) .5)
+	      ((> prob .5)
+               (max (- 1
+		        (+ (- 1 prob) 
+		           (* (/ (- 10 (sqrt (fake-reciprocal *temperature*))) 
+			         100)
+			      (- 1 (- 1 prob)))))
+	            .5))))
+  result)

papers/resources/best.py

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+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)
+
+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):
+    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)
+

papers/resources/entropy.py

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+import math
+
+def _entropy(temp, prob):
+    if prob == 0 or prob == 0.5 or temp == 0:
+        return prob
+    if prob < 0.5:
+        return 1.0 - _original(temp, 1.0 - prob)
+    coldness = 100.0 - temp
+    a = math.sqrt(coldness)
+    c = (10 - a) / 100
+    f = (c + 1) * prob
+    return -f * math.log2(f)

papers/resources/original.py

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+import math
+
+def _original(temp, prob):
+    if prob == 0 or prob == 0.5 or temp == 0:
+        return prob
+    if prob < 0.5:
+        return 1.0 - _original(temp, 1.0 - prob)
+    coldness = 100.0 - temp
+    a = math.sqrt(coldness)
+    c = (10 - a) / 100
+    f = (c + 1) * prob
+    return max(f, 0.5)

papers/resources/weighted.py

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+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 _weighted(temp, prob, iprob, prob)
+
+# Uses .5 instead of 1-prob
+def _fifty_converge(temp, prob):
+    return _weighted(temp, prob, .5, prob)
+
+# Curves to the average of the (1-p) and .5
+def _soft_curve(temp, prob):
+    return min(1, _weighted(temp, prob, (1.5-prob)/2, prob))
+
+# Curves to the weighted average of the (1-p) and .5
+def _weighted_soft_curve(temp, prob):
+    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