Adds adjustment formulas and embeds them in paper

papers/formulas/best.py

@@ -0,0 +1,21 @@
+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/formulas/entropy.py

@@ -0,0 +1,12 @@
+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/formulas/original.py

@@ -0,0 +1,12 @@
+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/formulas/weighted.py

@@ -0,0 +1,28 @@
+def _weighted(temp, prob, s, u, alpha=1, beta=1):
+    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

papers/paper.tex

@@ -150,7 +150,10 @@ The original formulas being used to do this were overly complicated.
 In summary, many formulas were tested in a spreadsheet, and an optimal one was chosen that replicated the desired behavior.
 []
 
-\lstinputlisting[language=Python]{test.py}
+\lstinputlisting[language=Python]{formulas/original.py}
+\lstinputlisting[language=Python]{formulas/entropy.py}
+\lstinputlisting[language=Python]{formulas/weighted.py}
+\lstinputlisting[language=Python]{formulas/best.py}
 
 \subsection{Steps/plan}