From b028ee53d9ee494579bbb1ba9b1cc8e29b911721 Mon Sep 17 00:00:00 2001
From: Connor Olding <cloningdonor@gmail.com>
Date: Wed, 25 May 2016 11:37:34 -0700
Subject: [PATCH] .

---
 atttt.py | 10 +---------
 basic.py | 40 +++++++++++++++-------------------------
 2 files changed, 16 insertions(+), 34 deletions(-)

diff --git a/atttt.py b/atttt.py
index f9f0af7..02f662c 100755
--- a/atttt.py
+++ b/atttt.py
@@ -60,7 +60,7 @@ class ATTTT():
 class PatternBrain(Brain):
 
     def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
+        super().__init__(*args, padding='~', **kwargs)
         self.tokens = []
 
 
@@ -147,11 +147,6 @@ class PatternBrain(Brain):
             token_value = "".join(self.resolve_tokens(most_common[0]))
             new_id = self.new_token(token_value)
 
-            if len("".join(self.tokens.values())) > len(all_items):
-                # this might not ever occur
-                lament('preventing token dictionary from growing larger than source')
-                break
-
             # replace the most common two-token sequence
             # with one token to represent both
             found = np.all(sequences == most_common[0], axis=1)
@@ -175,8 +170,6 @@ class PatternBrain(Brain):
             lament("new token id {:5} occurs {:8} times: \"{}\"".format(
                 new_id, len(here[0]), self.tokens[new_id]))
 
-        # TODO: find unused tokens?
-
         # reconstruct all_items out of the sequences
         all_items = sequences.reshape(-1)[::2][1:].copy()
         return all_items
@@ -198,7 +191,6 @@ class PatternBrain(Brain):
             all_items = self.merge_all(all_items, merges, min_count)
 
         # begin the actual learning
-        self.padding = '~'
         self.reset()
         np_item = []
         for i in all_items:
diff --git a/basic.py b/basic.py
index 417a5ab..0dc1416 100755
--- a/basic.py
+++ b/basic.py
@@ -9,25 +9,24 @@ def normalize(counter):
     s = float(sum(v))
     m = float(max(v))
     del v
-    d = {}
-    for c, cnt in counter.items():
-        d[c] = (cnt/s, cnt/m)
-    return d
-#   return [(c, cnt/s, cnt/m) for c, cnt in counter.items()]
+    return [(c, cnt/s, cnt/m) for c, cnt in counter.items()]
 
 
 def normalize_sorted(counter):
-    # mostly just for debugging i guess?
+    # if the elements were unsorted,
+    # we couldn't use our lazy method (subtraction) of selecting tokens
+    # and temperature would correspond to arbitrary tokens
+    # instead of more/less common tokens.
     return sorted(normalize(counter), key=lambda t: t[1], reverse=True)
 
 
 # http://nbviewer.jupyter.org/gist/yoavg/d76121dfde2618422139
 class Brain:
 
-    def __init__(self, order=1, temperature=0.5):
+    def __init__(self, padding, order=1, temperature=0.5):
         self.order = order
         self.temperature = temperature
-        self.padding = None
+        self.padding = padding
 
         self.reset()
 
@@ -51,21 +50,13 @@ class Brain:
 
     @temperature.setter
     def temperature(self, value):
+        assert(0 < value < 1)
         self._temperature = value
 
-        if value == 1:
-            # TODO: proper distribution stuff
-            self.random = lambda count: np.random.random(count)**2
-        elif value == 0:
-            self.random = np.random.random
-        else:
-            # +0.25 = -0.0
-            # +0.50 = +0.5
-            # +0.75 = +1.0
-            point75 = 1
-            const = (point75 * 2 - 1) / math.atanh(0.75 * 2 - 1)
-            unbound = (math.atanh((1 - value) * 2 - 1) * const + 1) / 2
-            self.random = easytruncnorm(0, 1, unbound, 0.25).rvs
+        a = 1 - value * 2
+        # http://www.mathopenref.com/graphfunctions.html?fx=(a*x-x)/(2*a*x-a-1)&sg=f&sh=f&xh=1&xl=0&yh=1&yl=0&ah=1&al=-1&a=0.5
+        tweak = lambda x: (a * x - x) / (2 * a * x - a - 1)
+        self.random = lambda n: 1 - tweak(np.random.random(n))
 
 
     def learn_all(self, items):
@@ -102,7 +93,7 @@ class Brain:
 
     def update(self):
         if self.dirty and self._machine:
-            self.machine = {hist:normalize(items)
+            self.machine = {hist: normalize_sorted(items)
                             for hist, items in self._machine.items()}
             self.dirty = False
 
@@ -116,9 +107,8 @@ class Brain:
             return None
 
         x = self.random(1)
-        for c, v in dist.items():
-        #   if x <= v: # this is a bad idea
-            x = x - v[0]
+        for c, cs, cm in dist:
+            x = x - cs
             if x <= 0:
                 return c