.

atttt.py

@@ -7,6 +7,10 @@ from misc import *
 from basic import Brain
 
 
+def align(x, alignment):
+    return (x + alignment // 2) // alignment * alignment
+
+
 def uniq_rows(a, return_index=False, return_inverse=False, return_counts=False):
     # via http://stackoverflow.com/a/16973510
     # black magic wrapper around np.unique
@@ -66,7 +70,10 @@ class PatternBrain(Brain):
 
     def resolve_tokens(self, tokens):
         # positive values are just unicode characters
-        return [o < 0 and self.tokens[o] or chr(o) for o in tokens]
+        if isinstance(tokens, int) or isinstance(tokens, np.int32):
+            return tokens < 0 and self.tokens[tokens] or chr(tokens)
+        else:
+            return [o < 0 and self.tokens[o] or chr(o) for o in tokens]
 
 
     def new_token(self, value):
@@ -75,6 +82,46 @@ class PatternBrain(Brain):
         return new_id
 
 
+    @staticmethod
+    def prepare_items(items, pad=True):
+        new_items = []
+        for item in items:
+            item = item.strip('\n')
+            # assert that the number of sequences is a multiple of 2
+            # otherwise we can't .reshape() it to be two-dimensional later on
+            next_biggest = align(len(item) + 1, 2)
+            # initialize with padding (-1)
+            new_item = -np.ones(next_biggest, dtype=np.int32)
+            for i, c in enumerate(item):
+                new_item[i] = ord(c)
+            new_items.append(new_item)
+
+        # add an extra padding item to the head and tail
+        # to make it easier to convert from sequences back to items later on
+        if pad:
+            pad = -np.ones(1, dtype=np.int32)
+            new_items.insert(0, pad)
+            new_items.append(pad)
+
+        return np.concatenate(new_items)
+
+
+    def stat_tokens(self, all_items, skip_normal=False):
+        unique, counts = np.unique(all_items, return_counts=True)
+        count_order = np.argsort(counts)[::-1]
+        counts_descending = counts[count_order]
+        unique_descending = unique[count_order]
+        for i, token_id in enumerate(unique_descending):
+            if token_id == -1:
+                continue
+            if skip_normal and token_id >= 0:
+                continue
+            token = self.resolve_tokens(token_id)
+            lament("token id {:5} occurs {:8} times: \"{}\"".format(
+                token_id, counts_descending[i], token))
+        lament("total tokens: {:5}".format(i + 1))
+
+
     def merge_all(self, all_items, merges, min_count=2):
         # set up a 2d array to step through at half the row length;
         # this means double redundancy; to acquire all the sequences.
@@ -125,16 +172,17 @@ class PatternBrain(Brain):
             here = np.where(found)
             sequences = np.delete(sequences, here, axis=0)
 
-            print("new token id {:5} occurs {:8} times: \"{}\"".format(new_id, len(here[0]), self.tokens[new_id]))
+            lament("new token id {:5} occurs {:8} times: \"{}\"".format(
+                new_id, len(here[0]), self.tokens[new_id]))
 
-        # TODO: find unused tokens
+        # TODO: find unused tokens?
 
         # reconstruct all_items out of the sequences
         all_items = sequences.reshape(-1)[::2][1:].copy()
         return all_items
 
 
-    def learn_all(self, items, merges=0):
+    def learn_all(self, items, merges=0, stat=True):
         min_count = 2 # minimum number of occurences to stop creating tokens at
         if merges < 0:
             min_count = -merges
@@ -144,29 +192,7 @@ class PatternBrain(Brain):
 
         self.tokens = {-1: ''} # default with an empty padding token
 
-        # we need to assert that the number of sequences is a multiple of this
-        # otherwise we can't .reshape() it to be two-dimensional later on
-        alignment = 2
-        align = lambda x: (x + alignment // 2) // alignment * alignment
-
-        new_items = []
-        for item in items:
-            item = item.strip('\n')
-            # assert at least 1 padding character at the end
-            next_biggest = align(len(item) + 1)
-            # initialize with padding (-1)
-            new_item = -np.ones(next_biggest, dtype=np.int32)
-            for i, c in enumerate(item):
-                new_item[i] = ord(c)
-            new_items.append(new_item)
-
-        # add an extra padding item to the head and tail
-        # to make it easier to convert from sequences back to items later on
-        pad = -np.ones(1, dtype=np.int32)
-        new_items.insert(0, pad)
-        new_items.append(pad)
-
-        all_items = np.concatenate(new_items)
+        all_items = self.prepare_items(items)
 
         if merges > 0:
             all_items = self.merge_all(all_items, merges, min_count)
@@ -193,6 +219,9 @@ class PatternBrain(Brain):
                 np_item.append(i)
         self.update()
 
+        if merges != 0 and stat:
+            self.stat_tokens(all_items)
+
 
 def run(pname, args, env):
     if not 1 <= len(args) <= 2:

misc.py

@@ -8,12 +8,4 @@ def die(*args, **kwargs):
     sys.exit(1)
 
 
-def easytruncnorm(lower=0, upper=1, loc=0.5, scale=0.25):
-    import scipy.stats as stats
-    a = (lower - loc) / scale
-    b = (upper - loc) / scale
-    return stats.truncnorm(a=a, b=b, loc=loc, scale=scale)
-
-
-# only make some things visible to "from misc import *"
 __all__ = [o for o in locals() if type(o) != 'module' and not o.startswith('_')]