diff --git a/atttt.py b/atttt.py
new file mode 100755
index 0000000..02f662c
--- /dev/null
+++ b/atttt.py
@@ -0,0 +1,280 @@
+#!/usr/bin/env python3
+
+import sys
+import numpy as np
+
+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
+    return_any = return_index or return_inverse or return_counts
+    if not return_any:
+        np.unique(a.view(np.dtype((np.void, a.dtype.itemsize * a.shape[1])))).view(a.dtype).reshape(-1, a.shape[1])
+    else:
+        void_dtype = np.dtype((np.void, a.dtype.itemsize * a.shape[1]))
+        ret = np.unique(a.view(void_dtype), return_index, return_inverse, return_counts)
+        return (ret[0].view(a.dtype).reshape(-1, a.shape[1]),) + ret[1:]
+
+
+class ATTTT():
+
+    def __init__(self, brain):
+        self.brain = brain
+        self.score = self._score
+
+
+    def _score(self, reply, maxn):
+        if len(reply) > maxn:
+            return -999999999
+
+        #return len(reply)
+        return 1
+
+
+    def reply(self, item=None, maxn=1000, include_scores=False, attempts=None):
+        if attempts == None:
+            # just guess some value that'll take roughly the same amount of time
+            attempts = int(2**12 / self.brain.order)
+            lament('attempts:', attempts)
+
+        replies = []
+        for i in range(attempts):
+            reply = "".join(self.brain.reply(item=item, maxn=maxn+1))
+            replies += [(reply, self.score(reply, maxn))]
+
+        result = sorted(replies, key=lambda t: t[1], reverse=True)[0]
+
+        if include_scores:
+            return result
+        else:
+            return result[0]
+
+
+class PatternBrain(Brain):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, padding='~', **kwargs)
+        self.tokens = []
+
+
+    def helper(self, v):
+        return (v,)
+
+
+    def resolve_tokens(self, tokens):
+        # positive values are just unicode characters
+        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):
+        new_id = -1 - len(self.tokens)
+        self.tokens[new_id] = value
+        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.
+        # we could instead .roll it later to get the other half.
+        # that would require less memory, but memory isn't really a concern.
+        sequences = all_items.repeat(2)[1:-1].reshape(-1, 2).copy()
+
+        for i in range(merges):
+            invalid = np.any(sequences == -1, axis=1)
+            valid_sequences = np.delete(sequences, np.where(invalid), axis=0)
+            unique, counts = uniq_rows(valid_sequences, return_counts=True)
+            count = counts.max()
+
+            most_common = (None, 1)
+            if count > most_common[1]:
+                seq = unique[counts == count][0]
+                most_common = (seq, count)
+
+            if most_common[0] is None or most_common[1] <= 1 or most_common[1] < min_count:
+                lament('no more valid sequences')
+                break
+
+            token_value = "".join(self.resolve_tokens(most_common[0]))
+            new_id = self.new_token(token_value)
+
+            # replace the most common two-token sequence
+            # with one token to represent both
+            found = np.all(sequences == most_common[0], axis=1)
+            before = np.roll(found, -1)
+            after = np.roll(found, 1)
+            # don't wrap around truth values
+            before[-1] = False
+            after[0] = False
+            # remove the "found" sequences
+            # and update the previous/next,
+            # not unlike a doubly-linked list.
+            befores = sequences[before].T.copy()
+            befores[1] = new_id
+            sequences[before] = befores.T
+            afters = sequences[after].T.copy()
+            afters[0] = new_id
+            sequences[after] = afters.T
+            here = np.where(found)
+            sequences = np.delete(sequences, here, axis=0)
+
+            lament("new token id {:5} occurs {:8} times: \"{}\"".format(
+                new_id, len(here[0]), self.tokens[new_id]))
+
+        # 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, stat=True):
+        min_count = 2 # minimum number of occurences to stop creating tokens at
+        if merges < 0:
+            min_count = -merges
+            merges = 65536 # arbitrary sanity value
+
+        # we'll use numpy matrices so this isn't nearly as disgustingly slow
+
+        self.tokens = {-1: ''} # default with an empty padding token
+
+        all_items = self.prepare_items(items)
+
+        if merges > 0:
+            all_items = self.merge_all(all_items, merges, min_count)
+
+        # begin the actual learning
+        self.reset()
+        np_item = []
+        for i in all_items:
+            if i == -1:
+                if len(np_item) == 0:
+                    continue
+                item = tuple()
+                for i in np_item:
+                    if i < 0:
+                        assert(i != -1)
+                        item += self.helper(self.tokens[i])
+                    else:
+                        item += self.helper(chr(i))
+                #die(np_item, item)
+                self.learn(item)
+                np_item = []
+            else:
+                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:
+        lament("usage: {} {{input file}} [savestate file]".format(pname))
+        return 1
+
+    args = dict(enumerate(args)) # just for the .get() method
+
+    fn = args[0]
+    state_fn = args.get(1, None)
+
+    # the number of lines to output.
+    count = int(env.get('COUNT', '8'))
+    # learn and sample using this number of sequential tokens.
+    order = int(env.get('ORDER', '2'))
+    # how experimental to be with sampling.
+    # probably doesn't work properly.
+    temperature = float(env.get('TEMPERATURE', '0.5'))
+    # the max character length of output. (not guaranteed)
+    maxn = int(env.get('MAXN', '240'))
+    # attempts to maximize scoring
+    attempts = int(env.get('ATTEMPTS', '-1'))
+    # if positive, maximum number of tokens to merge.
+    # if negative, minimum number of occurences to stop at.
+    merges = int(env.get('MERGES', '0'))
+
+    if attempts <= 0:
+        attempts = None
+
+    brain = PatternBrain(order=order, temperature=temperature)
+    tool = ATTTT(brain)
+
+    if state_fn:
+        lament('# loading')
+        try:
+            brain.load(state_fn, raw=False)
+        except FileNotFoundError:
+            lament('# no file to load. skipping')
+            pass
+
+    if brain and brain.new:
+        lament('# learning')
+        lines = open(fn).readlines()
+        brain.learn_all(lines, merges)
+
+    if brain and brain.new and state_fn:
+        lament('# saving')
+        brain.save(state_fn, raw=False)
+
+    lament('# replying')
+    for i in range(count):
+        #reply = tool.reply(maxn=maxn, raw=True, attempts=attempts)
+        #print('{:6.1f}\t{}'.format(reply[1], reply[0]))
+        print(tool.reply(maxn=maxn, attempts=attempts))
+
+    return 0
+
+
+if __name__ == '__main__':
+    import sys
+    import os
+    pname = len(sys.argv) > 0 and sys.argv[0] or ''
+    args = len(sys.argv) > 1 and sys.argv[1:] or []
+    sys.exit(run(pname, args, os.environ))
diff --git a/basic.py b/basic.py
new file mode 100755
index 0000000..0dc1416
--- /dev/null
+++ b/basic.py
@@ -0,0 +1,189 @@
+import math
+import numpy as np
+
+from misc import *
+
+
+def normalize(counter):
+    v = counter.values()
+    s = float(sum(v))
+    m = float(max(v))
+    del v
+    return [(c, cnt/s, cnt/m) for c, cnt in counter.items()]
+
+
+def normalize_sorted(counter):
+    # 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, padding, order=1, temperature=0.5):
+        self.order = order
+        self.temperature = temperature
+        self.padding = padding
+
+        self.reset()
+
+
+    def reset(self):
+        import collections as cool
+        # unnormalized
+        self._machine = cool.defaultdict(cool.Counter)
+        # normalized
+        self.machine = None
+
+        self.type = None
+        self.dirty = False
+        self.new = True
+
+
+    @property
+    def temperature(self):
+        return self._temperature
+
+
+    @temperature.setter
+    def temperature(self, value):
+        assert(0 < value < 1)
+        self._temperature = value
+
+        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):
+        for item in items:
+            self.learn(item)
+        self.update()
+
+
+    def learn(self, item):
+        assert(self.padding)
+
+        if self.type is None and item is not None:
+            self.type = type(item)
+        if type(item) is not self.type:
+            raise Exception("that's no good")
+
+        if self.type == type("string"):
+            item = item.strip()
+
+        if len(item) == 0:
+            return
+
+        pad = self.helper(self.padding) * self.order
+        item = pad + item + pad
+
+        stop = len(item) - self.order
+        if stop > 0:
+            for i in range(stop):
+                history, newitem = item[i:i+self.order], item[i+self.order]
+                self._machine[history][newitem] += 1
+
+        self.dirty = True
+
+
+    def update(self):
+        if self.dirty and self._machine:
+            self.machine = {hist: normalize_sorted(items)
+                            for hist, items in self._machine.items()}
+            self.dirty = False
+
+
+    def next(self, history):
+        history = history[-self.order:]
+
+        dist = self.machine.get(history, None)
+        if dist == None:
+            lament('warning: no value: {}'.format(history))
+            return None
+
+        x = self.random(1)
+        for c, cs, cm in dist:
+            x = x - cs
+            if x <= 0:
+                return c
+
+
+    # for overriding in subclasses
+    # in case the input tokens aren't strings (e.g. tuples)
+    def helper(self, v):
+        return v
+
+
+    def reply(self, item=None, maxn=1000):
+        assert(self.padding)
+        self.update()
+
+        history = self.helper(self.padding) * self.order
+
+        out = []
+        for i in range(maxn):
+            c = self.next(history)
+            if c.find(self.padding) != -1:
+                out.append(c.replace(self.padding, ''))
+                break
+            history = history[-self.order:] + self.helper(c)
+            out.append(c)
+
+        return out
+
+
+    def load(self, fn, raw=True):
+        import pickle
+        if type(fn) == type(''):
+            f = open(fn, 'rb')
+        else:
+            f = fn
+
+        d = pickle.load(f)
+
+        if d['order'] != self.order:
+            lament('warning: order mismatch. cancelling load.')
+            return
+        self.order = d['order']
+
+        if raw:
+            if not d.get('_machine'):
+                lament('warning: no _machine. cancelling load.')
+                return
+            self._machine = d['_machine']
+
+            self.dirty = True
+            self.update()
+        else:
+            if not d.get('machine'):
+                lament('warning: no machine. cancelling load.')
+                return
+            self.machine = d['machine']
+
+        self.new = False
+        if f != fn:
+            f.close()
+
+
+    def save(self, fn, raw=True):
+        import pickle
+        if type(fn) == type(''):
+            f = open(fn, 'wb')
+        else:
+            f = fn
+
+        d = {}
+        d['order'] = self.order
+        if raw:
+            d['_machine'] = self._machine
+        else:
+            d['machine'] = self.machine
+        pickle.dump(d, f)
+
+        if f != fn:
+            f.close()
diff --git a/misc.py b/misc.py
new file mode 100755
index 0000000..36da0d4
--- /dev/null
+++ b/misc.py
@@ -0,0 +1,11 @@
+import sys
+lament = lambda *args, **kwargs: print(*args, file=sys.stderr, **kwargs)
+
+
+def die(*args, **kwargs):
+    # just for ad-hoc debugging really
+    lament(*args, **kwargs)
+    sys.exit(1)
+
+
+__all__ = [o for o in locals() if type(o) != 'module' and not o.startswith('_')]