.

.dummy

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atttt.py

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+#!/usr/bin/env python3
+
+import sys
+import numpy as np
+
+from misc import *
+from basic import Brain
+
+
+def uniq_rows(a, return_index=False, return_inverse=False, return_counts=False):
+    # black magic wrapper around np.unique
+    # via np.dtype((np.void, a.dtype.itemsize * a.shape[1]))
+    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, raw=False, attempts=None):
+        if attempts == None:
+            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 raw:
+            return result
+        else:
+            return result[0]
+
+
+class PatternBrain(Brain):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.tokens = []
+
+
+    def helper(self, v):
+        return (v,)
+
+
+    def learn_all(self, items, merges=1):
+        min_count = 2
+        if merges < 0:
+            min_count = -merges
+            merges = 65536
+
+        # use numpy so this isn't nearly as disgustingly slow
+
+        int32_min = -2**(np.dtype(np.int32).itemsize * 8 - 1)
+        empty = int32_min
+        neg_lookup = {-1: ''} # default with padding
+
+        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)
+            # fill 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
+        # for easier conversion from sequence back to all_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)
+
+        if merges > 0:
+            # set up a 2d array to step through at half the row length,
+            # this means double redundancy, to acquire all the sequences.
+            # we don't have to .roll it later to get the other half,
+            # though that would require less memory.
+            sequences = all_items.repeat(2)[1:-1].reshape(-1, 2).copy()
+
+        for i in range(merges):
+            # learn
+            most_common = (None, 1)
+            # TODO: eventually check for empty here too
+            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()
+
+            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
+
+            new_id = -1 - len(neg_lookup)
+            neg_lookup[new_id] = "".join([o < 0 and neg_lookup[o] or chr(o) for o in most_common[0]])
+
+            if len("".join(neg_lookup.values())) > len(all_items):
+                lament('preventing dict from growing larger than source')
+                break
+
+            # replace our most common sequence in the sequences
+            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
+            # or remove padding
+            #before[0] = False
+            #after[-1] = False
+            # remove the "found" sequences
+            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
+            #sequences[found] = [empty, empty]
+            here = np.where(found)
+            sequences = np.delete(sequences, here, axis=0)
+
+            print("({:8}) new token: {:5} \"{}\"".format(len(here[0]), new_id, neg_lookup[new_id]))
+
+        if merges > 0:
+            # reconstruct all_items out of the sequences
+            all_items = sequences.reshape(-1)[::2][1:].copy()
+
+        self.padding = '~'
+        self.reset()
+        np_item = []
+        for i in all_items:
+        #for np_item in np.split(all_items, np.where(all_items == -1)):
+            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(neg_lookup[i])
+                    else:
+                        item += self.helper(chr(i))
+                #die(np_item, item)
+                self.learn(item)
+                np_item = []
+            elif i != empty:
+                np_item.append(i)
+        self.update()
+
+
+def run(pname, args, env):
+    if not 1 <= len(args) <= 2:
+        lament("usage: {} {{input file}} [state_fn file]".format(sys.argv[0]))
+        sys.exit(1)
+
+    args = dict(enumerate(args)) # for .get()
+
+    fn = args[0]
+    state_fn = args.get(1, None)
+
+    count = int(env.get('COUNT', '8'))
+    order = int(env.get('ORDER', '3'))
+    temperature = float(env.get('TEMPERATURE', '0'))
+    maxn = int(env.get('MAXN', '1000'))
+    attempts = int(env.get('ATTEMPTS', '-1'))
+    merges = int(env.get('MERGES', '0'))
+
+    if attempts <= 0:
+        attempts = None
+
+    brain = PatternBrain(order=order, temperature=temperature)
+    tool = ATTTT(brain)
+
+    lament('# loading')
+    if state_fn:
+        try:
+            brain.load(state_fn, raw=False)
+        except FileNotFoundError:
+            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:
+        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))
+
+
+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))

basic.py

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+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
+    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()]
+
+
+def normalize_sorted(counter):
+    # mostly just for debugging i guess?
+    return sorted(normalize(counter), key=lambda t: t[1], reverse=True)
+
+
+# http://nbviewer.jupyter.org/gist/yoavg/d76121dfde2618422139
+class Brain:
+
+    # TODO: don't default padding here, but make sure it's set before running
+    # the reason is it's the only place that's specific to a string anymore
+    def __init__(self, order=1, temperature=0.5, padding="~"):
+        self.order = order
+        self.padding = padding
+        self.temperature = temperature
+
+        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):
+        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
+
+
+    def learn_all(self, items):
+        for item in items:
+            self.learn(item)
+        self.update()
+
+
+    def learn(self, item):
+        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(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, v in dist.items():
+        #   if x <= v: # this is a bad idea
+            x = x - v[0]
+            if x <= 0:
+                return c
+
+
+    def helper(self, v):
+        return v
+
+
+    def reply(self, item=None, maxn=1000):
+        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()

misc.py

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+import sys
+lament = lambda *args, **kwargs: print(*args, file=sys.stderr, **kwargs)
+
+
+def die(*args, **kwargs):
+    lament(*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('_')]