notwa/gists
1
0
Fork 0
Code Issues Pull requests Releases Wiki Activity

.

Browse source
This commit is contained in:
Connor Olding 2016-05-25 07:31:48 -07:00
parent 28edd29072
commit eeb5d2941e
3 changed files with 100 additions and 68 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

156
atttt.py
View file

@ -8,8 +8,8 @@ from basic import Brain
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
# 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])
@ -34,8 +34,9 @@ class ATTTT():
return 1
def reply(self, item=None, maxn=1000, raw=False, attempts=None):
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)
@ -46,7 +47,7 @@ class ATTTT():
result = sorted(replies, key=lambda t: t[1], reverse=True)[0]
if raw:
if include_scores:
return result
else:
return result[0]
@ -63,56 +64,31 @@ class PatternBrain(Brain):
return (v,)
def learn_all(self, items, merges=1):
min_count = 2
if merges < 0:
min_count = -merges
merges = 65536
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]
# 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
def new_token(self, value):
new_id = -1 - len(self.tokens)
self.tokens[new_id] = value
return new_id
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()
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):
# 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()
most_common = (None, 1)
if count > most_common[1]:
seq = unique[counts == count][0]
most_common = (seq, count)
@ -121,45 +97,85 @@ class PatternBrain(Brain):
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]])
token_value = "".join(self.resolve_tokens(most_common[0]))
new_id = self.new_token(token_value)
if len("".join(neg_lookup.values())) > len(all_items):
lament('preventing dict from growing larger than source')
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 our most common sequence in the sequences
# 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
# or remove padding
#before[0] = False
#after[-1] = 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
#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]))
print("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
def learn_all(self, items, merges=0):
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
# 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)
if merges > 0:
# reconstruct all_items out of the sequences
all_items = sequences.reshape(-1)[::2][1:].copy()
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:
#for np_item in np.split(all_items, np.where(all_items == -1)):
if i == -1:
if len(np_item) == 0:
continue
@ -167,32 +183,40 @@ class PatternBrain(Brain):
for i in np_item:
if i < 0:
assert(i != -1)
item += self.helper(neg_lookup[i])
item += self.helper(self.tokens[i])
else:
item += self.helper(chr(i))
#die(np_item, item)
self.learn(item)
np_item = []
elif i != empty:
else:
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)
lament("usage: {} {{input file}} [savestate file]".format(pname))
return 1
args = dict(enumerate(args)) # for .get()
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'))
order = int(env.get('ORDER', '3'))
temperature = float(env.get('TEMPERATURE', '0'))
maxn = int(env.get('MAXN', '1000'))
# 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:
@ -201,11 +225,12 @@ def run(pname, args, env):
brain = PatternBrain(order=order, temperature=temperature)
tool = ATTTT(brain)
lament('# loading')
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:
@ -214,6 +239,7 @@ def run(pname, args, env):
brain.learn_all(lines, merges)
if brain and brain.new and state_fn:
lament('# saving')
brain.save(state_fn, raw=False)
lament('# replying')
@ -222,6 +248,8 @@ def run(pname, args, env):
#print('{:6.1f}\t{}'.format(reply[1], reply[0]))
print(tool.reply(maxn=maxn, attempts=attempts))
return 0
if __name__ == '__main__':
import sys

11
basic.py
View file

@ -24,12 +24,10 @@ def normalize_sorted(counter):
# 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="~"):
def __init__(self, order=1, temperature=0.5):
self.order = order
self.padding = padding
self.temperature = temperature
self.padding = None
self.reset()
@ -77,6 +75,8 @@ class Brain:
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:
@ -123,11 +123,14 @@ class Brain:
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

1
misc.py
View file

@ -3,6 +3,7 @@ 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)