280 lines
9.1 KiB
Python
Executable file
280 lines
9.1 KiB
Python
Executable file
#!/usr/bin/env python3
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import sys
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import numpy as np
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from misc import *
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from basic import Brain
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def align(x, alignment):
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return (x + alignment // 2) // alignment * alignment
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def uniq_rows(a, return_index=False, return_inverse=False, return_counts=False):
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# via http://stackoverflow.com/a/16973510
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# black magic wrapper around np.unique
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return_any = return_index or return_inverse or return_counts
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if not return_any:
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np.unique(a.view(np.dtype((np.void, a.dtype.itemsize * a.shape[1])))).view(a.dtype).reshape(-1, a.shape[1])
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else:
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void_dtype = np.dtype((np.void, a.dtype.itemsize * a.shape[1]))
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ret = np.unique(a.view(void_dtype), return_index, return_inverse, return_counts)
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return (ret[0].view(a.dtype).reshape(-1, a.shape[1]),) + ret[1:]
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class ATTTT():
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def __init__(self, brain):
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self.brain = brain
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self.score = self._score
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def _score(self, reply, maxn):
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if len(reply) > maxn:
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return -999999999
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#return len(reply)
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return 1
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def reply(self, item=None, maxn=1000, include_scores=False, attempts=None):
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if attempts == None:
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# just guess some value that'll take roughly the same amount of time
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attempts = int(2**12 / self.brain.order)
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lament('attempts:', attempts)
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replies = []
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for i in range(attempts):
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reply = "".join(self.brain.reply(item=item, maxn=maxn+1))
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replies += [(reply, self.score(reply, maxn))]
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result = sorted(replies, key=lambda t: t[1], reverse=True)[0]
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if include_scores:
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return result
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else:
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return result[0]
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class PatternBrain(Brain):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, padding='~', **kwargs)
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self.tokens = []
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def helper(self, v):
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return (v,)
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def resolve_tokens(self, tokens):
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# positive values are just unicode characters
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if isinstance(tokens, int) or isinstance(tokens, np.int32):
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return tokens < 0 and self.tokens[tokens] or chr(tokens)
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else:
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return [o < 0 and self.tokens[o] or chr(o) for o in tokens]
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def new_token(self, value):
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new_id = -1 - len(self.tokens)
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self.tokens[new_id] = value
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return new_id
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@staticmethod
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def prepare_items(items, pad=True):
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new_items = []
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for item in items:
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item = item.strip('\n')
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# assert that the number of sequences is a multiple of 2
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# otherwise we can't .reshape() it to be two-dimensional later on
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next_biggest = align(len(item) + 1, 2)
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# initialize with padding (-1)
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new_item = -np.ones(next_biggest, dtype=np.int32)
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for i, c in enumerate(item):
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new_item[i] = ord(c)
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new_items.append(new_item)
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# add an extra padding item to the head and tail
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# to make it easier to convert from sequences back to items later on
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if pad:
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pad = -np.ones(1, dtype=np.int32)
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new_items.insert(0, pad)
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new_items.append(pad)
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return np.concatenate(new_items)
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def stat_tokens(self, all_items, skip_normal=False):
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unique, counts = np.unique(all_items, return_counts=True)
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count_order = np.argsort(counts)[::-1]
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counts_descending = counts[count_order]
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unique_descending = unique[count_order]
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for i, token_id in enumerate(unique_descending):
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if token_id == -1:
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continue
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if skip_normal and token_id >= 0:
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continue
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token = self.resolve_tokens(token_id)
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lament("token id {:5} occurs {:8} times: \"{}\"".format(
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token_id, counts_descending[i], token))
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lament("total tokens: {:5}".format(i + 1))
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def merge_all(self, all_items, merges, min_count=2):
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# set up a 2d array to step through at half the row length;
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# this means double redundancy; to acquire all the sequences.
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# we could instead .roll it later to get the other half.
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# that would require less memory, but memory isn't really a concern.
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sequences = all_items.repeat(2)[1:-1].reshape(-1, 2).copy()
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for i in range(merges):
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invalid = np.any(sequences == -1, axis=1)
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valid_sequences = np.delete(sequences, np.where(invalid), axis=0)
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unique, counts = uniq_rows(valid_sequences, return_counts=True)
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count = counts.max()
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most_common = (None, 1)
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if count > most_common[1]:
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seq = unique[counts == count][0]
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most_common = (seq, count)
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if most_common[0] is None or most_common[1] <= 1 or most_common[1] < min_count:
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lament('no more valid sequences')
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break
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token_value = "".join(self.resolve_tokens(most_common[0]))
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new_id = self.new_token(token_value)
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# replace the most common two-token sequence
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# with one token to represent both
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found = np.all(sequences == most_common[0], axis=1)
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before = np.roll(found, -1)
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after = np.roll(found, 1)
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# don't wrap around truth values
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before[-1] = False
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after[0] = False
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# remove the "found" sequences
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# and update the previous/next,
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# not unlike a doubly-linked list.
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befores = sequences[before].T.copy()
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befores[1] = new_id
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sequences[before] = befores.T
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afters = sequences[after].T.copy()
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afters[0] = new_id
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sequences[after] = afters.T
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here = np.where(found)
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sequences = np.delete(sequences, here, axis=0)
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lament("new token id {:5} occurs {:8} times: \"{}\"".format(
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new_id, len(here[0]), self.tokens[new_id]))
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# reconstruct all_items out of the sequences
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all_items = sequences.reshape(-1)[::2][1:].copy()
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return all_items
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def learn_all(self, items, merges=0, stat=True):
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min_count = 2 # minimum number of occurences to stop creating tokens at
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if merges < 0:
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min_count = -merges
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merges = 65536 # arbitrary sanity value
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# we'll use numpy matrices so this isn't nearly as disgustingly slow
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self.tokens = {-1: ''} # default with an empty padding token
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all_items = self.prepare_items(items)
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if merges > 0:
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all_items = self.merge_all(all_items, merges, min_count)
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# begin the actual learning
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self.reset()
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np_item = []
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for i in all_items:
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if i == -1:
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if len(np_item) == 0:
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continue
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item = tuple()
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for i in np_item:
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if i < 0:
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assert(i != -1)
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item += self.helper(self.tokens[i])
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else:
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item += self.helper(chr(i))
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#die(np_item, item)
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self.learn(item)
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np_item = []
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else:
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np_item.append(i)
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self.update()
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if merges != 0 and stat:
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self.stat_tokens(all_items)
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def run(pname, args, env):
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if not 1 <= len(args) <= 2:
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lament("usage: {} {{input file}} [savestate file]".format(pname))
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return 1
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args = dict(enumerate(args)) # just for the .get() method
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fn = args[0]
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state_fn = args.get(1, None)
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# the number of lines to output.
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count = int(env.get('COUNT', '8'))
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# learn and sample using this number of sequential tokens.
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order = int(env.get('ORDER', '2'))
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# how experimental to be with sampling.
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# probably doesn't work properly.
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temperature = float(env.get('TEMPERATURE', '0.5'))
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# the max character length of output. (not guaranteed)
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maxn = int(env.get('MAXN', '240'))
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# attempts to maximize scoring
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attempts = int(env.get('ATTEMPTS', '-1'))
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# if positive, maximum number of tokens to merge.
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# if negative, minimum number of occurences to stop at.
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merges = int(env.get('MERGES', '0'))
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if attempts <= 0:
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attempts = None
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brain = PatternBrain(order=order, temperature=temperature)
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tool = ATTTT(brain)
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if state_fn:
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lament('# loading')
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try:
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brain.load(state_fn, raw=False)
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except FileNotFoundError:
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lament('# no file to load. skipping')
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pass
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if brain and brain.new:
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lament('# learning')
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lines = open(fn).readlines()
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brain.learn_all(lines, merges)
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if brain and brain.new and state_fn:
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lament('# saving')
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brain.save(state_fn, raw=False)
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lament('# replying')
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for i in range(count):
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#reply = tool.reply(maxn=maxn, raw=True, attempts=attempts)
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#print('{:6.1f}\t{}'.format(reply[1], reply[0]))
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print(tool.reply(maxn=maxn, attempts=attempts))
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return 0
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if __name__ == '__main__':
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import sys
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import os
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pname = len(sys.argv) > 0 and sys.argv[0] or ''
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args = len(sys.argv) > 1 and sys.argv[1:] or []
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sys.exit(run(pname, args, os.environ))
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