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import itertools | |
import re | |
import sys | |
from collections import Counter, defaultdict | |
from functools import reduce | |
import numpy as np | |
from aoc.helpers import timing, loc, Printer, loc_input | |
# ICE | |
_default_puzzle_input = "year_2021/sink/day_08.txt" | |
_default_test_input = "year_2021/sink/day_08_test.txt" | |
default_puzzle_input = loc_input(__file__, ".txt") | |
default_test_input = loc_input(__file__, "_test.txt") | |
default_test_input_2 = loc_input(__file__, "_test_2.txt") | |
class Translator(object): | |
def __init__(self): | |
self.words = [] | |
self.words_per_count = defaultdict(list) | |
self.seed = {} | |
self.key = defaultdict(set) | |
self.known_vector_indices = [(1, 2), (7, 3), (4, 4), (8, 7)] | |
self.known_vector_indices_map = {v: k for k, v in self.known_vector_indices} | |
def clear_seed(self): | |
self.seed.clear() | |
self.words_per_count.clear() | |
def _vectorise(self, word): | |
# Opted for frequency analysis. Vectorisation of character differences is less palatable. | |
corpus = "".join([word for words in self.words_per_count.values() for word in words]) | |
cumulative_freq = sum(corpus.count(char) for char in list(word)) | |
return cumulative_freq | |
def vectorise_seed(self): | |
for word in self.words_per_count[5]: | |
vector = self._vectorise(word) | |
self.seed[vector] = word | |
for word in self.words_per_count[6]: | |
vector = self._vectorise(word) | |
self.seed[vector] = word | |
def solve_vector(self, key_word, solution): | |
if len(key_word) in {len_ for _, len_ in self.known_vector_indices}: | |
return | |
vector = self._vectorise(key_word) | |
self.key[vector].add(solution) | |
def solve(self, word): | |
word_len = len(word) | |
if known_number := self.known_vector_indices_map.get(word_len): | |
return known_number | |
vector = self._vectorise(word) | |
if number_set := self.key.get(vector): | |
number, = number_set | |
return number | |
return 0 | |
def add_word(self, str_value): | |
self.words.append(str_value) | |
self.words_per_count[len(str_value)].append(str_value) | |
@timing | |
def solve_2(puzzle_input=None): | |
""" | |
Cipher solve of part 2. Works because every answer is listed in test except one (0) in the second part. | |
""" | |
translator = Translator() | |
result_counter = 0 | |
with open(loc(_default_test_input), "r") as fp: | |
key_lines = [i.strip() for i in fp.readlines()] | |
with open(loc(_default_puzzle_input), "r") as fp: | |
lines = [i.strip() for i in fp.readlines()] | |
for key_line in key_lines: | |
translator.clear_seed() | |
input_numbers_text, display_numbers_text = key_line.split(" | ") | |
diplay_numbers_, solutions_ = display_numbers_text.split(": ") | |
input_numbers = input_numbers_text.split() | |
display_numbers = diplay_numbers_.split() | |
solutions = list(solutions_) | |
output_solutions = list(zip(display_numbers, solutions)) | |
for number in input_numbers: | |
translator.add_word(number) | |
translator.vectorise_seed() | |
for word, key in output_solutions: | |
translator.solve_vector(word, int(key)) | |
for line in lines: | |
translator.clear_seed() | |
input_numbers_text, display_numbers_text = line.split(" | ") | |
input_numbers = input_numbers_text.split() | |
display_numbers = display_numbers_text.split() | |
for input_number in input_numbers: | |
translator.add_word(input_number) | |
translator.vectorise_seed() | |
result_counter += int("".join(str(translator.solve(display_number)) for display_number in display_numbers)) | |
return result_counter |
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