class HuffmanDecoder:
# Assuming content comes as a huge string.
def __init__(self, content):
self.header = content[:4096]
self.content = content[4096:]
self.get_frequencies()
self.tree = HuffmanTree(self.frequencies)
def get_frequencies(self):
gap = int(len(self.header) / 128)
cnts = [
int(self.header[i:i + gap], 2)
for i in range(0, len(self.header), gap)
]
self.frequencies = {chr(x): cnts[x] for x in range(128) if cnts[x] > 0}
def get_decoding(self):
cur = ''
decoded_list = []
for c in self.content:
cur += c
if self.tree.isDecodable(cur):
decoded_list.append(self.tree.getChar(cur))
cur = ''
return ''.join(decoded_list)
def create_huffman_tree(occurrences):
'''
Return a Huffman tree of the symbols given in `occurrences`.
:param occurrences: Number of occurrences of each symbol.
:type occurrences: dict
:return: Return a single Huffman tree (obtained with Huffman algorithm)\
of the symbols in `occurrences`.
:rtype: huffman_tree.HuffmanTre
:Examples:
>>> create_huffman_tree({'a': 4, 'b': 1, 'c': 2})
|bca:7|_<|bc:3|_<|b:1|, |c:2|>, |a:4|>
>>> create_huffman_tree({'a': 1, 'b': 1, 'c': 2})
|cab:4|_<|c:2|, |ab:2|_<|a:1|, |b:1|>>
'''
symbol_list = create_forest(occurrences)
tree_list = []
while len(tree_list) + len(symbol_list) != 1:
(elem1, elem2) = (pop_least_element(symbol_list, tree_list),\
pop_least_element(symbol_list, tree_list))
new_tree = HuffmanTree(left=elem1, right=elem2)
tree_list.append(new_tree)
if len(tree_list) == 1:
return tree_list[0]
return symbol_list[0]
def train_model(self, text_list):
if self.huffman is None:
if self.word_dict is None:
wc = WordCounter(text_list)
self.__generate_word_dict(wc.count_res.larger_than(5))
self.cutted_text_list = wc.text_list
self.huffman = HuffmanTree(self.word_dict, vec_len=self.vec_len)
print('word_dict and huffman tree already generated')
before = (self.win_len - 1) >> 1
after = self.win_len - 1 - before
if self.model == 'cbow':
method = self.__deal_gram_cbow
else:
method = self.__deal_gram_skipgram
if self.cutted_text_list:
total = len(self.cutted_text_list)
count = 0
for line in self.cutted_text_list:
line_len = len(line)
for i in range(line_len):
method(line[i], line[max(0, i-before):i] +
line[i+1: min(line_len, i+after+1)])
else:
for line in text_list:
line = list(jieba.cut(line, cut_all=False))
line_len = len(line_len)
for i in range(line_len):
method(line[i], line[max(0, i-before): i] +
line[i+1, min(line_len, i+after+1)])
print('word vector has been generated')
def __init__(self, input_file_name, min_count):
self.input_file_name = input_file_name
self.input_file = open(self.input_file_name, encoding='utf-8') # 数据文件
self.min_count = min_count # 要淘汰的低频数据的频度
self.wordId_frequency_dict = dict() # 词id-出现次数 dict
self.word_count = 0 # 单词数（重复的词只算1个）
self.word_count_sum = 0 # 单词总数 （重复的词 次数也累加）
self.id2word_dict = dict() # 词id-词 dict
self.word2id_dict = dict() # 词-词id dict
self.word_sequence = list()
self._init_dict() # 初始化字典
self.huffman_tree = HuffmanTree(self.wordId_frequency_dict) # 霍夫曼树
self.huffman_pos_path, self.huffman_neg_path = self.huffman_tree.get_all_pos_and_neg_path(
)
self.word_pairs_queue = deque()
# 结果展示
print('Word Count is:', self.word_count)
print('Word Count Sum is', self.word_count_sum)
def generate_codes(self) -> Dict[int, Deque[bool]]:
"""
Reads the whole input file and generates coding_table by the Huffman tree
:return: coding table where key is byte and value is deque of bits
"""
self.input_file.seek(0)
freq_table = {}
while True:
input_buffer = self.input_file.read(INPUT_BUFFER_SIZE)
if not input_buffer:
break
for byte in input_buffer:
if byte in freq_table:
freq_table[byte] += 1
else:
freq_table[byte] = 1
tree = HuffmanTree(freq_table)
return tree.generate_codes()
class HuffmanEncoder:
def __init__(self, content_source, type='PATH'):
self.get_content(content_source, type)
self.build_frequencies()
self.build_tree()
self.encode()
self.build_header()
def get_content(self, content_source, type):
if type == 'PATH':
f = open(content_source, 'r')
self.content = f.read()
f.close()
else:
self.content = content_source
# TODO: Figure out what to do with characters outside of 128 ASCII
self.content = ''.join(
list(filter(lambda x: ord(x) < 128, self.content)))
def build_frequencies(self):
self.frequencies = {}
for c in self.content:
if c not in self.frequencies:
self.frequencies[c] = 0
self.frequencies[c] += 1
def build_tree(self):
self.tree = HuffmanTree(self.frequencies)
def encode(self):
enc_list = [self.tree.getCode(c) for c in self.content]
self.encoding = ''.join(enc_list)
def build_header(self):
header_list = []
for i in range(128):
v = 0 if chr(i) not in self.frequencies else self.frequencies[chr(
i)]
b = bin(v)[2:]
to_append = '0' * (32 - len(b)) + b
header_list.append(to_append)
if chr(i) in self.frequencies:
assert int(to_append, 2) == self.frequencies[chr(i)]
self.header = ''.join(header_list)
# Prints the encoding as a binary string.
def get_encoding(self, header=True):
if header: return self.header + self.encoding
else: return self.encoding
def generate_codes(self) -> Dict[int, Deque[bool]]:
"""
Reads the whole input file and generates coding_table by the Huffman tree
:return: coding table where key is byte and value is deque of bits
"""
self.input_file.seek(0) # переключаемся на первый байт входного файла
freq_table = {
} # создаём пустую мапу частот, в котоорую в дальнейшем будем её записывать
while True:
input_buffer = self.input_file.read(
INPUT_BUFFER_SIZE
) # считываем и помещаем данные из инпут файла в буффер (в память)
if not input_buffer: # если ничего не считалось, т.е. файл пустой - выходим
break
for byte in input_buffer: # если считались данные, перебираем каждый байт этого файла
if byte in freq_table: # если данный байт уже есть в таблице частот, инкрементируем его значение
freq_table[byte] += 1
else: # если данного байта ещё нет в таблице частот, значит нужно его добавить и присвоить 1, как количество повторений
freq_table[byte] = 1
tree = HuffmanTree(
freq_table
) # строим дерево после формирования таблицы частот (внутри простроится дерево, и вернем коды которые получатся для каждого символа)
return tree.generate_codes() # возвращаем построенные коды
def create_huffman_tree(occurrences):
'''
Return a Huffman tree of the symbols given in `occurrences`.
:param occurrences: Number of occurrences of each symbol.
:type occurrences: dict
:return: Return a single Huffman tree (obtained with Huffman algorithm)\
of the symbols in `occurrences`.
:rtype: huffman_tree.HuffmanTre
:Examples:
>>> create_huffman_tree({'a': 1, 'b': 1, 'c': 2}) # doctest: +NORMALIZE_WHITESPACE
/--▮ 'b':1
/--◯ 'a, b':2
| \--▮ 'a':1
◯ 'c, a, b':4
\--▮ 'c':2
<BLANKLINE>
>>> create_huffman_tree({'a': 4, 'b': 1, 'c': 2}) # doctest: +NORMALIZE_WHITESPACE
/--▮ 'a':4
◯ 'b, c, a':7
| /--▮ 'c':2
\--◯ 'b, c':3
\--▮ 'b':1
<BLANKLINE>
>>> create_huffman_tree({97: 4, 98: 1, 99: 2}) # doctest: +NORMALIZE_WHITESPACE
/--▮ 97:4
◯ '98, 99, 97':7
| /--▮ 99:2
\--◯ '98, 99':3
\--▮ 98:1
<BLANKLINE>
'''
symbol_list = create_forest(occurrences)
tree_list = []
while len(tree_list) + len(symbol_list) != 1:
(elem1, elem2) = (pop_least_element(symbol_list, tree_list),\
pop_least_element(symbol_list, tree_list))
new_tree = HuffmanTree(left=elem1, right=elem2)
tree_list.append(new_tree)
if len(tree_list) == 1:
return tree_list[0]
return symbol_list[0]
def create_forest(occurrences):
'''
Create the initial list of Huffman trees based on the dictionary of
symbols given in parameter.
:param occurrences: Number of occurrences of each symbol.
:type occurrences: dict
:return: A list sorted in ascending order on the number of occurrences\
and on the symbols of Huffman trees of all symbols provided in\
`occurrences`.
:Examples:
>>> create_forest({'a': 4, 'c': 2, 'b': 1})
[|b:1|, |c:2|, |a:4|]
>>> create_forest({'e': 1, 'f': 1, 'g': 1, 'h': 1, 'a':2})
[|e:1|, |f:1|, |g:1|, |h:1|, |a:2|]
'''
sorted_occs = sorted(occurrences.items(), key=lambda item: (item[1], item[0]))
forest = [HuffmanTree(chr(leaf[0][0]),leaf[1]) for leaf in sorted_occs]
return forest
def create_forest(occurrences):
'''
Create the initial list of Huffman trees based on the dictionary of
symbols given in parameter.
:param occurrences: Number of occurrences of each symbol.
:type occurrences: dict
:return: A list sorted in ascending order on the number of occurrences\
and on the symbols of Huffman trees of all symbols provided in\
`occurrences`.
:Examples:
>>> create_forest({'a': 4, 'c': 2, 'b': 1})
[|b:1|, |c:2|, |a:4|]
>>> create_forest({'e': 1, 'f': 1, 'g': 1, 'h': 1, 'a':2})
[|e:1|, |f:1|, |g:1|, |h:1|, |a:2|]
'''
#key=lambda item: (item[1], item[0]) permet de trier par rapport à l'items 1 du dictionnaire
#sinon le trie va se faire par ordre lexicographique (a,b,c,...,z)
sorted_occs = sorted(occurrences.items(),
key=lambda item: (item[1], item[0]))
forest = [HuffmanTree(leaf[0], leaf[1]) for leaf in sorted_occs]
return forest
class InputData:
def __init__(self, input_file_name, min_count):
self.input_file_name = input_file_name
self.input_file = open(self.input_file_name) # 数据文件
self.index = 0
self.min_count = min_count # 要淘汰的低频数据的频度
self.wordId_frequency_dict = dict() # 词id-出现次数 dict
self.word_count = 0 # 单词数（重复的词只算1个）
self.word_count_sum = 0 # 单词总数 （重复的词 次数也累加）
self.sentence_count = 0 # 句子数
self.id2word_dict = dict() # 词id-词 dict
self.word2id_dict = dict() # 词-词id dict
self._init_dict() # 初始化字典
self.get_wordId_list()
self.huffman_tree = HuffmanTree(self.wordId_frequency_dict) # 霍夫曼树
self.huffman_pos_path, self.huffman_neg_path = self.huffman_tree.get_all_pos_and_neg_path()
self.word_pairs_queue = deque()
# 结果展示
print('Word Count is:', self.word_count)
print('Word Count Sum is', self.word_count_sum)
print('Sentence Count is:', self.sentence_count)
print('Tree Node is:', len(self.huffman_tree.huffman))
def _init_dict(self):
word_freq = dict()
# 统计 word_frequency
for line in self.input_file:
line = line.strip().split(' ') # 去首尾空格
self.word_count_sum += len(line)
self.sentence_count += 1
for word in line:
try:
word_freq[word] += 1
except:
word_freq[word] = 1
word_id = 0
# 初始化 word2id_dict,id2word_dict, wordId_frequency_dict字典
for per_word, per_count in word_freq.items():
if per_count < self.min_count: # 去除低频
self.word_count_sum -= per_count
continue
self.id2word_dict[word_id] = per_word
self.word2id_dict[per_word] = word_id
self.wordId_frequency_dict[word_id] = per_count
word_id += 1
self.word_count = len(self.word2id_dict)
# 获取mini-batch大小的 正采样对 (Xw,w) Xw为上下文id数组，w为目标词id。上下文步长为window_size，即2c = 2*window_size
def get_wordId_list(self):
self.input_file = open(self.input_file_name, encoding="utf-8")
sentence = self.input_file.readline()
wordId_list = [] # 一句中的所有word 对应的 id
sentence = sentence.strip().split(' ')
for i, word in enumerate(sentence):
if i % 1000000 == 0:
print(i, len(sentence))
try:
word_id = self.word2id_dict[word]
wordId_list.append(word_id)
except:
continue
self.wordId_list = wordId_list
def get_batch_pairs(self, batch_size, window_size):
while len(self.word_pairs_queue) < batch_size:
for _ in range(1000):
if self.index == len(self.wordId_list):
self.index = 0
wordId_w = self.wordId_list[self.index]
context_ids = []
for i in range(max(self.index - window_size, 0),
min(self.index + window_size + 1, len(self.wordId_list))):
if self.index == i: # 上下文=中心词 跳过
continue
context_ids.append(self.wordId_list[i])
self.word_pairs_queue.append((context_ids, wordId_w))
self.index += 1
result_pairs = [] # 返回mini-batch大小的正采样对
for _ in range(batch_size):
result_pairs.append(self.word_pairs_queue.popleft())
return result_pairs
def get_pairs(self, pos_pairs):
neg_word_pair = []
pos_word_pair = []
for pair in pos_pairs:
pos_word_pair += zip([pair[0]] * len(self.huffman_pos_path[pair[1]]), self.huffman_pos_path[pair[1]])
neg_word_pair += zip([pair[0]] * len(self.huffman_neg_path[pair[1]]), self.huffman_neg_path[pair[1]])
return pos_word_pair, neg_word_pair
# 估计数据中正采样对数，用于设定batch
def evaluate_pairs_count(self, window_size):
return self.word_count_sum * (2 * window_size - 1) - (self.sentence_count - 1) * (1 + window_size) * window_size
def __init__(self, content):
self.header = content[:4096]
self.content = content[4096:]
self.get_frequencies()
self.tree = HuffmanTree(self.frequencies)
freq = {}
for char in input_string:
if char not in freq:
freq[char] = 1
else:
freq[char] += 1
return freq
input_string = open("input.txt", "r").read()
probability = construct_probability(input_string)
encoding_array = {}
if len(probability) == 1:
encoding_array.update({list(probability)[0]: '0'})
else:
tree = HuffmanTree()
tree.get_nodes_heap(probability)
tree.construct_tree()
encoding_array = tree.get_codes()
# Encode
output_string = ""
for char in input_string:
output_string += encoding_array[char]
ouput = open('binary_output.bin', 'w')
ouput.write(output_string)
ouput.close()
# Decode
decoder = {}
for key in encoding_array:
def build_tree(self):
self.tree = HuffmanTree(self.frequencies)
class InputData:
def __init__(self, input_file_name, min_count):
self.input_file_name = input_file_name
self.input_file = open(self.input_file_name, encoding='utf-8') # 数据文件
self.min_count = min_count # 要淘汰的低频数据的频度
self.wordId_frequency_dict = dict() # 词id-出现次数 dict
self.word_count = 0 # 单词数（重复的词只算1个）
self.word_count_sum = 0 # 单词总数 （重复的词 次数也累加）
self.id2word_dict = dict() # 词id-词 dict
self.word2id_dict = dict() # 词-词id dict
self.word_sequence = list()
self._init_dict() # 初始化字典
self.huffman_tree = HuffmanTree(self.wordId_frequency_dict) # 霍夫曼树
self.huffman_pos_path, self.huffman_neg_path = self.huffman_tree.get_all_pos_and_neg_path(
)
self.word_pairs_queue = deque()
# 结果展示
print('Word Count is:', self.word_count)
print('Word Count Sum is', self.word_count_sum)
#print('Tree Node is:', len(self.huffman_tree.huffman))
def _init_dict(self):
word_freq = dict()
# 统计 word_frequency
for line in self.input_file:
line = " ".join(line.split('\n'))
line = re.sub(",", "", line)
line = re.split('\.', line)
line = " ".join(line).split()
self.word_count_sum += len(line)
self.word_sequence += line
for word in line:
try:
word_freq[word] += 1
except:
word_freq[word] = 1
word_id = 0
# 初始化 word2id_dict,id2word_dict, wordId_frequency_dict字典
for per_word, per_count in word_freq.items():
if per_count < self.min_count: # 去除低频
self.word_count_sum -= per_count
continue
self.id2word_dict[word_id] = per_word
self.word2id_dict[per_word] = word_id
self.wordId_frequency_dict[word_id] = per_count
word_id += 1
self.word_count = len(self.word2id_dict)
# 获取mini-batch大小的 正采样对 (Xw,w) Xw为上下文id数组，w为目标词id。上下文步长为window_size，即2c = 2*window_size
def get_batch_pairs(self, batch_size, window_size):
wordId_list = [] # 一句中的所有word 对应的 id
for word in self.word_sequence:
try:
word_id = self.word2id_dict[word]
wordId_list.append(word_id)
except:
continue
# 寻找正采样对 (context(w),w) 加入正采样队列
for i, wordId_w in enumerate(wordId_list):
context_ids = []
for j, wordId_u in enumerate(
wordId_list[max(i - window_size, 0):i + window_size + 1]):
j += max(i - window_size, 0)
assert wordId_w < self.word_count
assert wordId_u < self.word_count
if i == j: # 上下文=中心词 跳过
continue
elif max(0, i - window_size + 1) <= j <= min(
len(wordId_list), i + window_size - 1):
context_ids.append(wordId_u)
if len(context_ids) == 0:
continue
self.word_pairs_queue.append((context_ids, wordId_w))
result_pairs = [] # 返回mini-batch大小的正采样对
for _ in range(batch_size):
a = random.choice(self.word_pairs_queue)
result_pairs.append(a)
return result_pairs
def get_pairs(self, pos_pairs):
neg_word_pair = []
pos_word_pair = []
for pair in pos_pairs:
a = [pair[0]]
b = [pair[1]]
pos_word_pair += zip([pair[0]] *
len(self.huffman_pos_path[pair[1]]),
self.huffman_pos_path[pair[1]])
neg_word_pair += zip([pair[0]] *
len(self.huffman_neg_path[pair[1]]),
self.huffman_neg_path[pair[1]])
return pos_word_pair, neg_word_pair
