TF-IDFのメモ．
TfidfVectorizerを使ったほうがはるかに簡単なのですが，勉強も兼ねて．
おかしなところがあれば，優しく教えてください．

計算の対象となる文書

hoge.txt

white black red
white white black
white black black black
white

#準備
print(word_set)
['black', 'red', 'white']

print(doc_words)
[['white', 'black', 'red'], ['white', 'white', 'black'], ['white', 'black', 'black', 'black'], ['white']]

def tokenizer(word_set, doc_words):
    token_doc = []
    for words in doc_words:
        temp = []
        for w in words:
            temp.append(word_set.index(w))
        token_doc.append(temp)
    return token_doc

token_doc = tokenizer(word_set, doc_words)
#print(token_doc)

doc_num = len(token_doc)
#print(doc_num)
IDF = []
count = 0

import math

for j in range(len(word_set)):
    count = 0
    for d_list in token_doc:
        if j in d_list:
            count += 1
    IDF.append(math.log(doc_num / count) + 1)

TF_set = []
for doc in token_doc:
    TF = [0] * len(word_set)
    for t in doc:
        TF[t] += 1
    TF_set.append(TF)

TF_IDF_set = []
TF_IDF = []
for temp_TF in TF_set:
    for m in range(len(word_set)):
        TF_IDF.append(temp_TF[m] * IDF[m])
    TF_IDF_set.append(TF_IDF)
    TF_IDF = []

#結果
print(token_doc)
[[2, 0, 1], [2, 2, 0], [2, 0, 0, 0], [2]]

print(word_set)
['black', 'red', 'white']

print(TF_IDF_set)
[[1.2876820724517808, 2.386294361119891, 1.0], [1.2876820724517808, 0.0, 2.0], [3.8630462173553424, 0.0, 1.0], [0.0, 0.0, 1.0]]