#Teks harus sudah melalui proses word tokenizing terlebih dahulu.

def tf(sudahDiTokenize): #Term Frequency
    wordlist = sudahDiTokenize
    #flat_list = [item for sublist in wordlist for item in sublist] #bila memakai tf normalized
    #jumkata = len(flat_list) # bila memakai tf normalized
    wordfreq = {}
    for w in wordlist:
	    for o in w:
		    wordfreq[o] = wordfreq.get(o,0) + 1
    #wordfreq.update((x, y/jumkata) for x, y in wordfreq.items()) #Gunakan ini apabila memakai TF Normalized
    print(wordfreq)
    return wordfreq

def idf(sudahDiTokenize): #Inverse Term Frequency
    idf_values = {}
    jumdok = len(sudahDiTokenize)
    all_tokens_set = set([item for sublist in sudahDiTokenize for item in sublist])
    print(all_tokens_set)
    for tkn in all_tokens_set:
        contains_token = map(lambda doc: tkn in doc, sudahDiTokenize)
        idf_values[tkn] = math.log10(jumdok/(sum(contains_token)))
    print(idf_values)
    return idf_values

def tfxidf(tf,idf):
    hasil = {k: tf[k]*idf[k] for k in tf}
    print(hasil)