termFrequency2.js

function termFrequency(document){
    // calculates term frequency of each sentence
    words_without_stopwords = prettify(document);
    
    // gets rid of trailing spaces
    const sentences = document.split(".").map(item => item.trim());
    sentences[0] = sentences[0].substring(146);

    const TFVals = countWords(words_without_stopwords)
    const unique_words = uniqueWords(words_without_stopwords);

    // actually makes it TF values according to formula
    for (const [key, value] of Object.entries(TFVals)){
        TFVals[key] = TFVals[key] / words_without_stopwords.length;
    }

    // splits it up into sentences now
    var TFSentences = {};
    // for every sentence
    for (let i = 0; i <= sentences.length - 1; i ++){
        // for every word in that sentence
        let sentence_split_words = sentences[i].split(" ");
        // get the assiocated TF values of each word
        // temp.add is the "TF" value of a sentence, we need to divide it at the end
        let temp_add = 0.0;
        let words_no_stop_words_length = prettify(sentences[i]).length;
        for (let x = 0; x <= sentence_split_words.length - 1; x++){
            // get the assiocated TF value and add it to temp_add
            if (sentence_split_words[x].toLowerCase() in TFVals){
                // adds all the TF values up
                temp_add = temp_add + TFVals[sentence_split_words[x].toLowerCase()];
            }
            else{
                // nothing, since it's a stop word.
            }
        }
        // TF sentences divide by X number of items on top
        TFSentences[sentences[i]] = temp_add / words_no_stop_words_length;
    }
    
    return TFSentences;
}