def main(tfidf=True):
bib_data = get_bib_data()
at_mat, authors, term_list1, authors_cnt = get_author_by_term_mat(bib_data, tfreq=5, afreq=10)
yt_mat, years, term_list2, years_cnt = get_year_by_term_mat(bib_data, freq=5)
if tfidf:
at_mat = count_transform(at_mat)
yt_mat = count_transform(yt_mat)
def main(url_file, use_tfidf=True):
word_cnt, sites, site_urls = make_data.get_sites_words(url_file)
sw_mat, word_list = make_data.make_site_by_word_mat(word_cnt, sites, freq=5, percent=0.7)
X = sw_mat
if use_tfidf:
X = count_transform(sw_mat)
labels = ['Normal Deviate', 'MLTheory', 'CNET', 'BBC', 'CNN', 'JP', 'CNN-Tech', 'TechReview', 'NYT-Tech', 'Time-World', 'Mark-Reid']
clustering(X, labels, algo='hcluster', figname='hcluster_site_by_word_tfidf.png')
