def main():
clustered_corpus_path = 'clustered_corpus'
clustered_corpus = read_clustered_corpus(clustered_corpus_path)
corpus = merge_clustered_corpus_into_a_single_corpus(clustered_corpus)
target_file_path = 'target.txt'
text = read_text_file(target_file_path)
document = Document(text)
corpus = Corpus(corpus)
clustered_corpus = ClusteredCorpus(clustered_corpus)
candidate_to_rank_mapping = {}
candidate_to_params_mapping = {}
candidate_to_dfs_in_each_cluster_mapping = {}
for candidate in document.get_candidates():
tf = math.log(1.0 + document.get_tf_for(candidate), 10.0)
# tf = document.get_tf_for(candidate)
idf = math.log(1.0 + 1.0 / corpus.get_df_for(candidate), 2.0)
cu = clustered_corpus.get_cu_for(candidate)
rank = cu
# rank = tf * cu
# rank = tf * idf
dfs_in_each_cluster = clustered_corpus.get_dfs_in_each_cluster_for(candidate)
candidate_representative = corpus.get_representative_for(candidate)
candidate_to_rank_mapping[candidate_representative] = rank
candidate_to_params_mapping[candidate_representative] = (tf, idf, cu)
candidate_to_dfs_in_each_cluster_mapping[candidate_representative] = dfs_in_each_cluster
table = generate_table_based_on(
candidate_to_rank_mapping,
candidate_to_params_mapping,
candidate_to_dfs_in_each_cluster_mapping
)
save_as_file(table)
print('Done.')
