Formatting, cluster counting begins with 1.

import sys

print('[A] Loading ' + sys.argv[0] + '.\n')

import os # for reading files

import networkx as nx # for visualisation

from copy import deepcopy

from nltk.corpus import stopwords

import numpy as np # for calculations

import config

import spacy # for nlp

nlp = spacy.load('en') # standard english nlp

nlp = spacy.load('en') # standard english nlp

def frequencies(corpus_path, target):

    for f in files:

        if i % int(len(files)/23) == 0:

            file_ratio = i/len(files[:])

            max_node_ratio = len(node_freq)/max_nodes

            max_edge_ratio = len(edge_freq)/max_edges

            ratios = [file_ratio, max_node_ratio, max_edge_ratio]

            print(' ~{}%\tNodes: {}\tEdges: {}.'.format(int((max(ratios))*100), len(node_freq), len(edge_freq)))

        if len(node_freq) > max_nodes:

        #if no sense is found for a target word, we should assume that there only is one sense

        if len(H) == 0:

            result.append((0, idx))

            result.append((1, idx))

        else:

            else:

                result.append((np.argmax(scores), idx))

                result.append((np.argmax(scores)+1, idx))

    return result