def createNetFromSentences(self, sentences):
"This function creates the network starting from a set of sentences"
text_filter = TextFilter()
for sentence in sentences:
filtered_sentence = text_filter.filter_all(sentence)
tokens = nltk.word_tokenize(filtered_sentence)
single_tokens = list(set(tokens))
for token in single_tokens:
if not self.gr.has_node(token):
self.gr.add_node(str(token))
for i, token in enumerate(tokens):
if i != 0:
edge = (tokens[i - 1], token)
if not self.gr.has_edge(edge):
self.gr.add_edge(edge,
wt=1.0,
label=START_OCCURRENCES_NUM)
else:
#If the edge exists, the weight of the edge shall divided by the number of occurrences of the couple of terms.
#Therefore, we shall keep memory of the number of occurrences for each couple of terms.
number_of_occurrences = self.gr.edge_label(edge)
new_number_of_occurrences = number_of_occurrences + 1
self.gr.set_edge_label(edge, new_number_of_occurrences)
self.gr.set_edge_weight(edge,
wt=1.0 /
new_number_of_occurrences)
from SentenceNetVisitor import SentenceNetVisitor
from XMLReqManager import XMLReqManager
from SentenceNetCreator import SentenceNetCreator
from irutils.TextFilter import TextFilter
s1 = SentenceNetCreator()
n1 = s1.get_net()
v1 = SentenceNetVisitor(n1, s1.get_edge_start_weight(),
s1.get_start_occurrences_num())
xml_doc_handler = XMLReqManager('req_document.xsd', '2007 - eirene fun 7.xml')
req_document = xml_doc_handler.get_requirements_text()
terms_filter = TextFilter()
for sent in req_document:
filtered_sent = terms_filter.filter_all(sent)
path1, path_weight1 = v1.search_A_star(filtered_sent)
print 'now producing a random sentence according to the document learnt...'
print v1.get_random_sentence('network', 100)
