def get_evaluator(method='knn', k=3, distance_measure='euclidean',
kernel="rbf", nu=0.1, gamma=0.1, **kwargs):
"""
Returns an evaluator object with the given parameters.
See the individual evluators for configuration.
"""
distance = distances.get_distance(distance_measure, **kwargs)
if method == 'knn':
evaluator = knn.KNNEvaluator(distance=distance, k=int(k), **kwargs)
elif method == 'svm':
evaluator = svm.SVMEvaluator(**kwargs)
else:
raise NotImplementedError('Method %s not recognized' % method)
return evaluator
def get_evaluator(method='knn',
k=3,
distance_measure='euclidean',
kernel="rbf",
nu=0.1,
gamma=0.1,
**kwargs):
"""
Returns an evaluator object with the given parameters.
See the individual evluators for configuration.
"""
distance = distances.get_distance(distance_measure, **kwargs)
if method == 'knn':
evaluator = knn.KNNEvaluator(distance=distance, k=int(k), **kwargs)
elif method == 'svm':
evaluator = svm.SVMEvaluator(**kwargs)
else:
raise NotImplementedError('Method %s not recognized' % method)
return evaluator
def main():
#start Stanford NER
p = subprocess.Popen("java -mx1000m -cp stanford-ner/stanford-ner.jar edu.stanford.nlp.ie.NERServer -loadClassifier stanford-ner/classifiers/english.all.3class.distsim.crf.ser.gz -port 1239", shell=True)
#wait ten sec to make sure NER is up and running
time.sleep(10)
datafile = open('data/data.json', 'r')
data = json.load(datafile)
documents = []
for article in data['articles']:
document = Document(article)
documents.append(document)
distances.register_all_distances()
d = distances.get_distance("Cosine")
clus = Clusterer(documents, d, int(sys.argv[1]))
clus.process_documents()
print "Clustering finished ======================================================= \n"
clus.print_all_topics()
#kill NER
os.kill(p.pid+1, 9)