def cluster(model):
ranks, website_names, X = data.getBinnedHistograms(amount=amount, cut=config.cut, big=config.big)
N, D = X.shape
print "Each feature vector has dimension %d" % D
print "Training on %d samples" % N
clusters = model.fit_predict(X)
assert(len(clusters) == N)
websites = []
for i in range(len(clusters)):
websites.append((clusters[i], website_names[i]))
websites.sort()
print websites
numClusters = len(set(clusters))
print 'Number of clusters is %d' % numClusters
data.plotClusters(websites, amount, clusters=numClusters, xFactor=75, yFactor=25)
# Writes kmeans object to pickle
to = amount + '-histogram-clusters'
pickle_to = '../persist/%s.pkl' % to
joblib.dump(model, pickle_to)
# Writes clusters to csv
with open(to + '.csv', 'wb') as csvfile:
writer = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
for i in xrange(len(clusters)):
writer.writerow([clusters[i], ranks[i], website_names[i]])
