57 lines
1.3 KiB
Python
57 lines
1.3 KiB
Python
from infBack import get_vect as gv
|
|
import numpy as np
|
|
from sklearn.feature_extraction.text import TfidfTransformer
|
|
from sklearn.feature_extraction.text import CountVectorizer
|
|
from sklearn import cluster
|
|
from matplotlib import pyplot
|
|
import numpy as np
|
|
|
|
def stopWrdList():
|
|
sw = open('stop.words')
|
|
prue = []
|
|
prue.append(sw.readlines())
|
|
return [l.strip('\n\r') for l in prue[0]]
|
|
|
|
|
|
stop_words = stopWrdList()
|
|
|
|
dataVect = gv()
|
|
|
|
dataVect = np.array(dataVect)
|
|
|
|
corpus = dataVect[:, 2]
|
|
|
|
vectorizer = CountVectorizer(stop_words=stop_words)
|
|
transformer = TfidfTransformer(smooth_idf=False)
|
|
|
|
X = vectorizer.fit_transform(corpus)
|
|
|
|
del dataVect, corpus, stop_words
|
|
|
|
J = X.toarray()
|
|
|
|
tf_idf = transformer.fit_transform(J)
|
|
|
|
tf_idf_matrix = tf_idf.toarray()
|
|
|
|
k = 2
|
|
kmeans = cluster.KMeans(n_clusters=k)
|
|
kmeans.fit(J)
|
|
|
|
labels = kmeans.labels_
|
|
centroids = kmeans.cluster_centers_
|
|
|
|
for i in range(k):
|
|
# select only data observations with cluster label == i
|
|
ds = J[np.where(labels == i)]
|
|
# plot the data observations
|
|
pyplot.plot(ds[:,0],ds[:,1],'o')
|
|
# plot the centroids
|
|
lines = pyplot.plot(centroids[i, 0], centroids[i, 1], 'kx')
|
|
# make the centroid x's bigger
|
|
pyplot.setp(lines, ms=15.0)
|
|
pyplot.setp(lines, mew=2.0)
|
|
pyplot.show()
|
|
|
|
print(X.toarray())
|