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