sklearn kmeans example

#http://blog.csdn.net/github_36326955/article/details/54999627#-*-coding:utf-8-*-ImportNumPy as NPImportMatplotlib.pyplot as Plt fromSklearn.datasets.samples_generatorImportMake_blobsImportSklearnx,y= Make_blobs (n_samples=1000,n_features=2,centers=[[-1,-1],[0,0],[1,1],[2,2]],cluster_std=[0.4,0.2,0.2,0.2], Random_state=9) Plt.scatter (x[:,0],x[:,1],marker='o') plt.show () fromSklearn.clusterImportkmeansy_pred= Kmeans (n_clusters=2,random_state=9). Fit

Sklearn database example-Decision Tree Classification and sklearn database example Decision-Making Introduction of decision tree algorithm on Sklearn: http://scikit-learn.org/stable/modules/tree.html 1. Decision Tree: A non-parametric supervised learning method, mainly used

----tree----tree Score: 0.81tree Cross Avg. Score: 0.75 (+/- 0.09)tree Time: 0.90----forest_10----forest_10 Score: 0.83forest_10 Cross Avg. Score: 0.80 (+/- 0.10)forest_10 Time: 0.56----forest_100----forest_100 Score: 0.84forest_100 Cross Avg. Score: 0.80 (+/- 0.14)forest_100 Time: 5.38----svm_linear----svm_linear Score: 0.74svm_linear Cross Avg. Score: 0.65 (+/- 0.18)svm_linear Time: 0.15----svm_nusvc----svm_nusvc Score: 0.63svm_nusvc Cross Avg. Score: 0.55 (+/- 0.21)svm_nusvc Time: 1.62----bay

('res/'+ File,'W') Fout.write (line_res) fout.close ()defCipin (Self, fil_list): Corpus= [] forFilinchFil_list:ffout= Open ('res/'+fil,'R') Read_r=Ffout.read () ffout.close () corpus.append (read_r) Vectorizer=Countvectorizer () transformer=Tfidftransformer () TFIDF=Transformer.fit_transform (Vectorizer.fit_transform (corpus)) Word= Vectorizer.get_feature_names ()#keywords for all textWeight =Tfidf.toarray () forJinchRange (len (weight)): F= Open ('fes/'+FIL_LIST[J],'W') for

In mahout_in_action, a text clustering instance is provided and raw input data is provided. As the main application scenario of clustering algorithms-text classification, text information modeling is also a common problem. There is already a good modeling method in the field of information retrieval, which is the most common vector space model in the field of information retrieval. Term Frequency-inverse Document Frequency (TF-IDF): It is an enhancement to the TF method, and the importance of a