Update README.rst

kk7nc · web-flow · commit d8e93b002f36 · 2019-01-22T15:14:52.000-05:00
diff --git a/README.rst b/README.rst
@@ -561,43 +561,42 @@ Non-negative Matrix Factorization (NMF)
 .. code:: python
 
 
-  from sklearn.feature_extraction.text import TfidfVectorizer
-  import numpy as np
-  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
-
+    from sklearn.feature_extraction.text import TfidfVectorizer
+    import numpy as np
+    from sklearn.decomposition import NMF
 
-  def TFIDF(X_train, X_test, MAX_NB_WORDS=75000):
-      vectorizer_x = TfidfVectorizer(max_features=MAX_NB_WORDS)
-      X_train = vectorizer_x.fit_transform(X_train).toarray()
-      X_test = vectorizer_x.transform(X_test).toarray()
-      print("tf-idf with", str(np.array(X_train).shape[1]), "features")
-      return (X_train, X_test)
 
+    def TFIDF(X_train, X_test, MAX_NB_WORDS=75000):
+        vectorizer_x = TfidfVectorizer(max_features=MAX_NB_WORDS)
+        X_train = vectorizer_x.fit_transform(X_train).toarray()
+        X_test = vectorizer_x.transform(X_test).toarray()
+        print("tf-idf with", str(np.array(X_train).shape[1]), "features")
+        return (X_train, X_test)
 
-  from sklearn.datasets import fetch_20newsgroups
 
-  newsgroups_train = fetch_20newsgroups(subset='train')
-  newsgroups_test = fetch_20newsgroups(subset='test')
-  X_train = newsgroups_train.data
-  X_test = newsgroups_test.data
-  y_train = newsgroups_train.target
-  y_test = newsgroups_test.target
+    from sklearn.datasets import fetch_20newsgroups
 
-  X_train,X_test = TFIDF(X_train,X_test)
+    newsgroups_train = fetch_20newsgroups(subset='train')
+    newsgroups_test = fetch_20newsgroups(subset='test')
+    X_train = newsgroups_train.data
+    X_test = newsgroups_test.data
+    y_train = newsgroups_train.target
+    y_test = newsgroups_test.target
 
+    X_train,X_test = TFIDF(X_train,X_test)
 
 
-  LDA = LinearDiscriminantAnalysis(n_components=2000)
-  X_train_new = LDA.fit(X_train,y_train)
-  X_train_new =  LDA.transform(X_train)
-  X_test_new = LDA.transform(X_test)
 
-  print("train with old features: ",np.array(X_train).shape)
-  print("train with new features:" ,np.array(X_train_new).shape)
+    NMF_ = NMF(n_components=2000)
+    X_train_new = NMF_.fit(X_train)
+    X_train_new =  NMF_.transform(X_train)
+    X_test_new = NMF_.transform(X_test)
 
-  print("test with old features: ",np.array(X_test).shape)
-  print("test with new features:" ,np.array(X_test_new).shape)
+    print("train with old features: ",np.array(X_train).shape)
+    print("train with new features:" ,np.array(X_train_new).shape)
 
+    print("test with old features: ",np.array(X_test).shape)
+    print("test with new features:" ,np.array(X_test_new))
 
 output:
 
