title: 1-D LDA output dimension in Numpy
body: <p>I am trying to reduce dimensionality of a 2000-D data to 2D data. So I used the sample code here: <a href="http://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html" rel="nofollow">http://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html</a>
  But instead of a 2-D output I get a 1-D output from LDA. Does anyone have a suggestion for the cause?
  Here is my code:</p>
  
  <pre><code>lda = LinearDiscriminantAnalysis(n_components=2)
  X_r2 = lda.fit(X, y).transform(X)
  </code></pre>
  
  <p>The shapes of X, y, and X_r2 are as follows, respectively:</p>
  
  <pre><code>(33139, 2000) (33139,) (33139, 1)
  </code></pre>
  
  <p>As you see X_r2 is 1-D, wherase I expect it to be 2-D, because I have set <code>n_components=2</code>.</p>