title: Canonical Correlation Analysis in Python with sklearn
body: <p>I'm trying to use sklearn to carry out Canonical Correlation Analysis (CCA). I'm starting with the simple example that is included in the <a href="http://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.CCA.html#sklearn.cross_decomposition.CCA.score" rel="noreferrer">manual</a>:</p>
  
  <pre><code>from sklearn.cross_decomposition import CCA
  X = [[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [3.,5.,4.]]
  Y = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]]
  cca = CCA(n_components=1)
  cca.fit(X, Y)
  
  X_c, Y_c = cca.transform(X, Y)
  </code></pre>
  
  <p>I understand that in cca.x_weights_ I get the "canonical coefficents", i.e., the linear combinations of the original X variables (the columns of matrices "A" and "B" returned by <a href="http://www.mathworks.es/es/help/stats/canoncorr.html" rel="noreferrer">MATLAB</a>). However, where are the the "canonical correlations", i.e, the maximum correlation reached when applying the transformation given by the canonical coeficients (i.e., vector "r" returned by MATLAB). Is it possible to also get that in Python?</p>