title: Numpy covariance command returning matrix with more dimensions than input
body: <p>I have an arbitrary row vector "u" and an arbitrary matrix "e" as follows:</p>
  
  <pre><code>u = np.resize(np.array([8,3]),[1,2])
  e = np.resize(np.array([[2,2,5,5],[1, 6, 7, 4]]),[4,2])
  np.cov(u,e)
  
  array([[ 12.5,   0. ,   0. , -12.5,   7.5],
     [  0. ,   0. ,   0. ,   0. ,   0. ],
     [  0. ,   0. ,   0. ,   0. ,   0. ],
     [-12.5,   0. ,   0. ,  12.5,  -7.5],
     [  7.5,   0. ,   0. ,  -7.5,   4.5]])
  </code></pre>
  
  <p>The matrix that this returns is 5x5. This is confusing to me because the largest dimension of the inputs is only 4. </p>
  
  <p>Thus, this may be less of a numpy question and more of a math question...not sure... </p>