title: numpy covariance and covariance matrix by formula is producing different results
body: <p>I generate a matrix that I want to get the covariance of:</p>
  
  <pre><code>test=np.array([4,2,.6,4.2,2.1,.59,3.9,2,.58,4.3,2.1,.62,4.1,2.2,.63]).reshape(5,3)
  test 
  array([[ 4.  ,  2.  ,  0.6 ],
         [ 4.2 ,  2.1 ,  0.59],
         [ 3.9 ,  2.  ,  0.58],
         [ 4.3 ,  2.1 ,  0.62],
         [ 4.1 ,  2.2 ,  0.63]])
  </code></pre>
  
  <p>I calculate the covariance with the numpy function:</p>
  
  <pre><code>np.cov(test)
  array([[ 2.92      ,  3.098     ,  2.846     ,  3.164     ,  2.966     ],
         [ 3.098     ,  3.28703333,  3.0199    ,  3.3566    ,  3.1479    ],
         [ 2.846     ,  3.0199    ,  2.7748    ,  3.0832    ,  2.8933    ],
         [ 3.164     ,  3.3566    ,  3.0832    ,  3.4288    ,  3.2122    ],
         [ 2.966     ,  3.1479    ,  2.8933    ,  3.2122    ,  3.0193    ]])
  </code></pre>
  
  <p>This however is different than following the covariance formula:</p>
  
  <p><a href="https://i.stack.imgur.com/swQOK.gif" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/swQOK.gif" alt="enter image description here"></a></p>
  
  <pre><code>mean=np.mean(test,0)
  np.dot(test-mean,(test-mean).T)/(5-1)
  array([[ 0.004104, -0.002886,  0.006624, -0.005416, -0.002426],
         [-0.002886,  0.002649, -0.005316,  0.005044,  0.000509],
         [ 0.006624, -0.005316,  0.011744, -0.010496, -0.002556],
         [-0.005416,  0.005044, -0.010496,  0.010164,  0.000704],
         [-0.002426,  0.000509, -0.002556,  0.000704,  0.003769]])
  </code></pre>
  
  <p>This does not match the numpy calculations.
  In fact, I take a peek at the <a href="https://github.com/numpy/numpy/blob/v1.7.0/numpy/lib/function_base.py#L1947" rel="nofollow noreferrer">source code</a> and the equation is <code>(x-m) * (x-m).T.conj() / (N - 1)</code> which I believe I am implementing. </p>