title: k-means with selected initial centers
body: <p>I am trying to k-means clustering with selected initial centroids. 
  It says <a href="http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html" rel="noreferrer">here</a>
   that to specify your initial centers:</p>
  
  <pre><code>init : {‘k-means++’, ‘random’ or an ndarray} 
  </code></pre>
  
  <p>If an <code>ndarray</code> is passed, it should be of shape (<code>n_clusters</code>, <code>n_features</code>) and gives the initial centers.</p>
  
  <p>My code in Python:</p>
  
  <pre><code>X = np.array([[-19.07480000,  -8.536],
                [22.010800000,-10.9737],
                [12.659700000,19.2601]], np.float64)
  km = KMeans(n_clusters=3,init=X).fit(data)
  # print km
  centers = km.cluster_centers_
  print centers
  </code></pre>
  
  <p>Returns an error:</p>
  
  <pre><code>RuntimeWarning: Explicit initial center position passed: performing only one init in k-means instead of n_init=10
    n_jobs=self.n_jobs)
  </code></pre>
  
  <p>and return the same initial centers. Any idea how to form the initial centers so it can be accepted?</p>