title: Adding new points to the t-SNE model
body: <p>I try to use t-SNE algorithm in the scikit-learn:</p>
  
  <pre><code>import numpy as np
  from sklearn.manifold import TSNE
  X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
  model = TSNE(n_components=2, random_state=0)
  np.set_printoptions(suppress=True)
  model.fit_transform(X) 
  </code></pre>
  
  <p>Output:</p>
  
  <pre><code>array([[ 0.00017599,  0.00003993], #1
         [ 0.00009891,  0.00021913], 
         [ 0.00018554, -0.00009357],
         [ 0.00009528, -0.00001407]]) #2
  </code></pre>
  
  <p>After that I try to add some points with the coordinates exactly like in the first array <code>X</code> to the existing model:</p>
  
  <pre><code>Y = np.array([[0, 0, 0], [1, 1, 1]])
  model.fit_transform(Y) 
  </code></pre>
  
  <p>Output:</p>
  
  <pre><code>array([[ 0.00017882,  0.00004002], #1
         [ 0.00009546,  0.00022409]]) #2
  </code></pre>
  
  <p>But coords in the second array not equal to the first and last coords from the first array. </p>
  
  <p>I understand that this is the right behaviour, but <strong>how can I add new coords to the <code>model</code> and get the same coords in the output array for the same coords in the input array?</strong></p>
  
  <p>Also I still need to get closest points even after appending new points. </p>