title: Multidimensional Scaling Fitting in Numpy, Pandas and Sklearn (ValueError)
body: <p>I'm trying out multidimensional scaling with sklearn, pandas and numpy.  The data file Im using has 10 numerical columns and no missing values.  I am trying to take this ten dimensional data and visualize it in 2 dimensions with sklearn.manifold's multidimensional scaling as follows:</p>
  
  <pre><code>import numpy as np
  import pandas as pd
  from sklearn import manifold
  from sklearn.metrics import euclidean_distances
  
  seed = np.random.RandomState(seed=3)
  data = pd.read_csv('data/big-file.csv')
  
  #  start small dont take all the data, 
  #  its about 200k records
  subset = data[:10000]
  similarities = euclidean_distances(subset)
  
  mds = manifold.MDS(n_components=2, max_iter=3000, eps=1e-9, 
        random_state=seed, dissimilarity="precomputed", n_jobs=1)
  
  pos = mds.fit(similarities).embedding_
  </code></pre>
  
  <p>But I get this value error:</p>
  
  <pre><code>Traceback (most recent call last):
    File "demo/mds-demo.py", line 18, in &lt;module&gt;
      pos = mds.fit(similarities).embedding_
    File "/Users/dwilliams/Desktop/Anaconda/lib/python2.7/site-packages/sklearn/manifold/mds.py", line 360, in fit
      self.fit_transform(X, init=init)
    File "/Users/dwilliams/Desktop/Anaconda/lib/python2.7/site-packages/sklearn/manifold/mds.py", line 395, in fit_transform
  eps=self.eps, random_state=self.random_state)
    File "/Users/dwilliams/Desktop/Anaconda/lib/python2.7/site-packages/sklearn/manifold/mds.py", line 242, in smacof
  eps=eps, random_state=random_state)
    File "/Users/dwilliams/Desktop/Anaconda/lib/python2.7/site-packages/sklearn/manifold/mds.py", line 73, in _smacof_single
  raise ValueError("similarities must be symmetric")
  ValueError: similarities must be symmetric
  </code></pre>
  
  <p>I thought euclidean_distances returned a symmetric matrix.  What am I doing wrong and how do I fix it?</p>