title: in self._compute_kernel(X) raise ValueError("X.shape[0] should be equal to X.shape[1]")
body: <p>In my code in which X and y are the training data:</p>
  
  <pre><code>from sklearn.svm import SVC
  clf = SVC(kernel=lambda x,y:gauss_kernel(x, y, 100) )
  print(X.shape[0])
  print(X.shape[1])
  print(X.shape)
  
  clf.fit(X, y)
  </code></pre>
  
  <p>I get the following error:</p>
  
  <pre><code>211
  2
  (211, 2)
  /Users/mona/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py:547: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
    y = column_or_1d(y, warn=True)
  ---------------------------------------------------------------------------
  IndexError                                Traceback (most recent call last)
  &lt;ipython-input-23-1f163ab380a5&gt; in &lt;module&gt;()
        8 print(X.shape)
        9 
  ---&gt; 10 clf.fit(X, y)
       11 plot_data()
       12 plot_boundary(svm,-.5,.3,-.8,.6)
  
  ~/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py in fit(self, X, y, sample_weight)
      185 
      186         seed = rnd.randint(np.iinfo('i').max)
  --&gt; 187         fit(X, y, sample_weight, solver_type, kernel, random_seed=seed)
      188         # see comment on the other call to np.iinfo in this file
      189 
  
  ~/anaconda/lib/python3.6/site-packages/sklearn/svm/base.py in _dense_fit(self, X, y, sample_weight, solver_type, kernel, random_seed)
      226             X = self._compute_kernel(X)
      227 
  --&gt; 228             if X.shape[0] != X.shape[1]:
      229                 raise ValueError("X.shape[0] should be equal to X.shape[1]")
      230 
  </code></pre>
  
  <p>IndexError: tuple index out of range</p>
  
  <p>Here's the customized Gaussian Kernel I wrote:</p>
  
  <pre><code>import math
  def gauss_kernel(x1, x2, gamma):
      sigma = math.sqrt(gamma) 
      return np.exp(-np.sum((x1-x2)**2)/(2*sigma**2))
  </code></pre>
  
  <p>How should I fix this? When I look at SVM examples in sklearn, they basically do the same thing. I believe I am neglecting something small but can't pin down the problem when matching with sklearn examples. </p>