Gaussian Process scikit-learn - Exception
<p>I want to use Gaussian Processes to solve a regression task. My data is as follow : each X vector has a length of 37, and each Y vector has a length of 8.</p>

<p>I'm using the <code>sklearn</code>package in <code>Python</code> but trying to use gaussian processes leads to an <code>Exception</code>:</p>

<pre><code>from sklearn import gaussian_process

print "x :", x__
print "y :", y__

gp = gaussian_process.GaussianProcess(theta0=1e-2, thetaL=1e-4, thetaU=1e-1)
gp.fit(x__, y__) 
</code></pre>

<blockquote>
  <p>x : [[  136.   137.   137.   132.   130.   130.   132.   133.   134.<br>
  135.
      135.   134.   134.  1139.  1019.     0.     0.     0.     0.     0.
        0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
       70.    24.    55.     0.     9.     0.     0.]  [  136.   137.   137.   132.   130.   130.   132.   133.   134.   135.
      135.   134.   134.  1139.  1019.     0.     0.     0.     0.     0.
        0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
       70.    24.    55.     0.     9.     0.     0.]  [   82.    76.    80.   103.   135.   155.   159.   156.   145.   138.
      130.   122.   122.   689.   569.     0.     0.     0.     0.     0.
        0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
        0.     0.     0.     0.     0.     0.     0.]  [  156.   145.   138.   130.   122.   118.   113.   111.   105.   101.
       98.    95.    95.   759.   639.     0.     0.     0.     0.     0.
        0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
        0.     0.     0.     0.     0.     0.     0.]  [  112.   111.   111.   114.   114.   113.   114.   114.   112.   111.
      109.   109.   109.  1109.   989.     0.     0.     0.     0.     0.
        0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
        0.     0.     0.     0.     0.     0.     0.]  [  133.   130.   125.   124.   124.   123.   103.    87.    96.   121.
      122.   123.   123.   399.   279.     0.     0.     0.     0.     0.
        0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
        0.     0.     0.     0.     0.     0.     0.]  [  104.   109.   111.   106.    91.    86.   117.   123.   123.   120.
      121.   115.   115.   549.   429.     0.     0.     0.     0.     0.
        0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
        0.     0.     0.     0.     0.     0.     0.]  [  144.   138.   126.   122.   119.   118.   116.   114.   107.   105.
      106.   119.   119.   479.   359.     0.     0.     0.     0.     0.
        0.     0.     0.     0.     0.     0.     0.     0.     0.     0.
        0.     0.     0.     0.     0.     0.     0.]] </p>
  
  <p>y : [[  7.           9.          13.          30.          34.          37.
     36.          41.        ]  [  7.           9.          13.          30.          34.          37.
     36.          41.        ]  [ -4.          -9.         -17.         -21.         -27.         -28.   -28.         -20.        ]  [ -1.          -1.          -4.          -5.          20.          28.
     31.          23.        ]  [ -1.          -2.          -3.          -1.          -4.          -7.
      8.          58.        ]  [ -1.          -2.         -14.33333333 -14.         -13.66666667 -32.   -26.66666667  -1.        ]  [  1.           3.33333333   0.          -0.66666667   3.           6.
     22.          54.        ]  [ -2.          -8.         -11.         -17.         -17.         -16.   -16.         -23.        ]]</p>
  
  <p>--------------------------------------------------------------------------- Exception                                 Traceback (most recent call
  last)  in ()
       11 gp = gaussian_process.GaussianProcess(theta0=1e-2, thetaL=1e-4, thetaU=1e-1)
       12 
  ---> 13 gp.fit(x__, y__)</p>
  
  <p>/usr/local/lib/python2.7/site-packages/sklearn/gaussian_process/gaussian_process.pyc
  in fit(self, X, y)
      300         if (np.min(np.sum(D, axis=1)) == 0.
      301                 and self.corr != correlation.pure_nugget):
  --> 302             raise Exception("Multiple input features cannot have the same"
      303                             " target value.")
      304 </p>
  
  <p>Exception: Multiple input features cannot have the same target value.</p>
</blockquote>

<p>I've found <a href="https://github.com/scikit-learn/scikit-learn/issues/4916">some topics related to a <code>scikit-learn</code> issue</a>, but my version is up-to-date. </p>