text: Title: `test_expected_minimum` failure
  Body: One test for the expected minimum function fails somewhere deep in scipy. Any ideas on how to track this down?
  
  ```
  $ pytest --pdb -x -m 'not slow_test' skopt/tests/test_utils.py          (skopt)
  ============================= test session starts ==============================
  platform darwin -- Python 3.5.2, pytest-3.0.7, py-1.4.31, pluggy-0.4.0
  rootdir: /Users/thead/git/scikit-optimize, inifile:
  collected 3 items 
  
  skopt/tests/test_utils.py ..F
  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> traceback >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
  
      @pytest.mark.fast_test
      def test_expected_minimum():
          res = gp_minimize(bench3,
                            [(-2.0, 2.0)],
                            x0=[0.],
                            noise=0.0,
                            n_calls=20,
  >                         random_state=1)
  
  skopt/tests/test_utils.py:81: 
  _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
  skopt/optimizer/gp.py:238: in gp_minimize
      callback=callback, n_jobs=n_jobs)
  skopt/optimizer/base.py:249: in base_minimize
      result = optimizer.tell(next_x, next_y, fit=fit_model)
  skopt/optimizer/optimizer.py:407: in tell
      est.fit(self.space.transform(self.Xi), self.yi)
  skopt/learning/gaussian_process/gpr.py:194: in fit
      super(GaussianProcessRegressor, self).fit(X, y)
  ../../anaconda/envs/skopt/lib/python3.5/site-packages/sklearn/gaussian_process/gpr.py:217: in fit
      bounds))
  ../../anaconda/envs/skopt/lib/python3.5/site-packages/sklearn/gaussian_process/gpr.py:424: in _constrained_optimization
      fmin_l_bfgs_b(obj_func, initial_theta, bounds=bounds)
  ../../anaconda/envs/skopt/lib/python3.5/site-packages/scipy/optimize/lbfgsb.py:193: in fmin_l_bfgs_b
      **opts)
  ../../anaconda/envs/skopt/lib/python3.5/site-packages/scipy/optimize/lbfgsb.py:330: in _minimize_lbfgsb
      f, g = func_and_grad(x)
  ../../anaconda/envs/skopt/lib/python3.5/site-packages/scipy/optimize/lbfgsb.py:278: in func_and_grad
      f = fun(x, *args)
  ../../anaconda/envs/skopt/lib/python3.5/site-packages/scipy/optimize/optimize.py:289: in function_wrapper
      return function(*(wrapper_args + args))
  ../../anaconda/envs/skopt/lib/python3.5/site-packages/scipy/optimize/optimize.py:63: in __call__
      fg = self.fun(x, *args)
  ../../anaconda/envs/skopt/lib/python3.5/site-packages/sklearn/gaussian_process/gpr.py:194: in obj_func
      theta, eval_gradient=True)
  ../../anaconda/envs/skopt/lib/python3.5/site-packages/sklearn/gaussian_process/gpr.py:388: in log_marginal_likelihood
      L = cholesky(K, lower=True)  # Line 2
  ../../anaconda/envs/skopt/lib/python3.5/site-packages/scipy/linalg/decomp_cholesky.py:81: in cholesky
      check_finite=check_finite)
  ../../anaconda/envs/skopt/lib/python3.5/site-packages/scipy/linalg/decomp_cholesky.py:20: in _cholesky
      a1 = asarray_chkfinite(a)
  _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
  
  a = array([[ nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
           nan,  nan,  nan],
         [ nan,  na...nan,  nan,  nan],
         [ nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,  nan,
           nan,  nan,  nan]])
  dtype = None, order = None
  
      def asarray_chkfinite(a, dtype=None, order=None):
          """Convert the input to an array, checking for NaNs or Infs.
      
          Parameters
          ----------
          a : array_like
              Input data, in any form that can be converted to an array.  This
              includes lists, lists of tuples, tuples, tuples of tuples, tuples
              of lists and ndarrays.  Success requires no NaNs or Infs.
          dtype : data-type, optional
              By default, the data-type is inferred from the input data.
          order : {'C', 'F'}, optional
               Whether to use row-major (C-style) or
               column-major (Fortran-style) memory representation.
               Defaults to 'C'.
      
          Returns
          -------
          out : ndarray
              Array interpretation of `a`.  No copy is performed if the input
              is already an ndarray.  If `a` is a subclass of ndarray, a base
              class ndarray is returned.
      
          Raises
          ------
          ValueError
              Raises ValueError if `a` contains NaN (Not a Number) or Inf (Infinity).
      
          See Also
          --------
          asarray : Create and array.
          asanyarray : Similar function which passes through subclasses.
          ascontiguousarray : Convert input to a contiguous array.
          asfarray : Convert input to a floating point ndarray.
          asfortranarray : Convert input to an ndarray with column-major
                           memory order.
          fromiter : Create an array from an iterator.
          fromfunction : Construct an array by executing a function on grid
                         positions.
      
          Examples
          --------
          Convert a list into an array.  If all elements are finite
          ``asarray_chkfinite`` is identical to ``asarray``.
      
          >>> a = [1, 2]
          >>> np.asarray_chkfinite(a, dtype=float)
          array([1., 2.])
      
          Raises ValueError if array_like contains Nans or Infs.
      
          >>> a = [1, 2, np.inf]
          >>> try:
          ...     np.asarray_chkfinite(a)
          ... except ValueError:
          ...     print('ValueError')
          ...
          ValueError
      
          """
          a = asarray(a, dtype=dtype, order=order)
          if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all():
              raise ValueError(
  >               "array must not contain infs or NaNs")
  E           ValueError: array must not contain infs or NaNs
  
  ../../anaconda/envs/skopt/lib/python3.5/site-packages/numpy/lib/function_base.py:1022: ValueError
  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> entering PDB >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
  > /Users/thead/anaconda/envs/skopt/lib/python3.5/site-packages/numpy/lib/function_base.py(1022)asarray_chkfinite()
  -> "array must not contain infs or NaNs")
  ```