docstr: Invert the dimensions; i.e., for a dataset of dimension 1 [3, 2, 4],
  the points should have a dimension of 3 [[3], [2], [4]].
docstr_tok: ["Invert","the","dimensions",";","i.e.",",","for","a","dataset","of","dimension","1","[","3",",","2",",","4","]",",","the","points","should","have","a","dimension","of","3","[","[","3","]",",","[","2","]",",","[","4","]","]","."]
code: def test_evaluate_inv_dim(self):
      """
      Invert the dimensions; i.e., for a dataset of dimension 1 [3, 2, 4],
      the points should have a dimension of 3 [[3], [2], [4]].
      """
      np.random.seed(8765678)
      n_basesample = 50
      multidim_data = np.random.randn(n_basesample)
      kde = mlab.GaussianKDE(multidim_data)
      x2 = [[1], [2], [3]]
      with pytest.raises(ValueError):
          kde.evaluate(x2)
  
imports: from numpy.testing import assert_allclose, assert_almost_equal, assert_array_equal, assert_array_almost_equal_nulp
  import numpy
  import pytest
  from matplotlib import mlab
label_desc: Use image node_id 2 for calling the TestGaussianKDEEvaluate obj's underlying member method code with example usage: obj.test_evaluate_inv_dim() without return types