code: def test_incremental_pca_check_projection():
      # Test that the projection of data is correct.
      rng = np.random.RandomState(1999)
      n, p = 100, 3
      X = rng.randn(n, p) * 0.1
      X[:10] += np.array([3, 4, 5])
      Xt = 0.1 * rng.randn(1, p) + np.array([3, 4, 5])
      X = da.from_array(X, chunks=(3, -1))
      Xt = da.from_array(Xt, chunks=(4, 3))
  
      # Get the reconstruction of the generated data X
      # Note that Xt has the same "components" as X, just separated
      # This is what we want to ensure is recreated correctly
      Yt = IncrementalPCA(n_components=2).fit(X).transform(Xt)
      assert isinstance(Yt, da.Array)
  
      # Normalize
      Yt /= np.sqrt((Yt**2).sum())
  
      # Make sure that the first element of Yt is ~1, this means
      # the reconstruction worked as expected
      assert_almost_equal(np.abs(Yt[0][0]), 1.0, 1)
  
imports: import numpy
  import pytest
  from dask import array
  from sklearn import datasets, decomposition
  from sklearn.decomposition import PCA
  from dask_ml.decomposition import IncrementalPCA
  from dask_ml.utils import flip_vector_signs
label_desc: Use image node_id 3 for calling a global function with example usage: test_incremental_pca_check_projection() without return types