code: def test_actual_results_strategy_uniform():
      """Test that the actual results are the expected ones."""
      # Data
      X = [
          [0, 0, 0, 1, 0, 0, 1, 1, 1],
          [0, 1, 1, 1, 0, 0, 1, 1, 1],
          [0, 0, 0, 1, 0, 0, 0, 1, 0],
      ]
      y = [0, 0, 1]
  
      clf = SAXVSM(
          window_size=4,
          word_size=4,
          n_bins=2,
          strategy="uniform",
          numerosity_reduction=False,
          sublinear_tf=False,
      )
      decision_function_actual = clf.fit(X, y).decision_function(X)
  
      # X_bow = ["aaab aaba abaa baab aabb abbb",
      #          "abbb bbba bbaa baab aabb abbb",
      #          "aaab aaba abaa baaa aaab aaba"]
  
      assert clf.vocabulary_ == {
          0: "aaab",
          1: "aaba",
          2: "aabb",
          3: "abaa",
          4: "abbb",
          5: "baaa",
          6: "baab",
          7: "bbaa",
          8: "bbba",
      }
  
      freq = np.asarray(
          [
              [1, 1, 1, 1, 1, 0, 1, 0, 0],
              [0, 0, 1, 0, 2, 0, 1, 1, 1],
              [2, 2, 0, 1, 0, 1, 0, 0, 0],
          ]
      )
      tf = np.asarray(
          [[1, 1, 2, 1, 3, 0, 2, 1, 1], [2, 2, 0, 1, 0, 1, 0, 0, 0]]
      )
      idf = np.asarray(
          [
              1,
              1,
              log(2) + 1,
              1,
              log(2) + 1,
              log(2) + 1,
              log(2) + 1,
              log(2) + 1,
              log(2) + 1,
          ]
      )
      decision_function_desired = cosine_similarity(
          freq, tf * idf[None, :]
      )
      np.testing.assert_allclose(
          decision_function_actual,
          decision_function_desired,
          atol=1e-5,
          rtol=0.0,
      )
  
      pred_actual = clf.predict(X)
      pred_desired = cosine_similarity(freq, tf * idf[None, :]).argmax(
          axis=1
      )
      np.testing.assert_array_equal(pred_actual, pred_desired)
  
imports: import numpy
  from math import log
  from sklearn.metrics.pairwise import cosine_similarity
  from pyts.classification import SAXVSM
label_desc: Use image node_id 1 for calling a global function with example usage: test_actual_results_strategy_uniform() without return types