code: def test_actual_results_strategy_quantile():
      """Test that the actual results are the expected ones."""
      # Data
      X = [
          [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
          [0.0, 0.3, 0.2, 0.4, 0.1, 0.5, 0.6, 0.7, 0.8, 0.9],
          [0.0, 0.9, 0.1, 0.8, 0.2, 0.7, 0.3, 0.6, 0.4, 0.5],
      ]
      y = [0, 0, 1]
  
      clf = SAXVSM(
          window_size=4,
          word_size=4,
          n_bins=2,
          strategy="quantile",
          numerosity_reduction=False,
          sublinear_tf=False,
      )
      decision_function_actual = clf.fit(X, y).decision_function(X)
  
      # X_bow = ["aabb aabb aabb aabb aabb aabb aabb",
      #          "abab baba abab aabb aabb aabb aabb",
      #          "abab baba abab baba abab baba abab"]
  
      assert clf.vocabulary_ == {0: "aabb", 1: "abab", 2: "baba"}
  
      freq = np.asarray([[7, 0, 0], [4, 2, 1], [0, 4, 3]])
      tf = np.asarray([[11, 2, 1], [0, 4, 3]])
      idf = np.asarray([log(2) + 1, 1, 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.fit(X, y).predict(X)
      pred_desired = cosine_similarity(freq, tf * idf[None, :]).argmax(
          axis=1
      )
      np.testing.assert_array_equal(pred_actual, pred_desired)
  
      # Without IDF weighting
      clf = SAXVSM(
          window_size=4,
          word_size=4,
          n_bins=2,
          strategy="quantile",
          numerosity_reduction=False,
          sublinear_tf=False,
          use_idf=False,
      )
      decision_function_actual = clf.fit(X, y).decision_function(X)
  
      decision_function_desired = cosine_similarity(freq, tf)
      np.testing.assert_allclose(
          decision_function_actual,
          decision_function_desired,
          atol=1e-5,
          rtol=0.0,
      )
  
      pred_actual = clf.fit(X, y).predict(X)
      pred_desired = cosine_similarity(freq, tf).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 2 for calling a global function with example usage: test_actual_results_strategy_quantile() without return types