test_class.declarator: partsToBeTested = new int[] {1, 2, 3, 4, 5, 7}
test_class.modifier: private static final
test_class.original_string: private static final int[] partsToBeTested = new int[] {1, 2, 3, 4, 5, 7};
test_class.type: int[]
test_class.var_name: partsToBeTested
test_class.declarator: parts
test_class.modifier: @Parameterized.Parameter()
      public
test_class.original_string: @Parameterized.Parameter()
      public int parts;
test_class.type: int
test_class.var_name: parts
test_class.declarator: useIdx
test_class.modifier: @Parameterized.Parameter(1)
      public
test_class.original_string: @Parameterized.Parameter(1)
      public int useIdx;
test_class.type: int
test_class.var_name: useIdx
test_class.file: modules/ml/src/test/java/org/apache/ignite/ml/tree/DecisionTreeRegressionTrainerTest.java
test_class.identifier: DecisionTreeRegressionTrainerTest
test_case.body: @Test
      public void testFit() {
          int size = 100;
  
          Map<Integer, double[]> data = new HashMap<>();
  
          Random rnd = new Random(0);
          for (int i = 0; i < size; i++) {
              double x = rnd.nextDouble() - 0.5;
              data.put(i, new double[]{x, x > 0 ? 1 : 0});
          }
  
          DecisionTreeRegressionTrainer trainer = new DecisionTreeRegressionTrainer(1, 0)
              .withUsingIdx(useIdx == 1);
  
          DecisionTreeNode tree = trainer.fit(data, parts, new DoubleArrayVectorizer<Integer>().labeled(1));
  
          assertTrue(tree instanceof DecisionTreeConditionalNode);
  
          DecisionTreeConditionalNode node = (DecisionTreeConditionalNode) tree;
  
          assertEquals(0, node.getThreshold(), 1e-3);
  
          assertTrue(node.getThenNode() instanceof DecisionTreeLeafNode);
          assertTrue(node.getElseNode() instanceof DecisionTreeLeafNode);
  
          DecisionTreeLeafNode thenNode = (DecisionTreeLeafNode) node.getThenNode();
          DecisionTreeLeafNode elseNode = (DecisionTreeLeafNode) node.getElseNode();
  
          assertEquals(1, thenNode.getVal(), 1e-10);
          assertEquals(0, elseNode.getVal(), 1e-10);
      }
test_case.class_method_signature: DecisionTreeRegressionTrainerTest.testFit()
test_case.full_signature: @Test public void testFit()
test_case.identifier: testFit
test_case.invocations: nextDouble put withUsingIdx fit labeled assertTrue assertEquals getThreshold assertTrue getThenNode assertTrue getElseNode getThenNode getElseNode assertEquals getVal assertEquals getVal
test_case.modifiers: @Test public
test_case.parameters: ()
test_case.return: void
test_case.signature: void testFit()
focal_class.file: modules/ml/src/main/java/org/apache/ignite/ml/tree/DecisionTreeRegressionTrainer.java
focal_class.identifier: DecisionTreeRegressionTrainer
focal_class.class_method_signature: DecisionTreeRegressionTrainer.DecisionTreeRegressionTrainer(int maxDeep, double minImpurityDecrease)
focal_class.full_signature: public  DecisionTreeRegressionTrainer(int maxDeep, double minImpurityDecrease)
focal_class.identifier: DecisionTreeRegressionTrainer
focal_class.modifiers: public
focal_class.parameters: (int maxDeep, double minImpurityDecrease)
focal_class.signature:  DecisionTreeRegressionTrainer(int maxDeep, double minImpurityDecrease)
focal_class.class_method_signature: DecisionTreeRegressionTrainer.DecisionTreeRegressionTrainer(int maxDeep, double minImpurityDecrease,
          StepFunctionCompressor<MSEImpurityMeasure> compressor)
focal_class.full_signature: public  DecisionTreeRegressionTrainer(int maxDeep, double minImpurityDecrease,
          StepFunctionCompressor<MSEImpurityMeasure> compressor)
focal_class.identifier: DecisionTreeRegressionTrainer
focal_class.modifiers: public
focal_class.parameters: (int maxDeep, double minImpurityDecrease,
          StepFunctionCompressor<MSEImpurityMeasure> compressor)
focal_class.signature:  DecisionTreeRegressionTrainer(int maxDeep, double minImpurityDecrease,
          StepFunctionCompressor<MSEImpurityMeasure> compressor)
focal_class.class_method_signature: DecisionTreeRegressionTrainer.withUsingIdx(boolean usingIdx)
focal_class.full_signature: public DecisionTreeRegressionTrainer withUsingIdx(boolean usingIdx)
focal_class.identifier: withUsingIdx
focal_class.modifiers: public
focal_class.parameters: (boolean usingIdx)
focal_class.return: DecisionTreeRegressionTrainer
focal_class.signature: DecisionTreeRegressionTrainer withUsingIdx(boolean usingIdx)
focal_class.class_method_signature: DecisionTreeRegressionTrainer.getImpurityMeasureCalculator(
          Dataset<EmptyContext, DecisionTreeData> dataset)
focal_class.full_signature: @Override protected ImpurityMeasureCalculator<MSEImpurityMeasure> getImpurityMeasureCalculator(
          Dataset<EmptyContext, DecisionTreeData> dataset)
focal_class.identifier: getImpurityMeasureCalculator
focal_class.modifiers: @Override protected
focal_class.parameters: (
          Dataset<EmptyContext, DecisionTreeData> dataset)
focal_class.return: ImpurityMeasureCalculator<MSEImpurityMeasure>
focal_class.signature: ImpurityMeasureCalculator<MSEImpurityMeasure> getImpurityMeasureCalculator(
          Dataset<EmptyContext, DecisionTreeData> dataset)
focal_class.class_method_signature: DecisionTreeRegressionTrainer.withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
focal_class.full_signature: @Override public DecisionTreeRegressionTrainer withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
focal_class.identifier: withEnvironmentBuilder
focal_class.modifiers: @Override public
focal_class.parameters: (LearningEnvironmentBuilder envBuilder)
focal_class.return: DecisionTreeRegressionTrainer
focal_class.signature: DecisionTreeRegressionTrainer withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)
focal_class.superclass: extends DecisionTree<MSEImpurityMeasure>
focal_method.body: public DecisionTreeRegressionTrainer withUsingIdx(boolean usingIdx) {
          this.usingIdx = usingIdx;
          return this;
      }
focal_method.class_method_signature: DecisionTreeRegressionTrainer.withUsingIdx(boolean usingIdx)
focal_method.full_signature: public DecisionTreeRegressionTrainer withUsingIdx(boolean usingIdx)
focal_method.identifier: withUsingIdx
focal_method.modifiers: public
focal_method.parameters: (boolean usingIdx)
focal_method.return: DecisionTreeRegressionTrainer
focal_method.signature: DecisionTreeRegressionTrainer withUsingIdx(boolean usingIdx)