samoa-api/src/main/java/org/apache/samoa/learners/classifiers/rules/AMRulesRegressor.java [58:127]:
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  public FloatOption splitConfidenceOption = new FloatOption(
      "splitConfidence",
      'c',
      "Hoeffding Bound Parameter. The allowable error in split decision, values closer to 0 will take longer to decide.",
      0.0000001, 0.0, 1.0);

  public FloatOption tieThresholdOption = new FloatOption("tieThreshold",
      't', "Hoeffding Bound Parameter. Threshold below which a split will be forced to break ties.",
      0.05, 0.0, 1.0);

  public IntOption gracePeriodOption = new IntOption("gracePeriod",
      'g', "Hoeffding Bound Parameter. The number of instances a leaf should observe between split attempts.",
      200, 1, Integer.MAX_VALUE);

  public FlagOption DriftDetectionOption = new FlagOption("DoNotDetectChanges", 'H',
      "Drift Detection. Page-Hinkley.");

  public FloatOption pageHinckleyAlphaOption = new FloatOption(
      "pageHinckleyAlpha",
      'a',
      "The alpha value to use in the Page Hinckley change detection tests.",
      0.005, 0.0, 1.0);

  public IntOption pageHinckleyThresholdOption = new IntOption(
      "pageHinckleyThreshold",
      'l',
      "The threshold value (Lambda) to be used in the Page Hinckley change detection tests.",
      35, 0, Integer.MAX_VALUE);

  public FlagOption noAnomalyDetectionOption = new FlagOption("noAnomalyDetection", 'A',
      "Disable anomaly Detection.");

  public FloatOption multivariateAnomalyProbabilityThresholdOption = new FloatOption(
      "multivariateAnomalyProbabilityThresholdd",
      'm',
      "Multivariate anomaly threshold value.",
      0.99, 0.0, 1.0);

  public FloatOption univariateAnomalyProbabilityThresholdOption = new FloatOption(
      "univariateAnomalyprobabilityThreshold",
      'u',
      "Univariate anomaly threshold value.",
      0.10, 0.0, 1.0);

  public IntOption anomalyNumInstThresholdOption = new IntOption(
      "anomalyThreshold",
      'n',
      "The threshold value of anomalies to be used in the anomaly detection.",
      30, 0, Integer.MAX_VALUE); // num minimum of instances to detect anomalies anomalies. 15.

  public FlagOption unorderedRulesOption = new FlagOption("setUnorderedRulesOn", 'U',
      "unorderedRules.");

  public ClassOption numericObserverOption = new ClassOption("numericObserver",
      'z', "Numeric observer.",
      FIMTDDNumericAttributeClassLimitObserver.class,
      "FIMTDDNumericAttributeClassLimitObserver");

  public MultiChoiceOption predictionFunctionOption = new MultiChoiceOption(
      "predictionFunctionOption", 'P', "The prediction function to use.", new String[] {
          "Adaptative", "Perceptron", "Target Mean" }, new String[] {
          "Adaptative", "Perceptron", "Target Mean" }, 0);

  public FlagOption constantLearningRatioDecayOption = new FlagOption(
      "learningRatio_Decay_set_constant", 'd',
      "Learning Ratio Decay in Perceptron set to be constant. (The next parameter).");

  public FloatOption learningRatioOption = new FloatOption(
      "learningRatio", 's',
      "Constante Learning Ratio to use for training the Perceptrons in the leaves.", 0.025);
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samoa-api/src/main/java/org/apache/samoa/learners/classifiers/rules/HorizontalAMRulesRegressor.java [58:127]:
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  public FloatOption splitConfidenceOption = new FloatOption(
      "splitConfidence",
      'c',
      "Hoeffding Bound Parameter. The allowable error in split decision, values closer to 0 will take longer to decide.",
      0.0000001, 0.0, 1.0);

  public FloatOption tieThresholdOption = new FloatOption("tieThreshold",
      't', "Hoeffding Bound Parameter. Threshold below which a split will be forced to break ties.",
      0.05, 0.0, 1.0);

  public IntOption gracePeriodOption = new IntOption("gracePeriod",
      'g', "Hoeffding Bound Parameter. The number of instances a leaf should observe between split attempts.",
      200, 1, Integer.MAX_VALUE);

  public FlagOption DriftDetectionOption = new FlagOption("DoNotDetectChanges", 'H',
      "Drift Detection. Page-Hinkley.");

  public FloatOption pageHinckleyAlphaOption = new FloatOption(
      "pageHinckleyAlpha",
      'a',
      "The alpha value to use in the Page Hinckley change detection tests.",
      0.005, 0.0, 1.0);

  public IntOption pageHinckleyThresholdOption = new IntOption(
      "pageHinckleyThreshold",
      'l',
      "The threshold value (Lambda) to be used in the Page Hinckley change detection tests.",
      35, 0, Integer.MAX_VALUE);

  public FlagOption noAnomalyDetectionOption = new FlagOption("noAnomalyDetection", 'A',
      "Disable anomaly Detection.");

  public FloatOption multivariateAnomalyProbabilityThresholdOption = new FloatOption(
      "multivariateAnomalyProbabilityThresholdd",
      'm',
      "Multivariate anomaly threshold value.",
      0.99, 0.0, 1.0);

  public FloatOption univariateAnomalyProbabilityThresholdOption = new FloatOption(
      "univariateAnomalyprobabilityThreshold",
      'u',
      "Univariate anomaly threshold value.",
      0.10, 0.0, 1.0);

  public IntOption anomalyNumInstThresholdOption = new IntOption(
      "anomalyThreshold",
      'n',
      "The threshold value of anomalies to be used in the anomaly detection.",
      30, 0, Integer.MAX_VALUE); // num minimum of instances to detect anomalies. 15.

  public FlagOption unorderedRulesOption = new FlagOption("setUnorderedRulesOn", 'U',
      "unorderedRules.");

  public ClassOption numericObserverOption = new ClassOption("numericObserver",
      'z', "Numeric observer.",
      FIMTDDNumericAttributeClassLimitObserver.class,
      "FIMTDDNumericAttributeClassLimitObserver");

  public MultiChoiceOption predictionFunctionOption = new MultiChoiceOption(
      "predictionFunctionOption", 'P', "The prediction function to use.", new String[] {
          "Adaptative", "Perceptron", "Target Mean" }, new String[] {
          "Adaptative", "Perceptron", "Target Mean" }, 0);

  public FlagOption constantLearningRatioDecayOption = new FlagOption(
      "learningRatio_Decay_set_constant", 'd',
      "Learning Ratio Decay in Perceptron set to be constant. (The next parameter).");

  public FloatOption learningRatioOption = new FloatOption(
      "learningRatio", 's',
      "Constante Learning Ratio to use for training the Perceptrons in the leaves.", 0.025);
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