samoa-api/src/main/java/org/apache/samoa/learners/classifiers/rules/HorizontalAMRulesRegressor.java [81:132]:
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  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);

  public MultiChoiceOption votingTypeOption = new MultiChoiceOption(
      "votingType", 'V', "Voting Type.", new String[] {
          "InverseErrorWeightedVote", "UniformWeightedVote" }, new String[] {
          "InverseErrorWeightedVote", "UniformWeightedVote" }, 0);
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samoa-api/src/main/java/org/apache/samoa/learners/classifiers/rules/VerticalAMRulesRegressor.java [80:131]:
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  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);

  public MultiChoiceOption votingTypeOption = new MultiChoiceOption(
      "votingType", 'V', "Voting Type.", new String[] {
          "InverseErrorWeightedVote", "UniformWeightedVote" }, new String[] {
          "InverseErrorWeightedVote", "UniformWeightedVote" }, 0);
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