Package com.rapidminer.parameter

Examples of com.rapidminer.parameter.ParameterTypeBoolean

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  public List<ParameterType> getParameterTypes() {
     List<ParameterType> types = super.getParameterTypes();
     types.add(new ParameterTypeInt(PARAMETER_Min, "Value of minimal rating value. Range: integer; 0-+?; default: 1", 0, Integer.MAX_VALUE, 1, false));
     types.add(new ParameterTypeInt(PARAMETER_Range, "Range of possible rating values.  Range: integer; 1-+?; default: 4 ; Max Rating=Min Rating+Range;", 1, Integer.MAX_VALUE, 4, false));
     types.add(new ParameterTypeBoolean(PARAMETER_NORMAL, "Use random generator from normal distribution.  Range: boolean; default: false", false, false));
     types.add(new ParameterTypeDouble(PARAMETER_INIT_MEAN, "Initial mean, used in normal distribution mode only.  Range: double; 0-+?; default: 0.5", 0, Double.MAX_VALUE, 0.5, true));
     types.add(new ParameterTypeDouble(PARAMETER_INIT_STDEV, "Initial stdev, used in normal distribution mode only.  Range: double; 0-+?; default: 0.0010", 0, Double.MAX_VALUE, 0.0010, true));
     return types;
     }
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  public static final String PARAMETER_Updates="Online updates";
 
  public List<ParameterType> getParameterTypes() {
     List<ParameterType> types = super.getParameterTypes();
     types.add(new ParameterTypeBoolean(PARAMETER_Updates, "Use online model updates.  Range: boolean; default: false", false, false));
     return types;
     }
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  public List<ParameterType> getParameterTypes() {
     List<ParameterType> types = super.getParameterTypes();
     types.add(new ParameterTypeInt(PARAMETER_K, "The used number of nearest neighbors. Range: integer; 1-+?; default: 80", 1, Integer.MAX_VALUE, 80, false));
     types.add(new ParameterTypeBoolean(PARAMETER_WEIGHTED, "Use weighted Knn.  Range: boolean; default: false", false, false));
     return types;
     }
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     types.add(new ParameterTypeDouble(PARAMETER_LEARN_RATE, "Learning rate of algorithm.  Range: double; 0-+?; default: 0.01", 0, Double.MAX_VALUE, 0.01, false));
     types.add(new ParameterTypeInt(PARAMETER_NUM_ITER, "Number of iterations.  Range: integer; 1-+?; default: 30", 1, Integer.MAX_VALUE, 30, false));
     types.add(new ParameterTypeDouble(PARAMETER_REGULARIZATION, "Value of regularization parameter. Range: double; 0-+?; default: 0.015", 0, Double.MAX_VALUE, 0.015, true));
     types.add(new ParameterTypeDouble(PARAMETER_INIT_MEAN, "Initial mean.  Range: double; 0-+?; default: 0", 0, Double.MAX_VALUE, 0, true));
     types.add(new ParameterTypeDouble(PARAMETER_INIT_STDEV, "Initial stdev.  Range: double; 0-+?; default: 0.1", 0, Double.MAX_VALUE, 0.1, true));
     types.add(new ParameterTypeBoolean(PARAMETER_BOLD_DRIVER, "Use bold driver heuristics for learning rate adaption.  Range: boolean; default: false", false, true));
     types.add(new ParameterTypeBoolean(PARAMETER_MAE_OPTIMIZED, "Use biased matrix factorization optimized for mean average error (MAE).  Range: boolean; default: false", false, false));
     return types;
     }
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  }
 
  public List<ParameterType> getParameterTypes() {
     List<ParameterType> types = super.getParameterTypes();
     types.add(new ParameterTypeInt(PARAMETER_K, "The used number of nearest neighbors. Range: integer; 1-+?; default: 80", 1, Integer.MAX_VALUE, 80, false));
     types.add(new ParameterTypeBoolean(PARAMETER_WEIGHTED, "Use weighted Knn.  Range: boolean; default: false", false, false));
     return types;
     }
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     types.add(new ParameterTypeInt(PARAMETER_NUM_ITER, "Number of iterations.  Range: integer; 1-+?; default: 30", 1, Integer.MAX_VALUE, 30, false));
     types.add(new ParameterTypeDouble(PARAMETER_LEARN_RATE, "Learning rate of algorithm.  Range: double; 0-+?; default: 0.05", 0, Double.MAX_VALUE, 0.05, false));
     types.add(new ParameterTypeDouble(PARAMETER_INIT_MEAN, "Initial mean.  Range: double; 0-+?; default: 0", 0, Double.MAX_VALUE, 0, true));
     types.add(new ParameterTypeDouble(PARAMETER_INIT_STDEV, "Initial stdev.  Range: double; 0-+?; default: 0.1", 0, Double.MAX_VALUE, 0.1, true));
     types.add(new ParameterTypeInt(PARAMETER_FAST_SAMPLING, "Fast sampling memory limit, in MiB. Range: integer; 1-+?; default: 1024", 1, Integer.MAX_VALUE, 1024, true));
     types.add(new ParameterTypeBoolean(PARAMETER_BOLD_DRIVER, "Use bold driver heuristics for learning rate adaption.  Range: boolean; default: false", false, true));
   
   
     return types;
     }
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  public List<ParameterType> getParameterTypes() {
     List<ParameterType> types = super.getParameterTypes();
     types.add(new ParameterTypeInt(PARAMETER_N, "Desplay first n ranked items for users. Range: integer; 1-+?; default: 100", 1, Integer.MAX_VALUE, 100, false));
     types.add(new ParameterTypeBoolean(PARAMETER_Updates, "Use online model updates.  Range: boolean; default: false", false, false));
     return types;
     }
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Related Classes of com.rapidminer.parameter.ParameterTypeBoolean

  • com.rapidminer.ItemRecommendation.ApplyModel
  • com.rapidminer.ItemRecommendation.BPRMatrixFactorization
  • com.rapidminer.ItemRecommendation.ItemKnn
  • com.rapidminer.ItemRecommendation.UserKnn
  • com.rapidminer.operator.RatingPrediction.ApplyModel
  • com.rapidminer.operator.RatingPrediction.BMF
  • com.rapidminer.operator.RatingPrediction.RandomO
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