Package com.rapidminer.parameter

Examples of com.rapidminer.parameter.ParameterTypeDouble

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  }
 
  @Override
  public List<ParameterType> getParameterTypes() {
    List<ParameterType> types = super.getParameterTypes();
    types.add(new ParameterTypeDouble(PARAMETER_DEFAULT_WEIGHT, "The default weight for all models not specified in the list 'model_weights'.", 0.0d, Double.POSITIVE_INFINITY, 1.0d));
    types.add(new ParameterTypeList(PARAMETER_MODEL_WEIGHTS, "The weights for several models. Criteria weights not defined in this list are set to 'default_weight'.",
        new ParameterTypeString("operator_name", "The name of the operator."),
        new ParameterTypeDouble("model_weight", "The weight for this model.", 0.0d,
            Double.POSITIVE_INFINITY, 1.0d)));
    return types;
  }
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  }
 
  @Override
  public List<ParameterType> getParameterTypes() {
    List<ParameterType> types = super.getParameterTypes();
    types.add(new ParameterTypeDouble(PARAMETER_DEFAULT_WEIGHT, "The default weight for all models not specified in the list 'model_weights'.", 0.0d, Double.POSITIVE_INFINITY, 1.0d));
    types.add(new ParameterTypeList(PARAMETER_MODEL_WEIGHTS, "The weights for several models. Criteria weights not defined in this list are set to 'default_weight'.",
        new ParameterTypeString("operator_name", "The name of the operator."),
        new ParameterTypeDouble("model_weight", "The weight for this model.", 0.0d,
            Double.POSITIVE_INFINITY, 1.0d)));
    return types;
  }
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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 ParameterTypeInt(PARAMETER_NUM_FACTORS, "Number of latent factors. Range: integer; 1-+?; default: 10", 1, Integer.MAX_VALUE, 10, true));
     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));
     return types;
     }
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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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     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));
     ParameterType type = new ParameterTypeCategory(PARAMETER_CORRELATION_MODE, "Tipe of correlation used to calculate prediction.", CORRELATION_MODES, CORRELATION_MODE_COSINE);
      type.setExpert(false);
      types.add(type);
     types.add(new ParameterTypeDouble(PARAMETER_REGU, "Regularization parameter for user biases.  Range: double; 0-+?; default: 10 ;", 0, Double.MAX_VALUE, 10, true));
     types.add(new ParameterTypeDouble(PARAMETER_REGI, "Regularization parameter for item biases.  Range: double; 0-+?; default: 5 ;", 0, Double.MAX_VALUE, 5, true));
     types.add(new ParameterTypeDouble(PARAMETER_schrink, "Schrinkage regularization parameter.  Range: float; 0-+?; default: 10 ; used only in Pearson mode", 0, Float.MAX_VALUE, 10, true));
     return types;
     }
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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 ParameterTypeInt(PARAMETER_NUM_FACTORS, "Number of latent factors. Range: integer; 1-+?; default: 10", 1, Integer.MAX_VALUE, 10, true));
       types.add(new ParameterTypeDouble(PARAMETER_SCHRINKAGE, "Schrinkage. Range: double; 0-+?; default: 25", 0, Integer.MAX_VALUE, 25, true));
       types.add(new ParameterTypeDouble(PARAMETER_SENSIBILITY, "Sensibility. Range: double; 0-+?; default: 0.00001", 0, Integer.MAX_VALUE, 0.00001, true));
       types.add(new ParameterTypeInt(PARAMETER_NUM_ITER, "Number of iterations.  Range: integer; 1-+?; default: 10", 1, Integer.MAX_VALUE, 10, 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));
       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 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 ParameterTypeDouble(PARAMETER_REGU, "Regularization parameter for user biases.  Range: double; 0-+?; default: 10 ;", 0, Double.MAX_VALUE, 10, true));
         types.add(new ParameterTypeDouble(PARAMETER_REGI, "Regularization parameter for item biases.  Range: double; 0-+?; default: 5 ;", 0, Double.MAX_VALUE, 5, true));
         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 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 ParameterTypeDouble(PARAMETER_REGU, "Regularization parameter for user biases.  Range: double; 0-+?; default: 10 ;", 0, Double.MAX_VALUE, 10, true));
     types.add(new ParameterTypeDouble(PARAMETER_REGI, "Regularization parameter for item biases.  Range: double; 0-+?; default: 5 ;", 0, Double.MAX_VALUE, 5, true));
     types.add(new ParameterTypeDouble(PARAMETER_schrink, "Schrinkage regularization parameter.  Range: float; 0-+?; default: 10 ; used only in Pearson mode", 0, Float.MAX_VALUE, 10, true));
     ParameterType type = new ParameterTypeCategory(PARAMETER_CORRELATION_MODE, "Tipe of correlation used to calculate prediction.", CORRELATION_MODES, CORRELATION_MODE_COSINE);
      type.setExpert(false);
      types.add(type);
     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 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 ParameterTypeDouble(PARAMETER_REGU, "Regularization parameter for user biases.  Range: double; 0-+?; default: 10 ;", 0, Double.MAX_VALUE, 10, true));
       types.add(new ParameterTypeDouble(PARAMETER_REGI, "Regularization parameter for item biases.  Range: double; 0-+?; default: 5 ;", 0, Double.MAX_VALUE, 5, true));
       return types;
       }
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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 ParameterTypeInt(PARAMETER_NUM_FACTORS, "Number of latent factors. Range: integer; 1-+?; default: 10", 1, Integer.MAX_VALUE, 10, true));
     types.add(new ParameterTypeDouble(PARAMETER_BIAS_REG, "Bias regularization parameter.  Range: double; 0-+?; default: 0.0001", 0, Double.MAX_VALUE, 0.0001, true));
     types.add(new ParameterTypeDouble(PARAMETER_REG_U, "User regularization parameter.  Range: double; 0-+?; default: 0.015", 0, Double.MAX_VALUE, 0.015, true));
     types.add(new ParameterTypeDouble(PARAMETER_REG_I, "Item regularization parameter.  Range: double; 0-+?; default: 0.015", 0, Double.MAX_VALUE, 0.015, true));
     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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