Package com.rapidminer.parameter

Examples of com.rapidminer.parameter.ParameterTypeDouble

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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_numIter, "Number of iterations; 1-+?; default: 10", 1, Integer.MAX_VALUE, 10, false));
     types.add(new ParameterTypeDouble(PARAMETER_REGU, "Regularization parameter for user biases.  Range: double; 0-+?; default: 15 ;", 0, Double.MAX_VALUE, 15, true));
     types.add(new ParameterTypeDouble(PARAMETER_REGI, "Regularization parameter for item biases.  Range: double; 0-+?; default: 10 ;", 0, Double.MAX_VALUE, 10, true));
     return types;
     }
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  public List<ParameterType> getParameterTypes() {
     List<ParameterType> types = super.getParameterTypes();
 
     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", 0, Double.MAX_VALUE, 0, true));
     types.add(new ParameterTypeDouble(PARAMETER_REG_U, "User regularization parameter.  Range: double; 0-+?; default: 0.025", 0, Double.MAX_VALUE, 0.025, true));
     types.add(new ParameterTypeDouble(PARAMETER_REG_I, "Item regularization parameter.  Range: double; 0-+?; default: 0.025", 0, Double.MAX_VALUE, 0.025, true));
     types.add(new ParameterTypeDouble(PARAMETER_REG_J, "Negative item regularization parameter.  Range: double; 0-+?; default: 0.025", 0, Double.MAX_VALUE, 0.025, true));
     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_NUM_FACTORS, "Number of latent factors. Range: integer; 1-+?; default: 10", 1, Integer.MAX_VALUE, 10, true));
     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_CPOS, "C position: the weight/confidence that is put on positive observations. Range: double; 0-+?; default: 1", 0, Double.MAX_VALUE, 1, true));
     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_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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