| public GetBoolean ( String name, bool required, bool defaultValue ) : bool | ||
| name | String | The name of the double. |
| required | bool | True if this value is required. |
| defaultValue | bool | The default value. |
| return | bool | |
public bool GetBoolean(String name, bool required,
bool defaultValue)
{
String str = GetString(name, required, null);
if (str == null)
return defaultValue;
if (!str.Equals("true", StringComparison.InvariantCultureIgnoreCase) &&
!str.Equals("false", StringComparison.InvariantCultureIgnoreCase))
{
throw new EncogError("Property " + name
+ " has an invalid value of " + str
+ ", should be true/false.");
}
return str.Equals("true", StringComparison.InvariantCultureIgnoreCase);
}
} /// <summary>
/// Create a LMA trainer.
/// </summary>
///
/// <param name="method">The method to use.</param>
/// <param name="training">The training data to use.</param>
/// <param name="argsStr">The arguments to use.</param>
/// <returns>The newly created trainer.</returns>
public IMLTrain Create(IMLMethod method,
IMLDataSet training, String argsStr)
{
if (!(method is BasicNetwork))
{
throw new EncogError(
"LMA training cannot be used on a method of type: "
+ method.GetType().FullName);
}
IDictionary<String, String> args = ArchitectureParse.ParseParams(argsStr);
var holder = new ParamsHolder(args);
bool useReg = holder.GetBoolean(
MLTrainFactory.PropertyBayesianRegularization, false, false);
var result = new LevenbergMarquardtTraining(
(BasicNetwork) method, training) {UseBayesianRegularization = useReg};
return result;
}