本文整理汇总了C#中Configuration.setMeasuredValue方法的典型用法代码示例。如果您正苦于以下问题：C# Configuration.setMeasuredValue方法的具体用法？C# Configuration.setMeasuredValue怎么用？C# Configuration.setMeasuredValue使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Configuration的用法示例。
在下文中一共展示了Configuration.setMeasuredValue方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: performOneCommand
//.........这里部分代码省略.........
exp.addBinarySelection_Validation(vg.generateRandomVariants(GlobalState.varModel, treshold, modulu));
exp.addBinarySampling_Validation("random " + task);
}
else
{
exp.addBinarySelection_Learning(vg.generateRandomVariants(GlobalState.varModel, treshold, modulu));
exp.addBinarySampling_Learning("random " + task);
}
break;
}
case COMMAND_START_LEARNING:
{
InfluenceModel infMod = new InfluenceModel(GlobalState.varModel, GlobalState.currentNFP);
List<Configuration> configurations_Learning = new List<Configuration>();
List<Configuration> configurations_Validation = new List<Configuration>();
if (exp.TrueModel == null)
{
//List<List<BinaryOption>> availableBinary
//configurations_Learning = GlobalState.getMeasuredConfigs(exp.BinarySelections_Learning, exp.NumericSelection_Learning);
configurations_Learning = GlobalState.getMeasuredConfigs(Configuration.getConfigurations(exp.BinarySelections_Learning, exp.NumericSelection_Learning));
configurations_Learning = configurations_Learning.Distinct().ToList();
configurations_Validation = GlobalState.getMeasuredConfigs(Configuration.getConfigurations(exp.BinarySelections_Validation, exp.NumericSelection_Validation));
configurations_Validation = configurations_Validation.Distinct().ToList();
//break;//todo only to get the configurations that we haven't measured
} else
{
foreach (List<BinaryOption> binConfig in exp.BinarySelections_Learning)
{
if (exp.NumericSelection_Learning.Count == 0)
{
Configuration c = new Configuration(binConfig);
c.setMeasuredValue(GlobalState.currentNFP, exp.TrueModel.eval(c));
if (!configurations_Learning.Contains(c))
configurations_Learning.Add(c);
continue;
}
foreach (Dictionary<NumericOption, double> numConf in exp.NumericSelection_Learning)
{
Configuration c = new Configuration(binConfig, numConf);
c.setMeasuredValue(GlobalState.currentNFP, exp.TrueModel.eval(c));
if(GlobalState.varModel.configurationIsValid(c))
// if (!configurations_Learning.Contains(c))
configurations_Learning.Add(c);
}
}
}
if (configurations_Learning.Count == 0)
{
configurations_Learning = configurations_Validation;
}
if (configurations_Learning.Count == 0)
{
GlobalState.logInfo.log("The learning set is empty! Cannot start learning!");
break;
}
if (configurations_Validation.Count == 0)
{
configurations_Validation = configurations_Learning;
}
//break;
GlobalState.logInfo.log("Learning: " + "NumberOfConfigurationsLearning:" + configurations_Learning.Count + " NumberOfConfigurationsValidation:" + configurations_Validation.Count
+ " UnionNumberOfConfigurations:" + (configurations_Learning.Union(configurations_Validation)).Count());
// prepare the machine learning
exp.learning.init(infMod, exp.mlSettings);
exp.learning.setLearningSet(configurations_Learning);
exp.learning.setValidationSet(configurations_Validation);
exp.learning.learn();
}
break;
case COMMAND_SAMPLE_NEGATIVE_OPTIONWISE:
// TODO there are two different variants in generating NegFW configurations.
NegFeatureWise neg = new NegFeatureWise();
if (taskAsParameter.Contains(COMMAND_VALIDATION))
{
exp.addBinarySelection_Validation(neg.generateNegativeFW(GlobalState.varModel));
exp.addBinarySampling_Validation("newFW");
}
else
{
exp.addBinarySelection_Learning(neg.generateNegativeFW(GlobalState.varModel));//neg.generateNegativeFWAllCombinations(GlobalState.varModel));
exp.addBinarySampling_Learning("newFW");
}
break;
default:
return command;
}
return "";
}示例2: readCSVBinaryOptFormat
private static List<Configuration> readCSVBinaryOptFormat(string file, VariabilityModel model)
{
List<Configuration> result = new List<Configuration>();
StreamReader sr = new StreamReader(file);
while (!sr.EndOfStream)
{
var line = sr.ReadLine().Split(';');
List<BinaryOption> temp = new List<BinaryOption>();
for (int i = 0; i < line.Length - 1; i++)
{
BinaryOption b = model.getBinaryOption(line[i]);
temp.Add(b);
}
double value = Double.Parse(line[line.Length - 1].Replace(',', '.'));
var c = new Configuration(temp);
c.setMeasuredValue(GlobalState.currentNFP, value);
result.Add(c);
}
sr.Close();
return result;
}示例3: computeEvaluationDataSetBasedOnTrueModel
/// <summary>
/// The methods generates based on all binary sampling and 50% hyper sampling configurations for the validation set.
/// If we have the true model, we can just compute the true value of the nonfunctional property for a given configuration.
/// </summary>
private void computeEvaluationDataSetBasedOnTrueModel()
{
VariantGenerator vg = new VariantGenerator(null);
List<List<BinaryOption>> temp = vg.generateAllVariantsFast(GlobalState.varModel);
List<List<BinaryOption>> binaryConfigs = new List<List<BinaryOption>>();
//take only 10k
if (temp.Count > 1000)
{
GlobalState.logInfo.log("Found " + temp.Count + " configurations. Use only 1000.");
HashSet<int> picked = new HashSet<int>();
Random r = new Random(1);
for (int i = 0; i < 1000; i++)
{
int x = 0;
do
{
x = r.Next(1, temp.Count);
} while (picked.Contains(x));
picked.Add(x);
binaryConfigs.Add(temp[x]);
}
temp.Clear();
}
else
binaryConfigs = temp;
exp.addBinarySelection_Validation(binaryConfigs);
var expDesign = new HyperSampling(GlobalState.varModel.NumericOptions);
expDesign.Precision = 10;
expDesign.computeDesign();
exp.addNumericalSelection_Validation(expDesign.SelectedConfigurations);
var numericConfigs = expDesign.SelectedConfigurations;
foreach (List<BinaryOption> binConfig in binaryConfigs)
{
if (numericConfigs.Count == 0)
{
Configuration c = new Configuration(binConfig);
c.setMeasuredValue(GlobalState.currentNFP, exp.TrueModel.eval(c));
GlobalState.addConfiguration(c);
}
foreach (Dictionary<NumericOption, double> numConf in numericConfigs)
{
Configuration c = new Configuration(binConfig, numConf);
c.setMeasuredValue(GlobalState.currentNFP, exp.TrueModel.eval(c));
GlobalState.addConfiguration(c);
}
}
}