public List <Figure> Execute()
{
Dictionary <string, object> variableScope = new Dictionary <string, object>();
Dictionary <string, Function> functionScope = new Dictionary <string, Function>();
Function constructPoint = new ConstructPointFunction();
Function constructPlane = new ConstructPlaneFunction();
Function constructSphere = new ConstructSphereFunction();
Function intersection = new IntersectionFunction();
Function pointOn = new PointOnFunction();
Function binormal = new BinormalFunction();
Function constructLine = new ConstructLineFunction();
Function center = new CenterFunction();
Function nullFunc = new NullFunction();
Function space = new SpaceFunction();
functionScope.Add("point", constructPoint);
functionScope.Add("plane", constructPlane);
functionScope.Add("sphere", constructSphere);
functionScope.Add("intersection", intersection);
functionScope.Add("point_on", pointOn);
functionScope.Add("binormal", binormal);
functionScope.Add("line", constructLine);
functionScope.Add("center", center);
functionScope.Add("null", nullFunc);
functionScope.Add("space", space);
string[] files = Directory.GetFiles("Assets/Custom/Scripts/Euclid/Lib", "*.euclid");
foreach (string f in files)
{
RootStatement libRoot = ConstructAbstractSyntaxTree(Tokenize(ReadFromFile(f)));
libRoot.Run(variableScope, functionScope);
}
root.Run(variableScope, functionScope);
List <Figure> render = new List <Figure>();
foreach (string variable in variableScope.Keys)
{
if (variableScope[variable] is Figure)
{
Figure fig = variableScope[variable] as Figure;
if (fig.properties.ContainsKey("render"))
{
if (Mathf.Approximately((float)fig.properties["render"], 1))
{
render.Add(fig);
Debug.Log(fig);
fig.properties["name"] = variable;
}
}
}
}
return(render);
}
static void Main()
{
#region 参数
Constant.Gold = Gold.Top;
Constant.Similarity = Similarity.SMC;
Constant.Methods = new Method[] { Method.MLE, Method.Cascaded, };// Method.Aggregation, Method.OrdinaryCombination, Method.WeightedCombination, Method.ExpertiseCombination, Method.Probability
int[] groupsizes = new int[] { 3, 5, 10, 15, 30 };
Constant.Filter = Filter.Ordinary;
//TrainConstant.Corpus = Corpus.Love; NotTrainConstant.Corpus = Corpus.Apple;
TrainConstant.Corpus = Corpus.Apple; NotTrainConstant.Corpus = Corpus.Love;
//Constant.SourceTaxonomy = new Taxonomy(TaxonomyType.Ekman, Constant.EkmanLabelArray); Constant.TargetTaxonomy = new Taxonomy(TaxonomyType.Nakamura, Constant.NakaLabelArray);
Constant.SourceTaxonomy = new Taxonomy(TaxonomyType.Nakamura, Constant.NakaLabelArray); Constant.TargetTaxonomy = new Taxonomy(TaxonomyType.Ekman, Constant.EkmanLabelArray);
#endregion
#region Initialization (Train)
TrainConstant.SentenceList = GeneralFunction.SentenceList(TrainConstant.Corpus);
switch (TrainConstant.Corpus)
{
case Corpus.LoveSample:
InitializationFunction._InitializeLoveSample();
break;
case Corpus.Love:
InitializationFunction.InitializeLove(TrainConstant.SentenceList, ref TrainConstant.SourceWorkerList, ref TrainConstant.TargetWorkerList);
break;
case Corpus.AppleSample:
InitializationFunction._InitializeAppleSample(TrainConstant.SentenceList, ref TrainConstant.SourceWorkerList, ref TrainConstant.TargetWorkerList);
break;
case Corpus.Apple:
InitializationFunction.InitializeApple(TrainConstant.SentenceList, ref TrainConstant.SourceWorkerList, ref TrainConstant.TargetWorkerList);
break;
}
#endregion
#region Initialization (Other)
NotTrainConstant.SentenceList = GeneralFunction.SentenceList(NotTrainConstant.Corpus);
switch (NotTrainConstant.Corpus)
{
case Corpus.Love:
InitializationFunction.InitializeLove(NotTrainConstant.SentenceList, ref NotTrainConstant.SourceWorkerList, ref NotTrainConstant.TargetWorkerList);
break;
case Corpus.Apple:
InitializationFunction.InitializeApple(NotTrainConstant.SentenceList, ref NotTrainConstant.SourceWorkerList, ref NotTrainConstant.TargetWorkerList);
break;
}
#endregion
#region 过滤 （为了分组）
if (Constant.Filter != Filter.Ordinary)
{
SentenceProperty.SortedSourceWorkerSourceAnnotationCountDic(); //如果按照true label个数过滤worker的话，需要
SentenceProperty.SortedTargetWorkerTargetAnnotationCountDic(); //如果按照true label个数过滤worker的话，需要
}
InitializationFunction.FilterTargetWorker();
#endregion
#region PropertyGeneration （整体属性，与各组无关）
SentenceProperty.SortedSourceAnnotationCountDic();
SentenceProperty.SortedSourceLabelDic();
SentenceProperty.GoldSourceAnnotation();
GeneralFunction.OutputGoldBinarySourceAnnotations();
SentenceProperty.SortedTargetAnnotationCountDic();
SentenceProperty.SortedTargetLabelDic();
SentenceProperty.GoldTargetAnnotation();
GeneralFunction.OutputGoldBinaryTargetAnnotations();
SpaceFunction.OutputGoldRealTargetAnnotations();
CascadedFunction.OutputGoldRealTargetAnnotations();
#endregion
#region Statistic (For Paper)
//IDictionary<Label, double> numberOfEachLabel = PaperFunction.NumberOfEachLabel(SourceOrTarget.Source, Constant.SentenceList);
//int numberOfWorkers = PaperFunction.NumberOfWorkers(SourceOrTarget.Source, Constant.SentenceList);
//IDictionary<Annotation, double> numberOfEachAnnotation = PaperFunction.NumberOfEachAnnotation(SourceOrTarget.Target, TrainConstant.SentenceList);
#endregion
#region Algorithm
foreach (int groupsize in groupsizes)
{
InitializationFunction.Group(groupsize);
int numberOfGroups = 30 / groupsize;
foreach (Method method in Constant.Methods)
{
switch (method)
{
case Method.MLE:
double accuracy = 0;
for (int i = 0; i < numberOfGroups; ++i)
{
MLEFunction.Pr_T_S(i); //训练用（因为测试时不用计算，所以没有测试步骤）
accuracy += GeneralFunction.Accuracy(method);
GeneralFunction.OutputEstimatedBinaryTargetAnnotations(method, groupsize, i);
}
GeneralFunction.ConsoleAndFile(method + "," + accuracy / numberOfGroups);
break;
case Method.Cascaded:
accuracy = 0;
for (int i = 0; i < numberOfGroups; ++i)
{
CascadedFunction.Pr_t(i);
CascadedFunction.Pr_t_s(i);
CascadedFunction.OutputPr_t_s(groupsize, i);
CascadedFunction.Pr_T_S(); //测试用（训练在S内完成，并且只需要前面计算好的Pr_t和Pr_t_s，所以与分组无关）
accuracy += GeneralFunction.Accuracy(method);
GeneralFunction.OutputEstimatedBinaryTargetAnnotations(method, groupsize, i);
}
GeneralFunction.ConsoleAndFile(method + "," + accuracy / numberOfGroups);
#region Test Pr_T_t
//TargetAnnotation ta = new TargetAnnotation();
//ta.LabelAndTruthDic[Label.喜Joy] = true;
//TargetLabeltruth tl = new TargetLabeltruth(Label.喜Joy, true);
//double Pr_T_t = tl.Pr_T_t(ta);
// 验证和是1
//TargetLabeltruth tl = new TargetLabeltruth(Label.喜Joy, true);
//double Pr_T_t_ForAllT = 0;
//foreach (Label[] targetLabelArray in ProbabilityFunction.PowerSet(Constant.TargetTaxonomy.LabelArray))
//{
// TargetAnnotation targetAnnotation = new TargetAnnotation(targetLabelArray);
// Pr_T_t_ForAllT += tl.Pr_T_t(targetAnnotation);
//}
#endregion
#region Test Pr_t_s（验证和是1）
//TargetLabeltruth tltrue = new TargetLabeltruth(Label.哀Sadness, true);
//TargetLabeltruth tlflase = new TargetLabeltruth(Label.哀Sadness, false);
//SourceLabeltruth sltrue = new SourceLabeltruth(Label.Sadness, true);
//SourceLabeltruth slfalse = new SourceLabeltruth(Label.Sadness, false);
//double Pr_t_srue_t_true = ProbabilityConstant.Matrix[sltrue][tltrue];
//double Pr_t_srue_t_false = ProbabilityConstant.Matrix[sltrue][tlflase];
//double Pr_s_false_t_true = ProbabilityConstant.Matrix[slfalse][tltrue];
//double Pr_s_false_t_false = ProbabilityConstant.Matrix[slfalse][tlflase];
#endregion
#region Test MLE
//SourceAnnotation sa = new SourceAnnotation();
////sa.LabelAndTruthDic[Label.Anger] = true;
////sa.LabelAndTruthDic[Label.Sadness] = true;
//sa.LabelAndTruthDic[Label.Joy] = true;
////sa.LabelAndTruthDic[Label.Surprise] = true;
////sa.LabelAndTruthDic[Label.Fear] = true;
////sa.LabelAndTruthDic[Label.Disgust] = true;
//IDictionary<TargetAnnotation, double> targetAnnotationProbabilityDic = new Dictionary<TargetAnnotation, double>();
//foreach (Label[] targetLabelArray in ProbabilityFunction.PowerSet(Constant.TargetTaxonomy.LabelArray))
//{
// TargetAnnotation targetAnnotation = new TargetAnnotation(targetLabelArray);
// double probability = sa.Pr_T_S(targetAnnotation);
// if (probability != 0)
// targetAnnotationProbabilityDic.Add(targetAnnotation, probability);
//}
//IDictionary<TargetAnnotation, double> sortedElements = GeneralFunction.SortDictionary(targetAnnotationProbabilityDic);
#endregion
break;
#region Space
case Method.Aggregation:
SpaceFunction.AggregatedMatrix();
SpaceFunction.OutputMatrix(Method.Aggregation);
SpaceFunction.RealTargetAnnotations();
SpaceFunction.OutputEstimatedRealTargetAnnotations(method);
GeneralFunction.OutputEstimatedBinaryTargetAnnotations(method, groupsize, int.MaxValue);
GeneralFunction.ConsoleAndFile(method + "," + GeneralFunction.Accuracy(method));
break;
case Method.OrdinaryCombination: //0论文中OC
SpaceFunction.OrdinaryMatrix();
SpaceFunction.OutputMatrix(method);
SpaceFunction.RealTargetAnnotations();
SpaceFunction.OutputEstimatedRealTargetAnnotations(method);
GeneralFunction.OutputEstimatedBinaryTargetAnnotations(method, groupsize, int.MaxValue);
GeneralFunction.ConsoleAndFile(method + "," + GeneralFunction.Accuracy(method));
break;
case Method.WeightedCombination: //5（IMECS中WC）
SentenceProperty.OtherNonormalizeWeightDic();
SpaceFunction.WeightedMatrix(method);
SpaceFunction.OutputMatrix(method);
SpaceFunction.RealTargetAnnotations();
SpaceFunction.OutputEstimatedRealTargetAnnotations(method);
GeneralFunction.OutputEstimatedBinaryTargetAnnotations(method, groupsize, int.MaxValue);
GeneralFunction.ConsoleAndFile(method + "," + GeneralFunction.Accuracy(method));
break;
case Method.ExpertiseCombination:
TargetWorkerProperty.ExpertiseMatrix(1);
SpaceFunction.ExpertiseTransformationMatrix();
SpaceFunction.OutputMatrix(method);
SpaceFunction.RealTargetAnnotations();
SpaceFunction.OutputEstimatedRealTargetAnnotations(method);
GeneralFunction.OutputEstimatedBinaryTargetAnnotations(method, groupsize, int.MaxValue);
GeneralFunction.ConsoleAndFile(method + "," + GeneralFunction.Accuracy(method));
break;
case Method.TestExpertise:
TargetWorkerProperty.ExpertiseMatrix(10);
Test.TestExpertise(10);
break;
#endregion
#region 废弃方法
case Method.TemporaryNogeneralNonormalize: //1
SentenceProperty.TemporaryNonormalizeWeightDic();
SpaceFunction.WeightedMatrix(method);
SpaceFunction.OutputMatrix(method);
GeneralFunction.ConsoleAndFile(method + "," + GeneralFunction.Accuracy(method));
break;
//一个sentence内所有worker的影响和为1。
//结果与未Normalized不相同（理论上是相同的，可能是计算精度问题）
case Method.TemporaryNogeneralNormalize: //2(需要1)
SentenceProperty.TemporaryNormalizeWeightDic();
SpaceFunction.WeightedMatrix(method);
SpaceFunction.OutputMatrix(method);
GeneralFunction.ConsoleAndFile(method + "," + GeneralFunction.Accuracy(method));
break;
case Method.TemporaryGeneralNonormalize: //3(需要1)
TargetWorkerProperty.TemporaryNonormalizeWeight();
SpaceFunction.WeightedMatrix(method);
SpaceFunction.OutputMatrix(method);
GeneralFunction.ConsoleAndFile(method + "," + GeneralFunction.Accuracy(method));
break;
case Method.TemporaryGeneralNormalize: //4(需要3)
TargetWorkerProperty.TemporaryNormalizeWeight();
SpaceFunction.WeightedMatrix(method);
SpaceFunction.OutputMatrix(method);
GeneralFunction.ConsoleAndFile(method + "," + GeneralFunction.Accuracy(method));
break;
case Method.OtherNogeneralNormalize: //6（需要5）
SentenceProperty.OtherNormalizeWeightDic();
SpaceFunction.WeightedMatrix(method);
SpaceFunction.OutputMatrix(method);
GeneralFunction.ConsoleAndFile(method + "," + GeneralFunction.Accuracy(method));
break;
case Method.OtherGeneralNonormalize: //7（需要5）
TargetWorkerProperty.OtherNonormalizeWeight();
SpaceFunction.WeightedMatrix(method);
SpaceFunction.OutputMatrix(method);
GeneralFunction.ConsoleAndFile(method + "," + GeneralFunction.Accuracy(method));
break;
case Method.OtherGeneralNormalize:
TargetWorkerProperty.OtherGeneralWeight();
SpaceFunction.WeightedMatrix(method);
SpaceFunction.OutputMatrix(method);
GeneralFunction.ConsoleAndFile(method + "," + GeneralFunction.Accuracy(method));
break;
#endregion
}
}
}
#endregion
#region Output
StreamWriter Output = new StreamWriter("Output/" + Constant.Gold + "/" + TrainConstant.Corpus + "_" + Constant.SourceTaxonomy.Name + "/Accuracy.csv");
Output.Write(Constant.Output);
Output.Close();
Console.Write("Press any key...");
Console.Read();
#endregion
}
