public Turara_DistGenerator(int dim, double dist_weight, int main_index)
{
var turara = MyMath.MakeTurara(dim, dist_weight);
var dist = Vector <double> .Build.Dense(dim, turara.other);
dist[main_index] = turara.max;
this.MyCustomDistribution = new CustomDistribution(dist, dist_weight);
}
public SubjectManager Generate(OpinionSubject opinion_subject, double dist_weight, int correct_dim, double sensor_weight, EnvDistributionEnum env_dis_mode, int malicious_dim = 0, double malicious_dist_weight = 0.0)
{
CustomDistribution env_dist = null;
CustomDistribution env_malicious_dist = null;
switch (env_dis_mode)
{
case EnvDistributionEnum.Turara:
env_dist = new Turara_DistGenerator(opinion_subject.SubjectDimSize, dist_weight, correct_dim).Generate(); //maxとotherを計算して返す
env_malicious_dist = new Turara_DistGenerator(opinion_subject.SubjectDimSize, malicious_dist_weight, malicious_dim).Generate(); //同上
break;
case EnvDistributionEnum.Exponential:
env_dist = new Exponential_DistGenerator(opinion_subject.SubjectDimSize, dist_weight, correct_dim).Generate();
env_malicious_dist = new Exponential_DistGenerator(opinion_subject.SubjectDimSize, malicious_dist_weight, malicious_dim).Generate();
break;
case EnvDistributionEnum.Shitei:
env_dist = new Shitei_DistGenerator(opinion_subject.SubjectDimSize, dist_weight, correct_dim).Generate();
env_malicious_dist = new Shitei_DistGenerator(opinion_subject.SubjectDimSize, malicious_dist_weight, malicious_dim).Generate();
break;
}
Debug.Assert(env_dist != null); //計算できてなかったらエラー
Debug.Assert(env_malicious_dist != null);
var subject_tv = new OpinionSubject("good_tv", 3);
var subject_test = new OpinionSubject("test", opinion_subject.SubjectDimSize);
var subject_company = new OpinionSubject("good_company", 2);
double[] conv_array = { 1, 0, 0, 1, 1, 0 };
var conv_matrix = Matrix <double> .Build.DenseOfColumnMajor(2, 3, conv_array); //2*3の形にリシェイプ
var osm_env = new OpinionEnvironment()
.SetSubject(subject_test)
.SetCorrectDim(correct_dim) //正しい次元
.SetMaliciousDim(malicious_dim) //間違った次元
.SetSensorWeight(sensor_weight) //センサウェイト
.SetCustomDistribution(env_dist)
.SetMaliciousCustomDistribution(env_malicious_dist);
var subject_manager = new SubjectManager() //サブジェクトマネージャー生成
.AddSubject(subject_test)
.RegistConversionMatrix(subject_tv, subject_company, conv_matrix) //オピニオンにサブジェクトマネージャーを登録.サブジェクトマネージャーに意見交換クラスとしてこれらの情報を登録
.SetEnvironment(osm_env); //環境をセット
return(subject_manager);
}
public Exponential_DistGenerator(int dim, double dist_weight, int main_index) //意見数，
{
var range = Enumerable.Range(0, dim); //0から意見数-1(意見数分).要素も0,1,2,..
var dist_enumerable = range.Select((item, index) => Math.Pow(1 - dist_weight, index)); //1-dist_weightのindex乗
var sum = dist_enumerable.Sum();
var dist_list = dist_enumerable.Select(item => Math.Round(item / sum, 5)); //各要素をsumで割り算して小数第5位で丸める
var dist = Vector <double> .Build.Dense(dist_list.ToArray()); //double型のリストにコピー
var max_index = dist.MaximumIndex(); //最大値のindexを返す(同値の場合は最小のindex)
var max = dist[max_index]; //最大値取得
dist[max_index] = dist[main_index]; //max_indexとmain_indexを交換
dist[main_index] = max;
this.MyCustomDistribution = new CustomDistribution(dist, dist_weight); //意見を確立によって決定.distとdist_weightをセットしてCustomDistributionをセット
}
public Shitei_DistGenerator(int dim, double dist_weight, int main_index) //意見数，
{
var dist_list = new List <double>()
{
0.8, 0.2
};
var dist = Vector <double> .Build.Dense(dist_list.ToArray()); //double型のリストにコピー
var max_index = dist.MaximumIndex(); //最大値のindexを返す(同値の場合は最小のindex)
var max = dist[max_index]; //最大値取得
dist[max_index] = dist[main_index]; //max_indexとmain_indexを交換
dist[main_index] = max;
this.MyCustomDistribution = new CustomDistribution(dist, dist_weight); //意見を確立によって決定.distとdist_weightをセットしてCustomDistributionをセット
}
public static IDistribution GetDistribution(Enums.Distribuciones dist, Tuple <object, object, object> parameters)
{
IDistribution result = null;
switch (dist)
{
case Enums.Distribuciones.Uniforme:
result = new ContinuousUniformDistribution();
result.Initialize(parameters);
break;
case Enums.Distribuciones.Normal:
result = new NormalDistribution();
result.Initialize(parameters);
break;
case Enums.Distribuciones.Beta:
result = new BetaDistribution();
result.Initialize(parameters);
break;
case Enums.Distribuciones.BetaPERT:
result = new BetaPertDistribution();
result.Initialize(parameters);
break;
case Enums.Distribuciones.Binomial_Discreta:
result = new BinomialDistribution();
result.Initialize(parameters);
break;
case Enums.Distribuciones.Custom_Discreta:
result = new CustomDistribution();
result.Initialize(parameters);
break;
default:
break;
}
return(result);
}
private static CustomDistribution GetCustomDistribution(string distrName, string formulaPPN, int index)
{
var startIndex = index;
index += distrName.Length + 1;
while (index != formulaPPN.Length && IsDigit(formulaPPN[index]))
{
index++;
}
var customDistrFileName = formulaPPN.Substring(startIndex, index - startIndex);
var folderPath = Path.Combine(Directory.GetCurrentDirectory(), CUSTOM_DISTR_FOLDER_NAME);
var fileAndExtention = Directory.GetFiles(folderPath)
.Select(x => (Path.GetFileNameWithoutExtension(x), Path.GetExtension(x)))
.FirstOrDefault(x => x.Item1 == customDistrFileName);
var filePath = $"{fileAndExtention.Item1}{fileAndExtention.Item2}";
var data = File.ReadAllLines(Path.Combine(folderPath, filePath)).Select(x => double.Parse(x)).ToArray();
var customDistribution = new CustomDistribution(data);
return(customDistribution);
}
public CommonCuriocity_Experiment SetCustomDistribution(CustomDistribution custom_dist)
{
this.MyCustomDistribution = custom_dist;
return(this);
}
public Tuple <List <List <double> >, List <double>, List <Constraint>, List <double>, List <double>, OptDirectionEnum> GenerateProblem(int xCount, int alternativesCount, int constraintsCount, string optDirectionString, CustomDistribution distribution)
{
//define optimization direction
OptDirectionEnum optDirection;
switch (optDirectionString)
{
case "min": { optDirection = OptDirectionEnum.min; break; }
case "max": { optDirection = OptDirectionEnum.max; break; }
default: { optDirection = GenerateOptDirection(); break; }
}
//define ws
List <double> ws = new List <double>();
for (int i = 0; i < alternativesCount; i++)
{
ws.Add(distribution.Generate());
}
//define ls
List <double> ls = new List <double>();
for (int i = 0; i < alternativesCount; i++)
{
ls.Add(distribution.Generate());
}
//define numerators
List <List <double> > numerators = new List <List <double> >();
for (int i = 0; i < alternativesCount; i++)
{
List <double> c = new List <double>();
for (int j = 0; j < xCount; j++)
{
c.Add(distribution.Generate());
}
numerators.Add(c);
}
//define denominator
List <double> denominator = new List <double>();
for (int i = 0; i < xCount; i++)
{
denominator.Add(distribution.Generate());
}
//define constraints
List <Constraint> constraints = new List <Constraint>();
for (int i = 0; i < constraintsCount; i++)
{
List <double> constrCoefs = new List <double>();
for (int j = 0; j < xCount; j++)
{
constrCoefs.Add(distribution.Generate());
}
double freeValue = distribution.Generate();
SymbolEnum symbol = GenerateSymbolEnum();
constraints.Add(new Constraint(constrCoefs, symbol, freeValue));
}
return(new Tuple <List <List <double> >, List <double>, List <Constraint>, List <double>, List <double>, OptDirectionEnum>(numerators, denominator, constraints, ls, ws, optDirection));
}
public void GenerateIndividualProblem(int xCount, int alternativesCount, int constraintsCount, string optDirectionString, CustomDistribution distribution)
{
bool flag = false;
while (!flag)
{
try
{
var problem = _generateService.GenerateProblem(xCount, alternativesCount, constraintsCount, optDirectionString, distribution);
var result = _solveHelper.SolveProblem(problem.Item1, problem.Item2, problem.Item3, problem.Item4, problem.Item5, problem.Item6);
fileInput = WriteInputToFile(problem.Item1, problem.Item2, problem.Item3, problem.Item4, problem.Item5, problem.Item6.ToString());
fileOutput = WriteOutputToFile(result.Item1, result.Item2, result.Item3, result.Item4);
flag = true;
}
catch (Y0IsNullException)
{
flag = false;
}
catch (NoOptimumException)
{
flag = false;
}
}
}
public OpinionEnvironment SetMaliciousCustomDistribution(CustomDistribution custom_malicious_dist)
{
this.MyMaliciousCustomDistribution = custom_malicious_dist;
return(this);
}
public OpinionEnvironment SetCustomDistribution(CustomDistribution custom_dist)
{
this.MyCustomDistribution = custom_dist;
return(this);
}
static void Main(string[] args)
{
var path = @"C:\RandomData\data.txt";
var textData = File.ReadAllLines(path);
var distrData = textData.Select(t => double.Parse(t)).ToArray();
var customDistribution = new CustomDistribution(distrData) + new NormalDistribution(0, 1);
Console.WriteLine("\nПользовательское распределение");
Console.WriteLine(customDistribution.SummaryInfo);
var data = customDistribution.GetPDFDataForPlot().ToArray();
var series51 = MakeSeries(data);
var chart51 = MakeChart(series51, "x", "f(x)", "График f(x) пользовательского распределения",
SeriesChartType.Column, 1, -10, 10);
/////
//var sum = new NormalDistribution(2, 1) + new NormalDistribution(0, 1);
//Console.WriteLine("\nПользовательское распределение");
//Console.WriteLine(sum.SummaryInfo);
//var sumData = sum.GetPDFDataForPlot().ToArray();
//var series52 = MakeSeries(sumData);
//var chart52 = MakeChart(series52, "x", "f(x)", "График суммы двух нормальных",
// SeriesChartType.Column, 1, -10, 10);
////////////////////////////////////////////////////
var x = 0.17;
var gamma = Gamma.Function(x);
//var x = new double[]
//{
// -1, 0, 1, 2, 3, 4
//};
//var y = new double[x.Length];
//for (var i = 0; i < x.Length; i++)
//{
// y[i] = Math.Pow(x[i], 2);
//}
//var L0 = new UniformDistribution(9, 10);
//var L1 = new UniformDistribution(11, 12);
//var dt = new NormalDistribution(1, 2);
//var productOfNormalVars = (L1 / L0 - 1) / dt;
//////////////////////////////////////////////////////
//var customDistribution = new CauchyDistribution(0, 0.5);
//Console.WriteLine("\nПользовательское распределение");
//Console.WriteLine(customDistribution.SummaryInfo);
//var data = customDistribution.GetPDFDataForPlot(-5, 5).ToArray();
//var series51 = MakeSeries(data);
//var chart51 = MakeChart(series51, "x", "f(x)", "График f(x) пользовательского распределения",
// SeriesChartType.Column, 0.5, -5, 5);
//////////////////////////////////////////////////////
///
//var normalVar1 = new ExponentialDistribution(1.5);
////var normalVar1 = new UniformDistribution(0, 1) + new UniformDistribution(0, 1);
//var series61 = MakeSeries(normalVar1.GetPDFDataForPlot().ToArray());
//var chart61 = MakeChart(series61, "x", "f(x)", "График f(x) нормального распределения",
// SeriesChartType.Column, 1, -3, 5);
//var series62 = MakeSeries(normalVar1.GetCDFDataForPlot().ToArray());
//var chart62 = MakeChart(series62, "x", "F(x)", "График F(x) нормального распределения",
// SeriesChartType.Column, 1, -3, 5);
//Console.WriteLine("\nНормальное распределение");
//Console.WriteLine(normalVar1.SummaryInfo);
////////////////////////////////////////////////////
//var customDistribution = new CauchyDistribution(0, 0.5);
//var dataCount = 1000000;
//var dataSampling = new List<double>();
//for (var i = 0; i < dataCount; i++)
//{
// dataSampling.Add(customDistribution.GetNewRandomValue());
//}
//dataSampling.Sort();
//var (pdfValuesExpected, pdfValuesActual) = CalculateExpectedAndActualPDFValues(customDistribution, dataSampling.ToArray());
//var chiSquareTest = new Test(pdfValuesExpected.Select(p => p.Y).ToArray(), pdfValuesActual.Select(p => p.Y).ToArray(), pdfValuesExpected.Count() - 1);
//var series1 = MakeSeries(pdfValuesExpected);
//var chart1 = MakeChart(series1, "x", "f(x)", "pdfValuesExpected",
// SeriesChartType.Column, 1, -1, 30);
//var series2 = MakeSeries(pdfValuesActual);
//var chart2 = MakeChart(series2, "x", "f(x)", "pdfValuesActual",
// SeriesChartType.Column, 1, -1, 30);
////////////////////////////////////////////////////
var forms = MakeFormsFromCharts(new List <Chart> {
chart51
});
Application.EnableVisualStyles();
Application.Run(new MultiFormContext(forms.ToArray()));
Console.ReadKey();
}
public Normal_Experiment SetCustomDistribution(CustomDistribution custom_dist)
{
this.MyCustomDistribution = custom_dist;
return(this);
}
