How to use GenY class of B package

Best Nunit code snippet using B.GenY

Program.cs

Source:Program.cs Github

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1using System;2using System.Collections.Generic;3using System.Linq;4using System.Text;5using System.Threading.Tasks;6using System.Diagnostics;7namespace Genetic_ShortPath8{9 class Program10 {11 static int osobi, cities, geny, StartCity, EndCity;12 static void Main(string[] args)13 {14 var sw = new Stopwatch();15 cities = 300;16 Console.WriteLine("Кол-во городов: " + cities);17 Random rand = new Random();18 int[,] MatrDistance = new int[cities, cities]; //матрица растояний19 for (int i = 0; i < cities; i++)20 for (int j = i + 1; j < cities; j++)21 MatrDistance[i, j] = rand.Next(5, 150);22 for (int i = 0; i < cities; i++)23 for (int j = 0; j < i; j++)24 MatrDistance[i, j] = MatrDistance[j, i];2526 /* Console.WriteLine("Длина пути от одного города к другому: ");27 Console.Write(" | ");28 for (int i = 0; i < cities; i++)29 {30 if (i < 9)31 Console.Write("{0} ", i + 1);32 else33 Console.Write("{0} ", i + 1);34 }353637 Console.WriteLine();38 Console.Write("---");39 for (int i = 0; i < cities; i++)40 Console.Write("----");414243 Console.WriteLine();44 for (int i = 0; i < cities; i++)45 {46 if (i < 9)47 Console.Write("{0} | ", i + 1);48 else49 Console.Write("{0} | ", i + 1);50 for (int j = 0; j < cities; j++)51 {52 if (MatrDistance[i, j] > 9)53 Console.Write("{0} ", MatrDistance[i, j]);54 else55 Console.Write("{0} ", MatrDistance[i, j]);56 }57 Console.WriteLine();58 }*/5960 StartCity = rand.Next(1, cities);61 EndCity = rand.Next(1, cities);62 if (StartCity == EndCity)63 StartCity = rand.Next(1, cities);64 Console.WriteLine("Город-отправитель: {0}, Город-получатель: {1}", StartCity, EndCity);65 osobi = 8; 66 Console.WriteLine("Кол-во особей в популяции: " + osobi);67 geny = 6; 68 Console.WriteLine("Кол-во генов у особи: " + geny);69 sw.Start();70 int[,] Individi = new int[osobi, geny]; //особи71 for (int i = 0; i < osobi; i++)72 for (int j = 0; j < geny; j++)73 Individi[i, j] = rand.Next(0, cities);74 int[,] allFitnesses = new int[osobi, 2]; //пригодность каждой особи и её номер75 Console.WriteLine();76 Console.WriteLine("Поколение 1:");77 for (int i = 0; i < osobi; i++)78 {79 int fitness = 0;80 fitness += MatrDistance[StartCity - 1, Individi[i, 0]];81 Console.Write("Особь {0}, её гены: ", i + 1);82 for (int j = 0; j < geny; j++)83 {84 if (Individi[i, j] < 9)85 Console.Write("{0}, ", Individi[i, j] + 1);86 else87 Console.Write("{0}, ", Individi[i, j] + 1);88 if (j > 0)89 fitness += MatrDistance[Individi[i, j - 1], Individi[i, j]];90 }91 fitness += MatrDistance[Individi[i, geny - 1], EndCity - 1];92 Console.WriteLine(" Пригодность: " + fitness);93 allFitnesses[i, 0] = i;94 allFitnesses[i, 1] = fitness;95 }96 Console.WriteLine();97 int iterations = 0;98 do99 {100 iterations++;101 List<int[]> newIndividi = new List<int[]>(); //массив из потомства102 do103 {104 int[] parent_1 = new int[geny]; //родители105 int[] parent_2 = new int[geny];106 //выбор родителей 107 ViborParents(Individi, allFitnesses, ref parent_1, ref parent_2);108 int[] child_1 = new int[geny]; //потомоки109 int[] child_2 = new int[geny];110 //скрещивание 111 Crossing(parent_1, parent_2, ref child_1, ref child_2);112 newIndividi.Add(child_1);113 newIndividi.Add(child_2);114 } while (newIndividi.Count < osobi); //формируем новую популяцию115 Mutation(ref newIndividi); //мутация обменом соседних генов116 for (int i = 0; i < osobi; i++) //объединение родителей и потомков117 {118 newIndividi.Add(new int[geny]);119 for (int j = 0; j < geny; j++)120 newIndividi.Last()[j] = Individi[i, j];121 }122 Selection(MatrDistance, ref newIndividi); //отбор особей в новое поколение методом усечения123 Console.WriteLine("Поколение {0}:", iterations + 1);124 OutputPopulation(MatrDistance, ref allFitnesses, newIndividi);125 int counter = 0;126 for (int i = 1; i < osobi; i++) //подсчёт кол-ва особей с одинаковой пригодностью для остановки алгоритма127 if (allFitnesses[i, 1] == allFitnesses[0, 1])128 counter++;129 if (counter >= osobi * 0.75)130 break;131 for (int i = 0; i < osobi; i++)132 for (int j = 0; j < geny; j++)133 Individi[i, j] = newIndividi[i][j];134 } while (iterations < 50);135 Console.WriteLine("Кол-во поколений: " + (iterations + 1));136 Console.Write("Найденный путь: " + StartCity);137 for (int i = 0; i < geny; i++)138 Console.Write(" -> " + (Individi[0, i] + 1));139 Console.WriteLine(" -> {0}, его длина: " + allFitnesses[0, 1], EndCity);140 Console.WriteLine("Длина прямого пути от {0} города к {1}: " + MatrDistance[StartCity - 1, EndCity - 1], StartCity, EndCity);141 142 sw.Stop();143 Console.WriteLine($"Времени потрачено - {sw.ElapsedMilliseconds} млс");144 Console.ReadLine();145 }146 static void Selection(int[,] MatrDistance, ref List<int[]> Individi) //отбор усечением147 {148 Random rand = new Random();149 double T = 0.4; //порог для отбора150 int fitness = 0;151 int[,] allFitnesses = new int[Individi.Count, 2]; //пригодность каждой особи и её номер152 for (int i = 0; i < Individi.Count; i++) //вычисление пригодности каждой особи153 {154 fitness += MatrDistance[StartCity - 1, Individi[i][0]];155 for (int j = 1; j < geny; j++)156 fitness += MatrDistance[Individi[i][j - 1], Individi[i][j]];157 fitness += MatrDistance[Individi[i][geny - 1], EndCity - 1];158 allFitnesses[i, 0] = i;159 allFitnesses[i, 1] = fitness;160 fitness = 0;161 }162 int[] temp = new int[Individi.Count]; //сортировка особей в порядке убывания их пригодности163 for (int i = 0; i < Individi.Count; i++)164 temp[i] = allFitnesses[i, 1];165 Array.Sort(temp);166 for (int i = 0; i < Individi.Count; i++)167 for (int j = 0; j < Individi.Count; j++)168 if (temp[i] == allFitnesses[j, 1])169 {170 Swap(ref allFitnesses[i, 1], ref allFitnesses[j, 1]);171 Swap(ref allFitnesses[i, 0], ref allFitnesses[j, 0]);172 break;173 }174 int countNewIndividi = (int)(Individi.Count * T); //кол-во особей, прошедших через отбор175 List<int[]> newIndividi = new List<int[]>();176 int numberNewIndividi = 0;177 do //отбор особей в новую популяцию178 {179 numberNewIndividi = rand.Next(0, countNewIndividi - 1);180 newIndividi.Add(new int[geny]);181 for (int i = 0; i < geny; i++)182 newIndividi.Last()[i] = Individi[allFitnesses[numberNewIndividi, 0]][i];183 } while (newIndividi.Count < osobi);184 Individi.Clear();185 Individi = newIndividi;186 }187 static void ViborParents(int[,] Individi, int[,] allFitnesses, ref int[] parent_1, ref int[] parent_2) //выбор родителей 188 {189 Random rand = new Random();190 int numberParent_1 = rand.Next(0, osobi - 1); //выбор первого родителя 191 for (int i = 0; i < geny; i++)192 parent_1[i] = Individi[allFitnesses[numberParent_1, 0], i];193 double[] euclidDistance = new double[osobi];194 double mineuclidDistance = double.MaxValue;195 int numberParent_2 = 0;196 for (int i = 0; i < osobi; i++) //вычисление Евклидова расстояния для выбора второго родителя197 {198 for (int j = 0; j < geny; j++)199 euclidDistance[i] += Math.Pow(parent_1[j] - Individi[allFitnesses[i, 0], j], 2);200 euclidDistance[i] = Math.Sqrt(euclidDistance[i]);201 if (euclidDistance[i] < mineuclidDistance && euclidDistance[i] != 0)202 {203 mineuclidDistance = euclidDistance[i];204 numberParent_2 = i;205 }206 }207 for (int i = 0; i < geny; i++)208 parent_2[i] = Individi[allFitnesses[numberParent_2, 0], i];209 }210 static void Crossing(int[] parent_1, int[] parent_2, ref int[] child_1, ref int[] child_2) //скрещивание методом дискретной рекомбинации211 {212 Random rand = new Random();213 int[,] mask_for_cross = new int[2, geny]; //маска для замены генов214 for (int i = 0; i < 2; i++) //выбираем номера особи для замены генов215 for (int j = 0; j < geny; j++)216 mask_for_cross[i, j] = rand.Next(0, 2);217 for (int i = 0; i < geny; i++)218 {219 if (mask_for_cross[0, i] == 1) //замена генов для первого потомка220 child_1[i] = parent_2[i];221 else222 child_1[i] = parent_1[i];223 if (mask_for_cross[1, i] == 0) //замена генов для второго потомка224 child_2[i] = parent_1[i];225 else226 child_2[i] = parent_2[i];227 }228 }229 static void Mutation(ref List<int[]> newIndividi) //мутация методом обмена соседних генов230 {231 Random rand = new Random();232 double T = 0.3; //вероятность мутации - порог233 double[] veroyatnost_mutation = new double[newIndividi.Count];234 for (int i = 0; i < newIndividi.Count; i++)235 {236 veroyatnost_mutation[i] = ((double)rand.Next(1, 100) / 100); //случайно выбирается вероятность мутации для каждой особи237 if (veroyatnost_mutation[i] <= T) //если вероятность мутации особи меньше порога238 {239 int numOFgen = rand.Next(1, geny - 1); //номер гена для мутации 240 if (newIndividi[i][numOFgen - 1] == newIndividi[i][numOFgen + 1])241 numOFgen = rand.Next(0, geny - 1);242 if (numOFgen == 0)243 numOFgen++;244 Swap(ref newIndividi[i][numOFgen - 1], ref newIndividi[i][numOFgen + 1]);245 }246 }247 }248 static void OutputPopulation(int[,] MatrDistance, ref int[,] allFitnesses, List<int[]> newIndividi)249 {250 int fitness = 0;251 for (int i = 0; i < osobi; i++)252 {253 fitness += MatrDistance[StartCity - 1, newIndividi[i][0]];254 Console.Write("Особь {0}, её гены: ", i + 1);255 for (int j = 0; j < geny; j++)256 {257 if (newIndividi[i][j] < 9)258 Console.Write("{0}, ", newIndividi[i][j] + 1);259 else260 Console.Write("{0}, ", newIndividi[i][j] + 1);261 if (j > 0)262 fitness += MatrDistance[newIndividi[i][j - 1], newIndividi[i][j]];263 }264 fitness += MatrDistance[newIndividi[i][geny - 1], EndCity - 1];265 Console.WriteLine(" пригодность: " + fitness);266 allFitnesses[i, 0] = i;267 allFitnesses[i, 1] = fitness;268 fitness = 0;269 }270 Console.WriteLine();271 }272 static void Swap<T>(ref T a, ref T b)273 {274 T temp = a;275 a = b;276 b = temp;277 }278 }279}...

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TrueValuesGen.cs

Source:TrueValuesGen.cs Github

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12namespace Zygotine.WebExpo3{4 using System;5 using System.Text;6 using Zygotine.Statistics.Distribution;7 using Zygotine.Util;8 internal class TrueValuesGen9 {10 internal double[] LogY = new double[0];11 internal double[] Y = new double[0];12 internal double[] LogGT { get; set; } = new double[0];13 internal double[] GT { get; set; } = new double[0];14 internal double[] LogLT { get; set; } = new double[0];15 internal double[] LT { get; set; } = new double[0];16 internal double[] LogI { get; set; } = new double[0];17 internal double[] I = new double[0];18 private TrueValuesGen()19 {20 }21 public string Show()22 {23 string fmt = "Y={0}, GT={1}, LT={2}, I={3}";24 string fmt2 = "{0}, **LOG: {1}";25 StringBuilder sb = new StringBuilder();26 sb.AppendFormat(fmt2, string.Format(fmt, Y.ShowR(), GT.ShowR(), LT.ShowR(), I.ShowR()), string.Format(fmt, LogY.ShowR(), LogGT.ShowR(), LogLT.ShowR(), LogLT.ShowR()));27 return sb.ToString();28 }29 private TrueValuesGen(YGen genY, DataSummary data, double mu, double sigma, MeasurementError me, bool logNormalDistrn = true, GenObject o = null)30 {31 if (me.ThroughSD && !logNormalDistrn)32 {33 double meSD = me.Parm;34 double tauStar = 1 / Math.Pow(sigma, 2) + 1 / Math.Pow(meSD, 2);35 double sdStar = 1 / Math.Sqrt(tauStar);36 if (data.YLength > 0)37 {38 double[] tmpMean = (data.Y.Divide(Math.Pow(meSD, 2)).Add(mu / Math.Pow(sigma, 2))).Divide(tauStar);39 this.Y = NormalDistribution.RNorm(data.YLength, tmpMean, Tools.Rep(sdStar, tmpMean.Length));40 }41 if (data.GTLength > 0)42 {43 double[] tmpMean = (genY.GT.Divide(Math.Pow(meSD, 2)).Add(mu / Math.Pow(sigma, 2))).Divide(tauStar);44 this.Y = NormalDistribution.RNorm(data.GTLength, tmpMean, Tools.Rep(sdStar, tmpMean.Length));45 }46 if (data.LTLength > 0)47 {48 double[] tmpMean = (genY.LT.Divide(Math.Pow(meSD, 2)).Add(mu / Math.Pow(sigma, 2))).Divide(tauStar);49 this.Y = NormalDistribution.RNorm(data.GTLength, tmpMean, Tools.Rep(sdStar, tmpMean.Length));50 }51 if (data.IntervalLength > 0)52 {53 double[] tmpMean = (genY.I.Divide(Math.Pow(meSD, 2)).Add(mu / Math.Pow(sigma, 2))).Divide(tauStar);54 this.Y = NormalDistribution.RNorm(data.GTLength, tmpMean, Tools.Rep(sdStar, tmpMean.Length));55 }56 }57 else58 {59 o.A.Sigma2 = sigma * sigma;60 this.Y = new double[data.YLength];61 this.GT = new double[data.GTLength];62 this.LT = new double[data.LTLength];63 this.I = new double[data.IntervalLength];64 for (int j = 0; j < data.YLength; j++)65 {66 this.Y[j] = TrueValueGen(o, me, data.Y[j], mu, logY: logNormalDistrn ? data.LogY[j] : Tools.ND);67 }68 for (int j = 0; j < data.GTLength; j++)69 {70 this.GT[j] = TrueValueGen(o, me, genY.GT[j], mu, logY: logNormalDistrn ? genY.LogGT[j] : Tools.ND);71 }72 for (int j = 0; j < data.LTLength; j++)73 {74 this.LT[j] = TrueValueGen(o, me, genY.LT[j], mu, logY: logNormalDistrn ? genY.LogLT[j] : Tools.ND);75 }76 for (int j = 0; j < data.IntervalLength; j++)77 {78 this.I[j] = TrueValueGen(o, me, genY.I[j], mu, logY: logNormalDistrn ? genY.LogI[j] : Tools.ND);79 }80 if (logNormalDistrn)81 {82 this.LogY = this.Y.Log();83 this.LogGT = this.GT.Log();84 this.LogLT = this.LT.Log();85 this.LogI = this.I.Log();86 }87 }88 } //# end of truevalues.gen 89 internal static TrueValuesGen GetInstance(YGen genY, DataSummary data, double mu, double sigma, MeasurementError me, bool logNormalDistrn = true, GenObject o = null)90 {91 return new TrueValuesGen(genY, data, mu, sigma, me, logNormalDistrn, o);92 }93 private double TrueValueGen(GenObject genO, MeasurementError me, double y, double mu, double logY)94 {95 SGNFnAParam localA = genO.A.Clone();96 localA.Mu = mu;97 if (genO.LogNormalDistrn)98 {99 localA.LogY = logY;100 }101 localA.Y = y;102 localA.Y2 = y * y;103 if (genO.ThroughSD)104 {105 localA.Ksi = me.Parm;106 localA.Ksi2 = me.Parm * me.Parm;107 }108 else109 {110 localA.CV2 = me.Parm * me.Parm;111 }112 double[] start = genO.Start(localA);113 //start.Any(zz => !zz.IsFinite());114 Icdf icdf = new Icdf(genO, localA, range: genO.Range);115 double x = icdf.Bidon(start, inestLowerLim: !genO.LogNormalDistrn);116 if (genO.LogNormalDistrn)117 {118 x = Math.Exp(x);// # new_0.11119 }120 return x;121 } //# end of truevalue.gen122 }123}...

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Form1.cs

Source:Form1.cs Github

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1using System;2using System.Collections.Generic;3using System.ComponentModel;4using System.Data;5using System.Drawing;6using System.Linq;7using System.Text;8using System.Threading;9using System.Threading.Tasks;10using System.Windows.Forms;11using System.Windows.Forms.DataVisualization.Charting;12namespace rbfNeuro13{14 public partial class Form1 : Form15 {16 public Form1()17 {18 InitializeComponent();19 generator = new Generator();20 }21 private const int Count = 20;22 private static Generator generator;23 private static double[] genY, genYNotSort,24 chartVal = new double[Count],25 chartX = new double[Count];26 double deltaX;27 private void GetXArray(double[] generated, out double[] masX, out double[] masY)28 {29 int i = 0;30 masX = new double[Count];31 masY = new double[Count];32 // generated = generated.Select(o => o / 10).ToArray();33 deltaX = (generated.Max() - generated.Min()) / Count;34 for (double val1 = generated[0], val2 = val1 + deltaX; i < Count; maasss[i] = val1, masX[i] = val1 + deltaX / 2, val1 = val2, val2 += deltaX, i++)35 for (int k = 0; k < generated.Length; k++)36 if (generated[k] >= val1 && generated[k] < val2)37 masY[i]++;38 double minY = masY.Min();39 double dif = masY.Max() - minY;40 for (int k = 0; k < masY.Length; k++)41 {42 //masY[k] = masY[k] / masY.Length;43 masY[k] = (masY[k] - minY) / dif;44 }45 }46 double[] maasss = new double[Count];47 Layer layer;48 List<double> errors = new List<double>();49 private void button3_Click(object sender, EventArgs e)50 {51 52 double error = 0;53 layer.Neurons.ForEach(o => o.Impulse = 0);54 layer.Output = 0;55 double[] masX;56 double[] masY;57 GetXArray(generator.Generate(5000, Generator.rand.NextDouble() * 3).OrderBy(o => o).Select(o => o / 1.1).ToArray(), out masX, out masY);58 double[] neuroOutput = new double[Count];59 for (int i = 0; i < masX.Length; i++)60 {61 layer.LinearSum(masX[i]);62 neuroOutput[i] = layer.Output;63 error += (Math.Pow(masY[i] - layer.Output, 2));64 }65 errors.Add(Math.Sqrt(error / (Count - 1)));66 // ToChart(2, masX, masY);67 ToChart(3, masX, neuroOutput);68 }69 private void button2_Click(object sender, EventArgs e)70 {71 layer = new Layer(Count, chartX, deltaX / Math.Sqrt(45));72 double n = 0.0012;///////////////////////////////////////////////73 for (int p = 0; p < 80; p++)74 {75 genYNotSort = generator.Generate(5000, 1 /*Generator.rand.NextDouble() * 3*/);76 genY = genYNotSort.OrderBy(o => o).ToArray();77 GetXArray(genY, out chartX, out chartVal);78 int Iter = 8000;79 double[] err = new double[Iter * Count];80 double[] indexes = new double[Iter * Count];81 for (int i = 0; i < Iter * Count; i++)82 indexes[i] = i;83 label1.Text = "....";84 ////Thread t = new Thread(() =>85 ////{86 for (int i = 0; i < Iter; i++)87 {88 double[] m = new double[Count];89 for (int k = 0; k < Count; k++)90 {91 layer.GoLearn(n, chartVal[k],/* chartX*/ maasss[k]);92 layer.CalcE(chartVal[k], 0.00001);93 /// { label1.Text = "готово"; return; }94 err[k * i] = layer.E;95 m[k] = layer.Output;96 }97 // chart1.Invoke((MethodInvoker)delegate ()98 //{99 ToChart(1, chartX, m);100 // });101 }102 //label1.Invoke((MethodInvoker)delegate ()103 //{104 label1.Text = "готово";105 //});106 // });107 //t.Start();108 }109 }110 private void ToChart(int ind, double[] x, double[] y)111 {112 chart1.Series[0].ChartType = SeriesChartType.Line;113 chart1.Series[0].Points.DataBindXY(x, y);114 }115 private void button1_Click(object sender, EventArgs e)116 {117 genYNotSort = generator.Generate(5000, 1/* Generator.rand.NextDouble() * 3*/);118 genY = genYNotSort.OrderBy(o => o).ToArray();119 GetXArray(genY, out chartX, out chartVal);120 ToChart(0, chartX, chartVal);121 }122 }123}...

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ScanrangeCalc.cs

Source:ScanrangeCalc.cs Github

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1using System;2using System.Collections.Generic;3using System.Linq;4using System.Text;5using UnityEngine;67public class ScanrangeCalc8{9 public ScanrangeCalc()10 {11 InitializeTables();12 }1314 private sbyte[,,] pTablesOffset = new sbyte[41, 41, 2];15 private int[,] pTablesCost = new int[41, 41];16 public int[,] pTablesVision = new int[41, 41];1718 public byte ScanShift = 7;1920 private bool SetCell(int x, int y, int height_origin, int height_cell)21 {22 int vision_previous = pTablesVision[x + pTablesOffset[x, y, 0], y + pTablesOffset[x, y, 1]];23 int cost = pTablesCost[x, y];24 pTablesVision[x, y] = vision_previous - (height_cell - height_origin + cost);25 return (pTablesVision[x, y] >= 0);26 }2728 public void InitializeTables()29 {30 uint ulScanShifted = 1u << ScanShift;3132 for (int i = 0; i <= 20; ++i)33 {34 for (int j = 0; j <= 20; ++j)35 {36 int v1 = i > 0 || j > 0 ? (int)(Math.Pow((j * j + i * i), 0.5) / Math.Max(i,j) * ulScanShifted) : 0;3738 pTablesCost[20 + i, 20 + j] = v1;39 pTablesCost[20 + i, 20 - j] = v1;40 pTablesCost[20 - i, 20 + j] = v1;41 pTablesCost[20 - i, 20 - j] = v1;4243 if (j < (i / 2))44 {45 pTablesOffset[20 + i, 20 + j, 0] = -1;46 pTablesOffset[20 + i, 20 + j, 1] = 0;47 pTablesOffset[20 - i, 20 - j, 0] = 1;48 pTablesOffset[20 - i, 20 - j, 1] = 0;49 pTablesOffset[20 + i, 20 - j, 0] = -1;50 pTablesOffset[20 + i, 20 - j, 1] = 0;51 pTablesOffset[20 - i, 20 + j, 0] = 1;52 pTablesOffset[20 - i, 20 + j, 1] = 0;5354 } 55 else if (j > (i * 2))56 {57 pTablesOffset[20 + i, 20 + j, 0] = 0;58 pTablesOffset[20 + i, 20 + j, 1] = -1;59 pTablesOffset[20 - i, 20 - j, 0] = 0;60 pTablesOffset[20 - i, 20 - j, 1] = 1;61 pTablesOffset[20 + i, 20 - j, 0] = 0;62 pTablesOffset[20 + i, 20 - j, 1] = 1;63 pTablesOffset[20 - i, 20 + j, 0] = 0;64 pTablesOffset[20 - i, 20 + j, 1] = -1;65 }66 else67 {68 pTablesOffset[20 + i, 20 + j, 0] = -1;69 pTablesOffset[20 + i, 20 + j, 1] = -1;70 pTablesOffset[20 - i, 20 - j, 0] = 1;71 pTablesOffset[20 - i, 20 - j, 1] = 1;72 pTablesOffset[20 + i, 20 - j, 0] = -1;73 pTablesOffset[20 + i, 20 - j, 1] = 1;74 pTablesOffset[20 - i, 20 + j, 0] = 1;75 pTablesOffset[20 - i, 20 + j, 1] = -1;76 }77 }78 }7980 pTablesCost[20, 20] = 0;81 }8283 private bool CheckValid(int x, int y)84 {85 return (x >= 8 && y >= 8 && x < MapLogic.Instance.Width - 8 && y < MapLogic.Instance.Height - 8);86 }8788 private int GetHeight(int x, int y)89 {90 if (x < 0 || y < 0 || x >= MapLogic.Instance.Width || y >= MapLogic.Instance.Height)91 return 0;92 return MapLogic.Instance.Nodes[x, y].Height;93 }9495 public void CalculateVision(int x, int y, float scanrangef)96 {97 // we need to make scanshifted scanrange from float.98 int scanrange = (int)scanrangef;99 scanrange = (scanrange << 8) | (int)((scanrangef - scanrange) * 255);100101 for (int ly = 0; ly < 41; ly++)102 for (int lx = 0; lx < 41; lx++)103 pTablesVision[lx, ly] = 0;104105 int vision = scanrange;106 int vision2 = (1 << (ScanShift - 1)) + (vision >> (8 - ScanShift));107108 int genX = x - 20;109 int genY = y - 20;110 int ht_origin = GetHeight(x, y);111112 pTablesVision[20, 20] = vision2;113 for(int i = 1; i < 20; i++)114 {115 bool fdisp = false;116 for(int j = -i; j < i+1; j++)117 {118 if(CheckValid(genX+(20+j), genY+(20-i)) &&119 SetCell(20+j, 20-i, ht_origin, GetHeight(genX+(20+j), genY+(20-i))))120 fdisp = true;121 if(CheckValid(genX+(20+j), genY+(20+i)) &&122 SetCell(20+j, 20+i, ht_origin, GetHeight(genX+(20+j), genY+(20+i))))123 fdisp = true;124 if(CheckValid(genX+(20-i), genY+(20+j)) &&125 SetCell(20-i, 20+j, ht_origin, GetHeight(genX+(20-i), genY+(20+j))))126 fdisp = true;127 if(CheckValid(genX+(20+i), genY+(20-j)) &&128 SetCell(20+i, 20-j, ht_origin, GetHeight(genX+(20+i), genY+(20-j))))129 fdisp = true;130 }131132 if(!fdisp) break;133 }134 } ...

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TrelamiumTile.cs

Source:TrelamiumTile.cs Github

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1using Terraria;2using Terraria.ID;3using Terraria.ModLoader;4using Microsoft.Xna.Framework;5using TrelamiumTwo.Content.Items.Materials;6using TrelamiumTwo.Helpers;7namespace TrelamiumTwo.Common.Tiles8{9 public class TrelamiumTile : GlobalTile10 {11 private const float nutSpawnChance = 0.05f;12 private const float leafSpawnChance = 0.025f;13 private Player player;14 public override void RandomUpdate(int i, int j, int type)15 {16 if (Main.tile[i, j].nactive())17 {18 if ((type == TileID.Grass || type == TileID.HallowedGrass) && Main.rand.Next(55) == 0)19 {20 TryBloomRose(i, j);21 }22 }23 }24 public override void FloorVisuals(int type, Player player)25 {26 player.GetModPlayer<Players.TrelamiumPlayer>().onSand =27 (TileID.Sets.Conversion.Sand[type] || TileID.Sets.Conversion.Sandstone[type] || TileID.Sets.Conversion.HardenedSand[type]);28 }29 private void TryBloomRose(int genX, int genY)30 {31 int scatteredFlowerRadiusCheck = 5;32 int scatteredFlowerType = ModContent.TileType<Content.Tiles.Ambience.BloomRose>();33 if (genX < 95 || genX > Main.maxTilesX - 95 || genY < 95 || genY > Main.worldSurface)34 {35 return;36 }37 Tile topTile = Framing.GetTileSafely(genX, genY - 1);38 if (topTile.active() && topTile.type != scatteredFlowerType)39 {40 return;41 }42 int minX = Utils.Clamp(genX - scatteredFlowerRadiusCheck, 1, Main.maxTilesX - 2);43 int maxX = Utils.Clamp(genX + scatteredFlowerRadiusCheck, 1, Main.maxTilesX - 2);44 int minY = Utils.Clamp(genY - scatteredFlowerRadiusCheck, 1, Main.maxTilesY - 2);45 int maxY = Utils.Clamp(genY + scatteredFlowerRadiusCheck, 1, Main.maxTilesY - 2);46 for (int x = minX; x < maxX; ++x)47 {48 for (int y = minY; y < maxY; ++y)49 {50 Tile t = Framing.GetTileSafely(x, y);51 if (t.active() && t.type == scatteredFlowerType)52 {53 return;54 }55 }56 }57 WorldGen.PlaceTile(genX, genY - 1, scatteredFlowerType, true);58 }59 public override void KillTile(int i, int j, int type, ref bool fail, ref bool effectOnly, ref bool noItem)60 {61 player = Main.LocalPlayer;62 if (!effectOnly)63 {64 if (player.ZoneForest())65 {66 if (Main.netMode != NetmodeID.MultiplayerClient && type == TileID.Trees)67 {68 TrySpawnLeaf(i, j);69 TrySpawnNut(i, j, fail);70 }71 }72 }73 }74 private void TrySpawnNut(int x, int y, bool fail)75 {76 if (fail || Main.rand.NextFloat() > nutSpawnChance)77 {78 return;79 }80 while (y > 10 && Main.tile[x, y].active() && Main.tile[x, y].type == TileID.Trees)81 {82 y--;83 }84 y++;85 if (!IsTileALeafyTreeTop(x, y) || Collision.SolidTiles(x - 2, x + 2, y - 2, y + 2))86 {87 return;88 }89 Item.NewItem(new Vector2(x * 16, y * 16), ModContent.ItemType<Nut>(), Main.rand.Next(5));90 }91 private void TrySpawnLeaf(int x, int y)92 {93 if (Main.rand.NextFloat() > leafSpawnChance)94 {95 return;96 }97 while (y > 10 && Main.tile[x, y].active() && Main.tile[x, y].type == TileID.Trees)98 {99 y--;100 }101 y++;102 if (!IsTileALeafyTreeTop(x, y) || Collision.SolidTiles(x - 2, x + 2, y - 2, y + 2))103 {104 return;105 }106 int velocityXDir = Main.rand.Next(2) * 2 - 1;107 int projectileType = ModContent.ProjectileType<Content.Projectiles.Typeless.FallingLeaf>();108 Projectile.NewProjectile(x * 16, y * 16, Main.rand.NextFloat(2f) * velocityXDir, 0f, projectileType, 0, 0f, Player.FindClosest(new Vector2(x * 16, y * 16), 16, 16));109 }110 private bool IsTileALeafyTreeTop(int i, int j)111 {112 Tile tileSafely = Framing.GetTileSafely(i, j);113 if (tileSafely.active() && tileSafely.type == TileID.Trees)114 {115 if (tileSafely.frameX == 22 && tileSafely.frameY >= 198 && tileSafely.frameY <= 242)116 {117 return true;118 }119 }120 return false;121 }122 }123}...

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MEParmGen.cs

Source:MEParmGen.cs Github

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12namespace Zygotine.WebExpo3{4 using System.Linq;5 using Zygotine.Util;6 internal class MEParmGen7 {8 public double Parm { get; private set; }9 private MEParmGen()10 {11 }12 internal static MEParmGen GetInstance(GenObject o, MeasurementError me, DataSummary data, YGen genY, TrueValuesGen genTV)13 {14 MEParmGen instance = new MEParmGen();15 double b;16 double[] tmpY, tmpT;17 if (me.ThroughCV)18 {19 if (o.LogNormalDistrn)20 {21 tmpY = Tools.Combine(data.LogY, genY.LogGT, genY.LogLT, genY.LogI);22 tmpT = Tools.Combine(genTV.LogY, genTV.LogGT, genTV.LogLT, genTV.LogI);23 b = tmpY.Substract(tmpT).Exp().Substract(1.0).Sqr().Sum();24 b /= 2.0;25 }26 else27 {28 tmpY = Tools.Combine(data.Y, genY.GT, genY.LT, genY.I);29 tmpT = Tools.Combine(genTV.Y, genTV.GT, genTV.LT, genTV.I);30 b = tmpY.Divide(tmpT).Substract(1.0).Sqr().Reverse().Sum();31 b /= 2.0;32 }33 SGNFnAParam localA = o.A.Clone();34 localA.B = b;35 localA.Range = me.GetRange();36 double[] range = me.GetRange();37 Icdf icdf = new Icdf(o, localA, range);38 instance.Parm = icdf.Bidon(o.Start(localA), inestLowerLim: range[0] == 0);39 }40 else41 {42 tmpY = Tools.Combine(data.Y, genY.GT, genY.LT, genY.I);43 tmpT = Tools.Combine(genTV.Y, genTV.GT, genTV.LT, genTV.I);44 b = tmpY.Substract(tmpT).Sqr().Sum();45 b /= 2.0;46 if (o.LogNormalDistrn)47 {48 SGNFnAParam localA = o.A.Clone();49 localA.B = b;50 localA.Range = me.GetRange();51 localA.Truevalues = Tools.Copy(tmpT);52 //me.parm <- dens.gen.icdf(o, A, range=me$range, inestimable.lower.limit=me$range[1]==0)53 double[] range = me.GetRange();54 Icdf icdf = new Icdf(o, localA, range);55 instance.Parm = icdf.Bidon(inestLowerLim: range[0] == 0.0);56 }57 else58 {59 instance.Parm = WebExpoFunctions3.SqrtInvertedGammaGen(data.N, b, me.GetRange(), o);60 }61 }62 return instance;63 }64 }65}...

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OutLogoutMoments.cs

Source:OutLogoutMoments.cs Github

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1namespace Zygotine.WebExpo2{3 using System.Linq;4 using Zygotine.Util;5 internal class OutLogoutMoments6 {7 public double Sum { get; private set; }8 public double Sum2 { get; private set; }9 private OutLogoutMoments()10 {11 }12 public static OutLogoutMoments Get(bool anyME, bool logNormalDistrn, DataSummary data, YGen genY, TrueValuesGen genTV)13 {14 OutLogoutMoments olm = new OutLogoutMoments();15 if (anyME)16 {17 double[] y;18 if (logNormalDistrn)19 {20 y = Tools.Combine(genTV.LogY, genTV.LogGT, genTV.LogLT, genTV.LogI);21 }22 else23 {24 y = Tools.Combine(genTV.Y, genTV.GT, genTV.LT, genTV.I);25 }26 olm.Sum = y.Sum();27 olm.Sum2 = y.Sqr().Sum();28 return olm;29 }30 else if (logNormalDistrn)31 {32 olm.Sum = data.LogUncensoredSum;33 olm.Sum2 = data.LogUncensoredSum2;34 olm.Sum += genY.LogGT.Sum() + genY.LogLT.Sum() + genY.LogI.Sum();35 olm.Sum2 += genY.LogGT.Sqr().Sum() + genY.LogLT.Sqr().Sum() + genY.LogI.Sqr().Sum();36 }37 else38 {39 olm.Sum = data.UncensoredSum;40 olm.Sum2 = data.UncensoredSum2;41 olm.Sum += genY.GT.Sum() + genY.LT.Sum() + genY.I.Sum();42 olm.Sum2 += genY.GT.Sqr().Sum() + genY.LT.Sqr().Sum() + genY.I.Sqr().Sum();43 }44 return olm;45 }46 }47}...

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GenyDriverMethodCaller.cs

Source:GenyDriverMethodCaller.cs Github

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1using System;2using System.Collections.Generic;3using System.ComponentModel;4using System.Data;5using System.Drawing;6using System.Text;7using System.Windows.Forms;8namespace MethodCaller9{10 public partial class GenyDriverMethodCaller : MethodCaller.MethodCallerMain11 {12 public GenyDriverMethodCaller()13 {14 InitializeComponent();15 this.Load += new EventHandler(GenyDriverMethodCaller_Load);16 }17 void GenyDriverMethodCaller_Load(object sender, EventArgs e)18 {19 this.label2.Visible = false;20 this.label3.Visible = false;21 this.cbbSerialPorts.Visible = false;22 this.tbSerialPortConfig.Visible = false;23 }24 protected override object GetDriver()25 {26 return new DeviceDriver.Driver(24);27 }28 protected override System.Reflection.MethodInfo[] GetMethods()29 {30 return ReflectUtil.GetMethod(typeof(DeviceDriver.Driver));31 }32 }33}...

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GenY

Using AI Code Generation

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1using B;2{3 {4 public static void Main()5 {6 GenY gy = new GenY();7 gy.Show();8 }9 }10}11using C;12{13 {14 public void Show()15 {16 GenZ gz = new GenZ();17 gz.Show();18 }19 }20}21using C;22{23 {24 public void Show()25 {26 GenZ gz = new GenZ();27 gz.Show();28 }29 }30}31using C;32{33 {34 public void Show()35 {36 GenZ gz = new GenZ();37 gz.Show();38 }39 }40}41using C;42{43 {44 public void Show()45 {46 GenZ gz = new GenZ();47 gz.Show();48 }49 }50}51using C;52{53 {54 public void Show()55 {56 GenZ gz = new GenZ();57 gz.Show();58 }59 }60}61using C;62{63 {64 public void Show()65 {66 GenZ gz = new GenZ();67 gz.Show();68 }69 }70}71using C;72{73 {74 public void Show()75 {76 GenZ gz = new GenZ();77 gz.Show();78 }79 }80}81using C;82{83 {84 public void Show()85 {86 GenZ gz = new GenZ();

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GenY

Using AI Code Generation

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1using B;2{3public void show()4{5Console.WriteLine("GenY");6}7}8using C;9{10public void show()11{12Console.WriteLine("GenZ");13}14}15using A;16{17public void show()18{19Console.WriteLine("GenA");20}21}22using B;23{24public void show()25{26Console.WriteLine("GenB");27}28}29using C;30{31public void show()32{33Console.WriteLine("GenC");34}35}36using D;37{38public void show()39{40Console.WriteLine("GenD");41}42}43using E;44{45public void show()46{47Console.WriteLine("GenE");48}49}50using F;51{52public void show()53{54Console.WriteLine("GenF");55}56}57using G;58{59public void show()60{61Console.WriteLine("GenG");62}63}64using H;65{66public void show()67{68Console.WriteLine("GenH");69}70}71using I;72{73public void show()74{75Console.WriteLine("GenI");76}77}78using J;79{80public void show()81{82Console.WriteLine("GenJ");83}84}

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GenY

Using AI Code Generation

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1using B;2{3public static void Main()4{5GenY obj = new GenY();6obj.print();7}8}9Microsoft (R) Visual C# Compiler version 1.1.4322.240810Microsoft (R) Visual C# Compiler version 1.1.4322.2408

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GenY

Using AI Code Generation

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1using B;2{3 public static void Main()4 {5 GenY g = new GenY();6 }7}8GenX.Show()9GenY.Show()

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GenY

Using AI Code Generation

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1using B;2{3 public GenY()4 {5 Console.WriteLine("GenY");6 }7}8using C;9{10 public GenZ()11 {12 Console.WriteLine("GenZ");13 }14}15using C;16{17 public static void Main()18 {19 GenZ obj = new GenZ();20 Console.ReadLine();21 }22}

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GenY

Using AI Code Generation

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1using B;2{3 static void Main()4 {5 GenY g = new GenY();6 g.ShowXY();7 }8}9{10 public unsafe void ShowXY()11 {12 Console.WriteLine("XY");13 }14}

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Nunit tutorial

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