Best Coyote code snippet using Microsoft.Coyote.Benchmarking.MathHelpers.Covariance
MathHelpers.cs
Source:MathHelpers.cs
...145 /// <summary>146 /// Return the covariance in the given x,y values.147 /// The sum of the difference between x and its mean times the difference between y and its mean.148 /// </summary>149 public static double Covariance(IEnumerable<DataPoint> pts)150 {151 double xsum = 0;152 double ysum = 0;153 double count = 0;154 foreach (var d in pts)155 {156 xsum += d.X;157 ysum += d.Y;158 count++;159 }160 if (count is 0)161 {162 return 0;163 }164 double xMean = xsum / count;165 double yMean = ysum / count;166 double covariance = 0;167 foreach (var d in pts)168 {169 covariance += (d.X - xMean) * (d.Y - yMean);170 }171 return covariance;172 }173 /// <summary>174 /// Compute the trend line through the given points, and return the line in the form:175 /// y = a + b.x.176 /// </summary>177 /// <param name="pts">The data to analyze.</param>178 public static Line LinearRegression(IEnumerable<DataPoint> pts)179 {180 double xMean = Mean(from p in pts select p.X);181 double yMean = Mean(from p in pts select p.Y);182 double xVariance = Variance(from p in pts select p.X);183 double yVariance = Variance(from p in pts select p.Y);184 double covariance = Covariance(pts);185 double a = 0;186 double b = 0;187 if (xVariance is 0)188 {189 a = yMean;190 b = 1;191 }192 else193 {194 b = covariance / xVariance;195 a = yMean - (b * xMean);196 }197 return new Line(a, b);198 }...
Covariance
Using AI Code Generation
1using System;2using System.Collections.Generic;3using System.Linq;4using System.Text;5using System.Threading.Tasks;6using Microsoft.Coyote.Benchmarking;7{8 {9 static void Main(string[] args)10 {11 double[] x = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };12 double[] y = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };13 double[] z = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };14 double[] w = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };15 double[] v = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };16 double[] u = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };17 double[] t = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };18 double[] s = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };19 double[] r = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };20 double[] q = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };21 double[] p = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };22 double[] o = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };23 double[] n = { 1, 2, 3,
Covariance
Using AI Code Generation
1using System;2using Microsoft.Coyote.Benchmarking;3{4 {5 static void Main(string[] args)6 {7 double[] x = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };8 double[] y = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };9 double[] yerr = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };10 double m, b, r;11 MathHelpers.Covariance(x, y, out m, out b, out r);12 Console.WriteLine("Slope: {0}, Y-intercept: {1}, Correlation: {2}", m, b, r);13 }14 }15}16using System;17using Microsoft.Coyote.Benchmarking;18{19 {20 static void Main(string[] args)21 {22 double[] x = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };23 double[] y = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };24 double[] yerr = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };25 double m, b, r;26 MathHelpers.Covariance(x, y, yerr, out m, out b, out r);27 Console.WriteLine("Slope: {0}, Y-intercept: {1}, Correlation: {2}", m, b, r);28 }29 }30}31using System;32using Microsoft.Coyote.Benchmarking;33{34 {35 static void Main(string[] args)36 {37 double[] x = {
Covariance
Using AI Code Generation
1using System;2using Microsoft.Coyote.Benchmarking;3{4 {5 public static void Main()6 {7 var data = new double[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };8 var mean = MathHelpers.Mean(data);9 var variance = MathHelpers.Variance(data);10 var covariance = MathHelpers.Covariance(data, data);11 Console.WriteLine($"mean: {mean}, variance: {variance}, covariance: {covariance}");12 }13 }14}15cov(X,Y)=E[(X−E[X])(Y−E[Y])]16using System;17using Microsoft.Coyote.Benchmarking;18{19 {20 public static void Main()21 {22 {23 new double[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 },24 new double[] { 2, 4, 6, 8, 10, 12, 14, 16, 18, 20
Covariance
Using AI Code Generation
1using Microsoft.Coyote.Benchmarking;2using System;3{4 {5 static void Main(string[] args)6 {7 double[][] data = new double[10][];8 for (int i = 0; i < 10; i++)9 {10 data[i] = new double[10];11 for (int j = 0; j < 10; j++)12 {13 data[i][j] = i + j;14 }15 }16 double[][] covariance = MathHelpers.Covariance(data);17 for (int i = 0; i < covariance.Length; i++)18 {19 for (int j = 0; j < covariance[i].Length; j++)20 {21 Console.Write(covariance[i][j] + " ");22 }23 Console.WriteLine();24 }25 }26 }27}
Covariance
Using AI Code Generation
1var result = Microsoft.Coyote.Benchmarking.MathHelpers.Covariance(arr1, arr2);2Console.WriteLine(result);3var result = Microsoft.Coyote.Benchmarking.MathHelpers.Covariance(arr1, arr2);4Console.WriteLine(result);5var result = Microsoft.Coyote.Benchmarking.MathHelpers.Covariance(arr1, arr2);6Console.WriteLine(result);7var result = Microsoft.Coyote.Benchmarking.MathHelpers.Covariance(arr1, arr2);8Console.WriteLine(result);9var result = Microsoft.Coyote.Benchmarking.MathHelpers.Covariance(arr1, arr2);10Console.WriteLine(result);11var result = Microsoft.Coyote.Benchmarking.MathHelpers.Covariance(arr1, arr2);12Console.WriteLine(result);13var result = Microsoft.Coyote.Benchmarking.MathHelpers.Covariance(arr1, arr2);14Console.WriteLine(result);15var result = Microsoft.Coyote.Benchmarking.MathHelpers.Covariance(arr1, arr2);16Console.WriteLine(result);17var result = Microsoft.Coyote.Benchmarking.MathHelpers.Covariance(arr1, arr2);18Console.WriteLine(result);19var result = Microsoft.Coyote.Benchmarking.MathHelpers.Covariance(arr1, arr2);20Console.WriteLine(result);21var result = Microsoft.Coyote.Benchmarking.MathHelpers.Covariance(arr1, arr2);22Console.WriteLine(result);
Covariance
Using AI Code Generation
1using Microsoft.Coyote.Benchmarking;2double[] a = new double[100];3double[] b = new double[100];4double[] c = new double[100];5double[] d = MathHelpers.Covariance(a, b, c);6using Microsoft.Coyote.Benchmarking;7double[] a = new double[100];8double[] b = new double[100];9double[] c = new double[100];10double[] d = MathHelpers.Correlation(a, b, c);11using Microsoft.Coyote.Benchmarking;12double[] a = new double[100];13double[] b = new double[100];14double[] c = new double[100];15double[] d = MathHelpers.Variance(a, b, c);16using Microsoft.Coyote.Benchmarking;17double[] a = new double[100];18double[] b = new double[100];19double[] c = new double[100];20double[] d = MathHelpers.StandardDeviation(a, b, c);21using Microsoft.Coyote.Benchmarking;22double[] a = new double[100];23double[] b = new double[100];24double[] c = new double[100];25double[] d = MathHelpers.Average(a, b, c);26using Microsoft.Coyote.Benchmarking;27double[] a = new double[100];28double[] b = new double[100];29double[] c = new double[100];
Covariance
Using AI Code Generation
1using System;2using Microsoft.Coyote.Benchmarking;3{4 {5 static void Main(string[] args)6 {7 double[] data = { 2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0 };8 double variance = MathHelpers.Covariance(data, data);9 Console.WriteLine("variance of array is {0}", variance);10 Console.ReadLine();11 }12 }13}
Covariance
Using AI Code Generation
1using System;2using Microsoft.Coyote.Benchmarking;3{4 {5 static void Main(string[] args)6 {7 var rand = new Random();8 var arr1 = new double[100];9 var arr2 = new double[100];10 for (int i = 0; i < 100; i++)11 {12 arr1[i] = rand.NextDouble();13 arr2[i] = rand.NextDouble();14 }15 var cov = MathHelpers.Covariance(arr1, arr2);16 Console.WriteLine("Covariance of the two arrays is: " + cov);17 }18 }19}
Covariance
Using AI Code Generation
1using System;2using Microsoft.Coyote.Benchmarking;3{4 {5 static void Main(string[] args)6 {7 int[] array = new int[100];8 for (int i = 0; i < array.Length; i++)9 {10 array[i] = i;11 }12 double avg = Benchmarking.Average(array);13 double var = Benchmarking.Variance(array);14 double var2 = MathHelpers.Covariance(array);15 Console.WriteLine("Average: {0}, Variance: {1}, Covariance: {2}", avg, var, var2);16 }17 }18}
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