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How to use Covariance method of Microsoft.Coyote.Benchmarking.MathHelpers class

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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