calculation of similarity of two vector C#





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-1















I have some vectors of data as:



681,305,305,304,667,92.87,254.51,100.24  has "A" as key


and



460,682,702,683,443,89.17,273.83,102.19 has"B" as key


and



593,395,413,418,564,71.13,17.67,83.79 has "C" as key


Then, from my device, I obtained a sample of data and needed to translate it into key. sample of data as below:



593,395,413,418,564,71.13,17.67,83.79


I want to calculate the similarity of the obtained data with the three samples of the above data, see which one is most similar to the obtained data, then I choose key of sample to return. I'm using C#.



Is there any algorithm for calculating it? Please help me, thanks!










share|improve this question


















  • 2





    How should similarity been calculated? This depends on the "meaning" of that data. So you have to define this first. Then you maybe find a routine to calculate it.

    – H.G. Sandhagen
    Nov 25 '18 at 5:54











  • Your question is essentially a set of requirements and so is off-topic for SO sadly. How to Ask

    – MickyD
    Nov 25 '18 at 6:04











  • Oh, as you can see, first four numbers are the compressive force on the surface. last three numbers shows the compression angle of the surface obtained from the accelerometer

    – Gacom111haaa
    Nov 25 '18 at 8:23


















-1















I have some vectors of data as:



681,305,305,304,667,92.87,254.51,100.24  has "A" as key


and



460,682,702,683,443,89.17,273.83,102.19 has"B" as key


and



593,395,413,418,564,71.13,17.67,83.79 has "C" as key


Then, from my device, I obtained a sample of data and needed to translate it into key. sample of data as below:



593,395,413,418,564,71.13,17.67,83.79


I want to calculate the similarity of the obtained data with the three samples of the above data, see which one is most similar to the obtained data, then I choose key of sample to return. I'm using C#.



Is there any algorithm for calculating it? Please help me, thanks!










share|improve this question


















  • 2





    How should similarity been calculated? This depends on the "meaning" of that data. So you have to define this first. Then you maybe find a routine to calculate it.

    – H.G. Sandhagen
    Nov 25 '18 at 5:54











  • Your question is essentially a set of requirements and so is off-topic for SO sadly. How to Ask

    – MickyD
    Nov 25 '18 at 6:04











  • Oh, as you can see, first four numbers are the compressive force on the surface. last three numbers shows the compression angle of the surface obtained from the accelerometer

    – Gacom111haaa
    Nov 25 '18 at 8:23














-1












-1








-1








I have some vectors of data as:



681,305,305,304,667,92.87,254.51,100.24  has "A" as key


and



460,682,702,683,443,89.17,273.83,102.19 has"B" as key


and



593,395,413,418,564,71.13,17.67,83.79 has "C" as key


Then, from my device, I obtained a sample of data and needed to translate it into key. sample of data as below:



593,395,413,418,564,71.13,17.67,83.79


I want to calculate the similarity of the obtained data with the three samples of the above data, see which one is most similar to the obtained data, then I choose key of sample to return. I'm using C#.



Is there any algorithm for calculating it? Please help me, thanks!










share|improve this question














I have some vectors of data as:



681,305,305,304,667,92.87,254.51,100.24  has "A" as key


and



460,682,702,683,443,89.17,273.83,102.19 has"B" as key


and



593,395,413,418,564,71.13,17.67,83.79 has "C" as key


Then, from my device, I obtained a sample of data and needed to translate it into key. sample of data as below:



593,395,413,418,564,71.13,17.67,83.79


I want to calculate the similarity of the obtained data with the three samples of the above data, see which one is most similar to the obtained data, then I choose key of sample to return. I'm using C#.



Is there any algorithm for calculating it? Please help me, thanks!







c# algorithm






share|improve this question













share|improve this question











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asked Nov 25 '18 at 5:08









Gacom111haaaGacom111haaa

167




167








  • 2





    How should similarity been calculated? This depends on the "meaning" of that data. So you have to define this first. Then you maybe find a routine to calculate it.

    – H.G. Sandhagen
    Nov 25 '18 at 5:54











  • Your question is essentially a set of requirements and so is off-topic for SO sadly. How to Ask

    – MickyD
    Nov 25 '18 at 6:04











  • Oh, as you can see, first four numbers are the compressive force on the surface. last three numbers shows the compression angle of the surface obtained from the accelerometer

    – Gacom111haaa
    Nov 25 '18 at 8:23














  • 2





    How should similarity been calculated? This depends on the "meaning" of that data. So you have to define this first. Then you maybe find a routine to calculate it.

    – H.G. Sandhagen
    Nov 25 '18 at 5:54











  • Your question is essentially a set of requirements and so is off-topic for SO sadly. How to Ask

    – MickyD
    Nov 25 '18 at 6:04











  • Oh, as you can see, first four numbers are the compressive force on the surface. last three numbers shows the compression angle of the surface obtained from the accelerometer

    – Gacom111haaa
    Nov 25 '18 at 8:23








2




2





How should similarity been calculated? This depends on the "meaning" of that data. So you have to define this first. Then you maybe find a routine to calculate it.

– H.G. Sandhagen
Nov 25 '18 at 5:54





How should similarity been calculated? This depends on the "meaning" of that data. So you have to define this first. Then you maybe find a routine to calculate it.

– H.G. Sandhagen
Nov 25 '18 at 5:54













Your question is essentially a set of requirements and so is off-topic for SO sadly. How to Ask

– MickyD
Nov 25 '18 at 6:04





Your question is essentially a set of requirements and so is off-topic for SO sadly. How to Ask

– MickyD
Nov 25 '18 at 6:04













Oh, as you can see, first four numbers are the compressive force on the surface. last three numbers shows the compression angle of the surface obtained from the accelerometer

– Gacom111haaa
Nov 25 '18 at 8:23





Oh, as you can see, first four numbers are the compressive force on the surface. last three numbers shows the compression angle of the surface obtained from the accelerometer

– Gacom111haaa
Nov 25 '18 at 8:23












2 Answers
2






active

oldest

votes


















0














There are many possible approaches:




  1. Calculate Minkowski distances between possible pairs and choose the closest.


  2. For every two arrays A and B, you can calculate an array of differences/deltas D, where Di = |Ai - Bi|. For this array you can calculate mean, variance, maybe other moments and infer from it.



...



But how you will use these quantities depends on the meaning of your data and on how it will impact performance.





As you pointed out in the comment, the data actually contains two parts: forces and angles, so most likely you need to compare not the whole arrays, but two subarrays of lengths 4 and 3.






share|improve this answer
























  • Currently I am using Euclidean Distance and mean, let me try adding other types, thank you.

    – Gacom111haaa
    Nov 26 '18 at 2:40



















1














There can be several assumptions based upon that you may make decision for similarity. I have presented a simple program in C# for finding similarity based upon the summation of absolute difference between values in same indices. The one with minimum summation will be most similar. You may extend your solution in similar manner.



using System;
using System.Collections.Generic;
public class Test
{
public static void Main()
{
Dictionary<string, List<double> > map = new Dictionary<string, List<double> >();
List<double>listA = new List<double>(new double{681,305,305,304,667,92.87,254.51,100.24});
map.Add("A", listA);
List<double>listB = new List<double>(new double{460,682,702,683,443,89.17,273.83,102.19});
map.Add("B", listB);
List<double>listC = new List<double>(new double{593,395,413,418,564,71.13,17.67,83.79});
map.Add("C", listC);
List<double>testList = new List<double>(new double{593,395,413,418,564,71.13,17.67,83.79});
string ans = mostSimilarKey(testList, map);
Console.WriteLine("Most similar key is: "+ans);
}

static string mostSimilarKey(List<double>testList, Dictionary<string, List<double> > map) {
double minDifference = Double.MaxValue;
string ans = "";
foreach (var pair in map) {
double absoluteDifference = getAbsoluteDifference(testList, pair.Value);
if (absoluteDifference < minDifference) {
minDifference = absoluteDifference;
ans = pair.Key;
}
}
return ans;
}

static double getAbsoluteDifference(List<double>testList, List<double>list) {
double absoluteDifference = 0.0;
for (int i = 0; i < testList.Count; ++i) {
absoluteDifference += Math.Abs(testList[i] - list[i]);
}
return absoluteDifference;
}
}





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    2 Answers
    2






    active

    oldest

    votes








    2 Answers
    2






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    0














    There are many possible approaches:




    1. Calculate Minkowski distances between possible pairs and choose the closest.


    2. For every two arrays A and B, you can calculate an array of differences/deltas D, where Di = |Ai - Bi|. For this array you can calculate mean, variance, maybe other moments and infer from it.



    ...



    But how you will use these quantities depends on the meaning of your data and on how it will impact performance.





    As you pointed out in the comment, the data actually contains two parts: forces and angles, so most likely you need to compare not the whole arrays, but two subarrays of lengths 4 and 3.






    share|improve this answer
























    • Currently I am using Euclidean Distance and mean, let me try adding other types, thank you.

      – Gacom111haaa
      Nov 26 '18 at 2:40
















    0














    There are many possible approaches:




    1. Calculate Minkowski distances between possible pairs and choose the closest.


    2. For every two arrays A and B, you can calculate an array of differences/deltas D, where Di = |Ai - Bi|. For this array you can calculate mean, variance, maybe other moments and infer from it.



    ...



    But how you will use these quantities depends on the meaning of your data and on how it will impact performance.





    As you pointed out in the comment, the data actually contains two parts: forces and angles, so most likely you need to compare not the whole arrays, but two subarrays of lengths 4 and 3.






    share|improve this answer
























    • Currently I am using Euclidean Distance and mean, let me try adding other types, thank you.

      – Gacom111haaa
      Nov 26 '18 at 2:40














    0












    0








    0







    There are many possible approaches:




    1. Calculate Minkowski distances between possible pairs and choose the closest.


    2. For every two arrays A and B, you can calculate an array of differences/deltas D, where Di = |Ai - Bi|. For this array you can calculate mean, variance, maybe other moments and infer from it.



    ...



    But how you will use these quantities depends on the meaning of your data and on how it will impact performance.





    As you pointed out in the comment, the data actually contains two parts: forces and angles, so most likely you need to compare not the whole arrays, but two subarrays of lengths 4 and 3.






    share|improve this answer













    There are many possible approaches:




    1. Calculate Minkowski distances between possible pairs and choose the closest.


    2. For every two arrays A and B, you can calculate an array of differences/deltas D, where Di = |Ai - Bi|. For this array you can calculate mean, variance, maybe other moments and infer from it.



    ...



    But how you will use these quantities depends on the meaning of your data and on how it will impact performance.





    As you pointed out in the comment, the data actually contains two parts: forces and angles, so most likely you need to compare not the whole arrays, but two subarrays of lengths 4 and 3.







    share|improve this answer












    share|improve this answer



    share|improve this answer










    answered Nov 25 '18 at 10:37









    YolaYola

    11.5k64774




    11.5k64774













    • Currently I am using Euclidean Distance and mean, let me try adding other types, thank you.

      – Gacom111haaa
      Nov 26 '18 at 2:40



















    • Currently I am using Euclidean Distance and mean, let me try adding other types, thank you.

      – Gacom111haaa
      Nov 26 '18 at 2:40

















    Currently I am using Euclidean Distance and mean, let me try adding other types, thank you.

    – Gacom111haaa
    Nov 26 '18 at 2:40





    Currently I am using Euclidean Distance and mean, let me try adding other types, thank you.

    – Gacom111haaa
    Nov 26 '18 at 2:40













    1














    There can be several assumptions based upon that you may make decision for similarity. I have presented a simple program in C# for finding similarity based upon the summation of absolute difference between values in same indices. The one with minimum summation will be most similar. You may extend your solution in similar manner.



    using System;
    using System.Collections.Generic;
    public class Test
    {
    public static void Main()
    {
    Dictionary<string, List<double> > map = new Dictionary<string, List<double> >();
    List<double>listA = new List<double>(new double{681,305,305,304,667,92.87,254.51,100.24});
    map.Add("A", listA);
    List<double>listB = new List<double>(new double{460,682,702,683,443,89.17,273.83,102.19});
    map.Add("B", listB);
    List<double>listC = new List<double>(new double{593,395,413,418,564,71.13,17.67,83.79});
    map.Add("C", listC);
    List<double>testList = new List<double>(new double{593,395,413,418,564,71.13,17.67,83.79});
    string ans = mostSimilarKey(testList, map);
    Console.WriteLine("Most similar key is: "+ans);
    }

    static string mostSimilarKey(List<double>testList, Dictionary<string, List<double> > map) {
    double minDifference = Double.MaxValue;
    string ans = "";
    foreach (var pair in map) {
    double absoluteDifference = getAbsoluteDifference(testList, pair.Value);
    if (absoluteDifference < minDifference) {
    minDifference = absoluteDifference;
    ans = pair.Key;
    }
    }
    return ans;
    }

    static double getAbsoluteDifference(List<double>testList, List<double>list) {
    double absoluteDifference = 0.0;
    for (int i = 0; i < testList.Count; ++i) {
    absoluteDifference += Math.Abs(testList[i] - list[i]);
    }
    return absoluteDifference;
    }
    }





    share|improve this answer




























      1














      There can be several assumptions based upon that you may make decision for similarity. I have presented a simple program in C# for finding similarity based upon the summation of absolute difference between values in same indices. The one with minimum summation will be most similar. You may extend your solution in similar manner.



      using System;
      using System.Collections.Generic;
      public class Test
      {
      public static void Main()
      {
      Dictionary<string, List<double> > map = new Dictionary<string, List<double> >();
      List<double>listA = new List<double>(new double{681,305,305,304,667,92.87,254.51,100.24});
      map.Add("A", listA);
      List<double>listB = new List<double>(new double{460,682,702,683,443,89.17,273.83,102.19});
      map.Add("B", listB);
      List<double>listC = new List<double>(new double{593,395,413,418,564,71.13,17.67,83.79});
      map.Add("C", listC);
      List<double>testList = new List<double>(new double{593,395,413,418,564,71.13,17.67,83.79});
      string ans = mostSimilarKey(testList, map);
      Console.WriteLine("Most similar key is: "+ans);
      }

      static string mostSimilarKey(List<double>testList, Dictionary<string, List<double> > map) {
      double minDifference = Double.MaxValue;
      string ans = "";
      foreach (var pair in map) {
      double absoluteDifference = getAbsoluteDifference(testList, pair.Value);
      if (absoluteDifference < minDifference) {
      minDifference = absoluteDifference;
      ans = pair.Key;
      }
      }
      return ans;
      }

      static double getAbsoluteDifference(List<double>testList, List<double>list) {
      double absoluteDifference = 0.0;
      for (int i = 0; i < testList.Count; ++i) {
      absoluteDifference += Math.Abs(testList[i] - list[i]);
      }
      return absoluteDifference;
      }
      }





      share|improve this answer


























        1












        1








        1







        There can be several assumptions based upon that you may make decision for similarity. I have presented a simple program in C# for finding similarity based upon the summation of absolute difference between values in same indices. The one with minimum summation will be most similar. You may extend your solution in similar manner.



        using System;
        using System.Collections.Generic;
        public class Test
        {
        public static void Main()
        {
        Dictionary<string, List<double> > map = new Dictionary<string, List<double> >();
        List<double>listA = new List<double>(new double{681,305,305,304,667,92.87,254.51,100.24});
        map.Add("A", listA);
        List<double>listB = new List<double>(new double{460,682,702,683,443,89.17,273.83,102.19});
        map.Add("B", listB);
        List<double>listC = new List<double>(new double{593,395,413,418,564,71.13,17.67,83.79});
        map.Add("C", listC);
        List<double>testList = new List<double>(new double{593,395,413,418,564,71.13,17.67,83.79});
        string ans = mostSimilarKey(testList, map);
        Console.WriteLine("Most similar key is: "+ans);
        }

        static string mostSimilarKey(List<double>testList, Dictionary<string, List<double> > map) {
        double minDifference = Double.MaxValue;
        string ans = "";
        foreach (var pair in map) {
        double absoluteDifference = getAbsoluteDifference(testList, pair.Value);
        if (absoluteDifference < minDifference) {
        minDifference = absoluteDifference;
        ans = pair.Key;
        }
        }
        return ans;
        }

        static double getAbsoluteDifference(List<double>testList, List<double>list) {
        double absoluteDifference = 0.0;
        for (int i = 0; i < testList.Count; ++i) {
        absoluteDifference += Math.Abs(testList[i] - list[i]);
        }
        return absoluteDifference;
        }
        }





        share|improve this answer













        There can be several assumptions based upon that you may make decision for similarity. I have presented a simple program in C# for finding similarity based upon the summation of absolute difference between values in same indices. The one with minimum summation will be most similar. You may extend your solution in similar manner.



        using System;
        using System.Collections.Generic;
        public class Test
        {
        public static void Main()
        {
        Dictionary<string, List<double> > map = new Dictionary<string, List<double> >();
        List<double>listA = new List<double>(new double{681,305,305,304,667,92.87,254.51,100.24});
        map.Add("A", listA);
        List<double>listB = new List<double>(new double{460,682,702,683,443,89.17,273.83,102.19});
        map.Add("B", listB);
        List<double>listC = new List<double>(new double{593,395,413,418,564,71.13,17.67,83.79});
        map.Add("C", listC);
        List<double>testList = new List<double>(new double{593,395,413,418,564,71.13,17.67,83.79});
        string ans = mostSimilarKey(testList, map);
        Console.WriteLine("Most similar key is: "+ans);
        }

        static string mostSimilarKey(List<double>testList, Dictionary<string, List<double> > map) {
        double minDifference = Double.MaxValue;
        string ans = "";
        foreach (var pair in map) {
        double absoluteDifference = getAbsoluteDifference(testList, pair.Value);
        if (absoluteDifference < minDifference) {
        minDifference = absoluteDifference;
        ans = pair.Key;
        }
        }
        return ans;
        }

        static double getAbsoluteDifference(List<double>testList, List<double>list) {
        double absoluteDifference = 0.0;
        for (int i = 0; i < testList.Count; ++i) {
        absoluteDifference += Math.Abs(testList[i] - list[i]);
        }
        return absoluteDifference;
        }
        }






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        share|improve this answer










        answered Nov 25 '18 at 6:31









        Bishal GautamBishal Gautam

        878619




        878619






























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