Here are a couple of functions to calculate Euclidean distance between 2 points and similarity based on that distance. These are useful in the sort of algorithms described in the excellent book Programming Collective Intelligence
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Drawing;
namespace Algorithms
{
public class Distance
{
/// <summary>
/// Return the distance between 2 points
/// </summary>
public static double Euclidean(Point p1, Point p2)
{
return Math.Sqrt(Math.Pow(p1.X - p2.X, 2) + Math.Pow(p1.Y - p2.Y, 2));
}
/// <summary>
/// Calculates the similarity between 2 points using Euclidean distance.
/// Returns a value between 0 and 1 where 1 means they are identical
/// </summary>
public static double EuclideanSimilarity(Point p1, Point p2)
{
return 1/(1 + Euclidean(p1, p2));
}
}
}
And some tests:
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
using System.Text;
using Microsoft.VisualStudio.TestTools.UnitTesting;
namespace Algorithms.Test
{
[TestClass]
public class TestDistance
{
[TestMethod]
public void Test_Euclidean()
{
var p1 = new Point(5, 4);
var p2 = new Point(4, 1);
Assert.AreEqual(3.1622776601683795, Distance.Euclidean(p1, p2));
}
[TestMethod]
public void Test_EuclideanSimilarity()
{
var p1 = new Point(5, 4);
var p2 = new Point(4, 1);
Assert.AreEqual(0.2402530733520421, Distance.EuclideanSimilarity(p1, p2));
}
}
}
