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| 1 | +using System; |
| 2 | +using System.IO; |
| 3 | +using System.Linq; |
| 4 | +using System.Collections.Generic; |
| 5 | + |
| 6 | +namespace KNN |
| 7 | +{ |
| 8 | + class MainClass |
| 9 | + { |
| 10 | + const int k = 3; |
| 11 | + const int testingFraction = 4; |
| 12 | + |
| 13 | + public static void Main(string[] args) { |
| 14 | + var specimens = File.ReadAllLines("iris.data") |
| 15 | + .Select((l, i) => new { Data=ParseLine(l), Index = i}) |
| 16 | + .ToList(); |
| 17 | + |
| 18 | + var trainingSet = specimens.Where(x => x.Index % testingFraction != 0).Select(x => x.Data).ToList(); |
| 19 | + var testingSet = specimens.Where(x => x.Index % testingFraction == 0).Select(x => x.Data).ToList(); |
| 20 | + |
| 21 | + var correct = 0; |
| 22 | + foreach (var item in testingSet) { |
| 23 | + var predicted = Majority(FindKNearest(item, trainingSet)); |
| 24 | + Console.WriteLine("{0} read from file was predicted to be {1}", item.Class, predicted); |
| 25 | + if (predicted == item.Class) |
| 26 | + correct++; |
| 27 | + } |
| 28 | + Console.WriteLine("Accuracy: {0:0.00} %", ((double)correct * 100.0d / (double)testingSet.Count)); |
| 29 | + } |
| 30 | + |
| 31 | + static Specimen ParseLine(string line) { |
| 32 | + var data = line.Split(','); |
| 33 | + return new Specimen { |
| 34 | + SepalLength = double.Parse(data[0]), |
| 35 | + SepalWidth = double.Parse(data[1]), |
| 36 | + PetalLength = double.Parse(data[2]), |
| 37 | + PetalWidth = double.Parse(data[3]), |
| 38 | + Class = data[4] |
| 39 | + }; |
| 40 | + } |
| 41 | + |
| 42 | + static double Distance (Specimen a, Specimen b) { |
| 43 | + var d1 = Math.Pow(a.SepalLength - b.SepalLength, 2); |
| 44 | + var d2 = Math.Pow(a.SepalWidth - b.SepalWidth, 2); |
| 45 | + var d3 = Math.Pow(a.PetalLength - b.PetalLength, 2); |
| 46 | + var d4 = Math.Pow(a.PetalWidth - b.PetalWidth, 2); |
| 47 | + |
| 48 | + return d1 + d2 + d3 + d4; |
| 49 | + } |
| 50 | + |
| 51 | + static List<Specimen> FindKNearest (Specimen input, IEnumerable<Specimen> trainingSet) { |
| 52 | + return trainingSet.Select(t => new { Item = t, Dist = Distance(input, t)}) |
| 53 | + .OrderBy(d => d.Dist) |
| 54 | + .Take(k) |
| 55 | + .Select(x => x.Item) |
| 56 | + .ToList(); |
| 57 | + } |
| 58 | + |
| 59 | + static string Majority (List<Specimen> nearest) { |
| 60 | + var majorityGroup = nearest.GroupBy(x => x.Class) |
| 61 | + .OrderByDescending(g => g.Count()) |
| 62 | + .First(); |
| 63 | + var majorityClass = majorityGroup |
| 64 | + .First() |
| 65 | + .Class; |
| 66 | + return majorityClass; |
| 67 | + } |
| 68 | + } |
| 69 | +} |
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