Page 58 - MSDN Magazine, May 2019

The demo implementation of Euclidean distance is:
static double DistFunc(double[] item, double[] dataPoint) {
double sum = 0.0;
for (int i = 0; i < 2; ++i) {
double diff = item[i] - dataPoint[i+1];
Figure 3 The Weighted k-NN Demo Program
sum += diff * diff;
return Math.Sqrt(sum); }
Notice that the for-loop indexing is hardcoded with 2 for the number of predictor values, and the difference between item[i] and
}
using System; namespace WeightedKNN {
class WeightedKNNProgram {
static void Main(string[] args) {
Console.WriteLine("Begin weighted k-NN demo "); Console.WriteLine("Normalized age-expenses data: "); Console.WriteLine("[id = 0, 0.25, 0.43, class = 0]"); Console.WriteLine(" . . . ");
Console.WriteLine("[id = 32, 0.46, 0.22, class = 2]");
double[][] data = new double[33][];
data[0] = new double[] { 0, 0.25, 0.43, 0 }; data[1] = new double[] { 1, 0.26, 0.54, 0 }; data[2] = new double[] { 2, 0.27, 0.60, 0 }; data[3] = new double[] { 3, 0.37, 0.31, 0 }; data[4] = new double[] { 4, 0.38, 0.70, 0 }; data[5] = new double[] { 5, 0.49, 0.32, 0 }; data[6] = new double[] { 6, 0.46, 0.70, 0 }; data[7] = new double[] { 7, 0.55, 0.32, 0 }; data[8] = new double[] { 8, 0.58, 0.74, 0 }; data[9] = new double[] { 9, 0.67, 0.42, 0 }; data[10] = new double[] { 10, 0.69, 0.51, 0 }; data[11] = new double[] { 11, 0.66, 0.63, 0 };
data[12] = new double[] { 12, 0.29, 0.43, 1 }; data[13] = new double[] { 13, 0.35, 0.51, 1 }; data[14] = new double[] { 14, 0.39, 0.63, 1 }; data[15] = new double[] { 15, 0.47, 0.40, 1 }; data[16] = new double[] { 16, 0.48, 0.50, 1 }; data[17] = new double[] { 17, 0.45, 0.61, 1 }; data[18] = new double[] { 18, 0.55, 0.41, 1 }; data[19] = new double[] { 19, 0.57, 0.53, 1 }; data[20] = new double[] { 20, 0.56, 0.62, 1 }; data[21] = new double[] { 21, 0.68, 0.12, 1 }; data[22] = new double[] { 22, 0.78, 0.24, 1 }; data[23] = new double[] { 23, 0.86, 0.30, 1 }; data[24] = new double[] { 24, 0.86, 0.22, 1 }; data[25] = new double[] { 25, 0.86, 0.12, 1 }; data[26] = new double[] { 26, 0.78, 0.14, 1 };
data[27] = new double[] { 27, 0.28, 0.13, 2 }; data[28] = new double[] { 28, 0.25, 0.21, 2 }; data[29] = new double[] { 29, 0.39, 0.14, 2 }; data[30] = new double[] { 30, 0.37, 0.24, 2 }; data[31] = new double[] { 31, 0.45, 0.13, 2 }; data[32] = new double[] { 32, 0.46, 0.22, 2 };
double[] item = new double[] { 0.35, 0.38 };
Console.WriteLine("Nearest (k=6) to (0.35, 0.38):"); Analyze(item, data, 6, 3); // 3 classes
Console.WriteLine("End weighted k-NN demo ");
Console.ReadLine(); } // Main
// ------------------------------------------------------
static void Analyze(double[] item, double[][] data, int k, int c)
{
// 1. Compute all distances
int N = data.Length;
double[] distances = new double[N]; for (int i = 0; i < N; ++i)
distances[i] = DistFunc(item, data[i]);
// 2. Get ordering
int[] ordering = new int[N];
for (int i = 0; i < N; ++i) ordering[i] = i;
double[] distancesCopy = new double[N]; Array.Copy(distances, distancesCopy, distances.Length); Array.Sort(distancesCopy, ordering);
// 3. Show info for k nearest
double[] kNearestDists = new double[k]; for (int i = 0; i < k; ++i)
{
int idx = ordering[i]; Show(data[idx]); Console.WriteLine(" distance = " +
distances[idx].ToString("F4")); kNearestDists[i] = distances[idx];
}
// 4. Vote
double[] votes = new double[c]; // one per class double[] wts = MakeWeights(k, kNearestDists);
Console.WriteLine("Weights (inverse technique): "); for (int i = 0; i < wts.Length; ++i)
Console.Write(wts[i].ToString("F4") + " ");
Console.WriteLine("\n\nPredicted class: "); for (int i = 0; i < k; ++i) {
int idx = ordering[i];
int predClass = (int)data[idx][3]; votes[predClass] += wts[i] * 1.0;
}
for (int i = 0; i < c; ++i) Console.WriteLine("[" + i + "] " + votes[i].ToString("F4"));
} // Analyze
static double[] MakeWeights(int k, double[] distances) {
// Inverse technique
double[] result = new double[k]; // one perneighbor double sum = 0.0;
for (int i = 0; i < k; ++i)
{
result[i] = 1.0 / distances[i];
sum += result[i]; }
for (int i = 0; i < k; ++i) result[i] /= sum;
return result; }
static double DistFunc(double[] item, double[] dataPoint) {
double sum = 0.0;
for (int i = 0; i < 2; ++i) {
double diff = item[i] - dataPoint[i+1];
sum += diff * diff; }
return Math.Sqrt(sum); }
static void Show(double[] v) {
Console.Write("idx = " + v[0].ToString().PadLeft(3) + " (" + v[1].ToString("F2") + " " + v[2].ToString("F2") + ") class = " + v[3]);
}
} // Program } // ns
52 msdn magazine
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