private static string classify0(Vector inX, Matrix dataset, List<string> labels, int k)
{
Console.WriteLine("Input");
Console.WriteLine(inX.ToString());
Console.WriteLine("Data");
Console.WriteLine(dataset);
Console.WriteLine("Labels");
labels.ForEach(s => Console.Write(s+" "));
// Create difference matrix with same dimensions as the dataset
var diffMatrix = new Matrix(dataset.Rows,dataset.Cols);
for (int i = 0; i < dataset.Rows; i++)
{
diffMatrix[i]=inX;
}
diffMatrix = diffMatrix - dataset;
Console.WriteLine("Diff Matrix");
Console.WriteLine(diffMatrix.ToString());
// Square all the items
for (int i = 0; i < diffMatrix.Rows; i++)
{
var v = diffMatrix[i];
v.Each((d)=>Math.Pow(d,2.0));
diffMatrix[i] = v;
}
// Sum of each row and then square root
var sqDistances = diffMatrix.Sum(VectorType.Col); //Why is this a Column Summation?
sqDistances.Each(d => Math.Sqrt(d));
Console.WriteLine("Squared distances");
Console.WriteLine(sqDistances.ToString());
// get the sorted indices
var sortedIndices = Vector.SortOrder(sqDistances).Reverse().ToVector();
Console.WriteLine("Sorted Indices");
Console.WriteLine(sortedIndices);
var classCount = new Dictionary<string, double>();
// Now compare the first 'k' items that are closest
for (int i = 0; i < k; i++)
{
var votelabel = labels[(int)sortedIndices[i]];
if (!classCount.ContainsKey(votelabel))
{
classCount.Add(votelabel,1.0);
}
else
{
classCount[votelabel] += 1; // Increment count
}
}
// Order by the values descending and return the first Key
var orderedCount = classCount.OrderByDescending(kvp => kvp.Value).ToArray();
return orderedCount.First().Key;
}