// @Assumption: labels are stored as a natural range; that is, their values e [0, #classes[
/// <remarks>
/// Predicted labels are stored in the columns.
/// Actual labels are stored in the rows.
/// </remarks>
public static Matrix <int> Create(Array <ILabel> actualLabels, Array <ILabel> predictedLabels, int classCount)
{
if (actualLabels.Length != predictedLabels.Length)
{
throw new InvalidOperationException();
}
var absoluteConfusionMatrix = new MutableMatrix <int>(
rowCount: classCount,
columnCount: classCount);
var instanceCount = actualLabels.Length;
for (int instanceIndex = 0; instanceIndex < instanceCount; instanceIndex++)
{
var actual = ((SingleLabel)actualLabels[instanceIndex]).Value;
var predicted = ((SingleLabel)predictedLabels[instanceIndex]).Value;
var oldConfusionValue = absoluteConfusionMatrix.Get(
rowIndex: actual,
columnIndex: predicted);
absoluteConfusionMatrix.Set(
rowIndex: actual,
columnIndex: predicted,
oldConfusionValue + 1);
}
return(absoluteConfusionMatrix.ToMatrix());
}
private static MutableMatrix <float> ReadDataFile(string filename)
{
var recordCount = GetNumberOfRecords(filename);
var fieldCount = GetAndValidateNumberOfFields(filename);
var matrix = new MutableMatrix <float>(rowCount: recordCount, columnCount: fieldCount);
using var streamReader = new StreamReader(path: filename);
using var csvReader = new CsvReader(reader: streamReader, CultureInfo.InvariantCulture);
for (int i = 0; i < recordCount; i++)
{
csvReader.Read();
for (int j = 0; j < fieldCount; j++)
{
var fieldValue = csvReader.GetField(j);
var parsed = float.TryParse(fieldValue, out var parsedValue);
if (!parsed)
{
throw new InvalidOperationException($"Parsing error. Can't parse {fieldValue} as float. Line {i}. File: {filename}.");
}
matrix.Set(rowIndex: i, columnIndex: j, value: parsedValue);
}
}
return(matrix);
}
public static Matrix <double> ComputeEuclideanDistanceMatrix(Matrix <float> datasetInstaces)
{
var instanceCount = datasetInstaces.RowCount;
var distances = new MutableMatrix <double>(instanceCount, instanceCount);
Parallel.For(0, instanceCount, i => {
var lhs = datasetInstaces.GetRow(i);
for (int j = 0; j < instanceCount; j++)
{
var rhs = datasetInstaces.GetRow(j);
var distance = Euclidean(lhs, rhs);
distances.Set(i, j, distance);
}
});
return(distances.ToMatrix());
}