const int C = 3;
var data = new Matrix[C];

// input
for (int i = 0; i < C;   i)
{
    data[i] =
        File.ReadAllLines(String.Format("data/iris/data_{0}.csv", i), Encoding.UTF8)
            .Select(line =>
                line.Split(',').Select(s => Double.Parse(s)).ToRow())
            .ToMatrix();
}

Lda result = Func.Lda(data);

// Coefficients of linear discriminants
var ldc1 = new ColumnVector(result.Eigenvectors[0]);
var ldc2 = new ColumnVector(result.Eigenvectors[1]);
var coef = new Matrix(ldc1, ldc2);

for (int i = 0; i < C;   i)
{
    var m = data[i] * coef;   // calculate scores

    // output
    File.WriteAllText(String.Format("result_{0}.csv", i), Matrix.ToCsv(m), Encoding.UTF8);
}

var m = new Matrix(new[,] {
                    {86.0, 67.0},

                    // ...

                    {96.0, 61.0} });

// correlation matrix
var cor1 = Func.Correlate(m, Func.Target.EachColumn);

MatrixVisualizer.TestShowVisualizer(cor1);  // test method of DebuggerVisualizer

// normalization -> correlation matrix
var n = new Matrix(m);
n.Columns.ForEach((ci, cv) => {
    var avg = cv.Average;
    var std = Math.Sqrt(cv.UnbiasedVariance);
    cv.Apply((i, val) => (val - avg) / std);
});

var cor2 = Matrix.T(n) * n / (n.RowSize - 1);

MatrixVisualizer.TestShowVisualizer(cor2);