public static NDArrayGeneric <double> Std(this NDArrayGeneric <double> np, int axis = -1)
{
var std = new NDArrayGeneric <double>();
var mean = np.Mean(axis);
// axis == -1: DEFAULT; to compute the standard deviation of the flattened array.
if (axis == -1)
{
var sum = np.Data.Select(d => Math.Pow(Math.Abs(d - mean.Data[0]), 2)).Sum();
std.Data = new double[] { Math.Sqrt(sum / np.Size) };
}
// to compute mean by compressing row and row
else if (axis == 0)
{
double[] sumVec = new double[np.Shape.Shapes[1]];
for (int d = 0; d < np.Shape.Shapes[0]; d++)
{
for (int p = 0; p < np.Shape.Shapes[1]; p++)
{
sumVec[p] += np[d, p];
}
}
var puffer = mean.Data.ToList();
for (int d = 0; d < np.Shape.Shapes[1]; d++)
{
puffer.Add(sumVec[d] / np.Shape.Shapes[0]);
}
mean.Data = puffer.ToArray();
}
else if (axis == 1)
{
var puffer = mean.Data.ToList();
for (int d = 0; d < np.Shape.Shapes[0]; d++)
{
double rowSum = 0;
for (int p = 0; p < np.Shape.Shapes[1]; p++)
{
rowSum += np[d, p];
}
puffer.Add(rowSum / np.Shape.Shapes[1]);
}
mean.Data = puffer.ToArray();
}
return(std);
}
public void MeanAxisMinus1()
{
var np = new NDArrayGeneric <double>().arange(5, 1).reshape(2, 2);
Assert.IsTrue(Enumerable.SequenceEqual(np.Mean().Data, new double[] { 2.5 }));
}
