using System;

using System.Collections.Generic;

using System.Linq;

using System.Text;

namespace NumSharp.Core.Extensions

{

public static partial class NDArrayExtensions

{

public static NDArray<double> Std(this NDArray<double> np, int axis = -1)

{

var std = new NDArray<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;

}

}

}