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NdArray.Mean.cs
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61 lines (56 loc) · 1.92 KB
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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> Mean(this NDArray<double> np, int axis = -1)
{
var mean = new NDArray<double>();
mean.Data = new double[0];
// axis == -1: DEFAULT; to compute the mean of the flattened array.
if (axis == -1)
{
var sum = np.Data.Sum();
mean.Data = new double[] { sum / np.Size};
}
// to compute mean by compressing row and row
else if (axis == 0)
{
double[] sumVec = new double[np.Shape.Shapes[0]];
for (int d = 0; d < sumVec.Length; 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();
mean.Shape = new Shape(mean.Data.Length);
}
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();
mean.Shape = new Shape(mean.Data.Length);
}
return mean;
}
}
}