public void ConvoleSame()
{
var series1 = new NDArrayGeneric <double>();
series1.Data = new double[] { 1, 2, 3 };
series1.Shape = new Shape(3);
var series2 = new NDArrayGeneric <double>();
series2.Data = new double[] { 0, 1, 0.5 };
series2.Shape = new Shape(3);
var series3 = series1.Convolve(series2, "same");
double[] expectedResult = new double[] { 1, 2.5, 4 };
Assert.IsTrue(Enumerable.SequenceEqual(series3.Data.ToArray(), expectedResult));
}
/// <summary>
/// Convolution of 2 series
/// </summary>
/// <param name="numSharpArray1"></param>
/// <param name="numSharpArray2"></param>
/// <param name="mode"></param>
/// <returns></returns>
public static NDArrayGeneric <double> Convolve(this NDArrayGeneric <double> numSharpArray1, NDArrayGeneric <double> numSharpArray2, string mode = "full")
{
int nf = numSharpArray1.Shape.Shapes[0];
int ng = numSharpArray2.Shape.Shapes[0];
var numSharpReturn = new NDArrayGeneric <double>();
switch (mode)
{
case "full":
{
int n = nf + ng - 1;
var outArray = new double[n];
for (int idx = 0; idx < n; ++idx)
{
int jmn = (idx >= ng - 1) ? (idx - (ng - 1)) : 0;
int jmx = (idx < nf - 1) ? idx : nf - 1;
for (int jdx = jmn; jdx <= jmx; ++jdx)
{
outArray[idx] += (numSharpArray1[jdx] * numSharpArray2[idx - jdx]);
}
}
numSharpReturn.Data = outArray;
break;
}
case "valid":
{
var min_v = (nf < ng) ? numSharpArray1 : numSharpArray2;
var max_v = (nf < ng) ? numSharpArray2 : numSharpArray1;
int n = Math.Max(nf, ng) - Math.Min(nf, ng) + 1;
double[] outArray = new double[n];
for (int idx = 0; idx < n; ++idx)
{
int kdx = idx;
for (int jdx = (min_v.Shape.Shapes[0] - 1); jdx >= 0; --jdx)
{
outArray[idx] += min_v[jdx] * max_v[kdx];
++kdx;
}
}
numSharpReturn.Data = outArray;
break;
}
case "same":
{
// followed the discussion on
// https://stackoverflow.com/questions/38194270/matlab-convolution-same-to-numpy-convolve
// implemented numpy convolve because we follow numpy
var npad = numSharpArray2.Shape.Shapes[0] - 1;
if (npad % 2 == 1)
{
npad = (int)Math.Floor(((double)npad) / 2.0);
numSharpArray1.Data.ToList().AddRange(new double[npad + 1]);
var puffer = (new double[npad]).ToList();
puffer.AddRange(numSharpArray1.Data);
numSharpArray1.Data = puffer.ToArray();
numSharpArray1.Shape = new Shape(numSharpArray1.Data.Length);
}
else
{
npad = npad / 2;
var puffer = ((double[])numSharpArray1.Data).ToList();
puffer.AddRange(new double[npad]);
numSharpArray1.Data = puffer.ToArray();
numSharpArray1.Shape = new Shape(numSharpArray1.Data.Length);
puffer = (new double[npad]).ToList();
puffer.AddRange(numSharpArray1.Data);
numSharpArray1.Data = puffer.ToArray();
numSharpArray1.Shape = new Shape(numSharpArray1.Data.Length);
}
numSharpReturn = numSharpArray1.Convolve(numSharpArray2, "valid");
break;
}
}
numSharpReturn.Shape = new Shape(numSharpReturn.Data.Length);
return(numSharpReturn);
}