public static NDArray array <T>(this NumPy np, IEnumerable <T> array, int ndim = 1)
{
var nd = new NDArray(typeof(T));
nd.Set(array.ToArray());
nd.Shape = new Shape(new int[] { array.Count() });
return(nd);
}
public static NDArray array <T>(this NumPy np, System.Drawing.Bitmap image)
{
var imageArray = new NDArray(typeof(T));
var bmpd = image.LockBits(new System.Drawing.Rectangle(0, 0, image.Width, image.Height), System.Drawing.Imaging.ImageLockMode.ReadOnly, image.PixelFormat);
var dataSize = bmpd.Stride * bmpd.Height;
imageArray.Set(new byte[dataSize]);
System.Runtime.InteropServices.Marshal.Copy(bmpd.Scan0, (imageArray.Storage.values as byte[]), 0, imageArray.Size);
image.UnlockBits(bmpd);
imageArray.Shape = new Shape(new int[] { bmpd.Height, bmpd.Width, System.Drawing.Image.GetPixelFormatSize(image.PixelFormat) / 8 });
return(imageArray);
}
public NDArray delete(IEnumerable delete)
{
var np = new NumPy();
var sysArr = this.Storage.GetData();
NDArray res = null;
switch (sysArr)
{
case double[] castedSysArr:
{
var castedDelete = delete as double[];
res = np.array(castedSysArr.Where(x => !castedDelete.Contains(x)).ToArray());
break;
}
case float[] castedSysArr:
{
var castedDelete = delete as float[];
res = np.array(castedSysArr.Where(x => !castedDelete.Contains(x)).ToArray());
break;
}
case int[] castedSysArr:
{
var castedDelete = delete as int[];
res = np.array(castedSysArr.Where(x => !castedDelete.Contains(x)).ToArray());
break;
}
default:
{
throw new IncorrectTypeException();
}
}
return(res);
}
public NDArray permutation(int max)
{
var random = new Random();
int[] orders = new int[max];
var np = new NumPy().arange(max);
for (int i = 0; i < max; i++)
{
var pos = random.Next(0, max);
var zero = np.Data <int>(0);
np[0] = np.Data <int>(pos);
np[pos] = zero;
}
return(np);
}
public void normalize()
{
var np = new NumPy();
var min = this.min(0);
var max = this.max(0);
if (ndim == 2)
{
for (int col = 0; col < shape.Shapes[1]; col++)
{
double der = max.Data <double>(col) - min.Data <double>(col);
for (int row = 0; row < shape.Shapes[0]; row++)
{
this[row, col] = (Data <double>(row, col) - min.Data <double>(col)) / der;
}
}
}
else
{
throw new NotImplementedException();
}
}
public static NDArray array <T>(this NumPy np, T[][] data)
{
int size = data.Length * data[0].Length;
var all = new T[size];
int idx = 0;
for (int row = 0; row < data.Length; row++)
{
for (int col = 0; col < data[row].Length; col++)
{
all[idx] = data[row][col];
idx++;
}
}
var nd = new NDArray(typeof(T));
nd.Set(all.ToArray());
nd.Shape = new Shape(new int[] { data.Length, data[0].Length });
return(nd);
}
/// <summary>
/// Convolution of 2 series
/// </summary>
/// <param name="numSharpArray1"></param>
/// <param name="numSharpArray2"></param>
/// <param name="mode"></param>
/// <returns></returns>
public NDArray Convolve(NDArray numSharpArray2, string mode = "full")
{
int nf = this.shape.Shapes[0];
int ng = numSharpArray2.shape.Shapes[0];
if (shape.NDim > 1)
{
throw new IncorrectShapeException();
}
var numSharpReturn = new NDArray(typeof(double));
double[] np1 = this.Storage.GetData <double>();
double[] np2 = numSharpArray2.Storage.GetData <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] += (np1[jdx] * np2[idx - jdx]);
}
}
numSharpReturn.Storage = NDStorage.CreateByShapeAndType(numSharpReturn.dtype, new Shape(outArray.Length));
numSharpReturn.Storage.SetData(outArray);
break;
}
case "valid":
{
var min_v = (nf < ng) ? np1 : np2;
var max_v = (nf < ng) ? np2 : np1;
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.Length - 1); jdx >= 0; --jdx)
{
outArray[idx] += min_v[jdx] * max_v[kdx];
++kdx;
}
}
numSharpReturn.Storage = NDStorage.CreateByShapeAndType(numSharpReturn.dtype, new Shape(outArray.Length));
numSharpReturn.Storage.SetData(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;
double[] np1New = null;
if (npad % 2 == 1)
{
npad = (int)Math.Floor(((double)npad) / 2.0);
np1New = (double[])np1.Clone();
np1New.ToList().AddRange(new double[npad + 1]);
var puffer = (new double[npad]).ToList();
puffer.AddRange(np1New);
np1New = puffer.ToArray();
}
else
{
npad = npad / 2;
np1New = (double[])np1.Clone();
var puffer = np1New.ToList();
puffer.AddRange(new double[npad]);
np1New = puffer.ToArray();
puffer = (new double[npad]).ToList();
puffer.AddRange(np1New);
np1New = puffer.ToArray();
}
var numSharpNew = new NumPy().array(np1New, dtype);
numSharpReturn = numSharpNew.Convolve(numSharpArray2, "valid");
break;
}
}
return(numSharpReturn);
}
