public static NDStorage CreateByArray(Array values)
{
Type dtype = null;
if (!values.GetType().GetElementType().IsArray)
{
dtype = values.GetType().GetElementType();
}
else
{
throw new IncorrectShapeException();
}
int[] dims = new int[values.Rank];
for (int idx = 0; idx < dims.Length; idx++)
{
dims[idx] = values.GetLength(idx);
}
var storage = NDStorage.CreateByShapeAndType(dtype, new Shape(dims));
storage.values = Array.CreateInstance(dtype, values.Length);
storage.SetData(values);
return(storage);
}
public NDArray transpose()
{
var nd = new NDArray(dtype, new Shape(this.Storage.Shape.Shapes.Reverse().ToArray()));
if (ndim == 1)
{
nd.Storage = NDStorage.CreateByShapeAndType(dtype, new Shape(1, shape.Shapes[0]));
}
else if (ndim == 2)
{
for (int idx = 0; idx < nd.shape.Shapes[0]; idx++)
{
for (int jdx = 0; jdx < nd.shape.Shapes[1]; jdx++)
{
nd[idx, jdx] = this[jdx, idx];
}
}
}
else
{
throw new NotImplementedException();
}
return(nd);
}
public NDArray array(Array array, Type dtype = null, int ndim = 1)
{
dtype = (dtype == null) ? array.GetType().GetElementType() : dtype;
var nd = new NDArray(dtype);
if ((array.Rank == 1) && (!array.GetType().GetElementType().IsArray))
{
nd.Storage = NDStorage.CreateByShapeAndType(dtype, new Shape(new int[] { array.Length }));
nd.Storage.SetData(array);
}
else
{
throw new Exception("Method is not implemeneted for multidimensional arrays or jagged arrays.");
}
return(nd);
}
public new matrix transpose()
{
Storage.Shape = new Shape(this.Storage.Shape.Shapes.Reverse().ToArray());
var nd = new NDArray(this.dtype, this.shape);
switch (this.dtype.Name)
{
case "Double":
nd.Set(this.float64);
break;
case "Int32":
nd.Set(this.int32);
break;
}
if (ndim == 1)
{
Storage = NDStorage.CreateByShapeAndType(dtype, new Shape(1, shape.Shapes[0]));
Storage.SetData(nd.float64);
}
else if (ndim == 2)
{
for (int idx = 0; idx < shape.Shapes[0]; idx++)
{
for (int jdx = 0; jdx < shape.Shapes[1]; jdx++)
{
this[idx, jdx] = nd[jdx, idx];
}
}
}
else
{
throw new NotImplementedException();
}
return(this);
}
public NDArray ones(Type dtype = null, params int[] shapes)
{
dtype = (dtype == null) ? typeof(double) : dtype;
int dataLength = 1;
for (int idx = 0; idx < shapes.Length; idx++)
{
dataLength *= shapes[idx];
}
Array dataArray = Array.CreateInstance(dtype, dataLength);
for (int idx = 0; idx < dataLength; idx++)
{
dataArray.SetValue(1, idx);
}
this.Storage = NDStorage.CreateByShapeAndType(dtype, new Shape(shapes));
this.Storage.SetData(dataArray);
return(this);
}
public NDArray(Type dtype, params int[] shapes)
{
Storage = NDStorage.CreateByShapeAndType(dtype, new Shape(shapes));
}
public NDArray(Type dtype, Shape shape)
{
Storage = NDStorage.CreateByShapeAndType(dtype, shape);
}
public NDArray(Type dtype)
{
Storage = NDStorage.CreateByShapeAndType(dtype, new Shape(1));
}
public NDArray()
{
Storage = NDStorage.CreateByShapeAndType(typeof(double), new Shape(1));
}
/// <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);
}
