/// <summary>
/// Filter specific elements through select.
/// </summary>
/// <param name="select"></param>
/// <returns>Return a new NDArray with filterd elements.</returns>
public NDArrayGeneric <T> this[IList <int> select]
{
get
{
var n = new NDArrayGeneric <T>();
if (NDim == 1)
{
n.Data = new T[select.Count];
n.Shape = new Shape(select.Count);
for (int i = 0; i < select.Count; i++)
{
n[i] = this[select[i]];
}
}
else if (NDim == 2)
{
n.Data = new T[select.Count * Shape[1]];
n.Shape = new Shape(select.Count, Shape[1]);
for (int i = 0; i < select.Count; i++)
{
for (int j = 0; j < Shape[1]; j++)
{
n[i, j] = this[select[i], j];
}
}
}
else
{
throw new NotImplementedException();
}
return(n);
}
}
public NDArrayGeneric <T> this[Shape select]
{
get
{
if (select.NDim == NDim)
{
throw new Exception("Please use NDArray[m, n] to access element.");
}
else
{
int start = GetIndexInShape(select.Shapes.ToArray());
int length = Shape.DimOffset[select.NDim - 1];
var n = new NDArrayGeneric <T>();
Span <T> data = Data;
n.Data = data.Slice(start, length).ToArray();
int[] shape = new int[Shape.NDim - select.NDim];
for (int i = select.NDim; i < Shape.NDim; i++)
{
shape[i - select.NDim] = Shape[i];
}
n.Shape = new Shape(shape);
// n.Shape = new Shape(Shape.Shapes.ToArray().AsSpan().Slice(select.Length).ToArray());
return(n);
}
}
}
public NDArrayGeneric <T> arange(int start, int stop, int step = 1)
{
if (start > stop)
{
throw new Exception("parameters invalid");
}
switch (typeof(T).Name)
{
case "Int32":
{
var n = new NDArrayGeneric <int>();
n.arange(stop, start, step);
return(n as NDArrayGeneric <T>);
}
case "Double":
{
var n = new NDArrayGeneric <double>();
n.arange(stop, start, step);
return(n as NDArrayGeneric <T>);
}
default:
throw new NotImplementedException();
}
}
public static dynamic arange(object start, object stop, object step)
{
dynamic returnValue = null;
switch (start)
{
case int startCast:
{
int stopCast = (int)stop;
int stepCast = (int)step;
returnValue = new NumSharp.Core.NDArrayGeneric <int>().arange(stopCast, startCast, stepCast);
break;
}
case float startCast_:
{
int startCast = (int)start;
int stopCast = (int)stop;
int stepCast = (int)step;
returnValue = new NumSharp.Core.NDArrayGeneric <float>().arange(stopCast, startCast, stepCast);
break;
}
}
return(returnValue);
}
public static NDArrayGeneric <T> sin <T>(this NumPyGeneric <T> np, NDArrayGeneric <T> nd)
{
NDArrayGeneric <T> sinArray = new NDArrayGeneric <T>();
sinArray.Data = new T[nd.Size];
sinArray.Shape = new Shape(nd.Shape.Shapes);
for (int idx = 0; idx < nd.Size; idx++)
{
switch (nd[idx])
{
case double d:
sinArray[idx] = (T)(object)Math.Sin(d);
break;
case float d:
sinArray[idx] = (T)(object)Math.Sin(d);
break;
case Complex d:
sinArray[idx] = (T)(object)Complex.Sin(d);
break;
}
}
return(sinArray);
}
/// <summary>
/// Stack arrays in sequence vertically (row wise).
/// </summary>
/// <param name="nps"></param>
/// <returns></returns>
public static NDArrayGeneric <T> VStack <T>(this NDArrayGeneric <T> np1, params NDArrayGeneric <T>[] nps)
{
if (nps == null || nps.Length == 0)
{
throw new Exception("Input arrays can not be empty");
}
List <T> list = new List <T>();
NDArrayGeneric <T> np = new NDArrayGeneric <T>();
foreach (NDArrayGeneric <T> ele in nps)
{
if (nps[0].Shape != ele.Shape)
{
throw new Exception("Arrays mush have same shapes");
}
list.AddRange(ele.Data);
}
np.Data = list.ToArray();
if (nps[0].NDim == 1)
{
np.Shape = new Shape(new int[] { nps.Length, nps[0].Shape.Shapes[0] });
}
else
{
int[] shapes = nps[0].Shape.Shapes.ToArray();
shapes[0] *= nps.Length;
np.Shape = new Shape(shapes);
}
return(np);
}
public NDArrayGeneric <T> power(T exponent)
{
NDArrayGeneric <T> sinArray = new NDArrayGeneric <T>();
sinArray.Data = new T[this.Size];
sinArray.Shape = new Shape(this.Shape.Shapes);
switch (Data)
{
case double[] sinData:
{
for (int idx = 0; idx < sinData.Length; idx++)
{
sinArray[idx] = (T)(object)Math.Pow(sinData[idx], (double)(object)exponent);
}
break;
}
default:
{
throw new Exception("The operation is not implemented for the");
}
}
return(sinArray);
}
public NDArrayGeneric <T> inv()
{
NDArrayGeneric <T> npInv = new NDArrayGeneric <T>();
npInv.Shape = new Shape(this.Shape.Shapes);
npInv.Data = new T[this.Data.Length];
switch (this)
{
case NDArrayGeneric <double> np:
{
NDArrayGeneric <double> npInvDouble = npInv as NDArrayGeneric <double>;
double[][] matrix = np.ToDotNetArray <double[][]>();
double[][] matrixInv = MatrixInv.InverseMatrix(matrix);
for (int idx = 0; idx < npInv.Shape.Shapes[0]; idx++)
{
for (int jdx = 0; jdx < npInv.Shape.Shapes[1]; jdx++)
{
npInvDouble[idx, jdx] = matrixInv[idx][jdx];
}
}
break;
}
default:
{
throw new Exception("This method was not implemented for this Type : " + typeof(T).Name);
}
}
return(npInv);
}
public static NDArrayGeneric <T> array <T>(this NumPyGeneric <T> np, IEnumerable <T> array, int ndim = 1)
{
var nd = new NDArrayGeneric <T>();
nd.Data = array.ToArray();
nd.Shape = new Shape(new int[] { nd.Data.Length });
return(nd);
}
public NDArrayGeneric <T> array(T[] data)
{
var n = new NDArrayGeneric <T>();
n.Data = data;
n.Shape = new Shape(new int[] { data.Length });
return(n);
}
public NDArrayGeneric <T> log()
{
NDArrayGeneric <T> logArray = new NDArrayGeneric <T>();
logArray.Data = new T[this.Data.Length];
logArray.Shape = new Shape(this.Shape.Shapes);
switch (logArray.Data)
{
case double[] logData:
{
double[] npData = this.Data as double[];
for (int idx = 0; idx < npData.Length; idx++)
{
logData[idx] = Math.Log(npData[idx]);
}
break;
}
case float[] logData:
{
double[] npData = this.Data as double[];
for (int idx = 0; idx < npData.Length; idx++)
{
// boxing necessary because Math.Sin just for double
logData[idx] = (float)Math.Log(npData[idx]);
}
break;
}
case Complex[] logData:
{
Complex[] npData = this.Data as Complex[];
for (int idx = 0; idx < npData.Length; idx++)
{
// boxing necessary because Math.Sin just for double
logData[idx] = Complex.Log(npData[idx]);
}
break;
}
default:
{
throw new Exception("The operation is not implemented for the");
}
}
return(logArray);
}
public static NDArrayGeneric <Byte> array <T>(this NumPyGeneric <T> np, System.Drawing.Bitmap image)
{
NDArrayGeneric <Byte> imageArray = new NDArrayGeneric <Byte>();
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.Data = new byte[dataSize];
System.Runtime.InteropServices.Marshal.Copy(bmpd.Scan0, imageArray.Data, 0, imageArray.Data.Length);
image.UnlockBits(bmpd);
imageArray.Shape = new Shape(new int[] { bmpd.Height, bmpd.Width, System.Drawing.Image.GetPixelFormatSize(image.PixelFormat) / 8 });
return(imageArray);
}
/// <summary>
/// Return a new array of given shape and type, filled with ones.
/// </summary>
/// <typeparam name="T"></typeparam>
/// <param name="np"></param>
/// <param name="shape"></param>
/// <returns></returns>
public static NDArrayGeneric <T> ones <T>(this NumPyGeneric <T> np, Shape shape)
{
var nd = new NDArrayGeneric <T>();
nd.Shape = shape;
switch (default(T))
{
case int data:
nd.Data = Enumerable.Range(0, nd.Size).Select(x => (T)(object)1).ToArray();
break;
case double data:
nd.Data = Enumerable.Range(0, nd.Size).Select(x => (T)(object)1.0).ToArray();
break;
}
return(nd);
}
public NDArrayGeneric <T> zeros(params int[] shape)
{
switch (typeof(T).Name)
{
case "Int32":
{
var n = new NDArrayGeneric <int>();
n.Zeros(shape);
return(n as NDArrayGeneric <T>);
}
case "Double":
{
var n = new NDArrayGeneric <double>();
n.Zeros(shape);
return(n as NDArrayGeneric <T>);
}
default:
throw new NotImplementedException();
}
}
public NDArrayGeneric <T> array(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 n = new NDArrayGeneric <T>();
n.Data = all;
n.Shape = new Shape(new int[] { data.Length, data[0].Length });
return(n);
}
public static NDArrayGeneric <NDArrayGeneric <T> > sin <T>(this NumPyGeneric <T> np, NDArrayGeneric <NDArrayGeneric <T> > nd)
{
var sinArray = new NDArrayGeneric <NDArrayGeneric <T> >();
sinArray.Data = new NDArrayGeneric <T> [nd.Size];
sinArray.Shape = new Shape(nd.Shape.Shapes);
for (int idx = 0; idx < nd.Size; idx++)
{
switch (default(T))
{
case double d:
sinArray[idx] = new NDArrayGeneric <T>
{
Data = new T[] { (T)(object)Math.Sin(d) },
Shape = new Shape(1)
};
break;
}
}
return(sinArray);
}
/// <summary>
/// Draw random samples from a normal (Gaussian) distribution.
/// </summary>
/// <param name="loc">Mean of the distribution</param>
/// <param name="scale">Standard deviation of the distribution</param>
/// <param name="size"></param>
/// <returns></returns>
public NDArrayGeneric <double> normal(double loc, double scale, params int[] size)
{
if (size.Length == 0)
{
throw new Exception("d cannot be empty.");
}
NDArrayGeneric <double> array = new NDArrayGeneric <double>();
Random rand = new Random(); //reuse this if you are generating many
array.Shape = new Shape(size);
array.Data = new double[array.Shape.Size];
for (int i = 0; i < array.Shape.Size; i++)
{
double u1 = 1.0 - rand.NextDouble(); //uniform(0,1] random doubles
double u2 = 1.0 - rand.NextDouble();
double randStdNormal = Math.Sqrt(-2.0 * Math.Log(u1)) *
Math.Sin(2.0 * Math.PI * u2); //random normal(0,1)
double randNormal = loc + scale * randStdNormal; //random normal(mean,stdDev^2)
array.Data[i] = randNormal;
}
return(array);
}
public NDArrayGeneric <NDArrayGeneric <T> > this[Slice select]
{
get
{
var result = new NDArrayGeneric <NDArrayGeneric <T> >();
result.Data = new NDArrayGeneric <T> [select.Length];
result.shape = new Shape(select.Length);
int[] shape = new int[Shape.Length];
for (int i = 0; i < Shape.Length; i++)
{
if (i == 0)
{
shape[i] = select.Step;
}
else
{
shape[i] = Shape[i];
}
}
int index = 0;
var list = new NDArrayGeneric <T>();
for (int s = select.Start; s < select.Stop; s += select.Step)
{
var n = new NDArrayGeneric <T>();
Span <T> data = Data;
n.Data = data.Slice(s, select.Step).ToArray();
n.Shape = new Shape(shape);
result.Data[index] = n;
index++;
}
return(result);
}
}
public NDArrayGeneric <T> transpose()
{
var np = new NDArrayGeneric <T>();
np.Data = new T[this.Data.Length];
if (NDim == 1)
{
np.Shape = new Shape(1, Shape.Shapes[0]);
}
else
{
np.Shape = new Shape(this.Shape.Shapes.Reverse().ToArray());
for (int idx = 0; idx < np.shape.Shapes[0]; idx++)
{
for (int jdx = 0; jdx < np.shape.Shapes[1]; jdx++)
{
np[idx, jdx] = this[jdx, idx];
}
}
}
return(np);
}
public NDArrayGeneric <double> amin(NDArrayGeneric <double> np, int?axis = null)
{
return(np.AMin(axis));
}
/// <summary> /// Overload /// </summary> /// <param name="select"></param> /// <returns></returns> public NDArrayGeneric <T> this[NDArrayGeneric <int> select] => this[select.Data.ToList()];
public NDArrayGeneric <double> max(NDArrayGeneric <double> nd)
{
return(nd.Max());
}
public NDArrayGeneric <double> power(NDArrayGeneric <double> nd, double exponent)
{
return(nd.power(exponent));
}
public NDArrayGeneric <double> absolute(NDArrayGeneric <double> np)
{
return(np.Absolute());
}
/// <summary>
///
/// </summary>
/// <param name="np1"></param>
/// <param name="np2"></param>
/// <typeparam name="TData"></typeparam>
/// <returns></returns>
public NDArrayGeneric <T> dot(NDArrayGeneric <T> np2)
{
if ((this.Shape.Length == 1) & (np2.Shape.Length == 1))
{
if (this.Shape.Shapes[0] != np2.Shape.Shapes[0])
{
throw new Exception("The Dot method does not work with this shape or was not already implemented.");
}
else
{
np2.Shape = new Shape(np2.Data.Length, 1);
this.Shape = new Shape(1, this.Data.Length);
}
}
else
if (this.Shape.Shapes[1] != np2.Shape.Shapes[0])
{
throw new Exception("The Dot method does not work with this shape or was not already implemented.");
}
int iterator = this.Shape.Shapes[1];
int dim0 = this.Shape.Shapes[0];
int dim1 = np2.Shape.Shapes[1];
NDArrayGeneric <T> prod = new NDArrayGeneric <T>();
prod.Shape = new Shape(dim0, dim1);
prod.Data = new T[prod.Shape.Size];
switch (this.Data)
{
case double[] np1Array:
{
double[] result = prod.Data as double[];
double[] np2Array = np2.Data as double[];
for (int idx = 0; idx < prod.Data.Length; idx++)
{
int puffer1 = idx / dim1;
int puffer2 = idx % dim1;
int puffer3 = puffer1 * iterator;
for (int kdx = 0; kdx < iterator; kdx++)
{
result[idx] += np1Array[puffer3 + kdx] * np2Array[dim1 * kdx + puffer2];
}
}
break;
}
case float[] np1Array:
{
float[] result = prod.Data as float[];
float[] np2Array = np2.Data as float[];
for (int idx = 0; idx < prod.Data.Length; idx++)
{
int puffer1 = idx / dim1;
int puffer2 = idx % dim1;
int puffer3 = puffer1 * iterator;
for (int kdx = 0; kdx < iterator; kdx++)
{
result[idx] += np1Array[puffer3 + kdx] * np2Array[dim1 * kdx + puffer2];
}
}
break;
}
case Complex[] np1Array:
{
Complex[] result = prod.Data as Complex[];
Complex[] np2Array = np2.Data as Complex[];
for (int idx = 0; idx < prod.Data.Length; idx++)
{
int puffer1 = idx / dim1;
int puffer2 = idx % dim1;
int puffer3 = puffer1 * iterator;
for (int kdx = 0; kdx < iterator; kdx++)
{
result[idx] += np1Array[puffer3 + kdx] * np2Array[dim1 * kdx + puffer2];
}
}
break;
}
case Quaternion[] np1Array:
{
Quaternion[] result = prod.Data as Quaternion[];
Quaternion[] np2Array = np2.Data as Quaternion[];
for (int idx = 0; idx < prod.Data.Length; idx++)
{
int puffer1 = idx / dim1;
int puffer2 = idx % dim1;
int puffer3 = puffer1 * iterator;
for (int kdx = 0; kdx < iterator; kdx++)
{
result[idx] += np1Array[puffer3 + kdx] * np2Array[dim1 * kdx + puffer2];
}
}
break;
}
default:
{
throw new Exception("The Dot method is not implemented for the " + typeof(T).Name);
}
}
if ((this.Shape.Length == 1) & (np2.Shape.Length == 1))
{
this.Shape = new Shape(this.Data.Length);
np2.Shape = new Shape(np2.Data.Length);
prod.Shape = new Shape(1);
}
return(prod);
}
public NDArrayGeneric <double> hstack(params NDArrayGeneric <double>[] nps)
{
var n = new NDArrayGeneric <double>();
return(n.HStack(nps));
}
public NDArrayGeneric <int> reshape(NDArrayGeneric <int> np, params int[] shape)
{
np.Shape = new Shape(shape);
return(np);
}
public static NDArrayGeneric <T> operator +(NDArrayGeneric <T> np1, T scalar)
{
NDArrayGeneric <T> sum = new NDArrayGeneric <T>();
sum.Shape = np1.Shape;
sum.Data = new T[np1.Data.Length];
switch (scalar)
{
case double scalarDouble:
{
double[] np1Array = np1.Data as double[];
double[] sumArray = sum.Data as double[];
// for is faster than linq
for (int idx = 0; idx < sumArray.Length; idx++)
{
sumArray[idx] = np1Array[idx] + scalarDouble;
}
break;
}
case float scalarFloat:
{
float[] np1Array = np1.Data as float[];
float[] sumArray = sum.Data as float[];
// for is faster than linq
for (int idx = 0; idx < sumArray.Length; idx++)
{
sumArray[idx] = np1Array[idx] + scalarFloat;
}
break;
}
case Complex scalarComplex:
{
Complex[] np1Array = np1.Data as Complex[];
Complex[] sumArray = sum.Data as Complex[];
// for is faster than linq
for (int idx = 0; idx < sumArray.Length; idx++)
{
sumArray[idx] = np1Array[idx] + scalarComplex;
}
break;
}
case Quaternion scalarQuaternion:
{
Quaternion[] np1Array = np1.Data as Quaternion[];
Quaternion[] sumArray = sum.Data as Quaternion[];
// for is faster than linq
for (int idx = 0; idx < sumArray.Length; idx++)
{
sumArray[idx] = np1Array[idx] + scalarQuaternion;
}
break;
}
default:
{
throw new Exception("The operation is not implemented for the " + typeof(T).Name);
}
}
return((NDArrayGeneric <T>)sum);
}
public static NDArrayGeneric <T> ones_like <T>(this NumPyGeneric <T> np, NDArrayGeneric <T> nd, string order = "C")
{
return(np.ones(new Shape(nd.Shape.Shapes)));
}
