/// <summary>
/// Compute the truth value of x1 OR x2 element-wise.
/// </summary>
/// <param name="lhs">Input boolean array.</param>
/// <param name="rhs">Input boolean array.</param>
/// <returns>Returns True if the arrays are equal.</returns>
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.logical_or.html</remarks>
public static unsafe NDArray <bool> logical_or(NDArray lhs, NDArray rhs)
{
#if _REGEN1
if (lhs.typecode != rhs.typecode)
{
throw new NotImplementedException("please make sure operands have the same data type");
}
else if (lhs.typecode == NPTypeCode.Boolean)
{
if (lhs.Shape.IsScalar && rhs.Shape.IsScalar)
{
return(NDArray.Scalar(*(bool *)lhs.Address || *(bool *)rhs.Address).MakeGeneric <bool>());
}
var(BroadcastedLeftShape, BroadcastedRightShape) = DefaultEngine.Broadcast(lhs.Shape, rhs.Shape);
var lhs_address = (bool *)lhs.Address;
var rhs_address = (bool *)rhs.Address;
var ret = new NDArray <bool>(new Shape(BroadcastedLeftShape.dimensions), true);
Shape retShape = ret.Shape;
//iterate
var ret_address = (bool *)ret.Address;
var incr = new NDCoordinatesIncrementor(BroadcastedLeftShape.dimensions); //doesn't matter which side it is.
int[] current = incr.Index;
do
{
*(ret_address + retShape.GetOffset(current)) = (*(lhs_address + BroadcastedLeftShape.GetOffset(current))) || *(rhs_address + BroadcastedRightShape.GetOffset(current));
} while (incr.Next() != null);
return(ret);
}
public void broadcast_accessing()
{
var left = _create_arange(5, 5);
var right = _create_arange(5, 1);
var x = DefaultEngine.Broadcast(left.Shape, right.Shape);
left.Storage.SetShapeUnsafe(x.LeftShape);
right.Storage.SetShapeUnsafe(x.RightShape);
var ret = new NDArray(typeof(int), x.LeftShape);
foreach ((int i1, int i2) in indexes2(5, 5))
{
ret.SetValue(left.GetInt32(i1, i2) + right.GetInt32(i1, i2), i1, i2);
Console.WriteLine($"{ret.GetInt32(i1, i2)} = {left.GetInt32(i1, i2)} {right.GetInt32(i1, i2)}");
}
Console.WriteLine(ret.ToString(false));
left = _create_arange(2, 5, 5);
right = _create_arange(1, 1, 5);
x = DefaultEngine.Broadcast(left.Shape, right.Shape);
left.Storage.SetShapeUnsafe(x.LeftShape);
right.Storage.SetShapeUnsafe(x.RightShape);
ret = new NDArray(typeof(int), x.LeftShape);
foreach ((int i1, int i2, int i3) in indexes3(2, 5, 5))
{
ret.SetValue(left.GetInt32(i1, i2, i3) + right.GetInt32(i1, i2, i3), i1, i2, i3);
Console.WriteLine($"{ret.GetInt32(i1, i2, i3)} = {left.GetInt32(i1, i2)} {right.GetInt32(i1, i2, i3)}");
}
Console.WriteLine(ret.ToString(false));
}
public void basics_broadcasting_narrays()
{
Shape[] ret;
ret = DefaultEngine.Broadcast(new Shape[] { new Shape(5, 5), Shape.Scalar });
ret[0].dimensions.Should().ContainInOrder(5, 5);
ret[1].dimensions.Should().ContainInOrder(5, 5);
ret[1].strides.Should().AllBeEquivalentTo(0);
indexes2(5, 5).Select(i => ret[1].GetOffset(i.i1, i.i2)).Should().AllBeEquivalentTo(0);
indexes2(5, 5).Select(i => ret[0].GetOffset(i.i1, i.i2))
.Should().BeInAscendingOrder().And.Subject.First().Should().Be(0);
ret = DefaultEngine.Broadcast(new Shape[] { Shape.Scalar, new Shape(5, 5) });
ret[0].dimensions.Should().ContainInOrder(5, 5);
ret[1].dimensions.Should().ContainInOrder(5, 5);
ret[0].strides.Should().AllBeEquivalentTo(0);
indexes2(5, 5).Select(i => ret[0].GetOffset(i.i1, i.i2)).Should().AllBeEquivalentTo(0);
indexes2(5, 5).Select(i => ret[1].GetOffset(i.i1, i.i2))
.Should().BeInAscendingOrder().And.Subject.First().Should().Be(0);
ret = DefaultEngine.Broadcast(new Shape[] { new Shape(5, 5), new Shape(5, 5) });
ret[0].dimensions.Should().ContainInOrder(5, 5);
ret[1].dimensions.Should().ContainInOrder(5, 5);
ret[0].strides.Should().ContainInOrder(5, 1);
ret[1].strides.Should().ContainInOrder(5, 1);
ret = DefaultEngine.Broadcast(new Shape[] { new Shape(5, 5, 3), new Shape(5, 3) });
ret[0].dimensions.Should().ContainInOrder(5, 5, 3);
ret[1].dimensions.Should().ContainInOrder(5, 5, 3);
ret = DefaultEngine.Broadcast(new Shape[] { new Shape(256, 256, 3), new Shape(3) });
ret[0].dimensions.Should().ContainInOrder(256, 256, 3);
ret[1].dimensions.Should().ContainInOrder(256, 256, 3);
ret = DefaultEngine.Broadcast(new Shape[] { new Shape(8, 1, 6, 1), new Shape(7, 1, 5) });
ret[0].dimensions.Should().ContainInOrder(8, 7, 6, 5);
ret[1].dimensions.Should().ContainInOrder(8, 7, 6, 5);
ret = DefaultEngine.Broadcast(new Shape[] { new Shape(5, 4), new Shape(1) });
ret[0].dimensions.Should().ContainInOrder(5, 4);
ret[1].dimensions.Should().ContainInOrder(5, 4);
indexes2(5, 4).Select(i => ret[0].GetOffset(i.i1, i.i2))
.Should().BeInAscendingOrder().And.Subject.First().Should().Be(0);
indexes2(5, 4).Select(i => ret[1].GetOffset(i.i1, i.i2)).Should().AllBeEquivalentTo(0);
ret = DefaultEngine.Broadcast(new Shape[] { new Shape(5, 4), new Shape(4) });
ret[0].dimensions.Should().ContainInOrder(5, 4);
ret[1].dimensions.Should().ContainInOrder(5, 4);
ret = DefaultEngine.Broadcast(new Shape[] { new Shape(15, 3, 5), new Shape(15, 1, 5) });
ret[0].dimensions.Should().ContainInOrder(15, 3, 5);
ret[1].dimensions.Should().ContainInOrder(15, 3, 5);
ret = DefaultEngine.Broadcast(new Shape[] { new Shape(15, 3, 5), new Shape(3, 1) });
ret[0].dimensions.Should().ContainInOrder(15, 3, 5);
ret[1].dimensions.Should().ContainInOrder(15, 3, 5);
new Action(() => DefaultEngine.ResolveReturnShape(new Shape(3), new Shape(4))).Should().Throw <Exception>();
new Action(() => DefaultEngine.ResolveReturnShape(new Shape(2, 1), new Shape(8, 4, 3))).Should().Throw <Exception>();
}
/// <summary>
/// Broadcast any number of arrays against each other.
/// </summary>
/// <param name="ndArrays">The arrays to broadcast.</param>
/// <returns>These arrays are views on the original arrays. They are typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location. If you need to write to the arrays, make copies first.</returns>
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.broadcast_arrays.html</remarks>
public static NDArray[] broadcast_arrays(params NDArray[] ndArrays)
{
int len = ndArrays.Length;
int i;
var inputShapes = new Shape[len];
for (i = 0; i < len; i++)
{
inputShapes[i] = ndArrays[i].Shape;
}
var outputShapes = DefaultEngine.Broadcast(inputShapes);
var list = new NDArray[len];
for (i = 0; i < len; i++)
{
list[i] = new NDArray(UnmanagedStorage.CreateBroadcastedUnsafe(ndArrays[i].Storage, outputShapes[i]));
}
return(list);
}
/// <summary>
/// Aplies the (x, y) => z projection delegate to each element of left and right <see cref="NDArray"/> arguments.
/// </summary>
/// <typeparam name="TLElement">The type of a left <see cref="NDArray"/> element.</typeparam>
/// <typeparam name="TRElement">The type of a right <see cref="NDArray"/> element.</typeparam>
/// <typeparam name="TResult">The type of a resultint <see cref="NDArray"/> element.</typeparam>
/// <param name="lhs">The left <see cref="NDArray"/> to pass to the projector.</param>
/// <param name="rhs">The right <see cref="NDArray"/> to pass to the projector.</param>
/// <param name="operator">The projection delegate that handles the array elements.</param>
/// <returns>The resulting <see cref="NDArray"/> that contains the projection result.</returns>
public static unsafe NDArray BinaryOperation <TLElement, TRElement, TResult>(NDArray lhs, NDArray rhs, BinaryOperator <TLElement, TRElement, TResult> @operator)
where TLElement : unmanaged
where TRElement : unmanaged
where TResult : unmanaged
{
if (lhs is null)
{
throw new ArgumentNullException(nameof(lhs));
}
if (rhs is null)
{
throw new ArgumentNullException(nameof(rhs));
}
if (@operator is null)
{
throw new ArgumentNullException(nameof(@operator));
}
if (typeof(TLElement).GetTypeCode() != lhs.GetTypeCode)
{
throw new ArgumentException($"The left argument array type {lhs.GetTypeCode} does not match the expected type {typeof(TLElement).GetTypeCode()}.", nameof(lhs));
}
if (typeof(TRElement).GetTypeCode() != rhs.GetTypeCode)
{
throw new ArgumentException($"The right argument array type {rhs.GetTypeCode} does not match the expected type {typeof(TLElement).GetTypeCode()}.", nameof(rhs));
}
var(ls, rs) = DefaultEngine.Broadcast(lhs.Shape, rhs.Shape);
var resultShape = ls.Clean();
var result = new NDArray(typeof(TResult).GetTypeCode(), resultShape);
var lhsAddress = (TLElement *)lhs.Address;
var rhsAddress = (TRElement *)rhs.Address;
var resultAddress = (TResult *)result.Address;
switch ((l : ls.IsLinear, r : rs.IsLinear))
{
/// <summary>
/// Broadcast two arrays against each other.
/// </summary>
/// <param name="lhs">An array to broadcast.</param>
/// <param name="rhs">An array to broadcast.</param>
/// <returns>These arrays are views on the original arrays. They are typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location. If you need to write to the arrays, make copies first.</returns>
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.broadcast_arrays.html</remarks>
public static (NDArray Lhs, NDArray Rhs) broadcast_arrays(NDArray lhs, NDArray rhs)
{
var(leftShape, rightShape) = DefaultEngine.Broadcast(lhs.Shape, rhs.Shape);
return(new NDArray(UnmanagedStorage.CreateBroadcastedUnsafe(lhs.Storage, leftShape)),
new NDArray(UnmanagedStorage.CreateBroadcastedUnsafe(rhs.Storage, rightShape)));
}
/// <summary>
/// Broadcast an array to a new shape.
/// </summary>
/// <param name="from">The UnmanagedStorage to broadcast.</param>
/// <param name="against">The shape to broadcast against.</param>
/// <returns>These arrays are views on the original arrays. They are typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location. If you need to write to the arrays, make copies first.</returns>
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.broadcast_to.html</remarks>
public static NDArray broadcast_to(UnmanagedStorage from, UnmanagedStorage against)
{
return(new NDArray(UnmanagedStorage.CreateBroadcastedUnsafe(from, DefaultEngine.Broadcast(from.Shape, against.Shape).LeftShape)));
}
/// <summary>
/// Broadcast an shape against an other new shape.
/// </summary>
/// <param name="from">The shape that is to be broadcasted</param>
/// <param name="against">The shape that'll be used to broadcast <paramref name="from"/> shape</param>
/// <returns>A readonly view on the original array with the given shape. It is typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location.</returns>
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.broadcast_to.html</remarks>
public static Shape broadcast_to(Shape from, UnmanagedStorage against)
{
return(DefaultEngine.Broadcast(from, against.Shape).LeftShape);
}
/// <summary>
/// Broadcast an shape against an other new shape.
/// </summary>
/// <param name="from">The shape that is to be broadcasted</param>
/// <param name="against">The shape that'll be used to broadcast <paramref name="from"/> shape</param>
/// <returns>A readonly view on the original array with the given shape. It is typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location.</returns>
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.broadcast_to.html</remarks>
public static Shape broadcast_to(Shape from, NDArray against)
{
return(DefaultEngine.Broadcast(from, against.Shape).LeftShape);
}
