public void Case2_Axis4()
{
var shape = new Shape(1, 2, 1, 1, 3);
var sh = new NDCoordinatesAxisIncrementor(ref shape, 4);
var slices = sh.Slices;
slices.Should().ContainInOrder(Slice.Index(0), Slice.Index(0), Slice.Index(0), Slice.Index(0), Slice.All);
sh.Next().Should().ContainInOrder(Slice.Index(0), Slice.Index(1), Slice.Index(0), Slice.Index(0), Slice.All);
sh.Next().Should().BeNull();
}
public override NDArray ReduceArgMax(NDArray arr, int? axis_)
{
//in order to iterate an axis:
//consider arange shaped (1,2,3,4) when we want to summarize axis 1 (2nd dimension which its value is 2)
//the size of the array is [1, 2, n, m] all shapes after 2nd multiplied gives size
//the size of what we need to reduce is the size of the shape of the given axis (shape[axis])
var shape = arr.Shape;
if (shape.IsEmpty)
return arr;
if (shape.IsScalar || (shape.size == 1 && shape.NDim == 1))
return NDArray.Scalar(0);
if (axis_ == null)
return NDArray.Scalar(argmax_elementwise(arr));
var axis = axis_.Value;
while (axis < 0)
axis = arr.ndim + axis; //handle negative axis
if (axis >= arr.ndim)
throw new ArgumentOutOfRangeException(nameof(axis));
if (shape[axis] == 1)
{
//if the given div axis is 1 and can be squeezed out.
return np.squeeze_fast(arr, axis);
}
//handle keepdims
Shape axisedShape = Shape.GetAxis(shape, axis);
//prepare ret
var ret = new NDArray(NPTypeCode.Int32, axisedShape, false);
var iterAxis = new NDCoordinatesAxisIncrementor(ref shape, axis);
var iterRet = new NDCoordinatesIncrementor(ref axisedShape);
var iterIndex = iterRet.Index;
var slices = iterAxis.Slices;
#if _REGEN
#region Compute
switch (arr.GetTypeCode)
{
%foreach supported_numericals,supported_numericals_lowercase%
case NPTypeCode.#1:
{
int at;
do
{
var iter = arr[slices].AsIterator<#2>();
public void Case1_Axis1()
{
var shape = new Shape(2, 3, 3);
var sh = new NDCoordinatesAxisIncrementor(ref shape, 1);
var slices = sh.Slices;
slices.Should().ContainInOrder(Slice.Index(0), Slice.All, Slice.Index(0));
sh.Next().Should().ContainInOrder(Slice.Index(0), Slice.All, Slice.Index(1));
sh.Next().Should().ContainInOrder(Slice.Index(0), Slice.All, Slice.Index(2));
sh.Next().Should().ContainInOrder(Slice.Index(1), Slice.All, Slice.Index(0));
sh.Next().Should().ContainInOrder(Slice.Index(1), Slice.All, Slice.Index(1));
sh.Next().Should().ContainInOrder(Slice.Index(1), Slice.All, Slice.Index(2));
sh.Next().Should().BeNull();
}
public override NDArray ReduceAMin(NDArray arr, int? axis_, bool keepdims = false, NPTypeCode? typeCode = null)
{
//in order to iterate an axis:
//consider arange shaped (1,2,3,4) when we want to summarize axis 1 (2nd dimension which its value is 2)
//the size of the array is [1, 2, n, m] all shapes after 2nd multiplied gives size
//the size of what we need to reduce is the size of the shape of the given axis (shape[axis])
var shape = arr.Shape;
if (shape.IsEmpty)
return arr;
if (shape.IsScalar || shape.size == 1 && shape.dimensions.Length == 1)
{
var r = typeCode.HasValue ? Cast(arr, typeCode.Value, true) : arr.Clone();
if (keepdims)
r.Storage.ExpandDimension(0);
else if (!r.Shape.IsScalar && r.Shape.size == 1 && r.ndim == 1)
r.Storage.Reshape(Shape.Scalar);
return r;
}
if (axis_ == null)
{
var r = NDArray.Scalar(amin_elementwise(arr, typeCode));
if (keepdims)
r.Storage.ExpandDimension(0);
else if (!r.Shape.IsScalar && r.Shape.size == 1 && r.ndim == 1)
r.Storage.Reshape(Shape.Scalar);
return r;
}
var axis = axis_.Value;
while (axis < 0)
axis = arr.ndim + axis; //handle negative axis
if (axis >= arr.ndim)
throw new ArgumentOutOfRangeException(nameof(axis));
if (shape[axis] == 1)
{
//if the given div axis is 1 and can be squeezed out.
if (keepdims)
return new NDArray(arr.Storage.Alias());
return np.squeeze_fast(arr, axis);
}
//handle keepdims
Shape axisedShape = Shape.GetAxis(shape, axis);
//prepare ret
var ret = new NDArray(typeCode ?? arr.GetTypeCode, axisedShape, false);
var iterAxis = new NDCoordinatesAxisIncrementor(ref shape, axis);
var iterRet = new NDCoordinatesIncrementor(ref axisedShape);
var iterIndex = iterRet.Index;
var slices = iterAxis.Slices;
#if _REGEN1
#region Compute
switch (arr.GetTypeCode)
{
%foreach supported_numericals,supported_numericals_lowercase%
case NPTypeCode.#1:
{
switch (ret.GetTypeCode)
{
%foreach supported_numericals,supported_numericals_lowercase,supported_numericals_defaultvals%
case NPTypeCode.#101:
{
do
{
var iter = arr[slices].AsIterator<#2>();
public override NDArray ReduceProduct(NDArray arr, int? axis_, bool keepdims = false, NPTypeCode? typeCode = null)
{
//in order to iterate an axis:
//consider arange shaped (1,2,3,4) when we want to summarize axis 1 (2nd dimension which its value is 2)
//the size of the array is [1, 2, n, m] all shapes after 2nd multiplied gives size
//the size of what we need to reduce is the size of the shape of the given axis (shape[axis])
var shape = arr.Shape;
if (shape.IsEmpty || shape.size==0)
return NDArray.Scalar(1, (typeCode ?? arr.typecode));
if (shape.IsScalar || (shape.size == 1 && shape.NDim == 1))
{
var r = NDArray.Scalar(typeCode.HasValue ? Converts.ChangeType(arr.GetAtIndex(0), typeCode.Value) : arr.GetAtIndex(0));
if (keepdims)
r.Storage.ExpandDimension(0);
return r;
}
if (axis_ == null)
{
var r = NDArray.Scalar(product_elementwise(arr, typeCode));
if (keepdims)
r.Storage.ExpandDimension(0);
else if (!r.Shape.IsScalar && r.Shape.size == 1 && r.ndim == 1)
r.Storage.Reshape(Shape.Scalar);
return r;
}
var axis = axis_.Value;
while (axis < 0)
axis = arr.ndim + axis; //handle negative axis
if (axis >= arr.ndim)
throw new ArgumentOutOfRangeException(nameof(axis));
if (shape[axis] == 1)
{
//if the given div axis is 1 and can be squeezed out.
if (keepdims)
return new NDArray(arr.Storage.Alias());
return np.squeeze_fast(arr, axis);
}
//handle keepdims
Shape axisedShape = Shape.GetAxis(shape, axis);
var retType = typeCode ?? (arr.GetTypeCode.GetAccumulatingType());
//prepare ret
var ret = new NDArray(retType, axisedShape, false);
var iterAxis = new NDCoordinatesAxisIncrementor(ref shape, axis);
var iterRet = new NDCoordinatesIncrementor(ref axisedShape);
var iterIndex = iterRet.Index;
var slices = iterAxis.Slices;
//resolve the accumulator type
#if _REGEN1
#region Compute
switch (arr.GetTypeCode)
{
%foreach supported_numericals,supported_numericals_lowercase%
case NPTypeCode.#1:
{
switch (retType)
{
%foreach supported_numericals,supported_numericals_lowercase,supported_numericals_onevales%
case NPTypeCode.#101:
{
do
{
var slice = arr[slices];
var iter = slice.AsIterator<#2>();
var moveNext = iter.MoveNext;
var hasNext = iter.HasNext;
|#102 sum = #103;
while (hasNext())
sum *= (#102) moveNext();
ret.Set#101(Converts.To#101(sum), iterIndex);
} while (iterAxis.Next() != null && iterRet.Next() != null);
break;
}
