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Added np.std, more performance optimization and bug fixes
- Performance optimization for scalar-related cloning and math-ops - Fixed various issues with all DefaultEngine's reduction methods. - Revamped expand_dims - Added a faster logic for Default.Cast when casting a scalar.
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Lines changed: 15377 additions & 3096 deletions

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‎examples/NeuralNetwork.NumSharp/Layers/Activations/ReLU.cs‎

Lines changed: 1 addition & 1 deletion
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@@ -22,7 +22,7 @@ public override void Forward(NDArray x)
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public override void Backward(NDArray grad)
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{
25-
InputGrad = grad * (Input > 0);
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InputGrad = grad * (NDArray)(Input > 0);
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}
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}
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}

‎src/NumSharp.Core/APIs/np.amax.cs‎

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@@ -0,0 +1,46 @@
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using System;
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namespace NumSharp
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{
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public static partial class np
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{
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/// <summary>
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/// Return the maximum of an array or maximum along an axis.
9+
/// </summary>
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/// <typeparam name="T">the type expected as a return, cast is performed if necessary.</typeparam>
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/// <returns>Maximum of a. If axis is None, the result is a scalar value. If axis is given, the result is an array of dimension a.ndim - 1.</returns>
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/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.amax.html</remarks>
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public static T amax<T>(NDArray a) where T : unmanaged => a.amax<T>();
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/// <summary>
16+
/// Return the maximum of an array or maximum along an axis.
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/// </summary>
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/// <param name="axis">Axis or axes along which to operate.</param>
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/// <param name="keepdims">If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.</param>
20+
/// <param name="dtype">the type expected as a return, null will remain the same dtype.</param>
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/// <returns>Maximum of a. If axis is None, the result is a scalar value. If axis is given, the result is an array of dimension a.ndim - 1.</returns>
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/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.amax.html</remarks>
23+
public static NDArray amax(NDArray a, int? axis = null, bool keepdims = false, Type dtype = null)
24+
{
25+
if (!axis.HasValue)
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return a.amax(dtype);
27+
return a.amax(axis.Value, keepdims, dtype);
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}
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/// <summary>
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/// Return the maximum of an array or maximum along an axis.
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/// </summary>
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/// <param name="axis">Axis or axes along which to operate.</param>
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/// <param name="keepdims">If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.</param>
35+
/// <param name="dtype">the type expected as a return, null will remain the same dtype.</param>
36+
/// <returns>Maximum of a. If axis is None, the result is a scalar value. If axis is given, the result is an array of dimension a.ndim - 1.</returns>
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/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.amax.html</remarks>
38+
public static NDArray max(NDArray a, int? axis = null, bool keepdims = false, Type dtype = null)
39+
{
40+
if (!axis.HasValue)
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return a.amax(dtype);
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return a.amax(axis.Value, keepdims, dtype);
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}
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}
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}

‎src/NumSharp.Core/APIs/np.blas.cs‎

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Original file line numberDiff line numberDiff line change
@@ -16,9 +16,9 @@ public static partial class np
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/// <param name="y"></param>
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/// <returns></returns>
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public static NDArray dot(NDArray x, NDArray y)
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=> BackendFactory.GetEngine().Dot(x, y);
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=> x.TensorEngine.Dot(x, y);
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public static NDArray matmul(NDArray x, NDArray y)
22-
=> BackendFactory.GetEngine().MatMul(x, y);
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=> x.TensorEngine.MatMul(x, y);
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}
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}

‎src/NumSharp.Core/APIs/np.math.cs‎

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Original file line numberDiff line numberDiff line change
@@ -9,25 +9,25 @@ public static partial class np
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{
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/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.add.html</remarks>
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public static NDArray add(in NDArray x1, in NDArray x2)
12-
=> BackendFactory.GetEngine().Add(x1, x2);
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=> x1.TensorEngine.Add(x1, x2);
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/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.divide.html</remarks>
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public static NDArray divide(in NDArray x1, in NDArray x2)
16-
=> BackendFactory.GetEngine().Divide(x1, x2);
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=> x1.TensorEngine.Divide(x1, x2);
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/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.true_divide.html</remarks>
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public static NDArray true_divide(in NDArray x1, in NDArray x2)
2020
{
21-
return BackendFactory.GetEngine().Divide(x1, x2);
21+
return x1.TensorEngine.Divide(x1, x2);
2222
}
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2424
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.multiply.html</remarks>
2525
public static NDArray multiply(in NDArray x1, in NDArray x2)
26-
=> BackendFactory.GetEngine().Multiply(x1, x2);
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=> x1.TensorEngine.Multiply(x1, x2);
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/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.subtract.html</remarks>
2929
public static NDArray subtract(in NDArray x1, in NDArray x2)
30-
=> BackendFactory.GetEngine().Subtract(x1, x2);
30+
=> x1.TensorEngine.Subtract(x1, x2);
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/// <summary>
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/// Sum of array elements over a given axis.
@@ -39,7 +39,7 @@ public static NDArray subtract(in NDArray x1, in NDArray x2)
3939
/// <returns>An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. If an output array is specified, a reference to out is returned.</returns>
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/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html</remarks>
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public static NDArray sum(in NDArray a)
42-
=> BackendFactory.GetEngine().Sum(a, axis: null, typeCode: null, keepdims: false);
42+
=> a.TensorEngine.Sum(a, axis: null, typeCode: null, keepdims: false);
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/// <summary>
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/// Sum of array elements over a given axis.
@@ -51,7 +51,7 @@ public static NDArray sum(in NDArray a)
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/// <returns>An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. If an output array is specified, a reference to out is returned.</returns>
5252
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html</remarks>
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public static NDArray sum(in NDArray a, int axis)
54-
=> BackendFactory.GetEngine().Sum(a, axis: axis, typeCode: null, keepdims: false);
54+
=> a.TensorEngine.Sum(a, axis: axis, typeCode: null, keepdims: false);
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/// <summary>
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/// Sum of array elements over a given axis.
@@ -63,7 +63,7 @@ public static NDArray sum(in NDArray a, int axis)
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/// <returns>An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. If an output array is specified, a reference to out is returned.</returns>
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/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html</remarks>
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public static NDArray sum(in NDArray a, bool keepdims)
66-
=> BackendFactory.GetEngine().Sum(a, axis: null, typeCode: null, keepdims: keepdims);
66+
=> a.TensorEngine.Sum(a, axis: null, typeCode: null, keepdims: keepdims);
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/// <summary>
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/// Sum of array elements over a given axis.
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7575
/// <returns>An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. If an output array is specified, a reference to out is returned.</returns>
7676
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html</remarks>
7777
public static NDArray sum(in NDArray a, int? axis, bool keepdims)
78-
=> BackendFactory.GetEngine().Sum(a, axis: axis, typeCode: null, keepdims: keepdims);
78+
=> a.TensorEngine.Sum(a, axis: axis, typeCode: null, keepdims: keepdims);
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/// <summary>
8181
/// Sum of array elements over a given axis.
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8787
/// <returns>An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. If an output array is specified, a reference to out is returned.</returns>
8888
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html</remarks>
8989
public static NDArray sum(in NDArray a, int? axis, bool keepdims, Type dtype)
90-
=> BackendFactory.GetEngine().Sum(a, axis: axis, typeCode: dtype?.GetTypeCode(), keepdims: keepdims);
90+
=> a.TensorEngine.Sum(a, axis: axis, typeCode: dtype?.GetTypeCode(), keepdims: keepdims);
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/// <summary>
9393
/// Sum of array elements over a given axis.
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9999
/// <returns>An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. If an output array is specified, a reference to out is returned.</returns>
100100
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html</remarks>
101101
public static NDArray sum(in NDArray a, int? axis, bool keepdims, NPTypeCode? typeCode)
102-
=> BackendFactory.GetEngine().Sum(a, axis: axis, typeCode: typeCode, keepdims: keepdims);
102+
=> a.TensorEngine.Sum(a, axis: axis, typeCode: typeCode, keepdims: keepdims);
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/// <summary>
105105
/// Sum of array elements over a given axis.
@@ -111,7 +111,7 @@ public static NDArray sum(in NDArray a, int? axis, bool keepdims, NPTypeCode? ty
111111
/// <returns>An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. If an output array is specified, a reference to out is returned.</returns>
112112
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html</remarks>
113113
public static NDArray sum(in NDArray a, int? axis, Type dtype)
114-
=> BackendFactory.GetEngine().Sum(a, axis: axis, typeCode: dtype?.GetTypeCode(), keepdims: false);
114+
=> a.TensorEngine.Sum(a, axis: axis, typeCode: dtype?.GetTypeCode(), keepdims: false);
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116116
/// <summary>
117117
/// Sum of array elements over a given axis.
@@ -122,7 +122,7 @@ public static NDArray sum(in NDArray a, int? axis, Type dtype)
122122
/// <returns>An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. If an output array is specified, a reference to out is returned.</returns>
123123
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html</remarks>
124124
public static NDArray sum(in NDArray a, int? axis, NPTypeCode? typeCode)
125-
=> BackendFactory.GetEngine().Sum(a, axis: axis, typeCode: typeCode, keepdims: false);
125+
=> a.TensorEngine.Sum(a, axis: axis, typeCode: typeCode, keepdims: false);
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127127
/// <summary>
128128
/// Sum of array elements over a given axis.
@@ -134,7 +134,7 @@ public static NDArray sum(in NDArray a, int? axis, NPTypeCode? typeCode)
134134
/// <returns>An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. If an output array is specified, a reference to out is returned.</returns>
135135
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html</remarks>
136136
public static NDArray sum(in NDArray a, Type dtype)
137-
=> BackendFactory.GetEngine().Sum(a, axis: null, typeCode: dtype?.GetTypeCode(), keepdims: false);
137+
=> a.TensorEngine.Sum(a, axis: null, typeCode: dtype?.GetTypeCode(), keepdims: false);
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139139
/// <summary>
140140
/// Sum of array elements over a given axis.
@@ -146,7 +146,7 @@ public static NDArray sum(in NDArray a, Type dtype)
146146
/// <returns>An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. If an output array is specified, a reference to out is returned.</returns>
147147
/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html</remarks>
148148
public static NDArray sum(in NDArray a, NPTypeCode? typeCode)
149-
=> BackendFactory.GetEngine().Sum(a, axis: null, typeCode: typeCode, keepdims: false);
149+
=> a.TensorEngine.Sum(a, axis: null, typeCode: typeCode, keepdims: false);
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151151
/// <summary>
152152
/// Calculate the absolute value element-wise.
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194194
/// Array elements raised to given powers, element-wise.
195195
/// </summary>
196196
public static NDArray power(NDArray x, ValueType y)
197-
=> BackendFactory.GetEngine().Power(x, y);
197+
=> x.TensorEngine.Power(x, y);
198198

199199
//public static NDArray power<T>(NDArray nd, T exponent)
200200
// => nd.power<T>(exponent);

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