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Added full implementation for np.clip
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8 files changed

+1367
-10
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‎src/NumSharp.Core/Backends/Default/Math/Default.Clip.cs

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‎src/NumSharp.Core/Backends/Default/Math/Default.ClipNDArray.cs

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‎src/NumSharp.Core/Backends/TensorEngine.cs

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@@ -80,6 +80,11 @@ public abstract class TensorEngine
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public abstract NDArray Sinh(in NDArray nd, Type dtype);
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public abstract NDArray Sinh(in NDArray nd, NPTypeCode? typeCode = null);
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public abstract NDArray Clip(in NDArray lhs, in ValueType min, in ValueType max, Type dtype);
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public abstract NDArray Clip(in NDArray lhs, in ValueType min, in ValueType max, NPTypeCode? typeCode = null);
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public abstract NDArray ClipNDArray(in NDArray lhs, in NDArray min, in NDArray max, Type dtype);
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public abstract NDArray ClipNDArray(in NDArray lhs, in NDArray min, in NDArray max, NPTypeCode? typeCode = null);
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#endregion
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#region Logic

‎src/NumSharp.Core/Creation/np.broadcast_to.cs

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@@ -19,8 +19,8 @@ public static Shape broadcast_to(Shape from, Shape against)
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/// <summary>
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/// Broadcast an array to a new shape.
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/// </summary>
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/// <param name="lhs">An array to broadcast.</param>
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/// <param name="rhs">An array to broadcast.</param>
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/// <param name="from">The NDArray to broadcast.</param>
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/// <param name="against">The shape to broadcast against.</param>
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/// <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>
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/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.broadcast_to.html</remarks>
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public static NDArray broadcast_to(NDArray from, Shape against)

‎src/NumSharp.Core/Math/np.clip.cs

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using System;
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using NumSharp.Backends;
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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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/// Clip (limit) the values in an array.<br></br>
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/// Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of [0, 1] is specified, values smaller than 0 become 0, and values larger than 1 become 1.
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/// </summary>
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/// <param name="a">Array containing elements to clip.</param>
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/// <param name="a_max">Maximum value. If None, clipping is not performed on upper interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="a_min">Minimum value. If None, clipping is not performed on lower interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="outType">The dtype the returned ndarray should be of, only non integer values are supported.</param>
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/// <returns>An array with the elements of a, but where values &lt; a_min are replaced with a_min, and those &gt; a_max with a_max.</returns>
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/// <remarks>https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.clip.html</remarks>
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public static NDArray clip(in NDArray a, ValueType a_min, ValueType a_max, NPTypeCode? outType = null)
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=> a.TensorEngine.Clip(a, a_min, a_max, outType);
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/// <summary>
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/// Clip (limit) the values in an array.<br></br>
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/// Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of [0, 1] is specified, values smaller than 0 become 0, and values larger than 1 become 1.
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/// </summary>
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/// <param name="a">Array containing elements to clip.</param>
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/// <param name="a_max">Maximum value. If None, clipping is not performed on upper interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="a_min">Minimum value. If None, clipping is not performed on lower interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="outType">The dtype the returned ndarray should be of, only non integer values are supported.</param>
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/// <returns>An array with the elements of a, but where values &lt; a_min are replaced with a_min, and those &gt; a_max with a_max.</returns>
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/// <remarks>https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.clip.html</remarks>
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public static NDArray clip(in NDArray a, ValueType a_min, ValueType a_max, Type outType)
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=> a.TensorEngine.Clip(a, a_min, a_max, outType);
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/// <summary>
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/// Clip (limit) the values in an array.<br></br>
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/// Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of [0, 1] is specified, values smaller than 0 become 0, and values larger than 1 become 1.
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/// </summary>
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/// <param name="a">Array containing elements to clip.</param>
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/// <param name="a_max">Maximum value. If None, clipping is not performed on upper interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="a_min">Minimum value. If None, clipping is not performed on lower interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="outType">The dtype the returned ndarray should be of, only non integer values are supported.</param>
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/// <returns>An array with the elements of a, but where values &lt; a_min are replaced with a_min, and those &gt; a_max with a_max.</returns>
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/// <remarks>https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.clip.html</remarks>
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public static NDArray clip(in NDArray a, NDArray a_min, NDArray a_max, NPTypeCode? outType = null)
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=> a.TensorEngine.ClipNDArray(a, a_min, a_max, outType);
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/// <summary>
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/// Clip (limit) the values in an array.<br></br>
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/// Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of [0, 1] is specified, values smaller than 0 become 0, and values larger than 1 become 1.
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/// </summary>
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/// <param name="a">Array containing elements to clip.</param>
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/// <param name="a_max">Maximum value. If None, clipping is not performed on upper interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="a_min">Minimum value. If None, clipping is not performed on lower interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="outType">The dtype the returned ndarray should be of, only non integer values are supported.</param>
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/// <returns>An array with the elements of a, but where values &lt; a_min are replaced with a_min, and those &gt; a_max with a_max.</returns>
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/// <remarks>https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.clip.html</remarks>
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public static NDArray clip(in NDArray a, NDArray a_min, NDArray a_max, Type outType)
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=> a.TensorEngine.ClipNDArray(a, a_min, a_max, outType);
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/// <summary>
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/// Clip (limit) the values in an array.<br></br>
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/// Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of [0, 1] is specified, values smaller than 0 become 0, and values larger than 1 become 1.
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/// </summary>
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/// <param name="a">Array containing elements to clip.</param>
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/// <param name="a_max">Maximum value. If None, clipping is not performed on upper interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="a_min">Minimum value. If None, clipping is not performed on lower interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="outType">The dtype the returned ndarray should be of, only non integer values are supported.</param>
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/// <returns>An array with the elements of a, but where values &lt; a_min are replaced with a_min, and those &gt; a_max with a_max.</returns>
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/// <remarks>https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.clip.html</remarks>
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public static NDArray clip<TMax>(in NDArray a, NDArray a_min, TMax a_max, NPTypeCode? outType = null) where TMax : unmanaged
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=> a.TensorEngine.ClipNDArray(a, a_min, NDArray.Scalar(a_max), outType);
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/// <summary>
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/// Clip (limit) the values in an array.<br></br>
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/// Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of [0, 1] is specified, values smaller than 0 become 0, and values larger than 1 become 1.
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/// </summary>
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/// <param name="a">Array containing elements to clip.</param>
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/// <param name="a_max">Maximum value. If None, clipping is not performed on upper interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="a_min">Minimum value. If None, clipping is not performed on lower interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="outType">The dtype the returned ndarray should be of, only non integer values are supported.</param>
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/// <returns>An array with the elements of a, but where values &lt; a_min are replaced with a_min, and those &gt; a_max with a_max.</returns>
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/// <remarks>https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.clip.html</remarks>
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public static NDArray clip<TMax>(in NDArray a, NDArray a_min, TMax a_max, Type outType) where TMax : unmanaged
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=> a.TensorEngine.ClipNDArray(a, a_min, NDArray.Scalar(a_max), outType);
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/// <summary>
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/// Clip (limit) the values in an array.<br></br>
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/// Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of [0, 1] is specified, values smaller than 0 become 0, and values larger than 1 become 1.
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/// </summary>
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/// <param name="a">Array containing elements to clip.</param>
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/// <param name="a_max">Maximum value. If None, clipping is not performed on upper interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="a_min">Minimum value. If None, clipping is not performed on lower interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="outType">The dtype the returned ndarray should be of, only non integer values are supported.</param>
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/// <returns>An array with the elements of a, but where values &lt; a_min are replaced with a_min, and those &gt; a_max with a_max.</returns>
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/// <remarks>https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.clip.html</remarks>
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public static NDArray clip<TMin>(in NDArray a, TMin a_min, NDArray a_max, NPTypeCode? outType = null) where TMin : unmanaged
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=> a.TensorEngine.ClipNDArray(a, NDArray.Scalar(a_min), a_max, outType);
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/// <summary>
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/// Clip (limit) the values in an array.<br></br>
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/// Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of [0, 1] is specified, values smaller than 0 become 0, and values larger than 1 become 1.
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/// </summary>
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/// <param name="a">Array containing elements to clip.</param>
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/// <param name="a_max">Maximum value. If None, clipping is not performed on upper interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="a_min">Minimum value. If None, clipping is not performed on lower interval edge. Not more than one of a_min and a_max may be None.</param>
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/// <param name="outType">The dtype the returned ndarray should be of, only non integer values are supported.</param>
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/// <returns>An array with the elements of a, but where values &lt; a_min are replaced with a_min, and those &gt; a_max with a_max.</returns>
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/// <remarks>https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.clip.html</remarks>
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public static NDArray clip<TMin>(in NDArray a, TMin a_min, NDArray a_max, Type outType) where TMin : unmanaged
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=> a.TensorEngine.ClipNDArray(a, NDArray.Scalar(a_min), a_max, outType);
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}
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}

‎test/NumSharp.Benchmark/Unmanaged/NestingCalls.cs

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break;
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case NPTypeCode.String:
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break;
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case NPTypeCode.NDArray:
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break;
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case NPTypeCode.Complex:
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b = a + 1 - 1;
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break;

‎test/NumSharp.UnitTest/Math/np.clip.Test.cs

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using System;
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using System.Collections.Generic;
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using System.Text;
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using FluentAssertions;
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using Microsoft.VisualStudio.TestTools.UnitTesting;
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using NumSharp.UnitTest.Utilities;
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namespace NumSharp.UnitTest
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{
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[TestClass]
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public class np_clip_test
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{
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[TestMethod]
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public void Case1()
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{
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var a = np.arange(12).reshape(3, 4);
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var max = np.repeat(8, 12).reshape(3, 4);
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np.clip(a, 3, max).Should().BeOfValues(3, 3, 3, 3, 4, 5, 6, 7, 8, 8, 8, 8).And.BeShaped(3, 4);
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}
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[TestMethod]
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public void Case2()
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{
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var a = np.arange(12).reshape(3, 4);
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var max = np.repeat(8, 12).reshape(3, 4);
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np.clip(a, max, null).Should().BeOfValues(8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 10, 11).And.BeShaped(3, 4);
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}
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[TestMethod]
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public void Case3()
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{
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var a = np.arange(12).reshape(3, 4);
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var max = np.repeat(8, 12).reshape(3, 4);
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np.clip(a, null, max).Should().BeOfValues(0, 1, 2, 3, 4, 5, 6, 7, 8, 8, 8, 8).And.BeShaped(3, 4);
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}
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}
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}

‎test/NumSharp.UnitTest/Utilities/ArraysTests.cs

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Arrays.Create(NPTypeCode.Int32, 1000).Should().BeOfType<int[]>().And.HaveCount(1000);
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}
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[TestMethod]
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public void Create_4()
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{
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Arrays.Create(NPTypeCode.NDArray, 1000).Should().BeOfType<NDArray[]>().And.HaveCount(1000);
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}
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[TestMethod]
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public void Insert_0()
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{

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