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implemented 1)np.meshgrid 2) np.broadcast_array 3) _broadcast_to 4) _broadcast_shape
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5 files changed

Lines changed: 205 additions & 5 deletions

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‎src/NumSharp.Core/Casting/Broadcast.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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namespace NumSharp.Core.Casting
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{
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public class Broadcast
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{
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public Shape shape { get; set; }
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public int nd { get; set; }
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public int ndim { get; set; }
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public int size { get; set; }
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// public int index { get; set; }
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// public int numiters { get; set; }
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public Broadcast(int nd, int ndim, Shape shape, int size)
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{
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this.nd = nd;
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this.ndim = ndim;
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this.size = size;
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this.shape = shape;
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}
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}
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}

‎src/NumSharp.Core/Creation/broadcast.cs‎

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using NumSharp.Core.Casting;
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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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namespace NumSharp.Core
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{
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public static partial class np
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{/// <summary>
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///
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/// </summary>
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/// <param name="nd1"> 1-D arrays representing the coordinates of a grid </param>
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/// <param name="nd2"> 1-D arrays representing the coordinates of a grid</param>
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/// <returns></returns>
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public static Broadcast broadcast(NDArray nd1, NDArray nd2)
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{
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int nd1maxAxis = nd1.shape[0] > nd1.shape[1] ? nd1.shape[0] : nd1.shape[1];
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int nd2maxAxis = nd2.shape[0] > nd2.shape[1] ? nd2.shape[0] : nd2.shape[1];
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Shape shape = new Shape(nd1maxAxis, nd2maxAxis);
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int nd = 2;
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int ndim = 2;
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int size = nd1maxAxis * nd2maxAxis;
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return new Broadcast(nd, ndim, shape, size);
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}
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}
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}

‎src/NumSharp.Core/Creation/broadcast_arrays.cs‎

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using NumSharp.Core.Casting;
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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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namespace NumSharp.Core
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{
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public static partial class np
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{
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public static NDArray[] broadcast_arrays(NDArray nd1, NDArray nd2, bool subok = false)
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{
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NDArray args1 = np.array(nd1);
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NDArray args2 = np.array(nd2);
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var shape = _broadcast_shape(args1, args2);
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if (nd1.shape == shape && nd2.shape == shape)
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{
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return new NDArray[] { nd1, nd2 };
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}
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return new NDArray[] { _broadcast_to(nd1, shape, subok, false) };
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}
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private static Shape _broadcast_shape(NDArray nd1, NDArray nd2)
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{
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Broadcast b = np.broadcast(nd1, nd2);
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return b.shape;
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}
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private static NDArray _broadcast_to(NDArray nd, Shape shape, bool subok, bool rdonly)
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{
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double[,] table = new double[shape.Dimensions[0], shape.Dimensions[1]];
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if (nd.shape[0] == 1)
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{// (1,2,3)
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for (int i = 0; i < shape.Dimensions[0]; i++)
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{
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for (int j = 0; j < shape.Dimensions[1]; j++)
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{
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table[i, j] = (double)nd.Storage.GetData(0, j);
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}
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}
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}
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else if (nd.shape[1] == 1)
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{
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for (int i = 0; i < shape.Dimensions[0]; i++)
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{
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for (int j = 0; j < shape.Dimensions[1]; j++)
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{
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table[i, j] = (double)nd.Storage.GetData(i, 0);
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}
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}
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}
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return new NDArray(table);
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}
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}
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}

‎src/NumSharp.Core/Creation/np.asanyarray.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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namespace NumSharp.Core
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{
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public static partial class np
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{
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public static NDArray asanyarray(string data)
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{
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var nd = new NDArray(typeof(string), new int[0]);
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nd.Storage.SetData(new string[] { data });
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return nd;
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}
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public static NDArray asanyarray<T>(T data) where T : struct
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{
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var nd = new NDArray(typeof(T), new int[0]);
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nd.Storage.SetData(new T[] { data });
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return nd;
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}
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public static NDArray asanyarray(string[] data, int ndim = 1)
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{
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var nd = new NDArray(typeof(string), data.Length);
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nd.Storage.SetData(data);
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return nd;
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}
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public static NDArray asanyarray<T>(T[] data, int ndim = 1) where T : struct
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{
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var nd = new NDArray(typeof(T), data.Length);
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nd.Storage.SetData(data);
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return nd;
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}
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}
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}

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

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using System.Collections.Generic;
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using System.Text;
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namespace NumSharp.Core.Creation
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namespace NumSharp.Core
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{
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static partial class np
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{
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/// .. versionchanged:: 1.9
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/// 1-D and 0-D cases are allowed.
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/// </summary>
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/// <param name="x1"></param>
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/// <param name="x2"></param>
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/// <param name="x1"> 1-D arrays representing the coordinates of a grid</param>
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/// /// <param name="x2"> 1-D arrays representing the coordinates of a grid</param>
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/// <returns></returns>
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public static NDArray meshgrid(NDArray x1, NDArray x2)
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public static (NDArray, NDArray) meshgrid(NDArray x1, NDArray x2, Kwargs kwargs =null)
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{
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throw new NotFiniteNumberException();
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if (kwargs == null)
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{
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kwargs = new Kwargs();
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}
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int ndim = 2;
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var s0 = (1,1);
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var output = new NDArray[]{ np.asanyarray(x1).reshape(-1,1), np.asanyarray(x2).reshape(1,-1)};
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if (kwargs.indexing == "xy" && ndim > 1)
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{
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// Switch first and second axis
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output[0].reshape(1,-1);
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output[1].reshape(-1, 1);
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}
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if (!kwargs.sparse)
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{
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// Return the full N-D matrix(not only the 1 - D vector)
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output = np.broadcast_arrays(output[0], output[1], true);
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}
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if (kwargs.copy)
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{
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}
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return (output[0], output[1]);
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}
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}
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public class Kwargs
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{
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public string indexing { get; set; }
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public bool sparse { get; set; }
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public bool copy { get; set; }
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/// <summary>
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/// Kwargs constructor
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/// </summary>
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/// <param name="indexing"> {'xy', 'ij'}, optional Cartesian('xy', default) or matrix('ij') indexing of output.</param>
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/// <param name="sparse">If True a sparse grid is returned in order to conserve memory. Default is False.</param>
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/// <param name="copy">If False, a view into the original arrays are returned in order to conserve memory.
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/// Default is True.Please note that sparse= False, copy= False`` will likely return non-contiguous arrays.
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/// Furthermore, more than one element of a broadcast array may refer to a single memory location.
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/// If you need to write to the arrays, make copies first.</param>
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public Kwargs(string indexing, bool sparse, bool copy)
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{
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this.indexing = indexing;
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this.sparse = sparse;
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this.copy = copy;
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}
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public Kwargs()
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{
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this.indexing = "xy";
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this.sparse = false;
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this.copy = true;
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}
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}
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}

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