add spmatrix class. #190

Author: Oceania2018

src/NumSharp.Core/Sparse/_cs_matrix.cs

@@ -0,0 +1,49 @@
+using NumSharp.Core;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace NumSharp.Core.Sparse
+{
+    /// <summary>
+    /// base matrix class for compressed row and column oriented matrices
+    /// </summary>
+    public partial class _cs_matrix
+    {
+        public NDArray data { get; set; }
+
+        public int ndim => data.ndim;
+
+        public Type dtype => data.dtype;
+
+        public Shape shape => (Shape)data.Storage.Shape;
+
+        public int maxprint { get; set; }
+
+        public int nnz
+        {
+            get
+            {
+                switch (data.dtype.Name)
+                {
+                    case "Int32":
+                        return data.Data<int>().Length;
+                    case "Double":
+                        return data.Data<double>().Length;
+                }
+
+                return 0;
+            }
+        }
+
+        public bool has_canonical_format { get; set; }
+
+        public bool has_sorted_indices { get; set; }
+
+        public string format { get; set; }
+
+        public NDArray indices { get; set; }
+
+        public NDArray indptr { get; set; }
+    }
+}

src/NumSharp.Core/Sparse/_cs_matrix.mul.cs

@@ -0,0 +1,28 @@
+using NumSharp.Core;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace NumSharp.Core.Sparse
+{
+    public partial class _cs_matrix
+    {
+        public static matrix operator *(_cs_matrix m1, matrix m2)
+        {
+            m1.transpose();
+            var (M, N) = m1.shape.BiShape;
+
+            var result = np.zeros(M);
+
+            spmatrix.csc_matvec(M, N, m1.indptr.Data<int>(), m1.indices.Data<int>(), m1.data.Data<double>(), m2.Data<double>(), result);
+
+            m1.transpose();
+            return np.asmatrix(np.asmatrix(result).transpose());
+        }
+
+        public static matrix operator *(matrix m2, _cs_matrix m1)
+        {
+            return m1 * m2;
+        }
+    }
+}

src/NumSharp.Core/Sparse/_cs_matrix.sum.cs

@@ -0,0 +1,20 @@
+using NumSharp.Core;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace NumSharp.Core.Sparse
+{
+    public partial class _cs_matrix
+    {
+        /// <summary>
+        /// Sum the matrix over the given axis.  If the axis is None, sum over both rows and columns, returning a scalar.
+        /// </summary>
+        /// <param name="axis"></param>
+        /// <returns></returns>
+        public matrix sum(int? axis = null)
+        {
+            return spmatrix.sum(this, axis: 0, dtype: dtype);
+        }
+    }
+}

src/NumSharp.Core/Sparse/_cs_matrix.transpose.cs

@@ -0,0 +1,25 @@
+using NumSharp.Core;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+
+namespace NumSharp.Core.Sparse
+{
+    public partial class _cs_matrix
+    {
+        public _cs_matrix transpose()
+        {
+            if (data.ndim == 1)
+            {
+                data = data.transpose();
+            }
+            else
+            {
+                data.reshape(data.shape.Reverse().ToArray());
+            }
+
+            return this;
+        }
+    }
+}

src/NumSharp.Core/Sparse/csr_matrix.cs

@@ -0,0 +1,25 @@
+using NumSharp.Core;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace NumSharp.Core.Sparse
+{
+    public partial class csr_matrix : _cs_matrix
+    {
+        public csr_matrix(NDArray data, NDArray indices, NDArray indptr, Shape shape = null, Type dtype = null)
+        {
+            maxprint = 50;
+            format = "csf";
+
+            this.data = data;
+            this.indices = indices;
+            this.indptr = indptr;
+
+            if(shape != null)
+            {
+                data.reshape(shape);
+            }
+        }
+    }
+}

src/NumSharp.Core/Sparse/csr_matrix.sort_indices.cs

@@ -0,0 +1,43 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace NumSharp.Core.Sparse
+{
+    public partial class csr_matrix
+    {
+        public void sort_indices()
+        {
+            int indptrstart = 0;
+            int indptrend = 0;
+            int tmpindicesvalue = 0;
+            double tmpdatavalue = 0;
+
+            for (int i = 1; i < indptr.size; i++)
+            {
+                indptrend = indptr.Data<int>()[i] - 1;
+
+                for (int j = indptrstart; j < indptrend; j++)
+                {
+                    
+                    if (indices.Data<int>()[j + 1] < indices.Data<int>()[j])
+                    {
+                        // switch indices
+                        tmpindicesvalue = indices.Data<int>()[j];
+                        indices.Data<int>()[j] = indices.Data<int>()[j + 1];
+                        indices.Data<int>()[j + 1] = tmpindicesvalue;
+
+                        // switch value
+                        tmpdatavalue = data.Data<double>()[j];
+                        data.Data<double>()[j] = data.Data<double>()[j + 1];
+                        data.Data<double>()[j + 1] = tmpdatavalue;
+                    }
+                }
+
+                indptrstart = indptr.Data<int>()[i];
+            }
+
+            has_sorted_indices = true;
+        }
+    }
+}

src/NumSharp.Core/Sparse/sparse.cs

@@ -0,0 +1,23 @@
+using NumSharp.Core;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace NumSharp.Core.Sparse
+{
+    public class sparse
+    {
+        public csr_matrix diags(NDArray[] diagonals, int[] offsets = null, Shape shape = null, string format = null, Type dtype = null)
+        {
+            var (m, n) = shape.BiShape;
+            var data_arr = np.zeros(m);
+
+            for (int j = 0; j < diagonals.Length; j++)
+            {
+                var diagonal = diagonals[j];
+            }
+
+            return null;
+        }
+    }
+}

src/NumSharp.Core/Sparse/spmatrix.cs

@@ -0,0 +1,40 @@
+using NumSharp.Core;
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace NumSharp.Core.Sparse
+{
+    public class spmatrix
+    {
+        public static matrix sum(_cs_matrix mx, int? axis = null, Type dtype = null)
+        {
+            var (m, n) = mx.shape.BiShape;
+            matrix ret = null;
+
+            // sum over columns
+            if (axis == 0)
+            {
+                var matrix = np.asmatrix(np.ones(new Shape(1, m), dtype));
+                ret = matrix * mx;
+            }
+
+            return ret;
+        }
+
+        public static void csc_matvec(int n_row, int n_col, int[] Ap, int[] Ai, double[] Ax, double[] Xx, NDArray Yx)
+        {
+            for (int j = 0; j < n_col; j++)
+            {
+                int col_start = Ap[j];
+                int col_end = Ap[j + 1];
+
+                for (int ii = col_start; ii < col_end; ii++)
+                {
+                    int i = Ai[ii];
+                    Yx.Data<double>()[i] += Ax[ii] * Xx[j];
+                }
+            }
+        }
+    }
+}

src/NumSharp.PowerShell/NumSharp.PowerShell.csproj

@@ -7,10 +7,10 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Microsoft.PowerShell.SDK" Version="6.1.1">
+    <PackageReference Include="Microsoft.PowerShell.SDK" Version="6.1.2">
         <PrivateAssets>all</PrivateAssets>
     </PackageReference>
-    <PackageReference Include="NumSharp" Version="0.6.4" />
+    <PackageReference Include="NumSharp" Version="0.6.5" />
     <PackageReference Include="System.Drawing.Common" Version="4.5.1" />
   </ItemGroup>
 

src/NumSharp.Python/NumSharp.Python.csproj

@@ -1,7 +1,7 @@
 <Project Sdk="Microsoft.NET.Sdk">
 
   <ItemGroup>
-    <PackageReference Include="NumSharp" Version="0.6.4" />
+    <PackageReference Include="NumSharp" Version="0.6.5" />
     <PackageReference Include="System.Numerics.Vectors" Version="4.5.0" />
   </ItemGroup>