Merge remote-tracking branch 'upstream/master'

Author: dotchris90

src/NumSharp.Core/Casting/Implicit/NdArray.Implicit.ValueTypes.cs

@@ -46,6 +46,14 @@ public static implicit operator NDArray(double d)
             return ndArray;
         }
 
+        public static implicit operator NDArray(short d)
+        {
+            var ndArray = new NDArray(typeof(short), new int[0]);
+            ndArray.Storage.SetData(new short[] { d });
+
+            return ndArray;
+        }
+
         public static implicit operator NDArray(int d)
         {
             var ndArray = new NDArray(typeof(int),new int[0]);

src/NumSharp.Core/NDStorage.cs

@@ -3,6 +3,7 @@
 using NumSharp.Core;
 using System;
 using System.Linq;
+using System.Runtime.InteropServices;
 
 namespace NumSharp.Core
 {
@@ -177,17 +178,7 @@ public int DTypeSize
         {
             get
             {
-                switch (_DType.Name)
-                {
-                    case "Int32":
-                        return sizeof(int);
-                    case "Single":
-                        return sizeof(float);
-                    case "Double":
-                        return sizeof(double);
-                    default:
-                        return 0;
-                }
+                return Marshal.SizeOf(_DType);
             }
         }
         /// <summary>

src/NumSharp.Core/NumSharp.Core.csproj

@@ -10,16 +10,14 @@
     <Description>NumSharp is the fundamental package for scientific computing with dot NET. It has implemented the arange, array, max, min, reshape, normalize, unique and random interfaces and so on. More and more interfaces will be added to the library gradually. If you want to use .NET to get started with machine learning, NumSharp will be your best tool library.</Description>
     <PackageProjectUrl>https://github.com/SciSharp</PackageProjectUrl>
     <Copyright>Apache 2.0</Copyright>
-    <RepositoryUrl>https://github.com/Oceania2018/NumSharp</RepositoryUrl>
-    <PackageReleaseNotes>Added ReShape interface for Shape.
-Added the water equation example.
-Added np.mgrid API.
-Add axis arg in ndarray.roll.</PackageReleaseNotes>
-    <AssemblyVersion>0.6.2.0</AssemblyVersion>
-    <FileVersion>0.6.2.0</FileVersion>
+    <RepositoryUrl>https://github.com/SciSharp/NumSharp</RepositoryUrl>
+    <PackageReleaseNotes>Add public static implicit operator NDArray(short d)
+Add T randn&lt;T&gt;()</PackageReleaseNotes>
+    <AssemblyVersion>0.6.4.0</AssemblyVersion>
+    <FileVersion>0.6.4.0</FileVersion>
     <RepositoryType>git</RepositoryType>
     <PackageTags>NumPy, NumSharp, MachineLearning, Math, Scientific, Numeric</PackageTags>
-    <Version>0.6.2</Version>
+    <Version>0.6.4</Version>
     <PackageLicenseUrl>LICENSE</PackageLicenseUrl>
     <LangVersion>latest</LangVersion>
     <PackageIconUrl>https://avatars3.githubusercontent.com/u/44989469?s=200&amp;v=4</PackageIconUrl>

src/NumSharp.Core/Random/np.random.randn.cs

@@ -16,6 +16,23 @@ public NDArray randn(params int[] size)
             return stardard_normal(size);
         }
 
+        /// <summary>
+        /// Scalar value
+        /// </summary>
+        /// <typeparam name="T"></typeparam>
+        /// <returns></returns>
+        public T randn<T>()
+        {
+            var nd = stardard_normal();
+            switch (typeof(T).Name)
+            {
+                case "Double":
+                    return (T)Convert.ChangeType(nd.Data<double>()[0], typeof(T));
+                default:
+                    throw new NotImplementedException("randn");
+            }
+        }
+
         /// <summary>
         /// Draw random samples from a normal (Gaussian) distribution.
         /// </summary>
@@ -25,8 +42,6 @@ public NDArray randn(params int[] size)
         /// <returns></returns>
         public NDArray normal(double loc, double scale, params int[] dims)
         {
-            if (dims.Length == 0)
-                throw new Exception("d cannot be empty.");
             var array = new NDArray(typeof(double), new Shape(dims));
             Random rand = new Random(); //reuse this if you are generating many