From 1b1a50371b0829363d1f9c469aedbe727a6ec41f Mon Sep 17 00:00:00 2001
From: Visagan Guruparan <103048@smsassist.com>
Date: Sun, 18 Jun 2023 22:46:36 -0500
Subject: [PATCH] np update square and dot product

---
 src/TensorFlowNET.Core/APIs/tf.math.cs        | 15 ++++++++--
 src/TensorFlowNET.Core/Binding.Util.cs        | 23 ++++++++++++++-
 src/TensorFlowNET.Core/NumPy/Numpy.Math.cs    | 21 ++++++++++++++
 .../TensorFlowNET.UnitTest/Numpy/Math.Test.cs | 29 ++++++++++++++++++-
 4 files changed, 84 insertions(+), 4 deletions(-)

diff --git a/src/TensorFlowNET.Core/APIs/tf.math.cs b/src/TensorFlowNET.Core/APIs/tf.math.cs
index 75253700..0e53d938 100644
--- a/src/TensorFlowNET.Core/APIs/tf.math.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.math.cs
@@ -14,6 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
+using Tensorflow.NumPy;
 using Tensorflow.Operations;
 
 namespace Tensorflow
@@ -42,7 +43,6 @@ namespace Tensorflow
 
             public Tensor multiply(Tensor x, Tensor y, string name = null)
                 => math_ops.multiply(x, y, name: name);
-
             public Tensor divide_no_nan(Tensor a, Tensor b, string name = null)
                 => math_ops.div_no_nan(a, b);
 
@@ -452,7 +452,18 @@ namespace Tensorflow
         /// <returns></returns>
         public Tensor multiply<Tx, Ty>(Tx x, Ty y, string name = null)
             => gen_math_ops.mul(ops.convert_to_tensor(x), ops.convert_to_tensor(y), name: name);
-
+        /// <summary>
+        /// return scalar product
+        /// </summary>
+        /// <typeparam name="Tx"></typeparam>
+        /// <typeparam name="Ty"></typeparam>
+        /// <param name="x"></param>
+        /// <param name="y"></param>
+        /// <param name="axes"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public Tensor dot_prod<Tx, Ty>(Tx x, Ty y, NDArray axes, string name = null)
+            => math_ops.tensordot(convert_to_tensor(x), convert_to_tensor(y), axes, name: name);
         public Tensor negative(Tensor x, string name = null)
             => gen_math_ops.neg(x, name);
 
diff --git a/src/TensorFlowNET.Core/Binding.Util.cs b/src/TensorFlowNET.Core/Binding.Util.cs
index 8df39334..e414ef6e 100644
--- a/src/TensorFlowNET.Core/Binding.Util.cs
+++ b/src/TensorFlowNET.Core/Binding.Util.cs
@@ -486,7 +486,28 @@ namespace Tensorflow
                     throw new NotImplementedException("");
             }
         }
-
+        public static NDArray GetFlattenArray(NDArray x)
+        {
+            switch (x.GetDataType())
+            {
+                case TF_DataType.TF_FLOAT:
+                    x = x.ToArray<float>();
+                    break;
+                case TF_DataType.TF_DOUBLE:
+                    x = x.ToArray<double>();
+                    break;
+                case TF_DataType.TF_INT16:
+                case TF_DataType.TF_INT32:
+                    x = x.ToArray<int>();
+                    break;
+                case TF_DataType.TF_INT64:
+                    x = x.ToArray<long>();
+                    break;
+                default:
+                    break;
+            }
+            return x;
+        }
         public static TF_DataType GetDataType(this object data)
         {
             var type = data.GetType();
diff --git a/src/TensorFlowNET.Core/NumPy/Numpy.Math.cs b/src/TensorFlowNET.Core/NumPy/Numpy.Math.cs
index ea85048f..5bc97952 100644
--- a/src/TensorFlowNET.Core/NumPy/Numpy.Math.cs
+++ b/src/TensorFlowNET.Core/NumPy/Numpy.Math.cs
@@ -49,9 +49,30 @@ namespace Tensorflow.NumPy
         [AutoNumPy]
         public static NDArray prod<T>(params T[] array) where T : unmanaged
             => new NDArray(tf.reduce_prod(new NDArray(array)));
+        [AutoNumPy]
+        public static NDArray dot(NDArray x1, NDArray x2, NDArray? axes = null, string? name = null)
+        {
+            //if axes mentioned
+            if (axes != null)
+            {
+                return new NDArray(tf.dot_prod(x1, x2, axes, name));
+            }
+            if (x1.shape.ndim > 1)
+            {
+                x1 = GetFlattenArray(x1);
+            }
+            if (x2.shape.ndim > 1)
+            {
+                x2 = GetFlattenArray(x2);
+            }
+            //if axes not mentioned, default 0,0
+            return new NDArray(tf.dot_prod(x1, x2, axes: new int[] { 0, 0 }, name));
 
+        }
         [AutoNumPy]
         public static NDArray power(NDArray x, NDArray y) => new NDArray(tf.pow(x, y));
+        [AutoNumPy]
+        public static NDArray square(NDArray x) => new NDArray(tf.square(x));
 
         [AutoNumPy]
         public static NDArray sin(NDArray x) => new NDArray(math_ops.sin(x));
diff --git a/test/TensorFlowNET.UnitTest/Numpy/Math.Test.cs b/test/TensorFlowNET.UnitTest/Numpy/Math.Test.cs
index 32b517e4..65cdaedd 100644
--- a/test/TensorFlowNET.UnitTest/Numpy/Math.Test.cs
+++ b/test/TensorFlowNET.UnitTest/Numpy/Math.Test.cs
@@ -65,7 +65,34 @@ namespace TensorFlowNET.UnitTest.NumPy
             var y = np.power(x, 3);
             Assert.AreEqual(y, new[] { 0, 1, 8, 27, 64, 125 });
         }
-         [TestMethod]
+        [TestMethod]
+        public void square()
+        {
+            var x = np.arange(6);
+            var y = np.square(x);
+            Assert.AreEqual(y, new[] { 0, 1, 4, 9, 16, 25 });
+        }
+        [TestMethod]
+        public void dotproduct()
+        {
+            var x1 = new NDArray(new[] { 1, 2, 3 });
+            var x2 = new NDArray(new[] { 4, 5, 6 });
+            double result1 = np.dot(x1, x2);
+            NDArray y1 = new float[,] {
+                                    { 1.0f, 2.0f, 3.0f },
+                                    { 4.0f, 5.1f,6.0f },
+                                    { 4.0f, 5.1f,6.0f }
+                                };
+            NDArray y2 = new float[,] {
+                                    { 3.0f, 2.0f, 1.0f },
+                                    { 6.0f, 5.1f, 4.0f },
+                                    { 6.0f, 5.1f, 4.0f }
+                                };
+            double result2 = np.dot(y1, y2);
+            Assert.AreEqual(result1, 32);
+            Assert.AreEqual(Math.Round(result2, 2), 158.02);
+        }
+        [TestMethod]
        public void maximum()
         {
             var x1 = new NDArray(new[,] { { 1, 2, 3 }, { 4, 5.1, 6 } });