From e5205d5afda5f72a45905dc4e02177d63b092c52 Mon Sep 17 00:00:00 2001
From: Roland Grinis <roland.grinis@grinisrit.com>
Date: Sat, 16 Jan 2021 20:29:47 +0000
Subject: [PATCH] Corrected readme file

---
 kmath-torch/README.md | 33 ++++++++++++++++++++++-----------
 1 file changed, 22 insertions(+), 11 deletions(-)

diff --git a/kmath-torch/README.md b/kmath-torch/README.md
index f4341f29c..4d463f5bc 100644
--- a/kmath-torch/README.md
+++ b/kmath-torch/README.md
@@ -1,18 +1,22 @@
 # LibTorch extension (`kmath-torch`)
 
-This is a `Kotlin/Native` module, with only `linuxX64` supported so far. This library wraps some of the [PyTorch C++ API](https://pytorch.org/cppdocs), focusing on integrating `Aten` & `Autograd` with `KMath`. 
+This is a `Kotlin/Native` module, with only `linuxX64` supported so far. This library wraps some of
+the [PyTorch C++ API](https://pytorch.org/cppdocs), focusing on integrating `Aten` & `Autograd` with `KMath`.
 
 ## Installation
+
 To install the library, you have to build & publish locally `kmath-core`, `kmath-memory` with `kmath-torch`:
+
 ```
 ./gradlew -q :kmath-core:publishToMavenLocal :kmath-memory:publishToMavenLocal :kmath-torch:publishToMavenLocal
 ```
 
 This builds `ctorch`, a C wrapper for `LibTorch` placed inside:
 
-`~/.konan/third-party/kmath-torch-0.2.0-dev-4/cpp-build` 
+`~/.konan/third-party/kmath-torch-0.2.0-dev-4/cpp-build`
 
 You will have to link against it in your own project. Here is an example of build script for a standalone application:
+
 ```kotlin
 //build.gradle.kts
 plugins {
@@ -59,6 +63,7 @@ kotlin {
     }
 }
 ```
+
 ```kotlin
 //settings.gradle.kts
 pluginManagement {
@@ -76,13 +81,15 @@ pluginManagement {
 
 ## Usage
 
-Tensors are implemented over the `MutableNDStructure`. They can only be instantiated through provided factory methods and require scoping:
+Tensors are implemented over the `MutableNDStructure`. They can only be instantiated through provided factory methods
+and require scoping:
+
 ```kotlin
 TorchTensorRealAlgebra {
 
     val realTensor: TorchTensorReal = copyFromArray(
         array = (1..10).map { it + 50.0 }.toList().toDoubleArray(),
-        shape = intArrayOf(2,5)
+        shape = intArrayOf(2, 5)
     )
     println(realTensor)
 
@@ -94,23 +101,27 @@ TorchTensorRealAlgebra {
     println(gpuRealTensor)
 }
 ```
+
 Enjoy a high performance automatic differentiation engine:
+
 ```kotlin
 TorchTensorRealAlgebra {
     val dim = 10
     val device = TorchDevice.TorchCPU //or TorchDevice.TorchCUDA(0)
     val x = randNormal(shape = intArrayOf(dim), device = device)
-    // x is the variable
-    x.requiresGrad = true
-    
-    val X = randNormal(shape = intArrayOf(dim,dim), device = device)
-    val Q = X + X.transpose(0,1)
+
+    val X = randNormal(shape = intArrayOf(dim, dim), device = device)
+    val Q = X + X.transpose(0, 1)
     val mu = randNormal(shape = intArrayOf(dim), device = device)
-    
+
     // expression to differentiate w.r.t. x
-    val f = 0.5 * (x dot (Q dot x)) + (mu dot x) + 25.3
+    val f = x.withGrad {
+        0.5 * (x dot (Q dot x)) + (mu dot x) + 25.3
+    }
     // value of the gradient at x
     val gradf = f grad x
+    // value of the hessian at x
+    val hessf = f hess x
 }
 ```