[systemml] branch master updated: [DOC] Add documentation for builtin sigmoid func.

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The following commit(s) were added to refs/heads/master by this push:
     new 8d020fb  [DOC] Add documentation for builtin sigmoid func.
8d020fb is described below

commit 8d020fb9f807750b07d08398caa2d433305819b6
Author: Parul Damalu <po...@gmail.com>
AuthorDate: Mon Jun 1 08:29:14 2020 +0530

    [DOC] Add documentation for builtin sigmoid func.
    
    Closes #929.
---
 dev/docs/builtins-reference.md | 29 +++++++++++++++++++++++++++++
 1 file changed, 29 insertions(+)

diff --git a/dev/docs/builtins-reference.md b/dev/docs/builtins-reference.md
index b3effb1..3627bd8 100644
--- a/dev/docs/builtins-reference.md
+++ b/dev/docs/builtins-reference.md
@@ -27,9 +27,11 @@ limitations under the License.
     * [`lmDS`-Function](#lmds-function)
     * [`lmCG`-Function](#lmcg-function)
     * [`lmpredict`-Function](#lmpredict-function)
+    * [`sigmoid`-Function](#sigmoid-function)
     * [`steplm`-Function](#steplm-function)
     * [`slicefinder`-Function](#slicefinder-function)
     
+    
 # Introduction
 
 The DML (Declarative Machine Learning) language has built-in functions which enable access to both low- and high-level functions
@@ -244,6 +246,33 @@ w = lm(X = X, y = y)
 yp = lmpredict(X, w)
 ```
 
+## `sigmoid`-Function
+
+The Sigmoid function is a type of activation function, and also defined as a squashing function which limit the output 
+to a range between 0 and 1, which will make these functions useful in the prediction of probabilities.
+
+### Usage
+```r
+sigmoid(X)
+```
+
+### Arguments
+| Name  | Type           | Default  | Description |
+| :---- | :------------- | -------- | :---------- |
+| X     | Matrix[Double] | required | Matrix of feature vectors. |
+
+
+### Returns
+| Type           | Description |
+| :------------- | :---------- |
+| Matrix[Double] | 1-column matrix of weights. |
+
+### Example
+```r
+X = rand (rows = 20, cols = 10)
+Y = sigmoid(X)
+```
+
 ## `steplm`-Function
 
 The `steplm`-function (stepwise linear regression) implements a classical forward feature selection method.