[28/47] incubator-systemml git commit: Update dml-language-reference.md

[du...@apache.org]

Update dml-language-reference.md

Added "seed" description to rand().

Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml/commit/0ad76f6c
Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/0ad76f6c
Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml/diff/0ad76f6c

Branch: refs/heads/gh-pages
Commit: 0ad76f6c050cd0a24b903e9bbd9c1acdacc52bb7
Parents: 442e83a
Author: Shirish Tatikonda <st...@us.ibm.com>
Authored: Wed Sep 23 12:32:35 2015 -0700
Committer: Shirish Tatikonda <st...@us.ibm.com>
Committed: Wed Sep 23 12:32:35 2015 -0700

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 dml-language-reference.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
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http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/0ad76f6c/dml-language-reference.md
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diff --git a/dml-language-reference.md b/dml-language-reference.md
index c553a58..de73ab2 100644
--- a/dml-language-reference.md
+++ b/dml-language-reference.md
@@ -534,7 +534,7 @@ min() <br/> max() | Return the minimum/maximum cell value in matrix | Input: mat
 min() <br/> max() | Return the minimum/maximum cell values of two matrices, matrix and scalar, or scalar value of two scalars. | Input: matrices or scalars <br/> Output: matrix or scalar | With x,y, z as scalars, and X, Y, Z as matrices: <br/> Z = min (X, Y) <br/> Z = min (X, y) <br/> z = min(x,y)
 nrow(), <br/> ncol(), <br/> length() | Return the number of rows, number of columns, or number of cells in matrix respectively. | Input: matrix <br/> Output: scalar | nrow(X)
 prod() | Return the product of all cells in matrix | Input: matrix <br/> Output: scalarj | prod(X)
-rand() | Generates a random matrix | Input: (rows=&lt;value&gt;, cols=&lt;value&gt;, min=&lt;value&gt;, max=&lt;value&gt;, sparsity=&lt;value&gt;, pdf=&lt;string&gt;) <br/> rows/cols: Number of rows/cols (expression) <br/> min/max: Min/max value for cells (either constant value, or variable that evaluates to constant value) <br/> sparsity: fraction of non-zero cells (constant value) <br/> pdf: “uniform” (min, max) distribution, or “normal” (0,1) distribution; or “poisson” (lambda=1) distribution. string; default value is “uniform”. Note that, for the Poisson distribution, users can provide the mean/lambda parameter as follows: <br/> rand(rows=1000,cols=1000, pdf=”poisson”, lambda=2.5). <br/> The default value for lambda is 1. <br/> Output: matrix | X = rand(rows=10, cols=20, min=0, max=1, pdf="uniform", sparsity=0.2) <br/> The example generates a 10 x 20 matrix, with cell values uniformly chosen at random between 0 and 1, and approximately 20% of cells will have
  non-zero values.
+rand() | Generates a random matrix | Input: (rows=&lt;value&gt;, cols=&lt;value&gt;, min=&lt;value&gt;, max=&lt;value&gt;, sparsity=&lt;value&gt;, pdf=&lt;string&gt;, seed=&lt;value&gt;) <br/> rows/cols: Number of rows/cols (expression) <br/> min/max: Min/max value for cells (either constant value, or variable that evaluates to constant value) <br/> sparsity: fraction of non-zero cells (constant value) <br/> pdf: “uniform” (min, max) distribution, or “normal” (0,1) distribution; or “poisson” (lambda=1) distribution. string; default value is “uniform”. Note that, for the Poisson distribution, users can provide the mean/lambda parameter as follows: <br/> rand(rows=1000,cols=1000, pdf=”poisson”, lambda=2.5). <br/> The default value for lambda is 1. <br/> seed: Every invocation of rand() internally generates a random seed with which the cell values are generated. One can optionally provide a seed when repeatability is desired.  <br/> Output: matrix | X = rand(rows=1
 0, cols=20, min=0, max=1, pdf="uniform", sparsity=0.2) <br/> The example generates a 10 x 20 matrix, with cell values uniformly chosen at random between 0 and 1, and approximately 20% of cells will have non-zero values.
 removeEmpty() | Removes all empty rows or columns from the input matrix target X according to the specified margin. | Input : (target= X &lt;matrix&gt;, margin="...") <br/> Output : &lt;matrix&gt; <br/> Valid values for margin are "rows" or "cols". | A = removeEmpty(target=X, margin="rows")
 replace() | Creates a copy of input matrix X, where all values that are equal to the scalar pattern s1 are replaced with the scalar replacement s2. | Input : (target= X &lt;matrix&gt;, pattern=&lt;scalar&gt;, replacement=&lt;scalar&gt;) <br/> Output : &lt;matrix&gt; <br/> If s1 is NaN, then all NaN values of X are treated as equal and hence replaced with s2. Positive and negative infinity are treated as different values. | A = replace(target=X, pattern=s1, replacement=s2)
 seq() | Creates a single column vector with values starting from &lt;from&gt;, to &lt;to&gt;, in increments of &lt;increment&gt; | Input: (&lt;from&gt;, &lt;to&gt;, &lt;increment&gt;) <br/> Output: &lt;matrix&gt; | S = seq (10, 200, 10)