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Posted to commits@systemml.apache.org by de...@apache.org on 2015/12/17 22:29:56 UTC
incubator-systemml-website git commit: Add PNMF algorithm example to
main page
Repository: incubator-systemml-website
Updated Branches:
refs/heads/master 4e09d0a0f -> 5181cbeaf
Add PNMF algorithm example to main page
Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml-website/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml-website/commit/5181cbea
Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml-website/tree/5181cbea
Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml-website/diff/5181cbea
Branch: refs/heads/master
Commit: 5181cbeaf66cc23525880fc014530c416ed93636
Parents: 4e09d0a
Author: Deron Eriksson <de...@us.ibm.com>
Authored: Thu Dec 17 13:29:05 2015 -0800
Committer: Deron Eriksson <de...@us.ibm.com>
Committed: Thu Dec 17 13:29:05 2015 -0800
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index.md | 21 +++++++++++----------
1 file changed, 11 insertions(+), 10 deletions(-)
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http://git-wip-us.apache.org/repos/asf/incubator-systemml-website/blob/5181cbea/index.md
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diff --git a/index.md b/index.md
index c614244..9b1078f 100644
--- a/index.md
+++ b/index.md
@@ -9,25 +9,26 @@ tagline: Apache Project !
## Apache SystemML
-SystemML provides declarative large-scale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations, to distributed computations on Apache Hadoop and Apache Spark.
+SystemML provides declarative large-scale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single-node, in-memory computations, to distributed computations on Apache Hadoop and Apache Spark.
### Algorithm Customizability
-ML algorithms are expressed in a R or Python syntax, that includes linear algebra primitives, statistical functions, and ML-specific constructs. This high-level language significantly increases the productivity of data scientists as it provides (1) full flexibility in expressing custom analytics, and (2) data independence from the underlying input formats and physical data representations. Automatic optimization according to data characteristics such as distribution on the disk file system, and sparsity as well as processing characteristics in the distributed environment like number of nodes, CPU, memory per node, ensures both efficiency and scalability.
+ML algorithms are expressed in an R-like or Python-like syntax that includes linear algebra primitives, statistical functions, and ML-specific constructs. This high-level language significantly increases the productivity of data scientists as it provides (1) full flexibility in expressing custom analytics, and (2) data independence from the underlying input formats and physical data representations. Automatic optimization according to data and cluster characteristics ensures both efficiency and scalability.
+**Poisson Nonnegative Matrix Factorization in SystemML's R-like Syntax**
{% highlight r %}
-m = matrix("1 2 3 4 5 6 7 8 9 10 11 12", rows=4, cols=3)
-for (i in 1:nrow(m)) {
- for (j in 1:ncol(m)) {
- n = m[i,j]
- print('[' + i + ',' + j + ']:' + as.scalar(n))
- }
+while (iter < max_iterations) {
+ iter = iter + 1;
+ H = (H * (t(W) %*% (V/(W%*%H)))) / t(colSums(W));
+ W = (W * ((V/(W%*%H)) %*% t(H))) / t(rowSums(H));
+ obj = as.scalar(colSums(W) %*% rowSums(H)) - sum(V * log(W%*%H));
+ print("iter=" + iter + " obj=" + obj);
}
{% endhighlight %}
### Multiple Execution Modes
-SystemML computations can be executed in a variety of different modes. To begin with, SystemML can be operated in Standalone mode on a single machine, allowing data scientists to develop algorithms locally without need of a distributed cluster. Algorithms can be distributed across Hadoop or Spark. This flexibility allows the utilization of an organization's existing resources and expertise. In addition, SystemML can be operated via Java, Scala, and Python. SystemML also features an embedded API for scoring models.
+SystemML computations can be executed in a variety of different modes. To begin with, SystemML can be operated in Standalone mode on a single machine, allowing data scientists to develop algorithms locally without need of a distributed cluster. Algorithms can be distributed across Hadoop or Spark. This flexibility allows the utilization of an organization's existing resources and expertise. In addition, SystemML can be operated via Java and Scala. SystemML also features an embedded API for scoring models.
{% highlight sh %}
// Standalone
@@ -42,5 +43,5 @@ hadoop jar SystemML.jar -f test.dml
### Automatic Optimization
-Algorithms specified in DML are dynamically compiled and optimized based on data and cluster characteristics using rule-based and cost-based optimization techniques. The optimizer automatically generates hybrid runtime execution plans ranging from in-memory single-node execution to distributed computations on Spark or Hadoop. This ensures both efficiency and scalability. Automatic optimization reduces or eliminates the need to hand-tune distributed runtime execution plans and system configurations.
+Algorithms specified in DML and PyDML are dynamically compiled and optimized based on data and cluster characteristics using rule-based and cost-based optimization techniques. The optimizer automatically generates hybrid runtime execution plans ranging from in-memory single-node execution to distributed computations on Spark or Hadoop. This ensures both efficiency and scalability. Automatic optimization reduces or eliminates the need to hand-tune distributed runtime execution plans and system configurations.