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QuickStart
==========

Let's take a look at some code examples.

Matrix Operations
-----------------

Making use of SystemDS, let us multiply an Matrix with an scalar:

.. include:: ../code/getting_started/simpleExamples/multiply.py
  :start-line: 20
  :code: python

As output we get

.. code-block::

  [[13.02 13.02 13.02 13.02 13.02 13.02 13.02 13.02 13.02 13.02]
   [13.02 13.02 13.02 13.02 13.02 13.02 13.02 13.02 13.02 13.02]
   [13.02 13.02 13.02 13.02 13.02 13.02 13.02 13.02 13.02 13.02]
   [13.02 13.02 13.02 13.02 13.02 13.02 13.02 13.02 13.02 13.02]
   [13.02 13.02 13.02 13.02 13.02 13.02 13.02 13.02 13.02 13.02]]

The Python SystemDS package is compatible with numpy arrays.
Let us do a quick element-wise matrix multiplication of numpy arrays with SystemDS.
Remember to first start up a new terminal:

.. include:: ../code/getting_started/simpleExamples/multiplyMatrix.py
  :start-line: 20
  :code: python

More complex operations
-----------------------

SystemDS provides algorithm level functions as built-in functions to simplify development.
One example of this is l2SVM, a high level functions for Data-Scientists. Let's take a look at l2svm:

.. include:: ../code/getting_started/simpleExamples/l2svm.py
  :start-line: 20
  :code: python

The output should be similar to

.. code-block::

  [[ 0.02033445]
   [-0.00324092]
   [ 0.0014692 ]
   [ 0.02649209]
   [-0.00616902]
   [-0.0095087 ]
   [ 0.01039221]
   [-0.0011352 ]
   [-0.01686351]
   [-0.03839821]]

To get the full performance of SystemDS one can modify the script to only use internal functionality,
instead of using numpy arrays that have to be transfered into systemDS.
The above script transformed goes like this:

.. include:: ../code/getting_started/simpleExamples/l2svm_internal.py
  :start-line: 20
  :code: python

When reading in datasets for processing it is highly recommended that you read from inside systemds using 
sds.read("file"), since this avoid the transferring of numpy arrays.
