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.. software distributed under the License is distributed on an
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.. KIND, either express or implied.  See the License for the
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Algorithms
==========

SystemDS support different Machine learning algorithms out of the box.

As an example the lm algorithm can be used as follows:

.. code-block:: python

  # Import numpy and SystemDS matrix
  import numpy as np
  from systemds.context import SystemDSContext
  from systemds.operator.algorithm import lm

  # Set a seed
  np.random.seed(0)
  # Generate matrix of feature vectors
  features = np.random.rand(10, 15)
  # Generate a 1-column matrix of response values
  y = np.random.rand(10, 1)

  # compute the weights
  with SystemDSContext() as sds:
    weights = lm(sds.from_numpy(features), sds.from_numpy(y)).compute()
    print(weights)

The output should be similar to

.. code-block:: python

  [[-0.11538199]
  [-0.20386541]
  [-0.39956035]
  [ 1.04078623]
  [ 0.4327084 ]
  [ 0.18954599]
  [ 0.49858968]
  [-0.26812763]
  [ 0.09961844]
  [-0.57000751]
  [-0.43386048]
  [ 0.55358873]
  [-0.54638565]
  [ 0.2205885 ]
  [ 0.37957689]]

.. automodule:: systemds.operator.algorithm
  :members: