systemml.random package

Submodules

systemml.random.sampling module

systemml.random.sampling.normal(loc=0.0, scale=1.0, size=(1, 1), sparsity=1.0)

Draw random samples from a normal (Gaussian) distribution.

Parameters:
  • loc (Mean ("centre") of the distribution.) –
  • scale (Standard deviation (spread or "width") of the distribution.) –
  • size (Output shape (only tuple of length 2, i.e. (m, n), supported)) –
  • sparsity (Sparsity (between 0.0 and 1.0)) –

Examples

>>> import systemml as sml
>>> import numpy as np
>>> sml.setSparkContext(sc)
>>> from systemml import random
>>> m1 = sml.random.normal(loc=3, scale=2, size=(3,3))
>>> m1.toNumPy()
array([[ 3.48857226,  6.17261819,  2.51167259],
       [ 3.60506708, -1.90266305,  3.97601633],
       [ 3.62245706,  5.9430881 ,  2.53070413]])
systemml.random.sampling.uniform(low=0.0, high=1.0, size=(1, 1), sparsity=1.0)

Draw samples from a uniform distribution.

Parameters:
  • low (Lower boundary of the output interval.) –
  • high (Upper boundary of the output interval.) –
  • size (Output shape (only tuple of length 2, i.e. (m, n), supported)) –
  • sparsity (Sparsity (between 0.0 and 1.0)) –

Examples

>>> import systemml as sml
>>> import numpy as np
>>> sml.setSparkContext(sc)
>>> from systemml import random
>>> m1 = sml.random.uniform(size=(3,3))
>>> m1.toNumPy()
array([[ 0.54511396,  0.11937437,  0.72975775],
       [ 0.14135946,  0.01944448,  0.52544478],
       [ 0.67582422,  0.87068849,  0.02766852]])
systemml.random.sampling.poisson(lam=1.0, size=(1, 1), sparsity=1.0)

Draw samples from a Poisson distribution.

Parameters:
  • lam (Expectation of interval, should be > 0.) –
  • size (Output shape (only tuple of length 2, i.e. (m, n), supported)) –
  • sparsity (Sparsity (between 0.0 and 1.0)) –

Examples

>>> import systemml as sml
>>> import numpy as np
>>> sml.setSparkContext(sc)
>>> from systemml import random
>>> m1 = sml.random.poisson(lam=1, size=(3,3))
>>> m1.toNumPy()
array([[ 1.,  0.,  2.],
       [ 1.,  0.,  0.],
       [ 0.,  0.,  0.]])

Module contents

Random Number Generation

Univariate distributions
normal Normal / Gaussian distribution.
poisson Poisson distribution.
uniform Uniform distribution.
systemml.random.normal(loc=0.0, scale=1.0, size=(1, 1), sparsity=1.0)

Draw random samples from a normal (Gaussian) distribution.

Parameters:
  • loc (Mean ("centre") of the distribution.) –
  • scale (Standard deviation (spread or "width") of the distribution.) –
  • size (Output shape (only tuple of length 2, i.e. (m, n), supported)) –
  • sparsity (Sparsity (between 0.0 and 1.0)) –

Examples

>>> import systemml as sml
>>> import numpy as np
>>> sml.setSparkContext(sc)
>>> from systemml import random
>>> m1 = sml.random.normal(loc=3, scale=2, size=(3,3))
>>> m1.toNumPy()
array([[ 3.48857226,  6.17261819,  2.51167259],
       [ 3.60506708, -1.90266305,  3.97601633],
       [ 3.62245706,  5.9430881 ,  2.53070413]])
systemml.random.uniform(low=0.0, high=1.0, size=(1, 1), sparsity=1.0)

Draw samples from a uniform distribution.

Parameters:
  • low (Lower boundary of the output interval.) –
  • high (Upper boundary of the output interval.) –
  • size (Output shape (only tuple of length 2, i.e. (m, n), supported)) –
  • sparsity (Sparsity (between 0.0 and 1.0)) –

Examples

>>> import systemml as sml
>>> import numpy as np
>>> sml.setSparkContext(sc)
>>> from systemml import random
>>> m1 = sml.random.uniform(size=(3,3))
>>> m1.toNumPy()
array([[ 0.54511396,  0.11937437,  0.72975775],
       [ 0.14135946,  0.01944448,  0.52544478],
       [ 0.67582422,  0.87068849,  0.02766852]])
systemml.random.poisson(lam=1.0, size=(1, 1), sparsity=1.0)

Draw samples from a Poisson distribution.

Parameters:
  • lam (Expectation of interval, should be > 0.) –
  • size (Output shape (only tuple of length 2, i.e. (m, n), supported)) –
  • sparsity (Sparsity (between 0.0 and 1.0)) –

Examples

>>> import systemml as sml
>>> import numpy as np
>>> sml.setSparkContext(sc)
>>> from systemml import random
>>> m1 = sml.random.poisson(lam=1, size=(3,3))
>>> m1.toNumPy()
array([[ 1.,  0.,  2.],
       [ 1.,  0.,  0.],
       [ 0.,  0.,  0.]])