183 lines
6 KiB
ReStructuredText
183 lines
6 KiB
ReStructuredText
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numpy.random
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============
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Random numbers drawn specific distributions can be generated by
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instantiating a ``Generator`` object, and calling its methods. The
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module defines the following three functions:
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1. `numpy.random.Generator.normal <#normal>`__
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2. `numpy.random.Generator.random <#random>`__
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3. `numpy.random.Generator.uniform <#uniform>`__
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The ``Generator`` object, when instantiated, takes a single integer as
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its argument. This integer is the seed, which will be fed to the 32-bit
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or 64-bit routine. More details can be found under
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https://www.pcg-random.org/index.html. The generator is a standard
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``python`` object that keeps track of its state.
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``numpy``: https://numpy.org/doc/stable/reference/random/index.html
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normal
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------
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A random set of number from the ``normal`` distribution can be generated
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by calling the generator’s ``normal`` method. The method takes three
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optional arguments, ``loc=0.0``, the centre of the distribution,
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``scale=1.0``, the width of the distribution, and ``size=None``, a tuple
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containing the shape of the returned array. In case ``size`` is
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``None``, a single floating point number is returned.
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The ``normal`` method of the ``Generator`` object is based on the
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`Box-Muller
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transform <https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform>`__.
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``numpy``:
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https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.normal.html
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.. code::
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# code to be run in micropython
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from ulab import numpy as np
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rng = np.random.Generator(123456)
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print(rng)
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# return single number from a distribution of scale 1, and location 0
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print(rng.normal())
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print(rng.normal(loc=20.0, scale=10.0, size=(3,3)))
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# same as above, with positional arguments
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print(rng.normal(20.0, 10.0, (3,3)))
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.. parsed-literal::
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Gnerator() at 0x7fa9dae05340
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-6.285246229407202
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array([[24.95816273705659, 15.2670302229426, 14.81001577336041],
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[20.17589833056986, 23.14539083787544, 26.37772041367461],
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[41.94894234387275, 37.11027030608206, 25.65889562100477]], dtype=float64)
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array([[21.52562779033434, 12.74685887865834, 24.08404670765186],
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[4.728112596365396, 7.667757906857082, 21.61576094228444],
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[2.432338873595267, 27.75945683572574, 5.730827584659245]], dtype=float64)
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random
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------
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A random set of number from the uniform distribution in the interval [0,
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1] can be generated by calling the generator’s ``random`` method. The
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method takes two optional arguments, ``size=None``, a tuple containing
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the shape of the returned array, and ``out``. In case ``size`` is
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``None``, a single floating point number is returned.
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``out`` can be used, if a floating point array is available. An
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exception will be raised, if the array is not of ``float`` ``dtype``, or
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if both ``size`` and ``out`` are supplied, and there is a conflict in
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their shapes.
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If ``size`` is ``None``, a single floating point number will be
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returned.
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``numpy``:
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https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.random.html
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.. code::
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# code to be run in micropython
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from ulab import numpy as np
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rng = np.random.Generator(123456)
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print(rng)
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# returning new objects
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print(rng.random())
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print('\n', rng.random(size=(3,3)))
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# supplying a buffer
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a = np.array(range(9), dtype=np.float).reshape((3,3))
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print('\nbuffer array before:\n', a)
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rng.random(out=a)
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print('\nbuffer array after:\n', a)
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.. parsed-literal::
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Gnerator() at 0x7f299de05340
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6.384615058863119e-11
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array([[0.4348157846574171, 0.7906325931024071, 0.878697619856133],
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[0.8738606263361598, 0.4946080034142021, 0.7765890156101152],
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[0.1770783715717074, 0.02080447648492112, 0.1053837559005948]], dtype=float64)
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buffer array before:
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array([[0.0, 1.0, 2.0],
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[3.0, 4.0, 5.0],
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[6.0, 7.0, 8.0]], dtype=float64)
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buffer array after:
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array([[0.8508024287393201, 0.9848489829156055, 0.7598167589604003],
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[0.782995698302952, 0.2866337782847831, 0.7915884498022229],
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[0.4614071706315902, 0.4792657443088592, 0.1581582066230718]], dtype=float64)
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uniform
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-------
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``uniform`` is similar to ``random``, except that the interval over
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which the numbers are distributed can be specified, while the ``out``
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argument cannot. In addition to ``size`` specifying the shape of the
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output, ``low=0.0``, and ``high=1.0`` are accepted arguments. With the
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indicated defaults, ``uniform`` is identical to ``random``, which can be
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seen from the fact that the first 3-by-3 tensor below is the same as the
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one produced by ``rng.random(size=(3,3))`` above.
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If ``size`` is ``None``, a single floating point number will be
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returned.
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``numpy``:
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https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.uniform.html
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.. code::
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# code to be run in micropython
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from ulab import numpy as np
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rng = np.random.Generator(123456)
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print(rng)
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print(rng.uniform())
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# returning numbers between 0, and 1
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print('\n', rng.uniform(size=(3,3)))
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# returning numbers between 10, and 20
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print('\n', rng.uniform(low=10, high=20, size=(3,3)))
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# same as above, without the keywords
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print('\n', rng.uniform(10, 20, (3,3)))
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.. parsed-literal::
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Gnerator() at 0x7f1891205340
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6.384615058863119e-11
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array([[0.4348157846574171, 0.7906325931024071, 0.878697619856133],
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[0.8738606263361598, 0.4946080034142021, 0.7765890156101152],
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[0.1770783715717074, 0.02080447648492112, 0.1053837559005948]], dtype=float64)
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array([[18.5080242873932, 19.84848982915605, 17.598167589604],
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[17.82995698302952, 12.86633778284783, 17.91588449802223],
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[14.6140717063159, 14.79265744308859, 11.58158206623072]], dtype=float64)
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array([[14.3380400319162, 12.72487657409978, 15.77119643621117],
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[13.61835831436355, 18.96062889255558, 15.78847796795966],
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[12.59435855187034, 17.68262037443622, 14.77943040598734]], dtype=float64)
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