Random numbers drawn specific distributions can be generated by instantiating a Generator object, and calling its methods. The module defines the following three functions:
The Generator object, when instantiated, takes a single integer as its argument. This integer is the seed, which will be fed to the 32-bit or 64-bit routine. More details can be found under https://www.pcg-random.org/index.html. The generator is a standard python object that keeps track of its state.
numpy: https://numpy.org/doc/stable/reference/random/index.html
A random set of number from the normal distribution can be generated by calling the generator’s normal method. The method takes three optional arguments, loc=0.0, the centre of the distribution, scale=1.0, the width of the distribution, and size=None, a tuple containing the shape of the returned array. In case size is None, a single floating point number is returned.
The normal method of the Generator object is based on the Box-Muller transform.
numpy: https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.normal.html
# code to be run in micropython from ulab import numpy as np rng = np.random.Generator(123456) print(rng) # return single number from a distribution of scale 1, and location 0 print(rng.normal()) print(rng.normal(loc=20.0, scale=10.0, size=(3,3))) # same as above, with positional arguments print(rng.normal(20.0, 10.0, (3,3)))
Gnerator() at 0x7fa9dae05340
-6.285246229407202
array([[24.95816273705659, 15.2670302229426, 14.81001577336041],
[20.17589833056986, 23.14539083787544, 26.37772041367461],
[41.94894234387275, 37.11027030608206, 25.65889562100477]], dtype=float64)
array([[21.52562779033434, 12.74685887865834, 24.08404670765186],
[4.728112596365396, 7.667757906857082, 21.61576094228444],
[2.432338873595267, 27.75945683572574, 5.730827584659245]], dtype=float64)
random¶
A random set of number from the uniform distribution in the interval [0, 1] can be generated by calling the generator’s random method. The method takes two optional arguments, size=None, a tuple containing the shape of the returned array, and out. In case size is None, a single floating point number is returned.
out can be used, if a floating point array is available. An exception will be raised, if the array is not of float dtype, or if both size and out are supplied, and there is a conflict in their shapes.
If size is None, a single floating point number will be returned.
numpy: https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.random.html
# code to be run in micropython
from ulab import numpy as np
rng = np.random.Generator(123456)
print(rng)
# returning new objects
print(rng.random())
print('\n', rng.random(size=(3,3)))
# supplying a buffer
a = np.array(range(9), dtype=np.float).reshape((3,3))
print('\nbuffer array before:\n', a)
rng.random(out=a)
print('\nbuffer array after:\n', a)
Gnerator() at 0x7f299de05340
6.384615058863119e-11
array([[0.4348157846574171, 0.7906325931024071, 0.878697619856133],
[0.8738606263361598, 0.4946080034142021, 0.7765890156101152],
[0.1770783715717074, 0.02080447648492112, 0.1053837559005948]], dtype=float64)
buffer array before:
array([[0.0, 1.0, 2.0],
[3.0, 4.0, 5.0],
[6.0, 7.0, 8.0]], dtype=float64)
buffer array after:
array([[0.8508024287393201, 0.9848489829156055, 0.7598167589604003],
[0.782995698302952, 0.2866337782847831, 0.7915884498022229],
[0.4614071706315902, 0.4792657443088592, 0.1581582066230718]], dtype=float64)
uniform¶
uniform is similar to random, except that the interval over which the numbers are distributed can be specified, while the out argument cannot. In addition to size specifying the shape of the output, low=0.0, and high=1.0 are accepted arguments. With the indicated defaults, uniform is identical to random, which can be seen from the fact that the first 3-by-3 tensor below is the same as the one produced by rng.random(size=(3,3)) above.
If size is None, a single floating point number will be returned.
numpy: https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.uniform.html
# code to be run in micropython
from ulab import numpy as np
rng = np.random.Generator(123456)
print(rng)
print(rng.uniform())
# returning numbers between 0, and 1
print('\n', rng.uniform(size=(3,3)))
# returning numbers between 10, and 20
print('\n', rng.uniform(low=10, high=20, size=(3,3)))
# same as above, without the keywords
print('\n', rng.uniform(10, 20, (3,3)))
Gnerator() at 0x7f1891205340
6.384615058863119e-11
array([[0.4348157846574171, 0.7906325931024071, 0.878697619856133],
[0.8738606263361598, 0.4946080034142021, 0.7765890156101152],
[0.1770783715717074, 0.02080447648492112, 0.1053837559005948]], dtype=float64)
array([[18.5080242873932, 19.84848982915605, 17.598167589604],
[17.82995698302952, 12.86633778284783, 17.91588449802223],
[14.6140717063159, 14.79265744308859, 11.58158206623072]], dtype=float64)
array([[14.3380400319162, 12.72487657409978, 15.77119643621117],
[13.61835831436355, 18.96062889255558, 15.78847796795966],
[12.59435855187034, 17.68262037443622, 14.77943040598734]], dtype=float64)
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4