JsonRandom#
- class pams.utils.JsonRandom(prng)[source]#
random generator from json.
- The following direction can be used for config as randomized values:
[a, b]: uniform distribution started from a and ended to b. Not that the value should be int, this automatically converted into int. This always satisfy \(a \leq x < b\){"const": [a]}: constant value. Always set to a.{"uniform": [a, b]}: same as [a, b]{"normal": [u, s]}: normal distribution whose mean and deviation is u and s.{"expon": [lam]}: exponential distribution whose mean and deviation is lam.
Examples
>>> from pams.utils.json_random import JsonRandom >>> import random >>> jr = JsonRandom(prng=random.Random(42)) >>> [jr.random([10, 20]) for x in range(10)] [16.39426798457884, 10.25010755222667, 12.750293183691193, 12.232107381488227, 17.364712141640126, 16.766994874229113, 18.921795677048454, 10.869388326294162, 14.219218196852704, 10.297972194380703] >>> [jr.random({"const": [10]}) for x in range(10)] [10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0] >>> [jr.random({"uniform": [10, 20]}) for x in range(10)] [12.186379748036034, 15.053552881033625, 10.265359696838637, 11.988376506866485, 16.49884437779523, 15.449414806032166, 12.204406220406966, 15.892656838759088, 18.094304566778266, 10.064987596780611] >>> [jr.random({"normal": [0, 1]}) for x in range(10)] [0.5317762204008692, -1.453545298008678, -0.3122773171445598, 0.49036253259352475, 0.8734043853794468, -0.2406296726551354, 0.3765998586879102, 0.24821344932841446, 0.7823268087036421, -1.1132222142481727] >>> [jr.random({"expon": [3]}) for x in range(10)] [0.642818017709456, 0.9452346835236866, 1.869586994011895, 0.08175668259806873, 2.9143451561160503, 1.7824008841046926, 0.5611413226153803, 1.4412784552296345, 0.4465202669419299, 1.6479086846872075]