Random numbers

class RandomDistribution(distribution, parameters_pos=None, rng=None, **parameters_named)[source]

Class which defines a next(n) method which returns an array of n random numbers from a given distribution.

Arguments:
distribution:
the name of a random number distribution.
parameters_pos:
parameters of the distribution, provided as a tuple. For the correct ordering, see random.available_distributions.
rng:
if present, should be a NumpyRNG, GSLRNG or NativeRNG object.
parameters_named:
parameters of the distribution, provided as keyword arguments.

Parameters may be provided either through parameters_pos or through parameters_named, but not both. All parameters must be provided, there are no default values. Parameter names are, in general, as used in Wikipedia.

Examples:

>>> rd = RandomDistribution('uniform', (-70, -50))
>>> rd = RandomDistribution('normal', mu=0.5, sigma=0.1)
>>> rng = NumpyRNG(seed=8658764)
>>> rd = RandomDistribution('gamma', k=2.0, theta=5.0, rng=rng)

Available distributions:

Name Parameters Comments
binomial n, p  
gamma k, theta  
exponential beta  
lognormal mu, sigma  
normal mu, sigma  
normal_clipped mu, sigma, low, high Values outside (low, high) are redrawn
normal_clipped_to_boundary mu, sigma, low, high Values below/above low/high are set to low/high
poisson lambda_ Trailing underscore since lambda is a Python keyword
uniform low, high  
uniform_int low, high  
vonmises mu, kappa  
next(n=None, mask_local=None)[source]

Return n random numbers from the distribution.

class NumpyRNG(seed=None, parallel_safe=True)[source]

Bases: pyNN.random.WrappedRNG

Wrapper for the numpy.random.RandomState class (Mersenne Twister PRNG).

translations = {'binomial': ('binomial', {'p': 'p', 'n': 'n'}), 'lognormal': ('lognormal', {'mu': 'mean', 'sigma': 'sigma'}), 'gamma': ('gamma', {'k': 'shape', 'theta': 'scale'}), 'uniform': ('uniform', {'high': 'high', 'low': 'low'}), 'vonmises': ('vonmises', {'mu': 'mu', 'kappa': 'kappa'}), 'normal_clipped_to_boundary': ('normal_clipped_to_boundary', {'mu': 'mu', 'sigma': 'sigma', 'low': 'low', 'high': 'high'}), 'normal_clipped': ('normal_clipped', {'mu': 'mu', 'sigma': 'sigma', 'low': 'low', 'high': 'high'}), 'uniform_int': ('randint', {'high': 'high', 'low': 'low'}), 'poisson': ('poisson', {'lambda_': 'lam'}), 'exponential': ('exponential', {'beta': 'scale'}), 'normal': ('normal', {'mu': 'loc', 'sigma': 'scale'})}
normal_clipped(mu=0.0, sigma=1.0, low=-inf, high=inf, size=None)[source]
normal_clipped_to_boundary(mu=0.0, sigma=1.0, low=-inf, high=inf, size=None)[source]
describe()
next(n=None, distribution=None, parameters=None, mask_local=None)

Return n random numbers from the specified distribution.

If:
  • n is None, return a float,
  • n >= 1, return a Numpy array,
  • n < 0, raise an Exception,
  • n is 0, return an empty array.

If called with distribution=None, returns uniformly distributed floats in the range [0, 1)

class GSLRNG(seed=None, type='mt19937', parallel_safe=True)[source]

Bases: pyNN.random.WrappedRNG

Wrapper for the GSL random number generators.

translations = {'binomial': ('binomial', {'p': 'p', 'n': 'n'}), 'lognormal': ('lognormal', {'mu': 'zeta', 'sigma': 'sigma'}), 'gamma': ('gamma', {'k': 'k', 'theta': 'theta'}), 'uniform': ('flat', {'high': 'b', 'low': 'a'}), 'poisson': ('poisson', {'lambda_': 'mu'}), 'normal_clipped': ('normal_clipped', {'mu': 'mu', 'sigma': 'sigma', 'low': 'low', 'high': 'high'}), 'uniform_int': ('uniform_int', {'high': 'high', 'low': 'low'}), 'exponential': ('exponential', {'beta': 'mu'}), 'normal': ('normal', {'mu': 'mu', 'sigma': 'sigma'})}
uniform_int(low, high, size=None)[source]
gamma(k, theta, size=None)[source]
normal(mu=0.0, sigma=1.0, size=None)[source]
normal_clipped(mu=0.0, sigma=1.0, low=-inf, high=inf, size=None)[source]
describe()
next(n=None, distribution=None, parameters=None, mask_local=None)

Return n random numbers from the specified distribution.

If:
  • n is None, return a float,
  • n >= 1, return a Numpy array,
  • n < 0, raise an Exception,
  • n is 0, return an empty array.

If called with distribution=None, returns uniformly distributed floats in the range [0, 1)

class NativeRNG(seed=None)[source]

Bases: pyNN.random.AbstractRNG

Signals that the simulator’s own native RNG should be used. Each simulator module should implement a class of the same name which inherits from this and which sets the seed appropriately.

next(n=None, distribution=None, parameters=None, mask_local=None)

Return n random numbers from the specified distribution.

If:
  • n is None, return a float,
  • n >= 1, return a Numpy array,
  • n < 0, raise an Exception,
  • n is 0, return an empty array.

If called with distribution=None, returns uniformly distributed floats in the range [0, 1)

Adapting a different random number generator to work with PyNN

Todo

write this