def poisson(lam=1.0, size=None):
'''Return dataset of given shape (or a single number) with samples taken
from a Poisson distribution of parameter lam
'''
if size is None:
return _random.poisson(lam, [1]).getObject([0])
return _random.poisson(lam, _asiter(size))
def exponential(scale=1.0, size=None):
'''Return dataset of given shape (or a single number) with samples taken
from an exponential distribution of parameter scale
'''
if size is None:
return _random.exponential(scale, [1]).getObject([0])
return _random.exponential(scale, _asiter(size))
def randn(*shape):
'''Return dataset of given shape (or a single number) with samples taken
from a normal distribution of zero mean and unit variance
'''
if len(shape) == 0:
return _random.randn([1]).getObject([0])
return _random.randn(shape)
def rand(*shape):
'''Return dataset of given shape (or a single number) with samples taken
from a uniform distribution between 0 and 1
'''
if len(shape) == 0:
return _random.rand([1]).getObject([0])
return _random.rand(shape)
def seed(seed=None):
'''Set seed to given value (or a value based on the current time in
milliseconds since the Epoch)
'''
if seed is None:
import time
seed = int(time.time()*1000)
_random.seed(seed)
def random_integers(low, high=None, size=None):
'''Return dataset of given shape (or a single number) with samples taken
from a discrete distribution of integers in the range [low, high]
'''
if high is None:
high = low
low = 0
if size is None:
return _random.random_integers(low, high, [1]).getObject([0])
return _random.random_integers(low, high, _asiter(size))