I could not find a built-in function in Python to generate a log uniform distribution given a min and max value (the R equivalent is here), something like: loguni[n, exp(min), exp(max), base] that returns n log uniformly distributed in the range exp(min) and exp(max).
The closest I found though was numpy.random.uniform
.
From http://ecolego.facilia.se/ecolego/show/Log-Uniform%20Distribution:
In a loguniform distribution, the logtransformed random variable is assumed to be uniformly distributed.
Thus
logU(a, b) ~ exp(U(log(a), log(b))
Thus, we could create a log-uniform distribution using numpy
:
def loguniform(low=0, high=1, size=None):
return np.exp(np.random.uniform(low, high, size))
If you want to choose a different base, we could define a new function as follows:
def lognuniform(low=0, high=1, size=None, base=np.e):
return np.power(base, np.random.uniform(low, high, size))
EDIT: @joaoFaria's answer is also correct.
def loguniform(low=0, high=1, size=None):
return scipy.stats.reciprocal(np.exp(low), np.exp(high)).rvs(size)