def __init__(self, rate=1):
"""Create Exp(rate) distribution.
:param rate First shape parameter of exp
"""
assert rate > 0
self.rate = rate
self.UG = UniformDist()
super().__init__(ContinuousSpace(0, np.inf, open_brackets=False))
def __init__(self, shape=1, scale=1):
"""Create Ga(shape,scale) distribution.
:param shape Shape of Ga(shape,scale)
:param scale Scale of Ga(shape,scale)
"""
self.shape = shape
self.scale = scale
self.UG = UniformDist()
if shape >= 1: self.NG = NormalDist()
super().__init__(ContinuousSpace(0, np.inf, open_brackets=False))
def __init__(self, mean=0, var=1):
"""Create LogN(mean, var) distribution.
:param mean Center of the gaussian
:param var Variance around the center.
"""
self.mean = mean
self.var = var
# create random generators
self.NG = NormalDist(mean, var)
super().__init__(ContinuousSpace(0, np.inf))
def __init__(self, m=1, n=1):
"""Create F(m,n) distribution.
:param m Degrees of freedom
:param n Degrees of freedom
"""
# save params
self.m = m
self.n = n
self.BG = BetaDist(m / 2, n / 2)
super().__init__(ContinuousSpace(0, np.inf, open_brackets=False))
def __init__(self, mean=0, var=1):
"""Create N(mean, var) distribution.
:param mean Center of the gaussian
:param var Variance around the center.
"""
self.mean = mean
self.var = var
# create random generators
self.EG = ExpDist()
self.UG = UniformDist()
super().__init__(ContinuousSpace(-np.inf, np.inf))
def __init__(self, loc=1, scale=1):
"""Creates Gumbel(loc,scale) distribution.
:param loc Location of the distribution.
:param scale Scale of the distribution
"""
# save params
self.loc = loc
self.scale = scale
# create generator
self.UG = UniformDist()
super().__init__(ContinuousSpace(-np.inf, np.inf))
def __init__(self, v = 1, loc = 0, scale = 1):
"""Create t(v,loc,scale) distribution.
:param v Degrees of Freedom
"""
# save params
self.v = v
self.loc = loc
self.scale = scale
# create generator
self.UG = UniformDist()
super().__init__(ContinuousSpace(-np.inf, np.inf))
def __init__(self, shape=1, scale=1):
"""Creates Pareto(shape,scale) distribution.
:param shape Shape of the distribution.
:param scale Scale of the distribution
"""
# save params
self.shape = shape
self.scale = scale
# create generator
self.UG = UniformDist()
super().__init__(ContinuousSpace(0, np.inf, open_brackets=False))
def __init__(self, loc=0, scale=1):
"""Creates Laplace(loc,scale) distribution.
:param loc Location of the distribution.
:param scale Scale of the distribution
"""
# save params
self.loc = loc
self.scale = scale
# create generator
self.NG = NormalDist()
self.EG = ExpDist()
super().__init__(ContinuousSpace(-np.inf, np.inf))
def __init__(self, a=1, b=1):
"""Create Beta(a,b) distribution.
:param a First shape parameter of beta
:param b Second shape parameter of beta
"""
assert a > 0 and b > 0
# save params
self.a = a
self.b = b
# create two distributions when one wants to sample
self.GaG = GammaDist(self.a, 1)
self.GbG = GammaDist(self.b, 1)
# define the space of the distribution
super().__init__(ContinuousSpace(0, 1))