def __init__(self, rate=1):
"""Create Poi(rate) distribution.
:param rate The rate parameter.
"""
# save params
assert 0 < rate
self.rate = rate
# create distribution for sampling
self.UG = UniformDist()
super().__init__(DiscreteSpace(0, np.inf))
def __init__(self, p=0.5):
"""Create Ber(p) distribution.
:param p Probability that random var is true
"""
# save params
assert 0 <= p <= 1
self.p = p
# create distribution for sampling
self.UG = UniformDist()
super().__init__(DiscreteSpace([0, 1]))
def __init__(self, a=1, b=3):
"""Create U(K) distribution.
:param rate The rate parameter.
"""
# save params
assert b >= a
self.a = a
self.b = b
# create distribution for sampling
self.UG = UniformDist()
super().__init__(DiscreteSpace(a, b + 1))
def __init__(self, n = 1, p = 0.5):
"""Create Geom(p) distribution.
:param n Which should be summed
:param p Probability that random var is true
"""
# save params
assert 0 <= p <= 1
assert 1 <= n
self.p = p
self.n = n
# create distribution for sampling
self.UG = UniformDist()
super().__init__(DiscreteSpace(1, np.inf))
def __init__(self, n=1, p=0.5):
"""Create Bin(n, p) distribution.
:param n Which should be summed
:param p Probability that random var is true
"""
# save params
assert 0 <= p <= 1
assert 1 <= n
self.p = p
self.n = n
# create distribution for sampling
self.BG = BernDist(p)
super().__init__(DiscreteSpace(np.arange(n + 1)))
def __init__(self, alpha, A):
"""Create DPH(alpha, A) distribution.
:param alpha probability vector 1xm
:param A mxm matrix such that (I - A) is invertible.
"""
# save params
assert np.sum(alpha) == 1
assert np.shape(A)[0] == np.shape(A)[1] == len(alpha)
self.alpha = np.expand_dims(alpha, 0)
self.A = np.array(A)
self.m = np.shape(A)[1]
# create distribution for sampling
self.UG = UniformDist()
super().__init__(DiscreteSpace(1, np.inf))
def __init__(self, r=10, p=0.5):
"""Create NegBin(r, p) distribution.
:param r Which should be summed
:param p Probability that random var is true
"""
# save params
assert 0 <= p <= 1
assert 0 <= r
self.r = r
self.p = p
# create distribution for sampling
self.GG = GammaDist(r, p / (1 - p))
self.PG = PoissonDist(0.1)
super().__init__(DiscreteSpace(0, np.inf))
def __init__(self, n=20, r=150, N=300):
"""Create Hyp(n, r, N) distribution.
:param n offset vars
:param r binomaial offset
:param N biggest number.
"""
# save params
self.r = r
self.n = n
self.N = N
# generate upper and lower bound
lb = np.maximum(0, r + n - N)
ub = np.minimum(n, r)
# create distribution for sampling
self.UG = UniformDist()
super().__init__(DiscreteSpace(lb, ub + 1))