def random(mu, lmbda):
nu = normal.random(0, 1)
y = nu * nu
x = mu + (mu * mu * y /
(2 * lmbda)) - (mu /
(2 * lmbda)) * math.sqrt(4 * mu * lmbda * y +
mu * mu * y * y)
z = r.random()
if (z <= (mu / (mu + x))):
return x
return mu * mu / x
def random(delta, gmma, xi, lmbda):
return lmbda * math.sinh((normal.random(0, 1) - gmma) / delta) + xi
def random(k):
avg_=0
for _ in range(k):
avg_+=math.pow(normal.random(0,1),2)
return avg_
def random(m, S):
A = np.asmatrix(la.sqrtm(S))
z = np.zeros(m.shape)
for i in range(m.shape[0]):
z[i][0] = normal.random(0, 1)
return m + np.dot(A, z)
def random(mu, sigma2, lmbda):
return normal.random(mu, sigma2) + exponential.random(lmbda)
def random(mu,sigma):
return math.exp(normal.random(mu,sigma))
def random(aa, bb):
n = normal.random(0, 1)
return bb * (aa * n / 2 + math.sqrt((aa * n / 2)**2 + 1))**2
def random(m,s):
return abs(normal.random(m,s))
def random(aa, bb, mu, lmbda):
n = invgamma.random(aa, bb)
return normal.random(mu, math.sqrt(n / lmbda))
def random():
n = ds.rg0()
while (n == 1):
n = ds.rg0()
return normal.random(0, 1) / n
def random(mu, sigma, nu):
sum_ = 0
for _ in range(nu):
sum_ += normal.random(mu, sigma)
return sum_
def random(mu,nu):
return (normal.random(0,1)+mu)/math.sqrt(chi2.random(nu)/nu)
def random(a, mu, sigma):
n = normal.random(mu, sigma**2)
while n < a:
n = normal.random(mu, sigma**2)
return n
def random(a,b,mu,sigma):
n=normal.random(mu,sigma**2)
while (n<a or n>b):
n=normal.random(mu,sigma**2)
return n
def random(delta, gmma, xi, lmbda):
v = (normal.random(0, 1) - gmma) / delta
return xi + lmbda * math.exp(v)
def random(b,t):
w=exponential.random(1/b)
return 2*t/(1-t**2)*w+math.sqrt(2/(1-t**2))*math.sqrt(w)*normal.random(0,1)
def random(a, s, sigma):
return normal.random(0, sigma) + cauchy.random(a, s)
def random(aa, bb, mu, lmbda):
n = gamma.random(aa, bb)
return normal.random(mu, math.sqrt(1 / (lmbda * n)))
def random(b, mu, sigma):
n = normal.random(mu, sigma**2)
while n > b:
n = normal.random(mu, sigma**2)
return n
def random(t, nu):
return normal.random(0, 1) + t * math.sqrt(chi2.random(nu) / nu)
def random(mu, sigma):
n = normal.random(mu, sigma)
if (n < 0):
return 0
return n
def random(s, m):
return normal.random(0, s) / math.sqrt(gamma.random(1 / 2, m - 1 / 2))
def random(a, b, nu1, nu2):
return (b * normal.random(0, 1) +
a * math.sqrt(chi2.random(nu1) / nu1)) / math.sqrt(
chi2.random(nu2) / nu2)
