def __init__(self, df1=1, df2=1, lmbda=0):
d1 = NoncentralChiSquareDistr(df1, lmbda) / df1
d2 = ChiSquareDistr(df2) / df2
super(NoncentralFDistr, self).__init__(d1, d2)
self.df1 = df1
self.df2 = df2
self.lmbda = lmbda
def __init__(self, alpha=1, beta=1, lmbda=0):
d = 1 + ChiSquareDistr(2.0 * beta) / NoncentralChiSquareDistr(
2 * alpha, lmbda)
super(NoncentralBetaDistr, self).__init__(d)
self.alpha = alpha
self.beta = beta
self.lmbda = lmbda
def __init__(self, df, lmbda=0):
assert df >= 1
d1 = NormalDistr(sqrt(lmbda))**2
if df > 1:
d2 = ChiSquareDistr(df - 1)
else:
d2 = ZeroDistr()
super(NoncentralChiSquareDistr, self).__init__(d1, d2)
self.df = df
self.lmbda = lmbda
def __new__(cls, df, lmbda=0):
assert df >= 1
d1 = NormalDistr(sqrt(lmbda))**2
if df == 1:
return d1
d2 = ChiSquareDistr(df - 1)
ncc2 = super(NoncentralChiSquareDistr, cls).__new__(cls, d1, d2)
super(NoncentralChiSquareDistr, ncc2).__init__(d1, d2)
ncc2.df = df
ncc2.lmbda = lmbda
return ncc2
def __init__(self, df=2, mu=0):
d1 = NormalDistr(mu, 1)
d2 = distr_sqrt(ChiSquareDistr(df) / df)
super(NoncentralTDistr, self).__init__(d1, d2)
self.df = df
self.mu = mu