def __init__(self, A, B):
a = not isinstance(A, Dist) or 1 and len(A)
b = not isinstance(B, Dist) or 1 and len(B)
length = max(a,b)
Dist.__init__(self, A=A, B=B,
_length=length, _advance=True)
def __init__(self, A, B):
if isinstance(A, Dist):
assert not A.dependent()
if isinstance(B, Dist):
assert not B.dependent()
a = not isinstance(A, Dist) or 1 and len(A)
b = not isinstance(B, Dist) or 1 and len(B)
length = max(a, b)
Dist.__init__(self, A=A, B=B,
_length=length, _advance=True)
def __init__(self, A, B):
a = not isinstance(A, Dist) or 1 and len(A)
b = not isinstance(B, Dist) or 1 and len(B)
if isinstance(A, Dist):
assert not np.any(np.prod(A.range(), 0)<0)
if isinstance(B, Dist):
assert not np.any(np.prod(B.range(), 0)<0)
length = max(a,b)
Dist.__init__(self, A=A, B=B,
_length=length, _advance=True)
def __init__(self, dist, trans):
"""
Parameters
----------
dist : Dist
Distribution to wrap the copula around
trans : Dist
The copula wrapper. [0,1]^D \into [0,1]^D
"""
Dist.__init__(self, dist=dist, trans=trans, _advance=True, _length=len(trans))
def __init__(self, loc=[0,0], scale=[[1,.5],[.5,1]]):
loc, scale = np.asfarray(loc), np.asfarray(scale)
assert len(loc)==len(scale)
dist = joint.Iid(normal(), len(loc))
C = np.linalg.cholesky(scale)
Ci = np.linalg.inv(C)
Dist.__init__(self, dist=dist, loc=loc, C=C, Ci=Ci,
scale=scale, _length=len(scale), _advance=True)
def __init__(self, dist, N):
"""
Parameters
----------
dist : Dist
Input variable. Must have `len(dist)==1`.
N : int
Number of variable in the output.
"""
assert len(dist)==1 and N>1
Dist.__init__(self, dist=dist, _length=N)
def __init__(self, R, ordering=None):
"R symmetric & positive definite matrix"
if ordering is None:
ordering = range(len(R))
ordering = np.array(ordering)
P = np.eye(len(R))[ordering]
R = np.dot(P, np.dot(R, P.T))
R = np.linalg.cholesky(R)
R = np.dot(P.T, np.dot(R, P))
Ci = np.linalg.inv(R)
Dist.__init__(self, C=R, Ci=Ci, _length=len(R))
def __init__(self, *args):
"""
Parameters
----------
*args : [Dist, ..., Dist]
Set of univariate distributions to join into joint.
"""
assert np.all([isinstance(_, Dist) for _ in args])
prm = {"_%03d" % i:args[i] for i in range(len(args))}
Dist.__init__(self, _advance=True, _length=len(args), **prm)
self.sorting = []
for dist in self.G:
if dist in args:
self.sorting.append(args.index(dist))
def __init__(self, dist):
Dist.__init__(self, dist=dist, _length=len(dist),
_advance=True)
def __init__(self, c, s):
Dist.__init__(self, c=c, s=s)
def __init__(self, N, theta=0.5, eps=1e-6):
theta = float(theta)
assert -1 <= theta < 1
Dist.__init__(self, th=theta, _length=N, eps=eps)
def __init__(self, N, theta=1.0, eps=1e-6):
theta = float(theta)
Dist.__init__(self, th=theta, eps=eps, _length=N)
def __init__(self, lo=0, up=1):
Dist.__init__(self, lo=lo, up=up)
def __init__(self, c=1):
Dist.__init__(self, c=c)
def __init__(self, dist, C):
Dist.__init__(self, dist=dist, C=C,
Ci=np.linalg.inv(C),
_length=len(dist), _advance=True)
def __init__(self):
Dist.__init__(self)
def __init__(self, c):
Dist.__init__(self, c=c)
def __init__(self, lam):
Dist.__init__(self, lam=lam)
def __init__(self, a, b, mu, sigma):
Dist.__init__(self, a=a, b=b)
self.norm = normal()*sigma+mu
self.fa = self.norm.fwd(a)
self.fb = self.norm.fwd(b)
def __init__(self, b):
Dist.__init__(self, b=b)
def __init__(self, nu):
Dist.__init__(self, nu=nu)
def __init__(self):
Dist.__init__(self)
def __init__(self, df, nc):
Dist.__init__(self, df=df, nc=nc)
def __init__(self, dist):
assert isinstance(dist, Dist)
assert np.all(dist.range()>=0)
Dist.__init__(self, dist=dist, _length=len(dist),
_advance=True)
def __init__(self, mu):
Dist.__init__(self, mu=mu)
def __init__(self, dfn, dfd, nc):
Dist.__init__(self, dfn=dfn, dfd=dfd, nc=nc)
def __init__(self, a=1):
Dist.__init__(self, a=a)
def __init__(self, A):
Dist.__init__(self, A=A,
_length=len(A), _advance=True)
def __init__(self, a=1, loc=[0,0], scale=[[1,.5],[.5,1]]):
loc, scale = np.asfarray(loc), np.asfarray(scale)
C = np.linalg.cholesky(scale)
Ci = np.linalg.inv(C)
Dist.__init__(self, a=a, C=C, Ci=Ci, loc=loc, _length=len(C))
def __init__(self, dist, n=2):
assert isinstance(dist, Dist)
assert np.all(dist.range()>=0)
assert n>0 and n!=1
Dist.__init__(self, dist=dist, n=n,
_length=len(dist), _advance=True)
def __init__(self, c=1., d=1.):
Dist.__init__(self, c=c, d=d)
def __init__(self, dist, N):
N = int(N)
Dist.__init__(self, dist=dist, N=N,
_length=len(dist)*N)
def __init__(self, df=1):
Dist.__init__(self, df=df)
def __init__(self, N, theta=1.0, eps=1e-6):
Dist.__init__(self, th=float(theta), _length=N, eps=eps)
def __init__(self, a=1):
Dist.__init__(self, a=a)
def __init__(self, N, theta, eps=1e-6):
"theta!=0"
theta = float(theta)
assert theta != 0
Dist.__init__(self, th=theta, _length=N, eps=eps)
def __init__(self, b=1):
Dist.__init__(self, b=b)
def __init__(self, N, theta, eps=1e-6):
"theta in [1,inf)"
theta = float(theta)
assert theta >= 1
Dist.__init__(self, th=theta, _length=N, eps=eps)
def __init__(self, a=1, b=1, c=1):
Dist.__init__(self, a=a, b=b, c=c)
def __init__(self, a, R):
self.MV = mvstudentt(a, np.zeros(len(R)), R)
self.UV = student_t(a)
Dist.__init__(self, _length=len(R))
def __init__(self, x, a, c):
Dist.__init__(self, a=a, c=c)
def __init__(self, dist):
lo,up = dist.range()
assert np.all(lo>=-1) and np.all(up<=1)
Dist.__init__(self, dist=dist, _length=len(dist),
_advance=True)
def __init__(self, k, s):
Dist.__init__(self, k=k, s=s)
