def __init__(self, dist, trans):
"""
Args:
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, 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, loc=[0,0], scale=[[1,.5],[.5,1]]):
loc, scale = np.asfarray(loc), np.asfarray(scale)
assert len(loc)==len(scale)
dist = chaospy.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, trans):
"""
Args:
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, 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, 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.graph:
if dist in args:
self.sorting.append(args.index(dist))
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.graph:
if dist in args:
self.sorting.append(args.index(dist))
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 = chaospy.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, 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, df, nc):
Dist.__init__(self, df=df, nc=nc)
def __init__(self, mu):
Dist.__init__(self, mu=mu)
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, nu):
Dist.__init__(self, nu=nu)
def __init__(self, df=1):
Dist.__init__(self, df=df)
def __init__(self, c=1.):
Dist.__init__(self, c=c)
def __init__(self, c=0):
Dist.__init__(self, c=c)
def __init__(self, a=.5):
assert np.all(a >= 0) and np.all(a <= 1)
Dist.__init__(self, a=a)
def __init__(self, a=1, c=1):
Dist.__init__(self, a=a, c=c)
def __init__(self, b=1):
Dist.__init__(self, b=b)
def __init__(self, a=1):
Dist.__init__(self, a=a)
def __init__(self, df=1):
Dist.__init__(self, df=df)
def __init__(self):
Dist.__init__(self)
def __init__(self, lo=0, up=1):
Dist.__init__(self, lo=lo, up=up)
def __init__(self, x, a, c):
Dist.__init__(self, a=a, c=c)
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):
Dist.__init__(self, dist=dist, _length=len(dist), _advance=True)
def __init__(self, b=1):
Dist.__init__(self, b=b)
def __init__(self, c=1., d=1.):
Dist.__init__(self, c=c, d=d)
def __init__(self):
Dist.__init__(self)
def __init__(self, a=1, b=1):
Dist.__init__(self, a=a, b=b)
def __init__(self, lo=0, up=1):
Dist.__init__(self, lo=lo, up=up)
def __init__(self, a):
Dist.__init__(a=a)
def __init__(self, k, s):
Dist.__init__(self, k=k, s=s)
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, dfn, dfd, nc):
Dist.__init__(self, dfn=dfn, dfd=dfd, nc=nc)
def __init__(self, N, theta=1., eps=1e-6):
theta = float(theta)
Dist.__init__(self, th=theta, eps=eps, _length=N)
def __init__(self, c, s):
Dist.__init__(self, c=c, s=s)
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, a=.5):
assert np.all(a>=0) and np.all(a<=1)
Dist.__init__(self, a=a)
def __init__(self, a):
Dist.__init__(a=a)
def __init__(self, a, R):
self.MV = chaospy.dist.mvstudentt(a, np.zeros(len(R)), R)
self.UV = chaospy.dist.student_t(a)
Dist.__init__(self, _length=len(R))
def __init__(self, a=1, b=1):
assert np.all(a>0) and np.all(b>0)
Dist.__init__(self, a=a, b=b)
def __init__(self, N, theta=1., eps=1e-6):
Dist.__init__(self, th=float(theta), _length=N, eps=eps)
def __init__(self, lam):
Dist.__init__(self, lam=lam)
def __init__(self, N, theta=.5, eps=1e-6):
theta = float(theta)
assert -1 <= theta < 1
Dist.__init__(self, th=theta, _length=N, eps=eps)
def __init__(self, c=1., d=1.):
Dist.__init__(self, c=c, d=d)
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, a=1):
Dist.__init__(self, a=a)
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, a=1, b=1, c=1):
Dist.__init__(self, a=a, b=b, c=c)
def __init__(self, a, R):
self.MV = chaospy.dist.mvstudentt(a, np.zeros(len(R)), R)
self.UV = chaospy.dist.student_t(a)
Dist.__init__(self, _length=len(R))
def __init__(self, c=1):
Dist.__init__(self, c=c)
def __init__(self, N, theta=1.0, eps=1e-6):
Dist.__init__(self, th=float(theta), _length=N, eps=eps)
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, 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, dist):
assert np.all(dist.range() >= 1)
Dist.__init__(self, dist=dist, _length=len(dist), _advance=True)
def __init__(self, a=1, b=1):
assert np.all(a > 0) and np.all(b > 0)
Dist.__init__(self, a=a, b=b)
