def __init__(self,
a,
b,
nu_0,
S_0,
M_0,
blocksizes=None,
K_0=None,
niter=10,
**kwargs):
blocksizes = np.ones(M_0.shape[1],dtype=np.int64) \
if blocksizes is None else blocksizes
self.niter = niter
self.blocksizes = np.array(blocksizes)
self.starts = cumsum(blocksizes, strict=True)
self.stops = cumsum(blocksizes, strict=False)
self.a = np.repeat(a, len(blocksizes))
self.b = np.repeat(b, len(blocksizes))
self.nu_0 = nu_0
self.S_0 = S_0
self.M_0 = M_0
if K_0 is None:
self.resample_K()
else:
self.K_0 = K_0
super(ARDRegression, self).__init__(K_0=self.K_0,
nu_0=nu_0,
S_0=S_0,
M_0=M_0,
**kwargs)
def __init__(
self, a,b,nu_0,S_0,M_0,
blocksizes=None,K_0=None,niter=10,**kwargs):
blocksizes = np.ones(M_0.shape[1],dtype=np.int64) \
if blocksizes is None else blocksizes
self.niter = niter
self.blocksizes = np.array(blocksizes)
self.starts = cumsum(blocksizes,strict=True)
self.stops = cumsum(blocksizes,strict=False)
self.a = np.repeat(a,len(blocksizes))
self.b = np.repeat(b,len(blocksizes))
self.nu_0 = nu_0
self.S_0 = S_0
self.M_0 = M_0
if K_0 is None:
self.resample_K()
else:
self.K_0 = K_0
super(ARDRegression,self).__init__(
K_0=self.K_0,nu_0=nu_0,S_0=S_0,M_0=M_0,**kwargs)