class AlphaLayer(nn.Module):
"""
Alpha persistence layer
Parameters:
maxdim : maximum homology dimension (default=0)
alg : algorithm
'hom' = homology (default)
'cohom' = cohomology
"""
def __init__(self, maxdim=0, alg='hom'):
super(AlphaLayer, self).__init__()
self.maxdim = maxdim
self.fnobj = FlagDiagram()
self.alg = alg
def forward(self, x):
xnp = x.cpu().detach().numpy()
complex = None
if xnp.shape[1] == 1:
xnp = xnp.flatten()
complex = delaunay_complex_1d(xnp)
else:
complex = delaunay_complex(xnp, maxdim=self.maxdim + 1)
complex.initialize()
dgms = self.fnobj.apply(complex, x, self.maxdim, self.alg)
return dgms, True
class RipsLayer(nn.Module):
"""
Rips persistence layer
Parameters:
n : number of points
maxdim : maximum homology dimension (default=1)
"""
def __init__(self, n, maxdim=1):
super(RipsLayer, self).__init__()
self.maxdim = maxdim
self.complex = clique_complex(n, maxdim + 1)
self.complex.initialize()
self.fnobj = FlagDiagram()
def forward(self, x):
dgms = self.fnobj.apply(self.complex, x, self.maxdim)
return dgms, True
class AlphaLayer(nn.Module):
"""
Alpha persistence layer
Parameters:
maxdim : maximum homology dimension (default=0)
"""
def __init__(self, maxdim=0):
super(AlphaLayer, self).__init__()
self.maxdim = maxdim
self.fnobj = FlagDiagram()
def forward(self, x):
xnp = x.data.numpy()
complex = None
if xnp.shape[1] == 1:
xnp = xnp.flatten()
complex = delaunay_complex_1d(xnp)
else:
complex = delaunay_complex(xnp, maxdim=self.maxdim+1)
complex.initialize()
dgms = self.fnobj.apply(complex, x, self.maxdim)
return dgms, True
def __init__(self, maxdim=0):
super(AlphaLayer, self).__init__()
self.maxdim = maxdim
self.fnobj = FlagDiagram()
def __init__(self, n, maxdim=1):
super(RipsLayer, self).__init__()
self.maxdim = maxdim
self.complex = clique_complex(n, maxdim + 1)
self.complex.initialize()
self.fnobj = FlagDiagram()