def forward(self, x):
#print('x in:' , x.shape)
rad1 = 0.7272
radius = rad1
adjacency_method = 'cyflann'
adjacency_kwds = {'radius': radius}
affinity_method = 'gaussian'
affinity_kwds = {'radius': radius}
laplacian_method = 'symmetricnormalized'
laplacian_kwds = {'scaling_epps': radius}
geom = Geometry(adjacency_method=adjacency_method,
adjacency_kwds=adjacency_kwds,
affinity_method=affinity_method,
affinity_kwds=affinity_kwds,
laplacian_method=laplacian_method,
laplacian_kwds=laplacian_kwds)
x = x.view(100, -1)
#print('before embedding:', x.shape)
geom.set_data_matrix(x)
spectral = SpectralEmbedding(n_components=10,
eigen_solver='amg',
geom=geom,
drop_first=False) # use 3 for spectral
embed_spectral = spectral.fit_transform(x)
embed_spectral = torch.from_numpy(embed_spectral).float()
#print('x_totorch:', embed_spectral.shape)
x = embed_spectral.view(-1, 10)
x = self.fc(x)
return x
def forward(self, x):
radius = 1.2
adjacency_method = 'cyflann'
adjacency_kwds = {'radius': radius}
affinity_method = 'gaussian'
affinity_kwds = {'radius': radius}
laplacian_method = 'symmetricnormalized'
laplacian_kwds = {'scaling_epps': radius}
geom = Geometry(adjacency_method=adjacency_method,
adjacency_kwds=adjacency_kwds,
affinity_method=affinity_method,
affinity_kwds=affinity_kwds,
laplacian_method=laplacian_method,
laplacian_kwds=laplacian_kwds)
x = x.view(100, -1)
#print(x.shape)
geom.set_data_matrix(x)
lle = LocallyLinearEmbedding(n_components=10,
eigen_solver='dense',
geom=geom)
embed_lle = lle.fit_transform(x)
embed_lle = torch.from_numpy(embed_lle).float()
x = embed_lle.view(-1, 10)
x = self.fc(x)
return x
def compute_geom_brute(self, diffusion_time, n_neighbors):
data = self.data
dim = self.dim
#set radius according to paper (i think this is dim not d)
radius = (diffusion_time * (diffusion_time * np.pi * 4)**(dim/2))**(0.5)
#set adjacency radius large enough that points beyond it have affinity close to zero
bigradius = 3 * radius
adjacency_method = 'brute'
adjacency_kwds = {'radius':bigradius}
affinity_method = 'gaussian'
affinity_kwds = {'radius':radius}
laplacian_method = 'geometric'
laplacian_kwds = {'scaling_epps':1}
geom = Geometry(adjacency_method=adjacency_method, adjacency_kwds=adjacency_kwds,
affinity_method=affinity_method, affinity_kwds=affinity_kwds,
laplacian_method=laplacian_method, laplacian_kwds=laplacian_kwds)
geom.set_data_matrix(data)
geom.adjacency_matrix = geom.compute_adjacency_matrix()
geom.laplacian_matrix = geom.compute_affinity_matrix()
geom.laplacian_matrix = self.get_laplacian(geom, radius)
return(geom)
def compute_geom(self, diffusion_time, n_neighbors):
data = self.data
dim = self.dim
#set radius according to paper (i think this is dim not d)
radius = (diffusion_time * (diffusion_time * np.pi * 4)**(dim/2))**(0.5)
#set adjacency radius large enough that points beyond it have affinity close to zero
bigradius = 3 * radius
adjacency_method = 'cyflann'
cyflann_kwds = {'index_type':'kdtrees', 'num_trees':10, 'num_checks':n_neighbors}
adjacency_kwds = {'radius':bigradius, 'cyflann_kwds':cyflann_kwds}
affinity_method = 'gaussian'
affinity_kwds = {'radius':radius}
laplacian_method = 'geometric'
laplacian_kwds = {'scaling_epps':radius}
geom = Geometry(adjacency_method=adjacency_method, adjacency_kwds=adjacency_kwds,
affinity_method=affinity_method, affinity_kwds=affinity_kwds,
laplacian_method=laplacian_method, laplacian_kwds=laplacian_kwds)
geom.set_data_matrix(data)
adjacency_matrix = geom.compute_adjacency_matrix()
laplacian_matrix = geom.compute_laplacian_matrix()
return(geom)
# Affinity an NxN (sparse) pairwise matrix insicated similarity between points
# Laplacian an NxN (sparse) pairwsie matrix containing geometric manifold information
radius = 5
adjacency_method = 'cyflann'
adjacency_kwds = {'radius': radius} # ignore distances above this radius
affinity_method = 'gaussian'
affinity_kwds = {'radius': radius} # A = exp(-||x - y||/radius^2)
laplacian_method = 'geometric'
laplacian_kwds = {
'scaling_epps': radius
} # scaling ensures convergence to Laplace-Beltrami operator
geom = Geometry(adjacency_method=adjacency_method,
adjacency_kwds=adjacency_kwds,
affinity_method=affinity_method,
affinity_kwds=affinity_kwds,
laplacian_method=laplacian_method,
laplacian_kwds=laplacian_kwds)
# You can/should also use the set_data_matrix, set_adjacency_matrix, set_affinity_matrix
# to send your data set (in whichever form it takes) this way.
geom.set_data_matrix(X)
# You can get the distance, affinity etc with e.g: Geometry.get_distance_matrix()
# you can update the keyword arguments passed inially using these functions
adjacency_matrix = geom.compute_adjacency_matrix()
# by defualt this is pass-by-reference. Use copy=True to get a copied version.
# If you don't want to pre-compute a Geometry you can pass a dictionary or geometry
# arguments to one of the embedding classes.
geom = {
