def new_edge_covariate(self, name):
self.edge_covariates[name] = EdgeCovariate(self.rnames, self.cnames)
return self.edge_covariates[name]
def initialize_offset(self):
self.offset = EdgeCovariate(self.rnames, self.cnames)
return self.offset
class Array:
def __init__(self, M, N):
self.M = M
self.N = N
self.rnames = np.array(['m_%d' % m for m in range(self.M)])
self.cnames = np.array(['n_%d' % n for n in range(self.N)])
self.array = sparse.lil_matrix((self.M, self.N), dtype = np.bool)
# Offset initially set to None so simpler offset-free model
# code can be used by default
self.offset = None
self.row_covariates = {}
self.col_covariates = {}
self.edge_covariates = {}
def __setitem__(self, index, x):
self.array.__setitem__(index, x)
def new_row_covariate(self, name, dtype = np.float64):
self.row_covariates[name] = NodeCovariate(self.rnames, dtype)
return self.row_covariates[name]
def new_col_covariate(self, name, dtype = np.float64):
self.col_covariates[name] = NodeCovariate(self.cnames, dtype)
return self.col_covariates[name]
def new_edge_covariate(self, name):
self.edge_covariates[name] = EdgeCovariate(self.rnames, self.cnames)
return self.edge_covariates[name]
def initialize_offset(self):
self.offset = EdgeCovariate(self.rnames, self.cnames)
return self.offset
def subarray(self, rinds = None, cinds = None):
if rinds is None:
rinds = np.arange(self.M)
if cinds is None:
cinds = np.arange(self.N)
sub_M = len(rinds)
sub_N = len(cinds)
sub = Array(sub_M, sub_N)
sub.rnames = self.rnames[rinds]
sub.cnames = self.cnames[cinds]
sub.array = self.array[rinds][:,cinds]
for row_covariate in self.row_covariates:
src = self.row_covariates[row_covariate]
sub.row_covariates[row_covariate] = src.subset(rinds)
for col_covariate in self.col_covariates:
src = self.col_covariates[col_covariate]
sub.col_covariates[col_covariate] = src.subset(cinds)
for edge_covariate in self.edge_covariates:
src = self.edge_covariates[edge_covariate]
sub.edge_covariates[edge_covariate] = src.subset(rinds, cinds)
if self.offset:
sub.offset = self.offset.subset(rinds, cinds)
return sub
# Syntactic sugar to make array generation look like object mutation
def generate(self, model, **opts):
self.array = model.generate(self, **opts)
def is_sparse(self):
return sparse.issparse(self.array)
def as_dense(self):
if self.is_sparse():
return np.asarray(self.array.todense())
else:
return self.array
def offset_extremes(self):
if self.offset is None:
self.initialize_offset()
# (Separately) sort rows and columns of adjacency matrix by
# increasing sum
A = self.as_dense()
r_ord = np.argsort(A.sum(1))
c_ord = np.argsort(A.sum(0))
A = A[r_ord][:,c_ord]
# Convenience function to set blocks of the offset
def set_offset_block(r, c, val):
# Replacing this with a vectorized operation will require
# a conversion to CSR. It's not clear if this is worth the
# overhead.
for i in r_ord[r]:
for j in c_ord[c]:
self.offset[i,j] = val
# Recursively examine for submatrices that will send
# corresponding EMLE parameter estimates to infinity
to_screen = [(np.arange(self.M), np.arange(self.N))]
while len(to_screen) > 0:
r_act, c_act = to_screen.pop()
if len(r_act) == 0 or len(c_act) == 0:
continue
A_act = A[r_act][:,c_act]
n_act = A_act.shape
# Cumulative sums allow for efficient bad substructure search
C_inc = np.cumsum(np.cumsum(A_act,1),0)
def reverse(x):
return x[range(n_act[0]-1,-1,-1)][:,range(n_act[1]-1,-1,-1)]
C_dec = reverse(np.cumsum(np.cumsum(reverse(1-A_act),1),0))
if C_inc[-1,-1] == 0:
set_offset_block(r_act, c_act, -np.inf)
continue
if C_dec[0,0] == 0:
set_offset_block(r_act, c_act, np.inf)
continue
align = np.zeros((n_act[0]+1,n_act[1]+1))
align[1:(n_act[0]+1)][:,1:(n_act[1]+1)] += (C_inc == 0)
align[0:n_act[0]][:,0:n_act[1]] *= (C_dec == 0)
splits = zip(*np.where(align))
if len(splits) == 0:
continue
i_split, j_split = splits[0]
set_offset_block(r_act[:i_split], c_act[:j_split], -np.inf)
set_offset_block(r_act[i_split:], c_act[j_split:], np.inf)
to_screen.append((r_act[i_split:], c_act[:j_split]))
to_screen.append((r_act[:i_split], c_act[j_split:]))
