def test_diag():
m = theano.sparse.csc_matrix()
d = sp.diag(m)
f = theano.function([m], d)
f2 = theano.function([m], d.shape)
for K in 1, 5:
np_matrix = numpy.asarray(numpy.reshape(range(K ** 2), (K, K)), dtype=theano.config.floatX)
diag = numpy.diagonal(np_matrix)
sp_matrix = scipy.sparse.csc_matrix(np_matrix)
assert numpy.all(diag == f(sp_matrix))
assert f2(sp_matrix) == diag.shape
def test_diag():
m = theano.sparse.csc_matrix()
d = sp.diag(m)
f = theano.function([m], d)
f2 = theano.function([m], d.shape)
for K in 1, 5:
np_matrix = numpy.asarray(numpy.reshape(range(K**2), (K, K)),
dtype=theano.config.floatX)
diag = numpy.diagonal(np_matrix)
sp_matrix = scipy.sparse.csc_matrix(np_matrix)
assert numpy.all(diag == f(sp_matrix))
assert f2(sp_matrix) == diag.shape
def d(x):
sp_x = theano.sparse.csc_from_dense(x)
diag_x = sp.diag(sp_x)
return diag_x.sum()
def d(x):
sp_x = theano.sparse.csc_from_dense(x)
diag_x = sp.diag(sp_x)
return diag_x.sum()
