Closed
Description
It may be nice to have constructors to create Vector or Matrix from "dense" a.k.a "full" data. It should be able to handle numpy arrays, list of lists, and scalars. I think scalars are important to create iso-valued objects.
Creating full objects from numpy arrays is more difficult than it ought to be in GraphBLAS. For example, see Matrix._from_dense.
Relatedly, we could add a method to create a diagonal matrix from an array or scalar (for example, to create an identity matrix).
These are all possible to do today, such as:
A = Matrix(a.dtype, nrows=a.shape[0], ncols=a.shape[1])
A[:, :] = a
# vs
A = Matrix.from_foo(a)and
A = Matrix(nrows=nrows, ncols=ncols)
A << scalar
# vs
A = Matrix.from_foo(scalar, nrows=nrows, ncols=ncols)and
v = Vector(a.dtype, size=a.size)
v[:] = a
A = v.diag()
# vs
A = Vector.from_foo(a).diag()
# vs
A = Vector.from_coo(np.arange(n), a, size=n).diag()
# vs
A = Matrix.from_coo(np.arange(n), np.arange(n), a, nrows=n, ncols=n)
# vs
A = Matrix.from_bar(a)(Note that there are suitesparse-specific constructors such as Matrix.ss.import_fullr)
Possible signatures:
def from_foo(values, dtype=None, *, nrows=None, ncols=None, name=None):
... # create full matrix from array, list of lists, or scalar
# what about rowwise or columnwise? Should we add `order="rowwise"` keyword?
def from_bar(values, dtype=None, *, k=0, nrows=None, ncols=None, name=None):
... # create matrix with data on the kth diagonal from array, list, or scalar (or Vector?)Also related is gb.io.from_numpy, whose behavior we could reconsider.