def _train_matrix_cd(mat: CSR, this: np.ndarray, other: np.ndarray,
reg: float):
"""
One half of an explicit ALS training round using coordinate descent.
Args:
mat: the :math:`m \\times n` matrix of ratings
this: the :math:`m \\times k` matrix to train
other: the :math:`n \\times k` matrix of sample features
reg: the regularization term
"""
nr = mat.nrows
nf = other.shape[1]
assert mat.ncols == other.shape[0]
assert mat.nrows == this.shape[0]
assert this.shape[1] == nf
frob = 0.0
for i in prange(nr):
cols = mat.row_cs(i)
if len(cols) == 0:
continue
vals = mat.row_vs(i)
w = this[i, :].copy()
_rr_solve(other, cols, vals, w, reg * len(cols), 2)
delta = this[i, :] - w
frob += np.dot(delta, delta)
this[i, :] = w
return np.sqrt(frob)
def test_unit_norm(csr: CSR):
# assume(spm.nnz >= 10)
backup = csr.copy()
m2 = csr.normalize_rows('unit')
assert len(m2) == csr.nrows
assert m2.dtype == csr.values.dtype
for i in range(csr.nrows):
vs = csr.row_vs(i)
bvs = backup.row_vs(i)
if len(vs) > 0:
assert m2[i] == approx(np.linalg.norm(bvs))
if m2[i] > 0:
assert np.linalg.norm(vs) == approx(1.0)
assert vs * m2[i] == approx(backup.row_vs(i))
else:
assert all(np.isnan(vs))
else:
assert m2[i] == 0.0