QR.conditional(QR[k, k] != 0.0, Qcompute(j, k, n, QR, Q))
return Q
def get_r(QR, Rdiag):
n = None # TODO: What is this supposed to be?
c = QR.context()
R = c.matrix(float, (n, n), 0.0)
for (i, j) in getindices(n, n):
if i < j:
R[i, j] = QR[i, j]
elif i == j:
R[i, j] = Rdiag[i]
return R
if __name__ == "__main__":
c = p.context()
A = c.matrix(float, (100, 100))
p.randomize(A)
QR, Rdiag = qr_decomp(A)
Q = get_q(QR, Rdiag)
R = get_r(QR, Rdiag)
[v.write(p.stdout) for v in (QR, Rdiag, Q, R)]
p.compute(c)
def get_u(LU):
# m = LU.shape[0] # row
n = LU.shape[1] # col
c = LU.context()
U = c.matrix(float, n, n)
for (i, j) in zip(range(n), range(n)):
if i <= j:
U[i, j] = LU[i, j]
else:
U[i, j] = 0.0
return U
if __name__ == "__main__":
c = pnx.context()
A = pnx.matrix(float, 100, 100)
pnx.random(A)
Bmat, LU = lu_decomp(A)
L = get_l(LU)
U = get_u(LU)
[v.write(pnx.stdout) for v in [Bmat, LU, L, U]]
pnx.compute(c)