def norm(x: ndarray,
ord: tp.Optional[int] = None,
axis: tp.Optional[int] = None,
keepdims: bool = False) -> float:
"""
Matrix or vector norm.
"""
if axis is not None:
raise ValueError("axis != None is not supported")
p = 1.0
q = 1.0
if ord == 2:
S = svd(x, compute_uv=False)
return S.max()
else:
if ord is None:
norm_type = af.NORM.VECTOR_2
elif isinstance(ord, Number):
if ord == float("inf") or ord == np.inf:
norm_type = af.NORM.MATRIX_INF
elif ord == 1:
norm_type = af.NORM.MATRIX_1
else:
norm_type = af.NORM.VECTOR_P
p = ord
else:
raise ValueError(f"ord = {ord} is not supported")
return af.norm(x._af_array, norm_type=norm_type, p=p, q=q)
def simple_lapack(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(5, 5)
l, u, p = af.lu(a)
display_func(l)
display_func(u)
display_func(p)
p = af.lu_inplace(a, "full")
display_func(a)
display_func(p)
a = af.randu(5, 3)
q, r, t = af.qr(a)
display_func(q)
display_func(r)
display_func(t)
af.qr_inplace(a)
display_func(a)
a = af.randu(5, 5)
a = af.matmulTN(a, a.copy()) + 10 * af.identity(5, 5)
R, info = af.cholesky(a)
display_func(R)
print_func(info)
af.cholesky_inplace(a)
display_func(a)
a = af.randu(5, 5)
ai = af.inverse(a)
display_func(a)
display_func(ai)
x0 = af.randu(5, 3)
b = af.matmul(a, x0)
x1 = af.solve(a, b)
display_func(x0)
display_func(x1)
p = af.lu_inplace(a)
x2 = af.solve_lu(a, p, b)
display_func(x2)
print_func(af.rank(a))
print_func(af.det(a))
print_func(af.norm(a, af.NORM.EUCLID))
print_func(af.norm(a, af.NORM.MATRIX_1))
print_func(af.norm(a, af.NORM.MATRIX_INF))
print_func(af.norm(a, af.NORM.MATRIX_L_PQ, 1, 1))
a = af.randu(10, 10)
display_func(a)
u, s, vt = af.svd(a)
display_func(af.matmul(af.matmul(u, af.diag(s, 0, False)), vt))
u, s, vt = af.svd_inplace(a)
display_func(af.matmul(af.matmul(u, af.diag(s, 0, False)), vt))
def simple_lapack(verbose=False):
display_func = _util.display_func(verbose)
print_func = _util.print_func(verbose)
a = af.randu(5,5)
l,u,p = af.lu(a)
display_func(l)
display_func(u)
display_func(p)
p = af.lu_inplace(a, "full")
display_func(a)
display_func(p)
a = af.randu(5,3)
q,r,t = af.qr(a)
display_func(q)
display_func(r)
display_func(t)
af.qr_inplace(a)
display_func(a)
a = af.randu(5, 5)
a = af.matmulTN(a, a) + 10 * af.identity(5,5)
R,info = af.cholesky(a)
display_func(R)
print_func(info)
af.cholesky_inplace(a)
display_func(a)
a = af.randu(5,5)
ai = af.inverse(a)
display_func(a)
display_func(ai)
x0 = af.randu(5, 3)
b = af.matmul(a, x0)
x1 = af.solve(a, b)
display_func(x0)
display_func(x1)
p = af.lu_inplace(a)
x2 = af.solve_lu(a, p, b)
display_func(x2)
print_func(af.rank(a))
print_func(af.det(a))
print_func(af.norm(a, af.NORM.EUCLID))
print_func(af.norm(a, af.NORM.MATRIX_1))
print_func(af.norm(a, af.NORM.MATRIX_INF))
print_func(af.norm(a, af.NORM.MATRIX_L_PQ, 1, 1))
print(info) af.cholesky_inplace(a) af.display(a) a = af.randu(5, 5) ai = af.inverse(a) af.display(a) af.display(ai) x0 = af.randu(5, 3) b = af.matmul(a, x0) x1 = af.solve(a, b) af.display(x0) af.display(x1) p = af.lu_inplace(a) x2 = af.solve_lu(a, p, b) af.display(x2) print(af.rank(a)) print(af.det(a)) print(af.norm(a, af.AF_NORM_EUCLID)) print(af.norm(a, af.AF_NORM_MATRIX_1)) print(af.norm(a, af.AF_NORM_MATRIX_INF)) print(af.norm(a, af.AF_NORM_MATRIX_L_PQ, 1, 1))
m = n = None
k = 1 # timing iterations
phase_unwrapped = unwrap_dct(phase_wrapped) # warm-up
af.device.sync()
t0 = time()
for i in range(k):
phase_unwrapped = unwrap_dct(phase_wrapped)
af.device.sync()
t1 = time() - t0
af.device.device_gc()
coord = None
print('time:\t%fs' % (t1 / k))
print('norm:\t%f' % af.norm(phase_unwrapped - phase_orig))
fig, ((ax1, ax2), (ax3, ax4)) = pl.subplots(2, 2)
im1 = ax1.imshow(phase_orig.__array__())
pl.colorbar(im1, ax=ax1)
ax1.set_title('original phase, SNR=%i' % snr)
im2 = ax2.imshow(phase_wrapped.__array__())
pl.colorbar(im2, ax=ax2)
ax2.set_title('wrapped phase')
im3 = ax3.imshow(phase_unwrapped.__array__())
pl.colorbar(im3, ax=ax3)
ax3.set_title('unwrapped phase')
im4 = ax4.imshow((phase_unwrapped - phase_orig).__array__())
pl.colorbar(im4, ax=ax4)
ax4.set_title('difference')
pl.show()
def computeCost(self, x):
return af.norm(af.moddims(x, np.prod(x.shape)),
norm_type=af.NORM.VECTOR_1)
