def assert_pogs_prox(self, lib, blas_handle, solver, f, f_py, g, g_py,
local_vars):
"""proximal operator application test
set
x^{k+1/2} = prox_{g, rho}(x^k - xt^k)
y^{k+1/2} = prox_{f, rho}(y^k - yt^k)
in C and Python, check that results agree
"""
m = len(f_py)
n = len(g_py)
DIGITS = 7 - 2 * lib.FLOAT
RTOL = 10**(-DIGITS)
ATOLM = RTOL * m**0.5
ATOLN = RTOL * n**0.5
z = solver.contents.z
rho = solver.contents.rho
self.assertCall( lib.prox(blas_handle, f, g, z, rho) )
self.load_all_local(lib, local_vars, solver)
f_list = [lib.function(*f_) for f_ in f_py]
g_list = [lib.function(*f_) for f_ in g_py]
for i in xrange(len(f_py)):
f_list[i].a *= local_vars.d[i]
f_list[i].d *= local_vars.d[i]
f_list[i].e *= local_vars.d[i]
for j in xrange(len(g_py)):
g_list[j].a /= local_vars.e[j]
g_list[j].d /= local_vars.e[j]
g_list[j].e /= local_vars.e[j]
x_arg = local_vars.x - local_vars.xt
y_arg = local_vars.y - local_vars.yt
x_out = prox_eval_python(g_list, rho, x_arg)
y_out = prox_eval_python(f_list, rho, y_arg)
self.assertVecEqual( local_vars.x12, x_out, ATOLN, RTOL )
self.assertVecEqual( local_vars.y12, y_out, ATOLM, RTOL )
def test_eval(self):
m, n = self.shape
scal = self.scalefactor
a = 10 * np.random.rand(m)
b = np.random.rand(m)
c = np.random.rand(m)
d = np.random.rand(m)
e = np.random.rand(m)
x_rand = np.random.rand(m)
for (gpu, single_precision) in self.CONDITIONS:
lib = self.libs.get(single_precision=single_precision, gpu=gpu)
if lib is None:
continue
self.register_exit(lib.ok_device_reset)
DIGITS = 7 - 2 * single_precision
RTOL = 10**(-DIGITS)
ATOLM = RTOL * m**0.5
ATOLN = RTOL * n**0.5
f, f_py, f_ptr = self.register_fnvector(lib, m, 'f')
x, x_py, x_ptr = self.register_vector(lib, m, 'x')
xout, xout_py, xout_ptr = self.register_vector(lib, m, 'xout')
# populate
x_py += x_rand
self.assertCall(lib.vector_memcpy_va(x, x_ptr, 1))
for hkey, hval in list(lib.function_enums.dict.items()):
if self.VERBOSE_TEST:
print(hkey)
print(hkey)
# avoid domain errors with randomly generated data
if 'Log' in hkey or 'Exp' in hkey or 'Entr' in hkey:
continue
for i in range(m):
f_py[i] = lib.function(hval, a[i], b[i], c[i], d[i], e[i])
self.assertCall(lib.function_vector_memcpy_va(f, f_ptr))
# function evaluation
f_list = [lib.function(*f_) for f_ in f_py]
funcval_py = func_eval_python(f_list, x_rand)
funcval_c = np.zeros(1).astype(lib.pyfloat)
funcval_c_ptr = funcval_c.ctypes.data_as(lib.ok_float_p)
self.assertCall(lib.function_eval_vector(f, x, funcval_c_ptr))
if funcval_c[0] in (np.inf, np.nan):
self.assertTrue(1)
else:
self.assertScalarEqual(funcval_py, funcval_c, RTOL)
# proximal operator evaluation, random rho
rho = 5 * np.random.rand()
prox_py = prox_eval_python(f_list, rho, x_rand)
self.assertCall(lib.prox_eval_vector(f, rho, x, xout))
self.assertCall(lib.vector_memcpy_av(xout_ptr, xout, 1))
self.assertVecEqual(xout_py, prox_py, ATOLM, RTOL)
self.free_vars('f', 'x', 'xout')
self.assertCall(lib.ok_device_reset())
def test_eval(self):
m, n = self.shape
scal = self.scalefactor
a = 10 * np.random.rand(m)
b = np.random.rand(m)
c = np.random.rand(m)
d = np.random.rand(m)
e = np.random.rand(m)
x_rand = np.random.rand(m)
for (gpu, single_precision) in self.CONDITIONS:
lib = self.libs.get(single_precision=single_precision, gpu=gpu)
if lib is None:
continue
self.register_exit(lib.ok_device_reset)
DIGITS = 7 - 2 * single_precision
RTOL = 10**(-DIGITS)
ATOLM = RTOL * m**0.5
ATOLN = RTOL * n**0.5
f, f_py, f_ptr = self.register_fnvector(lib, m, 'f')
x, x_py, x_ptr = self.register_vector(lib, m, 'x')
xout, xout_py, xout_ptr = self.register_vector(lib, m, 'xout')
# populate
x_py += x_rand
self.assertCall( lib.vector_memcpy_va(x, x_ptr, 1) )
for hkey, hval in lib.function_enums.dict.items():
if self.VERBOSE_TEST:
print hkey
print hkey
# avoid domain errors with randomly generated data
if 'Log' in hkey or 'Exp' in hkey or 'Entr' in hkey:
continue
for i in xrange(m):
f_py[i] = lib.function(hval, a[i], b[i], c[i], d[i], e[i])
self.assertCall( lib.function_vector_memcpy_va(f, f_ptr) )
# function evaluation
f_list = [lib.function(*f_) for f_ in f_py]
funcval_py = func_eval_python(f_list, x_rand)
funcval_c = np.zeros(1).astype(lib.pyfloat)
funcval_c_ptr = funcval_c.ctypes.data_as(lib.ok_float_p)
self.assertCall( lib.function_eval_vector(f, x,
funcval_c_ptr) )
if funcval_c[0] in (np.inf, np.nan):
self.assertTrue( 1 )
else:
self.assertScalarEqual( funcval_py, funcval_c, RTOL )
# proximal operator evaluation, random rho
rho = 5 * np.random.rand()
prox_py = prox_eval_python(f_list, rho, x_rand)
self.assertCall( lib.prox_eval_vector(f, rho, x, xout) )
self.assertCall( lib.vector_memcpy_av(xout_ptr, xout, 1) )
self.assertVecEqual( xout_py, prox_py, ATOLM, RTOL )
self.free_vars('f', 'x', 'xout')
self.assertCall( lib.ok_device_reset() )
