def exercise_increasing_dimensions(self):
print("Scaling with m and m/n: ", end=' ')
n_tests = 0
for sigma, a in self.matrices():
m, n = a.focus()
if self.show_progress:
if not n_tests: print()
print((m, n), end=' ')
sys.stdout.flush()
svd = self.klass(a, self.accumulate_u, self.accumulate_v)
if self.show_progress:
print('!', end=' ')
sys.stdout.flush()
sigma = sigma.select(flex.sort_permutation(sigma, reverse=True))
delta = (svd.sigma - sigma).norm() / sigma.norm() / min(
m, n) / self.eps
assert delta < 10
n_tests += 1
if not self.exercise_tntbx:
if self.show_progress: print()
continue
svd = tntbx.svd_m_ge_n_double(a)
if self.show_progress:
print('!')
sys.stdout.flush()
sigma = sigma.select(flex.sort_permutation(sigma, reverse=True))
delta = ((svd.singular_values() - sigma).norm() / sigma.norm() /
min(m, n) / self.eps)
assert delta < 10
if self.show_progress: print()
print("%i done" % n_tests)
def exercise_increasing_dimensions(self):
print "Scaling with m and m/n: ",
n_tests = 0
for sigma, a in self.matrices():
m, n = a.focus()
if self.show_progress:
if not n_tests: print
print (m,n),
sys.stdout.flush()
svd = scitbx.linalg.svd.real(a, self.accumulate_u, self.accumulate_v)
if self.show_progress:
print '!',
sys.stdout.flush()
sigma = sigma.select(flex.sort_permutation(sigma, reverse=True))
delta = (svd.sigma - sigma).norm()/sigma.norm()/min(m,n)/self.eps
assert delta < 10
n_tests += 1
if not self.exercise_tntbx:
if self.show_progress: print
continue
svd = tntbx.svd_m_ge_n_double(a)
if self.show_progress:
print '!'
sys.stdout.flush()
sigma = sigma.select(flex.sort_permutation(sigma, reverse=True))
delta = ((svd.singular_values() - sigma).norm()
/sigma.norm()/min(m,n)/self.eps)
assert delta < 10
if self.show_progress: print
print "%i done" % n_tests