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
def time(self):
from libtbx.easy_profile import easy_profile
self.scitbx_report = []
self.tntbx_report = []
prof_scitbx = easy_profile(self.klass,
file_name='svd.py',
func_name='__init__',
line=None)
if tntbx is not None:
prof_tntbx = easy_profile(lambda m: tntbx.svd_m_ge_n_double(m),
file_name='tst_svd.py',
func_name='<lambda>',
line=None)
for sigma, a in self.matrices():
m, n = a.focus()
if self.show_progress:
print((m, n), end=' ')
sys.stdout.flush()
flops = min(2 * m * n**2 - 2 * n**3 / 3, m * n**2 + n**3)
runs = max(int(1e4 / flops), 1)
if tntbx:
prof_tntbx.runs = runs
sys.stdout.flush()
t = prof_tntbx.time(a)
if self.show_progress:
print('!', end=' ')
self.tntbx_report.append((m, n, t))
prof_scitbx.runs = runs
t = prof_scitbx.time(a, accumulate_u=True, accumulate_v=True)
if self.show_progress:
print('!')
self.scitbx_report.append((m, n, t))
print('scitbx = {')
print(', '.join(
["{%i, %i, %f}" % (m, n, t) for m, n, t in self.scitbx_report]))
print('};')
if tntbx:
print('(***************************************************)')
print('(***************************************************)')
print('tntbx = {')
print(', '.join(
["{%i, %i, %f}" % (m, n, t) for m, n, t in self.tntbx_report]))
print('};')
def time(self):
from libtbx.easy_profile import easy_profile
self.scitbx_report = []
self.tntbx_report = []
prof_scitbx = easy_profile(scitbx.linalg.svd.real,
file_name='svd.py', func_name='__init__',
line=None)
if tntbx is not None:
prof_tntbx = easy_profile(lambda m: tntbx.svd_m_ge_n_double(m),
file_name='tst_svd.py', func_name='<lambda>',
line=None)
for sigma, a in self.matrices():
m, n = a.focus()
if self.show_progress:
print (m,n),
sys.stdout.flush()
flops = min(2*m*n**2 - 2*n**3/3, m*n**2 + n**3)
runs = max(int(1e4/flops), 1)
if tntbx:
prof_tntbx.runs = runs
sys.stdout.flush()
t = prof_tntbx.time(a)
if self.show_progress:
print '!',
self.tntbx_report.append((m, n, t))
prof_scitbx.runs = runs
t = prof_scitbx.time(a, accumulate_u=True, accumulate_v=True)
if self.show_progress:
print '!'
self.scitbx_report.append((m, n, t))
print 'scitbx = {'
print ', '.join([ "{%i, %i, %f}" % (m, n, t)
for m, n, t in self.scitbx_report ])
print '};'
if tntbx:
print '(***************************************************)'
print '(***************************************************)'
print 'tntbx = {'
print ', '.join([ "{%i, %i, %f}" % (m, n, t)
for m, n, t in self.tntbx_report ])
print '};'
