def test_mod_inverse_correctness():
assert_equal(utils.mod_inverse(a=3, n=37), 25)
assert_equal(utils.mod_inverse(a=7, n=26), 15)
assert_equal(utils.mod_inverse(a=1, n=10), 1)
def outer_loop(params,
x_f,
filter_location,
filter_estimation,
simulation=None):
permute = np.empty(params.total_loops)
permute_b = np.empty(params.total_loops)
x_samp = []
# for i in xrange(params.total_loops):
# if i < params.location_loops:
# x_samp.append(np.zeros(params.B_location, dtype=np.complex128))
# else:
# x_samp.append(np.zeros(params.B_estimation, dtype=np.complex128))
hits_found = 0
hits = np.zeros(params.n)
scores = np.zeros(params.n)
# Inner loop
for i in xrange(params.total_loops):
a = 0
b = 0
# GCD test
# http://en.wikipedia.org/wiki/GCD_test
while utils.gcd(a, params.n) != 1:
a = np.random.randint(params.n)
# print 'check', a, params.n, utils.gcd(a, params.n)
ai = utils.mod_inverse(a, params.n)
permute[i] = ai
permute_b[i] = b
perform_location = i < params.location_loops
if perform_location:
current_filter = filter_location
current_B = params.B_location
else:
current_filter = filter_estimation
current_B = params.B_estimation
inner_loop_locate_result = inner_loop_locate(
x=x_f,
n=params.n,
filt=current_filter,
B=current_B,
B_threshold=params.B_threshold,
a=a,
ai=ai,
b=b)
x_samp.append(inner_loop_locate_result['x_samp'])
# assert x_samp[i].shape == inner_loop_locate_result['x_samp'].shape
# print i, x_samp[i].shape, inner_loop_locate_result['x_samp'].shape
assert inner_loop_locate_result['J'].size == params.B_threshold
if perform_location:
inner_loop_filter_result = inner_loop_filter(
J=inner_loop_locate_result['J'],
B=current_B,
B_threshold=params.B_threshold,
loop_threshold=params.loop_threshold,
n=params.n,
a=a,
hits_found=hits_found,
hits=hits,
scores=scores)
hits_found = inner_loop_filter_result['hits_found']
hits = inner_loop_filter_result['hits']
scores = inner_loop_filter_result['scores']
# print params.B_threshold, inner_loop_locate_result['J'][0], inner_loop_locate_result['J'][1], hits_found
print('Number of candidates: {0}'.format(hits_found))
# Estimate values
answers = estimate_values(hits=hits,
hits_found=hits_found,
x_samp=x_samp,
loops=params.total_loops,
n=params.n,
permute=permute,
B_location=params.B_location,
B_estimation=params.B_estimation,
filter_location=filter_location,
filter_estimation=filter_estimation,
location_loops=params.location_loops)
# Reconstructed signal
x_f = np.zeros(params.n)
for location, value in answers.iteritems():
print 'got', int(location), np.abs(value)
x_f[int(location)] = np.abs(value)
# Counts
xc = np.zeros(params.n)
for i in xrange(params.n):
xc[i] = scores[i] * 1. / params.total_loops
# debug
if simulation is not None:
fig = plt.figure()
ax = fig.gca()
ax.plot(
simulation.t,
xc,
'-x',
simulation.t,
simulation.x_f * params.n,
'-x',
simulation.t,
x_f * params.n,
'-.x',
)
ax.legend(('counts', 'true signal', 'reconstruction'))
ax.set_xlim(right=simulation.t.shape[-1] - 1)
return {'x_f': x_f, 'answers': answers}
def test_mod_inverse_correctness():
assert_equal(utils.mod_inverse(a=3, n=37), 25)
assert_equal(utils.mod_inverse(a=7, n=26), 15)
assert_equal(utils.mod_inverse(a=1, n=10), 1)
def outer_loop(params, x_f, filter_location, filter_estimation, simulation=None):
permute = np.empty(params.total_loops)
permute_b = np.empty(params.total_loops)
x_samp = []
# for i in xrange(params.total_loops):
# if i < params.location_loops:
# x_samp.append(np.zeros(params.B_location, dtype=np.complex128))
# else:
# x_samp.append(np.zeros(params.B_estimation, dtype=np.complex128))
hits_found = 0
hits = np.zeros(params.n)
scores = np.zeros(params.n)
# Inner loop
for i in xrange(params.total_loops):
a = 0
b = 0
# GCD test
# http://en.wikipedia.org/wiki/GCD_test
while utils.gcd(a, params.n) != 1:
a = np.random.randint(params.n)
# print 'check', a, params.n, utils.gcd(a, params.n)
ai = utils.mod_inverse(a, params.n)
permute[i] = ai
permute_b[i] = b
perform_location = i < params.location_loops
if perform_location:
current_filter = filter_location
current_B = params.B_location
else:
current_filter = filter_estimation
current_B = params.B_estimation
inner_loop_locate_result = inner_loop_locate(
x=x_f,
n=params.n,
filt=current_filter,
B=current_B,
B_threshold=params.B_threshold,
a=a,
ai=ai,
b=b
)
x_samp.append(inner_loop_locate_result['x_samp'])
# assert x_samp[i].shape == inner_loop_locate_result['x_samp'].shape
# print i, x_samp[i].shape, inner_loop_locate_result['x_samp'].shape
assert inner_loop_locate_result['J'].size == params.B_threshold
if perform_location:
inner_loop_filter_result = inner_loop_filter(
J=inner_loop_locate_result['J'],
B=current_B,
B_threshold=params.B_threshold,
loop_threshold=params.loop_threshold,
n=params.n,
a=a,
hits_found=hits_found,
hits=hits,
scores=scores
)
hits_found = inner_loop_filter_result['hits_found']
hits = inner_loop_filter_result['hits']
scores = inner_loop_filter_result['scores']
# print params.B_threshold, inner_loop_locate_result['J'][0], inner_loop_locate_result['J'][1], hits_found
print('Number of candidates: {0}'.format(hits_found))
# Estimate values
answers = estimate_values(
hits=hits,
hits_found=hits_found,
x_samp=x_samp,
loops=params.total_loops,
n=params.n,
permute=permute,
B_location=params.B_location,
B_estimation=params.B_estimation,
filter_location=filter_location,
filter_estimation=filter_estimation,
location_loops=params.location_loops
)
# Reconstructed signal
x_f = np.zeros(params.n)
for location, value in answers.iteritems():
print 'got', int(location), np.abs(value)
x_f[int(location)] = np.abs(value)
# Counts
xc = np.zeros(params.n)
for i in xrange(params.n):
xc[i] = scores[i] * 1./ params.total_loops
# debug
if simulation is not None:
fig = plt.figure()
ax = fig.gca()
ax.plot(
simulation.t, xc, '-x',
simulation.t, simulation.x_f * params.n, '-x',
simulation.t, x_f * params.n, '-.x',
)
ax.legend(
(
'counts',
'true signal',
'reconstruction'
)
)
ax.set_xlim(right=simulation.t.shape[-1]-1)
return {
'x_f': x_f,
'answers': answers
}
