def rate_of_recovery():
print 'Computing rate of recovery of OMP'
n = 256
m = 64
trials = 200
ks = range(2, 21, 2)
succs = []
for k in ks:
succ = 0
print 'Running trials for k = %d' % k
for _ in range(trials):
x = make_sparse_x(n, k)
A = make_A(m, n, normalize=True)
y = np.dot(A, x)
x_hat = omp(A, y)
error = np.linalg.norm(x - x_hat)
if error < 1e-3:
succ += 1
succs.append(float(succ) / trials)
plt.figure()
plt.plot(ks, succs)
plt.xlabel('sparsity k')
plt.ylabel('rate of recovery')
plt.title('Rate of recovery usigin OMP\n n = %d m = %d' % (n, m))
plt.ylim(-0.1, 1.1)
plt.show()
def rate_of_recovery():
print 'Computing rate of recovery of BOMP and OMP'
n = 256
m = 64
J = 3
trials = 100
ks = range(1, 16)
succs_omp, succs_bomp = [], []
for k in ks:
succ_omp, succ_bomp = 0, 0
print 'Running trials for k = %d' % k
for _ in range(trials):
x = make_group_sparse_x(n, k, J)
A = make_A(m, n, normalize=True)
y = np.dot(A, x)
x_hat_omp = omp(A, y)
x_hat_bomp = bomp(A, y, J)
error_omp = np.linalg.norm(x - x_hat_omp)
error_bomp = np.linalg.norm(x - x_hat_bomp)
if error_omp < 1e-3:
succ_omp += 1
if error_bomp < 1e-3:
succ_bomp += 1
succs_omp.append(float(succ_omp) / trials)
succs_bomp.append(float(succ_bomp) / trials)
plt.figure()
plt.plot(ks, succs_omp, label='omp')
plt.plot(ks, succs_bomp, label='bomp')
plt.legend()
plt.xlabel('group sparsity k')
plt.ylabel('rate of recovery')
plt.title('Rate of recovery usigin OMP\n n = %d m = %d J = %d' % (n, m, J))
plt.ylim(-0.1, 1.1)
plt.show()
def rate_of_recovery():
print 'Computing rate of recovery of OMP'
n = 256
m = 64
trials = 200
ks = range(2, 21, 2)
succs = []
for k in ks:
succ = 0
print 'Running trials for k = %d' % k
for _ in range(trials):
x = make_sparse_x(n, k)
A = make_A(m, n, normalize=True)
y = np.dot(A, x)
x_hat = omp(A, y)
error = np.linalg.norm(x - x_hat)
if error < 1e-3:
succ += 1
succs.append(float(succ) / trials)
plt.figure()
plt.plot(ks, succs)
plt.xlabel('sparsity k')
plt.ylabel('rate of recovery')
plt.title('Rate of recovery usigin OMP\n n = %d m = %d' % (n, m))
plt.ylim(-0.1, 1.1)
plt.show()
def rate_of_recovery():
print 'Computing rate of recovery of BOMP and OMP'
n = 256
m = 64
J = 3
trials = 100
ks = range(1, 16)
succs_omp, succs_bomp = [], []
for k in ks:
succ_omp, succ_bomp = 0, 0
print 'Running trials for k = %d' % k
for _ in range(trials):
x = make_group_sparse_x(n, k, J)
A = make_A(m, n, normalize=True)
y = np.dot(A, x)
x_hat_omp = omp(A, y)
x_hat_bomp = bomp(A, y, J)
error_omp = np.linalg.norm(x - x_hat_omp)
error_bomp = np.linalg.norm(x - x_hat_bomp)
if error_omp < 1e-3:
succ_omp += 1
if error_bomp < 1e-3:
succ_bomp += 1
succs_omp.append(float(succ_omp) / trials)
succs_bomp.append(float(succ_bomp) / trials)
plt.figure()
plt.plot(ks, succs_omp, label='omp')
plt.plot(ks, succs_bomp, label='bomp')
plt.legend()
plt.xlabel('group sparsity k')
plt.ylabel('rate of recovery')
plt.title('Rate of recovery usigin OMP\n n = %d m = %d J = %d' % (n, m, J))
plt.ylim(-0.1, 1.1)
plt.show()