def wcosamp(Operator, y, w, s, eta, maxiter):
x = np.zeros(Operator.n)
u = np.zeros(Operator.n)
U_old = []
last_norm = np.inf
k = 0
while k < s:
if k > maxiter:
print("WCoSaMP did not converge after {0} iterations.".format(k))
break
x, S = weighted_quasi_abslargest(u, 3 * s, w)
U = list(set(S).union(weighted_quasi_abslargest(Operator.apply_adj(y - Operator.apply(x)), 2 * (3 * s), w)[1]))
if set(U) == set(U_old):
break
SubMatrix = Operator.genSubMatrix(U)
u = np.zeros(Operator.n)
u[U] = np.linalg.lstsq(SubMatrix, y)[0]
U_old = U
k = k + 1
return Result(x, k, "Weighted CoSaMP")
def whtp(Operator, y, w, s, eta, maxiter):
x = np.zeros(Operator.n)
S_old = np.array([])
k = 0
while True:
if k > maxiter:
print('WHTP did not converge after {0} iterations.'.format(k))
break
dummy, S = weighted_quasi_abslargest(x + Operator.apply_adj(y - Operator.apply(x)), 3 * s, w)
print('Norm of the error {0}, while the objective norm is {1} and the ratio is {2}'.format(np.linalg.norm(Operator.apply(x) - y),np.linalg.norm(y),np.linalg.norm(Operator.apply(x) - y)/np.linalg.norm(y)))
if set(S) == set(S_old) or np.linalg.norm(Operator.apply(x) - y)/np.linalg.norm(y) <= eta:
print('WHTP Converged after {0} iterations. Norm of residual {1}'.format(k,np.linalg.norm(Operator.apply(x) - y)))
break
SubMatrix = Operator.genSubMatrix(S)
x = np.zeros(Operator.n)
x[S] = np.linalg.lstsq(SubMatrix, y)[0]
S_old = S
k = k + 1
return Result(x, k, 'Weighted HTP')
def wiht(Operator, y, w, s, eta, maxiter):
x = np.zeros(Operator.n)
last_norm = 0
k = 0
while np.linalg.norm(Operator.apply(x) - y) > eta:
residuum = y - Operator.apply(x)
cur_norm = np.linalg.norm(residuum)
x, dummy = weighted_quasi_abslargest(x + Operator.apply_adj(residuum), 3 * s, w)
last_norm = cur_norm
k = k + 1
if k > maxiter:
print('WIHT did not converge after {0} iterations.'.format(k))
break
return Result(x, k, 'Weighted Iterative Hard Thresholding')
