def NormL1_project(x,weights,tau):
if all(np.isreal(x)):
return oneProjector(x,weights,tau)
else:
xa = abs(x)
idx = xa < np.spacing(1)
xc = oneProjector(xa,weights,tau)
xc = xc / xa
xc[idx] = 0
return x * xc
def NormL1_project(x, weights, tau):
if all(np.isreal(x)):
return oneProjector(x, weights, tau)
else:
xa = abs(x)
idx = xa < np.spacing(1)
xc = oneProjector(xa, weights, tau)
xc = xc / xa
xc[idx] = 0
return x * xc
def NormGroupL2_project(groups,x,weights,tau):
if all(np.isreal(x)):
xa = np.sqrt(np.sum(groups * x**2.,axis=1))
else:
xa = np.sqrt(np.sum(groups * abs(x)**2.,axis=1))
idx = xa < spacing(1)
xc = oneProjector(xa,weights,tau)
xc = xc / xa
xc[idx] = 0
return dot(np.conj(groups.T),xc)*x
def NormGroupL2_project(groups, x, weights, tau):
if all(np.isreal(x)):
xa = np.sqrt(np.sum(groups * x**2., axis=1))
else:
xa = np.sqrt(np.sum(groups * abs(x)**2., axis=1))
idx = xa < spacing(1)
xc = oneProjector(xa, weights, tau)
xc = xc / xa
xc[idx] = 0
return dot(np.conj(groups.T), xc) * x
def NormL12_project(g,x,weights,tau):
m = round(np.size(x) / g)
n = g
x = reshape(x,m,n)
if all(np.isreal(x)):
xa = np.sqrt(np.sum(x**2,axis=1))
else:
xa = np.sqrt(np.sum(abs(x)**2,axis=1))
idx = xa < spacing(1)
xc = oneProjector(xa,weights,tau)
xc = xc / xa
xc[idx] = 0
x = np.sparse.spdiags(xc,0,m,m)*x
return x.flatten()
def NormL12_project(g, x, weights, tau):
m = round(np.size(x) / g)
n = g
x = reshape(x, m, n)
if all(np.isreal(x)):
xa = np.sqrt(np.sum(x**2, axis=1))
else:
xa = np.sqrt(np.sum(abs(x)**2, axis=1))
idx = xa < spacing(1)
xc = oneProjector(xa, weights, tau)
xc = xc / xa
xc[idx] = 0
x = np.sparse.spdiags(xc, 0, m, m) * x
return x.flatten()