def L1_min(Phi, y, K):
# inequalities matrix with
M, Nf = Phi.shape
G = matrx(
rearrange_G(
scipy.linalg.block_diag(*[
matrix([[-1, -1], [1, -1.0]]),
] * Nf)))
# objective function row-matrix
c = matrx(hstack([ones(Nf), zeros(Nf)]))
# RHS for inequalities
h = matrx([
0.0,
] * (Nf * 2), (Nf * 2, 1), 'd')
# equality constraint matrix
A = matrx(hstack([Phi * 0, Phi]))
# RHS for equality constraints
b = matrx(y)
# suppress standard output
#old_stdout = sys.stdout
#sys.stdout = mystdout = StringIO()
print hstack([Phi * 0, Phi]).shape
sol = solvers.lp(c, G, h, A, b)
# restore standard output
#sys.stdout = old_stdout
sln = array(sol['x']).flatten()[:Nf].round(4)
return sln
def L1_min(Phi,y,K):
# inequalities matrix with
M,Nf = Phi.shape
G=matrx(rearrange_G(scipy.linalg.block_diag(*[matrix([[-1,-1],[1,-1.0]]),]*Nf) ))
# objective function row-matrix
c=matrx(np.hstack([ones(Nf),zeros(Nf)]))
# RHS for inequalities
h = matrx([0.0,]*(Nf*2),(Nf*2,1),'d')
# equality constraint matrix
A = matrix(hstack([Phi*0,Phi]))
# RHS for equality constraints
b=matrix(y)
# suppress standard output
old_stdout = sys.stdout
sys.stdout = mystdout = StringIO()
sol = solvers.lp(c, G, h,A,b)
# restore standard output
sys.stdout = old_stdout
sln = array(sol['x']).flatten()[:Nf].round(4)
return sln
Nf = len(ftime)
N = 2 * Nf
time_samples = np.array(range(0, N / 2, 2))
#print time_samples.shape,ftime.shape,N,Nf
half_indexed_time_samples = (array(time_samples) / 2).astype(int)
Phi = dftmatrix(N / 2).real * Q_rmatrix(N)
Phi_i = Phi[half_indexed_time_samples, :]
# inequalities matrix with
G = matrx(
rearrange_G(
scipy.linalg.block_diag(*[
matrix([[-1, -1], [1, -1.0]]),
] * N)))
# objective function row-matrix
#c=matrx(hstack([100*ones(N),zeros(N)]))
#if i==0:
c = matrx(hstack([ones(N), zeros(N)]))
#else:
#c=matrix(hstack(ff,zeros(N)))
# RHS for inequalities
h = matrx([
0.0,
] * (N * 2), (N * 2, 1), 'd')
# equality constraint matrix
A = matrx(hstack([Phi_i, Phi_i * 0]))
# RHS for equality constraints
Nf=len(ftime) N=2*Nf time_samples=np.array(range(0,N/2,2)) #print time_samples.shape,ftime.shape,N,Nf half_indexed_time_samples = (array(time_samples)/2).astype(int) Phi = dftmatrix(N/2).real*Q_rmatrix(N) Phi_i = Phi[half_indexed_time_samples,:] # inequalities matrix with G=matrx(rearrange_G(scipy.linalg.block_diag(*[matrix([[-1,-1],[1,-1.0]]),]*N) )) # objective function row-matrix #c=matrx(hstack([100*ones(N),zeros(N)])) #if i==0: c=matrx(hstack([ones(N),zeros(N)])) #else: #c=matrix(hstack(ff,zeros(N))) # RHS for inequalities h = matrx([0.0,]*(N*2),(N*2,1),'d') # equality constraint matrix A = matrx(hstack([Phi_i,Phi_i*0])) # RHS for equality constraints b=matrx(ftime[half_indexed_time_samples]) sol = solvers.lp(c, G, h,A,b)
