def sparc_codebook(L, M, n,P):
Ax, Ay, _ = block_sub_fht(n, M, L, seed=None, ordering=None) # seed must be explicit
def Ab(b):
return Ax(b).reshape(-1, 1) / np.sqrt(n)
def Az(z):
return Ay(z).reshape(-1, 1) / np.sqrt(n)
return Ab, Az
def SparcCodebook(self, L, ml, N):
"""
Generate SPARC Codebook for CS encoding
:param L: number of sections
:param ml: length of each section
:param N: number of channel uses (real DOF)
"""
# generate Hadamard matrices
self.__numBlockRows = N
self.__Ax, self.__Ay, _ = block_sub_fht(N,
ml,
L,
ordering=None,
seed=None)
def sparc_codebook(L, M, n):
"""
Create sensing matrix via randomly sampling the rows of Hadamard matrices
:param L: number of sections
:param M: length of each section
:param n: number of channel uses (real d.o.f.)
"""
Ax, Ay, _ = block_sub_fht(n, M, L, seed=None,
ordering=None) # seed must be explicit
def Ab(b):
return Ax(b).reshape(-1, 1) / np.sqrt(n)
def Az(z):
return Ay(z).reshape(-1, 1) / np.sqrt(n)
return Ab, Az
def sparcCodebook(self, n):
"""
Create sensing matrix via randomly sampling the rows of Hadamard matrices
:param n: number of rows in matrix
"""
# Initialize important parameters
M = self.__M
L = self.__L if self.__SensingMatrixType == 'Dense' else 1
self.__numBlockRows = n
# Create SPARC-like codebook
Ax, Ay, _ = block_sub_fht(n, M, L, seed=None,
ordering=None) # seed must be explicit
def Ab(b):
return Ax(b).reshape(-1, 1) / np.sqrt(n)
def Az(z):
return Ay(z).reshape(-1, 1) / np.sqrt(n)
return Ab, Az