def extend_callgrad(x, Q, P, M, L, r, mu, sigma, sm, maxdegree, vecsize, experiment, opt_var, small_k):
#opt_var elemnts
# 1. pwm
# 2. sm
# 3. sigma
# 0: no optimization variable
# 1: optimization variable
#example: opt_var =[1,0,1] === pwm,sigma
if (opt_var[0] == 1):
r = utils.converter(x[:vecsize[0]], P, M)
if (opt_var[1] == 1):
sm = utils.listconverter(x[vecsize[0] :vecsize[1]], P, M)
if (opt_var[2] == 1):
sigma = utils.listconverter(x[vecsize[1]:vecsize[2]], P, M)
if (opt_var[3] == 1):
mu = utils.listconverter(x[vecsize[2]:], P, M)
gradmu, gradsig, gradpwm, gradsm = extend_gradient(Q, P, M, L, r, mu, sigma, sm, maxdegree, opt_var, small_k)
gradient = []
if (opt_var[0] == 1):
gradpwm, vecsize1 = utils.matr2vec(gradpwm, P, M)
gradient = np.concatenate((gradient, gradpwm))
if (opt_var[1] == 1):
gradsm, vecsize2 = utils.list2vec(gradsm, P, M)
gradient = np.concatenate((gradient, gradsm))
if (opt_var[2] == 1):
gradsig, vecsize3 = utils.list2vec(gradsig, P, M)
gradient = np.concatenate((gradient, gradsig))
if (opt_var[3] == 1):
gradmu, vecsize4 = utils.list2vec(gradmu, P, M)
gradient = np.concatenate((gradient, gradmu))
return gradient