def set_Q_noise_solve(rule, vk, y, virusobjs, p_theta_eta, Na):
t_Q=tm.time()
WEIGHT_INDEX = 5 # the index of the abscissa weights in the rule data structure
vo = virusobjs # use a shorter name inside the function
""" initialize variables """
Ny = y.shape[0]
Nv = y.shape[1]
Nzi = rule.shape[0]
Nc = vo[0].cbar.shape[0]
""" compute the integral $$Q$$ Eq. (178) its gradient and Hessian """
Qem = 0
A=0
B=0
# compute the expectation with numerical integrations
eta=0
nu=vo[eta].nu
# unneeded since the lndet_fast function is commented out detV=prod(nu);
# read tilde b
b_fn = 'callti.out.read_by_C' # file contains \tilde{b} values computed from mathematica
# lmax = np.max(vo[eta].clnp.l)
lmax = find_lmax(vo[eta].clnp.l)
print lmax
tilde_b = rd_b(b_fn, lmax)
for n in xrange(0,Nzi):
Rabc = euler2R(rule[n, 0:3])
L=setL_nord(Rabc, vo[eta].clnp.l, vo[eta].clnp.n, vk, vo[eta].Htable, vo[eta].map_unique2lp,tilde_b)
""" Use fast lndet algorithm """
Lc = np.dot(L,vo[eta].cbar) # \mu = L*c;
y_Lc = y - Lc
wri = np.dot(rule[n,WEIGHT_INDEX],p_theta_eta[eta][0][:,n])
A=A+np.sum(wri)
B=B+np.dot(np.sum(y_Lc**2,axis=0),wri)
A=A*Na*Na
noise_var=B/A
sys.stdout.write('set_Q_noise_solve time: %d\n'%(tm.time()-t_Q))
return noise_var
# copied from funcs for debugging and modification
# omitted here for simplicity
# returns a matrix L
pass
def setHQem(nu): # rule 2D matrix; vk 2D
data = si.io.loadmat('fmin.mat', squeeze_me=True, struct_as_record=False)
t_Q = tm.time() #clock:matlab function, return the date and time
WEIGHT_INDEX = 5
vo = data['vobj']
Ny = data['y'].shape[0]
Nv = data['y'].shape[1]
rule = data['EM_iter'].rule
vk = data['vk']
noise_var = data['pixelnoisevar']
p_theta_eta = data['p_theta_eta']
y = data['y']
Nzi = data['EM_iter'].rule.shape[0]
tilde_b = data['tilde_b']
eta = data['eta']
# print (type(vo[0].flags[0]))
Nc = vo[0].cbar.shape[0]
#vo, a cell array, each element is a virus object
#cbar, a vector of double precision, attribute of the object
#matlab mathwork
HQem = np.zeros((Nc, Nc), float) #numpy array
#eta = 0
nu = vo[eta].nu
if len(nu.shape)<2:
d1 = nu.shape[0]
nu = si.reshape(nu, [d1, 1])
lndetV = np.sum(np.log(nu))
#b_fn = 'callti.out.read_by_C'
#lmax = vo[eta].clnp.l.max(0) #il in matlab is l in python, ndarray
#tilde_b = fun.rd_b(b_fn, lmax)
for n in range(Nzi): #n an index for a matrix
Rabc = fun.euler2R(rule[n, 0:3]) #rule is a matrix
L = fun.setL_nord(Rabc, vo[eta].clnp.l, vo[eta].clnp.n, vk, vo[eta].Htable, vo[eta].map_unique2lp,
tilde_b) #L is a ndarray
#clnp.in happens to be a keyword in python, how to fix this.
lndet_Sigma = fun.lndet_fast(noise_var, L, nu, lndetV) #ndarray
Lc = np.dot(L, vo[eta].cbar) #cbar is a list. Transferred to a ndarray before multiplication. Output is a ndarray.
y_Lc = (y.transpose()-Lc).transpose() #np.substract(y, Lc) Good! Translate bxsfun() in Python
wri = np.dot(rule[n][WEIGHT_INDEX], p_theta_eta[eta][:, n]) #.*, element-wise; *, dot product in matlab ???No matrix, all ndarrays???
sum_wri = np.sum(wri)
D, M = fun.LT_SigmaInv_fast(noise_var, L, vo[eta].nu, y_Lc) #D, M is ndarray according to translation of Yu
repwri = np.tile(np.sqrt(wri.T), (Nc, 1))
D = D * repwri
D = np.dot(D, D.T)
y_Lc_Sigma = fun.y_Lc_Sigma_fast(noise_var, L, nu, y_Lc) #ndarray
HQem = HQem + (-2 * D + M * sum_wri) * M
HQem = -HQem
# sys.stdout.write('set_Q_dV_unc time: %d\n' % (tm.time() - t_Q))
return HQem
def setQem(nu ): # rule 2D matrix; vk 2D
data = si.io.loadmat('fmin.mat', squeeze_me=True, struct_as_record=False)
if any( nuele <= 0 for nuele in nu ):
print("element in nu <=0")
t_Q = tm.time() #clock:matlab function, return the date and time
WEIGHT_INDEX = 5
vo = data['vobj']
rule = data['EM_iter'].rule
vk = data['vk']
noise_var = data['pixelnoisevar']
p_theta_eta = data['p_theta_eta']
y = data['y']
Nzi = data['EM_iter'].rule.shape[0]
tilde_b = data['tilde_b']
eta = data['eta']
# vo = vobj
# rule = rule
# vk = vk
# noise_var = pixelnoisevar
# p_theta_eta = p_theta_eta
# y = y
# Nzi = shape
# print (type(vo[0].flags[0]))
Nc = vo[0].cbar.shape[0]
#vo, a cell array, each element is a virus object
#cbar, a vector of double precision, attribute of the object
#matlab mathwork
Qem = 0
gQem = np.zeros((Nc,), float) #numpy array
#eta = 0
# nu = vo[eta].nu
#easy for testing with matlab data, I think we should
# delete this code after testing
if len(nu.shape)<2:
d1 = nu.shape[0]
nu = si.reshape(nu, [d1, 1])
lndetV = np.sum(np.log(nu))
#b_fn = 'callti.out.read_by_C'
#lmax = vo[eta].clnp.l.max(0) #il in matlab is l in python, ndarray
#############################
#tilde_b = fun.rd_b(b_fn, lmax)
for n in range(Nzi): #n an index for a matrix
################################
Rabc = fun.euler2R(rule[n, 0:3]) #rule is a matrix
L = fun.setL_nord(Rabc, vo[eta].clnp.l, vo[eta].clnp.n, vk, vo[eta].Htable, vo[eta].map_unique2lp,
tilde_b) #L is a ndarray
#clnp.in happens to be a keyword in python, how to fix this.
lndet_Sigma = fun.lndet_fast(noise_var, L, nu, lndetV) #ndarray
Lc = np.dot(L, vo[eta].cbar) #cbar is a list. Transferred to a ndarray before multiplication. Output is a ndarray.
y_Lc = (y.transpose()-Lc).transpose()
#change [eta][0][:,n]->[eta][:,n]
wri = np.dot(rule[n][WEIGHT_INDEX], p_theta_eta[eta][:, n]) #.*, element-wise; *, dot product in matlab ???No matrix, all ndarrays???
sum_wri = np.sum(wri)
D, M = fun.LT_SigmaInv_fast(noise_var, L, vo[eta].nu, y_Lc) #D, M is ndarray according to translation of Yu
repwri = np.tile(np.sqrt(wri.T), (Nc, 1))
D = D * repwri
D = np.dot(D, D.T)
y_Lc_Sigma = fun.y_Lc_Sigma_fast(noise_var, L, nu, y_Lc) #ndarray
Qem = Qem + (-0.5) * (np.dot(lndet_Sigma + y_Lc_Sigma, wri))
gQem = gQem + np.diag(D - np.dot(sum_wri, M))
Qem = -Qem
gQem = -gQem
temp = np.around(-Qem, decimals=16)
sys.stdout.write('Q = %.16f\n'% temp)
sys.stdout.write('set_Q_dV_unc time: %d\n' % (tm.time() - t_Q))
return Qem, gQem