def probMxl(paramFix, paramRnd, xFix, xFix_transBool, xFix_trans, nFix, xRnd,
xRnd_transBool, xRnd_trans, nRnd, nInd, rowsPerInd, map_obs_to_ind,
map_avail_to_obs):
vFix = 0
vRnd = 0
if nFix > 0:
if xFix_transBool: paramFix = transFix(paramFix, xFix_trans)
vFix = xFix @ paramFix
vFix = xFix[:,0] * paramFix[0] \
+ np.exp(paramFix[1]) * (xFix[:,1] + xFix[:,2:] @ paramFix[2:])
if nRnd > 0:
if xRnd_transBool: paramRnd = transRnd(paramRnd, xRnd_trans)
paramRndPerRow = np.repeat(paramRnd, rowsPerInd, axis=0)
#vRnd = paramRndPerRow[:,0] * (xRnd[:,0] + np.sum(xRnd[:,1:] * paramRndPerRow[:,1:], axis = 1))
vRnd = xRnd[:,0] * paramRndPerRow[:,0] \
+ paramRndPerRow[:,1] * (xRnd[:,1] + np.sum(xRnd[:,2:] * paramRndPerRow[:,2:], axis = 1))
v = vFix + vRnd
ev = np.exp(v)
ev[ev > 1e+200] = 1e+200
ev[ev < 1e-300] = 1e-300
nev = map_avail_to_obs.T @ ev + 1
pChosen = 1 / nev
lPChosen = np.log(pChosen)
lPInd = map_obs_to_ind.T @ lPChosen
return lPInd
def mcmcChainPredW(
chainID, seed,
mcmc_iterSampleThin, mcmc_disp,
modelName,
xFix, xFix_transBool, xFix_trans, nFix,
xRnd, xRnd_transBool, xRnd_trans, nRnd,
xRnd2, xRnd2_transBool, xRnd2_trans, nRnd2,
nInd, nObs, nRow,
rowsPerInd, map_avail_to_obs, chosenIdx, nonChosenIdx):
np.random.seed(seed + chainID)
###
#Retrieve draws
###
fileName = modelName + '_draws_chain' + str(chainID + 1) + '.hdf5'
file = h5py.File(fileName, "r")
paramFix_store = None
paramRnd_store = None
paramRnd2_store = None
if nFix: paramFix_store = np.array(file['paramFix_store'])
if nRnd: paramRnd_store = np.array(file['paramRnd_store'][:,:nInd,:])
if nRnd2: paramRnd2_store = np.array(file['paramRnd2_store'][:,:nInd,:])
###
#Simulate
###
pPred = np.zeros((mcmc_iterSampleThin, nRow + nObs))
vFix = 0; vRnd = 0; vRnd2 = 0;
for i in np.arange(mcmc_iterSampleThin):
if nFix:
paramFix = np.array(paramFix_store[i,:])
if xFix_transBool: paramFix = transFix(paramFix, xFix_trans)
#vFix = xFix @ paramFix
if nRnd:
paramRnd = paramRnd_store[i,:,:]
if xRnd_transBool: paramRnd = transRnd(paramRnd, xRnd_trans)
paramRndPerRow = np.repeat(paramRnd, rowsPerInd, axis = 0)
#vRnd = np.sum(xRnd * paramRndPerRow, axis = 1)
if nRnd2:
paramRnd2 = paramRnd2_store[i,:,:]
if xRnd2_transBool: paramRnd2 = transRnd(paramRnd2, xRnd2_trans)
paramRnd2PerRow = np.repeat(paramRnd2, rowsPerInd, axis = 0)
#vRnd2 = np.sum(xRnd2 * paramRnd2PerRow, axis = 1)
#v = vFix + vRnd + vRnd2
v = xRnd[:,0] * paramRndPerRow[:,0] \
+ paramRndPerRow[:,1] * (xRnd[:,1] + np.sum(xRnd[:,2:] * paramRndPerRow[:,2:], axis = 1))
pPred[i,:] = pPredMxl(v, map_avail_to_obs, 1, chosenIdx, nonChosenIdx)
if ((i + 1) % mcmc_disp) == 0:
print('Chain ' + str(chainID + 1) + '; iteration: ' + str(i + 1) + ' (predictive simulation)')
sys.stdout.flush()
return pPred
def probMxl(paramFix, paramRnd, paramRnd2, xFix, xFix_transBool, xFix_trans,
nFix, xRnd, xRnd_transBool, xRnd_trans, nRnd, xRnd2,
xRnd2_transBool, xRnd2_trans, nRnd2, nInd, rowsPerInd,
map_obs_to_ind, map_avail_to_obs):
vFix = 0
vRnd = 0
vRnd2 = 0
if nFix > 0:
if xFix_transBool: paramFix = transFix(paramFix, xFix_trans)
vFix = xFix @ paramFix
if nRnd > 0:
if xRnd_transBool: paramRnd = transRnd(paramRnd, xRnd_trans)
paramRndPerRow = np.repeat(paramRnd, rowsPerInd, axis=0)
vRnd = np.sum(xRnd * paramRndPerRow, axis=1)
if nRnd2 > 0:
if xRnd2_transBool: paramRnd2 = transRnd(paramRnd2, xRnd_trans)
paramRnd2PerRow = np.repeat(paramRnd2, rowsPerInd, axis=0)
vRnd2 = np.sum(xRnd2 * paramRnd2PerRow, axis=1)
v = vFix + vRnd + vRnd2
"""
if xRnd_transBool: paramRnd = transRnd(paramRnd, xRnd_trans)
paramRndPerRow = np.repeat(paramRnd, rowsPerInd, axis = 0)
if xRnd2_transBool: paramRnd2 = transRnd(paramRnd2, xRnd2_trans)
paramRnd2PerRow = np.repeat(paramRnd2, rowsPerInd, axis = 0)
v = xRnd[:,0] * paramRndPerRow[:,0] \
-paramRndPerRow[:,1] * (xRnd[:,1] + np.sum(xRnd2[:,2:] * paramRnd2PerRow[:,2:], axis = 1))
"""
ev = np.exp(v)
ev[ev > 1e+200] = 1e+200
ev[ev < 1e-300] = 1e-300
nev = map_avail_to_obs.T @ ev + 1
pChosen = 1 / nev
lPChosen = np.log(pChosen)
lPInd = map_obs_to_ind.T @ lPChosen
return lPInd
def mcmcChainPredB(chainID, seed, mcmc_iterSampleThin, mcmc_disp, nTakes, nSim,
modelName, xFix, xFix_transBool, xFix_trans, nFix, sim_xRnd,
xRnd_transBool, xRnd_trans, nRnd, sim_xRnd2,
xRnd2_transBool, xRnd2_trans, nRnd2, nInd, nObs, nRow,
sim_rowsPerInd, sim_map_avail_to_obs, chosenIdx,
nonChosenIdx):
np.random.seed(seed + chainID)
###
#Retrieve draws
###
fileName = modelName + '_draws_chain' + str(chainID + 1) + '.hdf5'
file = h5py.File(fileName, "r")
paramFix_store = None
zeta_store = None
Omega_store = None
zeta2_store = None
Omega2_store = None
pi_store = None
nComp = None
if nFix:
paramFix_store = np.array(file['paramFix_store'])
if nRnd:
zeta_store = np.array(file['zeta_store'])
Omega_store = np.array(file['Omega_store'])
if nRnd2:
zeta2_store = np.array(file['zeta2_store'])
Omega2_store = np.array(file['Omega2_store'])
pi_store = np.array(file['pi_store'])
nComp = pi_store.shape[1]
###
#Simulate
###
pPred = np.zeros((mcmc_iterSampleThin, nRow + nObs))
vFix = 0
vRnd = 0
vRnd2 = 0
for i in np.arange(mcmc_iterSampleThin):
if nFix:
paramFix = paramFix_store[i, :]
if xFix_transBool: paramFix = transFix(paramFix, xFix_trans)
vFix = np.tile(xFix @ paramFix, (nSim, ))
if nRnd:
zeta_tmp = zeta_store[i, :]
ch_tmp = np.linalg.cholesky(Omega_store[i, :, :])
if nRnd2:
zeta2_tmp = zeta2_store[i, :, :]
Omega2_tmp = Omega2_store[i, :, :, :]
pi_tmp = pi_store[i, :]
pPred_take = 0
for t in np.arange(nTakes):
if nRnd:
paramRnd = zeta_tmp + (
ch_tmp @ np.random.randn(nRnd, nInd * nSim)).T
if xRnd_transBool: paramRnd = transRnd(paramRnd, xRnd_trans)
paramRndPerRow = np.repeat(paramRnd, sim_rowsPerInd, axis=0)
#vRnd = np.sum(sim_xRnd * paramRndPerRow, axis = 1)
if nRnd2:
q = np.random.choice(np.arange(nComp),
size=nInd * nSim,
replace=True,
p=pi_tmp)
paramRnd2 = monrnd(zeta2_tmp, Omega2_tmp, q)
if xRnd2_transBool:
paramRnd2 = transRnd(paramRnd2, xRnd2_trans)
paramRnd2PerRow = np.repeat(paramRnd2, sim_rowsPerInd, axis=0)
#vRnd2 = np.sum(sim_xRnd2 * paramRnd2PerRow, axis = 1)
#v = vFix + vRnd + vRnd2
v = sim_xRnd[:,0] * paramRndPerRow[:,0] \
+ paramRndPerRow[:,1] * (sim_xRnd[:,1] + np.sum(sim_xRnd2 * paramRnd2PerRow, axis = 1))
pPred_take += pPredMxl(v, sim_map_avail_to_obs, nSim, chosenIdx,
nonChosenIdx)
pPred[i, :] = pPred_take / nTakes
if ((i + 1) % mcmc_disp) == 0:
print('Chain ' + str(chainID + 1) + '; iteration: ' + str(i + 1) +
' (predictive simulation)')
sys.stdout.flush()
return pPred
def probGrMxl(param, sim_xFix, xFix_transBool, xFix_trans, nFix, sim_xRndUc,
xRndUc_transBool, xRndUc_trans, nRndUc, sim_xRndCo,
xRndCo_transBool, xRndCo_trans, nRndCo, chIdx, drawsUcTake,
drawsCoTake, nDrawsMem, nInd, sim_rowsPerInd, sim_map_obs_to_ind,
sim_map_avail_to_obs, sim_map_ind_to_avail,
sim_map_draws_to_ind):
###
#Utility
###
vFix = 0
vRndUc = 0
vRndCo = 0
u = 0
if nFix > 0:
l = u
u += nFix
paramFix = np.array(param[l:u])
if xFix_transBool: paramFix = np.array(transFix(paramFix, xFix_trans))
vFix = sim_xFix @ paramFix
if nRndUc > 0:
l = u
u += nRndUc
paramRndUc_mu = np.array(param[l:u])
l = u
u += nRndUc
paramRndUc_sd = np.array(param[l:u])
paramRndUc = paramRndUc_mu + paramRndUc_sd * drawsUcTake
if xRndUc_transBool:
paramRndUc = np.array(transRnd(paramRndUc, xRndUc_trans))
paramRndUcPerRow = np.repeat(paramRndUc, sim_rowsPerInd, axis=0)
vRndUc = np.sum(sim_xRndUc * paramRndUcPerRow, axis=1)
if nRndCo > 0:
l = u
u += nRndCo
paramRndCo_mu = np.array(param[l:u])
l = u
paramRndCo_ch = np.zeros((nRndCo, nRndCo))
paramRndCo_ch[chIdx] = np.array(param[l:])
paramRndCo = paramRndCo_mu + (paramRndCo_ch @ drawsCoTake.T).T
if xRndCo_transBool:
paramRndCo = np.array(transRnd(paramRndCo, xRndCo_trans))
paramRndCoPerRow = np.repeat(paramRndCo, sim_rowsPerInd, axis=0)
vRndCo = np.sum(sim_xRndCo * paramRndCoPerRow, axis=1)
v = vFix + vRndUc + vRndCo
###
#Probability
###
ev = np.exp(v)
ev[ev > 1e+200] = 1e+200
ev[ev < 1e-200] = 1e-200
nev = sim_map_avail_to_obs.T @ ev + 1
nnev = sim_map_avail_to_obs * nev
pChosen = 1 / nev
pChosen[pChosen < 1e-200] = 1e-200
pNonChosen = ev / nnev
pNonChosen[pNonChosen < 1e-200] = 1e-200
lPChosen = np.log(pChosen)
lPInd = sim_map_obs_to_ind.T @ lPChosen
pIndVec = np.exp(lPInd)
pInd = pIndVec.reshape((nDrawsMem, nInd)).sum(axis=0)
###
#Gradient
###
def calcGradient(der):
frac = -pNonChosen.reshape((-1, 1)) * der
summation = sim_map_ind_to_avail @ frac
prod = pIndVec.reshape((-1, 1)) * summation
sgr = sim_map_draws_to_ind @ prod
return sgr
sgrFix = np.empty((nInd, 0))
sgrRndUc_mu = np.empty((nInd, 0))
sgrRndUc_sd = np.empty((nInd, 0))
sgrRndCo_mu = np.empty((nInd, 0))
sgrRndCo_ch = np.empty((nInd, 0))
if nFix > 0:
derFix = sim_xFix
if xFix_transBool:
derFix = np.array(transDerFix(derFix, paramFix, xFix_trans))
sgrFix = calcGradient(derFix)
if nRndUc > 0:
derRndUc_mu, derRndUc_sd = derRnd(nInd, nDrawsMem, nRndUc,
xRndUc_transBool, paramRndUc,
xRndUc_trans, chIdx, drawsUcTake,
sim_rowsPerInd, True)
derRndUc_mu *= sim_xRndUc
derRndUc_sd *= sim_xRndUc
sgrRndUc_mu = calcGradient(derRndUc_mu)
sgrRndUc_sd = calcGradient(derRndUc_sd)
if nRndCo > 0:
derRndCo_mu, derRndCo_ch = derRnd(nInd, nDrawsMem, nRndCo,
xRndCo_transBool, paramRndCo,
xRndCo_trans, chIdx, drawsCoTake,
sim_rowsPerInd, False)
derRndCo_mu *= sim_xRndCo
derRndCo_ch *= sim_xRndCo[:, chIdx[0]]
sgrRndCo_mu = calcGradient(derRndCo_mu)
sgrRndCo_ch = calcGradient(derRndCo_ch)
sgr = np.concatenate(
(sgrFix, sgrRndUc_mu, sgrRndUc_sd, sgrRndCo_mu, sgrRndCo_ch), axis=1)
return pInd, sgr
def probGrMxl(param, sim_xFix, xFix_transBool, xFix_trans, nFix, sim_xRndUc,
xRndUc_transBool, xRndUc_trans, nRndUc, sim_xRndCo,
xRndCo_transBool, xRndCo_trans, nRndCo, chIdx, sim_xRnd2Uc,
xRnd2Uc_transBool, xRnd2Uc_trans, nRnd2Uc, sim_xRnd2Co,
xRnd2Co_transBool, xRnd2Co_trans, nRnd2Co, ch2Idx, drawsUcTake,
drawsCoTake, nDrawsMem, drawsUcBTake, drawsCoBTake, nDrawsBMem,
drawsUcWTake, drawsCoWTake, nDrawsWMem, nInd, nObs,
simB_rowsPerInd, simB_obsPerInd, simW_rowsPerObs,
simW_map_avail_to_obs, simW_map_drawsB_to_obs):
###
#Utility
###
vFix = 0
vRndUc = 0
vRndCo = 0
vRnd2Uc = 0
vRnd2Co = 0
u = 0
if nFix > 0:
l = u
u += nFix
paramFix = np.array(param[l:u])
if xFix_transBool: paramFix = np.array(transFix(paramFix, xFix_trans))
vFix = sim_xFix @ paramFix #Good
if nRndUc > 0:
l = u
u += nRndUc
paramRndUc_mu = np.array(param[l:u])
l = u
u += nRndUc
paramRndUc_sd = np.array(param[l:u])
paramRndUc = paramRndUc_mu + paramRndUc_sd * drawsUcTake
if xRndUc_transBool:
paramRndUc = np.array(transRnd(paramRndUc, xRndUc_trans))
paramRndUcPerRow = np.tile(
np.repeat(paramRndUc, simB_rowsPerInd, axis=0),
(nDrawsWMem, 1)) #Good
vRndUc = np.sum(sim_xRndUc * paramRndUcPerRow, axis=1)
if nRndCo > 0:
l = u
u += nRndCo
paramRndCo_mu = np.array(param[l:u])
l = u
u += int((nRndCo * (nRndCo + 1)) / 2)
paramRndCo_ch = np.zeros((nRndCo, nRndCo))
paramRndCo_ch[chIdx] = np.array(param[l:u])
paramRndCo = paramRndCo_mu + (paramRndCo_ch @ drawsCoTake.T).T
if xRndCo_transBool:
paramRndCo = np.array(transRnd(paramRndCo, xRndCo_trans))
paramRndCoPerRow = np.tile(
np.repeat(paramRndCo, simB_rowsPerInd, axis=0),
(nDrawsWMem, 1)) #Good
vRndCo = np.sum(sim_xRndCo * paramRndCoPerRow, axis=1)
if nRnd2Uc > 0:
l = u
u += nRnd2Uc
paramRnd2Uc_mu = np.array(param[l:u])
l = u
u += nRnd2Uc
paramRnd2Uc_sdB = np.array(param[l:u])
l = u
u += nRnd2Uc
paramRnd2Uc_sdW = np.array(param[l:u])
paramRnd2Uc_ind = paramRnd2Uc_mu + paramRnd2Uc_sdB * drawsUcBTake #Good
paramRnd2Uc_obs = paramRnd2Uc_sdW * drawsUcWTake #Good
paramRnd2Uc = np.tile(
np.repeat(paramRnd2Uc_ind, simB_obsPerInd, axis=0),
(nDrawsWMem, 1)) + paramRnd2Uc_obs #Good
if xRnd2Uc_transBool:
paramRnd2Uc = np.array(transRnd(paramRnd2Uc, xRnd2Uc_trans))
paramRnd2UcPerRow = np.repeat(paramRnd2Uc, simW_rowsPerObs,
axis=0) #Good
vRnd2Uc = np.sum(sim_xRnd2Uc * paramRnd2UcPerRow, axis=1)
if nRnd2Co > 0:
l = u
u += nRnd2Co
paramRnd2Co_mu = np.array(param[l:u])
l = u
u += int((nRnd2Co * (nRnd2Co + 1)) / 2)
paramRnd2Co_chB = np.zeros((nRnd2Co, nRnd2Co))
paramRnd2Co_chB[ch2Idx] = np.array(param[l:u])
l = u
u += int((nRnd2Co * (nRnd2Co + 1)) / 2)
paramRnd2Co_chW = np.zeros((nRnd2Co, nRnd2Co))
paramRnd2Co_chW[ch2Idx] = np.array(param[l:u])
paramRnd2Co_ind = paramRnd2Co_mu + (
paramRnd2Co_chB @ drawsCoBTake.T).T #Good
paramRnd2Co_obs = (paramRnd2Co_chW @ drawsCoWTake.T).T #Good
paramRnd2Co = np.tile(
np.repeat(paramRnd2Co_ind, simB_obsPerInd, axis=0),
(nDrawsWMem, 1)) + paramRnd2Co_obs #Good
if xRnd2Co_transBool:
paramRnd2Co = np.array(transRnd(paramRnd2Co, xRnd2Co_trans))
paramRnd2CoPerRow = np.repeat(paramRnd2Co, simW_rowsPerObs,
axis=0) #Good
vRnd2Co = np.sum(sim_xRnd2Co * paramRnd2CoPerRow, axis=1)
v = vFix + vRndUc + vRndCo + vRnd2Uc + vRnd2Co
###
#Probability
###
ev = np.exp(v)
ev[ev > 1e+200] = 1e+200
ev[ev < 1e-200] = 1e-200
nev = simW_map_avail_to_obs.T @ ev + 1
nnev = simW_map_avail_to_obs @ nev
sim_pChosen = 1 / nev
sim_pChosen[sim_pChosen < 1e-200] = 1e-200
sim_pNonChosen = ev / nnev
sim_pNonChosen[sim_pNonChosen < 1e-200] = 1e-200
pChosen = sim_pChosen.reshape(
(nDrawsWMem, nDrawsBMem, nObs)).sum(axis=0).reshape((-1, 1))
###
#Gradient
###
def calcGradient(der):
t1 = -sim_pNonChosen.reshape((-1, 1)) * der
t2 = simW_map_avail_to_obs.T @ t1
numer = sim_pChosen.reshape((-1, 1)) * t2
sgr = simW_map_drawsB_to_obs @ numer
return sgr
sgrFix = np.empty((nObs * nDrawsBMem, 0))
sgrRndUc_mu = np.empty((nObs * nDrawsBMem, 0))
sgrRndUc_sd = np.empty((nObs * nDrawsBMem, 0))
sgrRndCo_mu = np.empty((nObs * nDrawsBMem, 0))
sgrRndCo_ch = np.empty((nObs * nDrawsBMem, 0))
sgrRnd2Uc_mu = np.empty((nObs * nDrawsBMem, 0))
sgrRnd2Uc_sdB = np.empty((nObs * nDrawsBMem, 0))
sgrRnd2Uc_sdW = np.empty((nObs * nDrawsBMem, 0))
sgrRnd2Co_mu = np.empty((nObs * nDrawsBMem, 0))
sgrRnd2Co_chB = np.empty((nObs * nDrawsBMem, 0))
sgrRnd2Co_chW = np.empty((nObs * nDrawsBMem, 0))
if nFix > 0:
derFix = sim_xFix
if xFix_transBool:
derFix = np.array(transDerFix(derFix, paramFix, xFix_trans))
sgrFix = calcGradient(derFix)
if nRndUc > 0:
derRndUc_mu, derRndUc_sd = derRnd(nInd, nDrawsBMem, nRndUc,
xRndUc_transBool, paramRndUc,
xRndUc_trans, chIdx, drawsUcTake,
simB_rowsPerInd, True)
derRndUc_mu = np.tile(derRndUc_mu, (nDrawsWMem, 1))
derRndUc_sd = np.tile(derRndUc_sd, (nDrawsWMem, 1))
derRndUc_mu *= sim_xRndUc
derRndUc_sd *= sim_xRndUc
sgrRndUc_mu = calcGradient(derRndUc_mu)
sgrRndUc_sd = calcGradient(derRndUc_sd)
if nRndCo > 0:
derRndCo_mu, derRndCo_ch = derRnd(nInd, nDrawsBMem, nRndCo,
xRndCo_transBool, paramRndCo,
xRndCo_trans, chIdx, drawsCoTake,
simB_rowsPerInd, False)
derRndCo_mu = np.tile(derRndCo_mu, (nDrawsWMem, 1))
derRndCo_ch = np.tile(derRndCo_ch, (nDrawsWMem, 1))
derRndCo_mu *= sim_xRndCo
derRndCo_ch *= sim_xRndCo[:, chIdx[0]]
sgrRndCo_mu = calcGradient(derRndCo_mu)
sgrRndCo_ch = calcGradient(derRndCo_ch)
if nRnd2Uc > 0:
derRnd2Uc_mu, derRnd2Uc_sdB = derRnd(nInd, nDrawsBMem, nRnd2Uc,
xRnd2Uc_transBool,
paramRnd2Uc_ind, xRnd2Uc_trans,
ch2Idx, drawsUcBTake,
simB_rowsPerInd, True)
derRnd2Uc_mu = np.tile(derRnd2Uc_mu, (nDrawsWMem, 1))
derRnd2Uc_sdB = np.tile(derRnd2Uc_sdB, (nDrawsWMem, 1))
derRnd2Uc_sdW = derRnd_ch(nObs, nDrawsMem, nRnd2Uc, xRnd2Uc_transBool,
paramRnd2Uc_obs, xRnd2Uc_trans, ch2Idx,
drawsUcBTake, simW_rowsPerObs, True)
derRnd2Uc_mu *= sim_xRnd2Uc
derRnd2Uc_sdB *= sim_xRnd2Uc
derRnd2Uc_sdW *= sim_xRnd2Uc
sgrRnd2Uc_mu = calcGradient(derRnd2Uc_mu)
sgrRnd2Uc_sdB = calcGradient(derRnd2Uc_sdB)
sgrRnd2Uc_sdW = calcGradient(derRnd2Uc_sdW)
if nRnd2Co > 0:
derRnd2Co_mu, derRnd2Co_chB = derRnd(nInd, nDrawsBMem, nRnd2Co,
xRnd2Co_transBool,
paramRnd2Co_ind, xRnd2Co_trans,
ch2Idx, drawsCoBTake,
simB_rowsPerInd, False)
derRnd2Co_mu = np.tile(derRnd2Co_mu, (nDrawsWMem, 1))
derRnd2Co_chB = np.tile(derRnd2Co_chB, (nDrawsWMem, 1))
derRnd2Co_chW = derRnd_ch(nObs, nDrawsMem, nRnd2Co, xRnd2Co_transBool,
paramRnd2Co_obs, xRnd2Co_trans, ch2Idx,
drawsCoWTake, simW_rowsPerObs, False)
derRnd2Co_mu *= sim_xRnd2Co
derRnd2Co_chB *= sim_xRnd2Co[:, ch2Idx[0]]
derRnd2Co_chW *= sim_xRnd2Co[:, ch2Idx[0]]
sgrRnd2Co_mu = calcGradient(derRnd2Co_mu)
sgrRnd2Co_chB = calcGradient(derRnd2Co_chB)
sgrRnd2Co_chW = calcGradient(derRnd2Co_chW)
sgr = np.concatenate(
(sgrFix, sgrRndUc_mu, sgrRndUc_sd, sgrRndCo_mu, sgrRndCo_ch,
sgrRnd2Uc_mu, sgrRnd2Uc_sdB, sgrRnd2Uc_sdW, sgrRnd2Co_mu,
sgrRnd2Co_chB, sgrRnd2Co_chW),
axis=1)
return pChosen, sgr
