def saveset(name, noparam=False):
# u, B, singleFreqAR, dt, stNz, x, dN, prbs
xprbsdN = _N.empty((N + 1, 3))
xprbsdN[:, 0] = x[:]
xprbsdN[:, 1] = prbs[:]
xprbsdN[:, 2] = dN[:]
_N.savetxt(resFN("xprbsdN.dat", dir=name, create=True),
xprbsdN,
fmt="%.5e")
if not noparam:
fp = open(resFN("params.py", dir=name, create=True), "w")
fp.write("u=%.3f\n" % u)
fp.write("beta=%s\n" % arrstr(beta))
fp.write("ARcoeff=_N.array(%s)\n" % str(ARcoeff))
fp.write("alfa=_N.array(%s)\n" % str(alfa))
fp.write("# ampAngRep=%s\n" % ampAngRep(alfa))
fp.write("dt=%.2e\n" % dt)
fp.write("stNz=%.3e\n" % stNz)
fp.write("absrefr=%d\n" % absrefr)
fp.close()
def gibbsSamp(
self,
smpls_fn_incl_trls=False): ########################### GIBBSSAMPH
global interrupted
oo = self
signal.signal(signal.SIGINT, signal_handler)
print("****!!!!!!!!!!!!!!!! dohist %s" % str(oo.dohist))
ooTR = oo.TR
ook = oo.k
ooN = oo.N
_kfar.init(oo.N, oo.k, oo.TR)
oo.x00 = _N.array(oo.smpx[:, 2])
oo.V00 = _N.zeros((ooTR, ook, ook))
if oo.dohist:
oo.loghist = _N.zeros(oo.Hbf.shape[0])
else:
print("fixed hist is")
print(oo.loghist)
print("oo.mcmcRunDir %s" % oo.mcmcRunDir)
if oo.mcmcRunDir is None:
oo.mcmcRunDir = ""
elif (len(oo.mcmcRunDir) > 0) and (oo.mcmcRunDir[-1] != "/"):
oo.mcmcRunDir += "/"
ARo = _N.zeros((ooTR, ooN + 1))
kpOws = _N.empty((ooTR, ooN + 1))
lv_f = _N.zeros((ooN + 1, ooN + 1))
lv_u = _N.zeros((ooTR, ooTR))
Bii = _N.zeros((ooN + 1, ooN + 1))
#alpC.reverse()
# F_alfa_rep = alpR + alpC already in right order, no?
Wims = _N.empty((ooTR, ooN + 1, ooN + 1))
Oms = _N.empty((ooTR, ooN + 1))
smWimOm = _N.zeros(ooN + 1)
smWinOn = _N.zeros(ooTR)
bConstPSTH = False
D_f = _N.diag(_N.ones(oo.B.shape[0]) * oo.s2_a) # spline
iD_f = _N.linalg.inv(D_f)
D_u = _N.diag(_N.ones(oo.TR) * oo.s2_u) # This should
iD_u = _N.linalg.inv(D_u)
iD_u_u_u = _N.dot(iD_u, _N.ones(oo.TR) * oo.u_u)
if oo.bpsth:
BDB = _N.dot(oo.B.T, _N.dot(D_f, oo.B))
DB = _N.dot(D_f, oo.B)
BTua = _N.dot(oo.B.T, oo.u_a)
it = -1
oous_rs = oo.us.reshape((ooTR, 1))
#runTO = ooNMC + oo.burn - 1 if (burns is None) else (burns - 1)
runTO = oo.ITERS - 1
oo.allocateSmp(runTO + 1, Bsmpx=oo.doBsmpx)
if cython_arc:
_arcfs.init(ooN + 1 - oo.ignr,
oo.k,
oo.TR,
oo.R,
oo.Cs,
oo.Cn,
aro=_cd.__NF__)
alpR = _N.array(oo.F_alfa_rep[0:oo.R])
alpC = _N.array(oo.F_alfa_rep[oo.R:])
else:
alpR = oo.F_alfa_rep[0:oo.R]
alpC = oo.F_alfa_rep[oo.R:]
BaS = _N.zeros(oo.N + 1) #_N.empty(oo.N+1)
# H shape 100 x 9
Hbf = oo.Hbf
RHS = _N.empty((oo.histknots, 1))
print("----------- histknots %d" % oo.histknots)
cInds = _N.arange(oo.iHistKnotBeginFixed, oo.histknots)
vInds = _N.arange(0, oo.iHistKnotBeginFixed)
#cInds = _N.array([4, 12, 13])
#vInds = _N.array([0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, ])
#vInds = _N.arange(0, oo.iHistKnotBeginFixed)
RHS[cInds, 0] = 0
Msts = []
for m in range(ooTR):
Msts.append(_N.where(oo.y[m] == 1)[0])
HcM = _N.ones((len(vInds), len(vInds)))
HbfExpd = _N.zeros((oo.histknots, ooTR, oo.N + 1))
#HbfExpd = _N.zeros((oo.histknots, ooTR, oo.Hbf.shape[0]))
# HbfExpd is 11 x M x 1200
# find the mean. For the HISTORY TERM
for i in range(oo.histknots):
for m in range(oo.TR):
sts = Msts[m]
HbfExpd[i, m, 0:sts[0]] = 0
for iss in range(len(sts) - 1):
t0 = sts[iss]
t1 = sts[iss + 1]
#HbfExpd[i, m, t0+1:t1+1] = Hbf[1:t1-t0+1, i]#Hbf[0:t1-t0, i]
HbfExpd[i, m, t0 + 1:t1 + 1] = Hbf[0:t1 - t0, i]
HbfExpd[i, m, sts[-1] + 1:] = 0
_N.dot(oo.B.T, oo.aS, out=BaS)
if oo.hS is None:
oo.hS = _N.zeros(oo.histknots)
if oo.dohist:
_N.dot(Hbf, oo.hS, out=oo.loghist)
oo.stitch_Hist(ARo, oo.loghist, Msts)
## ORDER OF SAMPLING
## f_xx, f_V
## DA: PG, kpOws
## history, build ARo
## psth
## offset
## DA: latent state
## AR coefficients
## q2
K = _N.empty((oo.TR, oo.N + 1, oo.k)) # kalman gain
iterBLOCKS = oo.ITERS // oo.peek
smpx_C_cont = _N.empty((oo.TR, oo.N + 1, oo.k)) # need C contiguous
# oo.smpx[:, 1+oo.ignr:, 0:ook], oo.smpx[:, oo.ignr:, 0:ook-1]
smpx_contiguous1 = _N.zeros((oo.TR, oo.N + 2, oo.k))
smpx_contiguous2 = _N.zeros((oo.TR, (oo.N + 1) + 2, oo.k - 1))
if (cython_inv_v == 3) or (cython_inv_v == 5):
oo.if_V = _N.array(oo.f_V)
oo.chol_L_fV = _N.array(oo.f_V)
###### Gibbs sampling procedure
ttts = _N.zeros((oo.ITERS, 9))
for itrB in range(iterBLOCKS):
it = itrB * oo.peek
if it > 0:
# 0.5*oo.fs because (dt*2) -> 1 corresponds to Fs/2
print("---------it: %(it)d mnStd %(mnstd).3f" % {
"it": itrB * oo.peek,
"mnstd": oo.mnStds[it - 1]
})
if not oo.noAR:
print(prt)
mnttt = _N.mean(ttts[0:it - 1], axis=0)
for ti in range(9):
print("t%(2)d-t%(1)d %(ttt).4f" % {
"1": ti + 1,
"2": ti + 2,
"ttt": mnttt[ti]
})
if interrupted:
break
for it in range(itrB * oo.peek, (itrB + 1) * oo.peek):
ttt1 = _tm.time()
itstore = it // oo.BsmpxSkp
# generate latent AR state
oo.f_x[:, 0] = oo.x00
if it == 0:
for m in range(ooTR):
oo.f_V[m, 0] = oo.s2_x00
else:
oo.f_V[:, 0] = _N.mean(oo.f_V[:, 1:], axis=1)
### PG latent variable sample
ttt2 = _tm.time()
for m in range(ooTR):
lw.rpg_devroye(oo.rn,
oo.smpx[m, 2:, 0] + oo.us[m] + BaS +
ARo[m] + oo.knownSig[m],
out=oo.ws[m]) ###### devryoe
ttt3 = _tm.time()
if ooTR == 1:
oo.ws = oo.ws.reshape(1, ooN + 1)
_N.divide(oo.kp, oo.ws, out=kpOws)
if oo.dohist:
O = kpOws - oo.smpx[..., 2:, 0] - oo.us.reshape(
(ooTR, 1)) - BaS - oo.knownSig
#print(oo.ws)
# for i in vInds:
# #print("i %d" % i)
# #print(_N.sum(HbfExpd[i]))
# for j in vInds:
# #print("j %d" % j)
# #print(_N.sum(HbfExpd[j]))
# HcM[i-iOf, j-iOf] = _N.sum(oo.ws*HbfExpd[i]*HbfExpd[j])
# RHS[i, 0] = _N.sum(oo.ws*HbfExpd[i]*O)
# for cj in cInds:
# RHS[i, 0] -= _N.sum(oo.ws*HbfExpd[i]*HbfExpd[cj])*RHS[cj, 0]
for ii in range(len(vInds)):
#print("i %d" % i)
#print(_N.sum(HbfExpd[i]))
i = vInds[ii]
for jj in range(len(vInds)):
j = vInds[jj]
#print("j %d" % j)
#print(_N.sum(HbfExpd[j]))
HcM[ii,
jj] = _N.sum(oo.ws * HbfExpd[i] * HbfExpd[j])
RHS[ii, 0] = _N.sum(oo.ws * HbfExpd[i] * O)
for cj in cInds:
RHS[ii, 0] -= _N.sum(
oo.ws * HbfExpd[i] * HbfExpd[cj]) * RHS[cj, 0]
# print("HbfExpd..............................")
# for i in range(oo.histknots):
# print(_N.sum(HbfExpd[i]))
# print("HcM..................................")
# print(HcM)
# print("RHS..................................")
# print(RHS[vInds])
vm = _N.linalg.solve(HcM, RHS[vInds])
Cov = _N.linalg.inv(HcM)
#print vm
#print(Cov)
#print(vm[:, 0])
cfs = _N.random.multivariate_normal(vm[:, 0], Cov, size=1)
RHS[vInds, 0] = cfs[0]
oo.smp_hS[it] = RHS[:, 0]
#RHS[2:6, 0] = vm[:, 0]
#vv = _N.dot(Hbf, RHS)
#print vv.shape
#print oo.loghist.shape
_N.dot(Hbf, RHS[:, 0], out=oo.loghist)
oo.smp_hist[it] = oo.loghist
oo.stitch_Hist(ARo, oo.loghist, Msts)
else:
oo.smp_hist[it] = oo.loghist
oo.stitch_Hist(ARo, oo.loghist, Msts)
# Now that we have PG variables, construct Gaussian timeseries
# ws(it+1) using u(it), F0(it), smpx(it)
# cov matrix, prior of aS
# oo.gau_obs = kpOws - BaS - ARo - oous_rs - oo.knownSig
# oo.gau_var =1 / oo.ws # time dependent noise
ttt4 = _tm.time()
if oo.bpsth:
Oms = kpOws - oo.smpx[..., 2:,
0] - ARo - oous_rs - oo.knownSig
_N.einsum("mn,mn->n", oo.ws, Oms, out=smWimOm) # sum over
ilv_f = _N.diag(_N.sum(oo.ws, axis=0))
# diag(_N.linalg.inv(Bi)) == diag(1./Bi). Bii = inv(Bi)
_N.fill_diagonal(lv_f, 1. / _N.diagonal(ilv_f))
lm_f = _N.dot(lv_f, smWimOm) # nondiag of 1./Bi are inf
# now sample
iVAR = _N.dot(oo.B, _N.dot(ilv_f, oo.B.T)) + iD_f
ttt4a = _tm.time()
VAR = _N.linalg.inv(iVAR) # knots x knots
ttt4b = _tm.time()
#iBDBW = _N.linalg.inv(BDB + lv_f) # BDB not diag
#Mn = oo.u_a + _N.dot(DB, _N.dot(iBDBW, lm_f - BTua))
# BDB + lv_f (N+1 x N+1)
# lm_f - BTua (N+1)
Mn = oo.u_a + _N.dot(
DB, _N.linalg.solve(BDB + lv_f, lm_f - BTua))
#t4c = _tm.time()
oo.aS = _N.random.multivariate_normal(Mn, VAR,
size=1)[0, :]
oo.smp_aS[it] = oo.aS
_N.dot(oo.B.T, oo.aS, out=BaS)
ttt5 = _tm.time()
######## per trial offset sample burns==None, only psth fit
Ons = kpOws - oo.smpx[..., 2:, 0] - ARo - BaS - oo.knownSig
# solve for the mean of the distribution
if not oo.bpsth: # if not doing PSTH, don't constrain offset, as there are no confounds controlling offset
_N.einsum("mn,mn->m", oo.ws, Ons,
out=smWinOn) # sum over trials
ilv_u = _N.diag(_N.sum(oo.ws, axis=1)) # var of LL
# diag(_N.linalg.inv(Bi)) == diag(1./Bi). Bii = inv(Bi)
_N.fill_diagonal(lv_u, 1. / _N.diagonal(ilv_u))
lm_u = _N.dot(
lv_u, smWinOn) # nondiag of 1./Bi are inf, mean LL
# now sample
iVAR = ilv_u + iD_u
VAR = _N.linalg.inv(iVAR) #
Mn = _N.dot(VAR, _N.dot(ilv_u, lm_u) + iD_u_u_u)
oo.us[:] = _N.random.multivariate_normal(Mn, VAR,
size=1)[0, :]
if not oo.bIndOffset:
oo.us[:] = _N.mean(oo.us)
oo.smp_u[it] = oo.us
else:
H = _N.ones((oo.TR - 1, oo.TR - 1)) * _N.sum(oo.ws[0])
uRHS = _N.empty(oo.TR - 1)
for dd in range(1, oo.TR):
H[dd - 1, dd - 1] += _N.sum(oo.ws[dd])
uRHS[dd - 1] = _N.sum(oo.ws[dd] * Ons[dd] -
oo.ws[0] * Ons[0])
MM = _N.linalg.solve(H, uRHS)
Cov = _N.linalg.inv(H)
oo.us[1:] = _N.random.multivariate_normal(MM, Cov, size=1)
oo.us[0] = -_N.sum(oo.us[1:])
if not oo.bIndOffset:
oo.us[:] = _N.mean(oo.us)
oo.smp_u[it] = oo.us
# Ons = kpOws - ARo
# _N.einsum("mn,mn->m", oo.ws, Ons, out=smWinOn) # sum over trials
# ilv_u = _N.diag(_N.sum(oo.ws, axis=1)) # var of LL
# # diag(_N.linalg.inv(Bi)) == diag(1./Bi). Bii = inv(Bi)
# _N.fill_diagonal(lv_u, 1./_N.diagonal(ilv_u))
# lm_u = _N.dot(lv_u, smWinOn) # nondiag of 1./Bi are inf, mean LL
# # now sample
# iVAR = ilv_u + iD_u
# VAR = _N.linalg.inv(iVAR) #
# Mn = _N.dot(VAR, _N.dot(ilv_u, lm_u) + iD_u_u_u)
# oo.us[:] = _N.random.multivariate_normal(Mn, VAR, size=1)[0, :]
# if not oo.bIndOffset:
# oo.us[:] = _N.mean(oo.us)
# oo.smp_u[:, it] = oo.us
ttt6 = _tm.time()
if not oo.noAR:
# _d.F, _d.N, _d.ks,
#_kfar.armdl_FFBS_1itrMP(oo.gau_obs, oo.gau_var, oo.Fs, _N.linalg.inv(oo.Fs), oo.q2, oo.Ns, oo.ks, oo.f_x, oo.f_V, oo.p_x, oo.p_V, oo.smpx, K)
oo.gau_obs = kpOws - BaS - ARo - oous_rs - oo.knownSig
oo.gau_var = 1 / oo.ws # time dependent noise
#print(oo.Fs)
#print(_N.linalg.inv(oo.Fs))
if (cython_inv_v == 2):
_kfar.armdl_FFBS_1itrMP(oo.gau_obs, oo.gau_var, oo.Fs,
_N.linalg.inv(oo.Fs), oo.q2,
oo.Ns, oo.ks, oo.f_x, oo.f_V,
oo.p_x, oo.p_V, smpx_C_cont, K)
else:
_kfar.armdl_FFBS_1itrMP(oo.gau_obs, oo.gau_var, oo.Fs,
_N.linalg.inv(oo.Fs), oo.q2,
oo.Ns, oo.ks, oo.f_x, oo.f_V,
oo.chol_L_fV, oo.if_V, oo.p_x,
oo.p_V, smpx_C_cont, K)
oo.smpx[:, 2:] = smpx_C_cont
oo.smpx[:, 1, 0:ook - 1] = oo.smpx[:, 2, 1:]
oo.smpx[:, 0, 0:ook - 2] = oo.smpx[:, 2, 2:]
if oo.doBsmpx and (it % oo.BsmpxSkp == 0):
oo.Bsmpx[it // oo.BsmpxSkp, :, 2:] = oo.smpx[:, 2:, 0]
#oo.Bsmpx[it // oo.BsmpxSkp, :, 2:] = oo.smpx[:, 2:, 0]
stds = _N.std(oo.smpx[:, 2 + oo.ignr:, 0], axis=1)
oo.mnStds[it] = _N.mean(stds, axis=0)
ttt7 = _tm.time()
#print("..................................")
#print(alpR)
#print(alpC)
#print(alpR)
#print(alpC)
# print(oo.smpx[0, 0:20, 0])
# print(oo.q2)
if cython_arc:
_N.copyto(smpx_contiguous1, oo.smpx[:, 1 + oo.ignr:])
_N.copyto(smpx_contiguous2, oo.smpx[:, oo.ignr:,
0:ook - 1])
#ARcfSmpl(int N, int k, int TR, AR2lims_nmpy, smpxU, smpxW, double[::1] q2, int R, int Cs, int Cn, complex[::1] valpR, complex[::1] valpC, double sig_ph0L, double sig_ph0H, double prR_s2)
oo.uts[itstore], oo.wts[itstore] = _arcfs.ARcfSmpl(
ooN + 1 - oo.ignr, ook, oo.TR, oo.AR2lims,
smpx_contiguous1, smpx_contiguous2, oo.q2, oo.R,
oo.Cs, oo.Cn, alpR, alpC, oo.sig_ph0L, oo.sig_ph0H,
0.2 * 0.2)
else:
oo.uts[itstore], oo.wts[itstore] = _arcfs.ARcfSmpl(
ooN + 1 - oo.ignr,
ook,
oo.AR2lims,
oo.smpx[:, 1 + oo.ignr:, 0:ook],
oo.smpx[:, oo.ignr:, 0:ook - 1],
oo.q2,
oo.R,
oo.Cs,
oo.Cn,
alpR,
alpC,
oo.TR,
aro=oo.ARord,
sig_ph0L=oo.sig_ph0L,
sig_ph0H=oo.sig_ph0H)
#oo.F_alfa_rep = alpR + alpC # new constructed
oo.F_alfa_rep[0:oo.R] = alpR
oo.F_alfa_rep[oo.R:] = alpC
prt, rank, f, amp = ampAngRep(oo.F_alfa_rep,
oo.dt,
f_order=True)
#print(f)
#print(amp)
ttt8 = _tm.time()
#print prt
#ut, wt = FilteredTimeseries(ooN+1, ook, oo.smpx[:, 1:, 0:ook], oo.smpx[:, :, 0:ook-1], oo.q2, oo.R, oo.Cs, oo.Cn, alpR, alpC, oo.TR)
#ranks[it] = rank
oo.allalfas[it] = oo.F_alfa_rep
for m in range(ooTR):
#oo.wts[m, it, :, :] = wt[m, :, :, 0]
#oo.uts[m, it, :, :] = ut[m, :, :, 0]
if not oo.bFixF:
oo.amps[it, :] = amp
oo.fs[it, :] = f
ttt9 = _tm.time()
oo.F0 = (-1 *
_Npp.polyfromroots(oo.F_alfa_rep)[::-1].real)[1:]
for tr in range(oo.TR):
oo.Fs[tr, 0] = oo.F0[:]
# sample u WE USED TO Do this after smpx
# u(it+1) using ws(it+1), F0(it), smpx(it+1), ws(it+1)
oo.a2 = oo.a_q2 + 0.5 * (ooTR * ooN + 2) # N + 1 - 1
#oo.a2 = 0.5*(ooTR*(ooN-oo.ignr) + 2) # N + 1 - 1
BB2 = oo.B_q2
#BB2 = 0
for m in range(ooTR):
# set x00
oo.x00[m] = oo.smpx[m, 2] * 0.1
##################### sample q2
rsd_stp = oo.smpx[m, 3 + oo.ignr:, 0] - _N.dot(
oo.smpx[m, 2 + oo.ignr:-1], oo.F0).T
#oo.rsds[it, m] = _N.dot(rsd_stp, rsd_stp.T)
BB2 += 0.5 * _N.dot(rsd_stp, rsd_stp.T)
oo.q2[:] = _ss.invgamma.rvs(oo.a2, scale=BB2)
oo.smp_q2[it] = oo.q2
ttt10 = _tm.time()
else:
ttt7 = ttt8 = ttt9 = ttt10 = ttt6
ttt10 = _tm.time()
ttts[it, 0] = ttt2 - ttt1
ttts[it, 1] = ttt3 - ttt2
ttts[it, 2] = ttt4 - ttt3
ttts[it, 3] = ttt5 - ttt4
ttts[it, 4] = ttt6 - ttt5
ttts[it, 5] = ttt7 - ttt6
ttts[it, 6] = ttt8 - ttt7
ttts[it, 7] = ttt9 - ttt8
ttts[it, 8] = ttt10 - ttt9
oo.last_iter = it
if it > oo.minITERS:
smps = _N.empty((3, it + 1))
smps[0, :it + 1] = oo.amps[:it + 1, 0]
smps[1, :it + 1] = oo.fs[:it + 1, 0]
smps[2, :it + 1] = oo.mnStds[:it + 1]
#frms = _mg.stationary_from_Z_bckwd(smps, blksz=oo.peek)
if _mg.stationary_test(oo.amps[:it + 1, 0],
oo.fs[:it + 1, 0],
oo.mnStds[:it + 1],
it + 1,
blocksize=oo.mg_blocksize,
points=oo.mg_points):
break
"""
fig = _plt.figure(figsize=(8, 8))
fig.add_subplot(3, 1, 1)
_plt.plot(range(1, it), oo.amps[1:it, 0], color="grey", lw=1.5)
_plt.plot(range(0, it), oo.amps[0:it, 0], color="black", lw=3)
_plt.ylabel("amp")
fig.add_subplot(3, 1, 2)
_plt.plot(range(1, it), oo.fs[1:it, 0]/(2*oo.dt), color="grey", lw=1.5)
_plt.plot(range(0, it), oo.fs[0:it, 0]/(2*oo.dt), color="black", lw=3)
_plt.ylabel("f")
fig.add_subplot(3, 1, 3)
_plt.plot(range(1, it), oo.mnStds[1:it], color="grey", lw=1.5)
_plt.plot(range(0, it), oo.mnStds[0:it], color="black", lw=3)
_plt.ylabel("amp")
_plt.xlabel("iter")
_plt.savefig("%(dir)stmp-fsamps%(it)d" % {"dir" : oo.mcmcRunDir, "it" : it+1})
fig.subplots_adjust(left=0.15, bottom=0.15, right=0.95, top=0.95)
_plt.close()
"""
#if it - frms > oo.stationaryDuration:
# break
oo.getComponents()
oo.dump_smps(0,
toiter=(it + 1),
dir=oo.mcmcRunDir,
smpls_fn_incl_trls=smpls_fn_incl_trls)
def gibbsSamp(self, N, ITER, obsvd, peek=50, skp=50):
"""
peek
"""
oo = self
oo.TR = 1
sig_ph0L = -1
sig_ph0H = 0 #
oo.obsvd = obsvd
oo.skp = skp
radians = buildLims(0, oo.freq_lims, nzLimL=1., Fs=oo.Fs)
AR2lims = 2*_N.cos(radians)
F_alfa_rep = initF(oo.R, oo.C, 0).tolist() # init F_alfa_rep
if ram:
alpR = _N.array(F_alfa_rep[0:oo.R], dtype=_N.complex)
alpC = _N.array(F_alfa_rep[oo.R:], dtype=_N.complex)
alpC_tmp = _N.array(F_alfa_rep[oo.R:], dtype=_N.complex)
else:
alpR = F_alfa_rep[0:oo.R]
alpC = F_alfa_rep[oo.R:]
alpC_tmp = list(F_alfa_rep[oo.R:])
q2 = _N.array([0.01])
oo.smpx = _N.empty((oo.TR, N+2, oo.k))
oo.fs = _N.empty((ITER//skp, oo.C))
oo.rs = _N.empty((ITER//skp, oo.R))
oo.amps = _N.empty((ITER//skp, oo.C))
oo.q2s = _N.empty(ITER//skp)
oo.uts = _N.empty((ITER//skp, oo.TR, oo.R, N+1, 1))
oo.wts = _N.empty((ITER//skp, oo.TR, oo.C, N+2, 1))
# oo.smpx[:, 1+oo.ignr:, 0:ook], oo.smpx[:, oo.ignr:, 0:ook-1]
if ram:
_arcfs.init(N, oo.k, 1, oo.R, oo.C, 0, aro=_cd.__NF__)
smpx_contiguous1 = _N.zeros((oo.TR, N + 1, oo.k))
smpx_contiguous2 = _N.zeros((oo.TR, N + 2, oo.k-1))
for n in range(N):
oo.smpx[0, n+2] = oo.obsvd[0, n:n+oo.k][::-1]
for m in range(oo.TR):
oo.smpx[0, 1, 0:oo.k-1] = oo.smpx[0, 2, 1:]
oo.smpx[0, 0, 0:oo.k-2] = oo.smpx[0, 2, 2:]
if ram:
_N.copyto(smpx_contiguous1,
oo.smpx[:, 1:])
_N.copyto(smpx_contiguous2,
oo.smpx[:, 0:, 0:oo.k-1])
oo.allalfas = _N.empty((ITER, oo.k), dtype=_N.complex)
for it in range(ITER):
itstore = it // skp
if it % peek == 0:
if it > 0:
print("%d -----------------" % it)
print(prt)
if ram:
oo.uts[itstore], oo.wts[itstore] = _arcfs.ARcfSmpl(N+1, oo.k, oo.TR, AR2lims, smpx_contiguous1, smpx_contiguous2, q2, oo.R, 0, oo.C, alpR, alpC, sig_ph0L, sig_ph0H, 0.2*0.2)
else:
oo.uts[itstore], oo.wts[itstore] = _arcfs.ARcfSmpl(N, oo.k, AR2lims, oo.smpx[:, 1:, 0:oo.k], oo.smpx[:, :, 0:oo.k-1], q2, oo.R, oo.C, 0, alpR, alpC, oo.TR, aro=ARord, sig_ph0L=sig_ph0L, sig_ph0H=sig_ph0H)
F_alfa_rep[0:oo.R] = alpR
F_alfa_rep[oo.R:] = alpC
oo.allalfas[it] = F_alfa_rep
#F_alfa_rep = alpR + alpC # new constructed
prt, rank, f, amp = ampAngRep(F_alfa_rep, oo.dt, f_order=True)
# reorder
if oo.freq_order:
# coh = _N.where(amp > 0.95)[0]
# slow= _N.where(f[coh] < f_thr)[0]
# # first, rearrange
for i in range(oo.C):
alpC_tmp[2*i] = alpC[rank[i]*2]
alpC_tmp[2*i+1] = alpC[rank[i]*2+1]
for i in range(oo.C):
alpC[2*i] = alpC_tmp[2*i]
alpC[2*i+1] = alpC_tmp[2*i+1]
oo.amps[itstore, :] = amp[rank]
oo.fs[itstore, :] = 0.5*(f[rank]/oo.dt)
else:
oo.amps[itstore, :] = amp
oo.fs[itstore, :] = 0.5*(f/oo.dt)
oo.rs[itstore] = alpR
F0 = (-1*_Npp.polyfromroots(F_alfa_rep)[::-1].real)[1:]
a2 = oo.a_q2 + 0.5*(oo.TR*N + 2) # N + 1 - 1
BB2 = oo.B_q2
for m in range(oo.TR):
# set x00
rsd_stp = oo.smpx[m, 3:, 0] - _N.dot(oo.smpx[m, 2:-1], F0).T
BB2 += 0.5 * _N.dot(rsd_stp, rsd_stp.T)
q2[:] = _ss.invgamma.rvs(a2, scale=BB2)
oo.q2s[itstore] = q2[0]
it0=0
it1=ITER
it0 = it0 // skp
it1 = it1 // skp
def gibbsSamp(self): ########################### GIBBSSAMPH
oo = self
print("****!!!!!!!!!!!!!!!! dohist %s" % str(oo.dohist))
ooTR = oo.TR
ook = oo.k
ooN = oo.N
_kfar.init(oo.N, oo.k, oo.TR)
oo.x00 = _N.array(oo.smpx[:, 2])
oo.V00 = _N.zeros((ooTR, ook, ook))
if oo.dohist:
oo.loghist = _N.zeros(oo.N + 1)
else:
print("fixed hist is")
print(oo.loghist)
print("oo.mcmcRunDir %s" % oo.mcmcRunDir)
if oo.mcmcRunDir is None:
oo.mcmcRunDir = ""
elif (len(oo.mcmcRunDir) > 0) and (oo.mcmcRunDir[-1] != "/"):
oo.mcmcRunDir += "/"
ARo = _N.zeros((ooTR, ooN + 1))
kpOws = _N.empty((ooTR, ooN + 1))
lv_f = _N.zeros((ooN + 1, ooN + 1))
lv_u = _N.zeros((ooTR, ooTR))
Bii = _N.zeros((ooN + 1, ooN + 1))
#alpC.reverse()
# F_alfa_rep = alpR + alpC already in right order, no?
Wims = _N.empty((ooTR, ooN + 1, ooN + 1))
Oms = _N.empty((ooTR, ooN + 1))
smWimOm = _N.zeros(ooN + 1)
smWinOn = _N.zeros(ooTR)
bConstPSTH = False
D_f = _N.diag(_N.ones(oo.B.shape[0]) * oo.s2_a) # spline
iD_f = _N.linalg.inv(D_f)
D_u = _N.diag(_N.ones(oo.TR) * oo.s2_u) # This should
iD_u = _N.linalg.inv(D_u)
iD_u_u_u = _N.dot(iD_u, _N.ones(oo.TR) * oo.u_u)
if oo.bpsth:
BDB = _N.dot(oo.B.T, _N.dot(D_f, oo.B))
DB = _N.dot(D_f, oo.B)
BTua = _N.dot(oo.B.T, oo.u_a)
it = -1
oous_rs = oo.us.reshape((ooTR, 1))
#runTO = ooNMC + oo.burn - 1 if (burns is None) else (burns - 1)
runTO = oo.ITERS - 1
oo.allocateSmp(runTO + 1, Bsmpx=oo.doBsmpx)
alpR = oo.F_alfa_rep[0:oo.R]
alpC = oo.F_alfa_rep[oo.R:]
BaS = _N.zeros(oo.N + 1) #_N.empty(oo.N+1)
# H shape 100 x 9
Hbf = oo.Hbf
RHS = _N.empty((oo.histknots, 1))
print("----------- histknots %d" % oo.histknots)
if oo.h0_1 > 1: # no spikes in first few time bins
print("!!!!!!! hist scenario 1")
#cInds = _N.array([0, 1, 5, 6, 7, 8, 9, 10])
#cInds = _N.array([0, 4, 5, 6, 7, 8, 9])
cInds = _N.array([0, 5, 6, 7, 8, 9])
#vInds = _N.array([2, 3, 4])
vInds = _N.array([1, 2, 3, 4])
RHS[cInds, 0] = 0
RHS[0, 0] = -5
elif oo.hist_max_at_0: # no refractory period
print("!!!!!!! hist scenario 2")
#cInds = _N.array([5, 6, 7, 8, 9, 10])
cInds = _N.array([
0,
4,
5,
6,
7,
8,
])
vInds = _N.array([1, 2, 3])
#vInds = _N.array([0, 1, 2, 3, 4])
RHS[cInds, 0] = 0
RHS[0, 0] = 0
else:
print("!!!!!!! hist scenario 3")
#cInds = _N.array([5, 6, 7, 8, 9, 10])
cInds = _N.array([
4,
5,
6,
7,
8,
9,
])
vInds = _N.array([
0,
1,
2,
3,
])
#vInds = _N.array([0, 1, 2, 3, 4])
RHS[cInds, 0] = 0
Msts = []
for m in range(ooTR):
Msts.append(_N.where(oo.y[m] == 1)[0])
HcM = _N.empty((len(vInds), len(vInds)))
HbfExpd = _N.zeros((oo.histknots, ooTR, oo.N + 1))
# HbfExpd is 11 x M x 1200
# find the mean. For the HISTORY TERM
for i in range(oo.histknots):
for m in range(oo.TR):
sts = Msts[m]
HbfExpd[i, m, 0:sts[0]] = 0
for iss in range(len(sts) - 1):
t0 = sts[iss]
t1 = sts[iss + 1]
#HbfExpd[i, m, t0+1:t1+1] = Hbf[1:t1-t0+1, i]#Hbf[0:t1-t0, i]
HbfExpd[i, m, t0 + 1:t1 + 1] = Hbf[0:t1 - t0, i]
HbfExpd[i, m, sts[-1] + 1:] = 0
_N.dot(oo.B.T, oo.aS, out=BaS)
if oo.hS is None:
oo.hS = _N.zeros(oo.histknots)
if oo.dohist:
_N.dot(Hbf, oo.hS, out=oo.loghist)
oo.stitch_Hist(ARo, oo.loghist, Msts)
## ORDER OF SAMPLING
## f_xx, f_V
## DA: PG, kpOws
## history, build ARo
## psth
## offset
## DA: latent state
## AR coefficients
## q2
K = _N.empty((oo.TR, oo.N + 1, oo.k)) # kalman gain
iterBLOCKS = oo.ITERS // oo.peek
smpx_tmp = _N.empty((oo.TR, oo.N + 1, oo.k))
###### Gibbs sampling procedure
for itrB in range(iterBLOCKS):
it = itrB * oo.peek
if it > 0:
print("it: %(it)d mnStd %(mnstd).3f" % {
"it": itrB * oo.peek,
"mnstd": oo.mnStds[it - 1]
})
#tttA = _tm.time()
for it in range(itrB * oo.peek, (itrB + 1) * oo.peek):
#ttt1 = _tm.time()
# generate latent AR state
oo.f_x[:, 0] = oo.x00
if it == 0:
for m in range(ooTR):
oo.f_V[m, 0] = oo.s2_x00
else:
oo.f_V[:, 0] = _N.mean(oo.f_V[:, 1:], axis=1)
### PG latent variable sample
#ttt2 = _tm.time()
for m in range(ooTR):
lw.rpg_devroye(oo.rn,
oo.smpx[m, 2:, 0] + oo.us[m] + BaS +
ARo[m] + oo.knownSig[m],
out=oo.ws[m]) ###### devryoe
#ttt3 = _tm.time()
if ooTR == 1:
oo.ws = oo.ws.reshape(1, ooN + 1)
_N.divide(oo.kp, oo.ws, out=kpOws)
if oo.dohist:
O = kpOws - oo.smpx[..., 2:, 0] - oo.us.reshape(
(ooTR, 1)) - BaS - oo.knownSig
if it == 2000:
_N.savetxt("it2000.dat", O)
iOf = vInds[0] # offset HcM index with RHS index.
#print(oo.ws)
for i in vInds:
#print("i %d" % i)
#print(_N.sum(HbfExpd[i]))
for j in vInds:
#print("j %d" % j)
#print(_N.sum(HbfExpd[j]))
HcM[i - iOf, j - iOf] = _N.sum(oo.ws * HbfExpd[i] *
HbfExpd[j])
RHS[i, 0] = _N.sum(oo.ws * HbfExpd[i] * O)
for cj in cInds:
RHS[i, 0] -= _N.sum(
oo.ws * HbfExpd[i] * HbfExpd[cj]) * RHS[cj, 0]
# print("HbfExpd..............................")
# print(HbfExpd)
# print("HcM..................................")
# print(HcM)
# print("RHS..................................")
# print(RHS[vInds])
vm = _N.linalg.solve(HcM, RHS[vInds])
Cov = _N.linalg.inv(HcM)
#print vm
#print(Cov)
#print(vm[:, 0])
cfs = _N.random.multivariate_normal(vm[:, 0], Cov, size=1)
RHS[vInds, 0] = cfs[0]
oo.smp_hS[:, it] = RHS[:, 0]
#RHS[2:6, 0] = vm[:, 0]
#vv = _N.dot(Hbf, RHS)
#print vv.shape
#print oo.loghist.shape
_N.dot(Hbf, RHS[:, 0], out=oo.loghist)
oo.smp_hist[:, it] = oo.loghist
oo.stitch_Hist(ARo, oo.loghist, Msts)
else:
oo.smp_hist[:, it] = oo.loghist
oo.stitch_Hist(ARo, oo.loghist, Msts)
# Now that we have PG variables, construct Gaussian timeseries
# ws(it+1) using u(it), F0(it), smpx(it)
# cov matrix, prior of aS
# oo.gau_obs = kpOws - BaS - ARo - oous_rs - oo.knownSig
# oo.gau_var =1 / oo.ws # time dependent noise
#ttt4 = _tm.time()
if oo.bpsth:
Oms = kpOws - oo.smpx[..., 2:,
0] - ARo - oous_rs - oo.knownSig
_N.einsum("mn,mn->n", oo.ws, Oms, out=smWimOm) # sum over
ilv_f = _N.diag(_N.sum(oo.ws, axis=0))
# diag(_N.linalg.inv(Bi)) == diag(1./Bi). Bii = inv(Bi)
_N.fill_diagonal(lv_f, 1. / _N.diagonal(ilv_f))
lm_f = _N.dot(lv_f, smWimOm) # nondiag of 1./Bi are inf
# now sample
iVAR = _N.dot(oo.B, _N.dot(ilv_f, oo.B.T)) + iD_f
#ttt4a = _tm.time()
VAR = _N.linalg.inv(iVAR) # knots x knots
#ttt4b = _tm.time()
#iBDBW = _N.linalg.inv(BDB + lv_f) # BDB not diag
#Mn = oo.u_a + _N.dot(DB, _N.dot(iBDBW, lm_f - BTua))
# BDB + lv_f (N+1 x N+1)
# lm_f - BTua (N+1)
Mn = oo.u_a + _N.dot(
DB, _N.linalg.solve(BDB + lv_f, lm_f - BTua))
#t4c = _tm.time()
oo.aS = _N.random.multivariate_normal(Mn, VAR,
size=1)[0, :]
oo.smp_aS[it, :] = oo.aS
_N.dot(oo.B.T, oo.aS, out=BaS)
#ttt5 = _tm.time()
######## per trial offset sample burns==None, only psth fit
Ons = kpOws - oo.smpx[..., 2:, 0] - ARo - BaS - oo.knownSig
# solve for the mean of the distribution
if not oo.bpsth: # if not doing PSTH, don't constrain offset, as there are no confounds controlling offset
_N.einsum("mn,mn->m", oo.ws, Ons,
out=smWinOn) # sum over trials
ilv_u = _N.diag(_N.sum(oo.ws, axis=1)) # var of LL
# diag(_N.linalg.inv(Bi)) == diag(1./Bi). Bii = inv(Bi)
_N.fill_diagonal(lv_u, 1. / _N.diagonal(ilv_u))
lm_u = _N.dot(
lv_u, smWinOn) # nondiag of 1./Bi are inf, mean LL
# now sample
iVAR = ilv_u + iD_u
VAR = _N.linalg.inv(iVAR) #
Mn = _N.dot(VAR, _N.dot(ilv_u, lm_u) + iD_u_u_u)
oo.us[:] = _N.random.multivariate_normal(Mn, VAR,
size=1)[0, :]
if not oo.bIndOffset:
oo.us[:] = _N.mean(oo.us)
oo.smp_u[:, it] = oo.us
else:
H = _N.ones((oo.TR - 1, oo.TR - 1)) * _N.sum(oo.ws[0])
uRHS = _N.empty(oo.TR - 1)
for dd in range(1, oo.TR):
H[dd - 1, dd - 1] += _N.sum(oo.ws[dd])
uRHS[dd - 1] = _N.sum(oo.ws[dd] * Ons[dd] -
oo.ws[0] * Ons[0])
MM = _N.linalg.solve(H, uRHS)
Cov = _N.linalg.inv(H)
oo.us[1:] = _N.random.multivariate_normal(MM, Cov, size=1)
oo.us[0] = -_N.sum(oo.us[1:])
if not oo.bIndOffset:
oo.us[:] = _N.mean(oo.us)
oo.smp_u[:, it] = oo.us
# Ons = kpOws - ARo
# _N.einsum("mn,mn->m", oo.ws, Ons, out=smWinOn) # sum over trials
# ilv_u = _N.diag(_N.sum(oo.ws, axis=1)) # var of LL
# # diag(_N.linalg.inv(Bi)) == diag(1./Bi). Bii = inv(Bi)
# _N.fill_diagonal(lv_u, 1./_N.diagonal(ilv_u))
# lm_u = _N.dot(lv_u, smWinOn) # nondiag of 1./Bi are inf, mean LL
# # now sample
# iVAR = ilv_u + iD_u
# VAR = _N.linalg.inv(iVAR) #
# Mn = _N.dot(VAR, _N.dot(ilv_u, lm_u) + iD_u_u_u)
# oo.us[:] = _N.random.multivariate_normal(Mn, VAR, size=1)[0, :]
# if not oo.bIndOffset:
# oo.us[:] = _N.mean(oo.us)
# oo.smp_u[:, it] = oo.us
#ttt6 = _tm.time()
if not oo.noAR:
# _d.F, _d.N, _d.ks,
#_kfar.armdl_FFBS_1itrMP(oo.gau_obs, oo.gau_var, oo.Fs, _N.linalg.inv(oo.Fs), oo.q2, oo.Ns, oo.ks, oo.f_x, oo.f_V, oo.p_x, oo.p_V, oo.smpx, K)
oo.gau_obs = kpOws - BaS - ARo - oous_rs - oo.knownSig
oo.gau_var = 1 / oo.ws # time dependent noise
_kfar.armdl_FFBS_1itrMP(oo.gau_obs, oo.gau_var, oo.Fs,
_N.linalg.inv(oo.Fs), oo.q2, oo.Ns,
oo.ks, oo.f_x, oo.f_V, oo.p_x,
oo.p_V, smpx_tmp, K)
oo.smpx[:, 2:] = smpx_tmp
oo.smpx[:, 1, 0:ook - 1] = oo.smpx[:, 2, 1:]
oo.smpx[:, 0, 0:ook - 2] = oo.smpx[:, 2, 2:]
if oo.doBsmpx and (it % oo.BsmpxSkp == 0):
oo.Bsmpx[:, it // oo.BsmpxSkp, 2:] = oo.smpx[:, 2:, 0]
#oo.Bsmpx[it // oo.BsmpxSkp, :, 2:] = oo.smpx[:, 2:, 0]
stds = _N.std(oo.smpx[:, 2 + oo.ignr:, 0], axis=1)
oo.mnStds[it] = _N.mean(stds, axis=0)
#ttt7 = _tm.time()
if not oo.bFixF:
#ARcfSmpl(oo.lfc, ooN+1-oo.ignr, ook, oo.AR2lims, oo.smpx[:, 1+oo.ignr:, 0:ook], oo.smpx[:, oo.ignr:, 0:ook-1], oo.q2, oo.R, oo.Cs, oo.Cn, alpR, alpC, oo.TR, prior=oo.use_prior, accepts=8, aro=oo.ARord, sig_ph0L=oo.sig_ph0L, sig_ph0H=oo.sig_ph0H)
ARcfSmpl(ooN + 1 - oo.ignr,
ook,
oo.AR2lims,
oo.smpx[:, 1 + oo.ignr:, 0:ook],
oo.smpx[:, oo.ignr:, 0:ook - 1],
oo.q2,
oo.R,
oo.Cs,
oo.Cn,
alpR,
alpC,
oo.TR,
prior=oo.use_prior,
accepts=8,
aro=oo.ARord,
sig_ph0L=oo.sig_ph0L,
sig_ph0H=oo.sig_ph0H)
oo.F_alfa_rep = alpR + alpC # new constructed
prt, rank, f, amp = ampAngRep(oo.F_alfa_rep,
f_order=True)
#print prt
#ut, wt = FilteredTimeseries(ooN+1, ook, oo.smpx[:, 1:, 0:ook], oo.smpx[:, :, 0:ook-1], oo.q2, oo.R, oo.Cs, oo.Cn, alpR, alpC, oo.TR)
#ranks[it] = rank
oo.allalfas[it] = oo.F_alfa_rep
for m in range(ooTR):
#oo.wts[m, it, :, :] = wt[m, :, :, 0]
#oo.uts[m, it, :, :] = ut[m, :, :, 0]
if not oo.bFixF:
oo.amps[it, :] = amp
oo.fs[it, :] = f
oo.F0 = (-1 *
_Npp.polyfromroots(oo.F_alfa_rep)[::-1].real)[1:]
for tr in range(oo.TR):
oo.Fs[tr, 0] = oo.F0[:]
# sample u WE USED TO Do this after smpx
# u(it+1) using ws(it+1), F0(it), smpx(it+1), ws(it+1)
oo.a2 = oo.a_q2 + 0.5 * (ooTR * ooN + 2) # N + 1 - 1
#oo.a2 = 0.5*(ooTR*(ooN-oo.ignr) + 2) # N + 1 - 1
BB2 = oo.B_q2
#BB2 = 0
for m in range(ooTR):
# set x00
oo.x00[m] = oo.smpx[m, 2] * 0.1
##################### sample q2
rsd_stp = oo.smpx[m, 3 + oo.ignr:, 0] - _N.dot(
oo.smpx[m, 2 + oo.ignr:-1], oo.F0).T
#oo.rsds[it, m] = _N.dot(rsd_stp, rsd_stp.T)
BB2 += 0.5 * _N.dot(rsd_stp, rsd_stp.T)
oo.q2[:] = _ss.invgamma.rvs(oo.a2, scale=BB2)
oo.smp_q2[:, it] = oo.q2
#ttt8 = _tm.time()
# print("--------------------------------")
# print ("t2-t1 %.4f" % (#ttt2-#ttt1))
# print ("t3-t2 %.4f" % (#ttt3-#ttt2))
# print ("t4-t3 %.4f" % (#ttt4-#ttt3))
# # print ("t4b-t4a %.4f" % (t4b-t4a))
# # print ("t4c-t4b %.4f" % (t4c-t4b))
# # print ("t4-t4c %.4f" % (t4-t4c))
# print ("t5-t4 %.4f" % (#ttt5-#ttt4))
# print ("t6-t5 %.4f" % (#ttt6-#ttt5))
# print ("t7-t6 %.4f" % (#ttt7-#ttt6))
# print ("t8-t7 %.4f" % (#ttt8-#ttt7))
#tttB = _tm.time()
#print("#tttB - #tttA %.4f" % (#tttB - #tttA))
oo.last_iter = it
if it > oo.minITERS:
smps = _N.empty((3, it + 1))
smps[0, :it + 1] = oo.amps[:it + 1, 0]
smps[1, :it + 1] = oo.fs[:it + 1, 0]
smps[2, :it + 1] = oo.mnStds[:it + 1]
#frms = _mg.stationary_from_Z_bckwd(smps, blksz=oo.peek)
if _mg.stationary_test(oo.amps[:it + 1, 0],
oo.fs[:it + 1, 0],
oo.mnStds[:it + 1],
it + 1,
blocksize=oo.mg_blocksize,
points=oo.mg_points):
break
"""
fig = _plt.figure(figsize=(8, 8))
fig.add_subplot(3, 1, 1)
_plt.plot(range(1, it), oo.amps[1:it, 0], color="grey", lw=1.5)
_plt.plot(range(0, it), oo.amps[0:it, 0], color="black", lw=3)
_plt.ylabel("amp")
fig.add_subplot(3, 1, 2)
_plt.plot(range(1, it), oo.fs[1:it, 0]/(2*oo.dt), color="grey", lw=1.5)
_plt.plot(range(0, it), oo.fs[0:it, 0]/(2*oo.dt), color="black", lw=3)
_plt.ylabel("f")
fig.add_subplot(3, 1, 3)
_plt.plot(range(1, it), oo.mnStds[1:it], color="grey", lw=1.5)
_plt.plot(range(0, it), oo.mnStds[0:it], color="black", lw=3)
_plt.ylabel("amp")
_plt.xlabel("iter")
_plt.savefig("%(dir)stmp-fsamps%(it)d" % {"dir" : oo.mcmcRunDir, "it" : it+1})
fig.subplots_adjust(left=0.15, bottom=0.15, right=0.95, top=0.95)
_plt.close()
"""
#if it - frms > oo.stationaryDuration:
# break
oo.dump_smps(0, toiter=(it + 1), dir=oo.mcmcRunDir)
oo.VIS = ARo # to examine this from outside
# AR coefficients
# modulus and angle
# roots
# here, I want to turn AR coefficient into modulus and angle pair
Fs = 1000
amps = _N.zeros(c12s.shape[0])
fs = _N.zeros(c12s.shape[0])
# lambda_u is n/2 for data length n
# lambda
for n in range(c12s.shape[0]):
bBdd, iBdd, mag, F_alfa_rep = _arl.ARroots(
_N.array([c12s[n, 0], c12s[n, 1]]))
prt, rank, f, amp = ampAngRep(F_alfa_rep, 1. / (Fs / 2), f_order=True)
amps[n] = amp[0]
fs[n] = f[0]
#
# my fs is between 0 and 1. lambda is between [2, lambda_u].
# w 2pi / 2 pi to 2pi/(N/2) = 4pi/N
w = fs * _N.pi
lam = 2 * _N.pi / w
fig = _plt.figure()
lam_x = _N.linspace(2, 52, 101)
dx = lam_x[1] - lam_x[0]
_plt.hist(lam, bins=lam_x, density=True, color="grey", edgecolor="grey")
A = _N.sum((_N.sin(2 * _N.pi / lam_x) / (lam_x * lam_x)) * dx)
def dirichletAllocate(self): ########################### GIBBSSAMP
oo = self
signal.signal(signal.SIGINT, signal_handler)
ooTR = oo.TR
ook = oo.k
ooN = oo.N
runTO = oo.ITERS - 1
oo.allocateSmp(runTO + 1, Bsmpx=oo.doBsmpx)
#oo.allocateSmp(oo.burn + oo.NMC)
oo.x00 = _N.array(oo.smpx[:, 2])
oo.V00 = _N.zeros((ooTR, ook, ook))
_kfar.init(oo.N, oo.k, oo.TR)
if oo.dohist:
oo.loghist = _N.zeros(oo.Hbf.shape[0])
else:
print("fixed hist is")
print(oo.loghist)
ARo = _N.zeros((ooTR, ooN + 1))
kpOws = _N.empty((ooTR, ooN + 1))
lv_f = _N.zeros((ooN + 1, ooN + 1))
lv_u = _N.zeros((ooTR, ooTR))
Bii = _N.zeros((ooN + 1, ooN + 1))
#alpC.reverse()
# F_alfa_rep = alpR + alpC already in right order, no?
Wims = _N.empty((ooTR, ooN + 1, ooN + 1))
Oms = _N.empty((ooTR, ooN + 1))
smWimOm = _N.zeros(ooN + 1)
smWinOn = _N.zeros(ooTR)
bConstPSTH = False
D_f = _N.diag(_N.ones(oo.B.shape[0]) * oo.s2_a) # spline
iD_f = _N.linalg.inv(D_f)
D_u = _N.diag(_N.ones(oo.TR) * oo.s2_u) # This should
iD_u = _N.linalg.inv(D_u)
iD_u_u_u = _N.dot(iD_u, _N.ones(oo.TR) * oo.u_u)
BDB = _N.dot(oo.B.T, _N.dot(D_f, oo.B))
DB = _N.dot(D_f, oo.B)
BTua = _N.dot(oo.B.T, oo.u_a)
it = 0
############################### MCMC LOOP ########################
### need pointer to oo.us, but reshaped for broadcasting to work
############################### MCMC LOOP ########################
oous_rs = oo.us.reshape((ooTR, 1)) # done for broadcasting rules
sd01 = _N.zeros((oo.nStates, oo.TR, oo.TR))
_N.fill_diagonal(sd01[0], oo.s[0])
_N.fill_diagonal(sd01[1], oo.s[1])
smpx01 = _N.zeros((oo.nStates, oo.TR, oo.N + 1))
zsmpx = _N.empty((oo.TR, oo.N + 1))
# zsmpx created
# PG
zd = _N.zeros((oo.TR, oo.TR))
izd = _N.zeros((oo.TR, oo.TR))
ll = _N.zeros(oo.nStates)
Bp = _N.empty((oo.nStates, oo.N + 1))
for m in range(ooTR):
oo.f_V[m, 0] = oo.s2_x00
oo.f_V[m, 1] = oo.s2_x00
THR = _N.empty(oo.TR)
dirArgs = _N.empty(oo.nStates) # dirichlet distribution args
expT = _N.empty(ooN + 1)
BaS = _N.dot(oo.B.T, oo.aS)
alpR = oo.F_alfa_rep[0:oo.R]
alpC = oo.F_alfa_rep[oo.R:]
print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
print(oo.F_alfa_rep)
print("*****************************")
print(alpR)
print(alpC)
oo.nSMP_smpxC = 0
if oo.mcmcRunDir is None:
oo.mcmcRunDir = ""
elif (len(oo.mcmcRunDir) > 0) and (oo.mcmcRunDir[-1] != "/"):
oo.mcmcRunDir += "/"
# H shape 100 x 9
Hbf = oo.Hbf
RHS = _N.empty((oo.histknots, 1))
cInds = _N.arange(oo.iHistKnotBeginFixed, oo.histknots)
vInds = _N.arange(0, oo.iHistKnotBeginFixed)
RHS[cInds, 0] = 0
Msts = []
for m in range(ooTR):
Msts.append(_N.where(oo.y[m] == 1)[0])
HcM = _N.empty((len(vInds), len(vInds)))
HbfExpd = _N.empty((oo.histknots, ooTR, oo.N + 1))
# HbfExpd is 11 x M x 1200
# find the mean. For the HISTORY TERM
for i in range(oo.histknots):
for m in range(oo.TR):
sts = Msts[m]
HbfExpd[i, m, 0:sts[0]] = 0
for iss in range(len(sts) - 1):
t0 = sts[iss]
t1 = sts[iss + 1]
HbfExpd[i, m, t0 + 1:t1 + 1] = Hbf[0:t1 - t0, i]
HbfExpd[i, m, sts[-1] + 1:] = 0
_N.dot(oo.B.T, oo.aS, out=BaS)
if oo.hS is None:
oo.hS = _N.zeros(oo.histknots)
_N.dot(Hbf, oo.hS, out=oo.loghist)
oo.stitch_Hist(ARo, oo.loghist, Msts)
K = _N.empty((oo.TR, oo.N + 1, oo.k)) # kalman gain
iterBLOCKS = oo.ITERS // oo.peek
smpx_tmp = _N.empty((oo.TR, oo.N + 1, oo.k))
## ORDER OF SAMPLING
## f_xx, f_V
## BINARY state
## DA: PG, kpOws
## history, build ARo
## psth
## offset
## DA: latent state
## AR coefficients
## q2
oo.gau_var = _N.array(oo.ws)
#iterBLOCKS = 1
#oo.peek = 1
arangeNp1 = _N.arange(oo.N + 1)
for itrB in range(iterBLOCKS):
it = itrB * oo.peek
if it > 0:
print(
"it: %(it)d mnStd %(mnstd).3f fs %(fs).3f m %(m).3f [%(0).2f,%(1).2f]"
% {
"it": itrB * oo.peek,
"mnstd": oo.mnStds[it - 1],
"fs": oo.fs[it - 1, 0],
"m": oo.m[0],
"0": oo.s[0],
"1": oo.s[1]
})
#tttA = _tm.time()
if interrupted:
break
for it in range(itrB * oo.peek, (itrB + 1) * oo.peek):
lowsts = _N.where(oo.Z[:, 0] == 1)
#print "lowsts %s" % str(lowsts)
t1 = _tm.time()
oo.f_x[:, 0] = oo.x00
if it == 0:
for m in range(ooTR):
oo.f_V[m, 0] = oo.s2_x00
else:
oo.f_V[:, 0] = _N.mean(oo.f_V[:, 1:], axis=1)
# generate latent AR state
if it > oo.startZ:
for tryZ in range(oo.nStates):
_N.dot(sd01[tryZ], oo.smpx[:, 2:, 0], out=smpx01[tryZ])
for m in range(oo.TR):
for tryZ in range(
oo.nStates
): # only allow certain trials to change
# calculate p0, p1 p0 = m_0 x PROD_n Ber(y_n | Z_j)
# = m_0 x _N.exp(_N.log( ))
# p0, p1 not normalized
# Ber(0 | ) and Ber(1 | )
_N.exp(smpx01[tryZ, m] + BaS + ARo[m] + oo.us[m] +
oo.knownSig[m],
out=expT)
Bp[0] = 1 / (1 + expT)
Bp[1] = expT / (1 + expT)
# z[:, 1] is state label
#ll[tryZ] = 0
ll[tryZ] = _N.sum(
_N.log(Bp[oo.y[m, arangeNp1], arangeNp1]))
ofs = _N.min(ll)
ll -= ofs
#nc = oo.m[0]*_N.exp(ll[0]) + oo.m[1]*_N.exp(ll[1])
nc = oo.m[0] + oo.m[1] * _N.exp(ll[1] - ll[0])
oo.Z[m, 0] = 0
oo.Z[m, 1] = 1
#THR[m] = (oo.m[0]*_N.exp(ll[0]) / nc)
THR[m] = (oo.m[0] / nc)
if _N.random.rand() < THR[m]:
oo.Z[m, 0] = 1
oo.Z[m, 1] = 0
oo.smp_zs[m, it] = oo.Z[m]
for m in oo.fxdz: ##### outside BM loop
oo.smp_zs[m, it] = oo.Z[m]
# Z set
_N.fill_diagonal(zd, oo.s[oo.Z[:, 1]])
_N.fill_diagonal(izd, 1. / oo.s[oo.Z[:, 1]])
_N.dot(zd, oo.smpx[..., 2:, 0], out=zsmpx)
###### sample m's
_N.add(oo.alp, _N.sum(oo.Z[oo.varz], axis=0), out=dirArgs)
oo.m[:] = _N.random.dirichlet(dirArgs)
oo.smp_ms[it] = oo.m
else: # turned off dirichlet, always allocate to low state
_N.fill_diagonal(zd, oo.s[oo.Z[:, 1]])
_N.fill_diagonal(izd, 1. / oo.s[oo.Z[:, 1]])
_N.dot(zd, oo.smpx[:, 2:, 0], out=zsmpx)
###### sample m's
oo.smp_ms[it] = oo.m
oo.smp_zs[:, it, 1] = 1
oo.smp_zs[:, it, 0] = 0
lwsts = _N.where(oo.Z[:, 0] == 1)[0]
hists = _N.where(oo.Z[:, 1] == 1)[0]
#print(zsmpx[0, 0:20])
#print(oo.smpx[0, 2:22, 0])
t3 = _tm.time()
###### PG generate
for m in range(ooTR):
### CHANGE 1
#lw.rpg_devroye(oo.rn, oo.smpx[m, 2:, 0] + oo.us[m] + BaS + ARo[m] + oo.knownSig[m], out=oo.ws[m]) ###### devryoe
lw.rpg_devroye(
oo.rn,
zsmpx[m] + oo.us[m] + BaS + ARo[m] + oo.knownSig[m],
out=oo.ws[m]) ###### devryoe ####TRD change
_N.divide(oo.kp, oo.ws, out=kpOws)
if oo.dohist:
#O = kpOws - oo.smpx[..., 2:, 0] - oo.us.reshape((ooTR, 1)) - BaS - oo.knownSig
O = kpOws - zsmpx - oo.us.reshape(
(ooTR, 1)) - BaS - oo.knownSig
for ii in range(len(vInds)):
#print("i %d" % i)
#print(_N.sum(HbfExpd[i]))
i = vInds[ii]
for jj in range(ii, len(vInds)):
j = vInds[jj]
#print("j %d" % j)
#print(_N.sum(HbfExpd[j]))
HcM[ii,
jj] = _N.sum(oo.ws * HbfExpd[i] * HbfExpd[j])
HcM[jj, ii] = HcM[ii, jj]
RHS[ii, 0] = _N.sum(oo.ws * HbfExpd[i] * O)
for cj in cInds:
RHS[ii, 0] -= _N.sum(
oo.ws * HbfExpd[i] * HbfExpd[cj]) * RHS[cj, 0]
vm = _N.linalg.solve(HcM, RHS[vInds])
Cov = _N.linalg.inv(HcM)
cfs = _N.random.multivariate_normal(vm[:, 0], Cov, size=1)
RHS[vInds, 0] = cfs[0]
oo.smp_hS[:, it] = RHS[:, 0]
#RHS[2:6, 0] = vm[:, 0]
#print HcM
#vv = _N.dot(Hbf, RHS)
#print vv.shape
#print oo.loghist.shape
_N.dot(Hbf, RHS[:, 0], out=oo.loghist)
oo.smp_hist[:, it] = oo.loghist
oo.stitch_Hist(ARo, oo.loghist, Msts)
######## PSTH sample Do PSTH after we generate zs
if oo.bpsth:
#Oms = kpOws - oo.smpx[..., 2:, 0] - ARo - oous_rs - oo.knownSig
Oms = kpOws - zsmpx - ARo - oous_rs - oo.knownSig
_N.einsum("mn,mn->n", oo.ws, Oms, out=smWimOm) # sum over
ilv_f = _N.diag(_N.sum(oo.ws, axis=0))
_N.fill_diagonal(lv_f, 1. / _N.diagonal(ilv_f))
lm_f = _N.dot(lv_f, smWimOm) # nondiag of 1./Bi are inf
# now sample
iVAR = _N.dot(oo.B, _N.dot(ilv_f, oo.B.T)) + iD_f
VAR = _N.linalg.inv(iVAR) # knots x knots
#iBDBW = _N.linalg.inv(BDB + lv_f) # BDB not diag
#Mn = oo.u_a + _N.dot(DB, _N.dot(iBDBW, lm_f - BTua))
Mn = oo.u_a + _N.dot(
DB, _N.linalg.solve(BDB + lv_f, lm_f - BTua))
oo.aS = _N.random.multivariate_normal(Mn, VAR,
size=1)[0, :]
oo.smp_aS[it, :] = oo.aS
#iBDBW = _N.linalg.inv(BDB + lv_f) # BDB not diag
#Mn = oo.u_a + _N.dot(DB, _N.dot(iBDBW, lm_f - BTua))
#oo.aS = _N.random.multivariate_normal(Mn, VAR, size=1)[0, :]
#oo.smp_aS[it, :] = oo.aS
else:
oo.aS[:] = 0
_N.dot(oo.B.T, oo.aS, out=BaS)
######## per trial offset sample
#Ons = kpOws - zsmpx - ARo - BaS - oo.knownSig
Ons = kpOws - oo.smpx[..., 2:, 0] - ARo - BaS - oo.knownSig
# solve for the mean of the distribution
H = _N.ones((oo.TR - 1, oo.TR - 1)) * _N.sum(oo.ws[0])
uRHS = _N.empty(oo.TR - 1)
for dd in range(1, oo.TR):
H[dd - 1, dd - 1] += _N.sum(oo.ws[dd])
uRHS[dd - 1] = _N.sum(oo.ws[dd] * Ons[dd] -
oo.ws[0] * Ons[0])
MM = _N.linalg.solve(H, uRHS)
Cov = _N.linalg.inv(H)
oo.us[1:] = _N.random.multivariate_normal(MM, Cov, size=1)
oo.us[0] = -_N.sum(oo.us[1:])
oo.smp_u[:, it] = oo.us
t4 = _tm.time()
#### Sample latent state
#oo.gau_obs = kpOws - BaS - ARo - oous_rs - oo.knownSig
oo.gau_obs = _N.dot(izd,
kpOws - BaS - ARo - oous_rs - oo.knownSig)
#oo.copyParams(oo.F0, oo.q2)
# (MxM) (MxN) = (MxN) (Rv is MxN)
_N.dot(_N.dot(izd, izd), 1. / oo.ws, out=oo.gau_var)
#oo.gau_var =1 / oo.ws
t5 = _tm.time()
_kfar.armdl_FFBS_1itrMP(oo.gau_obs, oo.gau_var, oo.Fs,
_N.linalg.inv(oo.Fs), oo.q2, oo.Ns,
oo.ks, oo.f_x, oo.f_V, oo.p_x, oo.p_V,
smpx_tmp, K)
oo.smpx[:, 2:] = smpx_tmp
oo.smpx[:, 1, 0:ook - 1] = oo.smpx[:, 2, 1:]
oo.smpx[:, 0, 0:ook - 2] = oo.smpx[:, 2, 2:]
if oo.doBsmpx and (it % oo.BsmpxSkp == 0):
oo.Bsmpx[:, it // oo.BsmpxSkp, 2:] = oo.smpx[:, 2:, 0]
stds = _N.std(oo.smpx[:, 2:, 0], axis=1)
oo.mnStds[it] = _N.mean(stds, axis=0)
if len(hists) == 0:
print("!!!!!! length hists is 0 before ARcfSmpl")
###
#_arcfs.ARcfSmpl(ooN+1, ook, oo.AR2lims, oo.smpx[:, 1:, 0:ook], oo.smpx[:, 0:, 0:ook-1], oo.q2, oo.R, oo.Cs, oo.Cn, alpR, alpC, oo.TR, prior=oo.use_prior, accepts=8, aro=oo.ARord, sig_ph0L=oo.sig_ph0L, sig_ph0H=oo.sig_ph0H)
_arcfs.ARcfSmpl(ooN + 1,
ook,
oo.AR2lims,
oo.smpx[hists, 1:, 0:ook],
oo.smpx[hists, 0:, 0:ook - 1],
oo.q2,
oo.R,
oo.Cs,
oo.Cn,
alpR,
alpC,
len(hists),
prior=oo.use_prior,
accepts=8,
aro=oo.ARord,
sig_ph0L=oo.sig_ph0L,
sig_ph0H=oo.sig_ph0H)
oo.F_alfa_rep = alpR + alpC # new constructed
prt, rank, f, amp = ampAngRep(oo.F_alfa_rep, f_order=True)
#ut, wt = FilteredTimeseries(ooN+1, ook, oo.smpx[:, 1:, 0:ook], oo.smpx[:, :, 0:ook-1], oo.q2, oo.R, oo.Cs, oo.Cn, alpR, alpC, oo.TR)
#ranks[it] = rank
oo.allalfas[it] = oo.F_alfa_rep
for m in range(ooTR):
#oo.wts[m, it, :, :] = wt[m, :, :, 0]
#oo.uts[m, it, :, :] = ut[m, :, :, 0]
if not oo.bFixF:
oo.amps[it, :] = amp
oo.fs[it, :] = f
oo.F0 = (-1 * _Npp.polyfromroots(oo.F_alfa_rep)[::-1].real)[1:]
for tr in range(oo.TR):
oo.Fs[tr, 0] = oo.F0[:]
#print "len(lwsts) %(l)d len(hists) %(h)d" % {"l" : len(lwsts), "h" : len(hists)}
# sts2chg = hists
# #sts2chg = lwsts
# #if (it > oo.startZ) and oo.doS and len(sts2chg) > 0:
# if oo.doS and len(sts2chg) > 0:
# AL = 0.5*_N.sum(oo.smpx[sts2chg, 2:, 0]*oo.smpx[sts2chg, 2:, 0]*oo.ws[sts2chg])
# #AL = 0.5*_N.sum(oo.smpx[sts2chg, 2:, 0]*oo.smpx[sts2chg, 2:, 0])
# BRL = kpOws[sts2chg] - BaS - oous_rs[sts2chg] - ARo[sts2chg] - oo.knownSig[sts2chg]
# BL = 0.5*_N.sum(oo.ws[sts2chg]*BRL*oo.smpx[sts2chg, 2:, 0])
# UL = BL / (2*AL)
# #sgL= 1/_N.sqrt(2*AL)
# sg2= 1./(2*AL)
# if it % 50 == 0:
# print("u %(u).3f %(s).3f" % {"u" : UL, "s" : _N.sqrt(sg2)})
# q2_pr = 0.25 # 0.05**2
# u_pr = 1.
# #u_pr = 0
# U = (u_pr * sg2 + UL * q2_pr) / (sg2 + q2_pr)
# sg= _N.sqrt((sg2*q2_pr) / (sg2 + q2_pr))
# #print "U %(U).4f UL %(UL).4f s %(s).3f" % {"U" : U, "s" : sg, "UL" : UL}
# if _N.isnan(U):
# print("U is nan UL %.4f" % UL)
# print("U is nan AL %.4f" % AL)
# print("U is nan BL %.4f" % BL)
# print("U is nan BaS ")
# print("hists")
# print(hists)
# print("lwsts")
# print(lwsts)
# oo.s[1] = U + sg*_N.random.randn()
# #oo.s[0] = U + sg*_N.random.randn()
# _N.fill_diagonal(sd01[0], oo.s[0])
# _N.fill_diagonal(sd01[1], oo.s[1])
# #print oo.s[1]
# oo.smp_ss[it] = oo.s[1]
# #oo.smp_ss[it] = oo.s[0]
#oo.a2 = oo.a_q2 + 0.5*(ooTR*ooN + 2) # N + 1 - 1
oo.a2 = oo.a_q2 + 0.5 * (len(hists) * ooN + 2) # N + 1 - 1
BB2 = oo.B_q2
#for m in range(ooTR):
for m in hists:
# set x00
#oo.x00[m] = oo.smpx[m, 2]*0.1
oo.x00[m] = oo.smpx[m, 2] * 0.1
##################### sample q2
rsd_stp = oo.smpx[m, 3:, 0] - _N.dot(
oo.smpx[m, 2:-1], oo.F0).T
BB2 += 0.5 * _N.dot(rsd_stp, rsd_stp.T)
oo.q2[:] = _ss.invgamma.rvs(oo.a2, scale=BB2)
oo.smp_q2[:, it] = oo.q2
t7 = _tm.time()
def setParams(self):
oo = self
# #generate initial values of parameters
#oo._d = _kfardat.KFARGauObsDat(oo.TR, oo.N, oo.k)
#oo._d.copyData(oo.y)
oo.Ns = _N.ones(oo.TR, dtype=_N.int) * oo.N
oo.ks = _N.ones(oo.TR, dtype=_N.int) * oo.k
oo.F = _N.zeros((oo.k, oo.k))
_N.fill_diagonal(oo.F[1:, 0:oo.k - 1], 1)
oo.F[0] = _N.random.randn(oo.k) / _N.arange(1, oo.k + 1)**2
oo.F[0, 0] = 0.8
oo.Fs = _N.zeros((oo.TR, oo.k, oo.k))
for tr in range(oo.TR):
oo.Fs[tr] = oo.F
oo.Ik = _N.identity(oo.k)
oo.IkN = _N.tile(oo.Ik, (oo.N + 1, 1, 1))
# need TR
# pr_x[:, 0] empty, not used
#oo.p_x = _N.empty((oo.TR, oo.N+1, oo.k, 1))
oo.p_x = _N.empty((oo.TR, oo.N + 1, oo.k))
oo.p_x[:, 0, 0] = 0
oo.p_V = _N.empty((oo.TR, oo.N + 1, oo.k, oo.k))
oo.p_Vi = _N.empty((oo.TR, oo.N + 1, oo.k, oo.k))
#oo.f_x = _N.empty((oo.TR, oo.N+1, oo.k, 1))
oo.f_x = _N.empty((oo.TR, oo.N + 1, oo.k))
oo.f_V = _N.empty((oo.TR, oo.N + 1, oo.k, oo.k))
#oo.s_x = _N.empty((oo.TR, oo.N+1, oo.k, 1))
oo.s_x = _N.empty((oo.TR, oo.N + 1, oo.k))
oo.s_V = _N.empty((oo.TR, oo.N + 1, oo.k, oo.k))
_N.fill_diagonal(oo.F[1:, 0:oo.k - 1], 1)
oo.G = _N.zeros((oo.k, 1))
oo.G[0, 0] = 1
oo.Q = _N.empty(oo.TR)
# baseFN_inter baseFN_comps baseFN_comps
radians = buildLims(oo.Cn, oo.freq_lims, nzLimL=1.)
oo.AR2lims = 2 * _N.cos(radians)
oo.smpx = _N.zeros((oo.TR, (oo.N + 1) + 2, oo.k)) # start at 0 + u
oo.ws = _N.empty((oo.TR, oo.N + 1), dtype=_N.float)
if oo.F_alfa_rep is None:
oo.F_alfa_rep = initF(oo.R, oo.Cs, oo.Cn,
ifs=oo.ifs).tolist() # init F_alfa_rep
print(ampAngRep(oo.F_alfa_rep))
if oo.q20 is None:
oo.q20 = 0.00077
oo.q2 = _N.ones(oo.TR) * oo.q20
oo.F0 = (-1 * _Npp.polyfromroots(oo.F_alfa_rep)[::-1].real)[1:]
oo.Fs = _N.zeros((oo.TR, oo.k, oo.k))
oo.F[0] = oo.F0
_N.fill_diagonal(oo.F[1:, 0:oo.k - 1], 1)
for tr in range(oo.TR):
oo.Fs[tr] = oo.F
######## Limit the amplitude to something reasonable
xE, nul = createDataAR(oo.N, oo.F0, oo.q20, 0.1)
mlt = _N.std(xE) / 0.5 # we want amplitude around 0.5
oo.q2 /= mlt * mlt
xE, nul = createDataAR(oo.N, oo.F0, oo.q2[0], 0.1)
w = 5
wf = gauKer(w)
gk = _N.empty((oo.TR, oo.N + 1))
fgk = _N.empty((oo.TR, oo.N + 1))
for m in range(oo.TR):
gk[m] = _N.convolve(oo.y[m], wf, mode="same")
gk[m] = gk[m] - _N.mean(gk[m])
gk[m] /= 5 * _N.std(gk[m])
fgk[m] = bpFilt(15, 100, 1, 135, 500, gk[m]) # we want
fgk[m, :] /= 3 * _N.std(fgk[m, :])
if oo.noAR:
oo.smpx[m, 2:, 0] = 0
else:
oo.smpx[m, 2:, 0] = fgk[m, :]
for n in range(2 + oo.k - 1, oo.N + 1 + 2): # CREATE square smpx
oo.smpx[m, n, 1:] = oo.smpx[m, n - oo.k + 1:n, 0][::-1]
for n in range(2 + oo.k - 2, -1, -1): # CREATE square smpx
oo.smpx[m, n, 0:oo.k - 1] = oo.smpx[m, n + 1, 1:oo.k]
oo.smpx[m, n, oo.k - 1] = _N.dot(oo.F0,
oo.smpx[m, n:n + oo.k,
oo.k - 2]) # no noise
if oo.bpsth:
psthKnts, apsth, aWeights = _spknts.suggestPSTHKnots(oo.dt,
oo.TR,
oo.N + 1,
oo.y.T,
iknts=4)
_N.savetxt("apsth.txt", apsth, fmt="%.4f")
_N.savetxt("psthKnts.txt", psthKnts, fmt="%.4f")
apprx_ps = _N.array(_N.abs(aWeights))
oo.u_a = -_N.log(1 / apprx_ps - 1)
# For oo.u_a, use the values we get from aWeights
print(psthKnts)
oo.B = patsy.bs(_N.linspace(0, (oo.t1 - oo.t0) * oo.dt,
(oo.t1 - oo.t0)),
knots=(psthKnts * oo.dt),
include_intercept=True) # spline basis
oo.B = oo.B.T # My convention for beta
oo.aS = _N.array(oo.u_a)
# fig = _plt.figure(figsize=(4, 7))
# fig.add_subplot(2, 1, 1)
# _plt.plot(apsth)
# fig.add_subplot(2, 1, 2)
# _plt.plot(_N.dot(oo.B.T, aWeights))
else:
oo.B = patsy.bs(_N.linspace(0, (oo.t1 - oo.t0) * oo.dt,
(oo.t1 - oo.t0)),
df=4,
include_intercept=True) # spline basis
oo.B = oo.B.T # My convention for beta
oo.aS = _N.zeros(4)
#oo.Hbf = patsy.bs(_N.linspace(0, (oo.N+1), oo.N+1, endpoint=False), knots=_N.array([oo.h0_1, oo.h0_2, oo.h0_3, oo.h0_4, oo.h0_5, int(0.7*(oo.N+1))]), include_intercept=True) # spline basisp
#farknot = oo.maxISI*2# < (oo.t1-oo.t0) if oo.maxISI*2 else int((oo.t1-oo.t0) *0.9)
farknot = oo.maxISI * 1.1 # < (oo.t1-oo.t0) if oo.maxISI*2 else int((oo.t1-oo.t0) *0.9)
if oo.hist_max_at_0:
print("!!!!!!!!!!!!!!!!!! here 1")
print(_N.array([oo.h0_1, oo.h0_2, oo.h0_3, oo.h0_4, farknot]))
oo.Hbf = patsy.bs(
_N.linspace(0, (oo.N + 1), oo.N + 1, endpoint=False),
knots=_N.array([oo.h0_1, oo.h0_2, oo.h0_3, oo.h0_4, farknot]),
include_intercept=True) # spline basisp
print(oo.Hbf)
else:
print("!!!!!!!!!!!!!!!!!! here 2")
print(
_N.array(
[oo.h0_1, oo.h0_2, oo.h0_3, oo.h0_4, oo.h0_5, farknot]))
oo.Hbf = patsy.bs(_N.linspace(0, (oo.N + 1),
oo.N + 1,
endpoint=False),
knots=_N.array([
oo.h0_1, oo.h0_2, oo.h0_3, oo.h0_4, oo.h0_5,
farknot
]),
include_intercept=True) # spline basisp
print(oo.Hbf)
