def __init__(self, t, N, M, xCond=None): C1 = 0.5 C2 = 3. # Model init xDomain = np.arange(2) # Load parameters region = 'dustBowl' #region = 'sahel' filename = 'parameters/'+region+'Sigma2_N35-55_W90-120_downsampled.csv' sigma2 = np.loadtxt(filename, delimiter=',') filename = 'parameters/'+region+'MuAb_N35-55_W90-120_downsampled.csv' muAb = np.loadtxt(filename, delimiter=',') filename = 'parameters/'+region+'MuNorm_N35-55_W90-120_downsampled.csv' muNorm = np.loadtxt(filename, delimiter=',') filename = 'processedData/'+region+'Yt'+str(t)+'_N35-55_W90-120_downsampled.csv' Y = np.loadtxt(filename, delimiter=',') I = Y.shape[0] J = Y.shape[1] # SMC init X = np.zeros( (N, I, J), dtype=bool ) ancestors = np.zeros( N ) logZ = 0. logW = np.zeros( N ) w = np.zeros( N ) ESS = np.zeros( J ) # --------------- # SMC # --------------- # SMC first iteration params = hlp.params(I = I, muAb = muAb[:,0], muNorm = muNorm[:,0], sigma2 = sigma2[:,0]) if xCond is not None: q = [nested.inner(params, Y[:,0], M, xCond[:,0]) for i in range(N)] else: q = [nested.inner(params, Y[:,0], M) for i in range(N)] logW = np.array([q[i].logZ for i in range(N)]) maxLogW = np.max(logW) w = np.exp(logW - maxLogW) logZ = maxLogW + np.log(np.sum(w)) - np.log(N) w /= np.sum(w) #print w.shape ESS[0] = 1/np.sum(w**2) #print 'ESS: ',ESS[0] #print 'First logZ: ',logZ ancestors = hlp.resampling(w) for i in range(N): X[i,:,0] = q[ancestors[i]].simulate() ## SMC MAIN LOOP for j in np.arange(1,J): #print j params = hlp.params(I = I, muAb = muAb[:,j], muNorm = muNorm[:,j], sigma2 = sigma2[:,j]) if xCond is not None: q = [nested.inner(params, Y[:,j], M, xCond[:,j], X[i,:,j-1]) for i in range(N)] else: q = [nested.inner(params, Y[:,j], M, xSpaceCond = X[i,:,j-1]) for i in range(N)] logW = np.array([q[i].logZ for i in range(N)]) maxLogW = np.max(logW) w = np.exp(logW - maxLogW) logZ += maxLogW + np.log(np.sum(w)) - np.log(N) #print 'j: ',j,' logZ',logZ #print 'logW: ',logW #print 'Y: ',Y[:,j] w /= np.sum(w) #print 'Max w: ',np.max(w) ESS[j] = 1/np.sum(w**2) #print 'ESS: ',ESS[j] ancestors = hlp.resampling(w) for i in range(N): X[i,:,j] = q[ancestors[i]].simulate() #print 'Last logZ: ',logZ ## Save init to class object self.N = N self.J = J self.I = I self.X = X self.logZ = logZ self.w = w self.xCond = xCond self.ESS = ESS
def runNested(d, tauPhi, N, M, nrRuns):
r"""Run NSMC filtering on high-dimensional LGSS.
Parameters
----------
d : int
State dimension.
tauPhi : float
Measurement precision.
N : int
Number of particles, 1st level.
M : int
Number of particles, 2nd level.
nrRuns : int
Number of independent runs of the algorithm.
Returns
-------
Saves E[X] and E[X**2] estimates.
"""
# Model init
a = 0.5
tauPsi = 1.
tauRho = 1.
params = hlp.params(a = a,tauPsi = tauPsi,tauRho = tauRho,tauPhi = tauPhi)
filename = 'simulatedData/d'+str(d)+'tauPhi'+str(tauPhi)+'y.txt'
y = np.loadtxt(filename)
T = y.shape[1]
for j in range(nrRuns):
# Algorithm init
logZ = np.zeros(N)
ancestors = np.zeros(N)
X = np.zeros((N,d))
ESS = np.zeros(T)
# Setup new file
filename = './results/paper/d'+str(d)+'_N'+str(N)+'_M'+str(M)+'tauPhi'+str(tauPhi)+'_nestedSMCrun'+str(j+1)+'.csv'
f = open(filename, 'w')
#f.write('Iter ESS E[x] E[x**2]\n')
f.close()
for t in range(T):
q = [ nsmc.nestedFAPF(params, y[:,t], M, X[i,:]) for i in range(N) ]
for i in range(N):
logZ[i] = q[i].logZ
maxLz = np.max(logZ)
w = np.exp(logZ-maxLz)
w /= np.sum(w)
ESS[t] = 1/np.sum(w**2)
ancestors = hlp.resampling(w)
for i in range(N):
X[i,:] = q[ancestors[i]].simulate(1,BS=True)
f = open(filename, 'a')
tmpVec = np.r_[t+1, ESS[t], np.mean(X,axis=0), np.mean(X**2,axis=0)]
np.savetxt(f, tmpVec.reshape((1,len(tmpVec))),delimiter=',')
f.close()
def runNested(d, tauPhi, N, M, nrRuns):
r"""Run NSMC filtering on high-dimensional LGSS.
Parameters
----------
d : int
State dimension.
tauPhi : float
Measurement precision.
N : int
Number of particles, 1st level.
M : int
Number of particles, 2nd level.
nrRuns : int
Number of independent runs of the algorithm.
Returns
-------
Saves E[X] and E[X**2] estimates.
"""
# Model init
a = 0.5
tauPsi = 1.
tauRho = 1.
params = hlp.params(a=a, tauPsi=tauPsi, tauRho=tauRho, tauPhi=tauPhi)
filename = 'simulatedData/d' + str(d) + 'tauPhi' + str(tauPhi) + 'y.txt'
y = np.loadtxt(filename)
T = y.shape[1]
for j in range(nrRuns):
# Algorithm init
logZ = np.zeros(N)
ancestors = np.zeros(N)
X = np.zeros((N, d))
ESS = np.zeros(T)
# Setup new file
filename = './results/paper/d' + str(d) + '_N' + str(N) + '_M' + str(
M) + 'tauPhi' + str(tauPhi) + '_nestedSMCrun' + str(j + 1) + '.csv'
f = open(filename, 'w')
#f.write('Iter ESS E[x] E[x**2]\n')
f.close()
for t in range(T):
q = [
nsmc.nestedFAPF(params, y[:, t], M, X[i, :]) for i in range(N)
]
for i in range(N):
logZ[i] = q[i].logZ
maxLz = np.max(logZ)
w = np.exp(logZ - maxLz)
w /= np.sum(w)
ESS[t] = 1 / np.sum(w**2)
ancestors = hlp.resampling(w)
for i in range(N):
X[i, :] = q[ancestors[i]].simulate(1, BS=True)
f = open(filename, 'a')
tmpVec = np.r_[t + 1, ESS[t],
np.mean(X, axis=0),
np.mean(X**2, axis=0)]
np.savetxt(f, tmpVec.reshape((1, len(tmpVec))), delimiter=',')
f.close()
def __init__(self, t, N, M, xCond=None):
C1 = 0.5
C2 = 3.
# Model init
xDomain = np.arange(2)
# Load parameters
region = 'dustBowl'
#region = 'sahel'
filename = 'parameters/' + region + 'Sigma2_N35-55_W90-120_downsampled.csv'
sigma2 = np.loadtxt(filename, delimiter=',')
filename = 'parameters/' + region + 'MuAb_N35-55_W90-120_downsampled.csv'
muAb = np.loadtxt(filename, delimiter=',')
filename = 'parameters/' + region + 'MuNorm_N35-55_W90-120_downsampled.csv'
muNorm = np.loadtxt(filename, delimiter=',')
filename = 'processedData/' + region + 'Yt' + str(
t) + '_N35-55_W90-120_downsampled.csv'
Y = np.loadtxt(filename, delimiter=',')
I = Y.shape[0]
J = Y.shape[1]
# SMC init
X = np.zeros((N, I, J), dtype=bool)
ancestors = np.zeros(N)
logZ = 0.
logW = np.zeros(N)
w = np.zeros(N)
ESS = np.zeros(J)
# ---------------
# SMC
# ---------------
# SMC first iteration
params = hlp.params(I=I,
muAb=muAb[:, 0],
muNorm=muNorm[:, 0],
sigma2=sigma2[:, 0])
if xCond is not None:
q = [
nested.inner(params, Y[:, 0], M, xCond[:, 0]) for i in range(N)
]
else:
q = [nested.inner(params, Y[:, 0], M) for i in range(N)]
logW = np.array([q[i].logZ for i in range(N)])
maxLogW = np.max(logW)
w = np.exp(logW - maxLogW)
logZ = maxLogW + np.log(np.sum(w)) - np.log(N)
w /= np.sum(w)
#print w.shape
ESS[0] = 1 / np.sum(w**2)
#print 'ESS: ',ESS[0]
#print 'First logZ: ',logZ
ancestors = hlp.resampling(w)
for i in range(N):
X[i, :, 0] = q[ancestors[i]].simulate()
## SMC MAIN LOOP
for j in np.arange(1, J):
#print j
params = hlp.params(I=I,
muAb=muAb[:, j],
muNorm=muNorm[:, j],
sigma2=sigma2[:, j])
if xCond is not None:
q = [
nested.inner(params, Y[:, j], M, xCond[:, j],
X[i, :, j - 1]) for i in range(N)
]
else:
q = [
nested.inner(params, Y[:, j], M, xSpaceCond=X[i, :, j - 1])
for i in range(N)
]
logW = np.array([q[i].logZ for i in range(N)])
maxLogW = np.max(logW)
w = np.exp(logW - maxLogW)
logZ += maxLogW + np.log(np.sum(w)) - np.log(N)
#print 'j: ',j,' logZ',logZ
#print 'logW: ',logW
#print 'Y: ',Y[:,j]
w /= np.sum(w)
#print 'Max w: ',np.max(w)
ESS[j] = 1 / np.sum(w**2)
#print 'ESS: ',ESS[j]
ancestors = hlp.resampling(w)
for i in range(N):
X[i, :, j] = q[ancestors[i]].simulate()
#print 'Last logZ: ',logZ
## Save init to class object
self.N = N
self.J = J
self.I = I
self.X = X
self.logZ = logZ
self.w = w
self.xCond = xCond
self.ESS = ESS
