def __init__(self, y, F, models, order, prior_model_prob,
m0=None, C0=None, n0=None, s0=None, approx_steps=1.):
self.approx_steps = approx_steps
self.dates = y.index
self.y = np.array(y)
self.nobs = len(y)
if self.y.ndim == 1:
pass
else:
raise Exception
F = np.array(F)
if F.ndim == 1:
F = F.reshape((len(F), 1))
self.F = F
self.ndim = self.F.shape[1]
self.nmodels = len(models)
self.names = order
self.models = [models[name] for name in order]
self.prior_model_prob = np.array([prior_model_prob[name]
for name in order])
# only can do one step back for now
self.approx_steps = 1
# self.approx_steps = int(approx_steps)
# set up result storage for all the models
self.marginal_prob = nan_array(self.nobs + 1, self.nmodels)
self.post_prob = nan_array(self.nobs + 1,
self.nmodels, self.nmodels)
self.mu_mode = nan_array(self.nobs + 1, self.nmodels, self.ndim)
self.mu_forc_mode = nan_array(self.nobs + 1, self.nmodels, self.ndim)
self.mu_scale = nan_array(self.nobs + 1, self.nmodels,
self.ndim, self.ndim)
self.mu_forc_var = nan_array(self.nobs + 1, self.nmodels, self.nmodels,
self.ndim, self.ndim)
self.forecast = np.zeros((self.nobs + 1, self.nmodels))
# set in initial values
self.marginal_prob[0] = self.prior_model_prob
self.mu_mode[0] = m0
self.mu_scale[0] = C0
# observation variance stuff
self.df = n0 + np.arange(self.nobs + 1) # precompute
self.var_est = nan_array(self.nobs + 1, self.nmodels)
self.var_scale = nan_array(self.nobs + 1, self.nmodels)
self.var_est[0] = s0
self.var_scale[0] = s0 * n0
# forecasts are computed via mixture for now
self.forc_var = nan_array(self.nobs, self.nmodels, self.nmodels)
self._compute_parameters()
def _fill_updates(self, func, shape):
total_shape = (self.nmodels, self.nmodels) + shape
result = nan_array(*total_shape)
for jt in range(self.nmodels):
for jtp in range(self.nmodels):
result[jt, jtp] = np.squeeze(func(jt, jtp))
return result
def _fill_updates(self, func, shape):
total_shape = (self.nmodels, self.nmodels) + shape
result = nan_array(*total_shape)
for jt in range(self.nmodels):
for jtp in range(self.nmodels):
result[jt, jtp] = np.squeeze(func(jt, jtp))
return result
def _collapse_params(self, pstar, mt, Ct):
coll_C = nan_array(self.nmodels, self.ndim, self.ndim)
coll_m = nan_array(self.nmodels, self.ndim)
for jt in range(self.nmodels):
C = np.zeros((self.ndim, self.ndim))
m = np.zeros(self.ndim)
# collapse modes
for jtp in range(self.nmodels):
m += pstar[jt, jtp] * mt[jt, jtp]
coll_m[jt] = m
# collapse scales
for jtp in range(self.nmodels):
mdev = coll_m[jt] - mt[jt, jtp]
C += pstar[jt, jtp] * (Ct[jt, jtp] + np.outer(mdev, mdev))
coll_C[jt] = C
return coll_m, coll_C
def _collapse_var(self, St, post_prob, marginal_post):
result = nan_array(self.nmodels)
# TODO: vectorize
for jt in range(self.nmodels):
prec = 0
for jtp in range(self.nmodels):
prec += post_prob[jt, jtp] / (St[jt, jtp] * marginal_post[jt])
result[jt] = 1 / prec
return result
def _collapse_params(self, pstar, mt, Ct):
coll_C = nan_array(self.nmodels, self.ndim, self.ndim)
coll_m = nan_array(self.nmodels, self.ndim)
for jt in range(self.nmodels):
C = np.zeros((self.ndim, self.ndim))
m = np.zeros(self.ndim)
# collapse modes
for jtp in range(self.nmodels):
m += pstar[jt, jtp] * mt[jt, jtp]
coll_m[jt] = m
# collapse scales
for jtp in range(self.nmodels):
mdev = coll_m[jt] - mt[jt, jtp]
C += pstar[jt, jtp] * (Ct[jt, jtp] + np.outer(mdev, mdev))
coll_C[jt] = C
return coll_m, coll_C
def _collapse_var(self, St, post_prob, marginal_post):
result = nan_array(self.nmodels)
# TODO: vectorize
for jt in range(self.nmodels):
prec = 0
for jtp in range(self.nmodels):
prec += post_prob[jt, jtp] / (St[jt, jtp] * marginal_post[jt])
result[jt] = 1 / prec
return result
def __init__(self,
y,
F,
models,
order,
prior_model_prob,
m0=None,
C0=None,
n0=None,
s0=None,
approx_steps=1.):
self.approx_steps = approx_steps
self.dates = y.index
self.y = np.array(y)
self.nobs = len(y)
if self.y.ndim == 1:
pass
else:
raise Exception
F = np.array(F)
if F.ndim == 1:
F = F.reshape((len(F), 1))
self.F = F
self.ndim = self.F.shape[1]
self.nmodels = len(models)
self.names = order
self.models = [models[name] for name in order]
self.prior_model_prob = np.array(
[prior_model_prob[name] for name in order])
# only can do one step back for now
self.approx_steps = 1
# self.approx_steps = int(approx_steps)
# set up result storage for all the models
self.marginal_prob = nan_array(self.nobs + 1, self.nmodels)
self.post_prob = nan_array(self.nobs + 1, self.nmodels, self.nmodels)
self.mu_mode = nan_array(self.nobs + 1, self.nmodels, self.ndim)
self.mu_forc_mode = nan_array(self.nobs + 1, self.nmodels, self.ndim)
self.mu_scale = nan_array(self.nobs + 1, self.nmodels, self.ndim,
self.ndim)
self.mu_forc_var = nan_array(self.nobs + 1, self.nmodels, self.nmodels,
self.ndim, self.ndim)
self.forecast = np.zeros((self.nobs + 1, self.nmodels))
# set in initial values
self.marginal_prob[0] = self.prior_model_prob
self.mu_mode[0] = m0
self.mu_scale[0] = C0
# observation variance stuff
self.df = n0 + np.arange(self.nobs + 1) # precompute
self.var_est = nan_array(self.nobs + 1, self.nmodels)
self.var_scale = nan_array(self.nobs + 1, self.nmodels)
self.var_est[0] = s0
self.var_scale[0] = s0 * n0
# forecasts are computed via mixture for now
self.forc_var = nan_array(self.nobs, self.nmodels, self.nmodels)
self._compute_parameters()
def filter_python(Y, F, G, delta, beta, df0, v0, m0, C0):
"""
Univariate DLM update equations with unknown observation variance
delta : state discount
beta : variance discount
"""
# cdef:
# Py_ssize_t i, t, nobs, ndim
# ndarray[double_t, ndim=1] df, Q, S
# ndarray a, C, R, mode
# ndarray at, mt, Ft, At, Ct, Rt
# double_t obs, ft, e, nt, dt, St, Qt
nobs = len(Y)
ndim = len(G)
mode = nan_array(nobs + 1, ndim)
a = nan_array(nobs + 1, ndim)
C = nan_array(nobs + 1, ndim, ndim)
R = nan_array(nobs + 1, ndim, ndim)
S = nan_array(nobs + 1)
Q = nan_array(nobs)
df = nan_array(nobs + 1)
mode[0] = mt = m0
C[0] = Ct = C0
df[0] = nt = df0
S[0] = St = v0
dt = df0 * v0
# allocate result arrays
for i in range(nobs):
obs = Y[i]
# column vector, for W&H notational consistency
# Ft = F[i]
Ft = F[i:i+1].T
# advance index: y_1 through y_nobs, 0 is prior
t = i + 1
# derive innovation variance from discount factor
at = mt
Rt = Ct
if t > 1:
# only discount after first time step?
if G is not None:
at = np.dot(G, mt)
Rt = chain_dot(G, Ct, G.T) / delta
else:
Rt = Ct / delta
# Qt = chain_dot(Ft.T, Rt, Ft) + St
Qt = chain_dot(Ft.T, Rt, Ft) + St
At = np.dot(Rt, Ft) / Qt
# forecast theta as time t
ft = np.dot(Ft.T, at)
e = obs - ft
# update mean parameters
mode[t] = mt = at + np.dot(At, e)
dt = beta * dt + St * e * e / Qt
nt = beta * nt + 1
St = dt / nt
S[t] = St
Ct = (S[t] / S[t-1]) * (Rt - np.dot(At, At.T) * Qt)
Ct = (Ct + Ct.T) / 2 # symmetrize
df[t] = nt
Q[t-1] = Qt
C[t] = Ct
a[t] = at
R[t] = Rt
return mode, a, C, df, S, Q, R
def _mvfilter_python(Y, F, G, V, delta, beta, df0, v0, m0, C0):
"""
Matrix-variate DLM update equations
V : V_t sequence
"""
nobs, k = Y.shape
p = F.shape[1]
mode = nan_array(nobs + 1, p, k) # theta posterior mode
C = nan_array(nobs + 1, p, p) # theta posterior scale
a = nan_array(nobs + 1, p, k) # theta forecast mode
Q = nan_array(nobs) # variance multiplier term
D = nan_array(nobs + 1, k, k) # scale matrix
S = nan_array(nobs + 1, k, k) # covariance estimate D / n
df = nan_array(nobs + 1)
mode[0] = m0
C[0] = C0
df[0] = df0
S[0] = v0
Mt = m0
Ct = C0
n = df0
d = n + k - 1
Dt = D0
St = Dt / n
# allocate result arrays
for t in xrange(1, nobs + 1):
obs = Y[t - 1:t].T
Ft = F[t - 1:t].T
# derive innovation variance from discount factor
# only discount after first time step?
if G is not None:
at = np.dot(G, Mt)
Rt = chain_dot(G, Ct, G.T) / delta
else:
at = Mt
Rt = Ct / delta
et = obs - np.dot(at.T, Ft)
qt = chain_dot(Ft.T, Rt, Ft) + V
At = np.dot(Rt, Ft) / qt
# update mean parameters
n = beta * n
b = (n + k - 1) / d
n = n + 1
d = n + k - 1
Dt = b * Dt + np.dot(et, et.T) / qt
St = Dt / n
Mt = at + np.dot(At, et.T)
Ct = Rt - np.dot(At, At.T) * qt
C[t] = Ct; df[t] = n; S[t] = (St + St.T) / 2; mode[t] = Mt
D[t] = Dt; a[t] = at; Q[t-1] = qt
return mode, a, C, S, Q
def filter_python(Y, F, G, delta, beta, df0, v0, m0, C0):
"""
Univariate DLM update equations with unknown observation variance
delta : state discount
beta : variance discount
"""
# cdef:
# Py_ssize_t i, t, nobs, ndim
# ndarray[double_t, ndim=1] df, Q, S
# ndarray a, C, R, mode
# ndarray at, mt, Ft, At, Ct, Rt
# double_t obs, ft, e, nt, dt, St, Qt
nobs = len(Y)
ndim = len(G)
mode = nan_array(nobs + 1, ndim)
a = nan_array(nobs + 1, ndim)
C = nan_array(nobs + 1, ndim, ndim)
R = nan_array(nobs + 1, ndim, ndim)
S = nan_array(nobs + 1)
Q = nan_array(nobs)
df = nan_array(nobs + 1)
mode[0] = mt = m0
C[0] = Ct = C0
df[0] = nt = df0
S[0] = St = v0
dt = df0 * v0
# allocate result arrays
for i in range(nobs):
obs = Y[i]
# column vector, for W&H notational consistency
# Ft = F[i]
Ft = F[i:i + 1].T
# advance index: y_1 through y_nobs, 0 is prior
t = i + 1
# derive innovation variance from discount factor
at = mt
Rt = Ct
if t > 1:
# only discount after first time step?
if G is not None:
at = np.dot(G, mt)
Rt = chain_dot(G, Ct, G.T) / delta
else:
Rt = Ct / delta
# Qt = chain_dot(Ft.T, Rt, Ft) + St
Qt = chain_dot(Ft.T, Rt, Ft) + St
At = np.dot(Rt, Ft) / Qt
# forecast theta as time t
ft = np.dot(Ft.T, at)
e = obs - ft
# update mean parameters
mode[t] = mt = at + np.dot(At, e)
dt = beta * dt + St * e * e / Qt
nt = beta * nt + 1
St = dt / nt
S[t] = St
Ct = (S[t] / S[t - 1]) * (Rt - np.dot(At, At.T) * Qt)
Ct = (Ct + Ct.T) / 2 # symmetrize
df[t] = nt
Q[t - 1] = Qt
C[t] = Ct
a[t] = at
R[t] = Rt
return mode, a, C, df, S, Q, R
def _mvfilter_python(Y, F, G, V, delta, beta, df0, v0, m0, C0):
"""
Matrix-variate DLM update equations
V : V_t sequence
"""
nobs, k = Y.shape
p = F.shape[1]
mode = nan_array(nobs + 1, p, k) # theta posterior mode
C = nan_array(nobs + 1, p, p) # theta posterior scale
a = nan_array(nobs + 1, p, k) # theta forecast mode
Q = nan_array(nobs) # variance multiplier term
D = nan_array(nobs + 1, k, k) # scale matrix
S = nan_array(nobs + 1, k, k) # covariance estimate D / n
df = nan_array(nobs + 1)
mode[0] = m0
C[0] = C0
df[0] = df0
S[0] = v0
Mt = m0
Ct = C0
n = df0
d = n + k - 1
Dt = D0
St = Dt / n
# allocate result arrays
for t in xrange(1, nobs + 1):
obs = Y[t - 1:t].T
Ft = F[t - 1:t].T
# derive innovation variance from discount factor
# only discount after first time step?
if G is not None:
at = np.dot(G, Mt)
Rt = chain_dot(G, Ct, G.T) / delta
else:
at = Mt
Rt = Ct / delta
et = obs - np.dot(at.T, Ft)
qt = chain_dot(Ft.T, Rt, Ft) + V
At = np.dot(Rt, Ft) / qt
# update mean parameters
n = beta * n
b = (n + k - 1) / d
n = n + 1
d = n + k - 1
Dt = b * Dt + np.dot(et, et.T) / qt
St = Dt / n
Mt = at + np.dot(At, et.T)
Ct = Rt - np.dot(At, At.T) * qt
C[t] = Ct
df[t] = n
S[t] = (St + St.T) / 2
mode[t] = Mt
D[t] = Dt
a[t] = at
Q[t - 1] = qt
return mode, a, C, S, Q
