def _transparams(self, params):
"""
Transforms params to induce stationarity/invertability.
Reference
---------
Jones(1980)
"""
p = self.k_ar
k = self.k_trend
newparams = params.copy()
newparams[k:k+p] = _ar_transparams(params[k:k+p].copy())
return newparams
def _transparams(self, params):
"""
Transforms params to induce stationarity/invertability.
Reference
---------
Jones(1980)
"""
p = self.k_ar
k = self.k_trend
newparams = params.copy()
newparams[k:k+p] = _ar_transparams(params[k:k+p].copy())
return newparams
def _transparams(self, params):
"""
Transforms params to induce stationarity/invertability.
Reference
---------
Jones(1980)
"""
k_ar, k_ma = self.k_ar, self.k_ma
k = self.k_exog + self.k_trend
newparams = np.zeros_like(params)
# just copy exogenous parameters
if k != 0:
newparams[:k] = params[:k]
# AR Coeffs
if k_ar != 0:
newparams[k:k + k_ar] = _ar_transparams(params[k:k + k_ar].copy())
# MA Coeffs
if k_ma != 0:
newparams[k + k_ar:] = _ma_transparams(params[k + k_ar:].copy())
return newparams
def _transparams(self, params):
"""
Transforms params to induce stationarity/invertability.
Reference
---------
Jones(1980)
"""
k_ar, k_ma = self.k_ar, self.k_ma
k = self.k_exog + self.k_trend
newparams = np.zeros_like(params)
# just copy exogenous parameters
if k != 0:
newparams[:k] = params[:k]
# AR Coeffs
if k_ar != 0:
newparams[k:k+k_ar] = _ar_transparams(params[k:k+k_ar].copy())
# MA Coeffs
if k_ma != 0:
newparams[k+k_ar:] = _ma_transparams(params[k+k_ar:].copy())
return newparams
def test_ar_transparams():
arr = np.array([-1000.0, -100.0, -10.0, 1.0, 0.0, 1.0, 10.0, 100.0, 1000.0])
assert (not np.isnan(tools._ar_transparams(arr)).any())
loglikelihood = DiscrepancyLogLikelihood(
problem, armax_result, transparams=transparams
) ################# <-----Changed 21/10 #####################
logmodelprior = LogPrior[info_id](transform_to_model_param,
transform_from_model_param)
# Priors for discrepancy
logarmaprior = ArmaNormalCentredLogPrior(
armax_result,
0.25) # Note for Chon: Worth checking out more wider/narrower priors
logprior = pints.ComposedLogPrior(logmodelprior, logarmaprior)
logposterior = pints.LogPosterior(loglikelihood, logprior)
# Check logposterior is working fine
init_arma_ar = _ar_transparams(armax_result.arparams.copy(
)) ################# <-----Changed 21/10 #####################
init_arma_ma = _ma_transparams(armax_result.maparams.copy(
)) ################# <-----Changed 21/10 #####################
init_arma = np.append(
init_arma_ar,
init_arma_ma) ################# <-----Changed 21/10 #####################
priorparams = np.copy(info.base_param)
transform_priorparams = transform_from_model_param(priorparams)
priorparams = np.append(priorparams, init_arma)
transform_priorparams = np.append(transform_priorparams, init_arma)
print('Posterior at prior parameters: ', logposterior(transform_priorparams))
for _ in range(10):
assert(logposterior(transform_priorparams) ==\
logposterior(transform_priorparams))
def test_ar_transparams():
arr = np.array([-1000.0, -100.0, -10.0, 1.0, 0.0, 1.0, 10.0, 100.0, 1000.0])
assert (not np.isnan(tools._ar_transparams(arr)).any())
