//======================================================================
ArModel::ArModel(int number_of_lags)
: ParamPolicy(new GlmCoefs(Vector(number_of_lags, 0.0), true),
new UnivParams(1.0)),
DataPolicy(new ArSuf(number_of_lags)),
filter_coefficients_current_(false) {
Phi_prm()->add_observer([this]() { this->observe_phi(); });
}
//======================================================================
ArModel::ArModel(int number_of_lags)
: ParamPolicy(new VectorParams(number_of_lags, 0),
new UnivParams(1.0)),
DataPolicy(new ArSuf(number_of_lags)),
filter_coefficients_current_(false)
{
Phi_prm()->add_observer(boost::bind(&ArModel::observe_phi, this));
}
//======================================================================
ArStateModel::ArStateModel(const ArStateModel &rhs)
: Model(rhs),
StateModel(rhs),
ArModel(rhs),
state_transition_matrix_(new AutoRegressionTransitionMatrix(Phi_prm())),
state_variance_matrix_(new UpperLeftCornerMatrixParamView(
Phi_prm()->nvars_possible(), Sigsq_prm())),
state_error_expander_(
new FirstElementSingleColumnMatrix(rhs.number_of_lags())),
state_error_variance_matrix_(
new SingleSparseDiagonalElementMatrixParamView(1, Sigsq_prm(), 0)),
observation_matrix_(rhs.observation_matrix_),
initial_state_mean_(rhs.initial_state_mean_),
initial_state_variance_(rhs.initial_state_variance_),
stationary_initial_distribution_(rhs.stationary_initial_distribution_) {
DataPolicy::only_keep_sufstats();
}
//======================================================================
ArStateModel::ArStateModel(const ArStateModel &rhs)
: Model(rhs),
StateModel(rhs),
ArModel(rhs),
state_transition_matrix_(new AutoRegressionTransitionMatrix(Phi_prm())),
state_variance_matrix_(new UpperLeftCornerMatrix(
Phi_prm()->size(), 1.0)),
state_variance_is_current_(false),
observation_matrix_(rhs.observation_matrix_),
initial_state_mean_(rhs.initial_state_mean_),
initial_state_variance_(rhs.initial_state_variance_),
stationary_initial_distribution_(rhs.stationary_initial_distribution_)
{
DataPolicy::only_keep_sufstats();
Sigsq_prm()->add_observer(
boost::bind(&ArStateModel::observe_residual_variance,
this));
}
ArModel::ArModel(Ptr<VectorParams> phi, Ptr<UnivParams> sigsq)
: ParamPolicy(phi, sigsq),
DataPolicy(new ArSuf(phi->size())),
filter_coefficients_current_(false)
{
bool ok = check_stationary(phi->value());
if (!ok) {
report_error("Attempt to initialize ArModel with an illegal value "
"of the autoregression coefficients.");
}
Phi_prm()->add_observer(boost::bind(&ArModel::observe_phi, this));
}
ArModel::ArModel(const Ptr<GlmCoefs> &autoregression_coefficients,
const Ptr<UnivParams> &innovation_variance)
: ParamPolicy(autoregression_coefficients, innovation_variance),
DataPolicy(new ArSuf(autoregression_coefficients->size())),
filter_coefficients_current_(false) {
bool ok = check_stationary(autoregression_coefficients->value());
if (!ok) {
report_error(
"Attempt to initialize ArModel with an illegal value "
"of the autoregression coefficients.");
}
Phi_prm()->add_observer([this]() { this->observe_phi(); });
}
ArStateModel::ArStateModel(int number_of_lags)
: ArModel(number_of_lags),
state_transition_matrix_(new AutoRegressionTransitionMatrix(Phi_prm())),
state_variance_matrix_(
new UpperLeftCornerMatrixParamView(number_of_lags, Sigsq_prm())),
state_error_expander_(
new FirstElementSingleColumnMatrix(number_of_lags)),
state_error_variance_matrix_(
new SingleSparseDiagonalElementMatrixParamView(1, Sigsq_prm(), 0)),
observation_matrix_(number_of_lags),
initial_state_mean_(number_of_lags, 0.0),
initial_state_variance_(number_of_lags, 1.0),
stationary_initial_distribution_(false) {
observation_matrix_[0] = 1.0;
DataPolicy::only_keep_sufstats();
}
ArStateModel::ArStateModel(int number_of_lags)
: ArModel(number_of_lags),
state_transition_matrix_(
new AutoRegressionTransitionMatrix(Phi_prm())),
state_variance_matrix_(
new UpperLeftCornerMatrix(number_of_lags, 1.0)),
state_variance_is_current_(false),
observation_matrix_(number_of_lags),
initial_state_mean_(number_of_lags, 0.0),
initial_state_variance_(number_of_lags, 1.0),
stationary_initial_distribution_(false)
{
observation_matrix_[0] = 1.0;
DataPolicy::only_keep_sufstats();
Sigsq_prm()->add_observer(
boost::bind(&ArStateModel::observe_residual_variance,
this));
}
void ArModel::set_phi(const Vec &phi){
Phi_prm()->set(phi);
}
const Vec &ArModel::phi()const{
return Phi_prm()->value();
}
const Ptr<GlmCoefs> coef_prm() const override { return Phi_prm(); }
Ptr<GlmCoefs> coef_prm() override { return Phi_prm(); }
//======================================================================
uint ArStateModel::state_dimension() const {
return Phi_prm()->nvars_possible();
}
//======================================================================
uint ArStateModel::state_dimension()const{
return Phi_prm()->size();
}
