example::PoissonModel #

Poisson Model.
#include <poisson_model.h>

Public Functions #

	Name
	PoissonModel(const arma::mat & x, const arma::mat & z) Constructs and fits a new PoissonModel.
	PoissonModel(const arma::mat & C, const arma::vec & d) Constructs a new PoissonModel.
auto	simulate(const arma::mat & x, bool add_noise =false) simulates Poisson model

Additional inherited members #

Public Functions inherited from example::LinearModel

	Name
auto	C() const get output matrix (C)
auto	d() const get output bias (d)

Public Attributes inherited from example::LinearModel

	Name
const size_t	n_x number of input channels
const size_t	n_y number of output channels

Protected Attributes inherited from example::LinearModel

	Name
arma::mat	C_ output matrix
arma::vec	d_ output bias

Public Function Details #

PoissonModel #

PoissonModel(
    const arma::mat & x,
    const arma::mat & z
)

Parameters:

x Input data [n_x x n_t]
z Measured output data [n_y x n_t]

Given input/output data, constructs and fits a new PoissonModel:

\[ \mathbf{y}_t = \exp\left(\mathbf{C} \mathbf{x}_{t} + \mathbf{d}\right) \] \[ \mathbf{z}_t \sim \mathcal{N}\left(\mathbf{y}_t , \mathbf{R} \right) \]

where \( \mathbf{x}_{t} \) , \( \mathbf{y}_{t} \) , and \( \mathbf{z}_{t} \) the input, output, and measurement, respectively; \( \mathbf{C} \) is the output matrix; \( \mathbf{d} \) is the output bias; and \( \mathbf{R} \) is the covariance of the assumed Gaussian measurement noise.

PoissonModel #

PoissonModel(
    const arma::mat & C,
    const arma::vec & d
)

Parameters:

C output matrix
d output bias

Constructs a new PoissonModel from parameters:

\[ \mathbf{y}_t = \exp \left( \mathbf{C} \mathbf{x}_{t} + \mathbf{d} \right) \] \[ \mathbf{z}_t \sim \mathcal{N}\left(\mathbf{y}_t , \mathbf{R} \right) \]

simulate #

auto simulate(
    const arma::mat & x,
    bool add_noise =false
)

Parameters:

x input data
add_noise whether to add measurement noise

Return: model output

Updated on 4 March 2021 at 09:51:17 CST