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ForwardModel

nereids_fitting::forward_model

Trait ForwardModel 

Source 
pub trait ForwardModel {
    // Required methods
    fn predict(&self, params: &[f64]) -> Result<Vec<f64>, FittingError>;
    fn n_data(&self) -> usize;
    fn n_params(&self) -> usize;

    // Provided method
    fn jacobian(
        &self,
        _params: &[f64],
        _free_param_indices: &[usize],
        _y_current: &[f64],
    ) -> Option<Vec<Vec<f64>>> { ... }
}
Expand description

Solver-agnostic forward model.

Implementations provide the model prediction and (optionally) its analytical Jacobian. Solvers wrap this trait to compute solver-specific objectives (chi-squared, Poisson NLL, etc.).

Required Methods§

Source

fn predict(&self, params: &[f64]) -> Result<Vec<f64>, FittingError>

Predict model output for the given parameter vector.

Returns a vector of predicted values (transmission, counts, etc.) with length equal to the number of data points.

Source

fn n_data(&self) -> usize

Number of data points in the model output.

Source

fn n_params(&self) -> usize

Number of parameters (total, including fixed).

Provided Methods§

Source

fn jacobian( &self, _params: &[f64], _free_param_indices: &[usize], _y_current: &[f64], ) -> Option<Vec<Vec<f64>>>

Analytical Jacobian (column-major layout).

Returns a Vec of column vectors: result[j] is the j-th column (partial derivatives of all data points w.r.t. the j-th free parameter), so result[j][i] = ∂predict[i] / ∂params[free_param_indices[j]].

Only the columns corresponding to free_param_indices are needed. Returns None to signal “use finite differences” (the default).

y_current is the output of predict(params), provided so implementations can avoid redundant computation.

Implementors§