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//! The [`DataSet`] trait provides routines that can be used for
//! optimization of parameters of equations of state given
//! a `target` which can be values from experimental data or
//! other models.
use super::{EstimatorError, Loss};
use feos_core::Residual;
use ndarray::Array1;
use std::fmt;
use std::sync::Arc;
/// Utilities for working with experimental data.
///
/// Functionalities in the context of optimizations of
/// parameters of equations of state.
pub trait DataSet<E: Residual>: Send + Sync {
/// Return target quantity.
fn target(&self) -> &Array1<f64>;
/// Return the description of the target quantity.
fn target_str(&self) -> &str;
/// Return the descritions of the input quantities needed to compute the target.
fn input_str(&self) -> Vec<&str>;
/// Evaluation of the equation of state for the target quantity.
fn predict(&self, eos: &Arc<E>) -> Result<Array1<f64>, EstimatorError>;
/// Evaluate the cost function.
fn cost(&self, eos: &Arc<E>, loss: Loss) -> Result<Array1<f64>, EstimatorError> {
let mut cost = self.relative_difference(eos)?;
loss.apply(&mut cost);
let datapoints = cost.len();
Ok(cost / datapoints as f64)
}
/// Returns the number of experimental data points.
fn datapoints(&self) -> usize {
self.target().len()
}
/// Returns the relative difference between the equation of state and the experimental values.
fn relative_difference(&self, eos: &Arc<E>) -> Result<Array1<f64>, EstimatorError> {
let prediction = &self.predict(eos)?;
let target = self.target();
Ok((prediction - target) / target)
}
/// Returns the mean of the absolute relative difference between the equation of state and the experimental values.
fn mean_absolute_relative_difference(&self, eos: &Arc<E>) -> Result<f64, EstimatorError> {
Ok(self
.relative_difference(eos)?
.into_iter()
.filter(|&x| x.is_finite())
.enumerate()
.fold(0.0, |mean, (i, x)| mean + (x.abs() - mean) / (i + 1) as f64))
}
}
impl<E: Residual> fmt::Display for dyn DataSet<E> {
fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
write!(
f,
"DataSet(target: {}, input: {}, datapoints: {}",
self.target_str(),
self.input_str().join(", "),
self.datapoints()
)
}
}