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use crate::time_series::{TimeSeries, Style};
use crate::plotable::Plotable;
use plotlib::view::{View, ContinuousView};
use plotlib::repr::Plot;
#[derive(Clone)]
pub struct LinearRegression {
series: TimeSeries,
data: Vec<(f64, f64)>,
cons: f64,
slope: f64,
}
impl LinearRegression {
pub fn new(series: &TimeSeries) -> Self {
let mut new = Self{
series: series.clone(),
data: Vec::new(),
cons: 0.0,
slope: 0.0,
};
new.update_data();
new
}
fn update_data(&mut self) {
self.data = Vec::new();
self.slope = self.slope();
self.cons = self.cons();
let pairs = self.series.get_data();
for i in 0..pairs.len() {
self.data.push(
(pairs[i].0, self.calculate(pairs[i].0) )
)
}
}
pub fn slope(&self) -> f64 {
let mut sum_x = 0.0;
let mut sum_y = 0.0;
let mut sum_xy = 0.0;
let mut sum_x2 = 0.0;
let data = self.series.get_data();
for pair in data.iter() {
sum_x += pair.0;
sum_y += pair.1;
sum_xy += pair.0 * pair.1;
sum_x2 += pair.0.powi(2);
}
let count = data.len() as f64;
( (count * sum_xy) - (sum_x) * (sum_y) ) / (count * sum_x2 - sum_x.powi(2))
}
pub fn cons(&self) -> f64 {
let data = self.series.get_data();
let mut sum_x = 0.0;
let mut sum_y = 0.0;
for pair in data.iter() {
sum_x += pair.0;
sum_y += pair.1;
}
(sum_y - self.slope() * sum_x) / (data.len() as f64)
}
pub fn calculate(&self, x: f64) -> f64 {
(self.slope() * x) + self.cons()
}
pub fn as_time_series(&self) -> TimeSeries {
TimeSeries::from_pairs_vec(self.get_data())
}
pub fn get_data(&self) -> Vec<(f64, f64)> {
self.data.clone()
}
pub fn style(&self) -> Style {
self.series.style()
}
}
impl Plotable for LinearRegression {
fn plot(&self) -> Box<dyn View> {
let plot = self.as_plot();
Box::new(ContinuousView::new().add(plot))
}
fn as_plot(&self) -> Plot {
let mut plot = Plot::new(self.get_data());
plot
.point_style(self.style().point)
.line_style(self.style().line)
}
}