mirror of
https://github.com/storytold/deckcraft.git
synced 2026-10-29 04:56:58 -06:00
Nikita Rakitin
7e4e628b13
PowerPoint does not show R² for a moving average, and the value can be negative. The line is still drawn.
624 lines
22 KiB
Rust
624 lines
22 KiB
Rust
//! Least-squares trendlines: linear, exponential, logarithmic, polynomial, power, and a
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//! moving average. The fit is what the chart drawer plots and what the axis grows to include.
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use crate::chart::{ChartType, Series, Trendline, TrendlineType};
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/// A fitted trendline. `y` is `None` where the model is undefined (a log of a non-positive x,
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/// an exponential that overflowed).
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#[derive(Clone, Debug)]
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pub struct TrendFit {
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kind: TrendlineType,
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model: Model,
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r2: f64,
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x_min: f64,
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x_max: f64,
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}
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#[derive(Clone, Debug)]
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enum Model {
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/// `y = c[0] + c[1] x + c[2] x² + …`
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Poly(Vec<f64>),
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/// `y = c · e^(b x)`
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Exp { c: f64, b: f64 },
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/// `y = b + c · ln(x)`
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Log { b: f64, c: f64 },
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/// `y = c · x^b`
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Power { c: f64, b: f64 },
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/// Window averages in x order. No equation.
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Moving(Vec<(f64, f64)>),
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}
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impl Trendline {
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/// Fit `xs`/`ys` (paired). Non-finite pairs are dropped. `None` when the type needs more
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/// points than it was given, or the normal equations have no solution.
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pub fn fit(&self, xs: &[f64], ys: &[f64]) -> Option<TrendFit> {
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let mut pairs: Vec<(f64, f64)> =
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xs.iter().zip(ys).map(|(x, y)| (*x, *y)).filter(|(x, y)| x.is_finite() && y.is_finite()).take(10_000).collect();
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if pairs.len() < 2 {
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return None;
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}
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pairs.sort_by(|a, b| a.0.partial_cmp(&b.0).unwrap_or(std::cmp::Ordering::Equal));
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let (x_min, x_max) = (pairs.first().map(|p| p.0)?, pairs.last().map(|p| p.0)?);
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let intercept = self.intercept.filter(|v| v.is_finite());
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let (model, used_y, yhat) = match self.kind {
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TrendlineType::Linear => {
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let (xs, ys) = split(&pairs);
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let (b, m) = linreg(&xs, &ys, intercept)?;
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let yhat: Vec<f64> = xs.iter().map(|x| b + m * x).collect();
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(Model::Poly(vec![b, m]), ys, yhat)
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}
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TrendlineType::Poly => {
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let order = self.order.unwrap_or(2).clamp(2, 6) as usize;
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if pairs.len() < order + 1 {
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return None;
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}
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let (xs, ys) = split(&pairs);
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let coeff = polyfit(&xs, &ys, order, intercept)?;
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let yhat: Vec<f64> = xs.iter().map(|x| poly_y(&coeff, *x)).collect();
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(Model::Poly(coeff), ys, yhat)
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}
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TrendlineType::Exp => {
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let pairs: Vec<(f64, f64)> = pairs.into_iter().filter(|(_, y)| *y > 0.0).collect();
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if pairs.len() < 2 {
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return None;
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}
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let (xs, ln_y): (Vec<f64>, Vec<f64>) = pairs.iter().map(|(x, y)| (*x, y.ln())).unzip();
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let fixed = intercept.filter(|c| *c > 0.0).map(|c| c.ln());
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let (ln_c, b) = linreg(&xs, &ln_y, fixed)?;
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let c = ln_c.exp();
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if !c.is_finite() || c <= 0.0 {
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return None;
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}
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let ys: Vec<f64> = pairs.iter().map(|(_, y)| *y).collect();
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let yhat: Vec<f64> = xs.iter().map(|x| c * (b * x).exp()).collect();
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(Model::Exp { c, b }, ys, yhat)
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}
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TrendlineType::Log => {
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let pairs: Vec<(f64, f64)> = pairs.into_iter().filter(|(x, _)| *x > 0.0).collect();
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if pairs.len() < 2 {
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return None;
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}
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let (ln_x, ys): (Vec<f64>, Vec<f64>) = pairs.iter().map(|(x, y)| (x.ln(), *y)).unzip();
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let (b, c) = linreg(&ln_x, &ys, intercept)?;
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let yhat: Vec<f64> = ln_x.iter().map(|u| b + c * u).collect();
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(Model::Log { b, c }, ys, yhat)
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}
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TrendlineType::Power => {
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let pairs: Vec<(f64, f64)> = pairs.into_iter().filter(|(x, y)| *x > 0.0 && *y > 0.0).collect();
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if pairs.len() < 2 {
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return None;
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}
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let (ln_x, ln_y): (Vec<f64>, Vec<f64>) = pairs.iter().map(|(x, y)| (x.ln(), y.ln())).unzip();
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let fixed = intercept.filter(|c| *c > 0.0).map(|c| c.ln());
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let (ln_c, b) = linreg(&ln_x, &ln_y, fixed)?;
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let c = ln_c.exp();
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if !c.is_finite() || c <= 0.0 {
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return None;
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}
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let ys: Vec<f64> = pairs.iter().map(|(_, y)| *y).collect();
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let yhat: Vec<f64> = pairs.iter().map(|(x, _)| c * x.powf(b)).collect();
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(Model::Power { c, b }, ys, yhat)
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}
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TrendlineType::MovingAvg => {
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let period = self.period.unwrap_or(2).clamp(2, 255) as usize;
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if pairs.len() <= period {
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return None;
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}
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let mut pts = Vec::new();
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let mut ys = Vec::new();
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let mut yhat = Vec::new();
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for i in period - 1..pairs.len() {
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let window = &pairs[i + 1 - period..=i];
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let avg = window.iter().map(|(_, y)| y).sum::<f64>() / period as f64;
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if avg.is_finite() {
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pts.push((pairs[i].0, avg));
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ys.push(pairs[i].1);
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yhat.push(avg);
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}
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}
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if pts.len() < 2 {
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return None;
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}
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(Model::Moving(pts), ys, yhat)
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}
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};
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if yhat.iter().any(|v| !v.is_finite()) {
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return None;
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}
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// A moving average starts at the first full window, not at the first data point.
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let (x_min, x_max) = if let Model::Moving(pts) = &model {
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match (pts.first(), pts.last()) {
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(Some(a), Some(b)) => (a.0, b.0),
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_ => (x_min, x_max),
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}
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} else {
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(x_min, x_max)
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};
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Some(TrendFit { kind: self.kind, model, r2: r_squared(&used_y, &yhat), x_min, x_max })
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}
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/// The equation and R² lines this trendline asks to show. Empty when neither is on. A moving
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/// average has no equation, and PowerPoint does not show its R², so that label is left off
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/// even when the file asks for it.
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pub fn caption(&self, fit: &TrendFit) -> String {
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let mut lines = Vec::new();
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if self.disp_eq
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&& let Some(eq) = fit.equation()
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{
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lines.push(eq);
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}
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if self.disp_r_sqr && self.kind != TrendlineType::MovingAvg {
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lines.push(fit.r2_label());
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}
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lines.join("\n")
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}
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}
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impl TrendFit {
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pub fn y(&self, x: f64) -> Option<f64> {
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if !x.is_finite() {
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return None;
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}
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let y = match &self.model {
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Model::Poly(c) => poly_y(c, x),
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Model::Exp { c, b } => c * (b * x).exp(),
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Model::Log { b, c } => {
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if x <= 0.0 {
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return None;
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}
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b + c * x.ln()
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}
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Model::Power { c, b } => {
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if x <= 0.0 {
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return None;
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}
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c * x.powf(*b)
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}
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Model::Moving(_) => return None,
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};
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y.is_finite().then_some(y)
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}
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/// Samples from `x0` to `x1`, forecast included. A moving average returns its window points
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/// and ignores the forecast (Excel does not extend one).
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pub fn samples(&self, x0: f64, x1: f64) -> Vec<(f64, f64)> {
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let (x0, x1) = if x0 <= x1 { (x0, x1) } else { (x1, x0) };
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if !x0.is_finite() || !x1.is_finite() {
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return Vec::new();
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}
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if let Model::Moving(pts) = &self.model {
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return pts.iter().copied().filter(|(x, y)| x.is_finite() && y.is_finite()).take(10_000).collect();
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}
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if matches!(self.kind, TrendlineType::Linear) {
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return [x0, x1].into_iter().filter_map(|x| self.y(x).map(|y| (x, y))).collect();
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}
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let steps = 48;
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(0..=steps)
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.filter_map(|i| {
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let x = x0 + (x1 - x0) * i as f64 / steps as f64;
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self.y(x).map(|y| (x, y))
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})
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.collect()
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}
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/// Data x plus forecast, in category (or scatter x) units. Forecast is capped so a hostile
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/// value cannot walk off the chart.
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pub fn span(&self, forward: Option<f64>, backward: Option<f64>) -> (f64, f64) {
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let pad = |v: Option<f64>| v.filter(|v| v.is_finite() && *v > 0.0).map(|v| v.min(24.0)).unwrap_or(0.0);
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if matches!(self.kind, TrendlineType::MovingAvg) { (self.x_min, self.x_max) } else { (self.x_min - pad(backward), self.x_max + pad(forward)) }
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}
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pub fn equation(&self) -> Option<String> {
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let body = match &self.model {
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Model::Poly(c) => poly_equation(c),
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Model::Exp { c, b } => Some(format!("y = {}e^({}x)", num(*c), num(*b))),
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Model::Log { b, c } => poly_equation(&[*b, *c]).map(|s| s.replacen("x", "ln(x)", 1)),
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Model::Power { c, b } => {
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let mag = if (c.abs() - 1.0).abs() < 1e-6 { String::new() } else { num(c.abs()) };
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let sign = if *c < 0.0 { "-" } else { "" };
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Some(format!("y = {sign}{mag}{}", x_pow(*b)))
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}
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Model::Moving(_) => None,
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}?;
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Some(body)
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}
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pub fn r2_label(&self) -> String {
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let r = if self.r2.is_finite() { self.r2 } else { 0.0 };
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if (r - 1.0).abs() < 5e-4 { "R² = 1".to_string() } else { format!("R² = {r:.3}") }
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}
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}
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impl Series {
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/// Trendline y values, including a short forecast, that sit near this series. The axis uses
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/// them so a forecast is not clipped off the plot. A value many times the series' own range
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/// is left out, so a wild polynomial cannot flatten the chart.
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pub fn trendline_ys(&self, scatter: bool) -> Vec<f64> {
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let n = self.values.len().min(10_000);
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let mut xs = Vec::new();
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let mut ys = Vec::new();
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for i in 0..n {
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let Some(y) = self.values.get(i).copied().flatten().filter(|v| v.is_finite()) else { continue };
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let x = if scatter {
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let Some(x) = self.x_values.get(i).copied().flatten().filter(|v| v.is_finite()) else { continue };
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x
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} else {
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(i + 1) as f64
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};
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xs.push(x);
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ys.push(y);
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}
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let (data_lo, data_hi) = ys.iter().fold((f64::MAX, f64::MIN), |(lo, hi), y| (lo.min(*y), hi.max(*y)));
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if !data_lo.is_finite() || !data_hi.is_finite() {
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return Vec::new();
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}
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let span = (data_hi - data_lo).abs().max(1.0);
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let mid = (data_lo + data_hi) / 2.0;
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let mut out = Vec::new();
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for t in self.trendlines.iter().take(8) {
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let Some(fit) = t.fit(&xs, &ys) else { continue };
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let (x0, x1) = fit.span(t.forward, t.backward);
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for (_, y) in fit.samples(x0, x1) {
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if y.is_finite() && (y - mid).abs() <= span * 8.0 {
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out.push(y);
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}
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}
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}
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out
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}
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}
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/// Whether a series is plotted against numeric x values rather than category positions.
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pub fn series_is_scatter(kind: ChartType) -> bool {
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matches!(kind, ChartType::Scatter | ChartType::Bubble)
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}
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fn split(pairs: &[(f64, f64)]) -> (Vec<f64>, Vec<f64>) {
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pairs.iter().map(|(x, y)| (*x, *y)).unzip()
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}
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/// `y = b + m x`. `intercept` fixes `b`.
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fn linreg(xs: &[f64], ys: &[f64], intercept: Option<f64>) -> Option<(f64, f64)> {
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if xs.len() != ys.len() || xs.len() < 2 {
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return None;
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}
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if let Some(b) = intercept {
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let mut sxx = 0.0;
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let mut sxy = 0.0;
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for (x, y) in xs.iter().zip(ys) {
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sxx += x * x;
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sxy += x * (y - b);
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}
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if sxx.abs() < 1e-18 {
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return None;
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}
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let m = sxy / sxx;
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return (m.is_finite() && b.is_finite()).then_some((b, m));
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}
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let n = xs.len() as f64;
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let (mut sx, mut sy, mut sxx, mut sxy) = (0.0, 0.0, 0.0, 0.0);
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for (x, y) in xs.iter().zip(ys) {
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sx += x;
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sy += y;
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sxx += x * x;
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sxy += x * y;
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}
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let den = n * sxx - sx * sx;
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if den.abs() < 1e-18 {
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return None;
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}
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let m = (n * sxy - sx * sy) / den;
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let b = (sy - m * sx) / n;
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(m.is_finite() && b.is_finite()).then_some((b, m))
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}
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fn poly_y(coeff: &[f64], x: f64) -> f64 {
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let mut acc = 0.0;
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let mut xp = 1.0;
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for c in coeff {
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acc += c * xp;
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if !acc.is_finite() {
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return f64::NAN;
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}
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xp *= x;
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}
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acc
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}
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/// Polynomial through the normal equations. `x` is centred when the intercept is free, then
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/// expanded back to powers of `x`, so order 6 on category numbers stays solvable.
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#[allow(clippy::needless_range_loop)] // the index is the power in the normal equations
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fn polyfit(xs: &[f64], ys: &[f64], order: usize, intercept: Option<f64>) -> Option<Vec<f64>> {
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let n = xs.len();
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if n != ys.len() || !(1..=6).contains(&order) || n < order + 1 {
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return None;
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}
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if let Some(b0) = intercept {
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let m = order;
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let mut ata = vec![vec![0.0; m]; m];
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let mut atb = vec![0.0; m];
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for (x, y) in xs.iter().zip(ys) {
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let dy = y - b0;
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for i in 0..m {
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let xi = x.powi((i + 1) as i32);
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atb[i] += dy * xi;
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for j in i..m {
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ata[i][j] += xi * x.powi((j + 1) as i32);
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}
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}
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}
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for i in 0..m {
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for j in 0..i {
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ata[i][j] = ata[j][i];
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}
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}
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let solved = solve(ata, atb)?;
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let mut coeff = Vec::with_capacity(order + 1);
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coeff.push(b0);
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coeff.extend(solved);
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return coeff.iter().all(|v| v.is_finite()).then_some(coeff);
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}
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let xm = xs.iter().sum::<f64>() / n as f64;
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let us: Vec<f64> = xs.iter().map(|x| x - xm).collect();
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let m = order + 1;
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let mut ata = vec![vec![0.0; m]; m];
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let mut atb = vec![0.0; m];
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for (u, y) in us.iter().zip(ys) {
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for i in 0..m {
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let ui = if i == 0 { 1.0 } else { u.powi(i as i32) };
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atb[i] += y * ui;
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for j in i..m {
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let uj = if j == 0 { 1.0 } else { u.powi(j as i32) };
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ata[i][j] += ui * uj;
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}
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}
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}
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for i in 0..m {
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for j in 0..i {
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ata[i][j] = ata[j][i];
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}
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}
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let centred = solve(ata, atb)?;
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let coeff = expand_shift(¢red, xm);
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coeff.iter().all(|v| v.is_finite()).then_some(coeff)
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}
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/// `a[k]` is the coefficient of `(x - xm)^k`. Return coefficients of powers of `x`.
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#[allow(clippy::needless_range_loop)] // j is the power of x after the binomial shift
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fn expand_shift(a: &[f64], xm: f64) -> Vec<f64> {
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let mut c = vec![0.0; a.len()];
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for (k, ak) in a.iter().enumerate() {
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let mut binom = 1.0;
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for j in 0..=k {
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c[j] += ak * binom * (-xm).powi((k - j) as i32);
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if j < k {
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binom *= (k - j) as f64 / (j + 1) as f64;
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}
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}
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}
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c
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}
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#[allow(clippy::needless_range_loop)] // Gaussian elimination walks rows and columns by index
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fn solve(mut a: Vec<Vec<f64>>, mut b: Vec<f64>) -> Option<Vec<f64>> {
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let n = b.len();
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if n == 0 || n > 8 || a.len() != n {
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return None;
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}
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for col in 0..n {
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let mut piv = col;
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for r in col + 1..n {
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if a[r][col].abs() > a[piv][col].abs() {
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piv = r;
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}
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}
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if a[piv][col].abs() < 1e-12 {
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return None;
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}
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a.swap(col, piv);
|
|
b.swap(col, piv);
|
|
let div = a[col][col];
|
|
for c in col..n {
|
|
a[col][c] /= div;
|
|
}
|
|
b[col] /= div;
|
|
for r in 0..n {
|
|
if r == col {
|
|
continue;
|
|
}
|
|
let f = a[r][col];
|
|
if f == 0.0 {
|
|
continue;
|
|
}
|
|
for c in col..n {
|
|
a[r][c] -= f * a[col][c];
|
|
}
|
|
b[r] -= f * b[col];
|
|
}
|
|
}
|
|
b.iter().all(|v| v.is_finite()).then_some(b)
|
|
}
|
|
|
|
fn r_squared(ys: &[f64], yhat: &[f64]) -> f64 {
|
|
if ys.is_empty() || ys.len() != yhat.len() {
|
|
return 0.0;
|
|
}
|
|
let mean = ys.iter().sum::<f64>() / ys.len() as f64;
|
|
let mut ss_tot = 0.0;
|
|
let mut ss_res = 0.0;
|
|
for (y, h) in ys.iter().zip(yhat) {
|
|
ss_tot += (y - mean) * (y - mean);
|
|
ss_res += (y - h) * (y - h);
|
|
}
|
|
if ss_tot < 1e-18 {
|
|
return if ss_res < 1e-12 { 1.0 } else { 0.0 };
|
|
}
|
|
let r = 1.0 - ss_res / ss_tot;
|
|
if r.is_finite() { r } else { 0.0 }
|
|
}
|
|
|
|
fn num(v: f64) -> String {
|
|
if !v.is_finite() {
|
|
return "0".to_string();
|
|
}
|
|
let a = v.abs();
|
|
if a == 0.0 {
|
|
return "0".to_string();
|
|
}
|
|
if a >= 1.0e6 || a < 1.0e-3 {
|
|
return format!("{v:.2e}").replace('+', "");
|
|
}
|
|
let digits = if a >= 100.0 {
|
|
1
|
|
} else if a >= 10.0 {
|
|
2
|
|
} else {
|
|
3
|
|
};
|
|
let mut s = format!("{v:.prec$}", prec = digits);
|
|
if let Some(dot) = s.find('.') {
|
|
while s.ends_with('0') {
|
|
s.pop();
|
|
}
|
|
if s.len() == dot + 1 {
|
|
s.pop();
|
|
}
|
|
}
|
|
s
|
|
}
|
|
|
|
fn x_pow(p: f64) -> String {
|
|
if (p - 1.0).abs() < 1e-6 {
|
|
"x".to_string()
|
|
} else if (p - 2.0).abs() < 1e-6 {
|
|
"x²".to_string()
|
|
} else if (p - 3.0).abs() < 1e-6 {
|
|
"x³".to_string()
|
|
} else {
|
|
format!("x^{}", num(p))
|
|
}
|
|
}
|
|
|
|
fn poly_equation(coeff: &[f64]) -> Option<String> {
|
|
let scale = coeff.iter().map(|c| c.abs()).fold(0.0, f64::max).max(1.0);
|
|
let mut body = String::new();
|
|
let mut first = true;
|
|
for (k, c) in coeff.iter().enumerate().rev() {
|
|
if c.abs() < scale * 1e-8 {
|
|
continue;
|
|
}
|
|
let pow = match k {
|
|
0 => String::new(),
|
|
1 => "x".to_string(),
|
|
_ => x_pow(k as f64),
|
|
};
|
|
let mag = if pow.is_empty() {
|
|
num(c.abs())
|
|
} else if (c.abs() - 1.0).abs() < scale * 1e-8 {
|
|
pow
|
|
} else {
|
|
format!("{}{}", num(c.abs()), pow)
|
|
};
|
|
if first {
|
|
if *c < 0.0 {
|
|
body.push('-');
|
|
}
|
|
body.push_str(&mag);
|
|
first = false;
|
|
} else if *c < 0.0 {
|
|
body.push_str(" - ");
|
|
body.push_str(&mag);
|
|
} else {
|
|
body.push_str(" + ");
|
|
body.push_str(&mag);
|
|
}
|
|
}
|
|
if body.is_empty() {
|
|
body.push('0');
|
|
}
|
|
Some(format!("y = {body}"))
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
fn line(kind: TrendlineType) -> Trendline {
|
|
Trendline { kind, ..Default::default() }
|
|
}
|
|
|
|
#[test]
|
|
fn linear_recovers_slope_and_a_fixed_intercept() {
|
|
let xs = [1.0, 2.0, 3.0, 4.0];
|
|
let ys = [3.0, 5.0, 7.0, 9.0];
|
|
let fit = line(TrendlineType::Linear).fit(&xs, &ys).expect("fit");
|
|
assert!((fit.y(1.0).unwrap() - 3.0).abs() < 1e-9);
|
|
assert!((fit.y(4.0).unwrap() - 9.0).abs() < 1e-9);
|
|
assert_eq!(fit.equation().as_deref(), Some("y = 2x + 1"));
|
|
assert_eq!(fit.r2_label(), "R² = 1");
|
|
let mut forced = line(TrendlineType::Linear);
|
|
forced.intercept = Some(0.0);
|
|
let fit = forced.fit(&[1.0, 2.0], &[2.0, 4.0]).expect("fit");
|
|
assert!((fit.y(3.0).unwrap() - 6.0).abs() < 1e-6);
|
|
assert_eq!(fit.equation().as_deref(), Some("y = 2x"));
|
|
}
|
|
|
|
#[test]
|
|
fn exp_log_power_and_quadratic_round_their_points() {
|
|
let xs = [1.0, 2.0, 3.0, 4.0];
|
|
let exp = line(TrendlineType::Exp).fit(&xs, &[1.0, 2.0, 4.0, 8.0]).expect("exp");
|
|
for (x, y) in xs.iter().zip([1.0, 2.0, 4.0, 8.0]) {
|
|
assert!((exp.y(*x).unwrap() - y).abs() < 1e-6, "{x}");
|
|
}
|
|
assert!(exp.equation().unwrap().contains("e^("));
|
|
let log = line(TrendlineType::Log).fit(&xs, &[1.0_f64.ln(), 2.0_f64.ln(), 3.0_f64.ln(), 4.0_f64.ln()]).expect("log");
|
|
assert!((log.y(2.0).unwrap() - 2.0_f64.ln()).abs() < 1e-6);
|
|
assert!(log.equation().unwrap().contains("ln(x)"));
|
|
let power = line(TrendlineType::Power).fit(&xs, &[1.0, 4.0, 9.0, 16.0]).expect("power");
|
|
assert!((power.y(3.0).unwrap() - 9.0).abs() < 1e-6);
|
|
assert_eq!(power.equation().as_deref(), Some("y = x²"));
|
|
let mut poly = line(TrendlineType::Poly);
|
|
poly.order = Some(2);
|
|
let quad = poly.fit(&xs, &[1.0, 4.0, 9.0, 16.0]).expect("poly");
|
|
assert!((quad.y(3.0).unwrap() - 9.0).abs() < 1e-6);
|
|
assert_eq!(quad.equation().as_deref(), Some("y = x²"));
|
|
}
|
|
|
|
#[test]
|
|
fn moving_average_and_forecast_span() {
|
|
let fit = Trendline { kind: TrendlineType::MovingAvg, period: Some(2), ..Default::default() }
|
|
.fit(&[1.0, 2.0, 3.0, 4.0], &[2.0, 8.0, 4.0, 10.0])
|
|
.expect("ma");
|
|
let pts = fit.samples(0.0, 10.0);
|
|
assert_eq!(pts.len(), 3);
|
|
assert!((pts[0].1 - 5.0).abs() < 1e-9);
|
|
assert!((pts[1].1 - 6.0).abs() < 1e-9);
|
|
assert!((pts[2].1 - 7.0).abs() < 1e-9);
|
|
assert!(fit.equation().is_none());
|
|
let linear = line(TrendlineType::Linear).fit(&[1.0, 2.0], &[1.0, 2.0]).expect("line");
|
|
assert_eq!(linear.span(Some(1.5), Some(-3.0)), (1.0, 3.5));
|
|
assert_eq!(fit.span(Some(4.0), Some(4.0)), (2.0, 4.0));
|
|
}
|
|
|
|
#[test]
|
|
fn moving_average_shows_neither_equation_nor_r2() {
|
|
let t = Trendline { kind: TrendlineType::MovingAvg, period: Some(2), disp_eq: true, disp_r_sqr: true, ..Default::default() };
|
|
let fit = t.fit(&[1.0, 2.0, 3.0, 4.0], &[2.0, 8.0, 4.0, 10.0]).expect("ma");
|
|
assert!(fit.r2_label().starts_with("R² ="));
|
|
assert!(t.caption(&fit).is_empty(), "{}", t.caption(&fit));
|
|
}
|
|
|
|
#[test]
|
|
fn too_few_points_and_non_finite_input_fit_nothing() {
|
|
assert!(line(TrendlineType::Linear).fit(&[1.0], &[2.0]).is_none());
|
|
assert!(line(TrendlineType::Exp).fit(&[1.0, 2.0], &[-1.0, -2.0]).is_none());
|
|
assert!(line(TrendlineType::Poly).fit(&[1.0, 2.0], &[1.0, 2.0]).is_none());
|
|
assert!(line(TrendlineType::Linear).fit(&[f64::NAN, f64::INFINITY], &[1.0, 2.0]).is_none());
|
|
let mut t = line(TrendlineType::Linear);
|
|
t.disp_eq = true;
|
|
t.disp_r_sqr = true;
|
|
let fit = t.fit(&[1.0, 2.0, 3.0], &[1.0, 2.0, 3.0]).expect("fit");
|
|
assert_eq!(t.caption(&fit), "y = x\nR² = 1");
|
|
}
|
|
}
|