doc: updated expected perfs and model description

index.html

@@ -35,8 +35,8 @@ <h1>Going NUTS with WASM!</h1>
         target="_blank">here</a>.
 
       FYI with the stations <a href="https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USW00023293/detail"
-        target="_blank">USW00023293</a> (San Jose, CA), the sampling takes about 10 seconds. If you don't
-      want to wait, use the fake data provided and just run the sampling.
+        target="_blank">USW00023293</a> (San Jose, CA), the downloading takes few seconds and the sampling less than 1s.
+      If you don't want to wait, use the fake data provided and just run the sampling.
     </div>
 
     <input type="text" id="input_url" value="https://noaa-ghcn-pds.s3.amazonaws.com/csv/by_station/USW00023293.csv" />
@@ -179,11 +179,11 @@ <h1>Going NUTS with WASM!</h1>
       intercept
       (alpha) and the slope (beta). Sigma has a a flat prior.
       <p class="equation">
-        TMAX[DATE-DATE0] ~ Normal(alpha + beta * (DATE-DATE0), sigma) <br />
+        TMAX[DATE-DATEM] ~ Normal(alpha + beta * (DATE-DATEM), sigma) <br />
         alpha ~ Normal(0, 10) <br />
         beta ~ Normal(0, 10) <br />
         sigma ~ Uniform <br />
-        DATE0 is the first date in the dataset.
+        DATEM is the mean of the dates in the dataset.
       </p>
       Beta is then the trend in C/years.
     </div>

src/lib.rs

@@ -202,9 +202,9 @@ pub fn run_with(
 
     // Use the middle of the time period as reference
     // to prevent strong correlations between alpha and beta
-    let x0 = x.iter().sum::<f64>() / x.len() as f64;
+    let x_m = x.iter().sum::<f64>() / x.len() as f64;
 
-    let x = x.iter().map(|x| x - x0).collect::<Vec<_>>();
+    let x = x.iter().map(|x| x - x_m).collect::<Vec<_>>();
 
     let model = Regression::new(x.clone(), y.clone());