update readme

README.Rmd

@@ -20,7 +20,7 @@ knitr::opts_chunk$set(
 <!-- badges: start -->
 <!-- badges: end -->
 
-This is the package I use to optimize my coffee brewing with [Bayesian Optimization](https://www.youtube.com/watch?v=wZODGJzKmD0)
+This is the package I use to optimize my coffee brewing with [Bayesian Optimization](https://www.youtube.com/watch?v=wZODGJzKmD0).
 
 ## Installation
 
@@ -39,20 +39,16 @@ remotes::install_github('andrewGhazi/dyingforacup', type = "source")
 
 ## Example
 
-Give the `run_gp()` function a data frame of brew parameters with an 
+Give the `suggest_next()` function a data frame of brew parameters with ratings and it will suggest a point to try next that has high predicted probability of improving the rating.
 
 ```{r eval=FALSE}
 library(dyingforacup)
 
 
-dat = tibble::tribble(
-  ~grinder_setting, ~temp, ~bloom_time, ~rating,
-  # 8, 193, 25, 1.1,
-  7, 195, 20, -.7,
-  9, 179, 45, -1,
-  9, 195, 25, -.5,
-) 
-
+dat = data.frame(grinder_setting = c(8, 193, 25), 
+                 temp            = c(7, 195, 20), 
+                 bloom_time      = c(9, 179, 45), 
+                 rating          = c(1.1, -.7, -1))
 
 suggest_next(dat,
              iter_sampling = 4000, 

README.md

@@ -10,7 +10,7 @@ COFFEE?!](https://www.youtube.com/watch?v=RJC9DXQAd7U)
 <!-- badges: end -->
 
 This is the package I use to optimize my coffee brewing with [Bayesian
-Optimization](https://www.youtube.com/watch?v=wZODGJzKmD0)
+Optimization](https://www.youtube.com/watch?v=wZODGJzKmD0).
 
 ## Installation
 
@@ -31,15 +31,23 @@ remotes::install_github('andrewGhazi/dyingforacup', type = "source")
 
 ## Example
 
-Give the `run_gp()` function a data frame of brew parameters with an
+Give the `suggest_next()` function a data frame of brew parameters with
+ratings and it will suggest a point to try next that has high predicted
+probability of improving the rating.
 
 ``` r
 library(dyingforacup)
 
-dat = data.frame(grinder_setting = c(8, 193, 25), 
-                 temp = c(7, 195, 20), 
-                 bloom_time = c(9, 179, 45), 
-                 rating = c(1.1, -.7, -1))
 
-run_gp(dat)
+dat = data.frame(grinder_setting = c(8, 193, 25), 
+                 temp            = c(7, 195, 20), 
+                 bloom_time      = c(9, 179, 45), 
+                 rating          = c(1.1, -.7, -1))
+
+suggest_next(dat,
+             iter_sampling = 4000, 
+             refresh = 1250, 
+             show_exceptions = FALSE, 
+             adapt_delta = .95, 
+             parallel_chains = 4)
 ```