update readme

README.Rmd

@@ -69,7 +69,7 @@ There's a lot going on in that animation. This explains each panel:
   * light grey lines: posterior draws for the relationship from the GP 
   * grey ribbon: 90% predictive interval of a new coffee brewed at the given setting
   * red arrow & dashed line: the previous best observation shifted down by a set amount (`offset`, the length of the red arrow). 
-* 2nd panel, blue function: fit sd, the sd of the light grey lines
+* 2nd panel, blue function: fit sd, the sd of the light grey lines (which is not the same thing as the width of the grey ribbon)
 * 3rd panel, red function: expected improvement: the expectation value of a new coffee at the given setting falling above the dashed red line.
 * bottom pane, green function: the acquisition function, a weighted mixture of the two curves above. `lambda * blue + (1-lambda) * red = green`
   * orange diamond: maximum point on the acquisition function = next suggested point
@@ -93,10 +93,10 @@ dat = data.frame(grinder_setting = c(  8,    7,   9),
                  rating          = c(1.1, -0.7,  -1))
 
 suggest_next(dat,
-             iter_sampling = 1000, 
-             refresh = 0, 
-             offset = .33, 
-             lambda = .1,
+             lambda          = .1,    # exploration parameters
+             offset          = .33,
+             iter_sampling   = 1000,  # sampler parameters
+             refresh         = 100,
              show_exceptions = FALSE, 
              parallel_chains = 4)
 ```
@@ -115,6 +115,8 @@ $suggested
 ```
 This returns a list of MCMC draws, the acquisition function values over a grid of brew parameters, and a suggestion on where to go next. `offset` and `lambda` can be tweaked to control exploration vs exploitation, but expect to be suggested some combinations that result in really bad coffee sometimes (especially early on before it has collapsed the uncertainty along the outer edges of the parameter space). See `?suggest_next` for more detail on these function arguments and more.
 
+Once you have a grid point to try, use `suggest_tune()` to get a coffee-related song to listen to while you brew!
+
 ## TODO list
 
 Easy:

README.md

@@ -81,6 +81,7 @@ There’s a lot going on in that animation. This explains each panel:
   - red arrow & dashed line: the previous best observation shifted down
     by a set amount (`offset`, the length of the red arrow).
 - 2nd panel, blue function: fit sd, the sd of the light grey lines
+  (which is not the same thing as the width of the grey ribbon)
 - 3rd panel, red function: expected improvement: the expectation value
   of a new coffee at the given setting falling above the dashed red
   line.
@@ -123,10 +124,10 @@ dat = data.frame(grinder_setting = c(  8,    7,   9),
                  rating          = c(1.1, -0.7,  -1))
 
 suggest_next(dat,
-             iter_sampling = 1000, 
-             refresh = 0, 
-             offset = .33, 
-             lambda = .1,
+             lambda          = .1,    # exploration parameters
+             offset          = .33,
+             iter_sampling   = 1000,  # sampler parameters
+             refresh         = 100,
              show_exceptions = FALSE, 
              parallel_chains = 4)
 ```
@@ -146,8 +147,13 @@ This returns a list of MCMC draws, the acquisition function values over
 a grid of brew parameters, and a suggestion on where to go next.
 `offset` and `lambda` can be tweaked to control exploration vs
 exploitation, but expect to be suggested some combinations that result
-in really bad coffee sometimes. See `?suggest_next` for more detail on
-these function arguments and more.
+in really bad coffee sometimes (especially early on before it has
+collapsed the uncertainty along the outer edges of the parameter space).
+See `?suggest_next` for more detail on these function arguments and
+more.
+
+Once you have a grid point to try, use `suggest_tune()` to get a
+coffee-related song to listen to while you brew!
 
 ## TODO list