A guided tour path.

Source: R/tour-guided.r

Instead of choosing new projections at random like the grand tour, the guided tour always tries to find a projection that is more interesting than the current projection.

guided_tour(
  index_f,
  d = 2,
  alpha = 0.5,
  cooling = 0.99,
  max.tries = 25,
  max.i = Inf,
  search_f = search_geodesic,
  n_sample = 100,
  ...
)

Arguments

index_f

the index function to optimise.

d

target dimensionality

alpha

the initial size of the search window, in radians

cooling

the amount the size of the search window should be adjusted by after each step

max.tries

the maximum number of unsuccessful attempts to find a better projection before giving up

max.i

the maximum index value, stop search if a larger value is found

search_f

the search strategy to use: search_geodesic, search_better, search_better_random, search_polish. Default is search_geodesic.

n_sample

number of samples to generate if search_f is search_polish

...

arguments sent to the search_f

Details

Currently the index functions only work in 2d.

Usually, you will not call this function directly, but will pass it to a method that works with tour paths like animate, save_history or render.

See also

cmass, holes and lda_pp for examples of index functions. The function should take a numeric matrix and return a single number, preferably between 0 and 1. search_geodesic, search_better, search_better_random for different search strategies

Examples

flea_std <- apply(flea[,1:6], 2, function(x) (x-mean(x))/sd(x))
animate_xy(flea_std, guided_tour(holes()), sphere = TRUE)
#> Value  0.806   0.7 % better  - NEW BASIS
#> Using half_range 3.8

# \donttest{
animate_xy(flea_std, guided_tour(holes(), search_f = search_better_random), sphere = TRUE)
#> Old 0.7954998 
#> New 0.8025468 try 1 
#> Accept 
#> Using half_range 3.8

animate_dist(flea_std, guided_tour(holes(), 1), sphere = TRUE)
#> Value  0.828   6.3 % better  - NEW BASIS
#> Using half_range 3.8


animate_xy(flea_std, guided_tour(lda_pp(flea$species)), sphere = TRUE, col = flea$species)
#> Value  0.855   15.0 % better  - NEW BASIS
#> Using half_range 3.8

# save_history is particularly useful in conjunction with the
# guided tour as it allows us to look at the tour path in many different
# ways
f <- flea_std[, 1:3]
tries <- replicate(5, save_history(f, guided_tour(holes())), simplify = FALSE)
#> Value  0.909   24.4 % better  - NEW BASIS
#> Value  0.972   6.9 % better  - NEW BASIS
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> No better bases found after 25 tries.  Giving up.
#> Final projection: 
#> -0.975  -0.012  
#> -0.146  0.793  
#> 0.171  0.609  
#> Value  0.853   28.4 % better  - NEW BASIS
#> Value  0.946   10.8 % better  - NEW BASIS
#> Value  0.970   2.6 % better  - NEW BASIS
#> Value  0.972   0.2 % better  - NEW BASIS
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> No better bases found after 25 tries.  Giving up.
#> Final projection: 
#> -0.868  -0.444  
#> -0.479  0.626  
#> -0.134  0.641  
#> Value  0.896   18.5 % better  - NEW BASIS
#> Value  0.948   5.8 % better  - NEW BASIS
#> Value  0.967   2.0 % better  - NEW BASIS
#> Value  0.972   0.5 % better  - NEW BASIS
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> No better bases found after 25 tries.  Giving up.
#> Final projection: 
#> -0.712  0.669  
#> 0.404  0.639  
#> 0.574  0.380  
#> Value  0.762   3.2 % better  - NEW BASIS
#> Value  0.887   16.4 % better  - NEW BASIS
#> Value  0.969   9.2 % better  - NEW BASIS
#> Value  0.972   0.4 % better  - NEW BASIS
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> No better bases found after 25 tries.  Giving up.
#> Final projection: 
#> -0.415  0.879  
#> -0.766  -0.198  
#> -0.491  -0.434  
#> Value  0.883   18.0 % better  - NEW BASIS
#> Value  0.970   9.8 % better  - NEW BASIS
#> Value  0.972   0.2 % better  - NEW BASIS
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
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#> Value  0.972   0.0 % better 
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#> Value  0.972   0.0 % better 
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#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> Value  0.972   0.0 % better 
#> No better bases found after 25 tries.  Giving up.
#> Final projection: 
#> -0.878  0.447  
#> -0.442  -0.620  
#> -0.183  -0.645  
# }