CODEWITHSUNDEEP
algorithmslideshow
Javier Rodríguez
Javier Rodríguez

Reputation: 3

Slideshow Algorithm

I need to design an algorithm for a photo slideshow that is constantly receiving new images, so that the oldest pictures appear less in the presentation, until a balance between the old photos and those that have appeared.

I have thought that every image could have a counter of the number of times they have been shown and prioritize those pictures with the lowest value in that variable.

Any other ideas or solutions would be well received.

Upvotes: 0

Views: 378

Answers (2)

Lior Kogan
Lior Kogan

Reputation: 20608

You can achieve an overall near-uniform distribution (each image appears about the same number of times for the long run), but I wouldn't recommend doing it. Images that were available early would appear very very rarely later on. A better user experience would be to simply choose a random image from all the available images at each step.

If you still want near-uniform distribution for the long run, you should set the probability for any image based on the number of times it appeared so far. For example:

p(i) = 1 - count(i) / (max_count() + epsilon)

Here is a simple R code that simulates such process. 37 random images are selected before a new image becomes available. This process is repeated 3000 times:

h    <- 3000              # total images
eps  <- 0.001

t    <- integer(length=h) # t[i]: no. of instances of value i in r
r    <- c()               # proceded vector of indexes of images 
m    <- 0                 # highest number of appearances for an image

for (i in 1:h)
    for (j in 1:37)                                   # select 37 random images in range 1..i
    {
        v    <- sample(1:i, 1, prob=1-t[1:i]/(m+eps)) # select image i with weight 1-t[i]/(m+eps)
        r    <- c(r, v)                               # add to output vector

        t[v] <- t[v]+1                                # update appearances count
        m    <- max(m, t[v])                          # update highest number of appearances
    }

plot(table(r))

The output plot shows the number of times each image appeared:

epsilon = 0.001:

epsilon = 0.001

epsilon = 0.0001:

epsilon = 0.0001

If we look, for example at the indexes in the output vector in which, say, image #3 was selected:

> which(r==3)
 [1]    75    76    77    78    79    80    81    82    83    84    85    86    87    88    89    90    91    92    93    94
[21]    95    96    97    98    99   100   101   102   103   104   105   106   107   108   109   110   111  1189 34767 39377
[41] 70259

Note that if epsilon is very small, the sequence will seem less random (newer images are much preferred). For the long run however, any epsilon will do.

Upvotes: 1

Jessica d
Jessica d

Reputation: 743

Instead of a view counter, you could also try basing your algorithm on the timestamp that images were uploaded.

Upvotes: 0

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