As you might remember I have been working on creating Conway’s Game of Life.
I was able to predict the next state and get the neighboring states of a particular cell I am looking at, I changed next-generation to survived.
Here it is
(defn survived? [state row column]
(let [neighbors (get-neighbors state row column)
living-cell? (= 1 (get-in state [row column]))]
(or
(= 3 neighbors)
(and
living-cell?
(= 2 neighbors)))))
These are the rules of the game so let me tell you how I implemented them.
Some one very wise told me recently “the truth is in the code”. So Let me present to you the code first.
(defn evolve-cell [state row column]
(if (survived? state row column) 1 0))
(defn evolve [state]
(for [row (range (count state))]
(for [column (range (count state))]
(evolve-cell state row column))))
Let’s look at evolved cell, I see if it did survive then place a 1, and if it did not place a 0. Easy enough.
Now let’s look at evolve. I had been struggling with this function for a long time. I kep trying to complete this using doseq however I have learned that the function is particularly useful for side-effects and not this situation.
I adapted and went back to list comprehensions. Others have asked why I did not use (range row) for column in evolve. The reason why is because Instead of going through every cell in my matrix, it is a cone because while row is 0, so is column, which row is 1, column is 0 and 1…
This does not get every cell.
All in all I am pleased to have been able to complete this and represent it visually.
Probably my proudest Kata yet!
Best,
Merl