Solve algorithms dealing with randomness

Tonight I learned that random sample where each element in the sampling corpus
has an equal likelihood of being chosen is a brand of algorithms known as
"reservoir sampling".

- Implement random.shuffle(..)
- Implement random.choice(..)

Surprisingly, candidates are expected to encounter problems like this during
interviews.

1 files changed, 7 insertions, 0 deletions

diff --git a/scratch/facebook/hard/fisher-yates.py b/scratch/facebook/hard/fisher-yates.py
new file mode 100644
index 000000000000..200d1613ddcb
--- /dev/null
+++ b/scratch/facebook/hard/fisher-yates.py
@@ -0,0 +1,7 @@
+import random
+
+def shuffle(xs):
+    n = len(xs)
+    for i in range(n):
+        j = random.randint(i, n - 1)
+        xs[i], xs[j] = xs[j], xs[i]