chapter10/README.md

@@ -1,3 +0,0 @@
-# Approximation algorithm
-
--   Easy to write, fast to run, useful for obtaining approximate solutions for NP-hard problems.

chapter11/README.md

@@ -1,6 +0,0 @@
-# Dynamic Programming
-
-A programming technique for decomposing a problem into smaller discrete subproblems.
-
--   Useful when trying to optimize something given a constraint.
-    -   Example, items in a knapsack of size W that gives the greatest value.

chapter12/README.md

@@ -1,9 +0,0 @@
-# K-Nearest Neighbours
-
-Useful for classification, regression and feature extraction. By examining a data point against its K nearest neighbours we can:
-
--   categorize into a group
--   predict responses
--   convert the item into a list of features
-
-A good starting point for machine learning.

chapter4/binary.py

@@ -1,5 +1,6 @@
 import logging
 import random
+import time
 
 logging.basicConfig(level=logging.DEBUG)
 logger = logging.getLogger(__name__)
@@ -32,10 +33,11 @@ SAMPLE_SIZE = 1000
 numbers = random.sample(range(LOWER, UPPER), SAMPLE_SIZE)
 numbers.sort()
 
+start = time.time()
 result = None
 while result is None:
     guess = random.randrange(LOWER, UPPER)
     logger.debug(f"guess: {guess}")
     result = binary_search(numbers, 0, len(numbers) - 1, guess)
 
-print(f"Found {guess} at index {result}.")
+print(f"Found {guess} at index {result}. Running time {time.time() - start}")

chapter9/README.md

@@ -1,5 +0,0 @@
-# Shortest path for weighted graph (cost associated edges)
-
--   Dijkstra's algorithm works when all weights are non-negative
-    -   If there are negative weights use Bellman-Ford.
--   Priority queue + min heap is optimal when compared to a function that operates on a list.