clean up repo.

add more notes

.gitignore

@@ -160,3 +160,5 @@ cython_debug/
 #.idea/
 
 words_alpha.txt
+
+tests/

chapter1/README.md

@@ -0,0 +1,5 @@
+# Binary Search
+
+Repeatedly split the array checking if value is greater or less than the mid point. Stop when the exact value is found.
+
+It takes log N steps to reduce an array of size N to an array of size 1. Time complexity for this algorithm is `O(log N)`.

chapter1/binary.py

@@ -30,11 +30,14 @@ SAMPLE_SIZE = 1000
 numbers = random.sample(range(LOWER, UPPER), SAMPLE_SIZE)
 numbers.sort()
 
+seen = set()
+count = 0
 result = None
-while result is None:
+while not result:
     guess = random.randrange(LOWER, UPPER)
-    logger.debug(f"guess: {guess}")
-    result = binary_search(numbers, guess)
+    if guess not in seen:
+        count += 1
+        seen.add(guess)
+        result = binary_search(numbers, guess)
 
-
-print(f"Found {guess} at index {result}.")
+print(f"Found {guess} at index {result} after {count} attempts")

chapter1/ex1.1.py

@@ -1,7 +0,0 @@
-import math
-
-num_steps = int(math.log2(128))
-print(
-    f"A binary search would take maximum {num_steps} steps "
-    "to search a list of 128 items."
-)

chapter1/ex1.2.py

@@ -1,7 +0,0 @@
-import math
-
-num_steps = int(math.log2(128*2))
-print(
-    f"A binary search would take maximum {num_steps} steps "
-    "to search a list of 256 items."
-)

chapter2/README.md

@@ -0,0 +1,7 @@
+# Selection Sort
+
+We have to perform N swaps a total of N times. This takes N^N steps, therefore:
+
+This algorithm has time complexity `O(N^2)`
+
+Technically (`n –  1, n -  2 ... 2, 1` ~= N/2) swaps are performed but in BigO the constants are dropped.

chapter4/README.md

@@ -0,0 +1,12 @@
+# Recursion
+
+Recursive functions must have both:
+
+-   one or more base cases
+-   a recursive case
+
+The base cases are required to ensure the recursion stops when meeting a condition
+
+The recursive case adds functions onto the call stack and completes each one top down.
+
+Note. Quicksort should be implemented using a random pivot to ensure average runtimes.

chapter4/binary.py

@@ -32,10 +32,14 @@ SAMPLE_SIZE = 1000
 numbers = random.sample(range(LOWER, UPPER), SAMPLE_SIZE)
 numbers.sort()
 
+seen = set()
+count = 0
 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)
+    if guess not in seen:
+        count += 1
+        seen.add(guess)
+        result = binary_search(numbers, 0, len(numbers) - 1, guess)
 
-print(f"Found {guess} at index {result}.")
+print(f"Found {guess} at index {result} after {count} attempts.")

chapter6/README.md

@@ -0,0 +1,9 @@
+# Breadth-First Search
+
+Can tell you if there's a path between A and B and will find the shortest.
+
+In these examples, 1st degree Mango sellers are found before 2nd degree, 2nd before 3rd and so on.
+
+Visted nodes should be stored in a set to ensure no infinite loops.
+
+Running time for BFS on a directed graph: `O(V + E`) where V = vertices, E = edges.

chapter9/README.md

@@ -2,4 +2,4 @@
 
 -   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.
+-   The book demonstrates a function that operates on a list. Priority queue + min heap added for completeness.

chapter9/examples/heaptest.py

@@ -1,10 +0,0 @@
-import heapq
-
-customers = []
-heapq.heappush(customers, (2, "Harry"))
-heapq.heappush(customers, (3, "Charles"))
-heapq.heappush(customers, (1, "Riya"))
-heapq.heappush(customers, (4, "Stacy"))
-
-while customers:
-    print(heapq.heappop(customers))

chapter9/examples/list.py

@@ -1,14 +0,0 @@
-customers = []
-customers.append((2, "Harry"))  # no sort needed here because 1 item.
-customers.append((3, "Charles"))
-customers.sort(reverse=True)
-# Need to sort to maintain order
-customers.append((1, "Riya"))
-customers.sort(reverse=True)
-# Need to sort to maintain order
-customers.append((4, "Stacy"))
-customers.sort(reverse=True)
-
-while customers:
-    print(customers.pop(0))
-# Will print names in the order: Stacy, Charles, Harry, Riya.

chapter9/examples/priorityqueuetest.py

@@ -1,12 +0,0 @@
-from queue import PriorityQueue
-
-customers = (
-    PriorityQueue()
-)  # we initialise the PQ class instead of using a function to operate upon a list.
-customers.put((2, "Harry"))
-customers.put((3, "Charles"))
-customers.put((1, "Riya"))
-customers.put((4, "Stacy"))
-
-while customers:
-    print(customers.get())