remove print numbers

upd chapter_01/binary.py

README.md

@@ -40,13 +40,13 @@
 
 [chapter12][knn]
 
-[binary]: ./chapter1/
-[selection_sort]: ./chapter2/
-[recursion]: ./chapter3/
-[quick_sort]: ./chapter4/
-[bfs]: ./chapter6/
-[trees]: ./chapter7/
-[dijkstra]: ./chapter9/
-[greedy]: ./chapter10/
-[dynamic]: ./chapter11/
-[knn]: ./chapter12/
+[binary]: ./chapter_01/
+[selection_sort]: ./chapter_02/
+[recursion]: ./chapter_03/
+[quick_sort]: ./chapter_04/
+[bfs]: ./chapter_06/
+[trees]: ./chapter_07/
+[dijkstra]: ./chapter_09/
+[greedy]: ./chapter_10/
+[dynamic]: ./chapter_11/
+[knn]: ./chapter_12/

chapter1/binary.py

@@ -1,43 +0,0 @@
-import logging
-import random
-
-logging.basicConfig(level=logging.DEBUG)
-logger = logging.getLogger(__name__)
-
-
-def binary_search(arr, item):
-    low = 0
-    high = len(arr) - 1
-
-    while low <= high:
-        mid = (low + high) // 2
-        guess = arr[mid]
-        if guess == item:
-            return mid
-        elif guess > item:
-            high = mid - 1
-        else:
-            low = mid + 1
-
-    return None
-
-
-LOWER = 1000
-UPPER = 1000000
-SAMPLE_SIZE = 1000
-
-
-numbers = random.sample(range(LOWER, UPPER), SAMPLE_SIZE)
-numbers.sort()
-
-seen = set()
-count = 0
-result = None
-while not result:
-    guess = random.randrange(LOWER, UPPER)
-    if guess not in seen:
-        count += 1
-        seen.add(guess)
-        result = binary_search(numbers, guess)
-
-print(f"Found {guess} at index {result} after {count} attempts")

chapter_01/binary.py

@@ -0,0 +1,43 @@
+import logging
+import random
+
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
+
+
+class BinarySearch:
+    def __init__(self, arr):
+        self._arr = arr
+
+    def search(self, item):
+        low = 0
+        high = len(self._arr) - 1
+
+        while low <= high:
+            mid = (low + high) // 2
+            guess = self._arr[mid]
+            if guess == item:
+                return mid
+            elif guess > item:
+                high = mid - 1
+            else:
+                low = mid + 1
+
+        return None
+
+
+LOWER = 1000
+UPPER = 1000000
+SAMPLE_SIZE = 1000
+
+
+numbers = random.sample(range(LOWER, UPPER), SAMPLE_SIZE)
+numbers.extend([9000, 999999])
+numbers.sort()
+
+search = BinarySearch(numbers)
+print(search.search(42))
+print(search.search(9000))
+print(search.search(20000))
+print(search.search(60000))
+print(search.search(999999))

chapter_09/with_heap/ex171a.py

@@ -20,10 +20,15 @@ def dijkstra(graph, node):
     costs[node] = 0
     parents = {node: None for node in graph}
     queue = [(0, node)]
+    visited = set()
 
     while queue:
         current_cost, current_node = heapq.heappop(queue)
+        if current_node in visited:
+            continue
+
         logger.debug(f"node {current_node} with cost {current_cost} popped from pqueue")
+        visited.add(current_node)
 
         for next_node, weight in graph[current_node].items():
             new_cost = current_cost + weight

chapter_09/with_heap/ex171b.py

@@ -19,10 +19,15 @@ def dijkstra(graph, node):
     costs[node] = 0
     parents = {node: None for node in graph}
     queue = [(0, node)]
+    visited = set()
 
     while queue:
         current_cost, current_node = heapq.heappop(queue)
+        if current_node in visited:
+            continue
+
         logger.debug(f"node {current_node} with cost {current_cost} popped from pqueue")
+        visited.add(current_node)
 
         for next_node, weight in graph[current_node].items():
             new_cost = current_cost + weight

chapter_09/with_heap/ex171c.py

@@ -19,10 +19,15 @@ def dijkstra(graph, node):
     costs[node] = 0
     parents = {node: None for node in graph}
     queue = [(0, node)]
+    visited = set()
 
     while queue:
         current_cost, current_node = heapq.heappop(queue)
+        if current_node in visited:
+            continue
+
         logger.debug(f"node {current_node} with cost {current_cost} popped from pqueue")
+        visited.add(current_node)
 
         for next_node, weight in graph[current_node].items():
             new_cost = current_cost + weight