python: unit tests for binary search

.gitignore

@@ -40,3 +40,5 @@ highlights/package-lock.json
 *.sublime-workspace
 /.env
 atlassian-ide-plugin.xml
+__pycache__
+.vscode

01_introduction_to_algorithms/python/01_binary_search.py

@@ -1,28 +0,0 @@
-def binary_search(list, item):
-  # low and high keep track of which part of the list you'll search in.
-  low = 0
-  high = len(list) - 1
-
-  # While you haven't narrowed it down to one element ...
-  while low <= high:
-    # ... check the middle element
-    mid = (low + high) // 2
-    guess = list[mid]
-    # Found the item.
-    if guess == item:
-      return mid
-    # The guess was too high.
-    if guess > item:
-      high = mid - 1
-    # The guess was too low.
-    else:
-      low = mid + 1
-
-  # Item doesn't exist
-  return None
-
-my_list = [1, 3, 5, 7, 9]
-print(binary_search(my_list, 3)) # => 1
-
-# 'None' means nil in Python. We use to indicate that the item wasn't found.
-print(binary_search(my_list, -1)) # => None

01_introduction_to_algorithms/python/binary_search.py

@@ -0,0 +1,58 @@
+class BinarySearch():
+
+  def search_iterative(self, list, item):
+    # low and high keep track of which part of the list you'll search in.
+    low = 0
+    high = len(list) - 1
+
+    # While you haven't narrowed it down to one element ...
+    while low <= high:
+      # ... check the middle element
+      mid = (low + high) // 2
+      guess = list[mid]
+      # Found the item.
+      if guess == item:
+        return mid
+      # The guess was too high.
+      if guess > item:
+        high = mid - 1
+      # The guess was too low.
+      else:
+        low = mid + 1
+
+    # Item doesn't exist
+    return None
+
+  def search_recursive(self, list, low, high, item):
+    # Check base case 
+    if high >= low: 
+  
+        mid = (high + low) // 2
+        guess = list[mid]
+  
+        # If element is present at the middle itself 
+        if guess == item:
+            return mid 
+  
+        # If element is smaller than mid, then it can only 
+        # be present in left subarray 
+        elif guess > item: 
+            return self.search_recursive(list, low, mid - 1, item) 
+  
+        # Else the element can only be present in right subarray 
+        else: 
+            return self.search_recursive(list, mid + 1, high, item) 
+  
+    else: 
+        # Element is not present in the array 
+        return None
+
+if __name__ == "__main__":
+  # We must initialize the class to use the methods of this class
+  bs = BinarySearch()
+  my_list = [1, 3, 5, 7, 9]
+  
+  print(bs.search_iterative(my_list, 3)) # => 1
+
+  # 'None' means nil in Python. We use to indicate that the item wasn't found.
+  print(bs.search_iterative(my_list, -1)) # => None

01_introduction_to_algorithms/python/test_binary_search.py

@@ -0,0 +1,107 @@
+from binary_search import BinarySearch
+import unittest
+import json
+import time
+
+bs = BinarySearch()
+
+# Unloading all lists from a file
+with open("items.json", "r") as file:
+  data = json.load(file)
+
+# Setting values to created variables
+simple_list = data["simple_list"]
+list_with_10_items = data["list_with_10_items"]
+list_with_100_items = data["list_with_100_items"]
+list_with_1000_items = data["list_with_1000_items"]
+
+
+# Test cases to test Binary Search algorithm
+class TestBinarySearch(unittest.TestCase):
+
+  def setUp(self):
+    print (".......... %s" % self._testMethodName)
+
+  # Checking the implementation of iterative binary search
+  def test_iterative_binary_search_with_simple_list(self):
+    # ARRANGE
+    # You can check the index of each item in the items.json file
+    item, expected_index = 3, 1
+
+    # ACT
+    # Run the method we created and get the index of the item we were looking for
+    index = bs.search_iterative(simple_list, item) # => 1
+
+    # ASSERT
+    # Compare the resulting index with the expected index
+    self.assertEqual(expected_index, index) # => 1 == 1
+
+  def test_recoursive_binary_search_with_simple_list(self):
+    item, expected_index = 3, 1
+    # To run recursive search for an item, 
+    # we need to determine the minimum and maximum indexes of the list
+    low, high = 0, len(simple_list) - 1
+
+    index = bs.search_recursive(simple_list, low, high, item)
+
+    self.assertEqual(expected_index, index)
+
+  def test_search_for_nonexistent_item(self):
+    # Specifically set a number that is not in the list
+    item, expected_result = 100, None
+
+    # Trying to find an item that doesn't exist
+    index = bs.search_iterative(simple_list, item) # => None
+
+    self.assertEqual(expected_result, index)
+
+  def test_binary_search_and_linear_search_execution_time(self):
+    item, expected_index = 9887, 990
+
+    # Time required to search
+    start_time = time.time()
+    binary_search_index = bs.search_iterative(list_with_1000_items, item) # => None
+    bs_time = time.time() - start_time
+ 
+    # list.index(x) return the index in the list of the first item whose value is x. 
+    # It is an error if there is no such item.
+    # Complexity of list.index(x) is O(n)
+    start_time = time.time()
+    linear_search_index = list_with_1000_items.index(item)
+    ls_time = time.time() - start_time
+
+    self.assertEqual(expected_index, binary_search_index)
+    self.assertEqual(expected_index, linear_search_index)
+    self.assertTrue(bs_time < ls_time)
+
+    # print("--- Time required to search item at the ending ---")
+    # print("--- Linear Search %f seconds ---" % (ls_time))
+    # print("--- Binary Search %f seconds ---" % (bs_time))
+
+  def test_execution_time_for_item_at_the_beginning(self):
+    item, expected_index = 55, 10
+
+    # Binary search - time required to search
+    start_time = time.time()
+    binary_search_index = bs.search_iterative(list_with_1000_items, item) # => None
+    bs_time = time.time() - start_time
+ 
+    # Linear search - time required to search
+    start_time = time.time()
+    linear_search_index = list_with_1000_items.index(item)
+    ls_time = time.time() - start_time
+
+    self.assertEqual(expected_index, binary_search_index)
+    self.assertEqual(expected_index, linear_search_index)
+
+    # linear search will be faster, since the item we are looking for 
+    # is at the beginning of the list
+    self.assertTrue(bs_time > ls_time)
+
+    # print("--- Time required to search item at the beginning ---")
+    # print("--- Linear Search %f seconds ---" % (ls_time))
+    # print("--- Binary Search %f seconds ---" % (bs_time))
+
+  
+if __name__ == '__main__':
+    unittest.main()