Added first 3 tests

Project 3/project_3_search.py

@@ -67,11 +67,37 @@ class Search:
         else:
             return self.binary_search_recursive(arr, target, low, mid - 1)
         
-        
-### write your tests in main function
 def main():
-    pass
+    search = Search() #search object to call Search() class
 
+    """
+    Part 1: Implement a Linear Search in your language of choice [Python]. Use the following plan to test your implementation on an array [Python List] of 100 randomly generated values (in random order). Randomly generate 100 values, and use Linear Search to find the value 42. Have your search print the number of unsuccessful checks before finding the value 42 (or reporting not found). 
+    """
+    arr = search.generate_random_list(100) #generate the list of 100 random integers
+    search.linear_search_print(arr, 42) #search for the value 42 using linear search and print the number of checks it took to find 42
+
+    """
+    Part 2: Take the search function from exercise 1, and modify it to count and return the number of checks Linear Search takes to find the value 42 in a random array. Write a loop to repeat this experiment 100 times, and average the number of checks it takes to find a specific value. What is that number close to? How does it change if you increase the number of tests from 100 to 1,000?
+    """
+    total = 0
+    tests = 100
+
+    for _ in range(tests):
+        arr = search.generate_random_list(100) #generate the list of 100 random integers
+        total += search.linear_search_count(arr, 42) #search for the value 42 using linear search and add the number of checks it took to find 42 to the total
+    print("Average number of checks for 100 tests:", total / tests) #print the average number of checks it took to find 42 for 100 tests
+
+    """
+    Part 3: The reasoning used to determine the time complexity of Binary Search closely resembles similar arguments from chapter 2 on recursion. Implement Binary Search as a recursive algorithm by adding extra parameters for the high and low variables. Make sure your function is tail-recursive to facilitate tail-call optimization.
+    """
+    arr = sorted(search.generate_random_list(100)) #generate the list of 100 random integers and sort it for binary search
+    index = search.binary_search_recursive(arr, 42, 0, len(arr) - 1) #search for the value 42 using binary search and get the index of 42
+    print("Index of 42 in sorted array:", index) #print the index of 42 in the sorted array
+
+    """
+    Part 4: With your implementations of Linear and Binary Search, write some tests to generate a number of random queries. Calculate the total time to conduct n/2 queries on a randomly generated dataset. Be sure to include the sorting time for your Binary Search database before calculating the total time for all queries. Compare your result to the Linear Search total query time. Next, repeat this process for n, 2*n, and 4*n queries. At what number of queries does Sorting + Binary Search start to show an advantage over Linear Search?
+    """
+    
 #  only execute when you run this file directily
 if __name__ == "__main__":
     main()