Glasgow | 26-Jul-SDC | Shreef Ibrahim | Sprint 2 | Analyse Refactor Functions

Sprint-1/JavaScript/calculateSumAndProduct/calculateSumAndProduct.js

@@ -9,26 +9,27 @@
  *   "product": 30 // 2 * 3 * 5
  * }
  *
- * Time Complexity:
- * Space Complexity:
- * Optimal Time Complexity:
+ * Time Complexity: O(n)
+ * Space Complexity: O(1)
+ * Optimal Time Complexity: O(n)
  *
  * @param {Array<number>} numbers - Numbers to process
  * @returns {Object} Object containing running total and product
  */
 export function calculateSumAndProduct(numbers) {
   let sum = 0;
-  for (const num of numbers) {
-    sum += num;
-  }
-
   let product = 1;
   for (const num of numbers) {
-    product *= num;
+    sum += num;
+    product *= num
   }
-
   return {
     sum: sum,
     product: product,
   };
 }
+//Time Complexity: O(n)  The loop visits each element exactly once
+// Space complexity is O(1) he function uses a fixed number of variables, regardless of the input size.
+//  The returned object also has a fixed size.
+// Optimal time complexity is O(n) because every element must be examined
+// to guarantee the correct sum and product.

Sprint-1/JavaScript/findCommonItems/findCommonItems.js

@@ -1,14 +1,32 @@
 /**
  * Finds common items between two arrays.
- *
- * Time Complexity:
- * Space Complexity:
- * Optimal Time Complexity:
+ * 
+ * Time Complexity: O(n * m)
+ * Space Complexity: O(n)
+ * Optimal Time Complexity: O(n + m)
  *
  * @param {Array} firstArray - First array to compare
  * @param {Array} secondArray - Second array to compare
  * @returns {Array} Array containing unique common items
  */
-export const findCommonItems = (firstArray, secondArray) => [
-  ...new Set(firstArray.filter((item) => secondArray.includes(item))),
-];
+export const findCommonItems = (firstArray, secondArray) => {
+  const allowedItems = new Set(secondArray);
+
+  const uniqueValues = [
+    ...new Set(firstArray.filter((item) => allowedItems.has(item))),
+  ];
+  return uniqueValues;
+};
+
+console.log(findCommonItems([2, 5, 7], [5, 6, 7]));
+
+
+// Time Complexity: O(n + m) on average.
+// Building the Set takes O(m), and filtering the first array takes O(n)
+// on average because Set.has() provides efficient lookups.
+
+// Space Complexity: O(n + m).
+// The Set stores elements from secondArray, while the filter result
+// and the Set of unique common items require additional space.
+
+// Improvement: Reduces average time complexity from O(n * m) to O(n + m).

Sprint-1/JavaScript/hasPairWithSum/hasPairWithSum.js

@@ -4,18 +4,29 @@
  * Time Complexity:
  * Space Complexity:
  * Optimal Time Complexity:
+ * 
+ * Time Complexity: O(N^2)
+// Space Complexity: O(1)
+// Optimal Time Complexity: O(N)
  *
  * @param {Array<number>} numbers - Array of numbers to search through
  * @param {number} target - Target sum to find
  * @returns {boolean} True if pair exists, false otherwise
  */
 export function hasPairWithSum(numbers, target) {
-  for (let i = 0; i < numbers.length; i++) {
-    for (let j = i + 1; j < numbers.length; j++) {
-      if (numbers[i] + numbers[j] === target) {
-        return true;
-      }
-    }
+  const seenNumbers = new Set();
+
+  for (const currentNumber of numbers) {
+    const complement = target - currentNumber;
+
+    if (seenNumbers.has(complement)) return true;
+
+    seenNumbers.add(currentNumber);
   }
   return false;
+
 }
+
+
+// Time Complexity: O(n) We check each number once.
+// Space Complexity: O(n) The Set may store all n numbers.

Sprint-1/JavaScript/removeDuplicates/removeDuplicates.mjs

@@ -9,28 +9,21 @@
  * @returns {Array} New sequence with duplicates removed
  */
 export function removeDuplicates(inputSequence) {
-  const uniqueItems = [];
 
-  for (
-    let currentIndex = 0;
-    currentIndex < inputSequence.length;
-    currentIndex++
-  ) {
-    let isDuplicate = false;
-    for (
-      let compareIndex = 0;
-      compareIndex < uniqueItems.length;
-      compareIndex++
-    ) {
-      if (inputSequence[currentIndex] === uniqueItems[compareIndex]) {
-        isDuplicate = true;
-        break;
-      }
-    }
-    if (!isDuplicate) {
-      uniqueItems.push(inputSequence[currentIndex]);
-    }
-  }
+  return [...new Set(inputSequence)]
 
-  return uniqueItems;
 }
+// Original complexity:
+// The original approach typically uses nested loops to compare
+// each value with the remaining values in the sequence.
+// This results in O(n²) time complexity because each value
+// may be compared with every other value.
+
+
+// Refactor:
+// Use a Set to remove duplicate values while preserving the
+// order of their first occurrence.
+// The Set stores only unique values, and spreading the Set
+// into a new array creates the result.
+// Set insertion is O(1) on average, so processing all n
+// elements takes O(n) time overall.