Merge pull request #101 from sounmind/main

Evan: Week 5

Author: DaleSeo

3sum/evan.js

@@ -0,0 +1,60 @@
+/**
+ * @param {number[]} nums
+ * @return {number[][]}
+ */
+var threeSum = function (nums) {
+  const sorted = nums.sort((a, b) => a - b);
+  const result = [];
+
+  for (let i = 0; i < sorted.length; i++) {
+    const fixedNumber = sorted[i];
+    const previousFixedNumber = sorted[i - 1];
+
+    if (fixedNumber === previousFixedNumber) {
+      continue;
+    }
+
+    let [leftEnd, rightEnd] = [i + 1, sorted.length - 1];
+
+    while (leftEnd < rightEnd) {
+      const sum = fixedNumber + sorted[leftEnd] + sorted[rightEnd];
+
+      if (sum === 0) {
+        result.push([sorted[leftEnd], sorted[rightEnd], sorted[i]]);
+
+        while (
+          sorted[leftEnd + 1] === sorted[leftEnd] ||
+          sorted[rightEnd - 1] === sorted[rightEnd]
+        ) {
+          if (sorted[leftEnd + 1] === sorted[leftEnd]) {
+            leftEnd += 1;
+          }
+
+          if (sorted[rightEnd - 1] === sorted[rightEnd]) {
+            rightEnd -= 1;
+          }
+        }
+
+        leftEnd += 1;
+        rightEnd -= 1;
+      } else if (sum < 0) {
+        leftEnd += 1;
+      } else {
+        rightEnd -= 1;
+      }
+    }
+  }
+
+  return result;
+};
+
+/**
+ * Time Complexity: O(n^2)
+ * The algorithm involves sorting the input array, which takes O(n log n) time.
+ * The main part of the algorithm consists of a loop that runs O(n) times, and within that loop, there is a two-pointer technique that runs in O(n) time.
+ * Thus, the overall time complexity is O(n log n) + O(n^2), which simplifies to O(n^2).
+ *
+ * Space Complexity: O(n)
+ * The space complexity is O(n) due to the space needed for the sorted array and the result array.
+ * Although the sorting algorithm may require additional space, typically O(log n) for the in-place sort in JavaScript, the dominant term is O(n) for the result storage.
+ */

encode-and-decode-strings/evan.js

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+const DELIMITER = "#";
+
+/**
+ *
+ * @param {string[]} strs
+ * @returns {string}
+ */
+function encode(strs) {
+  return strs.map((s) => `${s.length}${DELIMITER}${s}`).join("");
+}
+
+/**
+ *
+ * @param {string} encodedStr
+ * @returns {string[]}
+ */
+function decode(encodedStr) {
+  const decodedStrings = [];
+  let currentIndex = 0;
+
+  while (currentIndex < encodedStr.length) {
+    let delimiterIndex = currentIndex;
+
+    while (encodedStr[delimiterIndex] !== DELIMITER) {
+      delimiterIndex += 1;
+    }
+
+    const strLength = parseInt(
+      encodedStr.substring(currentIndex, delimiterIndex)
+    );
+
+    decodedStrings.push(
+      encodedStr.substring(delimiterIndex + 1, delimiterIndex + 1 + strLength)
+    );
+
+    currentIndex = delimiterIndex + 1 + strLength;
+  }
+
+  return decodedStrings;
+}
+/**
+ * Time Complexity: O(n) where n is the length of the encoded string.
+ * Reason:
+ *  The inner operations (finding # and extracting substrings) are proportional to the size of the encoded segments
+ *  but are ultimately bounded by the total length of the input string.
+ *
+ * Space Complexity: O(k) where k is the total length of the decoded strings.
+ */
+
+/**
+ * Test cases
+ */
+const strs4 = ["longestword", "short", "mid", "tiny"];
+const encoded4 = encode(strs4);
+console.log(encoded4); // Output: "11#longestword5#short3#mid4#tiny"
+
+const decoded4 = decode(encoded4);
+console.log(decoded4); // Output: ["longestword", "short", "mid", "tiny"]

longest-consecutive-sequence/evan.js

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+/**
+ * @param {number[]} nums
+ * @return {number}
+ */
+var longestConsecutive = function (nums) {
+  const set = new Set(nums);
+  let longestStreak = 0;
+
+  for (const num of set) {
+    // Check if it's the start of a sequence
+    if (!set.has(num - 1)) {
+      let currentNum = num;
+      let currentStreak = 1;
+
+      // Find the length of the sequence
+      while (set.has(currentNum + 1)) {
+        currentNum += 1;
+        currentStreak += 1;
+      }
+
+      longestStreak = Math.max(longestStreak, currentStreak);
+    }
+  }
+
+  return longestStreak;
+};
+
+/**
+ * Time Complexity: O(n) where n is the length of the input array.
+ * Reason:
+ *   Creating the Set: O(n)
+ *   Iterating Through the Set: O(n)
+ *   Checking for the Start of a Sequence: O(n)
+ *   Finding the Length of the Sequence: O(n) across all sequences
+ *   Tracking the Longest Sequence: O(n)
+ *
+ * Space Complexity: O(n) because of the additional space used by the set.
+ */

product-of-array-except-self/evan.js

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+/**
+ * @param {number[]} nums
+ * @return {number[]}
+ */
+var productExceptSelf = function (nums) {
+  const length = nums.length;
+  const result = Array(length).fill(1);
+
+  let leftProduct = 1;
+  for (let i = 0; i < length; i++) {
+    result[i] = leftProduct;
+    leftProduct *= nums[i];
+  }
+
+  let rightProduct = 1;
+  for (let i = length - 1; i >= 0; i--) {
+    result[i] *= rightProduct;
+    rightProduct *= nums[i];
+  }
+
+  return result;
+};
+
+/**
+ * Time Complexity: O(n)
+ * The algorithm iterates through the nums array twice (two separate loops), each taking O(n) time.
+ * Hence, the overall time complexity is O(2n), which simplifies to O(n).
+ *
+ * Space Complexity: O(1)
+ * The algorithm uses a constant amount of extra space for the leftProduct and rightProduct variables.
+ * The result array is not considered extra space as it is required for the output.
+ * Therefore, the extra space complexity is O(1).
+ */

top-k-frequent-elements/evan.js

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+/**
+ * @param {number[]} nums
+ * @param {number} k
+ * @return {number[]}
+ */
+var topKFrequent = function (nums, k) {
+  const counter = new Map();
+
+  nums.forEach((num) => {
+    if (counter.has(num)) {
+      counter.set(num, counter.get(num) + 1);
+    } else {
+      counter.set(num, 1);
+    }
+  });
+
+  const sorted = [...counter.entries()].sort(
+    ([, freqA], [, freqB]) => freqB - freqA
+  );
+
+  return sorted.slice(0, k).map(([num]) => num);
+};
+
+/**
+ * Time Complexity: O(n log n)
+ * - Counting the frequency of each element takes O(n) time.
+ * - Sorting the entries by frequency takes O(n log n) time.
+ * - Extracting the top k elements and mapping them takes O(k) time.
+ * - Therefore, the overall time complexity is dominated by the sorting step, resulting in O(n log n).
+
+ * Space Complexity: O(n)
+ * - The counter map requires O(n) space to store the frequency of each element.
+ * - The sorted array also requires O(n) space.
+ * - Therefore, the overall space complexity is O(n).
+ */