[나리] WEEK 01 Solutions

contains-duplicate/naringst.js

@@ -0,0 +1,24 @@
+/**
+ * @param {number[]} nums
+ * @return {boolean}
+ */
+
+/**
+ * Runtime: 89ms, Memory: 63MB
+ *
+ * Time complexity: O(n)
+ *  - To find the length of an array: O(n)
+ *  - Array to Set: O(n)
+ *  - To find the size of a set: O(n)
+ * Space complexity: O(n)
+ *  - arrToSet: O(n)
+ *
+ * **/
+
+var containsDuplicate = function (nums) {
+  const arrLength = nums.length;
+  const arrToSet = new Set(nums);
+  const setLength = arrToSet.size;
+
+  return arrLength !== setLength;
+};

number-of-1-bits/naringst.js

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+/**
+ * @param {number} n
+ * @return {number}
+ */
+
+/**
+ * Runtime: 59ms, Memory: 50.76MB
+ *
+ * Time complexity: O(logN)
+ * -> While
+ * Space complexity: O(logN)
+ * -> To save binary.split()
+ *
+ * **/
+
+var hammingWeight = function (n) {
+  let binary = "";
+
+  while (n > 1) {
+    let left = n % 2;
+    binary += left.toString();
+    n = Math.floor(n / 2);
+  }
+  binary = binary + n.toString();
+
+  const count = binary.split("1").length - 1;
+
+  return count;
+};

palindromic-substrings/naringst.js

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+/**
+ * @param {string} s
+ * @return {number}
+ */
+
+/**
+ * Runtime: 1521ms, Memory: 56.61MB
+ *
+ * Time complexity: O(N^2)
+ * Space complexity: O(N^2)
+ *
+ * Note: necessary to think of an alternative approach
+ * **/
+
+function isPalindrome(subString) {
+  const len = subString.length;
+  for (let i = 0; i < len / 2; i++) {
+    if (subString[i] !== subString[len - 1 - i]) {
+      return false;
+    }
+    return true;
+  }
+}
+
+var countSubstrings = function (s) {
+  const n = s.length;
+  let answer = n;
+
+  for (let i = 0; i < n; i++) {
+    for (let j = i + 1; j < n; j++) {
+      let subString = s.slice(i, j + 1);
+      if (isPalindrome(subString)) {
+        answer += 1;
+      }
+    }
+  }
+
+  return answer;
+};

top-k-frequent-elements/naringst.js

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+/**
+ * @param {number[]} nums
+ * @param {number} k
+ * @return {number[]}
+ */
+
+/**
+ * Runtime: 64ms, Memory: 53.31MB
+ *
+ * Time complexity: O(NlogN)
+ *  - frquentEntries.sort: NlogN
+ * Space complexity: O(n)
+ *
+ * **/
+
+var topKFrequent = function (nums, k) {
+  let answer = [];
+
+  const frequent = {};
+  for (const num of nums) {
+    frequent[num] = frequent[num] ? frequent[num] + 1 : 1;
+  }
+
+  const frequentEntries = Object.entries(frequent);
+  frequentEntries.sort((a, b) => b[1] - a[1]);
+
+  const topK = frequentEntries.slice(0, k).map((i) => i[0]);
+
+  return topK;
+};