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completed all tasks #2

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89 changes: 54 additions & 35 deletions src/Main.java
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Original file line number Diff line number Diff line change
Expand Up @@ -6,52 +6,52 @@
public class Main {

// The time complexity is:
// YOUR ANSWER HERE
// O(n^2) (Oh n-squared)
public static void timesTable(int x) {
for(int i = 1; i <= x; i++) {
for(int j = 1; j <= x; j++) {
System.out.print(i*j + " ");
for(int i = 1; i <= x; i++) { //O(n)
for(int j = 1; j <= x; j++) { //*O(n)
System.out.print(i*j + " ");//O(1)
}
System.out.println();
System.out.println(); //O(n)
}
}

// The time complexity is:
// YOUR ANSWER HERE
// O(n)
public static void printLetters(String word) {
char[] letters = word.toCharArray();
char[] letters = word.toCharArray(); //O(1)

for (char letter : letters) {
System.out.println(letter);
for (char letter : letters) { //O(n)
System.out.println(letter); //O(1)
}
}

// The time complexity is:
// YOUR ANSWER HERE
// O(1)
public static boolean isBanned(String password) {
String[] bannedPasswords = {"password", "hello", "qwerty"};
boolean banned = false;
for(String bannedPassword : bannedPasswords) {
String[] bannedPasswords = {"password", "hello", "qwerty"}; //O(1)
boolean banned = false; //O(1)
for(String bannedPassword : bannedPasswords) { //O(1) - fixed-length list (would be O(n) if variable)
if(password.equals(bannedPassword)) {
banned = true;
banned = true; //O(1)
}
}
return banned;
}


// The time complexity is:
// YOUR ANSWER HERE
// O(n)
public static int computeProduct(int[] nums) {
int total = 1;
for(int num : nums) {
total *= num;
int total = 1; //O(1)
for(int num : nums) { //O(n)
total *= num; //O(1)
}
return total;
}

// The time complexity is:
// YOUR ANSWER HERE
// O(n) (calling computeProduct(nums) which is O(n))
public static void describeProduct(int[] nums) {
System.out.println("About to compute the product of the array...");
int product = computeProduct(nums);
Expand All @@ -60,7 +60,7 @@ public static void describeProduct(int[] nums) {


// The time complexity is:
// YOUR ANSWER HERE
// O(n)
public static int computeFactorial(int n) {
int result = 1;
for(int i = 1; i <= n; i++) {
Expand All @@ -71,6 +71,8 @@ public static int computeFactorial(int n) {

// Assume that the largest number is no bigger than the length
// of the array

//Time complexity is O(n^2)
public static void computeAllFactorials(int[] nums) {
for(int num : nums) {
int result = computeFactorial(num);
Expand All @@ -80,7 +82,7 @@ public static void computeAllFactorials(int[] nums) {


// The time complexity is:
// YOUR ANSWER HERE
// O(n) - contains() performs linear search
public static void checkIfContainedArrayList(ArrayList<String> arr, String target) {
if (arr.contains(target)) {
System.out.println(target + " is present in the list");
Expand All @@ -92,7 +94,7 @@ public static void checkIfContainedArrayList(ArrayList<String> arr, String targe

// assume n = wordsA.length = wordsB.length
// The time complexity is:
// YOUR ANSWER HERE
// O(n^2)
public static boolean containsOverlap(String[] wordsA, String[] wordsB) {
for(String wordA : wordsA) {
for(String wordB : wordsB) {
Expand All @@ -105,7 +107,7 @@ public static boolean containsOverlap(String[] wordsA, String[] wordsB) {
}

// The time complexity is:
// YOUR ANSWER HERE
// O(n)
public static boolean containsOverlap2(String[] wordsA, String[] wordsB) {
Set<String> wordsSet = new HashSet<>();
for(String word : wordsA) {
Expand All @@ -122,20 +124,20 @@ public static boolean containsOverlap2(String[] wordsA, String[] wordsB) {
}

// The time complexity is:
// YOUR ANSWER HERE
// O(n)
public static void printCharacters(char[] chars) {
for (int i = 0; i < chars.length; i++) {
char character = chars[i];
System.out.println("The character at index " + i + " is " + character);
}
}
// The time complexity is:
// YOUR ANSWER HERE
// O(1)
public static double computeAverage(double a, double b) {
return (a + b) / 2.0;
}
// The time complexity is:
// YOUR ANSWER HERE
// O(1)
public static void checkIfContainedHashSet(HashSet<String> set, String target)
{
if (set.contains(target)) {
Expand All @@ -150,7 +152,7 @@ public static void checkIfContainedHashSet(HashSet<String> set, String target)
// A queryName is given, and this method returns the corresponding email if it is found
// Otherwise, it returns "Person not found"
// What is the time complexity of this method?
// YOUR ANSWER HERE
// O(n)
public static String emailLookup(String[] names, String[] emails, String queryName) {
for(int i = 0; i < names.length; i++) {
if (names[i].equals(queryName)) {
Expand All @@ -165,14 +167,22 @@ public static String emailLookup(String[] names, String[] emails, String queryNa
// keys are names and the values are emails.
// Write this method to efficiently return the corresponding email or "Person not found" if appropriate
// What is the time complexity of your solution?
// YOUR ANSWER HERE
// O(1)
public static String emailLookupEfficient(HashMap<String, String> namesToEmails, String queryName) {
return null;
//use HashMap get method to find email associated with queryName
String email = namesToEmails.get(queryName);

//if email null
if (email == null) {
return "Person not found";
}

return email;
}

// What is the time complexity of this method?
// (assume the set and list have the same number of elements)
// YOUR ANSWER HERE
// O(n^2) (this method is inefficient/backwards)
public static boolean hasCommon(HashSet<String> wordSet, ArrayList<String> wordList) {
for(String word : wordSet) {
if(wordList.contains(word)) {
Expand All @@ -184,30 +194,39 @@ public static boolean hasCommon(HashSet<String> wordSet, ArrayList<String> wordL
// Rewrite hasCommon so it does the same thing as hasCommon, but with a better time complexity.
// Do not change the datatype of wordSet or wordList.
// What is the time complexity of your new solution?
// YOUR ANSWER HERE
// O(n)
public static boolean hasCommonEfficient(HashSet<String> wordSet, ArrayList<String> wordList) {
return false;
//iterate over the wordList (instead of over wordSet)
for (String word : wordList) {
//check if word present in wordSet
if (wordSet.contains(word)) {
return true;
}
}
return false; //return false if no common word found
}

// Suppose you are building a dashboard that displays real-time stock prices.
// You want to keep track of the current price of each stock, with the stock's ticker symbol as the key.
// The prices will be updated frequently throughout the day, and you need to efficiently update
// and access the current price for each stock. The order of the ticker symbols is not important.
// What would be a good choice of data structure?
// YOUR ANSWER HERE
// ** HashMap (efficient lookups O(1); efficient updates
// O(1); order of ticker symbols not important)

// Suppose you are building a music player application where users can create playlists.
// Songs can be added to the end of the playlist in the order the user chooses, and the user can
// skip to the next or previous song. Most operations involve adding songs and accessing them by
// their position in the playlist.
// What would be a good choice of data structure?
// YOUR ANSWER HERE
// ** LinkedList (efficient adding of songs especially at end of list; users usually listen to songs in sequential order, LinkedList is good for this; random access would be O(n) however)

// Suppose you are developing a search feature that keeps track of the user's
// recent search queries. You want to store the queries in the order they were made,
// so you can display them to the user for quick access. The number of recent searches is
// relatively small, and it is more important to preserve the order of the searches than
// to optimize for fast lookups or deletions.
// What would be a good choice of data structure?
// YOUR ANSWER HERE
// ** I would think a LinkedList would again be the
//best choice here (a LinkedList implemented as a queue actually); this would preserve order for a small number of queries and allow for efficient additions
}