Welcome back to the final lesson of our course! Today, we're enhancing our algorithmic problem-solving skills with Go's map. Maps can be vital components in an application similar to a social network, ensuring that each user's data is both distinctive and easily accessible. By the end of this lesson, you will be familiar with using Go's map to efficiently tackle complex problems, especially those involving large data sets. Congratulations on reaching this stage—your dedication and hard work have brought you here!

We start our journey with the Majority Element Finder. Given an array of integers, your task is to identify whether the array contains a "celebrity" element. This integer appears more frequently than any other; more formally, it appears more than n/2 times.

Why is this important? Say you're analyzing sales data to find the most sold product in an online marketplace — recognizing the majority product can simplify marketing initiatives and inventory management. This is the real-world significance of the majority element problem.

To efficiently solve this, we'll use Go's map: our sophisticated voting system. With it, we can tally each product's sales as we go through the list without having to repeatedly scan the entire list for each product. It's like having a veteran cashier who has mastered their regular customers' buying habits.

Here is the full solution:

Go
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1func FindMajorityElement(arr []int) int {
2    countMap := make(map[int]int) // Initialize a map to count occurrences of each number
3    majorityThreshold := len(arr) / 2 // Define the majority threshold
4
5    for _, num := range arr {
6        countMap[num]++ // Increment the count for the current number
7        if countMap[num] > majorityThreshold { // Check if current number exceeds majority threshold
8            return num // Return the number if it's found to be the majority
9        }
10    }
11    return -1 // Return -1 if no majority element is found
12}

In the above code snippet:

• Initialization begins with creating a map called countMap to record the occurrences of each number, where the key represents the number and the value represents its count.
• Next, we determine the majorityThreshold, which is set to half the size of the array.
• Then, we iterate over each number in the array. For every number encountered, we increment its count in the map, thereby keeping track of its occurrences within the array.
• When checking majority element, after each count update, we compare it against the majorityThreshold. If a number's count surpasses this threshold, it is immediately returned as the majority element.
• If no number meets the majority condition, the function concludes by returning -1 to signal the absence of a majority element.

Imagine you’re building a search function for an application, requiring a feature that quickly retrieves all documents containing a specific keyword. If you've ever used your browser's Ctrl+F function, you've taken advantage of such an index! Our task is to create a map that acts like an index by linking a keyword to all the documents where it appears.

This function is crucial for search engines or reference databases, providing instant results when a word or phrase is queried. Indexing is an unsung hero in big data, where speed is precious.

Our map becomes our digital librarian, swiftly cataloging each word and its occurrences. It's the magic behind the rapid search results we often take for granted.

Here is the full solution:

Go
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1func CreateKeywordIndex(docs []string) map[string][]int {
2    index := make(map[string][]int) // Initialize a map to store words and their document indices
3
4    for i, doc := range docs {
5        words := strings.Fields(doc) // Split the document into individual words
6
7        for _, word := range words {
8            index[word] = append(index[word], i) // Append the document index to the word's list
9        }
10    }
11    return index // Return the constructed index map
12}

Let's go over the code above:

• During initialization, we create a map where each key is a distinct word and each associated value is a list holding the indices of documents where the word appears.
• We iterate over each document, using an index variable to represent the document's position.
• We split each document into individual words using whitespace as a delimiter.
• For each word encountered, we append the current document's index to its list in the map to build a connection between the word and its corresponding documents.
• We return the fully constructed keyword index map to enable rapid document retrieval based on specified keywords.

In summary, we've addressed two distinct algorithmic challenges by leveraging Go's map. From identifying a majority element in a sea of data to assembling a comprehensive keyword index, you've now experienced the robustness and efficiency that map offers. Roll up your sleeves as we transition from theoretical knowledge to practical application!