Minimum Number of Pushes to Type Word II

class Solution {
public:
    int minimumPresses(string text) {
        unordered_map<char, int> charCount;
    
        for (char& ch : text) {
            ++charCount[ch];
        }
    
        priority_queue<int> counts;
    
        for (const auto& pair : charCount) {
            counts.push(pair.second);
        }
    
        int presses = 0;
        int countIndex = 0;
    
        while (!counts.empty()) {
            presses += (1 + (countIndex / 8)) * counts.top();
            counts.pop();
            countIndex++;
        }
    
        return presses;
    }
};

The given C++ code defines a method to calculate the minimum number of key presses needed to type a given word on a keyboard with a special frequency-based mechanism. Follow these insights to understand the procedure:

• The function minimumPresses accepts a string text and initializes a hash map charCount to count the frequency of each character in the input string.
• It traverses through each character in text, incrementing its count in charCount.
• A priority queue counts is then used to store these frequencies in descending order.
• An integer presses is used to tally the total key presses necessary. Another integer, countIndex, helps to calculate additional presses due to higher frequency characters placed farther in a sublist or secondary options.
• As the characters are retrieved from the priority queue:
  • The calculation (1 + (countIndex / 8)) * counts.top() adjusts the presses needed based on the position (main or sub-lists due to high frequency of characters).
  • The character count (frequency) is popped from the priority queue, and countIndex is incremented.
• Finally, the total calculated presses are returned.

This mechanism makes sure to minimize the number of key presses by prioritizing characters based on their frequency of occurrence, allowing more frequently used characters to be typed with fewer presses.

class Solution {
    
    public int countMinimumPresses(String text) {
        // Hashmap to store the frequency of characters
        Map<Character, Integer> charCount = new HashMap<>();
    
        // Loop through each character in the string and store frequency
        for (char ch : text.toCharArray()) {
            charCount.put(ch, charCount.getOrDefault(ch, 0) + 1);
        }
    
        // Priority queue to hold frequencies in decreasing order
        PriorityQueue<Integer> queue = new PriorityQueue<>(
            Collections.reverseOrder()
        );
        queue.addAll(charCount.values());
    
        int pushesRequired = 0;
        int order = 0;
    
        // Compute the total number of presses needed
        while (!queue.isEmpty()) {
            pushesRequired += (order / 8 + 1) * queue.poll();
            order++;
        }
    
        return pushesRequired;
    }
}

In the provided Java code, you aim to determine the minimum number of key presses needed to type out a given string on a hypothetical keyboard layout. This implementation utilizes several data structures from Java's Collections framework to efficiently calculate the result.

• HashMap (charCount): Stores the frequency of each character in the string. The frequency is captured by iterating through each character in the string and updating the count in the map.

• PriorityQueue (queue): Holds the frequencies of characters in descending order. This allows the most frequent characters to be processed first, simulating an efficient typing mechanism where frequently used keys are presumably easier to access or require fewer pushes.

• Calculation of Pushes:

  • After populating the queue with character frequencies, the code initializes variables to store the cumulative number of pushes (pushesRequired) and the order or position of each character (order).
  • A loop runs as long as there are elements in the queue. Within each iteration, it computes the number of pushes required for the current most frequent character.
  • The presses are calculated based on the character's order, optimized by the formula (order / 8 + 1). This implies that every set of eight characters (or presses), the number of pushes increases, reflecting perhaps a new row or a shift in keyboard layout where characters beyond the first set take more effort or time to type.
  • The total presses for each character are then accumulated into pushesRequired.

By the end of this execution, pushesRequired holds the total number of key presses needed to type the entire string according to the defined keyboard press mechanism.

class Solution:
    def calcMinimumPushes(self, text: str) -> int:
        # Count of each character
        char_count = Counter(text)
    
        # Max-heap setup using negative values
        max_heap = [-count for count in char_count.values()]
        heapq.heapify(max_heap)
    
        pushes = 0
        position = 0
    
        # Processing the max_heap to compute pushes
        while max_heap:
            pushes += (1 + (position // 8)) * (-heapq.heappop(max_heap))
            position += 1
        return pushes

The Python code defines a class Solution with a method calcMinimumPushes that calculates the minimum number of "pushes" or actions required to type a given word, provided as the argument text. Here's how the solution works:

• First, it calculates the frequency of each character in the word using Counter from the collections module.
• It converts these counts into a max-heap. In Python, since the heapq library only provides a min-heap, the code multiplies the count by -1 to simulate a max-heap.
• The program initializes two variables: pushes to store the total number of pushes, and position to keep track of the current character's position.
• It iterates over the heap:
  • For each character (or heap element), it calculates the pushes needed by considering both the frequency of the character and the position of the character in multiple of 8 (since each push sequence resets after every 8 characters).
  • It then pops the element from the heap, adding to the total push count.
• Finally, the method returns the total number of pushes calculated.

This approach efficiently computes the required pushes using a max-heap to prioritize characters with higher frequencies, thereby optimizing the number of push sequences needed for repeated characters.