Daily Temperatures

class Solution {
    public int[] forecastHeating(int[] temps) {
        int len = temps.length;
        int maxTemp = 0;
        int result[] = new int[len];
        
        for (int i = len - 1; i >= 0; i--) {
            int tempToday = temps[i];
            if (tempToday >= maxTemp) {
                maxTemp = tempToday;
                continue;
            }
            
            int delay = 1;
            while (temps[i + delay] <= tempToday) {
                // Update delay based on previously computed results
                delay += result[i + delay];
            }
            result[i] = delay;
        }
        
        return result;
    }
}

The function forecastHeating in Java is designed to solve the problem of determining how many days you must wait for a warmer temperature, given an array of daily temperatures. The process involves iterating backwards through the array of temperatures and using a greedy algorithm to compute the waiting days efficiently.

• Initialize an integer array result of the same length as the input temps to store the number of days until a warmer day occurs for each temperature in temps.

• Start from the last day, working backwards, to utilize previously computed results for future computations.

• For every day, compare the temperature to the maximum temperature observed so far. If it is equal or greater, update the maximum temperature and move on since no future temperature would be warmer.

• If a warmer day in the future exists, calculate the number of days until that day by using a while loop which increments through subsequent days. The loop utilizes result from future days to skip over days that don't need individual checking, speeding up the process by avoiding unnecessary comparisons.

• Store the required delay in the result array for each day.

Given this methodology, the function efficiently calculates and returns an array indicating the number of days one would wait for a warmer temperature for each day given in temps. The use of backward traversal and leveraging previously calculated values minimizes redundant checks and optimizes performance.

class Solution:
    def findWarmerDays(self, temps: List[int]) -> List[int]:
        len_temps = len(temps)
        max_temp = 0
        days_until_warmer = [0] * len_temps
        
        for today in range(len_temps - 1, -1, -1):
            current_temp = temps[today]
            if current_temp >= max_temp:
                max_temp = current_temp
                continue
            
            days_offset = 1
            while temps[today + days_offset] <= current_temp:
                # Jump to next warmer day using previously stored results
                days_offset += days_until_warmer[today + days_offset]
            days_until_warmer[today] = days_offset

        return days_until_warmer

The given Python code provides a solution for finding the number of days one must wait until experiencing a warmer temperature based on a list of daily temperatures. The function implements an efficient approach that traverses the list from the last day to the first, using a stack data structure implicitly to track the temperatures and their indices.

Understand the steps and principles behind the implementation:

1. Initialize len_temps to store the length of the input list temps.

2. Set max_temp to zero to hold the maximum temperature observed as the list is processed from the end.

3. Create a list days_until_warmer of the same length as temps, initialized with zeros. This list will eventually contain the result, showing for each day how many days one must wait until a warmer temperature.

4. Loop through the list in reverse using a for loop (starting from the last element and moving to the first). This reverse loop helps in building the solution from known future values.

   • For each day, compare the day's temperature to max_temp.
   • If the current day’s temperature is equal or greater than max_temp, update max_temp to the current day's temperature. There is no need for further checks as no warmer days exist after this point.
   • If the current day's temperature is less than max_temp, initialize days_offset to 1. This variable helps to find the next warmer day.
     • Use a while loop to skip to the next known warmer days using the results already stored in days_until_warmer.
     • Increment days_offset by the values stored in days_until_warmer at the appropriate indices, thereby jumping several days ahead in a single step if possible.
   • Store the final calculated days_offset for the current day in days_until_warmer.

5. Return the days_until_warmer list, which now contains the number of days one must wait for a warmer temperature for each day in the list.

This method optimizes the search for the next warmer day by avoiding the examination of each subsequent day individually through intelligent use of previously computed values, significantly speeding up the process in typical scenarios.