
    Design a predictive algorithm that calculates the expected hospital bed occupancy pattern for a specific future day (n) based on the hospital's cyclical admission-discharge model, where each day's occupancy is determined by the sum of occupancy rates from two previous key days, with the pattern following that day's position represents the optimal bed allocation number. The sequence starts with 1,1 for the first two days, and each subsequent day's value is determined by the sum of the values at positions P(P(n-1)) and P(n-P(n-1)), where P(n) represents the bed occupancy pattern for day n.
    

    
def sequence(n):
    """
    Design a predictive algorithm that calculates the expected hospital bed occupancy pattern for a specific future day (n) based on the hospital's cyclical admission-discharge model, where each day's occupancy is determined by the sum of occupancy rates from two previous key days, with the pattern following that day's position represents the optimal bed allocation number. The sequence starts with 1,1 for the first two days, and each subsequent day's value is determined by the sum of the values at positions P(P(n-1)) and P(n-P(n-1)), where P(n) represents the bed occupancy pattern for day n.
    

    """
def sequence(n):
    """
    Design a predictive algorithm that calculates the expected hospital bed occupancy pattern for a specific future day (n) based on the hospital's cyclical admission-discharge model, where each day's occupancy is determined by the sum of occupancy rates from two previous key days, with the pattern following that day's position represents the optimal bed allocation number. The sequence starts with 1,1 for the first two days, and each subsequent day's value is determined by the sum of the values at positions P(P(n-1)) and P(n-P(n-1)), where P(n) represents the bed occupancy pattern for day n.
    

    """
    if n == 1 or n == 2:
        return 1
    else:
        return sequence(sequence(n - 1)) + sequence(n - sequence(n - 1))