动态规划算法安排排课问题代码

Sure! Here's an example of how you can use dynamic programming to solve the course scheduling problem:

def schedule_courses(courses):
    n = len(courses)
    dp = [0] * (n+1)

    # Sort courses by end time
    courses.sort(key=lambda x: x[1])

    for i in range(1, n+1):
        # Find the last course that doesn't conflict with the current course
        j = i - 1
        while j >= 0 and courses[j][1] > courses[i-1][0]:
            j -= 1

        # Update the maximum number of courses that can be scheduled at this point
        dp[i] = max(dp[i-1], dp[j+1] + 1)

    return dp[n]

In this code, `courses` is a list of tuples where each tuple represents a course, and the first element is the start time of the course and the second element is the end time. The goal is to schedule as many non-overlapping courses as possible.

The algorithm sorts the courses by their end times. Then, it uses dynamic programming to calculate the maximum number of courses that can be scheduled up to each course. The `dp` array stores these maximum values. The inner loop finds the last course that doesn't conflict with the current course and updates the `dp` array accordingly.

Finally, the function returns the maximum number of courses that can be scheduled.

Keep in mind that this is just a simple example, and you may need to modify it based on your specific requirements or constraints.