[{"keyword":"\ub514\uc544","rank":0},{"keyword":"\ub9d0\ub538","rank":1},{"keyword":"\ubd89\uc740\uc0ac\ub9c9","rank":-1},{"keyword":"\ud2b8\ub9ad\uceec","rank":0},{"keyword":"\ube14\ub8e8\uc544\uce74","rank":0},{"keyword":"\ubc84\ud29c\ubc84","rank":0},{"keyword":"\ub514\uc5442","rank":0},{"keyword":"\uc18c\uc8042","rank":"new"},{"keyword":"\uc544\uc774\uc628","rank":2},{"keyword":"\uba85\uc870","rank":-2},{"keyword":"\ubd89\uc740","rank":4},{"keyword":"\ub9bc\ubc84\uc2a4","rank":2},{"keyword":"\uc778\ubc29","rank":"new"},{"keyword":"\uc820\ub808\uc2a4","rank":-4},{"keyword":"\ub358\ud30c","rank":3},{"keyword":"\ub358\uc804\ubc25","rank":-4},{"keyword":"\uc18c\uc804","rank":5},{"keyword":"\u3147\u3147\u3131","rank":-5},{"keyword":"\uc720\ud76c\uc655","rank":-2},{"keyword":"\ub2c8\ucf00","rank":-1},{"keyword":"\ud5e4\ube10\ud5ec\uc988","rank":"new"},{"keyword":"\ud658\uc728","rank":"new"}]
(IP보기클릭)58.77.***.***
문제를 정확히 이해 못 하신거 같네요. 이런거는 chatGPT 한테 물어보면서 조금씩 이해하면서 개선해 나가는게 빨라요. 님이 쓴 그대로 chatGPT 한테 물어보니 아래 코드 줬네요. import numpy as np def generate_particles(space_size, num_particles): particles = np.random.rand(space_size, space_size) # 가우시안 분포로 중간에 0 설정 mid_x = space_size // 2 mid_y = space_size // 2 std_dev = space_size // 6 particles[mid_x, mid_y] = 0 particles = np.random.normal(loc=0, scale=std_dev, size=(space_size, space_size)) particles[particles < 0] = 0 return particles def particle_filter(space_size, num_particles, num_iterations): for i in range(num_iterations): particles = generate_particles(space_size, num_particles) print(f"Iteration {i + 1}:\n{particles}\n") if __name__ == "__main__": space_size = 1000 num_particles = 100 num_iterations = 10 particle_filter(space_size, num_particles, num_iterations) 제 생각에 저 코드에다가 시각화 하는걸 추가하면 좋은 답안이 될 것 같네요.
(IP보기클릭)58.77.***.***
문제를 정확히 이해 못 하신거 같네요. 이런거는 chatGPT 한테 물어보면서 조금씩 이해하면서 개선해 나가는게 빨라요. 님이 쓴 그대로 chatGPT 한테 물어보니 아래 코드 줬네요. import numpy as np def generate_particles(space_size, num_particles): particles = np.random.rand(space_size, space_size) # 가우시안 분포로 중간에 0 설정 mid_x = space_size // 2 mid_y = space_size // 2 std_dev = space_size // 6 particles[mid_x, mid_y] = 0 particles = np.random.normal(loc=0, scale=std_dev, size=(space_size, space_size)) particles[particles < 0] = 0 return particles def particle_filter(space_size, num_particles, num_iterations): for i in range(num_iterations): particles = generate_particles(space_size, num_particles) print(f"Iteration {i + 1}:\n{particles}\n") if __name__ == "__main__": space_size = 1000 num_particles = 100 num_iterations = 10 particle_filter(space_size, num_particles, num_iterations) 제 생각에 저 코드에다가 시각화 하는걸 추가하면 좋은 답안이 될 것 같네요.