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无编辑摘要
Boids 模型已经被用于其他有趣应用。该系统已应用于互联网多频道广播电台的自动节目编排，它也被用于可视化信息和优化任务。<ref>{{cite conference| title= DJ-boids: emergent collective behavior as multichannel radio station programming| first1 = Jesús | last1 = Ibáñez| first2 = Antonio F. | last2 = Gómez-Skarmeta| first3 = Josep | last3 = Blat| date = 2003| booktitle = Proceedings of the 8th international conference on Intelligent User Interfaces| pages = 248–250| doi= 10.1145/604045.604089}}</ref>以及可视化信息<ref>{{cite conference| title= Time-Varying Data Visualization Using Information Flocking Boids| last = Moere | first = A V| date= 2004| booktitle = Proceedings of the IEEE Symposium on Information Visualization| pages= 97–104| doi= 10.1109/INFVIS.2004.65}}</ref>和优化任务。<ref>{{cite journal|first1 = Zhihua | last1 = Cui|first2 = Zhongzhi | last2 = Shi|title= Boid particle swarm optimisation|journal= International Journal of Innovative Computing and Applications|volume=2 |issue=2 |pages= 77–85|date=2009|doi= 10.1504/IJICA.2009.031778}}</ref>
Boids 模型已经被用于其他有趣应用。该系统已应用于互联网多频道广播电台的自动节目编排，它也被用于可视化信息和优化任务。<ref>{{cite conference| title= DJ-boids: emergent collective behavior as multichannel radio station programming| first1 = Jesús | last1 = Ibáñez| first2 = Antonio F. | last2 = Gómez-Skarmeta| first3 = Josep | last3 = Blat| date = 2003| booktitle = Proceedings of the 8th international conference on Intelligent User Interfaces| pages = 248–250| doi= 10.1145/604045.604089}}</ref>以及可视化信息<ref>{{cite conference| title= Time-Varying Data Visualization Using Information Flocking Boids| last = Moere | first = A V| date= 2004| booktitle = Proceedings of the IEEE Symposium on Information Visualization| pages= 97–104| doi= 10.1109/INFVIS.2004.65}}</ref>和优化任务。<ref>{{cite journal|first1 = Zhihua | last1 = Cui|first2 = Zhongzhi | last2 = Shi|title= Boid particle swarm optimisation|journal= International Journal of Innovative Computing and Applications|volume=2 |issue=2 |pages= 77–85|date=2009|doi= 10.1504/IJICA.2009.031778}}</ref>
==代码实现==
以下内容是使用BOIDS三层模型对鸟群运动进行了模拟，可以通过调节不同的参数获取到不同的实验现象，可以在这个基础上修改模型规则，以更好的模拟集群运动，本文提供一个模板。代码来源是[https://www.cnblogs.com/zhangqifire/p/6621441.html 沐雨橙风fire]所写的博客。
<source lang="python">
# -*- coding:utf-8 -*-
import argparse
import math
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from scipy.spatial.distance import squareform, pdist
from numpy.linalg import norm
width, height = 1920, 1080
N = 100 # number of birds
minDist = 100.0 # min dist of approach
maxRuleVel = 0.3 # max magnitude of velocities calculated by "rules"
maxVel = 3.0 # max magnitude of final velocity
class Birds:
"""
Simulates flock behaviour of birds, using the realistic-looking Boids model (1986)
"""
def __init__(self):
self.N = N
self.minDist = minDist
self.maxRuleVel = maxRuleVel
self.maxVel = maxVel
# Computing initial position and velocity
self.pos = [width / 2.0, height / 2.0] + 10 * np.random.rand(2 * N).reshape(N, 2)
# Create an array of N random variable angles in the range [0. 2pi]
angles = 2 * math.pi * np.random.rand(N)
# Random velocity vector [x,y] coordinates zip grouped
self.vel = np.array(list(zip(np.sin(angles), np.cos(angles))))
def savef(self):
with open("douban.txt", "a") as f:
f.write(str(self.pos.reshape(1, N*2)))
print str(self.pos.reshape(1, N*2))
f.close()
def tick(self, frameNum, pts, beak):
"""
Update the simulation by one time step
"""
# get pairwise distances
self.distMatrix = squareform(pdist(self.pos))
# apply rules:
self.vel += self.apply_rules()
self.limit(self.vel, self.maxVel)
self.pos += self.vel
self.apply_bc()
# update data
pts.set_data(self.pos.reshape(2 * self.N)[::2],
self.pos.reshape(2 * self.N)[1::2])
vec = self.pos + 10 * self.vel / self.maxVel
beak.set_data(vec.reshape(2 * self.N)[::2],
vec.reshape(2 * self.N)[1::2])
self.savef()
#print self.pos.reshape(2 * self.N)
#np.savetxt("x.txt", self.pos.reshape(1, 2*N))
def limit_vec(self, vec, max_val):
""" Limit magnitude of 2D vector """
mag = norm(vec)
if mag > max_val:
vec[0], vec[1] = vec[0] * max_val / mag, vec[1] * max_val / mag
def limit(self, x, max_val):
""" Limit magnitide of 2D vectors in array X to maxValue """
for vec in x:
self.limit_vec(vec, max_val)
def apply_bc(self):
""" Apply boundary conditions """
deltaR = 2.0
for coord in self.pos:
if coord[0] > width + deltaR:
coord[0] = - deltaR
if coord[0] < - deltaR:
coord[0] = width + deltaR
if coord[1] > height + deltaR:
coord[1] = - deltaR
if coord[1] < - deltaR:
coord[1] = height + deltaR
def apply_rules(self):
# apply rule #1 - Separation
D = self.distMatrix < 20.0
vel = self.pos * D.sum(axis=1).reshape(self.N, 1) - D.dot(self.pos)
self.limit(vel, self.maxRuleVel)
# different distance threshold
D = self.distMatrix < 50.0
# apply rule #2 - Alignment
vel2 = D.dot(self.vel)
self.limit(vel2, self.maxRuleVel)
vel += vel2
# apply rule #1 - Cohesion
vel3 = D.dot(self.pos) - self.pos
self.limit(vel3, self.maxRuleVel)
vel += vel3
return vel
def tick(frameNum, pts, beak, birds):
""" Update function for animation """
birds.tick(frameNum, pts, beak)
return pts, beak
def main():
print('Starting flock simulation...')
# Create birds
birds = Birds()
# Setup plot
fig = plt.figure()
ax = plt.axes(xlim=(0, width), ylim=(0, height))
pts, = ax.plot([], [], markersize=10, c='k', marker='o', ls='None')
beak, = ax.plot([], [], markersize=4, c='r', marker='o', ls='None')
anim = animation.FuncAnimation(fig, tick, fargs=(pts, beak, birds), interval=20)
# TODO: add a "button press" event handler to scatter birds
#anim.save('basic_animation.mp4', fps=30, extra_args=['-vcodec', 'libx264'])
plt.show(anim)
if __name__ == '__main__':
main()
</source>
==参见==
==参见==