mopso/plot.py at master · JIIAYU/mopso · GitHub

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#encoding: utf-8
import numpy as np
import os
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
#from mpl_toolkits.mplot3d import Axes3D
import fitness_funs as fit
class Plot_pareto:
    def __init__(self):
        #绘制测试函数的曲面，（x1，x2）表示两位度的输入，（y1，y2）表示两位的适应值，
        self.x1 = np.linspace(0,10,100)
        self.x2 = np.linspace(0,10,100)
        self.x1,self.x2 = np.meshgrid(self.x1,self.x2)#根据横纵坐标生成网格点
        self.m,self.n = np.shape(self.x1)
        self.y1,self.y2 = np.zeros((self.m,self.n)),np.zeros((self.m,self.n))
        for i in range(self.m):
            for j in  range(self.n):
                [self.y1[i,j],self.y2[i,j]] = fit.fitness_([self.x1[i,j],self.x2[i,j]])
        if os.path.exists('./img_txt') == False:
            os.makedirs('./img_txt')
            print ('创建文件夹img_txt:保存粒子群每一次迭代的图片')
    def show(self,in_,fitness_,archive_in,archive_fitness,i):
        #共3个子图，第1、2/子图绘制输入坐标与适应值关系，第3图展示pareto边界的形成过程
        fig = plt.figure('第'+str(i+1)+'次迭代',figsize = (17,5))

        ax1 = fig.add_subplot(131, projection='3d')
        ax1.set_xlabel('input_x1')
        ax1.set_ylabel('input_x2')
        ax1.set_zlabel('fitness_y1')
        ax1.plot_surface(self.x1,self.x2,self.y1,alpha = 0.6)
        ax1.scatter(in_[:,0],in_[:,1],fitness_[:,0],s=20, c='blue', marker=".")
        ax1.scatter(archive_in[:,0],archive_in[:,1],archive_fitness[:,0],s=50, c='red', marker=".")
        ax2 = fig.add_subplot(132, projection='3d')
        ax2.set_xlabel('input_x1')
        ax2.set_ylabel('input_x2')
        ax2.set_zlabel('fitness_y2')
        ax2.plot_surface(self.x1,self.x2,self.y2,alpha = 0.6)
        ax2.scatter(in_[:,0],in_[:,1],fitness_[:,1],s=20, c='blue', marker=".")
        ax2.scatter(archive_in[:,0],archive_in[:,1],archive_fitness[:,1],s=50, c='red', marker=".")
        ax3 = fig.add_subplot(133)
        ax3.set_xlim((0,1))
        ax3.set_ylim((0,1))
        ax3.set_xlabel('fitness_y1')
        ax3.set_ylabel('fitness_y2')
        ax3.scatter(fitness_[:,0],fitness_[:,1],s=10, c='blue', marker=".")
        ax3.scatter(archive_fitness[:,0],archive_fitness[:,1],s=30, c='red', marker=".",alpha = 1.0)
        #plt.show()
        plt.savefig('./img_txt/'+str(i+1)+'.png')
        print ('第'+str(i+1)+'次迭代的图片保存于 img_txt 文件夹')
        plt.close()