IntelligentAlgorithmScheduler/chaosTest.py at master · Sherryran08/IntelligentAlgorithmScheduler · GitHub

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# -*- coding: utf-8 -*-
# @Author : ZhaoKe
# @Time : 2021-04-22 11:22
from utils.Entities import Cloudlet, VM
import numpy as np
from matplotlib import pyplot as plt
from DPSOTaskScheduling import DPSO
from ChaosDPSOScheduler import ChaosDPSO
from ChaosReproducePSO import ChaosReproductionPSO
from ACScheduler import ACScheduler
from GAScheduler import GAScheduler
from utils.dataExamples import get_data_r2n3c12, get_data_r3n4c18
from utils.dataExamples import get_data_r2n5c20, get_data_r2n6c24, get_data_r2n7c28


def select_data(ind: int):
    data = None
    if ind == 0:
        data = get_data_r2n3c12(1)
    elif ind == 1:
        data = get_data_r3n4c18(1)
    elif ind == 2:
        data = get_data_r2n5c20(1)
    elif ind == 3:
        data = get_data_r2n6c24()
    elif ind == 4:
        data = get_data_r2n7c28()
    return data


def iterate(nodes, lets, saveind):
    population = 300
    MAX_GEN = 300

    crpso = ChaosReproductionPSO(lets, nodes, population_number=population, times=MAX_GEN)
    cpso = ChaosDPSO(lets, nodes, population_number=population, times=MAX_GEN)
    dpso = DPSO(cloudlets=lets, vms=nodes, population_number=population, times=MAX_GEN)
    ga = GAScheduler(lets, nodes, population_number=population, times=MAX_GEN)
    aco = ACScheduler(lets, nodes, population_number=population, times=MAX_GEN)

    crpso_generation = crpso.exec()
    cpso_generation = cpso.exec()
    dpso_generation = dpso.exec()
    ga_generation = ga.ga_main()
    aco_generation = aco.scheduler_main()
    res = [crpso_generation, cpso_generation, dpso_generation, ga_generation, aco_generation]
    np.savetxt("chaos-res-matrix/res-"+str(saveind)+".txt", res, delimiter=',', encoding="GB2312")
    # return crpso_generation[-1], cpso_generation[-1], dpso_generation[-1], ga_generation[-1], aco_generation[-1]


def test(ind: int):
    X = np.arange(5)
    y = np.zeros((5, 5))
    for i in range(5):
        data = select_data(i)
        res = iterate(data["nodes"], data["cloudlets"])
        y[:, i] = res
    np.savetxt("data.txt", y, delimiter=',', encoding="GB2312")
    # # plt.style.use("fivethirtyeight")
    # ax = plt.figure(ind)
    # p1, = plt.plot(range(5), y[0, :])
    # p2, = plt.plot(range(5), y[1, :])
    # p3, = plt.plot(range(5), y[2, :])
    # p4, = plt.plot(range(5), y[3, :])
    # p5, = plt.plot(range(5), y[4, :])
    #
    # ax.legend([p1, p2, p3, p4, p5], ["CRPSO", "CPSO", "DPSO", "GA", "ACO"], bbox_to_anchor=(1.01, 0.88), loc=2,
    #           borderaxespad=0)
    # # plt.rcParams['font.sans-serif'] = ['KaiTi']  # 指定默认字体
    # # plt.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显示为方块的问题
    # # plt.title("云计算任务调度-负载均衡得分图")
    # plt.xlabel("task scale")
    # plt.ylabel("fitness")
    # plt.subplots_adjust(right=1)
    # # plt.savefig('result0pic.png', dpi=300, format='png', bbox_inches='tight')  # bbox_inches="tight"解决X轴时间两个字不被保存的问题
    # # plt.savefig("linePlot.png", dpi=300)
    # plt.show()

#
# def stackPlotTest():
#     y = np.loadtxt("data.txt", delimiter=',')
#     labels = ["CRPSO", "CPSO", "DPSO", "GA", "ACO"]
#     colors = ["red", "green", "orange", "purple", "blue"]
#     order = [2, 4, 3, 1, 0]
#     new_data = []
#     new_data.append(y[order[0]])
#     for i in range(1, 5):
#         new_data.append(y[order[i]] - y[order[i - 1]])
#     new_color = [colors[order[0]], colors[order[1]], colors[order[2]], colors[order[3]], colors[order[4]]]
#     new_labels = [labels[order[0]], labels[order[1]], labels[order[2]], labels[order[3]], labels[order[4]]]
#     plt.stackplot(range(5), new_data[0], new_data[1], new_data[2], new_data[3], new_data[4], colors=new_color,
#                   labels=new_labels)
#     #
#     # plt.rcParams['font.sans-serif'] = ['KaiTi']  # 指定默认字体
#     # plt.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显示为方块的问题
#     # plt.xlabel("teak scale")
#     # plt.ylabel("fitness")
#     # plt.savefig('fiveAlgorithm-cnP3T3-'+str(ind)+'.png', dpi=300,
#     #             format='png', bbox_inches='tight')  # bbox_inches="tight"解决X轴时间两个字不被保存的问题
#     plt.show()


# def test():
#     NUM = 10
#     #分别使用五种方法进行运算，将结果存放在data0.txt中
#     for i in range(5):  # 5组数据
#         data = select_data(i)
#         y = np.zeros((NUM, 5))
#         for j in range(NUM):
#             res = iterate(data["nodes"], data["cloudlets"])
#             y[j, :] = res
#         np.savetxt("data"+str(5+i)+".txt", y, delimiter=',', encoding="GB2312")


def boxPlotTest(i: int):
    # 绘制箱型图
    y0 = np.loadtxt("datas/boxplotdata/data" + str(i) + ".txt", delimiter=',')
    labels = ["", "CRPSO", "CPSO", "DPSO", "GA", "ACO"]
    y0 = 1/y0
    # 共5组数据，分别绘制箱型图
    ax = plt.figure(i)
    plt.boxplot((y0[:, 0], y0[:, 1], y0[:, 2], y0[:, 3], y0[:, 4]))
    plt.ylabel("load balance", fontdict={'size': 18})
    plt.xticks(range(6), labels, size=16)
    plt.yticks(size=18)
    plt.show()
    # plt.savefig("boxplot" + str(i) + ".png")
    # plt.close(i)


def LinePlot(ind: int):  # 绘制折线图
    data = np.loadtxt("datas/lineplotdata/data" + str(ind) + ".txt", delimiter=",")
    # plt.style.use("fivethirtyeight")
    ax = plt.figure(ind)

    p0, = plt.plot(range(5), data[:, 0], "blue")
    p1, = plt.plot(range(5), data[:, 1], "orange")
    p2, = plt.plot(range(5), data[:, 2], "green")
    p3, = plt.plot(range(5), data[:, 3], "red")
    p4, = plt.plot(range(5), data[:, 4], "purple")

    # ax.legend([p0, p1, p2, p3, p4], ["CRPSO", "CPSO", "DPSO", "GA", "ACO"], bbox_to_anchor=(1.01, 0.88), loc=2,
    #           borderaxespad=0)
    ax.legend([p0, p1, p2, p3, p4], ["CRPSO", "CPSO", "DPSO", "GA", "ACO"])
    plt.rcParams['font.sans-serif'] = ['KaiTi']  # 指定默认字体
    plt.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显示为方块的问题
    plt.title("Cloudlets Scheduling Algorithm-Dataset")
    plt.xlabel("task scale")
    plt.ylabel("fitness")
    # plt.subplots_adjust(right=1)
    # plt.savefig('result0pic.png', dpi=300, format='png', bbox_inches='tight')  # bbox_inches="tight"解决X轴时间两个字不被保存的问题
    # plt.savefig("linePlot.png", dpi=300)
    plt.show()


def getOrder(ind: int):
    if ind == 1:
        return [4, 3, 2, 1, 0]
    elif ind == 2:
        return [3, 4, 2, 1, 0]
    elif ind == 3:
        return [3, 4, 2, 1, 0]
    elif ind == 4:
        return [3, 4, 1, 2, 0]
    elif ind == 5:
        return [3, 4, 2, 1, 0]
    elif ind == 6:
        return [3, 4, 1, 2, 0]
    elif ind == 7:
        return [3, 4, 1, 2, 0]


# 1 [4, 3, 1, 2, 0]
# 2 order = [3, 4, 2, 1, 0]
# 4 []
def stackPlotTest(ind: int):
    # 绘制堆叠图
    y = np.loadtxt("datas/lineplotdata/data" + str(ind) + ".txt", delimiter=',')
    labels = ["CRPSO", "CPSO", "DPSO", "GA", "ACO"]
    colors = ["#87CEFA", "#FFD700", "#98FB98", "#FF4500", "#9370DB"]
    order = getOrder(ind)
    new_data = []
    new_data.append(y[:, order[0]])
    for i in range(1, 5):
        new_data.append(y[:, order[i]] - y[:, order[i - 1]])
    new_color = [colors[order[0]], colors[order[1]], colors[order[2]], colors[order[3]], colors[order[4]]]
    new_labels = [labels[order[0]], labels[order[1]], labels[order[2]], labels[order[3]], labels[order[4]]]
    ax = plt.figure(ind)
    plt.stackplot(range(1, 6), new_data[0], new_data[1], new_data[2], new_data[3], new_data[4], colors=new_color,
                  labels=new_labels)
    ax.legend(new_labels)
    # plt.rcParams['font.sans-serif'] = ['KaiTi']  # 指定默认字体
    # plt.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显示为方块的问题
    # plt.title("Cloudlets Scheduling Algorithm-Dataset")
    plt.xlabel("teak scale")
    plt.ylabel("fitness")
    plt.xticks(size=12)
    plt.yticks(size=12)
    # ax.set_xticks([1.0, 2.0, 3.0, 4.0, 5.0])
    # plt.savefig('datas/lineplotdata/stack'+str(ind)+'.png', dpi=300,
    #             format='png', bbox_inches='tight')  # bbox_inches="tight"解决X轴时间两个字不被保存的问题
    plt.show()


if __name__ == '__main__':
    data = select_data(1)
    for i in range(20, 25):
        iterate(data["nodes"], data["cloudlets"], i)