How to use iter_classes method in avocado

Best Python code snippet using avocado_python

exp2..py

Source:exp2..py Github

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1# from typing_extensions import ParamSpec2import numpy as np3import matplotlib.pyplot as plt4from mpl_toolkits.mplot3d import Axes3D5 6def dist(v_A, v_B):7 """8 判断两个节点之间的一维距离9 :param v_A: A 二维向量10 :param v_B: B 二维向量11 :return: 一维距离12 """13 return np.sqrt(np.power((v_A[0] - v_B[0]), 2) + np.power((v_A[1] - v_B[1]), 2))14 15def node_factory(N, R, r, energy=50):16 """17 生成N个节点的拓扑网络18 :param N: 网络中节点个数19 :param R: 圆形拓扑半径20 :para r: 通信半径范围，超出此范围为某簇的孤立节点21 :param selected_flag: 标志:是否被选择为簇首-->初始化为022 :param energy: 能量23 :return: 节点集合nodes=[[x,y,e],[x,y,e]...]24 """25 nodes = []26 selected_flag = []27 iso = []28 # 中心sink节点29 sinknode = [0, 0, energy]30 nodes.append(sinknode)31 selected_flag.append(0)32 33 # 随机生成圆形拓扑网络34 i = 035 while i < N-1:36 # 在1*1矩阵生成[x,y]坐标37 node = [np.random.uniform(-1, 1)*R, np.random.uniform(-1, 1)*R, energy]38 if dist(node, sinknode) < R and dist(node, sinknode) > r:39 nodes.append(node)40 selected_flag.append(0)41 iso.append(1)42 i = i + 143 elif dist(node, sinknode) < R and dist(node, sinknode) < r:44 nodes.append(node)45 selected_flag.append(0)46 iso.append(0)47 i = i + 148 49 return nodes, selected_flag, iso50 51 52def sel_heads(r, nodes, flags):53 """54 使用leach协议，选取簇头（注意这里还没有开始进入正式的分簇，这里只选了簇头）55 :param r: 轮数56 :param nodes: 节点列表57 :param flags: 选择标志58 :param P: 比例因子59 :return: 簇首列表heads,簇成员列表members60 """61 # 阈值函数 Tn 使用leach计算62 P = 0.25 * (200 /​ len(nodes))63 Tn = P /​ (1 - P * (r % (1 /​ P)))64 heads = [] # 簇首列表65 members = [] # 簇成员列表66 n_head = 0 # 本轮簇首数67 rands = [np.random.random() for _ in range(len(nodes))] # 对每个节点生成对应的随机数，用于筛选簇头68 69 # 遍历随机数列表，选取簇首70 for i in range(len(nodes)):71 # 随机数低于阈值-->选为簇首72 if rands[i] <= Tn:73 flags[i] = 174 heads.append(nodes[i])75 n_head += 176 # 随机数高于阈值77 else:78 members.append(nodes[i])79 80 return heads, members81 82 83def classify(nodes, flag, r, mode=1, k=20):84 """85 对网络进行簇分类86 :param nodes: 节点列表87 :param flag: 节点标记88 :param mode: 0-->显示图片(死亡节点不显示) 1-->显示结束轮数89 :param k: 轮数90 :return: 簇分类结果列表 classes[[ç±»1..],[ç±»2...],......] [ç±»1...簇首...簇成员]91 """92 # 能量损耗模型的参数93 b = 2400 # 比特数94 e_elec = 5*np.power(10., -9)*10095 e_fs = 10*np.power(10., -12)*10096 e_mp = 0.0013*np.power(10., -12)*10097 d_0 = 4.0 # 阈值98 # k轮的有效集合: 无死亡节点99 iter_classes = []100 # 是否已有节点能量为0101 e_is_empty = 0102 103 # 迭代rè½®104 for r in range(k):105 # mode1: 若无死亡节点 继续迭代106 if e_is_empty == 0:107 # 获取簇首列表，簇成员列表108 heads, members = sel_heads(r,nodes,flag)109 110 # 建立簇类的列表111 if len(heads) == 0:112 break113 classes = [[] for _ in range(len(heads))]114 115 # 将簇首作为首节点添加到聚类列表中116 for i in range(len(heads)):117 classes[i].append(heads[i])118 119 # 簇分类:遍历节点node120 for member in members:121 122 # 选取距离最小的节点123 dist_min = 100000124 125 # 判断和每个簇首的距离126 for i in range(len(heads)):127 128 dist_heads = dist(member, heads[i])129 130 # 找到距离最小的簇头对应的heads下标i131 if dist_heads < dist_min:132 dist_min = dist_heads133 head_cla = i134 if dist_min==1:135 print("本轮没有簇首!")136 break137 # 添加到距离最小的簇首对应的聚类列表中138 classes[head_cla].append(member)139 140 # 正式的数据传输过程，使用能量消耗模型141 if int(member[2]) > 0 and int(heads[head_cla][2]) > 0:142 if dist_min < d_0:143 member[2] -= e_elec*b+e_fs*b*dist_min144 else:145 member[2] -= e_elec*b+e_mp*b*dist_min146 147 if dist([member[0], member[1]], [heads[head_cla][0], heads[head_cla][1]]) <= r:148 heads[head_cla][2] -= e_elec*b149 else:150 pass151 # heads[head_cla][2] -= e_elec*b152 else:153 e_is_empty = mode154 # break155 iter_classes.append(classes)156 157 else:158 print("第", r, "轮能量耗尽")159 break160 161 return iter_classes162 163 164def show_plt(classes, R, r):165 """166 显示分类图167 :param classes: [[ç±»1...],[ç±»2...]....]-->[簇首,成员,成员...]168 :param R: 圆形拓扑半径169 :para r: 通信半径范围，超出此范围为某簇的孤立节点170 :return:171 """172 fig = plt.figure()173 ax1 = plt.gca()174 175 # 设置标题176 ax1.set_title('WSN2')177 # 设置X轴标签178 plt.xlabel('X')179 # 设置Y轴标签180 plt.ylabel('Y')181 182 # 簇内的显示点图标及连线颜色，以得到较好的显示结果183 icon = ['o', '*', '.', 'x', '+', 's']184 color = ['b', 'g', 'c', 'y', 'm']185 186 x, y, e = [], [], []187 # 对不同的簇进行不同的显示，以得到较好的显示结果188 for i in range(len(classes)):189 centor = classes[i][0]190 x.append(centor[0])191 y.append(centor[1])192 e.append(centor[2])193 for point in classes[i]:194 if point[2] > 0 and dist(centor, point) < r:195 ax1.plot([centor[0], point[0]], [centor[1], point[1]], c=color[i % 5], marker=icon[i % 5], alpha=0.4)196 elif point[2] > 0 and dist(centor, point) > r:197 ax1.plot([centor[0], point[0]], [centor[1], point[1]], c='r', marker=icon[i % 5], alpha=0.4)198 else:199 pass200 a, b = (0., 0.)201 theta = np.arange(0, 2*np.pi, 0.01)202 x = a + R * np.cos(theta)203 y = b + R * np.sin(theta)204 plt.plot(x, y, 'y')205 # 显示206 plt.show()207 208def show_eninfo(iter_classes):209 fig = plt.figure()210 ax1 = Axes3D(fig)211 lastclass = iter_classes[-1]212 x, y, e = [], [], []213 # 将所有节点的剩余能量统计起来，用于后续能量三维图的显示214 for i in range(len(lastclass)):215 for point in lastclass[i]:216 x.append(point[0])217 y.append(point[1])218 e.append(point[2])219 # 需要进行数据类型转换list->ndarray，才能进行三维图像的显示220 x = np.array(x)221 y = np.array(y)222 e = np.array(e)223 # 显示三维图像224 print(f"各节点能量的标准差: {np.std(e):.4f}")225 ax1.plot_trisurf(x, y, e, cmap='rainbow')226 plt.show()227def run():228 """229 1、输入节点个数N230 2、node_factory(N,energy): 生成N个节点的列表，节点的能量初始化为energy231 3、classify(nodes,mode=1,k=100): 进行簇分类，返回所有簇的列表232 mode=0: 当节点死亡不停止，进行k次迭代，显示k张图，图中已死亡节点不标记233 mode=1: 当节点死亡停止，记录第一个节点死亡时的轮数，显示无死亡节点的图234 4、show_plt(classes): 迭代每次聚类结果，显示连线图235 :return:236 """237 # N = int(input("请输入节点个数:"))238 N = 300239 R = 20240 r = 5241 # 获取初始节点列表242 nodes, flag, iso = node_factory(N, R, r, energy=5)243 # 对节点列表进行簇分类,mode为模式 2种244 iter_classes = classify(nodes,flag, r, mode=1, k=200)245 # 迭代每次聚类结果，显示连线图246 for classes in iter_classes:247 # 显示分类结果248 show_plt(classes, R, r)249 show_eninfo(iter_classes)250if __name__ == '__main__':...

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clustering.py

Source:clustering.py Github

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1'''2 * @Author: zhikunch 3 * @Date: 2021-03-19 20:39:28 4 * @Last Modified by: zhikunch5 * @Last Modified time: 2021-03-19 20:43:396 '''7 # devide users into several clusters following leach protocal8import numpy as np9import matplotlib.pyplot as plt10def dist(v_A, v_B):11 """12 判断两个节点之间的距离13 :param v_A: A 二维向量14 :param v_B: B 二维向量15 :return: 距离16 """17 return np.sqrt(np.power((v_A[0] - v_B[0]), 2) + np.power((v_A[1] - v_B[1]), 2))18def node_factory(N):19 """20 生成N个节点的集合21 :param N:节点的数目22 :param nodes: 节点的集合23 :param selected_flag: 标志，是否被选择为簇首-->初始化为024 :return: 节点集合nodes=[[x,y],[x,y]...] + 标志 flag25 """26 nodes = []27 selected_flag = []28 for i in range(0, N):29 # 1*1生成[x,y]坐标30 node = [np.random.random(),np.random.random()]31 # print("生成的节点为：",node)32 nodes.append(node)33 # 初始化对应标志为034 selected_flag.append(0)35 return nodes, selected_flag36def sel_heads(r, nodes, flags):37 """38 根据阈值选取簇头节点39 ：param r: 轮数40 ：param nodes：节点列表41 ：param flags：选择标志42 ：param P：比例因子43 ：return：簇首列表heads，簇成员列表members44 """45 # 阈值函数Tn使用leach计算46 P = 0.1 * (100 /​ len(nodes))47 Tn = P /​ (1 - P * (r % (1/​P)))48 49 # 簇首列表50 heads = []51 # 成员列表52 members = []53 # 本轮簇首数54 n_head = 055 56 # 对每个节点生成对应的随机数57 rands = [np.random.random() for _ in range(len(nodes))]58 59 # 遍历随机数列表，选取簇首60 for i in range(len(nodes)):61 # 若此节点未被选择为簇首62 if flags[i] == 0:63 # 随机数低于阈值-->选为簇首64 if rands[i] <= Tn:65 flags[i] = 166 heads.append(nodes[i])67 n_head += 168 # 随机数高于阈值69 else:70 members.append(nodes[i])71 # 若此节点已经被选择过72 else:73 members.append(nodes[i])74 75# print("簇首为：", len(heads),"个")76# print("簇成员为：",len(members),"个")77 78 return heads, members79def classify(nodes, flag, k=1):80 """81 进行簇分类82 ：param nodes：节点列表83 ：param flag：节点标记84 ：param k：轮数85 ：return：簇分类结果列表 classes[[ç±»1..],[ç±»2..],......] [ç±»1...簇首...簇成员]86 """87 # k轮的集合88 global head_cla89 iter_classes = []90 # 迭代rè½®91 for r in range(k):92 # 获取簇首列表，簇成员列表93 heads, members = sel_heads(r, nodes, flag)94 95 # 建立簇类的列表96 classes = [[] for _ in range(len(heads))]97 98 # 将簇首作为首节点添加到聚类列表中99 for i in range(len(heads)):100 classes[i].append(heads[i])101 ch = np.argwhere(np.array(nodes) == heads[i])[0][0]102 print("head:",ch)103 # 簇分类：遍历节点node104 for n in range(len(members)):105 # 选取距离最小的节点106 dist_min = 1107 108 for i in range(len(heads)):109 dist_heads = dist(members[n], heads[i])110 111 # 找到距离最小的簇头对应的heads下标i112 if dist_heads < dist_min:113 dist_min = dist_heads114 head_cla = i115 # 0 个簇首的情况116 if dist_min == 1:117 print("本轮没有簇首！")118 break119 # 添加到距离最小的簇首对应的聚类列表中120 classes[head_cla].append(members[n])121 122 iter_classes.append(classes)123 # dict124 dict_clusters = {j:{} for j in range(len(iter_classes[0]))}125 for j in range(len(iter_classes[0])):126 # print(len(iter_classes[0][j]))127 idx = []128 for jj in range(len(iter_classes[0][j])):129 idx.append(np.argwhere(np.array(nodes)==iter_classes[0][j][jj])[0][0])130 dict_clusters[j] = idx131 return iter_classes, dict_clusters132def show_plt(classes):133 """134 显示分类图135 ：param classes：[[ç±»1..],[ç±»2..],....]-->[簇首，成员，成员...]136 : return: 137 """138 fig = plt.figure()139 ax1 = plt.gca()140 141 ax1.set_title('WSN1')142 plt.xlabel('X')143 plt.ylabel('Y')144 145 icon = ['o','*','.','x','+','s']146 color = ['r','b','g','c','y','m']147 148 # 对每个簇分类列表进行show149 for i in range(len(classes)):150 centor = classes[i][0]151 for point in classes[i]:152 ax1.plot([centor[0],point[0]],[centor[1],point[1]], c=color[i % 6], marker=icon[i % 5],alpha=0.4)153 plt.show()154def run_leach(cluster_num):155 """156 1、输入节点数N157 2、node_factory(N)：生成N个节点的列表158 3、classify(nodes, flags, k=10):进行k轮簇分类，flag标记的节点不再成为簇首，返回所有簇159 4、show_plt(classes):迭代每次聚类结果，显示连线图160 return：161 """162 N=100163 # 获取初始节点列表，选择标志列表164 nodes, flags = node_factory(N)165 # 对节点列表进行簇分类，k为迭代轮数166 iter_classes, dict_clusters = classify(nodes, flags, k=1)167 168 # for classes in iter_classes:169 # show_plt(classes)170 return dict_clusters171if __name__ == '__main__':172 clusters = run_leach()173 # print(len(classes[0]))...

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jarjar

Source:jarjar Github

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...6parser = argparse.ArgumentParser(description='jarjar')7parser.add_argument("jarsa")8parser.add_argument("jarsb")9args = parser.parse_args()10def iter_classes(jarname, jar):11 with zipfile.ZipFile(jar, 'r') as zip:12 for name in zip.namelist():13 if name.endswith('.class'):14 yield jarname, name15def iter_jars(top):16 if os.path.isdir(top):17 for root, dirs, files in os.walk(top):18 for name in files:19 if name.endswith(".jar"):20 yield from iter_classes(name, os.path.join(root, name))21 elif top.endswith('.zip'):22 with zipfile.ZipFile(top, 'r') as zip:23 for name in zip.namelist():24 if name.endswith('.jar'):25 with zip.open(name, 'r') as jar:26 yield from iter_classes(os.path.basename(name), jar)27 elif top.endswith('.jar'):28 yield from iter_classes(os.path.basename(top), top)29 else:30 raise NotImplementedError31def scan_jars(top):32 class2jars = defaultdict(set);33 for jarname, classname in iter_jars(top):34 class2jars[classname].add(jarname)35 return class2jars36a = scan_jars(args.jarsa)37b = scan_jars(args.jarsb)38intersection = set(a.keys()) & set(b.keys())39if not intersection:40 print("no overlap")41for cls in intersection:42 print(f"{cls} is duplicated")...

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