用中文解释代码def distance(start, end, name, diameter): m = len(name) link = np.zeros((m, m)) d = np.ones((m, m)) p_link_type = {} for mk in range(len(start)): # 管段数 mi = 0 mj = 0 num1 = 0 num2 = 0 for k in range(m): # 行数 if start[mk] == name[k]: mi = k num1 = 1 if end[mk] == name[k]: mj = k num2 = 1 if num1 + num2 == 2: link[mi, mj] = 1 d[mi, mj] = diameter[mk] break return link, d file_name = "数据/富满集输数据" df_node = pd.read_excel("{}.xlsx".format(file_name), "节点") df_link = pd.read_excel("{}.xlsx".format(file_name), "管段") time = 1 Num_N = len(df_node) # 节点数 NumDP = len(df_link) # 管道数 Ep, d= distance(df_link["起点"], df_link["终点"], df_node["名称"], df_link["管径"]
时间: 2024-04-28 09:23:51 浏览: 10
这段代码是一个 Python 函数,函数名为 distance,接受四个参数:start、end、name 和 diameter。函数中首先定义了两个数组 link 和 d,然后遍历 start 数组,对于每个元素,找到它在 name 数组中的位置,并将其保存在 mi 变量中;然后遍历 end 数组,找到它在 name 数组中的位置,并将其保存在 mj 变量中;如果 mi 和 mj 都不为 0,则将 link 数组中 mi 和 mj 位置的元素设为 1,将 d 数组中 mi 和 mj 位置的元素设为 diameter 数组中对应位置的元素。最后返回 link 和 d 数组。函数的作用是计算管道网络中各个节点之间的距离。代码中还读取了一个 Excel 文件,并将读取结果保存在 df_node 和 df_link 变量中。
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解释代码def distance(start, end, name, diameter): m = len(name) link = np.zeros((m, m)) d = np.ones((m, m)) p_link_type = {} for mk in range(len(start)): # 管段数 mi = 0 mj = 0 num1 = 0 num2 = 0 for k in range(m): # 行数 if start[mk] == name[k]: mi = k num1 = 1 if end[mk] == name[k]: mj = k num2 = 1 if num1 + num2 == 2: link[mi, mj] = 1 d[mi, mj] = diameter[mk] break return link, d file_name = "数据/富满集输数据" df_node = pd.read_excel("{}.xlsx".format(file_name), "节点") df_link = pd.read_excel("{}.xlsx".format(file_name), "管段") time = 1 Num_N = len(df_node) # 节点数 NumDP = len(df_link) # 管道数 Ep, d= distance(df_link["起点"], df_link["终点"], df_node["名称"], df_link["管径"]) print(Ep, d)
This code defines a function called `distance` that takes four parameters: `start`, `end`, `name`, and `diameter`. The `start` and `end` parameters are lists of starting and ending nodes for pipeline segments, respectively. The `name` parameter is a list of node names, and the `diameter` parameter is a list of pipeline diameters.
Within the function, the length of `name` is determined and two 2D arrays, `link` and `d`, are initialized with zeros and ones, respectively. The `link` array is used to store information about which nodes are connected, and the `d` array is used to store the diameter of each pipeline segment.
The function then iterates over each pipeline segment, finding the starting and ending nodes and setting the corresponding values in `link` and `d`. Finally, the function returns `link` and `d`.
The code then reads in two Excel sheets using the `pandas` library and calls the `distance` function with appropriate parameters. The resulting `link` and `d` arrays are printed to the console.
解释代码def KMeans(dataSet, k): m = np.shape(dataSet)[0] clusterAssment = np.mat(np.zeros((m, 2))) clusterChange = True
这是一个K均值聚类算法的Python代码。
首先,定义了一个名为KMeans的函数,接受两个参数dataSet和k,分别代表数据集和聚类数。
接下来,计算数据集的行数m,并创建一个大小为(m,2)的全零矩阵clusterAssment,用于记录每条数据所属的簇及其到簇中心的距离。
最后,初始化一个名为clusterChange的变量为True,用于表示聚类是否发生变化。