ceil((date('n')) / 3) - 1
时间: 2024-05-19 07:15:54 浏览: 14
This code calculates the current quarter of the year.
Explanation:
- date('n') returns the current month number (1 to 12)
- ceil() function rounds up the result of dividing the month number by 3 to the nearest integer. For example, if the current month is 4, the result of ceil(4/3) is 2, meaning we are in the second quarter.
- Finally, we subtract 1 from the result, since we want the quarter numbers to start from 0 instead of 1.
For example, if the current month is July (which is in the third quarter), the code would evaluate to:
ceil(7/3) - 1 = ceil(2.33) - 1 = 3 - 1 = 2
So the current quarter is 2, indicating we are in the third quarter of the year.
相关问题
解释一下下面一组代码每个代码的含义吧!代码是:a = function(e) { for (var t = "", n = 0; n < e; n++) t += o(16 * Math.random()); return s(t, e) }, s = function(e, t) { var n = ""; if (e.length < t) for (var r = 0; r < t - e.length; r++) n += "0"; return n + e }, o = function(e) { return Math.ceil(e).toString(16).toUpperCase() } function get_uuid() { var e = a(8), t = a(4), n = a(4), r = a(4), o = a(12), i = (new Date).getTime(); return e + "-" + t + "-" + n + "-" + r + "-" + o + s((i % 1e5).toString(), 5) + "infoc" }
这段代码实现了生成 UUID (通用唯一识别码) 的功能。
- `a = function(e) { ... }` 定义了一个名为 `a` 的函数,该函数的参数是 `e`。在函数体内,它会循环 `e` 次,每次都调用另一个函数 `o(16 * Math.random())`,并将返回值拼接起来,最终返回 `s(t, e)`。
- `s = function(e, t) { ... }` 定义了一个名为 `s` 的函数,该函数的参数是 `e` 和 `t`。如果 `e` 的长度小于 `t`,它会循环,每次都在 `n` 后面添加一个 "0",直到 `n` 的长度等于 `t` 为止。最终返回 `n + e`。
- `o = function(e) { ... }` 定义了一个名为 `o` 的函数,该函数的参数是 `e`。它会将 `e` 向上取整,然后转换成 16 进制,并转换为大写字母,最终返回。
- `get_uuid()` 函数定义了一个名为 `get_uuid` 的函数。在函数体内,它会调用 `a(8)`、`a(4)`、`a(4)`、`a(4)`、`a(12)`,并将返回值拼接起来,并在最后添加 `s((i % 1e5).toString(), 5) + "infoc"`。最终返回生成的 UUID。
%% 计算指标 INdex=[]; n=[]; for i=1:k A=NWP_cluster{i}; index=[]; for j=1:size(A,1) for x=1:size(A,2) index(j,x)=sum((A(j,:)-A(x,:)).^2)^0.5; end end INdex(k)=sum(sum(index))/(size(A,1)*size(A,2)-1)/2; n(k)=size(A,1)*size(A,2); end compactness=sum(INdex)/sum(n); disp(['紧致度为:',num2str(compactness)]) %% 找出原始不聚类的训练测试集 Label_test_first=[]; first_label=[]; Label_1=[L{1}' L{2}' L{3}']; for i=1:k Label=find(label==i); A=Label_1(find(label==i)); first_label{i}=Label(1+ceil(length(A)*5/6):end); A(1:ceil(length(A)*5/6))=[]; Label_test_first=[Label_test_first A]; end X=1:size(data,1); X(Label_test_first)=[]; Train_NWP_power_zhijie =[data(X,:) power_date(X,:)]; Test_NWP_power_zhijie =[data(Label_test_first,:) power_date(Label_test_first,:)]; csvwrite('不聚类的训练集.csv',Train_NWP_power_zhijie); csvwrite('不聚类的测试集.csv',Test_NWP_power_zhijie); %% 找出一重聚类结果的训练测试集 first_L1=[]; first_L2=[]; first_L3=[]; for i=1:k B=first_label{i}; L1_label=B(find(B<=length(L{1}))); L2_label=B(find(B<=length([L{1}' L{2}']))); L3_label=B(~ismember(B,L2_label)); L2_label=L2_label(~ismember(L2_label,L1_label)); first_L1=[first_L1;L1_label]; first_L2=[first_L2;L2_label]; first_L3=[first_L3;L3_label]; end first_cluster_test_1=Label_1(first_L1); first_cluster_test_2=Label_1(first_L2); first_cluster_test_3=Label_1(first_L3); first_cluster_train_1=Label_cluster{1}(~ismember(Label_cluster{1},first_cluster_test_1)); first_cluster_train_2=Label_cluster{2}(~ismember(Label_cluster{2},first_cluster_test_2)); first_cluster_train_3=Label_cluster{3}(~ismember(Label_cluster{3},first_cluster_test_3)); %% 划分出训练测试集 NWP_power_test_1=[data(first_cluster_test_1,:) power_date(first_cluster_test_1,:)]; NWP_power_test_2=[data(first_cluster_test_2,:) power_date(first_cluster_test_2,:)]; NWP_power_test_3=[data(first_cluster_test_3,:) power_date(first_cluster_test_3,:)]; NWP_power_train_1=[data(first_cluster_train_1,:) power_date(first_cluster_train_1,:)]; NWP_power_train_2=[data(first_cluster_train_2,:) power_date(first_cluster_train_2,:)]; NWP_power_train_3=[data(first_cluster_train_3,:) power_date(first_cluster_train_3,:)]; NWP_power_test=[{NWP_power_test_1} {NWP_power_test_2} {NWP_power_test_3}]; NWP_power_train=[{NWP_power_train_1} {NWP_power_train_2} {NWP_power_train_3}]; for i=1:k str_test=['NWP_power_test_',num2str(i),'.csv']; csvwrite(str_test,NWP_power_test{i}); str_train=['NWP_power_train_',num2str(i),'.csv']; csvwrite(str_train,NWP_power_train{i}); end
这部分代码是对聚类结果进行指标计算,并根据聚类结果将原始数据集划分为训练集和测试集。
首先,你计算了每个聚类类别的紧致度(compactness),通过计算样本之间的距离来衡量。然后,你将原始数据集中未被聚类的样本标签存储在`Label_test_first`中,并将剩余的样本作为不聚类的训练集和测试集,分别存储在`Train_NWP_power_zhijie`和`Test_NWP_power_zhijie`中。
接下来,你将一重聚类结果中每个类别的样本标签分别存储在`first_L1`、`first_L2`和`first_L3`中,并根据这些标签将一重聚类结果划分为训练集和测试集。训练集中的样本存储在`first_cluster_train_1`、`first_cluster_train_2`和`first_cluster_train_3`中,测试集中的样本存储在`first_cluster_test_1`、`first_cluster_test_2`和`first_cluster_test_3`中。
最后,你根据训练集和测试集的标签将数据集划分为不同的类别,并将每个类别的数据分别存储在`NWP_power_train`和`NWP_power_test`中,并将它们分别写入名为`NWP_power_train_1.csv`、`NWP_power_train_2.csv`、`NWP_power_train_3.csv`、`NWP_power_test_1.csv`、`NWP_power_test_2.csv`和`NWP_power_test_3.csv`的CSV文件中。
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