public String selectyearReportDataTest2(YearReportData yearReportData){ int l_iID = 1; List<YearReportData> yearList = jiguanService.selectyearReportYearData(yearReportData); //搜索年數據 //循環寫入子節點 for (YearReportData l_dataYear:yearList) { l_dataYear.setId(l_iID++); l_dataYear.setDate(l_dataYear.getDate()+"年"); List<YearReportData> quarterList = jiguanService.selectyearReportQuarterData(l_dataYear); //获取季度数据 for (YearReportData l_dataQuarter:quarterList) { l_dataQuarter.setId(l_iID++); l_dataQuarter.setDate(l_dataQuarter.getDate()+ "季度"); //获取对应季度的月数据 List<YearReportData> monthList = jiguanService.selectyearReportMonthData(l_dataQuarter); //搜索年报月数据 for (YearReportData l_dataMonth:monthList) { l_dataMonth.setId(l_iID++); l_dataMonth.setDate(l_dataMonth.getDate() + "月"); //获取对应月的日数据 List<YearReportData> dayList = jiguanService.selectyearReportMonthData(l_dataMonth); l_dataMonth.setChildren(dayList); } l_dataQuarter.setChildren(monthList); //季度中写入月数据 } l_dataYear.setChildren(quarterList); } Gson l_gson = new Gson(); //Gson方法初始化 String l_JsonAllData = l_gson.toJson(yearList); return l_JsonAllData; }这个方法中，定义的json树状结构中日期显示不对，年报中的年数据和日数据不对应，这个方法直接把数据库中所有的日期数据都加到了其中一年里边，我想要的json树状结构是年数据对应日数据，不要这种年数据混在一块的

AnalysisException: join condition 'CAST((o.rpc_num_iid = p.num_iid) AS INT)' of type int is not a boolean.;

这个错误是由于您在JOIN操作中使用了一个将表达式转换为整数类型的条件，但JOIN条件应该是布尔类型的（返回真或假）。您需要修改条件，使其返回一个布尔值。例如，您可以使用等于运算符来比较两个表达式是否相等，...

def dataSetBalanceAllocation(self): mnistDataSet = GetDataSet(self.data_set_name, self.is_iid) test_data = torch.tensor(mnistDataSet.test_data) test_label = torch.argmax(torch.tensor(mnistDataSet.test_label), dim=1) self.test_data_loader = DataLoader(TensorDataset( test_data, test_label), batch_size=100, shuffle=False) train_data = mnistDataSet.train_data train_label = mnistDataSet.train_label shard_size = mnistDataSet.train_data_size // self.num_of_clients // 2 shards_id = np.random.permutation(mnistDataSet.train_data_size // shard_size) #permutation：排列 for i in range(self.num_of_clients): shards_id1 = shards_id[i * 2] shards_id2 = shards_id[i * 2 + 1] data_shards1 = train_data[shards_id1 * shard_size: shards_id1 * shard_size + shard_size] data_shards2 = train_data[shards_id2 * shard_size: shards_id2 * shard_size + shard_size] label_shards1 = train_label[shards_id1 * shard_size: shards_id1 * shard_size + shard_size] label_shards2 = train_label[shards_id2 * shard_size: shards_id2 * shard_size + shard_size] local_data, local_label = np.vstack((data_shards1, data_shards2)), np.vstack((label_shards1, label_shards2)) # vstack：data和label拼接 local_label = np.argmax(local_label, axis=1) someone = client(TensorDataset(torch.tensor(local_data), torch.tensor(local_label)), self.dev) self.clients_set['client{}'.format(i)] = someone 解释这段代码

在每次迭代中，通过索引数组shards_id获取两个分片的索引shards_id1和shards_id2。然后，通过这些索引从训练数据和标签中选择对应的数据分片。将这些分片堆叠起来，形成本地的数据和标签，并将其封装为...

解释这段代码 def load_data_set(self, train_file, test_file): train_dict = defaultdict(list) test_dict = defaultdict(list) train_user_items_dict = defaultdict(list) train_item_users_dict = defaultdict(list) test_user_items_dict = defaultdict(list) test_item_users_dict = defaultdict(list) train_data = [] test_data = [] with open(train_file) as f_train: with open(test_file) as f_test: for l in f_train: uid, iid = l.strip().split('\t') train_dict[int(uid)].append(int(iid)-self.n_users) train_user_items_dict[int(uid)].append(int(iid)) train_item_users_dict[int(iid)].append(int(uid)) train_data.append((int(uid), int(iid))) for l in f_test.readlines(): uid, iid = l.strip().split('\t') test_dict[int(uid)].append(int(iid)-self.n_users) test_user_items_dict[int(uid)].append(int(iid)) test_item_users_dict[int(iid)].append(int(uid)) test_data.append((int(uid), int(iid))) return train_data, test_data, train_dict, test_dict, train_user_items_dict, train_item_users_dict, test_user_items_dict, test_item_users_dict

这是一段 Python 代码，load_data_set 函数的作用是从 train_file 和 test_file 中读入数据，并将数据处理成不同的字典形式，同时将数据分为训练集和测试集。具体来说，train_dict 和 test_dict 存储了用户和物品...

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score # 数据加载和预处理 train_data = pd.read_csv('D:/wy/train.csv') test_data = pd.read_csv('D:/wy/test.csv') # 特征选择和处理 features = ['uid', 'iid'] target = 'score' X_train = train_data[features] y_train = train_data[target] X_test = test_data[features] # 划分训练集和验证集 X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42) # 模型训练 model = LogisticRegression() model.fit(X_train, y_train) # 模型评估 y_val_pred = model.predict(X_val) accuracy = accuracy_score(y_val, y_val_pred) print("Validation Accuracy:", accuracy) # 预测和推荐 y_test_pred = model.predict(X_test) test_data['score'] = y_test_pred recommended_movies = test_data.sort_values(by='score', ascending=False) # 结果保存 recommended_movies[['uid', 'iid', 'score']].to_csv('D:/forecast_result.csv', index=False)

这段代码使用了Pandas和Scikit-learn库来构建一个基于逻辑回归的电影推荐系统。首先，它读取训练和测试数据，然后选择了一些特征。接着，训练数据被划分为训练集和验证集，用于训练和评估模型。...

select distinct(a.group_id) from jags_errorrecord a left join ucenter250.complat_group b on a.group_id = b.iid ;反选这条sql的数据

LEFT JOIN ucenter250.complat_group b ON a.group_id = b.iid WHERE b.iid IS NULL; 在原始 SQL 语句的基础上，我们添加了一个 WHERE 子句，使用 IS NULL 来找出没有匹配的记录。具体地，我们在左连接的结果...

GridSearchCV.init() got an unexpected keyword argument 'iid'

在Scikit-learn的GridSearchCV中，iid参数被用于指定是否考虑每个样本的权重。在Scikit-learn的版本0.22.2中，iid参数被弃用了，因此你需要将其从GridSearchCV的参数中删除。你可以尝试这样做： from sklearn....

AudioUtilities.IAudioEndpointVolumeCallback._iid_报错

如果你在使用 pycaw 库时遇到了 AttributeError: type object 'IAudioEndpointVolumeCallback' has no attribute '_iid_' 的错误，可能是因为你的 pycaw 版本过低。 pycaw 库的新版本中使用的是 ...

#include<imapi.h>怎么用

<imapi.h> 是 Windows 操作系统中的一个头文件，它包含了一些用于管理光盘和文件系统的 API 函数。如果你想在你的程序中使用这些函数，你需要在程序中包含这个头文件，并且链接 imapi.lib 库文件。以下是一个...

#ifndef DLLS_H #define DLLS_H #include <ActivityInterface.h> class MyWidget; class Dlls: public QObject, public ActivityInterface { Q_OBJECT Q_PLUGIN_METADATA(IID "ActivityInterface") Q_INTERFACES(ActivityInterface) public: Dlls(QObject* parent = nullptr); public: int id() const override; QString name() const override; QWidget* widget() const override; void initialize(const QString&) override; private: MyWidget* m_widget; }; #endif // DLLS_H

public: virtual ~ActivityInterface() {} virtual int id() const = 0; virtual QString name() const = 0; virtual QWidget* widget() const = 0; virtual void initialize(const QString&) = 0; }; Q_...

TypeError: GridSearchCV.init() got an unexpected keyword argument 'iid'

根据提供的引用内容，你遇到了一个TypeError: GridSearchCV.__init__() got an unexpected keyword argument 'iid'的错误。这个错误是由于GridSearchCV类的初始化函数中传入了一个未知的关键字参数'iid'导致的。 ...

def _fetch_lfw_people(data_folder_path, slice_=None, color=False, resize=None, min_faces_per_person=0): """Perform the actual data loading for the lfw people dataset This operation is meant to be cached by a joblib wrapper. """ # scan the data folder content to retain people with more that # min_faces_per_person face pictures person_names, file_paths = [], [] for person_name in sorted(listdir(data_folder_path)): folder_path = join(data_folder_path, person_name) if not isdir(folder_path): continue paths = [join(folder_path, f) for f in sorted(listdir(folder_path))] n_pictures = len(paths) if n_pictures >= min_faces_per_person: person_name = person_name.replace('_', ' ') person_names.extend([person_name] * n_pictures) file_paths.extend(paths) n_faces = len(file_paths) if n_faces == 0: raise ValueError("min_faces_per_person=%d is too restrictive" % min_faces_per_person) target_names = np.unique(person_names) target = np.searchsorted(target_names, person_names) faces = _load_imgs(file_paths, slice_, color, resize) # shuffle the faces with a deterministic RNG scheme to avoid having # all faces of the same person in a row, as it would break some # cross validation and learning algorithms such as SGD and online # k-means that make an IID assumption indices = np.arange(n_faces) np.random.RandomState(42).shuffle(indices) faces, target = faces[indices], target[indices] return faces, target, target_names

这段代码是 fetch_lfw_people() 函数的内部实现，用于加载 LFW 人脸数据集。函数会扫描指定路径下的所有子文件夹，从中选取至少有 min_faces_per_person 张人脸照片的人作为数据集的一部分。...

File "D:\PFL-Non-IID-master\dataset\generate_mnist.py", line 77, in <module> niid = True if sys.argv[1] == "noniid" else False IndexError: list index out of range这段话报错的原因是

这段话报错的原因是在运行Python脚本时，没有传递命令行参数，导致sys.argv列表的长度为1，而sys.argv[1]越界，引发了IndexError: list index out of range异常。在这段代码中，第77行使用了sys.argv[1]来...

Traceback (most recent call last): File "D:\PFL-Non-IID-master\dataset\generate_mnist.py", line 78, in <module> balance = True if sys.argv[2] == "balance" else False IndexError: list index out of range如何解决

niid = True if len(sys.argv) > 1 and sys.argv[1] == "noniid" else False balance = True if len(sys.argv) > 2 and sys.argv[2] == "balance" else False 这样代码就会首先检查命令行参数是否传递了 ...

File "D:\PFL-Non-IID-master\dataset\generate_mnist.py", line 79, in <module> partition = sys.argv[3] if sys.argv[3] != "-" else None IndexError: list index out of range如何解决

niid = True if len(sys.argv) > 1 and sys.argv[1] == "noniid" else False balance = True if len(sys.argv) > 2 and sys.argv[2] == "balance" else False partition = sys.argv[3] if len(sys.argv) > 3 and sys...

list = [] for r in result: list.append(dict(iid=r[0], rate=r[1])) print(list)解释·代码

这段代码的作用是将一个列表result中的元素按照字典格式(iid,rate)，转换成一个新的列表list，并打印输出这个新列表。其中，result中的每个元素包含了一个iid和一个rate值，通过for循环遍历result中的每个元素，用...

covcorr.zip_T matrix_random_收缩估计

收缩估计 % function sigma=...% x (t*n): t iid observations on n random variables % sigma (n*n): invertible covariance matrix estimator % Shrinks towards constant correlation matrix % de-mean returns

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File "D:\PFL-Non-IID-master\dataset\generate_mnist.py", line 77, in <module> niid = True if sys.argv[1] == "noniid" else False IndexError: list index out of range这段话报错的原因是

Traceback (most recent call last): File "D:\PFL-Non-IID-master\dataset\generate_mnist.py", line 78, in <module> balance = True if sys.argv[2] == "balance" else False IndexError: list index out of range如何解决

File "D:\PFL-Non-IID-master\dataset\generate_mnist.py", line 79, in <module> partition = sys.argv[3] if sys.argv[3] != "-" else None IndexError: list index out of range如何解决

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