sequential-guid:生成顺序唯一标识符的JavaScript库

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资源摘要信息:"sequential-guid:用于基于 node-uuid 为浏览器和 node.js 生成顺序唯一标识符的包" 知识点详细说明: 1. **包功能**: sequential-guid 是一个专门设计用来在浏览器和 Node.js 环境中生成顺序唯一标识符(Sequential GUID)的Node包。它的主要作用是在使用node-uuid库的基础上,提供生成特定版本的guid(版本1和版本4)的能力。 2. **GUID(全局唯一标识符)**: GUID是全局唯一标识符(Globally Unique Identifier)的缩写,是一种在计算机系统中用来唯一标识信息的技术标准。一个标准的GUID通常由32个16进制数字(0-9和A-F)组成,并且以连字符分为五组,形式为8-4-4-4-12的36个字符(包含连字符)。其设计目的是确保在任意时间和空间中都是唯一的。 3. **版本1和版本4**: 在GUID的标准中,不同的版本有不同的生成规则。版本1的GUID通常基于时间戳和节点标识符(比如网卡地址)生成,确保了很高的全球唯一性,但可能会暴露时间信息。版本4的GUID是随机生成的,通常使用随机数或伪随机数生成器来创建,这种GUID不含有时间信息,所以被认为对隐私更加友好。 4. **在浏览器中使用**: 使用bower工具来安装sequential-guid包,并且需要引入node-uuid作为依赖。通过在HTML中引入sequid.js脚本文件,可以使得GUID的生成在浏览器端实现。这使得前端JavaScript应用能够在不直接依赖Node.js环境下运行的情况下,依然可以生成顺序唯一标识符。 5. **在Node.js中使用**: 通过npm(Node包管理器)可以安装sequential-guid包,安装后通过require方法引入sequential-guid模块。之后,可以通过创建Uid类的新实例来生成顺序唯一标识符。这种方式适合服务器端的Node.js应用。 6. **JavaScript**: 该包是用JavaScript编写的,适用于多种JavaScript运行环境,包括浏览器和Node.js。因此,它广泛适用于Web开发的各种场景,无论是客户端还是服务器端。 7. **文件结构**: 给定的压缩包文件名称为“sequential-guid-master”。这表明该包遵循版本控制中常见的master(主分支)命名惯例,意味着这是一个主版本,通常包含最新的稳定功能和改进。 8. **依赖**: sequential-guid包依赖于另一个名为node-uuid的库,该库提供了生成和管理GUID的基本功能。因此,无论是安装在浏览器还是Node.js环境下,用户都需要确保node-uuid也已经被安装和正确配置。 9. **安全性和隐私**: 顺序唯一标识符在某些情况下可以提升隐私安全性,因为与时间戳无关的版本4GUID不会无意中泄露系统时间信息,这在安全性要求较高的应用中尤其重要。 综上所述,sequential-guid包为开发者提供了一种便捷的方式来在JavaScript环境中生成顺序唯一标识符,能够满足不同项目在不同环境下对唯一标识符的需求。通过这个包,开发者可以很容易地实现跨平台的唯一标识符生成,并利用依赖包node-uuid提供的基础功能,进一步扩展标识符的生成策略和应用。

2023-06-06 18:10:33,041 INFO sqoop.Sqoop: Running Sqoop version: 1.4.7 2023-06-06 18:10:33,075 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead. 2023-06-06 18:10:33,218 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset. 2023-06-06 18:10:33,218 INFO tool.CodeGenTool: Beginning code generation Loading class `com.mysql.jdbc.Driver'. This is deprecated. The new driver class is `com.mysql.cj.jdbc.Driver'. The driver is automatically registered via the SPI and manual loading of the driver class is generally unnecessary. 2023-06-06 18:10:33,782 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `user_log` AS t LIMIT 1 2023-06-06 18:10:33,825 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `user_log` AS t LIMIT 1 2023-06-06 18:10:33,834 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /opt/module/hadoop-3.1.4 注: /tmp/sqoop-root/compile/5f4cfb16d119de74d33f1a0d776d5ae0/user_log.java使用或覆盖了已过时的 API。 注: 有关详细信息, 请使用 -Xlint:deprecation 重新编译。 2023-06-06 18:10:35,111 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-root/compile/5f4cfb16d119de74d33f1a0d776d5ae0/user_log.jar 2023-06-06 18:10:35,125 WARN manager.MySQLManager: It looks like you are importing from mysql. 2023-06-06 18:10:35,126 WARN manager.MySQLManager: This transfer can be faster! Use the --direct 2023-06-06 18:10:35,126 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path. 2023-06-06 18:10:35,126 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql) 2023-06-06 18:10:35,130 ERROR tool.ImportTool: Import failed: No primary key could be found for table user_log. Please specify one with --split-by or perform a sequential import with '-m 1'.

2023-06-07 上传

class HorNet(nn.Module): # HorNet # hornet by iscyy/yoloair def __init__(self, index, in_chans, depths, dim_base, drop_path_rate=0.,layer_scale_init_value=1e-6, gnconv=[ partial(gnconv, order=2, s=1.0/3.0), partial(gnconv, order=3, s=1.0/3.0), partial(gnconv, order=4, s=1.0/3.0), partial(gnconv, order=5, s=1.0/3.0), # GlobalLocalFilter ], ): super().__init__() dims = [dim_base, dim_base * 2, dim_base * 4, dim_base * 8] self.index = index self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers hornet by iscyy/air stem = nn.Sequential( nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4), HorLayerNorm(dims[0], eps=1e-6, data_format="channels_first") ) self.downsample_layers.append(stem) for i in range(3): downsample_layer = nn.Sequential( HorLayerNorm(dims[i], eps=1e-6, data_format="channels_first"), nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2), ) self.downsample_layers.append(downsample_layer) self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiples bind residual blocks dummy dp_rates=[x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] if not isinstance(gnconv, list): gnconv = [gnconv, gnconv, gnconv, gnconv] else: gnconv = gnconv assert len(gnconv) == 4 cur = 0 for i in range(4): stage = nn.Sequential( *[HorBlock(dim=dims[i], drop_path=dp_rates[cur + j], layer_scale_init_value=layer_scale_init_value, gnconv=gnconv[i]) for j in range(depths[i])]# hornet by iscyy/air ) self.stages.append(stage) cur += depths[i] self.apply(self._init_weights)

2023-06-12 上传

为以下的每句代码做注释:class ResNet(nn.Module): def __init__(self, block, blocks_num, num_classes=1000, include_top=True): super(ResNet, self).__init__() self.include_top = include_top self.in_channel = 64 self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(self.in_channel) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, blocks_num[0]) self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2) self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2) self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2) if self.include_top: self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # output size = (1, 1) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') def _make_layer(self, block, channel, block_num, stride=1): downsample = None if stride != 1 or self.in_channel != channel * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(channel * block.expansion)) layers = [] layers.append(block(self.in_channel, channel, downsample=downsample, stride=stride)) self.in_channel = channel * block.expansion for _ in range(1, block_num): layers.append(block(self.in_channel, channel)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) if self.include_top: x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x

2023-05-19 上传

import numpy as np import matplotlib.pyplot as plt from keras.layers import Dense,LSTM,Dropout from keras.models import Sequential # 加载数据 X = np.load("X_od.npy") Y = np.load("Y_od.npy") # 数据归一化 max = np.max(X) X = X / max Y = Y / max # 划分训练集、验证集、测试集 train_x = X[:1000] train_y = Y[:1000] val_x = X[1000:1150] val_y = Y[1000:1150] test_x = X[1150:] test_y = Y # 构建LSTM模型 model = Sequential() model.add(LSTM(units=64, input_shape=(5, 109))) model.add(Dropout(0.2)) model.add(Dense(units=109, activation='linear')) model.summary() # 编译模型 model.compile(optimizer='adam', loss='mse') # 训练模型 history = model.fit(train_x, train_y, epochs=50, batch_size=32, validation_data=(val_x, val_y), verbose=1, shuffle=False) # 评估模型 test_loss = model.evaluate(test_x, test_y) print('Test loss:', test_loss) # 模型预测 train_predict = model.predict(train_x) val_predict = model.predict(val_x) test_predict = model.predict(test_x) # 预测结果可视化 plt.figure(figsize=(20, 8)) plt.plot(train_y[-100:], label='true') plt.plot(train_predict[-100:], label='predict') plt.legend() plt.title('Training set') plt.show() plt.figure(figsize=(20, 8)) plt.plot(val_y[-50:], label='true') plt.plot(val_predict[-50:], label='predict') plt.legend() plt.title('Validation set') plt.show() plt.figure(figsize=(20, 8)) plt.plot(test_y[:50], label='true') plt.plot(test_predict[:50], label='predict') plt.legend() plt.title('Test set') plt.show()如何用返回序列修改这段程序

2023-05-17 上传