AndroidAnnotations——Enhancing the Application class优化Application类

通过使用@EApplication注解,可以优化Android Application类,并利用多种AA注解增强应用功能,如系统服务、数据存储及后台任务等。从AndroidAnnotations 2.1开始,还可以通过@App注解注入Application实例。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

Enhancing the Application class 优化Application类


Since AndroidAnnotations 2.4


You can enhance your Android  Application class with the  @EApplication annotation:
你可以使用   @EApplication   注解优化你的Android   Application   类:
@EApplication
public class MyApplication extends Application {

}

You can then start using most AA annotations, except the ones related to views and extras:
然后你就可以使用大部分的AA注解,除了一些Views和extra相关的注解:

@EApplication
public class MyApplication extends Application {

  public void onCreate() {
    super.onCreate();
    initSomeStuff();
  }

  @SystemService
  NotificationManager notificationManager;

  @Bean
  MyEnhancedDatastore datastore;

  @RestService
  MyService myService;
 
  @Background
  void initSomeStuff() {
    // init some stuff in background
  }}

Injecting your application class注入你的Application类

Since AndroidAnnotations 2.1


You can inject the application class using the  @App annotation:
你可以用   @App   注解注入你的Application类:

@EActivity
public class MyActivity extends Activity {

  @App
  MyApplication application;

}

It also works for any kind of annotated component, such as  @EBean:
在任何其他注解组件中,比如   @EBean ,都可以使用:

@EBean
public class MyBean {

  @App
  MyApplication application;

}

Since AndroidAnnotations 3.0, the application class must be annotated with @EApplication.

本文档的简单示例下载

### Primer Optimization Method in Machine Learning In the context of machine learning and data science, primer optimization methods refer to techniques aimed at improving various aspects of model performance through systematic adjustments. These optimizations can involve selecting appropriate algorithms, tuning hyperparameters, enhancing feature selection processes, or refining training procedures. A key aspect of primer optimization involves pattern classification[^4]. This process focuses on accurately assigning predefined class labels to specific instances based on their features. By optimizing how these patterns are recognized and classified, models achieve better accuracy and efficiency when processing new data points. To illustrate a common primer optimization technique used within supervised learning frameworks: ```python from sklearn.model_selection import GridSearchCV from sklearn.svm import SVC # Define parameter grid for SVM classifier param_grid = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf']} # Initialize support vector classifier svc = SVC() # Create grid search object with cross-validation grid_search = GridSearchCV(estimator=svc, param_grid=param_grid, cv=5) # Fit the model using training dataset X_train and y_train grid_search.fit(X_train, y_train) # Output best parameters found during fitting procedure print(grid_search.best_params_) ``` This code snippet demonstrates an application of primer optimization by performing a grid search over specified parameter values for a Support Vector Classifier (SVC), thereby identifying optimal settings that enhance its ability to classify unseen samples effectively.
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值