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巧用fastjson自定义序列化类实现字段的转换

项目中突然需要增加另一个字段的查找,而这个查找需要查另一张表的记录。 但现在产品很多地方都要增加该字段,如何最快的实现该功能呢。 办法如下: 通过fastjson序列化时,增加该字段的序列化类,该序列化类通过CODE查找名称,并序列化到前端。 VO的功能修改 @Data public class SysDictItemSelectVo implements Serializable{ private static final long serialVersionUID = 1L; @JSONField(serializeUsing=ToSystemNameSerializer.class) private String systemName; public String getSystemName(){ return this.getCode(); } } 序列化的类的属性 import com.alibaba.fastjson.serializer.JSONSerializer; import com.alibaba.fastjson.serializer.ObjectSerializer; import com.alibaba.fastjson.serializer.SerializeWriter; import java.io.IOException; import java.lang.reflect.Type; public class ToSystemNameSerializer implements ObjectSerializer { public static final ToSystemNameSerializer instance = new ToSystemNameSerializer(); @Override public void write(JSONSerializer serializer, Object object, Object fieldName, Type fieldType, int features) throws IOException { SerializeWriter out = serializer.out; if (object == null) { out.writeNull(); return; } //原来的值code String strVal = object.toString(); System.out.println(strVal); //通过code查找相关名称,测试先写死值 String name="std"; out.writeString(name); } } 经测试,序列化正常,其他地方只需要在VO增加该注解即可.

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sklearn.feature_extraction.DictVectorizer将字典格式的数据转换为特征

class sklearn.feature_extraction. DictVectorizer ( dtype=<class ‘numpy.float64’> , separator=’=’ , sparse=True , sort=True ) Transforms lists of feature-value mappings to vectors. This transformer turns lists of mappings (dict-like objects ) of feature names to feature values into Numpy arrays or scipy. sparse matrices(稀疏矩阵) for use with scikit-learn estimators. When feature values are strings, this transformer will do a binary one-hot (aka one-of-K) coding: one boolean-valued feature is constructed for each of the possible string values that the feature can take on. For instance, a feature “f” that can take on the values “ham” and “spam” will become two features in the output, one signifying “f=ham”, the other “f=spam”. However, note that this transformer will only do a binary one-hot encoding when feature values are of type string. If categorical features are represented as numeric values such as int, the DictVectorizer can be followed by OneHotEncoder to complete binary one-hot encoding. Features that do not occur in a sample (mapping) will have a zero value in the resulting array/matrix. Read more in the User Guide . Parameters: dtype : callable, optional The type of feature values. Passed to Numpy array/scipy.sparse matrix constructors as the dtype argument. separator : string, optional Separator string used when constructing new features for one-hot coding. sparse : boolean, optional. Whether transform should produce scipy.sparse matrices. True by default. sort : boolean, optional. Whether feature_names_ and vocabulary_ should be sorted when fitting. True by default. Attributes: vocabulary_ : dict A dictionary mapping feature names to feature indices. feature_names_ : list A list of length n_features containing the feature names (e.g., “f=ham” and “f=spam”).

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腾讯云软件源

腾讯云软件源

为解决软件依赖安装时官方源访问速度慢的问题,腾讯云为一些软件搭建了缓存服务。您可以通过使用腾讯云软件源站来提升依赖包的安装速度。为了方便用户自由搭建服务架构,目前腾讯云软件源站支持公网访问和内网访问。

Nacos

Nacos

Nacos /nɑ:kəʊs/ 是 Dynamic Naming and Configuration Service 的首字母简称,一个易于构建 AI Agent 应用的动态服务发现、配置管理和AI智能体管理平台。Nacos 致力于帮助您发现、配置和管理微服务及AI智能体应用。Nacos 提供了一组简单易用的特性集,帮助您快速实现动态服务发现、服务配置、服务元数据、流量管理。Nacos 帮助您更敏捷和容易地构建、交付和管理微服务平台。

Spring

Spring

Spring框架(Spring Framework)是由Rod Johnson于2002年提出的开源Java企业级应用框架,旨在通过使用JavaBean替代传统EJB实现方式降低企业级编程开发的复杂性。该框架基于简单性、可测试性和松耦合性设计理念,提供核心容器、应用上下文、数据访问集成等模块,支持整合Hibernate、Struts等第三方框架,其适用范围不仅限于服务器端开发,绝大多数Java应用均可从中受益。

Rocky Linux

Rocky Linux

Rocky Linux(中文名:洛基)是由Gregory Kurtzer于2020年12月发起的企业级Linux发行版,作为CentOS稳定版停止维护后与RHEL(Red Hat Enterprise Linux)完全兼容的开源替代方案,由社区拥有并管理,支持x86_64、aarch64等架构。其通过重新编译RHEL源代码提供长期稳定性,采用模块化包装和SELinux安全架构,默认包含GNOME桌面环境及XFS文件系统,支持十年生命周期更新。

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