Stream Subgroup

本文通过Java Stream API对菜品数据进行分组、计数、查找热量最高的菜品等操作,展示了Stream API的强大功能。文章详细介绍了如何使用groupingBy、counting、maxBy和collectingAndThen等方法来处理复杂的数据分析任务。

Domain Object

public class Dish {
    public Dish(String name, boolean vegetarian, int calories, Type type) {
        this.name = name;
        this.vegetarian = vegetarian;
        this.calories = calories;
        this.type = type;
    }

    public String getName() {
        return name;
    }


    public int getCalories() {
        return calories;
    }

    public boolean isVegetarian() {
        return vegetarian;
    }

    public Type getType() {
        return type;
    }

    private final String name;

    private final boolean vegetarian;

    private final int calories;

    private final Type type;


    public enum Type {MEAT, FISH, OTHER}
}

Initial Data

private static List<Dish> menu = Arrays.asList(
            new Dish("pork", false, 800, Dish.Type.MEAT)
            , new Dish("beef", false, 700, Dish.Type.MEAT)
            , new Dish("chicken", false, 400, Dish.Type.MEAT)
            , new Dish("french fries", true, 530, Dish.Type.OTHER)
            , new Dish("rice", true, 350, Dish.Type.OTHER)
            , new Dish("season fruit", true, 120, Dish.Type.OTHER)
            , new Dish("pizza", true, 550, Dish.Type.OTHER)
            , new Dish("prawns", false, 300, Dish.Type.FISH)
            , new Dish("prawns", false, 300, Dish.Type.FISH)
            , new Dish("salmon", false, 450, Dish.Type.FISH) );

Collecting Data In Subgroups

Counting In Subgroups

void subGroup(){
        Map<Dish.Type, Long> map
                = menu.stream().collect(groupingBy(Dish::getType,
                counting()));
    }

Max in Subgroups

void groupThenMax(){
        Map<Dish.Type, Optional<Dish>> map
                = menu.stream().collect(groupingBy(Dish::getType
                , maxBy(Comparator.comparingInt(Dish::getCalories))));
    }

Adapting the Collector Result to a Different Type

void groupMaxThenChangeResult(){
        Map<Dish.Type, Dish> map = menu.stream().collect(groupingBy(Dish::getType,
                collectingAndThen(maxBy(Comparator.comparingInt(Dish::getCalories)),
                        Optional::get)
                ));
    }

在这里插入图片描述

public void dealPolicyByAgentLeave(List<TconMasterChaApprDomain> list) { String url = cfgPropertiesBean.getGlsAgentLeaveUrl(); // 按(oldAgentCode, newAgentCode)分组合并policyCode Map<String, List<TconMasterChaApprDomain>> groupedRecords = new HashMap<>(); for (TconMasterChaApprDomain domain : list) { String key = domain.getAgentServiceCode() + "|" + domain.getAgentServiceCodeAppr(); groupedRecords.computeIfAbsent(key, k -> new ArrayList<>()).add(domain); } // 遍历分组处理 for (Map.Entry<String, List<TconMasterChaApprDomain>> entry : groupedRecords.entrySet()) { List<TconMasterChaApprDomain> group = entry.getValue(); String[] keys = entry.getKey().split("\\|"); String oldAgentCode = keys[0]; String newAgentCode = keys[1]; // 分批次处理policyCode int batchSize = 900;//拼接后超过900个,每900个分批调用接口 int totalSize = group.size(); int totalBatches = (int) Math.ceil((double) totalSize / batchSize); for (int i = 0; i < totalSize; i += batchSize) { int end = Math.min(i + batchSize, totalSize); List<TconMasterChaApprDomain> subGroup = group.subList(i, end); // 拼接policyCode StringBuilder policyCodesBuilder = new StringBuilder(); for (int j = 0; j < subGroup.size(); j++) { policyCodesBuilder.append(subGroup.get(j).getPolicyCode()); if (j < subGroup.size() - 1) { policyCodesBuilder.append(","); } } String mergedPolicyCodes = policyCodesBuilder.toString(); try { GlsAgentLeaveDomain callDomain = new GlsAgentLeaveDomain(); GlsAgentRequestBody body = new GlsAgentRequestBody(); GlsAgentRequestHead head = new GlsAgentRequestHead(); // 设置请求头 head.setCallTime(DateUtil.getFormatDate(DateUtil.getCurrentDate(), "yyyy-MM-dd HH:mm:ss")); head.setContentType("JSON"); head.setServiceAction("PolicyAgentChangeSendSmsService"); head.setServiceSystem("GLS"); head.setSourceSystem("GLS-CHANNEL"); callDomain.setRequestHead(head); // 设置请求体 body.setPolicyCode(mergedPolicyCodes); body.setOldAgentCode(oldAgentCode); body.setNewAgentCode(newAgentCode); callDomain.setRequestBody(body); // 发送请求 String requestJson = JSONObject.fromObject(callDomain).toString(); HessianProxyFactory factory = new HessianProxyFactory(); GlsHessianServiceApi service = (GlsHessianServiceApi) factory.create(GlsHessianServiceApi.class, url); String result = service.process(requestJson); // 日志记录 LOGGER.info("服务业务员变更组: oldAgentCode={}, newAgentCode={}, 批次={}/{}", oldAgentCode, newAgentCode, i / batchSize + 1, totalBatches); LOGGER.info("处理结果: {}", result); // 处理响应并更新状态 if (StringUtils.isNotEmpty(result)) { JSONObject obj = JSONObject.fromObject(result); GlsAgentLeaveResponseDomain resultObj = (GlsAgentLeaveResponseDomain) JsonUtil.getDTO(obj.get("responseBody").toString(), GlsAgentLeaveResponseDomain.class); for (TconMasterChaApprDomain domain : subGroup) { domain.setSendFlag(resultObj.getReturnCode()); tconMasterChaApprDao.updateStatus(domain); } } } catch (Exception e) { LOGGER.error("服务业务员变更组处理失败: oldAgentCode={}, newAgentCode={}, 批次={}/{}", oldAgentCode, newAgentCode, i / batchSize + 1, totalBatches); LOGGER.error("错误信息:", e); } } } }复杂度太高,把 for (int j = 0; j < subGroup.size(); j++) { policyCodesBuilder.append(subGroup.get(j).getPolicyCode()); if (j < subGroup.size() - 1) { policyCodesBuilder.append(","); } }和 try { GlsAgentLeaveDomain callDomain = new GlsAgentLeaveDomain(); GlsAgentRequestBody body = new GlsAgentRequestBody(); GlsAgentRequestHead head = new GlsAgentRequestHead(); // 设置请求头 head.setCallTime(DateUtil.getFormatDate(DateUtil.getCurrentDate(), "yyyy-MM-dd HH:mm:ss")); head.setContentType("JSON"); head.setServiceAction("PolicyAgentChangeSendSmsService"); head.setServiceSystem("GLS"); head.setSourceSystem("GLS-CHANNEL"); callDomain.setRequestHead(head); // 设置请求体 body.setPolicyCode(mergedPolicyCodes); body.setOldAgentCode(oldAgentCode); body.setNewAgentCode(newAgentCode); callDomain.setRequestBody(body); // 发送请求 String requestJson = JSONObject.fromObject(callDomain).toString(); HessianProxyFactory factory = new HessianProxyFactory(); GlsHessianServiceApi service = (GlsHessianServiceApi) factory.create(GlsHessianServiceApi.class, url); String result = service.process(requestJson); // 日志记录 LOGGER.info("服务业务员变更组: oldAgentCode={}, newAgentCode={}, 批次={}/{}", oldAgentCode, newAgentCode, i / batchSize + 1, totalBatches); LOGGER.info("处理结果: {}", result); // 处理响应并更新状态 if (StringUtils.isNotEmpty(result)) { JSONObject obj = JSONObject.fromObject(result); GlsAgentLeaveResponseDomain resultObj = (GlsAgentLeaveResponseDomain) JsonUtil.getDTO(obj.get("responseBody").toString(), GlsAgentLeaveResponseDomain.class); for (TconMasterChaApprDomain domain : subGroup) { domain.setSendFlag(resultObj.getReturnCode()); tconMasterChaApprDao.updateStatus(domain); } } } catch (Exception e) { LOGGER.error("服务业务员变更组处理失败: oldAgentCode={}, newAgentCode={}, 批次={}/{}", oldAgentCode, newAgentCode, i / batchSize + 1, totalBatches); LOGGER.error("错误信息:", e); }分别抽出可以降低复杂度吗
10-31
内容概要:本文介绍了一个基于MATLAB实现的无人机三维路径规划项目,采用蚁群算法(ACO)与多层感知机(MLP)相结合的混合模型(ACO-MLP)。该模型通过三维环境离散化建模,利用ACO进行全局路径搜索,并引入MLP对环境特征进行自适应学习与启发因子优化,实现路径的动态调整与多目标优化。项目解决了高维空间建模、动态障碍规避、局部最优陷阱、算法实时性及多目标权衡等关键技术难题,结合并行计算与参数自适应机制,提升了路径规划的智能性、安全性和工程适用性。文中提供了详细的模型架构、核心算法流程及MATLAB代码示例,涵盖空间建模、信息素更新、MLP训练与融合优化等关键步骤。; 适合人群:具备一定MATLAB编程基础,熟悉智能优化算法与神经网络的高校学生、科研人员及从事无人机路径规划相关工作的工程师;适合从事智能无人系统、自动驾驶、机器人导航等领域的研究人员; 使用场景及目标:①应用于复杂三维环境下的无人机路径规划,如城市物流、灾害救援、军事侦察等场景;②实现飞行安全、能耗优化、路径平滑与实时避障等多目标协同优化;③为智能无人系统的自主决策与环境适应能力提供算法支持; 阅读建议:此资源结合理论模型与MATLAB实践,建议读者在理解ACO与MLP基本原理的基础上,结合代码示例进行仿真调试,重点关注ACO-MLP融合机制、多目标优化函数设计及参数自适应策略的实现,以深入掌握混合智能算法在工程中的应用方法。
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