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mdozmorov / HiC DataA (continuously updated) collection of references to Hi-C data. Predominantly human/mouse Hi-C data, with replicates.
PrateekKumarSingh / PythonDay-wise Python Learning resources from basic concepts to advanced Python applications such as data science and Machine learning. It also includes cheat-sheets, references which are logged daily to accelerate your learning.
vybenetwork / Solana Ohlc Candlestick Data ApiSolana OHLC Candlestick Data API: This repository demonstrates how to use the Vybe Solana OHLC candlestick data API to fetch, display, and export OHLC candlestick data for any Token-2022 or SPL token. Use this project as a reference implementation or starter kit for building Solana price charting UIs, backtesting pipelines, and on-chain analytics.
iNeuronai / Same Resume Year WiseThis is same resume for data scientist year wise so that everyone can prepare there own resume with this reference
vybenetwork / Solana Token Stats Metadata ApiSolana Token Stats Metadata API: This repository fetches a token's top traders (highest PNLs), top holders and whales, and comprehensive Solana token stats and metadata for any Token-2022 or SPL token. Use this project as a reference implementation or starter kit for building Solana analytics dashboards, token explorers, and on-chain data products.
vybenetwork / Solana Historical Trade Data ApiSolana Historical Trade Data API: This repository demonstrates how to use the Vybe Solana historical trade data API to fetch, filter, and analyze on-chain trade history for any Token-2022 or SPL token. Use this project as a reference implementation or starter kit for building data products such as trade explorers, execution/flow stats and more.
Aastha2104 / Parkinson Disease PredictionIntroduction Parkinson’s Disease is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting more than 10 million people worldwide. Parkinson’s is characterized primarily by the deterioration of motor and cognitive ability. There is no single test which can be administered for diagnosis. Instead, doctors must perform a careful clinical analysis of the patient’s medical history. Unfortunately, this method of diagnosis is highly inaccurate. A study from the National Institute of Neurological Disorders finds that early diagnosis (having symptoms for 5 years or less) is only 53% accurate. This is not much better than random guessing, but an early diagnosis is critical to effective treatment. Because of these difficulties, I investigate a machine learning approach to accurately diagnose Parkinson’s, using a dataset of various speech features (a non-invasive yet characteristic tool) from the University of Oxford. Why speech features? Speech is very predictive and characteristic of Parkinson’s disease; almost every Parkinson’s patient experiences severe vocal degradation (inability to produce sustained phonations, tremor, hoarseness), so it makes sense to use voice to diagnose the disease. Voice analysis gives the added benefit of being non-invasive, inexpensive, and very easy to extract clinically. Background Parkinson's Disease Parkinson’s is a progressive neurodegenerative condition resulting from the death of the dopamine containing cells of the substantia nigra (which plays an important role in movement). Symptoms include: “frozen” facial features, bradykinesia (slowness of movement), akinesia (impairment of voluntary movement), tremor, and voice impairment. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. Performance Metrics TP = true positive, FP = false positive, TN = true negative, FN = false negative Accuracy: (TP+TN)/(P+N) Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate Algorithms Employed Logistic Regression (LR): Uses the sigmoid logistic equation with weights (coefficient values) and biases (constants) to model the probability of a certain class for binary classification. An output of 1 represents one class, and an output of 0 represents the other. Training the model will learn the optimal weights and biases. Linear Discriminant Analysis (LDA): Assumes that the data is Gaussian and each feature has the same variance. LDA estimates the mean and variance for each class from the training data, and then uses properties of statistics (Bayes theorem , Gaussian distribution, etc) to compute the probability of a particular instance belonging to a given class. The class with the largest probability is the prediction. k Nearest Neighbors (KNN): Makes predictions about the validation set using the entire training set. KNN makes a prediction about a new instance by searching through the entire set to find the k “closest” instances. “Closeness” is determined using a proximity measurement (Euclidean) across all features. The class that the majority of the k closest instances belong to is the class that the model predicts the new instance to be. Decision Tree (DT): Represented by a binary tree, where each root node represents an input variable and a split point, and each leaf node contains an output used to make a prediction. Neural Network (NN): Models the way the human brain makes decisions. Each neuron takes in 1+ inputs, and then uses an activation function to process the input with weights and biases to produce an output. Neurons can be arranged into layers, and multiple layers can form a network to model complex decisions. Training the network involves using the training instances to optimize the weights and biases. Naive Bayes (NB): Simplifies the calculation of probabilities by assuming that all features are independent of one another (a strong but effective assumption). Employs Bayes Theorem to calculate the probabilities that the instance to be predicted is in each class, then finds the class with the highest probability. Gradient Boost (GB): Generally used when seeking a model with very high predictive performance. Used to reduce bias and variance (“error”) by combining multiple “weak learners” (not very good models) to create a “strong learner” (high performance model). Involves 3 elements: a loss function (error function) to be optimized, a weak learner (decision tree) to make predictions, and an additive model to add trees to minimize the loss function. Gradient descent is used to minimize error after adding each tree (one by one). Engineering Goal Produce a machine learning model to diagnose Parkinson’s disease given various features of a patient’s speech with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare various algorithms and parameters to determine the best model for predicting Parkinson’s. Dataset Description Source: the University of Oxford 195 instances (147 subjects with Parkinson’s, 48 without Parkinson’s) 22 features (elements that are possibly characteristic of Parkinson’s, such as frequency, pitch, amplitude / period of the sound wave) 1 label (1 for Parkinson’s, 0 for no Parkinson’s) Project Pipeline pipeline Summary of Procedure Split the Oxford Parkinson’s Dataset into two parts: one for training, one for validation (evaluate how well the model performs) Train each of the following algorithms with the training set: Logistic Regression, Linear Discriminant Analysis, k Nearest Neighbors, Decision Tree, Neural Network, Naive Bayes, Gradient Boost Evaluate results using the validation set Repeat for the following training set to validation set splits: 80% training / 20% validation, 75% / 25%, and 70% / 30% Repeat for a rescaled version of the dataset (scale all the numbers in the dataset to a range from 0 to 1: this helps to reduce the effect of outliers) Conduct 5 trials and average the results Data a_o a_r m_o m_r Data Analysis In general, the models tended to perform the best (both in terms of accuracy and Matthews Correlation Coefficient) on the rescaled dataset with a 75-25 train-test split. The two highest performing algorithms, k Nearest Neighbors and the Neural Network, both achieved an accuracy of 98%. The NN achieved a MCC of 0.96, while KNN achieved a MCC of 0.94. These figures outperform most existing literature and significantly outperform current methods of diagnosis. Conclusion and Significance These robust results suggest that a machine learning approach can indeed be implemented to significantly improve diagnosis methods of Parkinson’s disease. Given the necessity of early diagnosis for effective treatment, my machine learning models provide a very promising alternative to the current, rather ineffective method of diagnosis. Current methods of early diagnosis are only 53% accurate, while my machine learning model produces 98% accuracy. This 45% increase is critical because an accurate, early diagnosis is needed to effectively treat the disease. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. With an earlier diagnosis, much of this degradation could have been slowed or treated. My results are very significant because Parkinson’s affects over 10 million people worldwide who could benefit greatly from an early, accurate diagnosis. Not only is my machine learning approach more accurate in terms of diagnostic accuracy, it is also more scalable, less expensive, and therefore more accessible to people who might not have access to established medical facilities and professionals. The diagnosis is also much simpler, requiring only a 10-15 second voice recording and producing an immediate diagnosis. Future Research Given more time and resources, I would investigate the following: Create a mobile application which would allow the user to record his/her voice, extract the necessary vocal features, and feed it into my machine learning model to diagnose Parkinson’s. Use larger datasets in conjunction with the University of Oxford dataset. Tune and improve my models even further to achieve even better results. Investigate different structures and types of neural networks. Construct a novel algorithm specifically suited for the prediction of Parkinson’s. Generalize my findings and algorithms for all types of dementia disorders, such as Alzheimer’s. References Bind, Shubham. "A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction." International Journal of Computer Science and Information Technologies 6 (2015): n. pag. International Journal of Computer Science and Information Technologies. 2015. Web. 8 Mar. 2017. Brooks, Megan. "Diagnosing Parkinson's Disease Still Challenging." Medscape Medical News. National Institute of Neurological Disorders, 31 July 2014. Web. 20 Mar. 2017. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Hashmi, Sumaiya F. "A Machine Learning Approach to Diagnosis of Parkinson’s Disease."Claremont Colleges Scholarship. Claremont College, 2013. Web. 10 Mar. 2017. Karplus, Abraham. "Machine Learning Algorithms for Cancer Diagnosis." Machine Learning Algorithms for Cancer Diagnosis (n.d.): n. pag. Mar. 2012. Web. 20 Mar. 2017. Little, Max. "Parkinsons Data Set." UCI Machine Learning Repository. University of Oxford, 26 June 2008. Web. 20 Feb. 2017. Ozcift, Akin, and Arif Gulten. "Classifier Ensemble Construction with Rotation Forest to Improve Medical Diagnosis Performance of Machine Learning Algorithms." Computer Methods and Programs in Biomedicine 104.3 (2011): 443-51. Semantic Scholar. 2011. Web. 15 Mar. 2017. "Parkinson’s Disease Dementia." UCI MIND. N.p., 19 Oct. 2015. Web. 17 Feb. 2017. Salvatore, C., A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M. Morelli, M.c. Gilardi, and A. Quattrone. "Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy."Journal of Neuroscience Methods 222 (2014): 230-37. 2014. Web. 18 Mar. 2017. Shahbakhi, Mohammad, Danial Taheri Far, and Ehsan Tahami. "Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine."Journal of Biomedical Science and Engineering 07.04 (2014): 147-56. Scientific Research. July 2014. Web. 2 Mar. 2017. "Speech and Communication." Speech and Communication. Parkinson's Disease Foundation, n.d. Web. 22 Mar. 2017. Sriram, Tarigoppula V. S., M. Venkateswara Rao, G. V. Satya Narayana, and D. S. V. G. K. Kaladhar. "Diagnosis of Parkinson Disease Using Machine Learning and Data Mining Systems from Voice Dataset." SpringerLink. Springer, Cham, 01 Jan. 1970. Web. 17 Mar. 2017.
OpenInstitute / OpenDukaOpen Duka is a project designed by the Open Institute that will provide a freely accessible database of information on Kenyan entities. This information will provide citizens, journalists, and civic activists with a practical and easy-to-use tool to understand the ownership structure of the world they live in, demonstrating the practical applications of open information for normal citizens. It will serve as a core dataset for citizens, journalists, and civic activists who want to build 3rd party public transparency and public accountability apps or services, by allowing them to cross reference the Open Duka company shareholder data against other datasets.
langnico / Global Canopy Height ModelThis repository contains the code used in the paper: A high-resolution canopy height model of the Earth. Here, we developed a model to estimate canopy top height anywhere on Earth. The model estimates canopy top height for every Sentinel-2 image pixel and was trained using sparse GEDI LIDAR data as a reference.
barricklab / Breseqbreseq is a computational pipeline for finding mutations relative to a reference sequence using high-throughput DNA resequencing data. It is intended for haploid microbial genomes (<20 Mb). breseq is a command line tool implemented in C++ and R.
google / StarthinkerReference framework for building data workflows provided by Google. Accelerates authentication, logging, scheduling, and deployment of solutions using GCP. To borrow a tagline.. "The framework for professionals with deadlines."
hiimvikash / DSA EndGameI have started Data structures and Algorithms on April 1, 2021, and this repository will be containing my resources, tutorial, codes, and my approach to Qs, for future reference. As I'm in the learning process, this repository will be refreshed daily with my new bits of knowledge.
LarryDong / Event RepresentationEvent-based camera data representation and processing. Some common representations and reference codes.
jq-rs / Mles RsDistributed publish-subscribe data service and Mles protocol reference implementation on Rust, Warp and Serde
caijiahao / SpringMvcPlusMongo慕课网 首页 实战 路径 猿问 手记 登录 注册 11.11 Python 手记 \ 史上最全，最详idea搭建springdata+mongoDB+maven+springmvc 史上最全，最详idea搭建springdata+mongoDB+maven+springmvc 原创 2016-10-21 10:54:297759浏览2评论 作为IT届的小弟，本篇作为本人的第一篇手记，还希望各位大牛多多指点，以下均为个人学习所得，如有错误，敬请指正。本着服务IT小白的原则，该手记比较详细。由于最近使用postgre开发大型项目，发现了关系型数据库的弊端及查询效率之慢，苦心钻研之下，对nosql的mongoDB从无知到有了初步了解。 项目环境：win10+IntelliJ IDEA2016+maven3.3.9+MongoDB 3.2+JDK1.7+spring4.1.7 推荐网站（适合学习各种知识的基础）：http://www.runoob.com/ mongo安装请参考http://www.runoob.com/mongodb/mongodb-window-install.html 由于最近osChina的maven仓库挂掉，推荐大家使用阿里的镜像，速度快的飞起 maven配置：<localRepository>F:\.m2\repository</localRepository> <mirrors> <mirror> <id>alimaven</id> <name>aliyun maven</name> <url>http://maven.aliyun.com/nexus/content/groups/public/</url> <mirrorOf>central</mirrorOf> </mirror> </mirrors> 这里不实用idea自带maven插件，改用3.3.9 图片描述 项目结构：图片描述 这里dao与mongoDao分别为mongoDB的两种查询方式： dao为JPA的查询方式（请参考springdataJPA） mongoDao使用mongoTemplate，类似于关系型数据库使用的jdbcTemplate 不罗嗦，上代码 先看配置文件 spring-context.xm为最基本的spring配置 <?xml version="1.0" encoding="UTF-8"?> <beans xmlns="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:context="http://www.springframework.org/schema/context" xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd"> <!--扫描service包嗲所有使用注解的类型--> <context:component-scan base-package="com.lida.mongo"/> <!-- 导入mongodb的配置文件 --> <import resource="spring-mongo.xml" /> <!-- 开启注解 --> <context:annotation-config /> </beans> spring-web.xml为springmvc的基本配置 <?xml version="1.0" encoding="UTF-8"?> <beans xmlns="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:mvc="http://www.springframework.org/schema/mvc" xmlns:context="http://www.springframework.org/schema/context" xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd http://www.springframework.org/schema/mvc http://www.springframework.org/schema/mvc/spring-mvc-4.0.xsd http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context-4.0.xsd"> <!--配置springmvc--> <!--1.开启springmvc注解模式--> <!--简化配置： (1)主动注册DefaultAnnotationHandlerMapping,AnnotationMethodHandlerAdapter (2)提供一系列功能：数据绑定，数字和日期的format @NumberFormt @DataTimeFormat，xml json默认的读写支持--> <mvc:annotation-driven/> <!--servlet-mapping--> <!--2静态资源默认的servlet配置，（1）允许对静态资源的处理：js，gif （2）允许使用“/”做整体映射--> <!-- 容器默认的DefaultServletHandler处理 所有静态内容与无RequestMapping处理的URL--> <mvc:default-servlet-handler/> <!--3:配置jsp 显示viewResolver--> <bean class="org.springframework.web.servlet.view.InternalResourceViewResolver"> <property name="viewClass" value="org.springframework.web.servlet.view.JstlView"/> <property name="prefix" value="/WEB-INF/views/"/> <property name="suffix" value=".jsp"/> </bean> <!-- 4自动扫描且只扫描@Controller --> <context:component-scan base-package="com.lida.mongo.controller" /> <!-- 定义无需Controller的url<->view直接映射 --> <mvc:view-controller path="/" view-name="redirect:/goMongo/list"/> </beans> spring-mongo.xml为mongo配置 <?xml version="1.0" encoding="UTF-8"?> <beans xmlns="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:context="http://www.springframework.org/schema/context" xmlns:mongo="http://www.springframework.org/schema/data/mongo" xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans-3.0.xsd http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd http://www.springframework.org/schema/data/mongo http://www.springframework.org/schema/data/mongo/spring-mongo.xsd"> <!-- 加载mongodb的属性配置文件 --> <context:property-placeholder location="classpath:mongo.properties" /> <!-- spring连接mongodb数据库的配置 --> <mongo:mongo-client replica-set="${mongo.hostport}" id="mongo"> <mongo:client-options connections-per-host="${mongo.connectionsPerHost}" threads-allowed-to-block-for-connection-multiplier="${mongo.threadsAllowedToBlockForConnectionMultiplier}" connect-timeout="${mongo.connectTimeout}" max-wait-time="${mongo.maxWaitTime}" socket-timeout="${mongo.socketTimeout}"/> </mongo:mongo-client> <!-- mongo的工厂，通过它来取得mongo实例,dbname为mongodb的数据库名，没有的话会自动创建 --> <mongo:db-factory id="mongoDbFactory" dbname="mongoLida" mongo-ref="mongo" /> <!-- 只要使用这个调用相应的方法操作 --> <bean id="mongoTemplate" class="org.springframework.data.mongodb.core.MongoTemplate"> <constructor-arg name="mongoDbFactory" ref="mongoDbFactory" /> </bean> <!-- mongodb bean的仓库目录，会自动扫描扩展了MongoRepository接口的接口进行注入 --> <mongo:repositories base-package="com.lida.mongo" /> </beans> mongo.properties #mongoDB连接配置 mongo.hostport=127.0.0.1:27017 mongo.connectionsPerHost=8 mongo.threadsAllowedToBlockForConnectionMultiplier=4 #连接超时时间 mongo.connectTimeout=1000 #等待时间 mongo.maxWaitTime=1500 #Socket超时时间 mongo.socketTimeout=1500 pom.xml <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>com.liad</groupId> <artifactId>mongo</artifactId> <packaging>war</packaging> <version>1.0-SNAPSHOT</version> <name>mongo Maven Webapp</name> <url>http://maven.apache.org</url> <dependencies> <!--使用junit4，注解的方式测试--> <dependency> <groupId>junit</groupId> <artifactId>junit</artifactId> <version>4.11</version> <scope>test</scope> </dependency> <!--日志--> <!--日志 slf4j,log4j,logback,common-logging--> <!--slf4j是规范/接口--> <!--log4j,logback,common-logging是日志实现 本项目使用slf4j + logback --> <dependency> <groupId>org.slf4j</groupId> <artifactId>slf4j-api</artifactId> <version>1.7.12</version> </dependency> <!--实现slf4j并整合--> <dependency> <groupId>ch.qos.logback</groupId> <artifactId>logback-core</artifactId> <version>1.1.1</version> </dependency> <dependency> <groupId>ch.qos.logback</groupId> <artifactId>logback-classic</artifactId> <version>1.1.1</version> </dependency> <!--数据库相关--> <dependency> <groupId>mysql</groupId> <artifactId>mysql-connector-java</artifactId> <version>5.1.22</version> <!--maven工作范围 驱动在真正工作的时候使用，故生命周期改为runtime--> <scope>runtime</scope> </dependency> <!--servlet web相关--> <dependency> <groupId>taglibs</groupId> <artifactId>standard</artifactId> <version>1.1.2</version> </dependency> <dependency> <groupId>jstl</groupId> <artifactId>jstl</artifactId> <version>1.2</version> </dependency> <dependency> <groupId>com.fasterxml.jackson.core</groupId> <artifactId>jackson-databind</artifactId> <version>2.5.4</version> </dependency> <!--spring--> <!--spring核心--> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-core</artifactId> <version>4.1.7.RELEASE</version> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-beans</artifactId> <version>4.1.7.RELEASE</version> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-context</artifactId> <version>4.1.7.RELEASE</version> </dependency> <!--spring dao--> <dependency> <groupId>org.springframework.data</groupId> <artifactId>spring-data-mongodb</artifactId> <version>1.8.0.RELEASE</version> </dependency> <dependency> <groupId>org.mongodb</groupId> <artifactId>mongo-java-driver</artifactId> <version>3.2.2</version> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-tx</artifactId> <version>4.1.7.RELEASE</version> </dependency> <!--spring web--> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-web</artifactId> <version>4.1.7.RELEASE</version> </dependency> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-webmvc</artifactId> <version>4.1.7.RELEASE</version> </dependency> <!--spring test--> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-test</artifactId> <version>4.1.7.RELEASE</version> </dependency> <dependency> <groupId>commons-collections</groupId> <artifactId>commons-collections</artifactId> <version>3.2.2</version> </dependency> <dependency> <groupId>commons-fileupload</groupId> <artifactId>commons-fileupload</artifactId> <version>1.3.2</version> </dependency> <dependency> <groupId>commons-codec</groupId> <artifactId>commons-codec</artifactId> <version>1.10</version> </dependency> </dependencies> <dependencyManagement> <dependencies> <dependency> <groupId>org.springframework</groupId> <artifactId>spring-framework-bom</artifactId> <version>${spring.version}</version> <type>pom</type> <scope>import</scope> </dependency> <dependency> <groupId>net.sf.ehcache</groupId> <artifactId>ehcache-core</artifactId> <version>2.6.9</version> </dependency> </dependencies> </dependencyManagement> <build> <finalName>mongo</finalName> <plugins> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-compiler-plugin</artifactId> <configuration> <source>1.6</source> <target>1.6</target> </configuration> </plugin> </plugins> </build> </project> 两个实体类： /** * Created by DuLida on 2016/10/20. */ public class Address { private String city; private String street; private int num; public Address() { } public Address(String city, String street, int num) { this.city = city; this.street = street; this.num = num; } public String getCity() { return city; } public void setCity(String city) { this.city = city; } public String getStreet() { return street; } public void setStreet(String street) { this.street = street; } public int getNum() { return num; } public void setNum(int num) { this.num = num; } @Override public String toString() { return "Address{" + "city='" + city + '\'' + ", street='" + street + '\'' + ", num=" + num + '}'; } } /** * Created by DuLida on 2016/10/20. */ @Document(collection="person") public class Person implements Serializable { @Id private ObjectId id; private String name; private int age; private Address address; public Person() { } public Person( String name, int age, Address address) { this.name = name; this.age = age; this.address = address; } public ObjectId getId() { return id; } public void setId(ObjectId id) { this.id = id; } public String getName() { return name; } public void setName(String name) { this.name = name; } public int getAge() { return age; } public void setAge(int age) { this.age = age; } public Address getAddress() { return address; } public void setAddress(Address address) { this.address = address; } @Override public String toString() { return "Person{" + "id=" + id + ", name='" + name + '\'' + ", age=" + age + ", address=" + address + '}'; } } JPA的dao，注意这里只要继承MongoRepository不用写注解spring就能认识这是个Repository，MongoRepository提供了基本的增删改查，不用实现便可直接调用，例如testMongo的personDao.save(persons); public interface PersonDao extends MongoRepository<Person, ObjectId> { @Query(value = "{'age' : {'$gte' : ?0, '$lte' : ?1}, 'name':?2 }",fields="{ 'name' : 1, 'age' : 1}") List<Person> findByAge(int age1, int age2, String name); } mongoTemplate的dao /** * Created by DuLida on 2016/10/21. */ public interface PersonMongoDao { List<Person> findAll(); void insertPerson(Person user); void removePerson(String userName); void updatePerson(); List<Person> findForRequery(String userName); } @Repository("personMongoImpl") public class PersonMongoImpl implements PersonMongoDao { @Resource private MongoTemplate mongoTemplate; @Override public List<Person> findAll() { return mongoTemplate.findAll(Person.class,"person"); } @Override public void insertPerson(Person person) { mongoTemplate.insert(person,"person"); } @Override public void removePerson(String userName) { mongoTemplate.remove(Query.query(Criteria.where("name").is(userName)),"person"); } @Override public void updatePerson() { mongoTemplate.updateMulti(Query.query(Criteria.where("age").gt(3).lte(5)), Update.update("age",3),"person"); } @Override public List<Person> findForRequery(String userName) { return mongoTemplate.find(Query.query(Criteria.where("name").is(userName)),Person.class); } } JPA查询的测试类： /** * Created by DuLida on 2016/10/20. */ @RunWith(SpringJUnit4ClassRunner.class) //告诉junit spring配置文件 @ContextConfiguration({"classpath:spring/spring-context.xml","classpath:spring/spring-mongo.xml"}) public class PersonDaoTest { @Resource private PersonDao personDao; /*先往数据库中插入10个person*/ @Test public void testMongo() { List<Person> persons = new ArrayList<Person>(); for (int i = 0; i < 10; i++) { persons.add(new Person("name"+i,i,new Address("石家庄","裕华路",i))); } personDao.save(persons); } @Test public void findMongo() { System.out.println(personDao.findByAge(2,8,"name6")); } } mongoTemplate查询的测试类 /** * Created by DuLida on 2016/10/21. */ @RunWith(SpringJUnit4ClassRunner.class) //告诉junit spring配置文件 @ContextConfiguration({"classpath:spring/spring-context.xml","classpath:spring/spring-mongo.xml"}) public class MongoTemplateTest { @Resource private PersonMongoImpl personMongo; @Test public void testMongoTemplate() { //personMongo.insertPerson(new Person("wukong",24,new Address("石家庄","鑫达路",20))); //personMongo.removePerson("name3"); //personMongo.updatePerson(); //System.out.println(personMongo.findAll()); System.out.println(personMongo.findForRequery("wukong")); } } 注意测试前请先通过testMongo()向数据库中插入数据。 项目源码Git地址，仅供学习使用：https://github.com/dreamSmile/mongo.git 参考资料http://docs.spring.io/spring-data/mongodb/docs/current/reference/html/ 本文原创发布于慕课网 ，转载请注明出处，谢谢合作！ 相关标签：JAVAMongoDB 时间丶思考 天才小驴 你好小Song 陈词滥调1 4 人推荐 收藏 相关阅读 JAVA第三季1-9（模拟借书系统）作业 用pkp类，players类，playgame类三步教你写扑克牌游戏 Java入门第三季习题，简易扑克牌游戏 java学习第二季哒哒租车系统 Java入门第二季第六章练习题 请登录后，发表评论 评论（Enter+Ctrl） 全部评论2条 你好小Song2F 多数据源如何配置, 比如多个mongodb数据库再加mysql 1天前回复赞同0 时间丶思考 回复 你好小Song： 41分钟前 就在加一个datasource就行啊，原来mysql的datasource怎么加，现在就怎么加上就行，加上直接用。 回复 你好小Song1F 参考一下, 学习了. 2天前回复赞同0 时间丶思考 JAVA开发工程师 情劫难逃。 3篇手记 3推荐 作者的热门手记 神奇的Canvas贝塞尔曲线画心，程序员的表白 1021浏览18推荐3评论 深入探究setTimeout 和setInterval 44浏览1推荐0评论 网站首页企业合作人才招聘联系我们合作专区关于我们讲师招募常见问题意见反馈友情链接 Copyright © 2016 imooc.com All Rights Reserved | 京ICP备 13046642号-2
sisinflab / Adversarial Recommender Systems SurveyThe goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 74 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS or on generative models using GANs to improve their quality.
tech-notes-hub / Tech NotesTech Notes Hub: All-in-one technical notes & code snippets - covering design patterns, algorithms, data structures, AWS, and more. A centralized knowledge base for learning, reference, and daily use.
Gaurav14cs17 / DSAA well-organized repository covering core Data Structures and Algorithms, from fundamentals to advanced topics. It’s designed for systematic learning, coding interview preparation, and reference implementations, with topic-wise structure and clean, practical solutions
eiriklv / Json PopulateTool for populating JSON data with circular references. Sort of like Falcor and GraphQL, but for plain JSON with a transparent interface.
ept / Ddia2 ReferencesLiterature references for “Designing Data-Intensive Applications, 2nd edition”
