Linear regression, also known as Simple Linear Regression<\/em> is a regression algorithm that models the relationship between a dependent variable and one independent variable. A linear regression model shows a relationship that is linear or a sloped straight line, hence the name Simple Linear Regression<\/em>.<\/p>\n In Linear Regression, the depedent variable must be a real or continuous value. However, you can measure the indepedent variable on continuous or categorical values.<\/p>\n The following are the two main objectives of the Simple Linear Regression algorithm:<\/p>\n Model the relationship between two given variables, for example, job experience and salary, income and expenditure, etc.<\/p>\n<\/li>\n Predict new observations. For example, amount of revenue generated by a company based on investments in a year, weather forecasting based on temperature, etc.<\/p>\n<\/li>\n<\/ul>\n Simple Linear Regression can be expressed using the following formula:<\/p>\n Where,<\/p>\n Y- is the dependent variable.<\/p>\n a- is the intercept of the Regression line.<\/p>\n b- is the slope of the line.<\/p>\n X- is the independent variable.<\/p>\n In this section, we will be discussing how to implement the Simple Linear Regression algorithm in Java. The dataset to be used has two variables, that is, We will build a Linear Regression model from this dataset. We will then use the model to predict the salary of an individual based on their experience.<\/p>\n The data has been stored in a CSV file named The dataset has two columns, namely Let’s start by importing the libraries that we will need for this:<\/p>\n Next, let’s create a static Java class to represent the GridDB container to be used:<\/p>\n The above class is equivalent to a SQL table with two columns. The two variables represent the columns of the GridDB container.<\/p>\n Let us now connect to GridDB using Java. We will use the credentials of our GridDB installation as shown below:<\/p>\n We will be using the GridDB’s We have created an instance of the container and given it the name Let us now read the data from the See we have created an object of the static class and given it the name We need to use the data to implement a Linear Regression model, so let’s pull it from the GridDB container:<\/p>\n We have used the Now that we have the data, it’s time to implement a Linear Regression model that will tell us whether there is a correlation between the two variables of the dataset.<\/p>\n First, let’s import the libraries that we will need to use:<\/p>\n Next, let’s create a bufferred reader for the dataset. We will also create instances for the dataset:<\/p>\n Let us now use the dataset to implement a Linear Regression classifier:<\/p>\n Now that the model is ready, let us use it to make a prediction. We will be predicting the salary of the last instance in the dataset:<\/p>\n To compile and execute the model, you will need the Weka API. You can download it from the following URL:<\/p>\n http:\/\/www.java2s.com\/Code\/Jar\/w\/weka.htm<\/p>\n Next, login as the \/griddb_4.6.0-1_amd64\/usr\/griddb-4.6.0\/bin<\/p>\n Next, run the following command on your Linux terminal to set the path for the gridstore.jar file:<\/p>\n Next, run the following command to compile your Run the .class file that is generated by running the following command:<\/p>\n The model returned Introduction Linear regression, also known as Simple Linear Regression is a regression algorithm that models the relationship between a dependent variable and one independent variable. A linear regression model shows a relationship that is linear or a sloped straight line, hence the name Simple Linear Regression. In Linear Regression, the depedent variable must be a […]<\/p>\n","protected":false},"author":41,"featured_media":28009,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[121],"tags":[],"class_list":["post-46682","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"yoast_head":"\n\n
Y = a + bX<\/code><\/pre>\n<\/div>\nImplementing Linear Regression in Java<\/h2>\n
salary<\/code>, which is the dependent variable, and experience<\/code>, which is the independent variable.<\/p>\nStore the Data in GridDB<\/h3>\n
Salary_Data.csv<\/code>, but we need to write it into a GridDB container. Although we can still read the data directly from the CSV file, GridDB will improve the performance of our queries.<\/p>\nyearsExperience<\/code> and salary<\/code>.<\/p>\nimport java.io.IOException;\nimport java.util.Collection;\nimport java.util.Properties;\nimport java.util.Scanner;\nimport java.io.File;\n\n\nimport com.toshiba.mwcloud.gs.Collection;\nimport com.toshiba.mwcloud.gs.GSException;\nimport com.toshiba.mwcloud.gs.GridStore;\nimport com.toshiba.mwcloud.gs.GridStoreFactory;\nimport com.toshiba.mwcloud.gs.Query;\nimport com.toshiba.mwcloud.gs.RowKey;\nimport com.toshiba.mwcloud.gs.RowSet;<\/code><\/pre>\n<\/div>\npublic static class ExperienceSalary {\n @RowKey float yearsExperience;\n Double salary; \n} <\/code><\/pre>\n<\/div>\n Properties props = new Properties();\n props.setProperty(\"notificationAddress\", \"239.0.0.1\");\n props.setProperty(\"notificationPort\", \"31999\");\n props.setProperty(\"clusterName\", \"defaultCluster\");\n props.setProperty(\"user\", \"admin\");\n props.setProperty(\"password\", \"admin\");\n GridStore store = GridStoreFactory.getInstance().getGridStore(props);<\/code><\/pre>\n<\/div>\nExperienceSalary<\/code> container, so let’s select it:<\/p>\nCollection<String, ExperienceSalary> coll = store.putCollection(\"col01\", ExperienceSalary.class);<\/code><\/pre>\n<\/div>\ncoll<\/code>. We will use this instance name to refer to the container.<\/p>\nSalary_Data.csv<\/code> file and write it into the GridDB container:<\/p>\nFile file1 = new File(\"Salary_Data.csv\");\n Scanner sc = new Scanner(file1);\n String data = sc.next();\n \n while (sc.hasNext()){\n String scData = sc.next();\n String dataList[] = scData.split(\",\");\n String yearsExperience = dataList[0];\n String salary = dataList[1];\n \n \n ExperienceSalary es = new ExperienceSalary();\n \n es.yearsExperience = Float.parseFloat(yearsExperience);\n es.salary = Double.parseDouble(salary);\n \n coll.append(es);\n }<\/code><\/pre>\n<\/div>\nes<\/code>. The object has then been appended to the GridDB container.<\/p>\nRetrieve the Data from GridDB<\/h2>\n
Query<experiencesalary> query = coll.query(\"select *\");\n RowSet<\/experiencesalary><experiencesalary> rs = query.fetch(false);\n RowSet res = query.fetch();<\/experiencesalary><\/code><\/pre>\n<\/div>\nselect *<\/code> query to pull all the data stored in the GridDB container.<\/p>\nBuild a Linear Regression Model<\/h3>\n
import java.io.IOException;\n\nimport weka.classifiers.Evaluation;\nimport weka.classifiers.Classifier;\nimport weka.core.Instance;\nimport weka.core.Instances;\nimport weka.core.converters.ArffLoader; \n\nimport java.io.BufferedReader;\nimport java.io.FileReader;<\/code><\/pre>\n<\/div>\n BufferedReader bufferedReader\n = new BufferedReader(\n new FileReader(res));\n \n \/\/ Create dataset instances\n Instances datasetInstances\n = new Instances(bufferedReader);<\/code><\/pre>\n<\/div>\ndatasetInstances.setClassIndex(datasetInstances.numAttributes()-1);\n\n Classifier classifier = new weka.classifiers.functions.LinearRegression();\n \n classifier.buildClassifier(datasetInstances);<\/code><\/pre>\n<\/div>\nMake a Prediction<\/h2>\n
\/\/ Predicting the salary \n Instance predSalary = datasetInstances.lastInstance();\n double sal = classifier.classifyInstance(predSalary);\n System.out.println(\"The salary is : \"+sal);<\/code><\/pre>\n<\/div>\nCompile and Execute the Model<\/h2>\n
gsadm<\/code> user. Move your .java<\/code> file to the bin<\/code> folder of your GridDB located in the following path:<\/p>\nexport CLASSPATH=$CLASSPATH:\/home\/osboxes\/Downloads\/griddb_4.6.0-1_amd64\/usr\/griddb-4.6.0\/bin\/gridstore.jar<\/code><\/pre>\n<\/div>\n.java<\/code> file:<\/p>\njavac -cp weka-3-7-0\/weka.jar LinearRegression.java<\/code><\/pre>\n<\/div>\njava -cp .:weka-3-7-0\/weka.jar LinearRegression<\/code><\/pre>\n<\/div>\n122017.00<\/code>, which is very close to the real salary for the instance, that is, 121872.00<\/code>.<\/p>\n","protected":false},"excerpt":{"rendered":"