{"id":46684,"date":"2022-02-19T00:00:00","date_gmt":"2022-02-19T08:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/logistic-regression-algorithm-in-java\/"},"modified":"2025-11-13T12:55:48","modified_gmt":"2025-11-13T20:55:48","slug":"logistic-regression-algorithm-in-java","status":"publish","type":"post","link":"https:\/\/griddb.net\/en\/blog\/logistic-regression-algorithm-in-java\/","title":{"rendered":"Logistic Regression Algorithm in Java"},"content":{"rendered":"<h2>Introduction<\/h2>\n<p>Regression analysis is a technique used to determine the relationship between the dependent and the independent variable (s) for prediction purposes. It is a good tool for data modelling and analysis.<\/p>\n<p>There are different regression techniques. Our focus will be on Logistic Regression.<\/p>\n<p>Logistic Regression is suitable when there are more than one independent variables in a dataset. This technique requires large sample sizes as maximum likelihood estimates are less powerful compared to ordinary least squares at low sample sizes.<\/p>\n<p>In this article, we will be demonstrating how to implement Logistic Regression using Java and GridDB. The goal is to predict whether to play or not based on weather conditions.<\/p>\n<h2>Store the Data in GridDB<\/h2>\n<p>The data has been stored in a CSV file named &#8220;play.csv&#8221;. The dataset has 4 independent variables (outlook, temperature, humidity, windy), and 1 dependent variable (play). We need to write the dataset into GridDB and enjoy the benefits offered by GridDB like improved query performance.<\/p>\n<p>Let&#8217;s first import the libraries to be used for this:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-java\">import 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>\n<p>Now, let&#8217;s create a static Java class to represent the GridDB container to be used:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-java\">public static class Weather {\n     @RowKey String outlook;\n     String temperature; \n     String humidity;\n     String windy;\n     String play;\n}  <\/code><\/pre>\n<\/div>\n<p>See the above class as a SQL table with 5 columns. The 5 variables simply represents the columns of the GridDB container.<\/p>\n<p>We can now connect to our GridDB container from Java. The following code demonstrates this:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-java\">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>\n<p>The GridDB container to be used has the name <code>Weather<\/code>. Let&#8217;s select it:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-java\">Collection&lt;String, Weather> coll = store.putCollection(\"col01\", Weather.class);<\/code><\/pre>\n<\/div>\n<p>Everytime we need to use the <code>Weather<\/code> container, we will use its instance name <code>coll<\/code>.<\/p>\n<p>We can now read the data from the <code>play.csv<\/code> file and write it into the GridDB container:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-java\"> File file1 = new File(\"play.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 outlook = dataList[0];\n                        String temperature = dataList[1];\n                        String humidity = dataList[2];\n                        String windy = dataList[3];\n                        String play = dataList[4];\n                                                \n                        \n                        Weather wt = new Weather();\n                        wt.outlook = outlook;\n                        wt.temperature = temperature;\n                        wt.humidity = humidity;\n                        wt.windy = windy;\n                        wt.play = play;\n    \n                        \n                        \n                        coll.append(wt);\n                 }<\/code><\/pre>\n<\/div>\n<h2>Retrieve the Data<\/h2>\n<p>To use the data to build a Logistic Regression model, we need to pull it from the GridDB container. The following code can help you to accomplish this:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-java\">Query&lt;weather> query = coll.query(\"select *\");\n                RowSet&lt;\/weather>&lt;weather> rs = query.fetch(false);\n            RowSet res = query.fetch();&lt;\/weather><\/code><\/pre>\n<\/div>\n<p>The <code>select *<\/code> query helped us to retrieve all the data stored in the GridDB container.<\/p>\n<h2>Implement a Logistic Regression Model<\/h2>\n<p>Now that the data is ready, we can use it to implement a Logistic Regression model. The goal of the model is to help us determine whether we can play or not depending on the weather.<\/p>\n<p>Let&#8217;s first import the libraries that will help us to implement the model:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-java\">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<p>Let us create buffered reader and instances for the dataset.<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-java\">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>\n<p>We can now call the <code>Logistic()<\/code> function of Weka API to build a Logistic Regression model using the dataset:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-java\">datasetInstances.setClassIndex(datasetInstances.numAttributes()-1);\n\nClassifier classifier = new weka.classifiers.functions.Logistic();\n        \/** *\/\nclassifier.buildClassifier(datasetInstances);\nSystem.out.println(classifier);<\/code><\/pre>\n<\/div>\n<h2>Make a Prediction<\/h2>\n<p>We can use the above model to make a prediction. We will be using the last instance of the dataset to make a prediction as shown below:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-java\">Instance pred = datasetInstances.lastInstance();\n        double answer = classifier.classifyInstance(pred);\n        System.out.println(answer);<\/code><\/pre>\n<\/div>\n<h2>Compile and Run the Model<\/h2>\n<p>To compile and run the model, you will need the Weka API. Download it from the following URL:<\/p>\n<p>http:\/\/www.java2s.com\/Code\/Jar\/w\/weka.htm<\/p>\n<p>Next, login as the <code>gsadm<\/code> user. Move your <code>.java<\/code> file to the <code>bin<\/code> folder of your GridDB located in the following path:<\/p>\n<p>\/griddb_4.6.0-1_amd64\/usr\/griddb-4.6.0\/bin<\/p>\n<p>Run the following command on your Linux terminal to set the path for the gridstore.jar file:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">export CLASSPATH=$CLASSPATH:\/home\/osboxes\/Downloads\/griddb_4.6.0-1_amd64\/usr\/griddb-4.6.0\/bin\/gridstore.jar<\/code><\/pre>\n<\/div>\n<p>Next, use the following command to compile your <code>.java<\/code> file:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">javac -cp weka-3-7-0\/weka.jar WeatherPlay.java<\/code><\/pre>\n<\/div>\n<p>Run the .class file that is generated by running the following command:<\/p>\n<div class=\"clipboard\">\n<pre><code class=\"language-sh\">java -cp .:weka-3-7-0\/weka.jar WeatherPlay<\/code><\/pre>\n<\/div>\n<p>The model returned <code>1.0<\/code> for the prediction, which means that we can play.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Introduction Regression analysis is a technique used to determine the relationship between the dependent and the independent variable (s) for prediction purposes. It is a good tool for data modelling and analysis. There are different regression techniques. Our focus will be on Logistic Regression. Logistic Regression is suitable when there are more than one independent [&hellip;]<\/p>\n","protected":false},"author":41,"featured_media":28060,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[121],"tags":[],"class_list":["post-46684","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Logistic Regression Algorithm in Java | GridDB: Open Source Time Series Database for IoT<\/title>\n<meta name=\"description\" content=\"Introduction Regression analysis is a technique used to determine the relationship between the dependent and the independent variable (s) for prediction\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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