{"id":46715,"date":"2022-08-17T00:00:00","date_gmt":"2022-08-17T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/perceptron-neural-network-in-java-and-griddb\/"},"modified":"2025-11-13T12:56:08","modified_gmt":"2025-11-13T20:56:08","slug":"perceptron-neural-network-in-java-and-griddb","status":"publish","type":"post","link":"https:\/\/griddb.net\/en\/blog\/perceptron-neural-network-in-java-and-griddb\/","title":{"rendered":"Perceptron Neural Network in Java and GridDB"},"content":{"rendered":"

A multilayer perceptron refers to a feed forward artificial neural network model that maps a set of input data to a set of appropriate outputs.<\/p>\n

A multilayer perceptron is made up of many layers of nodes in a directed graph, with every layer being fully connected to the next layer.<\/p>\n

In this article, we will be implementing a perceptron neural network model in Java and GridDB. The purpose of the model will be to predict whether house prices are above or below the median house price value.<\/p>\n

The full source code and csv<\/code> file can be found here in our Github repo: https:\/\/github.com\/griddbnet\/Blogs\/tree\/perceptron_neural_network<\/a><\/p>\n

Data Description<\/h2>\n

The dataset to be used has 11 features. The first 10 attributes of the dataset will be the input features. Each of these inputs describe a feature of a house.<\/p>\n

The last attribute of the dataset is the feature that we will be predicting, which describes whether a house price is above the median price or not. A value of 1 means that the house price is above the median value while a value of 0 means that the house price is below the median value.<\/p>\n

The data has been stored in a CSV file named housepricedata.csv<\/code>.<\/p>\n

Store the Data in GridDB<\/h2>\n

Although we can still use the data from the file, GridDB offers a number of benefits over the CSV file. For instance, GridDB can return query results faster than a CSV file.<\/p>\n

That is why we have opted to store the data in GridDB.<\/p>\n

Let’s first import the libraries to help us store the data in GridDB:<\/p>\n

\n
import java.io.File;\nimport java.util.Scanner;\nimport java.io.IOException;\nimport java.util.Collection;\nimport java.util.Properties;\n\nimport com.toshiba.mwcloud.gs.Query;\nimport com.toshiba.mwcloud.gs.RowKey;\nimport com.toshiba.mwcloud.gs.RowSet;\nimport com.toshiba.mwcloud.gs.GridStore;\nimport com.toshiba.mwcloud.gs.Collection;\nimport com.toshiba.mwcloud.gs.GSException;\nimport com.toshiba.mwcloud.gs.GridStoreFactory;<\/code><\/pre>\n<\/div>\n
GridDB groups data into containers. Let us create a static Java class to represent the GridDB container where the data will be stored:<\/p>\n
\n
public static class Perceptron {\n     @RowKey String lotArea;\n     String overallQual;\n     String overallCond; \n     String totalBsmtSF;\n     String fullBath;\n     String halfBath;\n     String bedroomAbvGr;\n     String totRmsAbvGrd;\n     String fireplaces;\n     String garageArea;\n     String aboveMedianPrice;\n                  \n}    <\/code><\/pre>\n<\/div>\n
See the above static class as a SQL table. Every variable represents a single column in the GridDB container.<\/p>\n
We can now connect to GridDB from Java. This is possible provided the authentication process runs successfully. The connection can be done using the following code:<\/p>\n
\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>\n
Ensure that you use the correct authentication details depending on your GridDB settings.<\/p>\n
Let us now select the container into which the data is to be inserted:<\/p>\n
\n
        Collection<String, Perceptron> coll = store.putCollection(\"col01\", Perceptron.class);<\/code><\/pre>\n<\/div>\n
We have created an instance of the container. This instance can now be used to refer to the container.<\/p>\n
Let’s now pull the data from the CSV file and insert it into GridDB:<\/p>\n
\n
File file1 = new File(\"housepricedata.csv\");\n                Scanner sc = new Scanner(file1);\n                String data = sc.next();\n \n                while (sc.hasNext()){\n                    \n                        String scData = sc.next();\n                        String dataList[] = scData.split(\",\");\n                        String lotArea = dataList[0];\n                        String overallQual = dataList[1];\n                        String overallCond = dataList[2];\n                        String totalBsmtSF = dataList[3];\n                        String fullBath = dataList[4];\n                        String halfBath = dataList[5];\n                        String bedroomAbvGr = dataList[6];\n                        String totRmsAbvGrd = dataList[7];\n                        String fireplaces = dataList[8];\n                        String garageArea = dataList[9];\n                        String aboveMedianPrice = dataList[10];\n                        \n                                                              \n                        \n                        Perceptron pc = new Perceptron();\n                        pc.lotArea = Integer.parseInt(lotArea);\n                        pc.overallQual = Integer.parseInt(overallQual);\n                        pc.overallCond = Integer.parseInt(overallCond);\n                        pc.totalBsmtSF = Integer.parseInt(totalBsmtSF);\n                        pc.fullBath = Integer.parseInt(fullBath);\n                        pc.halfBath = Integer.parseInt(halfBath);\n                        pc.bedroomAbvGr = Integer.parseInt(bedroomAbvGr);\n                        pc.totRmsAbvGrd = Integer.parseInt(totRmsAbvGrd);\n                        pc.fireplaces = Integer.parseInt(fireplaces);\n                        pc.garageArea = Integer.parseInt(garageArea);\n                        pc.aboveMedianPrice = Integer.parseInt(aboveMedianPrice);\n                        \n                                               \n                        coll.append(pc);\n                 }<\/code><\/pre>\n<\/div>\n
The above code will pull the data from the CSV file and insert it into the GridDB container.<\/p>\n
Retrieve the Data<\/h2>\n
We want to use the data to implement a Perceptron neural network model. So, let’s pull the data from the GridDB container:<\/p>\n
\n
Query<perceptron> query = coll.query(\"select *\");\n   RowSet<\/perceptron><perceptron> rs = query.fetch(false);\n   RowSet res = query.fetch();<\/perceptron><\/code><\/pre>\n<\/div>\n
The select *<\/code> statement helped us to select all the data stored in the GridDB container.<\/p>\n
Fit the Perceptron Neural Network Model<\/h2>\n
We can now create a machine learning model using the dataset. Let’s first import the libraries to be used for this:<\/p>\n
\n
import java.io.BufferedReader;\nimport java.io.FileReader;\nimport weka.core.Instance;\nimport weka.core.Instances;\nimport java.math.BigDecimal;\nimport weka.classifiers.Evaluation;\nimport weka.classifiers.functions.MultilayerPerceptron;<\/code><\/pre>\n<\/div>\n
Create a buffered reader for the dataset:<\/p>\n
\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>\n
Next, we create an instance of the PerceptronNeuralNetwork<\/code> class:<\/p>\n
\n
datasetInstances.setClassIndex(datasetInstances.numAttributes()-1);\n           \/\/Instance of NN\n            PerceptronNeuralNetwork mlp = new PerceptronNeuralNetwork();<\/code><\/pre>\n<\/div>\n
Let’s now set the parameters for the neural network:<\/p>\n
\n
  \/\/Setting Parameters\n           mlp.setLearningRate(0.1);\n           mlp.setMomentum(0.2);\n           mlp.setTrainingTime(2000);\n           mlp.setHiddenLayers(\"3\");<\/code><\/pre>\n<\/div>\n
The above code has helped us to set parameters such as the learning rate, momentum, training time, and the number of hidden layers for the model.<\/p>\n
Let’s now build the classifier:<\/p>\n
\n
           mlp.buildClassifier(datasetInstances);<\/code><\/pre>\n<\/div>\n
Evaluate the Model<\/h2>\n
Let’s now evaluate the model to see how it performs. We will use the Evaluation()<\/code> function for this:<\/p>\n
\n
 Evaluation eval = new Evaluation(datasetInstances);\n           eval.evaluateModel(mlp, datasetInstances);\n           System.out.println(eval.toSummaryString()); \/\/Summary of Training<\/code><\/pre>\n<\/div>\n
We can now display the evaluation metrics:<\/p>\n
\n
\/\/display metrics\n            System.out.println(\"Correlation: \"+eval.correlationCoefficient());\n            System.out.println(\"Mean Absolute Error: \"+new BigDecimal(eval.meanAbsoluteError()));\n            System.out.println(\"Root Mean Squared Error: \"+eval.rootMeanSquaredError());\n            System.out.println(\"Relative Absolute Error: \"+eval.relativeAbsoluteError()+\"%\");\n            System.out.println(\"Root Relative Squared Error: \"+eval.rootRelativeSquaredError()+\"%\");\n            System.out.println(\"Instances: \"+eval.numInstances());<\/code><\/pre>\n<\/div>\n
Make a Prediction<\/h2>\n
Let’s use the last instance of the dataset to make a prediction. The goal is to know whether the house price is above or below the median price. A result of 1 means the house price is above the median price. A value of 0 means the house price is below the median price.<\/p>\n
\n
Instance pred = datasetInstances.lastInstance();\n        double answer = mlp.classifyInstance(pred);\n        System.out.println(answer);<\/code><\/pre>\n<\/div>\n
Compile and Run the Model<\/h2>\n
To compile and run the model, you will need the Weka API. Download it from the following URL:<\/p>\n
http:\/\/www.java2s.com\/Code\/Jar\/w\/weka.htm<\/p>\n
Next, login as the gsadm<\/code> user. Move your .java<\/code> file to the bin<\/code> folder of your GridDB located in the following path:<\/p>\n
\/griddb_4.6.0-1_amd64\/usr\/griddb-4.6.0\/bin<\/p>\n
Run the following command on your Linux terminal to set the path for the gridstore.jar file:<\/p>\n
\n
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
Next, use the following command to compile your .java<\/code> file:<\/p>\n
\n
javac -cp weka-3-7-0\/weka.jar PerceptronNeuralNetwork.java<\/code><\/pre>\n<\/div>\n
Run the .class file that is generated by running the following command:<\/p>\n
\n
java -cp .:weka-3-7-0\/weka.jar PerceptronNeuralNetwork<\/code><\/pre>\n<\/div>\n
The model returned 0<\/code> for the prediction. This means that the house price is below the median price.<\/p>\n","protected":false},"excerpt":{"rendered":"
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